From f79bebed8698a77b795511a438db7d9d8dbf0950 Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Sat, 5 Nov 2022 09:49:13 -0700
Subject: [PATCH 001/199] =?UTF-8?q?lint(ldm.invoke.generator):=20?=
 =?UTF-8?q?=F0=9F=9A=AE=20remove=20unused=20imports?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
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---
 ldm/invoke/generator/base.py        | 24 ++++++++++----------
 ldm/invoke/generator/embiggen.py    | 14 +++++++-----
 ldm/invoke/generator/img2img.py     | 10 ++++-----
 ldm/invoke/generator/inpaint.py     | 34 ++++++++++++++---------------
 ldm/invoke/generator/omnibus.py     | 16 ++++++--------
 ldm/invoke/generator/txt2img.py     |  3 +--
 ldm/invoke/generator/txt2img2img.py | 11 +++++-----
 7 files changed, 57 insertions(+), 55 deletions(-)

diff --git a/ldm/invoke/generator/base.py b/ldm/invoke/generator/base.py
index ba3172e9dc8..1e303530664 100644
--- a/ldm/invoke/generator/base.py
+++ b/ldm/invoke/generator/base.py
@@ -2,17 +2,19 @@
 Base class for ldm.invoke.generator.*
 including img2img, txt2img, and inpaint
 '''
-import torch
-import numpy as  np
-import random
 import os
 import os.path as osp
+import random
 import traceback
-from tqdm import tqdm, trange
+
+import numpy as np
+import torch
 from PIL import Image, ImageFilter, ImageChops
 import cv2 as cv
-from einops import rearrange, repeat
+from einops import rearrange
 from pytorch_lightning import seed_everything
+from tqdm import trange
+
 from ldm.invoke.devices import choose_autocast
 from ldm.util import rand_perlin_2d
 
@@ -103,7 +105,7 @@ def generate(self,prompt,init_image,width,height,sampler, iterations=1,seed=None
                 seed = self.new_seed()
 
         return results
-    
+
     def sample_to_image(self,samples)->Image.Image:
         """
         Given samples returned from a sampler, converts
@@ -166,12 +168,12 @@ def repaste_and_color_correct(self, result: Image.Image, init_image: Image.Image
             blurred_init_mask = pil_init_mask
 
         multiplied_blurred_init_mask = ImageChops.multiply(blurred_init_mask, self.pil_image.split()[-1])
-        
+
         # Paste original on color-corrected generation (using blurred mask)
         matched_result.paste(init_image, (0,0), mask = multiplied_blurred_init_mask)
         return matched_result
 
-        
+
 
     def sample_to_lowres_estimated_image(self,samples):
         # origingally adapted from code by @erucipe and @keturn here:
@@ -219,11 +221,11 @@ def get_noise(self,width,height):
         (txt2img) or from the latent image (img2img, inpaint)
         """
         raise NotImplementedError("get_noise() must be implemented in a descendent class")
-    
+
     def get_perlin_noise(self,width,height):
         fixdevice = 'cpu' if (self.model.device.type == 'mps') else self.model.device
         return torch.stack([rand_perlin_2d((height, width), (8, 8), device = self.model.device).to(fixdevice) for _ in range(self.latent_channels)], dim=0).to(self.model.device)
-    
+
     def new_seed(self):
         self.seed = random.randrange(0, np.iinfo(np.uint32).max)
         return self.seed
@@ -325,4 +327,4 @@ def save_sample(self, sample, filepath):
             os.makedirs(dirname, exist_ok=True)
         image.save(filepath,'PNG')
 
-        
+
diff --git a/ldm/invoke/generator/embiggen.py b/ldm/invoke/generator/embiggen.py
index dc6af35a6c9..0b9fda7ac29 100644
--- a/ldm/invoke/generator/embiggen.py
+++ b/ldm/invoke/generator/embiggen.py
@@ -3,14 +3,16 @@
 and generates with ldm.invoke.generator.img2img
 '''
 
+import numpy as np
 import torch
-import numpy as  np
+from PIL import Image
 from tqdm import trange
-from PIL               import Image
-from ldm.invoke.generator.base      import Generator
-from ldm.invoke.generator.img2img   import Img2Img
+
 from ldm.invoke.devices import choose_autocast
-from ldm.models.diffusion.ddim     import DDIMSampler
+from ldm.invoke.generator.base import Generator
+from ldm.invoke.generator.img2img import Img2Img
+from ldm.models.diffusion.ddim import DDIMSampler
+
 
 class Embiggen(Generator):
     def __init__(self, model, precision):
@@ -493,7 +495,7 @@ def make_image():
                     # Layer tile onto final image
                     outputsuperimage.alpha_composite(intileimage, (left, top))
             else:
-                print(f'Error: could not find all Embiggen output tiles in memory? Something must have gone wrong with img2img generation.')
+                print('Error: could not find all Embiggen output tiles in memory? Something must have gone wrong with img2img generation.')
 
             # after internal loops and patching up return Embiggen image
             return outputsuperimage
diff --git a/ldm/invoke/generator/img2img.py b/ldm/invoke/generator/img2img.py
index 1981b4eacb6..edcc855a290 100644
--- a/ldm/invoke/generator/img2img.py
+++ b/ldm/invoke/generator/img2img.py
@@ -2,15 +2,15 @@
 ldm.invoke.generator.img2img descends from ldm.invoke.generator
 '''
 
-import torch
-import numpy as  np
 import PIL
-from torch import Tensor
+import numpy as np
+import torch
 from PIL import Image
+from torch import Tensor
+
 from ldm.invoke.devices import choose_autocast
 from ldm.invoke.generator.base import Generator
-from ldm.models.diffusion.ddim import DDIMSampler
-from ldm.models.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent
+
 
 class Img2Img(Generator):
     def __init__(self, model, precision):
diff --git a/ldm/invoke/generator/inpaint.py b/ldm/invoke/generator/inpaint.py
index 7798ed00c5e..02bac7c999b 100644
--- a/ldm/invoke/generator/inpaint.py
+++ b/ldm/invoke/generator/inpaint.py
@@ -3,21 +3,21 @@
 '''
 
 import math
-import torch
-import torchvision.transforms as T
-import numpy as  np
-import cv2 as cv
+
 import PIL
+import cv2 as cv
+import numpy as np
+import torch
 from PIL import Image, ImageFilter, ImageOps, ImageChops
-from skimage.exposure.histogram_matching import match_histograms
-from einops import rearrange, repeat
-from ldm.invoke.devices             import choose_autocast
-from ldm.invoke.generator.img2img   import Img2Img
-from ldm.models.diffusion.ddim     import DDIMSampler
-from ldm.models.diffusion.ksampler import KSampler
+from einops import repeat
+
+from ldm.invoke.devices import choose_autocast
 from ldm.invoke.generator.base import downsampling
-from ldm.util import debug_image
+from ldm.invoke.generator.img2img import Img2Img
 from ldm.invoke.globals import Globals
+from ldm.models.diffusion.ddim import DDIMSampler
+from ldm.models.diffusion.ksampler import KSampler
+from ldm.util import debug_image
 
 infill_methods: list[str] = list()
 
@@ -59,7 +59,7 @@ def get_tile_images(self, image: np.ndarray, width=8, height=8):
             writeable=False
         )
 
-    def infill_patchmatch(self, im: Image.Image) -> Image:        
+    def infill_patchmatch(self, im: Image.Image) -> Image:
         if im.mode != 'RGBA':
             return im
 
@@ -128,7 +128,7 @@ def mask_edge(self, mask: Image, edge_size: int, edge_blur: int) -> Image:
         # Combine
         npmask = npgradient + npedge
 
-        # Expand 
+        # Expand
         npmask = cv.dilate(npmask, np.ones((3,3), np.uint8), iterations = int(edge_size / 2))
 
         new_mask = Image.fromarray(npmask)
@@ -221,7 +221,7 @@ def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
                 init_filled = init_filled.resize((inpaint_width, inpaint_height))
 
             debug_image(init_filled, "init_filled", debug_status=self.enable_image_debugging)
-            
+
             # Create init tensor
             init_image = self._image_to_tensor(init_filled.convert('RGB'))
 
@@ -251,10 +251,10 @@ def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
         # klms samplers not supported yet, so ignore previous sampler
         if isinstance(sampler,KSampler):
             print(
-                f">> Using recommended DDIM sampler for inpainting."
+                ">> Using recommended DDIM sampler for inpainting."
             )
             sampler = DDIMSampler(self.model, device=self.model.device)
-        
+
         sampler.make_schedule(
             ddim_num_steps=steps, ddim_eta=ddim_eta, verbose=False
         )
@@ -353,7 +353,7 @@ def sample_to_image(self, samples)->Image.Image:
 
         if self.pil_image is None or self.pil_mask is None:
             return gen_result
-        
+
         corrected_result = super().repaste_and_color_correct(gen_result, self.pil_image, self.pil_mask, self.mask_blur_radius)
         debug_image(corrected_result, "corrected_result", debug_status=self.enable_image_debugging)
 
diff --git a/ldm/invoke/generator/omnibus.py b/ldm/invoke/generator/omnibus.py
index 277e8970210..3a17062d013 100644
--- a/ldm/invoke/generator/omnibus.py
+++ b/ldm/invoke/generator/omnibus.py
@@ -1,14 +1,14 @@
 """omnibus module to be used with the runwayml 9-channel custom inpainting model"""
 
 import torch
-import numpy as  np
+from PIL import Image, ImageOps
 from einops import repeat
-from PIL import Image, ImageOps, ImageChops
+
 from ldm.invoke.devices import choose_autocast
-from ldm.invoke.generator.base import downsampling
 from ldm.invoke.generator.img2img import Img2Img
 from ldm.invoke.generator.txt2img import Txt2Img
 
+
 class Omnibus(Img2Img,Txt2Img):
     def __init__(self, model, precision):
         super().__init__(model, precision)
@@ -58,11 +58,9 @@ def get_make_image(
 
         self.mask_blur_radius = mask_blur_radius
 
-        t_enc = steps
-
         if init_image is not None and mask_image is not None: # inpainting
             masked_image = init_image * (1 - mask_image)  # masked image is the image masked by mask - masked regions zero
-            
+
         elif init_image is not None: # img2img
             scope = choose_autocast(self.precision)
 
@@ -99,7 +97,7 @@ def make_image(x_T):
                         device=model.device,
                         num_samples=num_samples,
                     )
-                    
+
                     c = model.cond_stage_model.encode(batch["txt"])
                     c_cat = list()
                     for ck in model.concat_keys:
@@ -164,10 +162,10 @@ def get_noise(self, width:int, height:int):
 
     def sample_to_image(self, samples)->Image.Image:
         gen_result = super().sample_to_image(samples).convert('RGB')
-        
+
         if self.pil_image is None or self.pil_mask is None:
             return gen_result
 
         corrected_result = super(Img2Img, self).repaste_and_color_correct(gen_result, self.pil_image, self.pil_mask, self.mask_blur_radius)
-        
+
         return corrected_result
diff --git a/ldm/invoke/generator/txt2img.py b/ldm/invoke/generator/txt2img.py
index ba49d2ef558..a04207259b8 100644
--- a/ldm/invoke/generator/txt2img.py
+++ b/ldm/invoke/generator/txt2img.py
@@ -3,9 +3,8 @@
 '''
 
 import torch
-import numpy as  np
+
 from ldm.invoke.generator.base import Generator
-from ldm.models.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent
 
 
 class Txt2Img(Generator):
diff --git a/ldm/invoke/generator/txt2img2img.py b/ldm/invoke/generator/txt2img2img.py
index 759ba2dba4e..3da42ebb8af 100644
--- a/ldm/invoke/generator/txt2img2img.py
+++ b/ldm/invoke/generator/txt2img2img.py
@@ -2,14 +2,15 @@
 ldm.invoke.generator.txt2img inherits from ldm.invoke.generator
 '''
 
-import torch
-import numpy as  np
 import math
+
+import torch
+from PIL import Image
+
 from ldm.invoke.generator.base import Generator
-from ldm.models.diffusion.ddim import DDIMSampler
 from ldm.invoke.generator.omnibus import Omnibus
-from ldm.models.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent
-from PIL import Image
+from ldm.models.diffusion.ddim import DDIMSampler
+
 
 class Txt2Img2Img(Generator):
     def __init__(self, model, precision):

From 1d43512d64b94db261db49287d516c392e815df0 Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Wed, 9 Nov 2022 10:08:49 -0800
Subject: [PATCH 002/199] initial commit of DiffusionPipeline class

---
 ldm/invoke/generator/diffusers_pipeline.py | 325 +++++++++++++++++++++
 1 file changed, 325 insertions(+)
 create mode 100644 ldm/invoke/generator/diffusers_pipeline.py

diff --git a/ldm/invoke/generator/diffusers_pipeline.py b/ldm/invoke/generator/diffusers_pipeline.py
new file mode 100644
index 00000000000..b13f85b645e
--- /dev/null
+++ b/ldm/invoke/generator/diffusers_pipeline.py
@@ -0,0 +1,325 @@
+import secrets
+from dataclasses import dataclass
+from typing import List, Optional, Union
+
+import torch
+from diffusers.models import AutoencoderKL, UNet2DConditionModel
+from diffusers.pipeline_utils import DiffusionPipeline
+from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
+from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
+from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
+from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
+
+
+@dataclass
+class PipelineIntermediateState:
+    run_id: str
+    step: int
+    timestep: int
+    latents: torch.Tensor
+    predicted_original: Optional[torch.Tensor] = None
+
+
+class StableDiffusionGeneratorPipeline(DiffusionPipeline):
+    r"""
+    Pipeline for text-to-image generation using Stable Diffusion.
+
+    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
+    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
+
+    Implementation note: This class started as a refactored copy of diffusers.StableDiffusionPipeline.
+    Hopefully future versions of diffusers provide access to more of these functions so that we don't
+    need to duplicate them here: https://github.com/huggingface/diffusers/issues/551#issuecomment-1281508384
+
+    Args:
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+        text_encoder ([`CLIPTextModel`]):
+            Frozen text-encoder. Stable Diffusion uses the text portion of
+            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
+            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
+        tokenizer (`CLIPTokenizer`):
+            Tokenizer of class
+            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
+        scheduler ([`SchedulerMixin`]):
+            A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of
+            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
+        safety_checker ([`StableDiffusionSafetyChecker`]):
+            Classification module that estimates whether generated images could be considered offsensive or harmful.
+            Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details.
+        feature_extractor ([`CLIPFeatureExtractor`]):
+            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
+    """
+
+    ID_LENGTH = 8
+
+    def __init__(
+        self,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        unet: UNet2DConditionModel,
+        scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
+        safety_checker: StableDiffusionSafetyChecker,
+        feature_extractor: CLIPFeatureExtractor,
+    ):
+        super().__init__()
+
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            tokenizer=tokenizer,
+            unet=unet,
+            scheduler=scheduler,
+            safety_checker=safety_checker,
+            feature_extractor=feature_extractor,
+        )
+
+    def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
+        r"""
+        Enable sliced attention computation.
+
+        When this option is enabled, the attention module will split the input tensor in slices, to compute attention
+        in several steps. This is useful to save some memory in exchange for a small speed decrease.
+
+        Args:
+            slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
+                When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
+                a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
+                `attention_head_dim` must be a multiple of `slice_size`.
+        """
+        if slice_size == "auto":
+            # half the attention head size is usually a good trade-off between
+            # speed and memory
+            slice_size = self.unet.config.attention_head_dim // 2
+        self.unet.set_attention_slice(slice_size)
+
+    def disable_attention_slicing(self):
+        r"""
+        Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
+        back to computing attention in one step.
+        """
+        # set slice_size = `None` to disable `attention slicing`
+        self.enable_attention_slicing(None)
+
+    def enable_xformers_memory_efficient_attention(self):
+        r"""
+        Enable memory efficient attention as implemented in xformers.
+
+        When this option is enabled, you should observe lower GPU memory usage and a potential speed up at inference
+        time. Speed up at training time is not guaranteed.
+
+        Warning: When Memory Efficient Attention and Sliced attention are both enabled, the Memory Efficient Attention
+        is used.
+        """
+        self.unet.set_use_memory_efficient_attention_xformers(True)
+
+    def disable_xformers_memory_efficient_attention(self):
+        r"""
+        Disable memory efficient attention as implemented in xformers.
+        """
+        self.unet.set_use_memory_efficient_attention_xformers(False)
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        prompt: Union[str, List[str]],
+        height: Optional[int] = 512,
+        width: Optional[int] = 512,
+        num_inference_steps: Optional[int] = 50,
+        guidance_scale: Optional[float] = 7.5,
+        generator: Optional[torch.Generator] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        **extra_step_kwargs,
+    ):
+        r"""
+        Function invoked when calling the pipeline for generation.
+
+        Args:
+            prompt (`str` or `List[str]`):
+                The prompt or prompts to guide the image generation.
+            height (`int`, *optional*, defaults to 512):
+                The height in pixels of the generated image.
+            width (`int`, *optional*, defaults to 512):
+                The width in pixels of the generated image.
+            num_inference_steps (`int`, *optional*, defaults to 50):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            guidance_scale (`float`, *optional*, defaults to 7.5):
+                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+                `guidance_scale` is defined as `w` of equation 2. of [Imagen
+                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
+                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
+                usually at the expense of lower image quality.
+            generator (`torch.Generator`, *optional*):
+                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
+                deterministic.
+            latents (`torch.FloatTensor`, *optional*):
+                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
+                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
+                tensor will ge generated by sampling using the supplied random `generator`.
+
+        Returns:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
+            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
+            When returning a tuple, the first element is a list with the generated images, and the second element is a
+            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
+            (nsfw) content, according to the `safety_checker`.
+        """
+        result = None
+        for result in self.generate(
+                prompt, height=height, width=width, num_inference_steps=num_inference_steps,
+                guidance_scale=guidance_scale, generator=generator, latents=latents,
+                **extra_step_kwargs):
+            pass  # discarding intermediates
+        if result is None:
+            raise AssertionError("why was that an empty generator?")
+        return result
+
+    def generate(
+        self,
+        prompt: Union[str, List[str]],
+        *,
+        opposing_prompt: Union[str, List[str]] = None,
+        height: Optional[int] = 512,
+        width: Optional[int] = 512,
+        num_inference_steps: Optional[int] = 50,
+        guidance_scale: Optional[float] = 7.5,
+        generator: Optional[torch.Generator] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        run_id: str = None,
+        **extra_step_kwargs,
+    ):
+        if isinstance(prompt, str):
+            batch_size = 1
+        else:
+            batch_size = len(prompt)
+
+        if height % 8 != 0 or width % 8 != 0:
+            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
+
+        if run_id is None:
+            run_id = secrets.token_urlsafe(self.ID_LENGTH)
+
+        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
+        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
+        # corresponds to doing no classifier free guidance.
+        do_classifier_free_guidance = guidance_scale > 1.0
+        text_embeddings = self.get_text_embeddings(prompt, opposing_prompt, do_classifier_free_guidance, batch_size)\
+            .to(self.unet.device)
+        self.scheduler.set_timesteps(num_inference_steps)
+        latents = self.prepare_latents(latents, batch_size, height, width,
+                                       generator, self.unet.dtype)
+
+        yield PipelineIntermediateState(run_id=run_id, step=-1, timestep=self.scheduler.num_train_timesteps,
+                                        latents=latents)
+        for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
+            step_output = self.step(t, latents, guidance_scale, text_embeddings, **extra_step_kwargs)
+            latents = step_output.prev_sample
+            yield PipelineIntermediateState(run_id=run_id, step=i, timestep=int(t), latents=latents,
+                                            predicted_original=step_output.pred_original_sample)
+
+        # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
+        torch.cuda.empty_cache()
+
+        image = self.decode_to_image(latents)
+        output = StableDiffusionPipelineOutput(images=image, nsfw_content_detected=[])
+        yield self.check_for_safety(output)
+
+    @torch.inference_mode()
+    def step(self, t, latents: torch.Tensor, guidance_scale, text_embeddings: torch.Tensor, **extra_step_kwargs):
+        do_classifier_free_guidance = guidance_scale > 1.0
+
+        # expand the latents if we are doing classifier free guidance
+        latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
+        latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+
+        # predict the noise residual
+        noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
+
+        # perform guidance
+        if do_classifier_free_guidance:
+            noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
+            noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+
+        # compute the previous noisy sample x_t -> x_t-1
+        return self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs)
+
+    @torch.inference_mode()
+    def check_for_safety(self, output):
+        if not getattr(self, 'feature_extractor') or not getattr(self, 'safety_checker'):
+            return output
+        images = output.images
+        safety_checker_output = self.feature_extractor(self.numpy_to_pil(images),
+                                                       return_tensors="pt").to(self.device)
+        screened_images, has_nsfw_concept = self.safety_checker(
+            images=images, clip_input=safety_checker_output.pixel_values)
+        return StableDiffusionPipelineOutput(screened_images, has_nsfw_concept)
+
+    @torch.inference_mode()
+    def decode_to_image(self, latents):
+        # scale and decode the image latents with vae
+        latents = 1 / 0.18215 * latents
+        image = self.vae.decode(latents).sample
+        image = (image / 2 + 0.5).clamp(0, 1)
+        image = image.cpu().permute(0, 2, 3, 1).numpy()
+        return image
+
+    @torch.inference_mode()
+    def get_text_embeddings(self,
+                            prompt: Union[str, List[str]],
+                            opposing_prompt: Union[str, List[str]],
+                            do_classifier_free_guidance: bool,
+                            batch_size: int):
+        # get prompt text embeddings
+        text_input = self._tokenize(prompt)
+
+        text_embeddings = self.text_encoder(text_input.input_ids)[0]
+        # get unconditional embeddings for classifier free guidance
+        if do_classifier_free_guidance:
+            # opposing prompt defaults to blank caption for everything in the batch
+            text_anti_input = self._tokenize(opposing_prompt or [""] * batch_size)
+            uncond_embeddings = self.text_encoder(text_anti_input.input_ids)[0]
+
+            # For classifier free guidance, we need to do two forward passes.
+            # Here we concatenate the unconditional and text embeddings into a single batch
+            # to avoid doing two forward passes
+            # FIXME: assert these two are the same size
+            text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
+        return text_embeddings
+
+    @torch.inference_mode()
+    def _tokenize(self, prompt: Union[str, List[str]]):
+        return self.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=self.tokenizer.model_max_length,
+            truncation=True,
+            return_tensors="pt",
+        )
+
+    def prepare_latents(self, latents, batch_size, height, width, generator, dtype):
+        # get the initial random noise unless the user supplied it
+        # Unlike in other pipelines, latents need to be generated in the target device
+        # for 1-to-1 results reproducibility with the CompVis implementation.
+        # However this currently doesn't work in `mps`.
+        latents_shape = (batch_size, self.unet.in_channels, height // 8, width // 8)
+        if latents is None:
+            latents = torch.randn(
+                latents_shape,
+                generator=generator,
+                device=self.unet.device,
+                dtype=dtype
+            )
+        else:
+            if latents.shape != latents_shape:
+                raise ValueError(
+                    f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
+        if latents.device != self.unet.device:
+            raise ValueError(f"Unexpected latents device, got {latents.device}, "
+                             f"expected {self.unet.device}")
+
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents *= self.scheduler.init_noise_sigma
+        return latents

From 58ea3bf4c8e5dc5475c17fa17bb94988f133fd1d Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Wed, 9 Nov 2022 11:33:19 -0800
Subject: [PATCH 003/199] spike: proof of concept using diffusers for txt2img

---
 .../environment-lin-cuda.yml                  | 10 +++
 .../requirements-base.txt                     |  2 +-
 ldm/invoke/generator/diffusers_pipeline.py    | 36 +++++++---
 ldm/invoke/generator/txt2img.py               | 66 ++++++++++---------
 4 files changed, 75 insertions(+), 39 deletions(-)

diff --git a/environments-and-requirements/environment-lin-cuda.yml b/environments-and-requirements/environment-lin-cuda.yml
index 17b2cddc95d..64bbabe902d 100644
--- a/environments-and-requirements/environment-lin-cuda.yml
+++ b/environments-and-requirements/environment-lin-cuda.yml
@@ -12,6 +12,7 @@ dependencies:
   - pytorch=1.12.1
   - cudatoolkit=11.6
   - pip:
+    - accelerate~=0.13
     - albumentations==0.4.3
     - dependency_injector==4.40.0
     - diffusers==0.6.0
@@ -39,6 +40,15 @@ dependencies:
     - torch-fidelity==0.3.0
     - torchmetrics==0.7.0
     - transformers==4.21.3
+    - diffusers~=0.7
+    - torchmetrics==0.7.0
+    - flask==2.1.3
+    - flask_socketio==5.3.0
+    - flask_cors==3.0.10
+    - dependency_injector==4.40.0
+    - eventlet
+    - getpass_asterisk
+    - kornia==0.6.0
     - git+https://github.com/openai/CLIP.git@main#egg=clip
     - git+https://github.com/Birch-san/k-diffusion.git@mps#egg=k-diffusion
     - git+https://github.com/invoke-ai/clipseg.git@relaxed-python-requirement#egg=clipseg
diff --git a/environments-and-requirements/requirements-base.txt b/environments-and-requirements/requirements-base.txt
index 9c306c42cab..8b7d83aa94a 100644
--- a/environments-and-requirements/requirements-base.txt
+++ b/environments-and-requirements/requirements-base.txt
@@ -1,7 +1,7 @@
 # pip will resolve the version which matches torch
 albumentations
 dependency_injector==4.40.0
-diffusers
+diffusers[torch]~=0.7
 einops
 eventlet
 facexlib
diff --git a/ldm/invoke/generator/diffusers_pipeline.py b/ldm/invoke/generator/diffusers_pipeline.py
index b13f85b645e..d4d60b761a7 100644
--- a/ldm/invoke/generator/diffusers_pipeline.py
+++ b/ldm/invoke/generator/diffusers_pipeline.py
@@ -1,6 +1,6 @@
 import secrets
 from dataclasses import dataclass
-from typing import List, Optional, Union
+from typing import List, Optional, Union, Callable
 
 import torch
 from diffusers.models import AutoencoderKL, UNet2DConditionModel
@@ -131,6 +131,7 @@ def __call__(
         guidance_scale: Optional[float] = 7.5,
         generator: Optional[torch.Generator] = None,
         latents: Optional[torch.FloatTensor] = None,
+        callback: Optional[Callable[[PipelineIntermediateState], None]] = None,
         **extra_step_kwargs,
     ):
         r"""
@@ -172,7 +173,22 @@ def __call__(
                 prompt, height=height, width=width, num_inference_steps=num_inference_steps,
                 guidance_scale=guidance_scale, generator=generator, latents=latents,
                 **extra_step_kwargs):
-            pass  # discarding intermediates
+            if callback is not None:
+                callback(result)
+        if result is None:
+            raise AssertionError("why was that an empty generator?")
+        return result
+
+    def image_from_embeddings(self, latents: torch.Tensor, num_inference_steps: int,
+                              text_embeddings: torch.Tensor, guidance_scale: float,
+                              *, callback: Callable[[PipelineIntermediateState], None]=None, run_id=None,
+                              **extra_step_kwargs) -> StableDiffusionPipelineOutput:
+        self.scheduler.set_timesteps(num_inference_steps)
+        result = None
+        for result in self.generate_from_embeddings(
+                latents, text_embeddings, guidance_scale, run_id, **extra_step_kwargs):
+            if callback is not None:
+                callback(result)
         if result is None:
             raise AssertionError("why was that an empty generator?")
         return result
@@ -199,9 +215,6 @@ def generate(
         if height % 8 != 0 or width % 8 != 0:
             raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
 
-        if run_id is None:
-            run_id = secrets.token_urlsafe(self.ID_LENGTH)
-
         # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
         # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
         # corresponds to doing no classifier free guidance.
@@ -209,16 +222,23 @@ def generate(
         text_embeddings = self.get_text_embeddings(prompt, opposing_prompt, do_classifier_free_guidance, batch_size)\
             .to(self.unet.device)
         self.scheduler.set_timesteps(num_inference_steps)
-        latents = self.prepare_latents(latents, batch_size, height, width,
-                                       generator, self.unet.dtype)
+        latents = self.prepare_latents(latents, batch_size, height, width, generator, self.unet.dtype)
 
+        yield from self.generate_from_embeddings(latents, text_embeddings, guidance_scale, run_id, **extra_step_kwargs)
+
+    def generate_from_embeddings(self, latents: torch.Tensor, text_embeddings: torch.Tensor, guidance_scale: float,
+                                 run_id: str = None, **extra_step_kwargs):
+        if run_id is None:
+            run_id = secrets.token_urlsafe(self.ID_LENGTH)
         yield PipelineIntermediateState(run_id=run_id, step=-1, timestep=self.scheduler.num_train_timesteps,
                                         latents=latents)
+        # NOTE: Depends on scheduler being already initialized!
         for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
             step_output = self.step(t, latents, guidance_scale, text_embeddings, **extra_step_kwargs)
             latents = step_output.prev_sample
+            predicted_original = getattr(step_output, 'pred_original_sample', None)
             yield PipelineIntermediateState(run_id=run_id, step=i, timestep=int(t), latents=latents,
-                                            predicted_original=step_output.pred_original_sample)
+                                            predicted_original=predicted_original)
 
         # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
         torch.cuda.empty_cache()
diff --git a/ldm/invoke/generator/txt2img.py b/ldm/invoke/generator/txt2img.py
index a04207259b8..a882b156713 100644
--- a/ldm/invoke/generator/txt2img.py
+++ b/ldm/invoke/generator/txt2img.py
@@ -1,10 +1,11 @@
 '''
 ldm.invoke.generator.txt2img inherits from ldm.invoke.generator
 '''
-
+import PIL.Image
 import torch
 
-from ldm.invoke.generator.base import Generator
+from .base import Generator
+from .diffusers_pipeline import StableDiffusionGeneratorPipeline
 
 
 class Txt2Img(Generator):
@@ -13,7 +14,8 @@ def __init__(self, model, precision):
 
     @torch.no_grad()
     def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
-                       conditioning,width,height,step_callback=None,threshold=0.0,perlin=0.0,**kwargs):
+                       conditioning,width,height,step_callback=None,threshold=0.0,perlin=0.0,
+                       **kwargs):
         """
         Returns a function returning an image derived from the prompt and the initial image
         Return value depends on the seed at the time you call it
@@ -22,38 +24,42 @@ def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
         self.perlin = perlin
         uc, c, extra_conditioning_info   = conditioning
 
-        @torch.no_grad()
-        def make_image(x_T):
-            shape = [
-                self.latent_channels,
-                height // self.downsampling_factor,
-                width  // self.downsampling_factor,
-            ]
+        # FIXME: this should probably be either passed in to __init__ instead of model & precision,
+        #     or be constructed in __init__ from those inputs.
+        pipeline = StableDiffusionGeneratorPipeline.from_pretrained(
+            "runwayml/stable-diffusion-v1-5",
+            revision="fp16", torch_dtype=torch.float16,
+            safety_checker=None,  # TODO
+            # scheduler=sampler + ddim_eta,  # TODO
+            # TODO: local_files_only=True
+        )
+        pipeline.unet.to("cuda")
+        pipeline.vae.to("cuda")
+
+        def make_image(x_T) -> PIL.Image.Image:
+            # FIXME: restore free_gpu_mem functionality
+            # if self.free_gpu_mem and self.model.model.device != self.model.device:
+            #     self.model.model.to(self.model.device)
 
-            if self.free_gpu_mem and self.model.model.device != self.model.device:
-                self.model.model.to(self.model.device)
-                                
-            sampler.make_schedule(ddim_num_steps=steps, ddim_eta=ddim_eta, verbose=False)
+            # FIXME: how the embeddings are combined should be internal to the pipeline
+            combined_text_embeddings = torch.cat([uc, c])
 
-            samples, _ = sampler.sample(
-                batch_size                   = 1,
-                S                            = steps,
-                x_T                          = x_T,
-                conditioning                 = c,
-                shape                        = shape,
-                verbose                      = False,
-                unconditional_guidance_scale = cfg_scale,
-                unconditional_conditioning   = uc,
-                extra_conditioning_info      = extra_conditioning_info,
-                eta                          = ddim_eta,
-                img_callback                 = step_callback,
-                threshold                    = threshold,
+            pipeline_output = pipeline.image_from_embeddings(
+                latents=x_T,
+                num_inference_steps=steps,
+                text_embeddings=combined_text_embeddings,
+                guidance_scale=cfg_scale,
+                callback=step_callback,
+                # TODO: extra_conditioning_info = extra_conditioning_info,
+                # TODO: eta = ddim_eta,
+                # TODO: threshold = threshold,
             )
 
-            if self.free_gpu_mem:
-                self.model.model.to("cpu")
+            # FIXME: restore free_gpu_mem functionality
+            # if self.free_gpu_mem:
+            #     self.model.model.to("cpu")
 
-            return self.sample_to_image(samples)
+            return pipeline.numpy_to_pil(pipeline_output.images)[0]
 
         return make_image
 

From dcfdb83513d8db6ece04a2b71bbe8354709f5ac1 Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Wed, 9 Nov 2022 15:23:42 -0800
Subject: [PATCH 004/199] doc: type hints for Generator

---
 ldm/invoke/generator/base.py | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/ldm/invoke/generator/base.py b/ldm/invoke/generator/base.py
index 1e303530664..0a60fea45f8 100644
--- a/ldm/invoke/generator/base.py
+++ b/ldm/invoke/generator/base.py
@@ -16,13 +16,19 @@
 from tqdm import trange
 
 from ldm.invoke.devices import choose_autocast
+from ldm.models.diffusion.ddpm import DiffusionWrapper
 from ldm.util import rand_perlin_2d
 
 downsampling = 8
 CAUTION_IMG = 'assets/caution.png'
 
-class Generator():
-    def __init__(self, model, precision):
+class Generator:
+    downsampling_factor: int
+    latent_channels: int
+    precision: str
+    model: DiffusionWrapper
+
+    def __init__(self, model: DiffusionWrapper, precision: str):
         self.model = model
         self.precision = precision
         self.seed = None

From 9b274bd57c5e4711729532a8d9ac9aac691fc4fd Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Wed, 9 Nov 2022 15:25:56 -0800
Subject: [PATCH 005/199] refactor(model_cache): factor out load_ckpt

---
 ldm/invoke/model_cache.py | 90 +++++++++++++++++++++------------------
 1 file changed, 48 insertions(+), 42 deletions(-)

diff --git a/ldm/invoke/model_cache.py b/ldm/invoke/model_cache.py
index 7d1654718a3..c730c7b7759 100644
--- a/ldm/invoke/model_cache.py
+++ b/ldm/invoke/model_cache.py
@@ -1,5 +1,5 @@
 '''
-Manage a cache of Stable Diffusion model files for fast switching. 
+Manage a cache of Stable Diffusion model files for fast switching.
 They are moved between GPU and CPU as necessary. If CPU memory falls
 below a preset minimum, the least recently used model will be
 cleared and loaded from disk when next needed.
@@ -51,7 +51,7 @@ def valid_model(self, model_name:str)->bool:
         identifier.
         '''
         return model_name in self.config
-    
+
     def get_model(self, model_name:str):
         '''
         Given a model named identified in models.yaml, return
@@ -66,7 +66,7 @@ def get_model(self, model_name:str):
             if model_name not in self.models: # make room for a new one
                 self._make_cache_room()
             self.offload_model(self.current_model)
-        
+
         if model_name in self.models:
             requested_model = self.models[model_name]['model']
             print(f'>> Retrieving model {model_name} from system RAM cache')
@@ -92,7 +92,7 @@ def get_model(self, model_name:str):
                 print(f'** restoring {self.current_model}')
                 self.get_model(self.current_model)
                 return
-        
+
         self.current_model = model_name
         self._push_newest_model(model_name)
         return {
@@ -102,7 +102,7 @@ def get_model(self, model_name:str):
             'hash': hash
         }
 
-    def default_model(self) -> str:
+    def default_model(self) -> str | None:
         '''
         Returns the name of the default model, or None
         if none is defined.
@@ -191,25 +191,13 @@ def add_model(self, model_name:str, model_attributes:dict, clobber=False) -> Non
         omega[model_name] = config
         if clobber:
             self._invalidate_cached_model(model_name)
-    
+
     def _load_model(self, model_name:str):
         """Load and initialize the model from configuration variables passed at object creation time"""
         if model_name not in self.config:
             print(f'"{model_name}" is not a known model name. Please check your models.yaml file')
 
         mconfig = self.config[model_name]
-        config = mconfig.config
-        weights = mconfig.weights
-        vae = mconfig.get('vae')
-        width = mconfig.width
-        height = mconfig.height
-
-        if not os.path.isabs(weights):
-            weights = os.path.normpath(os.path.join(Globals.root,weights))
-        # scan model
-        self.scan_model(model_name, weights)
-
-        print(f'>> Loading {model_name} from {weights}')
 
         # for usage statistics
         if self._has_cuda():
@@ -219,12 +207,39 @@ def _load_model(self, model_name:str):
         tic = time.time()
 
         # this does the work
-        if not os.path.isabs(config):
-            config = os.path.join(Globals.root,config)
-        omega_config = OmegaConf.load(config)
-        with open(weights,'rb') as f:
+        model_format = mconfig.get('format', 'ckpt')
+        if model_format == 'ckpt':
+            weights = mconfig.weights
+            print(f'>> Loading {model_name} from {weights}')
+            model, width, height, model_hash = self._load_ckpt_model(mconfig)
+        elif model_format == 'diffusers':
+            model, width, height, model_hash = self._load_diffusers_model(mconfig)
+        else:
+            raise NotImplementedError(f"Unknown model format {model_name}: {model_format}")
+
+        # usage statistics
+        toc = time.time()
+        print(f'>> Model loaded in', '%4.2fs' % (toc - tic))
+        if self._has_cuda():
+            print(
+                '>> Max VRAM used to load the model:',
+                '%4.2fG' % (torch.cuda.max_memory_allocated() / 1e9),
+                '\n>> Current VRAM usage:'
+                '%4.2fG' % (torch.cuda.memory_allocated() / 1e9),
+            )
+        return model, width, height, model_hash
+
+    def _load_ckpt_model(self, mconfig):
+        config = mconfig.config
+        weights = mconfig.weights
+        vae = mconfig.get('vae', None)
+        width = mconfig.width
+        height = mconfig.height
+
+        c = OmegaConf.load(config)
+        with open(weights, 'rb') as f:
             weight_bytes = f.read()
-        model_hash  = self._cached_sha256(weights,weight_bytes)
+        model_hash = self._cached_sha256(weights, weight_bytes)
         sd = torch.load(io.BytesIO(weight_bytes), map_location='cpu')
         del weight_bytes
         sd = sd['state_dict']
@@ -252,28 +267,19 @@ def _load_model(self, model_name:str):
         model.to(self.device)
         # model.to doesn't change the cond_stage_model.device used to move the tokenizer output, so set it here
         model.cond_stage_model.device = self.device
-        
+
         model.eval()
 
         for module in model.modules():
             if isinstance(module, (torch.nn.Conv2d, torch.nn.ConvTranspose2d)):
                 module._orig_padding_mode = module.padding_mode
 
-        # usage statistics
-        toc = time.time()
-        print(f'>> Model loaded in', '%4.2fs' % (toc - tic))
+        return model, width, height, model_hash
 
-        if self._has_cuda():
-            print(
-                '>> Max VRAM used to load the model:',
-                '%4.2fG' % (torch.cuda.max_memory_allocated() / 1e9),
-                '\n>> Current VRAM usage:'
-                '%4.2fG' % (torch.cuda.memory_allocated() / 1e9),
-            )
+    def _load_diffusers_model(self, mconfig):
+        raise NotImplementedError()  # return pipeline, width, height, model_hash
 
-        return model, width, height, model_hash
-        
-    def offload_model(self, model_name:str) -> None:
+    def offload_model(self, model_name:str):
         '''
         Offload the indicated model to CPU. Will call
         _make_cache_room() to free space if needed.
@@ -288,7 +294,7 @@ def offload_model(self, model_name:str) -> None:
         gc.collect()
         if self._has_cuda():
             torch.cuda.empty_cache()
-    
+
     def scan_model(self, model_name, checkpoint):
         # scan model
         print(f'>> Scanning Model: {model_name}')
@@ -318,7 +324,7 @@ def _make_cache_room(self) -> None:
             if least_recent_model is not None:
                 del self.models[least_recent_model]
                 gc.collect()
-        
+
     def print_vram_usage(self) -> None:
         if self._has_cuda:
             print('>> Current VRAM usage: ','%4.2fG' % (torch.cuda.memory_allocated() / 1e9))
@@ -353,12 +359,12 @@ def _invalidate_cached_model(self,model_name:str) -> None:
         if model_name in self.stack:
             self.stack.remove(model_name)
         self.models.pop(model_name,None)
-        
+
     def _model_to_cpu(self,model):
         if self.device != 'cpu':
             model.cond_stage_model.device = 'cpu'
             model.first_stage_model.to('cpu')
-            model.cond_stage_model.to('cpu') 
+            model.cond_stage_model.to('cpu')
             model.model.to('cpu')
             return model.to('cpu')
         else:
@@ -388,7 +394,7 @@ def _push_newest_model(self,model_name:str) -> None:
         with contextlib.suppress(ValueError):
             self.stack.remove(model_name)
         self.stack.append(model_name)
-        
+
     def _has_cuda(self) -> bool:
         return self.device.type == 'cuda'
 

From 4c3858e079255e096327e53ad2f7fbd653755782 Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Wed, 9 Nov 2022 17:17:52 -0800
Subject: [PATCH 006/199] model_cache: add ability to load a diffusers model
 pipeline

and update associated things in Generate & Generator to not instantly fail when that happens
---
 ldm/generate.py                            | 53 +++++++++++++++++++++-
 ldm/invoke/generator/base.py               |  8 ++--
 ldm/invoke/generator/diffusers_pipeline.py | 28 ++++++++++++
 ldm/invoke/generator/txt2img.py            | 13 +-----
 ldm/invoke/model_cache.py                  | 43 +++++++++++++++++-
 5 files changed, 126 insertions(+), 19 deletions(-)

diff --git a/ldm/generate.py b/ldm/generate.py
index 8d68314f386..04805439891 100644
--- a/ldm/generate.py
+++ b/ldm/generate.py
@@ -18,6 +18,8 @@
 import hashlib
 import cv2
 import skimage
+from diffusers import DiffusionPipeline, DDIMScheduler, LMSDiscreteScheduler, EulerDiscreteScheduler, \
+    EulerAncestralDiscreteScheduler
 
 from omegaconf import OmegaConf
 from ldm.invoke.generator.base import downsampling
@@ -402,7 +404,10 @@ def process_image(image,seed):
         width = width or self.width
         height = height or self.height
 
-        configure_model_padding(model, seamless, seamless_axes)
+        if isinstance(model, DiffusionPipeline):
+            configure_model_padding(model.unet, seamless, seamless_axes)
+        else:
+            configure_model_padding(model, seamless, seamless_axes)
 
         assert cfg_scale > 1.0, 'CFG_Scale (-C) must be >1.0'
         assert threshold >= 0.0, '--threshold must be >=0.0'
@@ -962,9 +967,15 @@ def sample_to_image(self, samples):
     def sample_to_lowres_estimated_image(self, samples):
         return self._make_base().sample_to_lowres_estimated_image(samples)
 
+    def _set_sampler(self):
+        if isinstance(self.model, DiffusionPipeline):
+            return self._set_scheduler()
+        else:
+            return self._set_sampler_legacy()
+
     # very repetitive code - can this be simplified? The KSampler names are
     # consistent, at least
-    def _set_sampler(self):
+    def _set_sampler_legacy(self):
         msg = f'>> Setting Sampler to {self.sampler_name}'
         if self.sampler_name == 'plms':
             self.sampler = PLMSSampler(self.model, device=self.device)
@@ -992,6 +1003,44 @@ def _set_sampler(self):
 
         print(msg)
 
+    def _set_scheduler(self):
+        msg = f'>> Setting Sampler to {self.sampler_name}'
+        default = self.model.scheduler
+        # TODO: Test me! Not all schedulers take the same args.
+        scheduler_args = dict(
+            num_train_timesteps=default.num_train_timesteps,
+            beta_start=default.beta_start,
+            beta_end=default.beta_end,
+            beta_schedule=default.beta_schedule,
+        )
+        trained_betas = getattr(self.model.scheduler, 'trained_betas')
+        if trained_betas is not None:
+            scheduler_args.update(trained_betas=trained_betas)
+        if self.sampler_name == 'plms':
+            raise NotImplementedError("What's the diffusers implementation of PLMS?")
+        elif self.sampler_name == 'ddim':
+            self.sampler = DDIMScheduler(**scheduler_args)
+        elif self.sampler_name == 'k_dpm_2_a':
+            raise NotImplementedError("no diffusers implementation of dpm_2 samplers")
+        elif self.sampler_name == 'k_dpm_2':
+            raise NotImplementedError("no diffusers implementation of dpm_2 samplers")
+        elif self.sampler_name == 'k_euler_a':
+            self.sampler = EulerAncestralDiscreteScheduler(**scheduler_args)
+        elif self.sampler_name == 'k_euler':
+            self.sampler = EulerDiscreteScheduler(**scheduler_args)
+        elif self.sampler_name == 'k_heun':
+            raise NotImplementedError("no diffusers implementation of Heun's sampler")
+        elif self.sampler_name == 'k_lms':
+            self.sampler = LMSDiscreteScheduler(**scheduler_args)
+        else:
+            msg = f'>> Unsupported Sampler: {self.sampler_name}, Defaulting to {default}'
+
+        print(msg)
+
+        if not hasattr(self.sampler, 'uses_inpainting_model'):
+            # FIXME: terrible kludge!
+            self.sampler.uses_inpainting_model = lambda: False
+
     def _load_img(self, img)->Image:
         if isinstance(img, Image.Image):
             image = img
diff --git a/ldm/invoke/generator/base.py b/ldm/invoke/generator/base.py
index 0a60fea45f8..c8b79f24e14 100644
--- a/ldm/invoke/generator/base.py
+++ b/ldm/invoke/generator/base.py
@@ -9,8 +9,8 @@
 
 import numpy as np
 import torch
-from PIL import Image, ImageFilter, ImageChops
-import cv2 as cv
+from PIL import Image, ImageFilter
+from diffusers import DiffusionPipeline
 from einops import rearrange
 from pytorch_lightning import seed_everything
 from tqdm import trange
@@ -26,9 +26,9 @@ class Generator:
     downsampling_factor: int
     latent_channels: int
     precision: str
-    model: DiffusionWrapper
+    model: DiffusionWrapper | DiffusionPipeline
 
-    def __init__(self, model: DiffusionWrapper, precision: str):
+    def __init__(self, model: DiffusionWrapper | DiffusionPipeline, precision: str):
         self.model = model
         self.precision = precision
         self.seed = None
diff --git a/ldm/invoke/generator/diffusers_pipeline.py b/ldm/invoke/generator/diffusers_pipeline.py
index d4d60b761a7..c9fff12e0cb 100644
--- a/ldm/invoke/generator/diffusers_pipeline.py
+++ b/ldm/invoke/generator/diffusers_pipeline.py
@@ -1,4 +1,5 @@
 import secrets
+import warnings
 from dataclasses import dataclass
 from typing import List, Optional, Union, Callable
 
@@ -309,6 +310,28 @@ def get_text_embeddings(self,
             text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
         return text_embeddings
 
+    def get_learned_conditioning(self, c: List[List[str]], return_tokens=True,
+                                 fragment_weights=None, **kwargs):
+        """
+        Compatibility function for ldm.models.diffusion.ddpm.LatentDiffusion.
+        """
+        assert return_tokens == True
+        if fragment_weights:
+            weights = fragment_weights[0]
+            if any(weight != 1.0 for weight in weights):
+                warnings.warn(f"fragment weights not implemented yet {fragment_weights}", stacklevel=2)
+
+        if kwargs:
+            warnings.warn(f"unsupported args {kwargs}", stacklevel=2)
+
+        text_fragments = c[0]
+        text_input = self._tokenize(text_fragments)
+
+        with torch.inference_mode():
+            token_ids = text_input.input_ids.to(self.text_encoder.device)
+            text_embeddings = self.text_encoder(token_ids)[0]
+        return text_embeddings, text_input.input_ids
+
     @torch.inference_mode()
     def _tokenize(self, prompt: Union[str, List[str]]):
         return self.tokenizer(
@@ -319,6 +342,11 @@ def _tokenize(self, prompt: Union[str, List[str]]):
             return_tensors="pt",
         )
 
+    @property
+    def channels(self) -> int:
+        """Compatible with DiffusionWrapper"""
+        return self.unet.in_channels
+
     def prepare_latents(self, latents, batch_size, height, width, generator, dtype):
         # get the initial random noise unless the user supplied it
         # Unlike in other pipelines, latents need to be generated in the target device
diff --git a/ldm/invoke/generator/txt2img.py b/ldm/invoke/generator/txt2img.py
index a882b156713..7b36d37df44 100644
--- a/ldm/invoke/generator/txt2img.py
+++ b/ldm/invoke/generator/txt2img.py
@@ -24,17 +24,8 @@ def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
         self.perlin = perlin
         uc, c, extra_conditioning_info   = conditioning
 
-        # FIXME: this should probably be either passed in to __init__ instead of model & precision,
-        #     or be constructed in __init__ from those inputs.
-        pipeline = StableDiffusionGeneratorPipeline.from_pretrained(
-            "runwayml/stable-diffusion-v1-5",
-            revision="fp16", torch_dtype=torch.float16,
-            safety_checker=None,  # TODO
-            # scheduler=sampler + ddim_eta,  # TODO
-            # TODO: local_files_only=True
-        )
-        pipeline.unet.to("cuda")
-        pipeline.vae.to("cuda")
+        pipeline = self.model
+        # TODO: customize a new pipeline for the given sampler (Scheduler)
 
         def make_image(x_T) -> PIL.Image.Image:
             # FIXME: restore free_gpu_mem functionality
diff --git a/ldm/invoke/model_cache.py b/ldm/invoke/model_cache.py
index c730c7b7759..56c0f74c109 100644
--- a/ldm/invoke/model_cache.py
+++ b/ldm/invoke/model_cache.py
@@ -4,6 +4,7 @@
 below a preset minimum, the least recently used model will be
 cleared and loaded from disk when next needed.
 '''
+from pathlib import Path
 
 import torch
 import os
@@ -20,6 +21,8 @@
 from typing import Union
 from omegaconf import OmegaConf
 from omegaconf.errors import ConfigAttributeError
+
+from ldm.invoke.generator.diffusers_pipeline import StableDiffusionGeneratorPipeline
 from ldm.util import instantiate_from_config, ask_user
 from ldm.invoke.globals import Globals
 from picklescan.scanner import scan_file_path
@@ -91,7 +94,7 @@ def get_model(self, model_name:str):
                 assert self.current_model,'** FATAL: no current model to restore to'
                 print(f'** restoring {self.current_model}')
                 self.get_model(self.current_model)
-                return
+                return None
 
         self.current_model = model_name
         self._push_newest_model(model_name)
@@ -277,7 +280,43 @@ def _load_ckpt_model(self, mconfig):
         return model, width, height, model_hash
 
     def _load_diffusers_model(self, mconfig):
-        raise NotImplementedError()  # return pipeline, width, height, model_hash
+        pipeline_args = {}
+
+        if 'repo_name' in mconfig:
+            name_or_path = mconfig['repo_name']
+            model_hash = "FIXME"
+            # model_hash = huggingface_hub.get_hf_file_metadata(url).commit_hash
+        elif 'path' in mconfig:
+            name_or_path = Path(mconfig['path'])
+            # FIXME: What should the model_hash be? A hash of the unet weights? Of all files of all
+            #     the submodels hashed together? The commit ID from the repo?
+            model_hash = "FIXME TOO"
+        else:
+            raise ValueError("Model config must specify either repo_name or path.")
+
+        print(f'>> Loading diffusers model from {name_or_path}')
+
+        if self.precision == 'float16':
+            print('   | Using faster float16 precision')
+            pipeline_args.update(revision="fp16", torch_dtype=torch.float16)
+        else:
+            # TODO: more accurately, "using the model's default precision."
+            #     How do we find out what that is?
+            print('   | Using more accurate float32 precision')
+
+        pipeline = StableDiffusionGeneratorPipeline.from_pretrained(
+            name_or_path,
+            safety_checker=None,  # TODO
+            # TODO: alternate VAE
+            # TODO: local_files_only=True
+            **pipeline_args
+        )
+        pipeline.to(self.device)
+
+        width = pipeline.vae.sample_size
+        height = pipeline.vae.sample_size
+
+        return pipeline, width, height, model_hash
 
     def offload_model(self, model_name:str):
         '''

From ae9b482acf6da63d8670942651a9aa573af4fb9a Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Wed, 9 Nov 2022 19:21:58 -0800
Subject: [PATCH 007/199] model_cache: fix model default image dimensions

---
 ldm/invoke/generator/txt2img.py | 1 -
 ldm/invoke/model_cache.py       | 4 ++--
 2 files changed, 2 insertions(+), 3 deletions(-)

diff --git a/ldm/invoke/generator/txt2img.py b/ldm/invoke/generator/txt2img.py
index 7b36d37df44..15650ccbd98 100644
--- a/ldm/invoke/generator/txt2img.py
+++ b/ldm/invoke/generator/txt2img.py
@@ -5,7 +5,6 @@
 import torch
 
 from .base import Generator
-from .diffusers_pipeline import StableDiffusionGeneratorPipeline
 
 
 class Txt2Img(Generator):
diff --git a/ldm/invoke/model_cache.py b/ldm/invoke/model_cache.py
index 56c0f74c109..6d8ce990f05 100644
--- a/ldm/invoke/model_cache.py
+++ b/ldm/invoke/model_cache.py
@@ -313,8 +313,8 @@ def _load_diffusers_model(self, mconfig):
         )
         pipeline.to(self.device)
 
-        width = pipeline.vae.sample_size
-        height = pipeline.vae.sample_size
+        width = pipeline.vae.block_out_channels[-1]
+        height = pipeline.vae.block_out_channels[-1]
 
         return pipeline, width, height, model_hash
 

From d55e22981aee5994eb5589e9ed7bb9a422b31e4f Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Thu, 10 Nov 2022 14:36:45 -0800
Subject: [PATCH 008/199] txt2img: support switching diffusers schedulers

---
 backend/modules/parameters.py   |  3 ++
 ldm/generate.py                 | 59 +++++++++++++++++----------------
 ldm/invoke/args.py              |  3 ++
 ldm/invoke/generator/txt2img.py |  2 +-
 ldm/invoke/model_cache.py       | 12 +++++++
 5 files changed, 50 insertions(+), 29 deletions(-)

diff --git a/backend/modules/parameters.py b/backend/modules/parameters.py
index 10af5ece3a6..130d04d64ac 100644
--- a/backend/modules/parameters.py
+++ b/backend/modules/parameters.py
@@ -12,6 +12,9 @@
     "k_heun",
     "k_lms",
     "plms",
+    # diffusers:
+    "ipndm",
+    "pndm",
 ]
 
 
diff --git a/ldm/generate.py b/ldm/generate.py
index 04805439891..9300458611c 100644
--- a/ldm/generate.py
+++ b/ldm/generate.py
@@ -19,7 +19,7 @@
 import cv2
 import skimage
 from diffusers import DiffusionPipeline, DDIMScheduler, LMSDiscreteScheduler, EulerDiscreteScheduler, \
-    EulerAncestralDiscreteScheduler
+    EulerAncestralDiscreteScheduler, PNDMScheduler, IPNDMScheduler
 
 from omegaconf import OmegaConf
 from ldm.invoke.generator.base import downsampling
@@ -1004,36 +1004,39 @@ def _set_sampler_legacy(self):
         print(msg)
 
     def _set_scheduler(self):
-        msg = f'>> Setting Sampler to {self.sampler_name}'
         default = self.model.scheduler
-        # TODO: Test me! Not all schedulers take the same args.
-        scheduler_args = dict(
-            num_train_timesteps=default.num_train_timesteps,
-            beta_start=default.beta_start,
-            beta_end=default.beta_end,
-            beta_schedule=default.beta_schedule,
+
+        higher_order_samplers = [
+            'k_dpm_2',
+            'k_dpm_2_a',
+            'k_heun',
+            'plms',   # Its first step is like Heun
+        ]
+        scheduler_map = dict(
+            ddim=DDIMScheduler,
+            ipndm=IPNDMScheduler,
+            k_euler=EulerDiscreteScheduler,
+            k_euler_a=EulerAncestralDiscreteScheduler,
+            k_lms=LMSDiscreteScheduler,
+            pndm=PNDMScheduler,
         )
-        trained_betas = getattr(self.model.scheduler, 'trained_betas')
-        if trained_betas is not None:
-            scheduler_args.update(trained_betas=trained_betas)
-        if self.sampler_name == 'plms':
-            raise NotImplementedError("What's the diffusers implementation of PLMS?")
-        elif self.sampler_name == 'ddim':
-            self.sampler = DDIMScheduler(**scheduler_args)
-        elif self.sampler_name == 'k_dpm_2_a':
-            raise NotImplementedError("no diffusers implementation of dpm_2 samplers")
-        elif self.sampler_name == 'k_dpm_2':
-            raise NotImplementedError("no diffusers implementation of dpm_2 samplers")
-        elif self.sampler_name == 'k_euler_a':
-            self.sampler = EulerAncestralDiscreteScheduler(**scheduler_args)
-        elif self.sampler_name == 'k_euler':
-            self.sampler = EulerDiscreteScheduler(**scheduler_args)
-        elif self.sampler_name == 'k_heun':
-            raise NotImplementedError("no diffusers implementation of Heun's sampler")
-        elif self.sampler_name == 'k_lms':
-            self.sampler = LMSDiscreteScheduler(**scheduler_args)
+
+        if self.sampler_name in scheduler_map:
+            sampler_class = scheduler_map[self.sampler_name]
+            msg = f'>> Setting Sampler to {self.sampler_name} ({sampler_class.__name__})'
+            self.sampler = sampler_class.from_config(
+                self.model_cache.model_name_or_path(self.model_name),
+                subfolder="scheduler"
+            )
+        elif self.sampler_name in higher_order_samplers:
+            msg = (f'>> Unsupported Sampler: {self.sampler_name} '
+                  f'— diffusers does not yet support higher-order samplers, '
+                  f'Defaulting to {default}')
+            self.sampler = default
         else:
-            msg = f'>> Unsupported Sampler: {self.sampler_name}, Defaulting to {default}'
+            msg = (f'>> Unsupported Sampler: {self.sampler_name} '
+                  f'Defaulting to {default}')
+            self.sampler = default
 
         print(msg)
 
diff --git a/ldm/invoke/args.py b/ldm/invoke/args.py
index e746e5bab3c..a58617a511a 100644
--- a/ldm/invoke/args.py
+++ b/ldm/invoke/args.py
@@ -108,6 +108,9 @@
     'k_heun',
     'k_lms',
     'plms',
+    # diffusers:
+    "ipndm",
+    "pndm",
 ]
 
 PRECISION_CHOICES = [
diff --git a/ldm/invoke/generator/txt2img.py b/ldm/invoke/generator/txt2img.py
index 15650ccbd98..219e8131724 100644
--- a/ldm/invoke/generator/txt2img.py
+++ b/ldm/invoke/generator/txt2img.py
@@ -24,7 +24,7 @@ def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
         uc, c, extra_conditioning_info   = conditioning
 
         pipeline = self.model
-        # TODO: customize a new pipeline for the given sampler (Scheduler)
+        pipeline.scheduler = sampler
 
         def make_image(x_T) -> PIL.Image.Image:
             # FIXME: restore free_gpu_mem functionality
diff --git a/ldm/invoke/model_cache.py b/ldm/invoke/model_cache.py
index 6d8ce990f05..8ad8e3913b1 100644
--- a/ldm/invoke/model_cache.py
+++ b/ldm/invoke/model_cache.py
@@ -318,6 +318,18 @@ def _load_diffusers_model(self, mconfig):
 
         return pipeline, width, height, model_hash
 
+    def model_name_or_path(self, model_name:str) -> str | Path:
+        if model_name not in self.config:
+            raise ValueError(f'"{model_name}" is not a known model name. Please check your models.yaml file')
+
+        mconfig = self.config[model_name]
+        if 'repo_name' in mconfig:
+            return mconfig['repo_name']
+        elif 'path' in mconfig:
+            return Path(mconfig['path'])
+        else:
+            raise ValueError("Model config must specify either repo_name or path.")
+
     def offload_model(self, model_name:str):
         '''
         Offload the indicated model to CPU. Will call

From 1e98f4bafce68ab71d46f48f4e1340e58f14300d Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Thu, 10 Nov 2022 15:27:25 -0800
Subject: [PATCH 009/199] diffusers: let the scheduler do its scaling of the
 initial latents

Remove IPNDM scheduler; it is not behaving.
---
 backend/modules/parameters.py              | 1 -
 ldm/invoke/args.py                         | 1 -
 ldm/invoke/generator/diffusers_pipeline.py | 2 ++
 3 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/backend/modules/parameters.py b/backend/modules/parameters.py
index 130d04d64ac..9055297671d 100644
--- a/backend/modules/parameters.py
+++ b/backend/modules/parameters.py
@@ -13,7 +13,6 @@
     "k_lms",
     "plms",
     # diffusers:
-    "ipndm",
     "pndm",
 ]
 
diff --git a/ldm/invoke/args.py b/ldm/invoke/args.py
index a58617a511a..f1b5e9029d8 100644
--- a/ldm/invoke/args.py
+++ b/ldm/invoke/args.py
@@ -109,7 +109,6 @@
     'k_lms',
     'plms',
     # diffusers:
-    "ipndm",
     "pndm",
 ]
 
diff --git a/ldm/invoke/generator/diffusers_pipeline.py b/ldm/invoke/generator/diffusers_pipeline.py
index c9fff12e0cb..bad21b09565 100644
--- a/ldm/invoke/generator/diffusers_pipeline.py
+++ b/ldm/invoke/generator/diffusers_pipeline.py
@@ -231,6 +231,8 @@ def generate_from_embeddings(self, latents: torch.Tensor, text_embeddings: torch
                                  run_id: str = None, **extra_step_kwargs):
         if run_id is None:
             run_id = secrets.token_urlsafe(self.ID_LENGTH)
+        # scale the initial noise by the standard deviation required by the scheduler
+        latents *= self.scheduler.init_noise_sigma
         yield PipelineIntermediateState(run_id=run_id, step=-1, timestep=self.scheduler.num_train_timesteps,
                                         latents=latents)
         # NOTE: Depends on scheduler being already initialized!

From 05a1d68ef4d6ea39688c8fd8d4eb30d726eb6b94 Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Thu, 10 Nov 2022 15:28:22 -0800
Subject: [PATCH 010/199] web server: update image_progress callback for
 diffusers data

---
 backend/invoke_ai_web_server.py            | 5 ++++-
 ldm/invoke/generator/diffusers_pipeline.py | 2 +-
 2 files changed, 5 insertions(+), 2 deletions(-)

diff --git a/backend/invoke_ai_web_server.py b/backend/invoke_ai_web_server.py
index ac8edc6a324..d525cf87f89 100644
--- a/backend/invoke_ai_web_server.py
+++ b/backend/invoke_ai_web_server.py
@@ -19,6 +19,7 @@
 from threading import Event
 
 from ldm.invoke.args import Args, APP_ID, APP_VERSION, calculate_init_img_hash
+from ldm.invoke.generator.diffusers_pipeline import PipelineIntermediateState
 from ldm.invoke.pngwriter import PngWriter, retrieve_metadata
 from ldm.invoke.prompt_parser import split_weighted_subprompts
 from ldm.invoke.generator.inpaint import infill_methods
@@ -847,7 +848,9 @@ def generate_images(
                 init_img_path = self.get_image_path_from_url(init_img_url)
                 generation_parameters["init_img"] = Image.open(init_img_path).convert('RGB')
 
-            def image_progress(sample, step):
+            def image_progress(progress_state: PipelineIntermediateState):
+                step = progress_state.step
+                sample = progress_state.latents
                 if self.canceled.is_set():
                     raise CanceledException
 
diff --git a/ldm/invoke/generator/diffusers_pipeline.py b/ldm/invoke/generator/diffusers_pipeline.py
index bad21b09565..20caecada0b 100644
--- a/ldm/invoke/generator/diffusers_pipeline.py
+++ b/ldm/invoke/generator/diffusers_pipeline.py
@@ -188,7 +188,7 @@ def image_from_embeddings(self, latents: torch.Tensor, num_inference_steps: int,
         result = None
         for result in self.generate_from_embeddings(
                 latents, text_embeddings, guidance_scale, run_id, **extra_step_kwargs):
-            if callback is not None:
+            if callback is not None and isinstance(result, PipelineIntermediateState):
                 callback(result)
         if result is None:
             raise AssertionError("why was that an empty generator?")

From e99faeb8d7d633c8a7017b7b6f26002127f3920e Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Fri, 11 Nov 2022 13:16:09 -0800
Subject: [PATCH 011/199] diffusers: restore prompt weighting feature

---
 ldm/invoke/generator/diffusers_pipeline.py | 29 ++++++++--------------
 ldm/modules/encoders/modules.py            | 14 +++++++----
 2 files changed, 19 insertions(+), 24 deletions(-)

diff --git a/ldm/invoke/generator/diffusers_pipeline.py b/ldm/invoke/generator/diffusers_pipeline.py
index 20caecada0b..6846ff84567 100644
--- a/ldm/invoke/generator/diffusers_pipeline.py
+++ b/ldm/invoke/generator/diffusers_pipeline.py
@@ -1,5 +1,4 @@
 import secrets
-import warnings
 from dataclasses import dataclass
 from typing import List, Optional, Union, Callable
 
@@ -11,6 +10,8 @@
 from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
 from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
 
+from ldm.modules.encoders.modules import WeightedFrozenCLIPEmbedder
+
 
 @dataclass
 class PipelineIntermediateState:
@@ -76,6 +77,11 @@ def __init__(
             safety_checker=safety_checker,
             feature_extractor=feature_extractor,
         )
+        # InvokeAI's interface for text embeddings and whatnot
+        self.clip_embedder = WeightedFrozenCLIPEmbedder(
+            tokenizer=self.tokenizer,
+            transformer=self.text_encoder
+        )
 
     def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
         r"""
@@ -312,27 +318,12 @@ def get_text_embeddings(self,
             text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
         return text_embeddings
 
-    def get_learned_conditioning(self, c: List[List[str]], return_tokens=True,
-                                 fragment_weights=None, **kwargs):
+    @torch.inference_mode()
+    def get_learned_conditioning(self, c: List[List[str]], *, return_tokens=True, fragment_weights=None):
         """
         Compatibility function for ldm.models.diffusion.ddpm.LatentDiffusion.
         """
-        assert return_tokens == True
-        if fragment_weights:
-            weights = fragment_weights[0]
-            if any(weight != 1.0 for weight in weights):
-                warnings.warn(f"fragment weights not implemented yet {fragment_weights}", stacklevel=2)
-
-        if kwargs:
-            warnings.warn(f"unsupported args {kwargs}", stacklevel=2)
-
-        text_fragments = c[0]
-        text_input = self._tokenize(text_fragments)
-
-        with torch.inference_mode():
-            token_ids = text_input.input_ids.to(self.text_encoder.device)
-            text_embeddings = self.text_encoder(token_ids)[0]
-        return text_embeddings, text_input.input_ids
+        return self.clip_embedder.encode(c, return_tokens=return_tokens, fragment_weights=fragment_weights)
 
     @torch.inference_mode()
     def _tokenize(self, prompt: Union[str, List[str]]):
diff --git a/ldm/modules/encoders/modules.py b/ldm/modules/encoders/modules.py
index be9f88cdd2e..ca9a027f138 100644
--- a/ldm/modules/encoders/modules.py
+++ b/ldm/modules/encoders/modules.py
@@ -239,22 +239,22 @@ class FrozenCLIPEmbedder(AbstractEncoder):
     def __init__(
         self,
         version='openai/clip-vit-large-patch14',
-        device=choose_torch_device(),
         max_length=77,
+        tokenizer=None,
+        transformer=None,
     ):
         super().__init__()
         cache = os.path.join(Globals.root,'models',version)
-        self.tokenizer = CLIPTokenizer.from_pretrained(
+        self.tokenizer = tokenizer or CLIPTokenizer.from_pretrained(
             version,
             cache_dir=cache,
             local_files_only=True
         )
-        self.transformer = CLIPTextModel.from_pretrained(
+        self.transformer = transformer or CLIPTextModel.from_pretrained(
             version,
             cache_dir=cache,
             local_files_only=True
         )
-        self.device = device
         self.max_length = max_length
         self.freeze()
 
@@ -460,6 +460,10 @@ def forward(self, text, **kwargs):
     def encode(self, text, **kwargs):
         return self(text, **kwargs)
 
+    @property
+    def device(self):
+        return self.transformer.device
+
 class WeightedFrozenCLIPEmbedder(FrozenCLIPEmbedder):
 
     fragment_weights_key = "fragment_weights"
@@ -548,7 +552,7 @@ def forward(self, text: list, **kwargs):
 
             #print(f"assembled tokens for '{fragments}' into tensor of shape {lerped_embeddings.shape}")
 
-            # append to batch 
+            # append to batch
             batch_z = lerped_embeddings.unsqueeze(0) if batch_z is None else torch.cat([batch_z, lerped_embeddings.unsqueeze(0)], dim=1)
             batch_tokens = tokens.unsqueeze(0) if batch_tokens is None else torch.cat([batch_tokens, tokens.unsqueeze(0)], dim=1)
 

From 97dd4a25898a0bc5faffbec7615ccc22f3fcd2fc Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Fri, 11 Nov 2022 13:17:36 -0800
Subject: [PATCH 012/199] diffusers: fix set-sampler error following model
 switch

---
 ldm/generate.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ldm/generate.py b/ldm/generate.py
index 9300458611c..d99954c2497 100644
--- a/ldm/generate.py
+++ b/ldm/generate.py
@@ -864,8 +864,8 @@ def set_model(self,model_name):
                 self.embedding_path, self.precision == 'float32' or self.precision == 'autocast'
             )
 
-        self._set_sampler()
         self.model_name = model_name
+        self._set_sampler()  # requires self.model_name to be set first
         return self.model
 
     def load_concepts(self,concepts:list[str]):

From b6b1a8d97ca55f179f0080e331328daa8bcb16ec Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Fri, 11 Nov 2022 16:25:27 -0800
Subject: [PATCH 013/199] diffusers: use InvokeAIDiffuserComponent for
 conditioning

---
 ldm/invoke/generator/diffusers_pipeline.py    | 160 +++++++++---------
 ldm/invoke/generator/txt2img.py               |  12 +-
 .../diffusion/shared_invokeai_diffusion.py    |   1 +
 3 files changed, 83 insertions(+), 90 deletions(-)

diff --git a/ldm/invoke/generator/diffusers_pipeline.py b/ldm/invoke/generator/diffusers_pipeline.py
index 6846ff84567..0bd096ff6b2 100644
--- a/ldm/invoke/generator/diffusers_pipeline.py
+++ b/ldm/invoke/generator/diffusers_pipeline.py
@@ -1,4 +1,5 @@
 import secrets
+import warnings
 from dataclasses import dataclass
 from typing import List, Optional, Union, Callable
 
@@ -10,6 +11,7 @@
 from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
 from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
 
+from ldm.models.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent
 from ldm.modules.encoders.modules import WeightedFrozenCLIPEmbedder
 
 
@@ -82,6 +84,7 @@ def __init__(
             tokenizer=self.tokenizer,
             transformer=self.text_encoder
         )
+        self.invokeai_diffuser = InvokeAIDiffuserComponent(self.unet, self._unet_forward)
 
     def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
         r"""
@@ -128,72 +131,36 @@ def disable_xformers_memory_efficient_attention(self):
         """
         self.unet.set_use_memory_efficient_attention_xformers(False)
 
-    @torch.no_grad()
-    def __call__(
-        self,
-        prompt: Union[str, List[str]],
-        height: Optional[int] = 512,
-        width: Optional[int] = 512,
-        num_inference_steps: Optional[int] = 50,
-        guidance_scale: Optional[float] = 7.5,
-        generator: Optional[torch.Generator] = None,
-        latents: Optional[torch.FloatTensor] = None,
-        callback: Optional[Callable[[PipelineIntermediateState], None]] = None,
-        **extra_step_kwargs,
-    ):
+    def image_from_embeddings(self, latents: torch.Tensor, num_inference_steps: int,
+                              text_embeddings: torch.Tensor, unconditioned_embeddings: torch.Tensor,
+                              guidance_scale: float,
+                              *, callback: Callable[[PipelineIntermediateState], None]=None,
+                              extra_conditioning_info: InvokeAIDiffuserComponent.ExtraConditioningInfo=None,
+                              run_id=None,
+                              **extra_step_kwargs) -> StableDiffusionPipelineOutput:
         r"""
         Function invoked when calling the pipeline for generation.
 
-        Args:
-            prompt (`str` or `List[str]`):
-                The prompt or prompts to guide the image generation.
-            height (`int`, *optional*, defaults to 512):
-                The height in pixels of the generated image.
-            width (`int`, *optional*, defaults to 512):
-                The width in pixels of the generated image.
-            num_inference_steps (`int`, *optional*, defaults to 50):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            guidance_scale (`float`, *optional*, defaults to 7.5):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
-            generator (`torch.Generator`, *optional*):
-                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
-                deterministic.
-            latents (`torch.FloatTensor`, *optional*):
-                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
-                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
-                tensor will ge generated by sampling using the supplied random `generator`.
-
-        Returns:
-            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
-            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
-            When returning a tuple, the first element is a list with the generated images, and the second element is a
-            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
-            (nsfw) content, according to the `safety_checker`.
+        :param latents: Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for
+            image generation. Can be used to tweak the same generation with different prompts.
+        :param num_inference_steps: The number of denoising steps. More denoising steps usually lead to a higher quality
+            image at the expense of slower inference.
+        :param text_embeddings:
+        :param guidance_scale: Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+            `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf).
+             Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate
+             images that are closely linked to the text `prompt`, usually at the expense of lower image quality.
+        :param callback:
+        :param extra_conditioning_info:
+        :param run_id:
+        :param extra_step_kwargs:
         """
-        result = None
-        for result in self.generate(
-                prompt, height=height, width=width, num_inference_steps=num_inference_steps,
-                guidance_scale=guidance_scale, generator=generator, latents=latents,
-                **extra_step_kwargs):
-            if callback is not None:
-                callback(result)
-        if result is None:
-            raise AssertionError("why was that an empty generator?")
-        return result
-
-    def image_from_embeddings(self, latents: torch.Tensor, num_inference_steps: int,
-                              text_embeddings: torch.Tensor, guidance_scale: float,
-                              *, callback: Callable[[PipelineIntermediateState], None]=None, run_id=None,
-                              **extra_step_kwargs) -> StableDiffusionPipelineOutput:
-        self.scheduler.set_timesteps(num_inference_steps)
+        self.scheduler.set_timesteps(num_inference_steps, device=self.unet.device)
         result = None
         for result in self.generate_from_embeddings(
-                latents, text_embeddings, guidance_scale, run_id, **extra_step_kwargs):
+                latents, text_embeddings, unconditioned_embeddings, guidance_scale,
+                extra_conditioning_info=extra_conditioning_info,
+                run_id=run_id, **extra_step_kwargs):
             if callback is not None and isinstance(result, PipelineIntermediateState):
                 callback(result)
         if result is None:
@@ -226,24 +193,40 @@ def generate(
         # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
         # corresponds to doing no classifier free guidance.
         do_classifier_free_guidance = guidance_scale > 1.0
-        text_embeddings = self.get_text_embeddings(prompt, opposing_prompt, do_classifier_free_guidance, batch_size)\
+        text_embeddings, unconditioned_embeddings = self.get_text_embeddings(prompt, opposing_prompt, do_classifier_free_guidance, batch_size)\
             .to(self.unet.device)
         self.scheduler.set_timesteps(num_inference_steps)
         latents = self.prepare_latents(latents, batch_size, height, width, generator, self.unet.dtype)
 
-        yield from self.generate_from_embeddings(latents, text_embeddings, guidance_scale, run_id, **extra_step_kwargs)
-
-    def generate_from_embeddings(self, latents: torch.Tensor, text_embeddings: torch.Tensor, guidance_scale: float,
-                                 run_id: str = None, **extra_step_kwargs):
+        yield from self.generate_from_embeddings(latents, text_embeddings, unconditioned_embeddings,
+                                                 guidance_scale, run_id=run_id, **extra_step_kwargs)
+
+    def generate_from_embeddings(
+            self,
+            latents: torch.Tensor,
+            text_embeddings: torch.Tensor,
+            unconditioned_embeddings: torch.Tensor,
+            guidance_scale: float,
+            *,
+            run_id: str = None,
+            extra_conditioning_info: InvokeAIDiffuserComponent.ExtraConditioningInfo = None,
+            **extra_step_kwargs):
         if run_id is None:
             run_id = secrets.token_urlsafe(self.ID_LENGTH)
         # scale the initial noise by the standard deviation required by the scheduler
         latents *= self.scheduler.init_noise_sigma
         yield PipelineIntermediateState(run_id=run_id, step=-1, timestep=self.scheduler.num_train_timesteps,
                                         latents=latents)
+
+        batch_size = latents.shape[0]
+        batched_t = torch.full((batch_size,), self.scheduler.timesteps[0],
+                               dtype=self.scheduler.timesteps.dtype, device=self.unet.device)
         # NOTE: Depends on scheduler being already initialized!
         for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
-            step_output = self.step(t, latents, guidance_scale, text_embeddings, **extra_step_kwargs)
+            batched_t.fill_(t)
+            step_output = self.step(batched_t, latents, guidance_scale,
+                                    text_embeddings, unconditioned_embeddings,
+                                    i, **extra_step_kwargs)
             latents = step_output.prev_sample
             predicted_original = getattr(step_output, 'pred_original_sample', None)
             yield PipelineIntermediateState(run_id=run_id, step=i, timestep=int(t), latents=latents,
@@ -257,23 +240,30 @@ def generate_from_embeddings(self, latents: torch.Tensor, text_embeddings: torch
         yield self.check_for_safety(output)
 
     @torch.inference_mode()
-    def step(self, t, latents: torch.Tensor, guidance_scale, text_embeddings: torch.Tensor, **extra_step_kwargs):
-        do_classifier_free_guidance = guidance_scale > 1.0
+    def step(self, t: torch.Tensor, latents: torch.Tensor, guidance_scale: float,
+             text_embeddings: torch.Tensor, unconditioned_embeddings: torch.Tensor,
+             step_index:int | None = None,
+             **extra_step_kwargs):
+        # invokeai_diffuser has batched timesteps, but diffusers schedulers expect a single value
+        timestep = t[0]
 
-        # expand the latents if we are doing classifier free guidance
-        latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
-        latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
+        # TODO: should this scaling happen here or inside self._unet_forward?
+        #     i.e. before or after passing it to InvokeAIDiffuserComponent
+        latent_model_input = self.scheduler.scale_model_input(latents, timestep)
 
         # predict the noise residual
-        noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample
-
-        # perform guidance
-        if do_classifier_free_guidance:
-            noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-            noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
+        noise_pred = self.invokeai_diffuser.do_diffusion_step(
+            latent_model_input, t,
+            unconditioned_embeddings, text_embeddings,
+            guidance_scale,
+            step_index=step_index)
 
         # compute the previous noisy sample x_t -> x_t-1
-        return self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs)
+        return self.scheduler.step(noise_pred, timestep, latents, **extra_step_kwargs)
+
+    def _unet_forward(self, latents, t, text_embeddings):
+        # predict the noise residual
+        return self.unet(latents, t, encoder_hidden_states=text_embeddings).sample
 
     @torch.inference_mode()
     def check_for_safety(self, output):
@@ -310,13 +300,10 @@ def get_text_embeddings(self,
             # opposing prompt defaults to blank caption for everything in the batch
             text_anti_input = self._tokenize(opposing_prompt or [""] * batch_size)
             uncond_embeddings = self.text_encoder(text_anti_input.input_ids)[0]
+        else:
+            uncond_embeddings = None
 
-            # For classifier free guidance, we need to do two forward passes.
-            # Here we concatenate the unconditional and text embeddings into a single batch
-            # to avoid doing two forward passes
-            # FIXME: assert these two are the same size
-            text_embeddings = torch.cat([uncond_embeddings, text_embeddings])
-        return text_embeddings
+        return text_embeddings, uncond_embeddings
 
     @torch.inference_mode()
     def get_learned_conditioning(self, c: List[List[str]], *, return_tokens=True, fragment_weights=None):
@@ -325,6 +312,11 @@ def get_learned_conditioning(self, c: List[List[str]], *, return_tokens=True, fr
         """
         return self.clip_embedder.encode(c, return_tokens=return_tokens, fragment_weights=fragment_weights)
 
+    @property
+    def cond_stage_model(self):
+        warnings.warn("legacy compatibility layer", DeprecationWarning)
+        return self.clip_embedder
+
     @torch.inference_mode()
     def _tokenize(self, prompt: Union[str, List[str]]):
         return self.tokenizer(
diff --git a/ldm/invoke/generator/txt2img.py b/ldm/invoke/generator/txt2img.py
index 219e8131724..f9af1ac3ed7 100644
--- a/ldm/invoke/generator/txt2img.py
+++ b/ldm/invoke/generator/txt2img.py
@@ -5,6 +5,7 @@
 import torch
 
 from .base import Generator
+from .diffusers_pipeline import StableDiffusionGeneratorPipeline
 
 
 class Txt2Img(Generator):
@@ -23,7 +24,8 @@ def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
         self.perlin = perlin
         uc, c, extra_conditioning_info   = conditioning
 
-        pipeline = self.model
+        # noinspection PyTypeChecker
+        pipeline: StableDiffusionGeneratorPipeline = self.model
         pipeline.scheduler = sampler
 
         def make_image(x_T) -> PIL.Image.Image:
@@ -31,16 +33,14 @@ def make_image(x_T) -> PIL.Image.Image:
             # if self.free_gpu_mem and self.model.model.device != self.model.device:
             #     self.model.model.to(self.model.device)
 
-            # FIXME: how the embeddings are combined should be internal to the pipeline
-            combined_text_embeddings = torch.cat([uc, c])
-
             pipeline_output = pipeline.image_from_embeddings(
                 latents=x_T,
                 num_inference_steps=steps,
-                text_embeddings=combined_text_embeddings,
+                text_embeddings=c,
+                unconditioned_embeddings=uc,
                 guidance_scale=cfg_scale,
                 callback=step_callback,
-                # TODO: extra_conditioning_info = extra_conditioning_info,
+                extra_conditioning_info=extra_conditioning_info,
                 # TODO: eta = ddim_eta,
                 # TODO: threshold = threshold,
             )
diff --git a/ldm/models/diffusion/shared_invokeai_diffusion.py b/ldm/models/diffusion/shared_invokeai_diffusion.py
index d748c9a6735..d6ec1ea44bf 100644
--- a/ldm/models/diffusion/shared_invokeai_diffusion.py
+++ b/ldm/models/diffusion/shared_invokeai_diffusion.py
@@ -35,6 +35,7 @@ def __init__(self, model, model_forward_callback:
         :param model: the unet model to pass through to cross attention control
         :param model_forward_callback: a lambda with arguments (x, sigma, conditioning_to_apply). will be called repeatedly. most likely, this should simply call model.forward(x, sigma, conditioning)
         """
+        self.conditioning = None
         self.model = model
         self.model_forward_callback = model_forward_callback
         self.cross_attention_control_context = None

From 95db6e80ee8c20196e637a2d1aa1421d3b931044 Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Sat, 12 Nov 2022 10:10:46 -0800
Subject: [PATCH 014/199] cross_attention_control: stub (no-op) implementations
 for diffusers

---
 ldm/invoke/generator/diffusers_pipeline.py    |  7 ++++
 .../diffusion/cross_attention_control.py      | 40 +++++++++++++------
 2 files changed, 34 insertions(+), 13 deletions(-)

diff --git a/ldm/invoke/generator/diffusers_pipeline.py b/ldm/invoke/generator/diffusers_pipeline.py
index 0bd096ff6b2..861bf22a7a7 100644
--- a/ldm/invoke/generator/diffusers_pipeline.py
+++ b/ldm/invoke/generator/diffusers_pipeline.py
@@ -213,6 +213,13 @@ def generate_from_embeddings(
             **extra_step_kwargs):
         if run_id is None:
             run_id = secrets.token_urlsafe(self.ID_LENGTH)
+
+        if extra_conditioning_info is not None and extra_conditioning_info.wants_cross_attention_control:
+            self.invokeai_diffuser.setup_cross_attention_control(extra_conditioning_info,
+                                                                 step_count=len(self.scheduler.timesteps))
+        else:
+            self.invokeai_diffuser.remove_cross_attention_control()
+
         # scale the initial noise by the standard deviation required by the scheduler
         latents *= self.scheduler.init_noise_sigma
         yield PipelineIntermediateState(run_id=run_id, step=-1, timestep=self.scheduler.num_train_timesteps,
diff --git a/ldm/models/diffusion/cross_attention_control.py b/ldm/models/diffusion/cross_attention_control.py
index a4362e07704..3284f990cef 100644
--- a/ldm/models/diffusion/cross_attention_control.py
+++ b/ldm/models/diffusion/cross_attention_control.py
@@ -1,4 +1,5 @@
 import enum
+import warnings
 from typing import Optional
 
 import torch
@@ -244,19 +245,32 @@ def attention_slice_wrangler(module, suggested_attention_slice:torch.Tensor, dim
 
         return attention_slice
 
-    for name, module in unet.named_modules():
-        module_name = type(module).__name__
-        if module_name == "CrossAttention":
-            module.identifier = name
-            module.set_attention_slice_wrangler(attention_slice_wrangler)
-            module.set_slicing_strategy_getter(lambda module, module_identifier=name: \
-                                                   context.get_slicing_strategy(module_identifier))
+    cross_attention_modules = [(name, module) for (name, module) in unet.named_modules()
+                                   if type(module).__name__ == "CrossAttention"]
+        for identifier, module in cross_attention_modules:
+            module.identifier = identifier
+            try:
+                module.set_attention_slice_wrangler(attention_slice_wrangler)
+                module.set_slicing_strategy_getter(
+                    lambda module: context.get_slicing_strategy(identifier)
+                )
+            except AttributeError as e:
+                if e.name == 'set_attention_slice_wrangler':
+                    warnings.warn(f"TODO: implement for {type(module)}")  # TODO
+                else:
+                    raise
 
 
 def remove_attention_function(unet):
-    # clear wrangler callback
-    for name, module in unet.named_modules():
-        module_name = type(module).__name__
-        if module_name == "CrossAttention":
-            module.set_attention_slice_wrangler(None)
-            module.set_slicing_strategy_getter(None)
+    cross_attention_modules = [module for (_, module) in unet.named_modules()
+                                   if type(module).__name__ == "CrossAttention"]
+        for module in cross_attention_modules:
+            try:
+                # clear wrangler callback
+                module.set_attention_slice_wrangler(None)
+                module.set_slicing_strategy_getter(None)
+            except AttributeError as e:
+                if e.name == 'set_attention_slice_wrangler':
+                    warnings.warn(f"TODO: implement for {type(module)}")  # TODO
+                else:
+                    raise

From 01ff1cff587861941f9822781bcfe80290bcc880 Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Sat, 12 Nov 2022 10:11:50 -0800
Subject: [PATCH 015/199] model_cache: let offload_model work with
 DiffusionPipeline, sorta.

---
 ldm/invoke/model_cache.py       | 12 ++++++++----
 ldm/modules/encoders/modules.py | 16 ++++++++++++----
 2 files changed, 20 insertions(+), 8 deletions(-)

diff --git a/ldm/invoke/model_cache.py b/ldm/invoke/model_cache.py
index 8ad8e3913b1..181c4d39b46 100644
--- a/ldm/invoke/model_cache.py
+++ b/ldm/invoke/model_cache.py
@@ -4,6 +4,7 @@
 below a preset minimum, the least recently used model will be
 cleared and loaded from disk when next needed.
 '''
+import warnings
 from pathlib import Path
 
 import torch
@@ -413,10 +414,13 @@ def _invalidate_cached_model(self,model_name:str) -> None:
 
     def _model_to_cpu(self,model):
         if self.device != 'cpu':
-            model.cond_stage_model.device = 'cpu'
-            model.first_stage_model.to('cpu')
-            model.cond_stage_model.to('cpu')
-            model.model.to('cpu')
+            try:
+                model.cond_stage_model.device = 'cpu'
+                model.first_stage_model.to('cpu')
+                model.cond_stage_model.to('cpu')
+                model.model.to('cpu')
+            except AttributeError as e:
+                warnings.warn(f"TODO: clean up legacy model-management: {e}")
             return model.to('cpu')
         else:
             return model
diff --git a/ldm/modules/encoders/modules.py b/ldm/modules/encoders/modules.py
index ca9a027f138..cf460519333 100644
--- a/ldm/modules/encoders/modules.py
+++ b/ldm/modules/encoders/modules.py
@@ -1,5 +1,7 @@
 import math
 import os.path
+from typing import Optional
+
 import torch
 import torch.nn as nn
 from functools import partial
@@ -235,13 +237,15 @@ def encode(self, x):
 
 class FrozenCLIPEmbedder(AbstractEncoder):
     """Uses the CLIP transformer encoder for text (from Hugging Face)"""
+    tokenizer: CLIPTokenizer
+    transformer: CLIPTextModel
 
     def __init__(
         self,
-        version='openai/clip-vit-large-patch14',
-        max_length=77,
-        tokenizer=None,
-        transformer=None,
+        version:str='openai/clip-vit-large-patch14',
+        max_length:int=77,
+        tokenizer:Optional[CLIPTokenizer]=None,
+        transformer:Optional[CLIPTextModel]=None,
     ):
         super().__init__()
         cache = os.path.join(Globals.root,'models',version)
@@ -464,6 +468,10 @@ def encode(self, text, **kwargs):
     def device(self):
         return self.transformer.device
 
+    @device.setter
+    def device(self, device):
+        self.transformer.to(device=device)
+
 class WeightedFrozenCLIPEmbedder(FrozenCLIPEmbedder):
 
     fragment_weights_key = "fragment_weights"

From aca6d213d77cb25c1ac24b2955d3a071dbe4c7d7 Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Sun, 13 Nov 2022 15:19:38 -0800
Subject: [PATCH 016/199] models.yaml.example: add diffusers-format model, set
 as default

---
 configs/models.yaml.example | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/configs/models.yaml.example b/configs/models.yaml.example
index 31401cd02d8..1eb2781f4a8 100644
--- a/configs/models.yaml.example
+++ b/configs/models.yaml.example
@@ -5,6 +5,11 @@
 # model requires a model config file, a weights file,
 # and the width and height of the images it
 # was trained on.
+diffusers-1.5:
+  description: Diffusers version of Stable Diffusion version 1.5
+  format: diffusers
+  repo_name: runwayml/stable-diffusion-v1-5
+  default: true
 stable-diffusion-1.5:
   description: The newest Stable Diffusion version 1.5 weight file (4.27 GB)
   weights: models/ldm/stable-diffusion-v1/v1-5-pruned-emaonly.ckpt

From c5274e6e50078497edafc20f5c8a0b7e8a935ba5 Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Sun, 13 Nov 2022 15:36:51 -0800
Subject: [PATCH 017/199] test-invoke-conda: use diffusers-format model
 test-invoke-conda: put huggingface-token where the library can use it

---
 .github/workflows/test-invoke-conda.yml | 71 +++++++++++++------------
 1 file changed, 36 insertions(+), 35 deletions(-)

diff --git a/.github/workflows/test-invoke-conda.yml b/.github/workflows/test-invoke-conda.yml
index 41f72c5a84d..009df909e00 100644
--- a/.github/workflows/test-invoke-conda.yml
+++ b/.github/workflows/test-invoke-conda.yml
@@ -1,17 +1,16 @@
 name: Test invoke.py
-on:
-  push:
-    branches:
-      - 'main'
-      - 'development'
-      - 'fix-gh-actions-fork'
-  pull_request:
-    branches:
-      - 'main'
-      - 'development'
+on: [push, pull_request]
 
 jobs:
   matrix:
+    # Run on:
+    # - pull requests
+    # - pushes to forks (will run in the forked project with that fork's secrets)
+    # - pushes to branches that are *not* pull requests
+    if: |
+      github.event_name == 'pull_request'
+      || github.repository != 'invoke-ai/InvokeAI'
+      || github.ref_protected
     strategy:
       matrix:
         stable-diffusion-model:
@@ -30,15 +29,19 @@ jobs:
           - environment-yaml: environment-mac.yml
             os: macos-12
             default-shell: bash -l {0}
-          - stable-diffusion-model: stable-diffusion-1.5
-            stable-diffusion-model-url: https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt
-            stable-diffusion-model-dl-path: models/ldm/stable-diffusion-v1
-            stable-diffusion-model-dl-name: v1-5-pruned-emaonly.ckpt
-    name: ${{ matrix.environment-yaml }} on ${{ matrix.os }}
+          # - stable-diffusion-model: https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt
+          #   stable-diffusion-model-dl-path: models/ldm/stable-diffusion-v1/sd-v1-4.ckpt
+          #   stable-diffusion-model-switch: stable-diffusion-1.4
+          - stable-diffusion-model: https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt
+            stable-diffusion-model-dl-path: models/ldm/stable-diffusion-v1/v1-5-pruned-emaonly.ckpt
+            stable-diffusion-model-switch: diffusers-1.5
+    name: ${{ matrix.os }} with ${{ matrix.stable-diffusion-model-switch }}
     runs-on: ${{ matrix.os }}
     env:
       CONDA_ENV_NAME: invokeai
       INVOKEAI_ROOT: '${{ github.workspace }}/invokeai'
+      PYTHONUNBUFFERED: 1
+      HAVE_SECRETS: ${{ secrets.HUGGINGFACE_TOKEN != '' }}
     defaults:
       run:
         shell: ${{ matrix.default-shell }}
@@ -55,6 +58,19 @@ jobs:
       - name: create environment.yml
         run: cp "environments-and-requirements/${{ matrix.environment-yaml }}" environment.yml
 
+      - name: Use Cached Stable Diffusion Model
+        id: cache-sd-model
+        uses: actions/cache@v3
+        env:
+          cache-name: huggingface-${{ matrix.stable-diffusion-model-switch }}
+        with:
+          path: ~/.cache/huggingface
+          key: ${{ env.cache-name }}
+
+      - name: Check model availability
+        if: steps.cache-sd-model.outputs.cache-hit != true && env.HAVE_SECRETS != 'true'
+        run: echo -e '\a ⛔ GitHub model cache not found, and no HUGGINGFACE_TOKEN is available. Will not be able to load Stable Diffusion.' ; exit 1
+
       - name: Use cached conda packages
         id: use-cached-conda-packages
         uses: actions/cache@v3
@@ -82,28 +98,13 @@ jobs:
         if: ${{ github.ref != 'refs/heads/main' && github.ref != 'refs/heads/development' }}
         run: echo "TEST_PROMPTS=tests/validate_pr_prompt.txt" >> $GITHUB_ENV
 
-      - name: Use Cached Stable Diffusion Model
-        id: cache-sd-model
-        uses: actions/cache@v3
-        env:
-          cache-name: cache-${{ matrix.stable-diffusion-model }}
-        with:
-          path: ${{ env.INVOKEAI_ROOT }}/${{ matrix.stable-diffusion-model-dl-path }}
-          key: ${{ env.cache-name }}
-
-      - name: Download ${{ matrix.stable-diffusion-model }}
-        id: download-stable-diffusion-model
-        if: ${{ steps.cache-sd-model.outputs.cache-hit != 'true' }}
-        run: |
-          mkdir -p "${{ env.INVOKEAI_ROOT }}/${{ matrix.stable-diffusion-model-dl-path }}"
-          curl \
-            -H "Authorization: Bearer ${{ secrets.HUGGINGFACE_TOKEN }}" \
-            -o "${{ env.INVOKEAI_ROOT }}/${{ matrix.stable-diffusion-model-dl-path }}/${{ matrix.stable-diffusion-model-dl-name }}" \
-            -L ${{ matrix.stable-diffusion-model-url }}
-
-      - name: run configure_invokeai.py
+      - name: run preload_models.py
         id: run-preload-models
         run: |
+          if [ "${HAVE_SECRETS}" == true ] ; then
+            mkdir -p ~/.huggingface
+            echo -n '${{ secrets.HUGGINGFACE_TOKEN }}' > ~/.huggingface/token
+          fi
           python scripts/configure_invokeai.py --no-interactive --yes
 
       - name: cat ~/.invokeai

From 94f57345e3b8637a6596e5108f6e16482067e1f8 Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Sun, 13 Nov 2022 23:35:33 -0800
Subject: [PATCH 018/199] environment-mac: upgrade to diffusers 0.7 (from 0.6)

this was already done for linux; mac must have been lost in the merge.
---
 environments-and-requirements/environment-mac.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/environments-and-requirements/environment-mac.yml b/environments-and-requirements/environment-mac.yml
index ca1dcc78868..95f714a73d6 100644
--- a/environments-and-requirements/environment-mac.yml
+++ b/environments-and-requirements/environment-mac.yml
@@ -22,7 +22,7 @@ dependencies:
 
   - albumentations=1.2
   - coloredlogs=15.0
-  - diffusers=0.6
+  - diffusers~=0.7
   - einops=0.3
   - eventlet
   - grpcio=1.46

From a6a766dfa2bdbfff35759546453fada09b4ced76 Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Tue, 15 Nov 2022 22:04:38 -0800
Subject: [PATCH 019/199] preload_models: explicitly load diffusers models

In non-interactive mode too, as long as you're logged in.
---
 .github/workflows/test-invoke-conda.yml | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/test-invoke-conda.yml b/.github/workflows/test-invoke-conda.yml
index 009df909e00..b690f190454 100644
--- a/.github/workflows/test-invoke-conda.yml
+++ b/.github/workflows/test-invoke-conda.yml
@@ -105,7 +105,9 @@ jobs:
             mkdir -p ~/.huggingface
             echo -n '${{ secrets.HUGGINGFACE_TOKEN }}' > ~/.huggingface/token
           fi
-          python scripts/configure_invokeai.py --no-interactive --yes
+          python scripts/preload_models.py \
+            --no-interactive \
+            --full-precision  # can't use fp16 weights without a GPU
 
       - name: cat ~/.invokeai
         id: cat-invokeai
@@ -113,6 +115,10 @@ jobs:
 
       - name: Run the tests
         id: run-tests
+        env:
+          HF_HUB_OFFLINE: 1
+          HF_DATASETS_OFFLINE: 1
+          TRANSFORMERS_OFFLINE: 1
         run: |
           time python scripts/invoke.py \
             --no-patchmatch \

From f3f6213b972867df13b41406a6b16e770d521052 Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Mon, 21 Nov 2022 16:46:32 -0800
Subject: [PATCH 020/199] fix(model_cache): don't check `model.config` in
 diffusers format

clean-up from recent merge.
---
 ldm/invoke/model_cache.py | 26 ++++++++++++++++----------
 1 file changed, 16 insertions(+), 10 deletions(-)

diff --git a/ldm/invoke/model_cache.py b/ldm/invoke/model_cache.py
index 181c4d39b46..199ef4307d3 100644
--- a/ldm/invoke/model_cache.py
+++ b/ldm/invoke/model_cache.py
@@ -4,29 +4,28 @@
 below a preset minimum, the least recently used model will be
 cleared and loaded from disk when next needed.
 '''
+import gc
+import hashlib
+import io
+import os
+import sys
+import time
+import traceback
 import warnings
 from pathlib import Path
 
 import torch
-import os
-import io
-import time
-import gc
-import hashlib
-import psutil
-import sys
 import transformers
-import traceback
 import textwrap
 import contextlib
 from typing import Union
 from omegaconf import OmegaConf
 from omegaconf.errors import ConfigAttributeError
+from picklescan.scanner import scan_file_path
 
 from ldm.invoke.generator.diffusers_pipeline import StableDiffusionGeneratorPipeline
-from ldm.util import instantiate_from_config, ask_user
 from ldm.invoke.globals import Globals
-from picklescan.scanner import scan_file_path
+from ldm.util import instantiate_from_config, ask_user
 
 DEFAULT_MAX_MODELS=2
 
@@ -240,6 +239,13 @@ def _load_ckpt_model(self, mconfig):
         width = mconfig.width
         height = mconfig.height
 
+        if not os.path.isabs(config):
+            config = os.path.join(Globals.root,config)
+        if not os.path.isabs(weights):
+            weights = os.path.normpath(os.path.join(Globals.root,weights))
+        # scan model
+        self._scan_model(model_name, weights)
+
         c = OmegaConf.load(config)
         with open(weights, 'rb') as f:
             weight_bytes = f.read()

From efbb807905c3005d789bcfe4f2dbaf4241edac29 Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Wed, 23 Nov 2022 17:38:31 -0800
Subject: [PATCH 021/199] diffusers integration: support img2img

---
 ldm/invoke/generator/diffusers_pipeline.py | 74 +++++++++++++++++-
 ldm/invoke/generator/img2img.py            | 89 +++++++++-------------
 2 files changed, 106 insertions(+), 57 deletions(-)

diff --git a/ldm/invoke/generator/diffusers_pipeline.py b/ldm/invoke/generator/diffusers_pipeline.py
index 861bf22a7a7..911de67601a 100644
--- a/ldm/invoke/generator/diffusers_pipeline.py
+++ b/ldm/invoke/generator/diffusers_pipeline.py
@@ -3,10 +3,12 @@
 from dataclasses import dataclass
 from typing import List, Optional, Union, Callable
 
+import PIL.Image
 import torch
 from diffusers.models import AutoencoderKL, UNet2DConditionModel
 from diffusers.pipeline_utils import DiffusionPipeline
 from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
+from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img import preprocess
 from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
 from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
 from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
@@ -210,6 +212,7 @@ def generate_from_embeddings(
             *,
             run_id: str = None,
             extra_conditioning_info: InvokeAIDiffuserComponent.ExtraConditioningInfo = None,
+            timesteps = None,
             **extra_step_kwargs):
         if run_id is None:
             run_id = secrets.token_urlsafe(self.ID_LENGTH)
@@ -220,16 +223,19 @@ def generate_from_embeddings(
         else:
             self.invokeai_diffuser.remove_cross_attention_control()
 
+        if timesteps is None:
+            timesteps = self.scheduler.timesteps
+
         # scale the initial noise by the standard deviation required by the scheduler
         latents *= self.scheduler.init_noise_sigma
         yield PipelineIntermediateState(run_id=run_id, step=-1, timestep=self.scheduler.num_train_timesteps,
                                         latents=latents)
 
         batch_size = latents.shape[0]
-        batched_t = torch.full((batch_size,), self.scheduler.timesteps[0],
-                               dtype=self.scheduler.timesteps.dtype, device=self.unet.device)
+        batched_t = torch.full((batch_size,), timesteps[0],
+                               dtype=timesteps.dtype, device=self.unet.device)
         # NOTE: Depends on scheduler being already initialized!
-        for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
+        for i, t in enumerate(self.progress_bar(timesteps)):
             batched_t.fill_(t)
             step_output = self.step(batched_t, latents, guidance_scale,
                                     text_embeddings, unconditioned_embeddings,
@@ -272,6 +278,68 @@ def _unet_forward(self, latents, t, text_embeddings):
         # predict the noise residual
         return self.unet(latents, t, encoder_hidden_states=text_embeddings).sample
 
+    def img2img_from_embeddings(self,
+                                init_image: Union[torch.FloatTensor, PIL.Image.Image],
+                                strength: float,
+                                num_inference_steps: int,
+                                text_embeddings: torch.Tensor, unconditioned_embeddings: torch.Tensor,
+                                guidance_scale: float,
+                                *, callback: Callable[[PipelineIntermediateState], None] = None,
+                                extra_conditioning_info: InvokeAIDiffuserComponent.ExtraConditioningInfo = None,
+                                run_id=None,
+                                noise_func=None,
+                                **extra_step_kwargs) -> StableDiffusionPipelineOutput:
+        device = self.unet.device
+        latents_dtype = text_embeddings.dtype
+        batch_size = 1
+        num_images_per_prompt = 1
+
+        if isinstance(init_image, PIL.Image.Image):
+            init_image = preprocess(init_image.convert('RGB'))
+
+        self.scheduler.set_timesteps(num_inference_steps, device=device)
+        timesteps = self._diffusers08_get_timesteps(num_inference_steps, strength)
+        latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
+
+        # 6. Prepare latent variables
+        latents = self.prepare_latents_from_image(init_image, latent_timestep, latents_dtype, device, noise_func)
+
+        result = None
+        for result in self.generate_from_embeddings(
+                latents, text_embeddings, unconditioned_embeddings, guidance_scale,
+                extra_conditioning_info=extra_conditioning_info,
+                timesteps=timesteps,
+                run_id=run_id, **extra_step_kwargs):
+            if callback is not None and isinstance(result, PipelineIntermediateState):
+                callback(result)
+        if result is None:
+            raise AssertionError("why was that an empty generator?")
+        return result
+
+    def prepare_latents_from_image(self, init_image, timestep, dtype, device, noise_func) -> torch.FloatTensor:
+        # can't quite use upstream StableDiffusionImg2ImgPipeline.prepare_latents
+        # because we have our own noise function
+        init_image = init_image.to(device=device, dtype=dtype)
+        with torch.inference_mode():
+            init_latent_dist = self.vae.encode(init_image).latent_dist
+            init_latents = init_latent_dist.sample()  # FIXME: uses torch.randn. make reproducible!
+        init_latents = 0.18215 * init_latents
+
+        noise = noise_func(init_latents)
+
+        return self.scheduler.add_noise(init_latents, noise, timestep)
+
+    def _diffusers08_get_timesteps(self, num_inference_steps, strength):
+        # get the original timestep using init_timestep
+        offset = self.scheduler.config.get("steps_offset", 0)
+        init_timestep = int(num_inference_steps * strength) + offset
+        init_timestep = min(init_timestep, num_inference_steps)
+
+        t_start = max(num_inference_steps - init_timestep + offset, 0)
+        timesteps = self.scheduler.timesteps[t_start:]
+
+        return timesteps
+
     @torch.inference_mode()
     def check_for_safety(self, output):
         if not getattr(self, 'feature_extractor') or not getattr(self, 'safety_checker'):
diff --git a/ldm/invoke/generator/img2img.py b/ldm/invoke/generator/img2img.py
index edcc855a290..6ea41fda33c 100644
--- a/ldm/invoke/generator/img2img.py
+++ b/ldm/invoke/generator/img2img.py
@@ -2,14 +2,10 @@
 ldm.invoke.generator.img2img descends from ldm.invoke.generator
 '''
 
-import PIL
-import numpy as np
 import torch
-from PIL import Image
-from torch import Tensor
 
-from ldm.invoke.devices import choose_autocast
 from ldm.invoke.generator.base import Generator
+from ldm.invoke.generator.diffusers_pipeline import StableDiffusionGeneratorPipeline
 
 
 class Img2Img(Generator):
@@ -25,66 +21,51 @@ def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
         """
         self.perlin = perlin
 
-        sampler.make_schedule(
-            ddim_num_steps=steps, ddim_eta=ddim_eta, verbose=False
-        )
-
-        if isinstance(init_image, PIL.Image.Image):
-            init_image = self._image_to_tensor(init_image.convert('RGB'))
-
-        scope = choose_autocast(self.precision)
-        with scope(self.model.device.type):
-            self.init_latent = self.model.get_first_stage_encoding(
-                self.model.encode_first_stage(init_image)
-            ) # move to latent space
-
-        t_enc = int(strength * steps)
         uc, c, extra_conditioning_info   = conditioning
 
+        # noinspection PyTypeChecker
+        pipeline: StableDiffusionGeneratorPipeline = self.model
+        pipeline.scheduler = sampler
+
         def make_image(x_T):
-            # encode (scaled latent)
-            z_enc = sampler.stochastic_encode(
-                self.init_latent,
-                torch.tensor([t_enc]).to(self.model.device),
-                noise=x_T
-            )
-            # decode it
-            samples = sampler.decode(
-                z_enc,
-                c,
-                t_enc,
-                img_callback = step_callback,
-                unconditional_guidance_scale=cfg_scale,
-                unconditional_conditioning=uc,
-                init_latent = self.init_latent, # changes how noising is performed in ksampler
-                extra_conditioning_info = extra_conditioning_info,
-                all_timesteps_count = steps
+            # FIXME: use x_T for initial seeded noise
+            pipeline_output = pipeline.img2img_from_embeddings(
+                init_image, strength, steps, c, uc, cfg_scale,
+                extra_conditioning_info=extra_conditioning_info,
+                noise_func=self.get_noise_like,
+                callback=step_callback
             )
 
-            return self.sample_to_image(samples)
+            return pipeline.numpy_to_pil(pipeline_output.images)[0]
 
         return make_image
 
-    def get_noise(self,width,height):
-        device      = self.model.device
-        init_latent = self.init_latent
-        assert init_latent is not None,'call to get_noise() when init_latent not set'
+    def get_noise_like(self, like: torch.Tensor):
+        device = like.device
         if device.type == 'mps':
-            x = torch.randn_like(init_latent, device='cpu').to(device)
+            x = torch.randn_like(like, device='cpu').to(device)
         else:
-            x = torch.randn_like(init_latent, device=device)
+            x = torch.randn_like(like, device=device)
         if self.perlin > 0.0:
-            shape = init_latent.shape
+            shape = like.shape
             x = (1-self.perlin)*x + self.perlin*self.get_perlin_noise(shape[3], shape[2])
         return x
 
-    def _image_to_tensor(self, image:Image, normalize:bool=True)->Tensor:
-        image = np.array(image).astype(np.float32) / 255.0
-        if len(image.shape) == 2:  # 'L' image, as in a mask
-            image = image[None,None]
-        else:                      # 'RGB' image
-            image = image[None].transpose(0, 3, 1, 2)
-        image = torch.from_numpy(image)
-        if normalize:
-            image = 2.0 * image - 1.0
-        return image.to(self.model.device)    
+    def get_noise(self,width,height):
+        # copy of the Txt2Img.get_noise
+        device         = self.model.device
+        if self.use_mps_noise or device.type == 'mps':
+            x = torch.randn([1,
+                                self.latent_channels,
+                                height // self.downsampling_factor,
+                                width  // self.downsampling_factor],
+                               device='cpu').to(device)
+        else:
+            x = torch.randn([1,
+                                self.latent_channels,
+                                height // self.downsampling_factor,
+                                width  // self.downsampling_factor],
+                               device=device)
+        if self.perlin > 0.0:
+            x = (1-self.perlin)*x + self.perlin*self.get_perlin_noise(width  // self.downsampling_factor, height // self.downsampling_factor)
+        return x

From ceb53ccdfbe8cd0be89ec379a8f38fbf088a12ca Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Wed, 23 Nov 2022 14:46:41 -0800
Subject: [PATCH 022/199] dev: upgrade to diffusers 0.8 (from 0.7.1)

We get to remove some code by using methods that were factored out in the base class.
---
 .../environment-lin-amd.yml                   |   2 +-
 .../environment-lin-cuda.yml                  |   2 +-
 .../environment-mac.yml                       |   2 +-
 .../environment-win-cuda.yml                  |   2 +-
 .../requirements-base.txt                     |   2 +-
 ldm/invoke/generator/diffusers_pipeline.py    | 142 +++---------------
 6 files changed, 28 insertions(+), 124 deletions(-)

diff --git a/environments-and-requirements/environment-lin-amd.yml b/environments-and-requirements/environment-lin-amd.yml
index 93344666d20..8f9beb44418 100644
--- a/environments-and-requirements/environment-lin-amd.yml
+++ b/environments-and-requirements/environment-lin-amd.yml
@@ -11,7 +11,7 @@ dependencies:
     - --extra-index-url  https://download.pytorch.org/whl/rocm5.2/
     - albumentations==0.4.3
     - dependency_injector==4.40.0
-    - diffusers==0.6.0
+    - diffusers~=0.8
     - einops==0.3.0
     - eventlet
     - flask==2.1.3
diff --git a/environments-and-requirements/environment-lin-cuda.yml b/environments-and-requirements/environment-lin-cuda.yml
index 64bbabe902d..9a6f3da25fa 100644
--- a/environments-and-requirements/environment-lin-cuda.yml
+++ b/environments-and-requirements/environment-lin-cuda.yml
@@ -15,7 +15,7 @@ dependencies:
     - accelerate~=0.13
     - albumentations==0.4.3
     - dependency_injector==4.40.0
-    - diffusers==0.6.0
+    - diffusers~=0.8
     - einops==0.3.0
     - eventlet
     - flask==2.1.3
diff --git a/environments-and-requirements/environment-mac.yml b/environments-and-requirements/environment-mac.yml
index 95f714a73d6..3409ae3c117 100644
--- a/environments-and-requirements/environment-mac.yml
+++ b/environments-and-requirements/environment-mac.yml
@@ -22,7 +22,7 @@ dependencies:
 
   - albumentations=1.2
   - coloredlogs=15.0
-  - diffusers~=0.7
+  - diffusers~=0.8
   - einops=0.3
   - eventlet
   - grpcio=1.46
diff --git a/environments-and-requirements/environment-win-cuda.yml b/environments-and-requirements/environment-win-cuda.yml
index f71ca666191..e9d293cee71 100644
--- a/environments-and-requirements/environment-win-cuda.yml
+++ b/environments-and-requirements/environment-win-cuda.yml
@@ -15,7 +15,7 @@ dependencies:
     - albumentations==0.4.3
     - basicsr==1.4.1
     - dependency_injector==4.40.0
-    - diffusers==0.6.0
+    - diffusers~=0.8
     - einops==0.3.0
     - eventlet
     - flask==2.1.3
diff --git a/environments-and-requirements/requirements-base.txt b/environments-and-requirements/requirements-base.txt
index 8b7d83aa94a..f3284474eb0 100644
--- a/environments-and-requirements/requirements-base.txt
+++ b/environments-and-requirements/requirements-base.txt
@@ -1,7 +1,7 @@
 # pip will resolve the version which matches torch
 albumentations
 dependency_injector==4.40.0
-diffusers[torch]~=0.7
+diffusers[torch]~=0.8
 einops
 eventlet
 facexlib
diff --git a/ldm/invoke/generator/diffusers_pipeline.py b/ldm/invoke/generator/diffusers_pipeline.py
index 911de67601a..4a4700232ca 100644
--- a/ldm/invoke/generator/diffusers_pipeline.py
+++ b/ldm/invoke/generator/diffusers_pipeline.py
@@ -5,8 +5,8 @@
 
 import PIL.Image
 import torch
+from diffusers import StableDiffusionPipeline
 from diffusers.models import AutoencoderKL, UNet2DConditionModel
-from diffusers.pipeline_utils import DiffusionPipeline
 from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
 from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img import preprocess
 from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
@@ -26,7 +26,7 @@ class PipelineIntermediateState:
     predicted_original: Optional[torch.Tensor] = None
 
 
-class StableDiffusionGeneratorPipeline(DiffusionPipeline):
+class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
     r"""
     Pipeline for text-to-image generation using Stable Diffusion.
 
@@ -67,10 +67,10 @@ def __init__(
         tokenizer: CLIPTokenizer,
         unet: UNet2DConditionModel,
         scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
-        safety_checker: StableDiffusionSafetyChecker,
-        feature_extractor: CLIPFeatureExtractor,
+        safety_checker: Optional[StableDiffusionSafetyChecker],
+        feature_extractor: Optional[CLIPFeatureExtractor],
     ):
-        super().__init__()
+        super().__init__(vae, text_encoder, tokenizer, unet, scheduler, safety_checker, feature_extractor)
 
         self.register_modules(
             vae=vae,
@@ -88,51 +88,6 @@ def __init__(
         )
         self.invokeai_diffuser = InvokeAIDiffuserComponent(self.unet, self._unet_forward)
 
-    def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
-        r"""
-        Enable sliced attention computation.
-
-        When this option is enabled, the attention module will split the input tensor in slices, to compute attention
-        in several steps. This is useful to save some memory in exchange for a small speed decrease.
-
-        Args:
-            slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
-                When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
-                a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
-                `attention_head_dim` must be a multiple of `slice_size`.
-        """
-        if slice_size == "auto":
-            # half the attention head size is usually a good trade-off between
-            # speed and memory
-            slice_size = self.unet.config.attention_head_dim // 2
-        self.unet.set_attention_slice(slice_size)
-
-    def disable_attention_slicing(self):
-        r"""
-        Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
-        back to computing attention in one step.
-        """
-        # set slice_size = `None` to disable `attention slicing`
-        self.enable_attention_slicing(None)
-
-    def enable_xformers_memory_efficient_attention(self):
-        r"""
-        Enable memory efficient attention as implemented in xformers.
-
-        When this option is enabled, you should observe lower GPU memory usage and a potential speed up at inference
-        time. Speed up at training time is not guaranteed.
-
-        Warning: When Memory Efficient Attention and Sliced attention are both enabled, the Memory Efficient Attention
-        is used.
-        """
-        self.unet.set_use_memory_efficient_attention_xformers(True)
-
-    def disable_xformers_memory_efficient_attention(self):
-        r"""
-        Disable memory efficient attention as implemented in xformers.
-        """
-        self.unet.set_use_memory_efficient_attention_xformers(False)
-
     def image_from_embeddings(self, latents: torch.Tensor, num_inference_steps: int,
                               text_embeddings: torch.Tensor, unconditioned_embeddings: torch.Tensor,
                               guidance_scale: float,
@@ -195,10 +150,17 @@ def generate(
         # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
         # corresponds to doing no classifier free guidance.
         do_classifier_free_guidance = guidance_scale > 1.0
-        text_embeddings, unconditioned_embeddings = self.get_text_embeddings(prompt, opposing_prompt, do_classifier_free_guidance, batch_size)\
-            .to(self.unet.device)
+
+        combined_embeddings = self._encode_prompt(prompt, device=self._execution_device, num_images_per_prompt=1,
+                            do_classifier_free_guidance=do_classifier_free_guidance,
+                            negative_prompt=opposing_prompt)
+        text_embeddings, unconditioned_embeddings = combined_embeddings.chunk(2)
         self.scheduler.set_timesteps(num_inference_steps)
-        latents = self.prepare_latents(latents, batch_size, height, width, generator, self.unet.dtype)
+        latents = self.prepare_latents(batch_size=batch_size, num_channels_latents=self.unet.in_channels,
+                                       height=height, width=width,
+                                       dtype=self.unet.dtype, device=self._execution_device,
+                                       generator=generator,
+                                       latents=latents)
 
         yield from self.generate_from_embeddings(latents, text_embeddings, unconditioned_embeddings,
                                                  guidance_scale, run_id=run_id, **extra_step_kwargs)
@@ -248,9 +210,10 @@ def generate_from_embeddings(
         # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
         torch.cuda.empty_cache()
 
-        image = self.decode_to_image(latents)
-        output = StableDiffusionPipelineOutput(images=image, nsfw_content_detected=[])
-        yield self.check_for_safety(output)
+        with torch.inference_mode():
+            image = self.decode_latents(latents)
+            output = StableDiffusionPipelineOutput(images=image, nsfw_content_detected=[])
+            yield self.check_for_safety(output, dtype=text_embeddings.dtype)
 
     @torch.inference_mode()
     def step(self, t: torch.Tensor, latents: torch.Tensor, guidance_scale: float,
@@ -340,46 +303,12 @@ def _diffusers08_get_timesteps(self, num_inference_steps, strength):
 
         return timesteps
 
-    @torch.inference_mode()
-    def check_for_safety(self, output):
-        if not getattr(self, 'feature_extractor') or not getattr(self, 'safety_checker'):
-            return output
-        images = output.images
-        safety_checker_output = self.feature_extractor(self.numpy_to_pil(images),
-                                                       return_tensors="pt").to(self.device)
-        screened_images, has_nsfw_concept = self.safety_checker(
-            images=images, clip_input=safety_checker_output.pixel_values)
+    def check_for_safety(self, output, dtype):
+        with torch.inference_mode():
+            screened_images, has_nsfw_concept = self.run_safety_checker(
+                output.images, device=self._execution_device, dtype=dtype)
         return StableDiffusionPipelineOutput(screened_images, has_nsfw_concept)
 
-    @torch.inference_mode()
-    def decode_to_image(self, latents):
-        # scale and decode the image latents with vae
-        latents = 1 / 0.18215 * latents
-        image = self.vae.decode(latents).sample
-        image = (image / 2 + 0.5).clamp(0, 1)
-        image = image.cpu().permute(0, 2, 3, 1).numpy()
-        return image
-
-    @torch.inference_mode()
-    def get_text_embeddings(self,
-                            prompt: Union[str, List[str]],
-                            opposing_prompt: Union[str, List[str]],
-                            do_classifier_free_guidance: bool,
-                            batch_size: int):
-        # get prompt text embeddings
-        text_input = self._tokenize(prompt)
-
-        text_embeddings = self.text_encoder(text_input.input_ids)[0]
-        # get unconditional embeddings for classifier free guidance
-        if do_classifier_free_guidance:
-            # opposing prompt defaults to blank caption for everything in the batch
-            text_anti_input = self._tokenize(opposing_prompt or [""] * batch_size)
-            uncond_embeddings = self.text_encoder(text_anti_input.input_ids)[0]
-        else:
-            uncond_embeddings = None
-
-        return text_embeddings, uncond_embeddings
-
     @torch.inference_mode()
     def get_learned_conditioning(self, c: List[List[str]], *, return_tokens=True, fragment_weights=None):
         """
@@ -406,28 +335,3 @@ def _tokenize(self, prompt: Union[str, List[str]]):
     def channels(self) -> int:
         """Compatible with DiffusionWrapper"""
         return self.unet.in_channels
-
-    def prepare_latents(self, latents, batch_size, height, width, generator, dtype):
-        # get the initial random noise unless the user supplied it
-        # Unlike in other pipelines, latents need to be generated in the target device
-        # for 1-to-1 results reproducibility with the CompVis implementation.
-        # However this currently doesn't work in `mps`.
-        latents_shape = (batch_size, self.unet.in_channels, height // 8, width // 8)
-        if latents is None:
-            latents = torch.randn(
-                latents_shape,
-                generator=generator,
-                device=self.unet.device,
-                dtype=dtype
-            )
-        else:
-            if latents.shape != latents_shape:
-                raise ValueError(
-                    f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
-        if latents.device != self.unet.device:
-            raise ValueError(f"Unexpected latents device, got {latents.device}, "
-                             f"expected {self.unet.device}")
-
-        # scale the initial noise by the standard deviation required by the scheduler
-        latents *= self.scheduler.init_noise_sigma
-        return latents

From b7864aa1a7924a2dc558b7dedaaaae788d2e86b5 Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Wed, 23 Nov 2022 20:30:06 -0800
Subject: [PATCH 023/199] refactor: remove backported img2img.get_timesteps

now that we can use it directly from diffusers 0.8.1
---
 ldm/invoke/generator/diffusers_pipeline.py | 18 ++++--------------
 1 file changed, 4 insertions(+), 14 deletions(-)

diff --git a/ldm/invoke/generator/diffusers_pipeline.py b/ldm/invoke/generator/diffusers_pipeline.py
index 4a4700232ca..5d6b66ccf01 100644
--- a/ldm/invoke/generator/diffusers_pipeline.py
+++ b/ldm/invoke/generator/diffusers_pipeline.py
@@ -5,7 +5,7 @@
 
 import PIL.Image
 import torch
-from diffusers import StableDiffusionPipeline
+from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline
 from diffusers.models import AutoencoderKL, UNet2DConditionModel
 from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
 from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img import preprocess
@@ -260,8 +260,9 @@ def img2img_from_embeddings(self,
         if isinstance(init_image, PIL.Image.Image):
             init_image = preprocess(init_image.convert('RGB'))
 
-        self.scheduler.set_timesteps(num_inference_steps, device=device)
-        timesteps = self._diffusers08_get_timesteps(num_inference_steps, strength)
+        img2img_pipeline = StableDiffusionImg2ImgPipeline(**self.components)
+        img2img_pipeline.scheduler.set_timesteps(num_inference_steps, device=device)
+        timesteps = img2img_pipeline.get_timesteps(num_inference_steps, strength, device=device)
         latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
 
         # 6. Prepare latent variables
@@ -292,17 +293,6 @@ def prepare_latents_from_image(self, init_image, timestep, dtype, device, noise_
 
         return self.scheduler.add_noise(init_latents, noise, timestep)
 
-    def _diffusers08_get_timesteps(self, num_inference_steps, strength):
-        # get the original timestep using init_timestep
-        offset = self.scheduler.config.get("steps_offset", 0)
-        init_timestep = int(num_inference_steps * strength) + offset
-        init_timestep = min(init_timestep, num_inference_steps)
-
-        t_start = max(num_inference_steps - init_timestep + offset, 0)
-        timesteps = self.scheduler.timesteps[t_start:]
-
-        return timesteps
-
     def check_for_safety(self, output, dtype):
         with torch.inference_mode():
             screened_images, has_nsfw_concept = self.run_safety_checker(

From 5cee2111be450d22e6a3c9b83b0d5773f17b73ee Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Thu, 24 Nov 2022 18:38:08 -0800
Subject: [PATCH 024/199] ci: use diffusers model

---
 .github/workflows/test-invoke-conda.yml | 15 ++----
 .github/workflows/test-invoke-pip.yml   | 71 +++++++++++++------------
 2 files changed, 42 insertions(+), 44 deletions(-)

diff --git a/.github/workflows/test-invoke-conda.yml b/.github/workflows/test-invoke-conda.yml
index b690f190454..e1c5928b2ea 100644
--- a/.github/workflows/test-invoke-conda.yml
+++ b/.github/workflows/test-invoke-conda.yml
@@ -14,7 +14,7 @@ jobs:
     strategy:
       matrix:
         stable-diffusion-model:
-          - 'stable-diffusion-1.5'
+          - 'diffusers-1.5'
         environment-yaml:
           - environment-lin-amd.yml
           - environment-lin-cuda.yml
@@ -29,13 +29,7 @@ jobs:
           - environment-yaml: environment-mac.yml
             os: macos-12
             default-shell: bash -l {0}
-          # - stable-diffusion-model: https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt
-          #   stable-diffusion-model-dl-path: models/ldm/stable-diffusion-v1/sd-v1-4.ckpt
-          #   stable-diffusion-model-switch: stable-diffusion-1.4
-          - stable-diffusion-model: https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt
-            stable-diffusion-model-dl-path: models/ldm/stable-diffusion-v1/v1-5-pruned-emaonly.ckpt
-            stable-diffusion-model-switch: diffusers-1.5
-    name: ${{ matrix.os }} with ${{ matrix.stable-diffusion-model-switch }}
+    name: ${{ matrix.environment-yaml }} on ${{ matrix.os }}
     runs-on: ${{ matrix.os }}
     env:
       CONDA_ENV_NAME: invokeai
@@ -62,7 +56,7 @@ jobs:
         id: cache-sd-model
         uses: actions/cache@v3
         env:
-          cache-name: huggingface-${{ matrix.stable-diffusion-model-switch }}
+          cache-name: huggingface-${{ matrix.stable-diffusion-model }}
         with:
           path: ~/.cache/huggingface
           key: ${{ env.cache-name }}
@@ -106,7 +100,7 @@ jobs:
             echo -n '${{ secrets.HUGGINGFACE_TOKEN }}' > ~/.huggingface/token
           fi
           python scripts/preload_models.py \
-            --no-interactive \
+            --no-interactive --yes \
             --full-precision  # can't use fp16 weights without a GPU
 
       - name: cat ~/.invokeai
@@ -116,6 +110,7 @@ jobs:
       - name: Run the tests
         id: run-tests
         env:
+          # Set offline mode to make sure configure preloaded successfully.
           HF_HUB_OFFLINE: 1
           HF_DATASETS_OFFLINE: 1
           TRANSFORMERS_OFFLINE: 1
diff --git a/.github/workflows/test-invoke-pip.yml b/.github/workflows/test-invoke-pip.yml
index 3866a4c32c1..baaa6f92a82 100644
--- a/.github/workflows/test-invoke-pip.yml
+++ b/.github/workflows/test-invoke-pip.yml
@@ -1,20 +1,20 @@
 name: Test invoke.py pip
-on:
-  push:
-    branches:
-      - 'main'
-      - 'development'
-  pull_request:
-    branches:
-      - 'main'
-      - 'development'
+on: [push, pull_request]
 
 jobs:
   matrix:
+    # Run on:
+    # - pull requests
+    # - pushes to forks (will run in the forked project with that fork's secrets)
+    # - pushes to branches that are *not* pull requests
+    if: |
+      github.event_name == 'pull_request'
+      || github.repository != 'invoke-ai/InvokeAI'
+      || github.ref_protected
     strategy:
       matrix:
         stable-diffusion-model:
-          - stable-diffusion-1.5
+          - diffusers-1.5
         requirements-file:
           - requirements-lin-cuda.txt
           - requirements-lin-amd.txt
@@ -32,10 +32,6 @@ jobs:
           - requirements-file: requirements-mac-mps-cpu.txt
             os: macOS-12
             default-shell: bash -l {0}
-          - stable-diffusion-model: stable-diffusion-1.5
-            stable-diffusion-model-url: https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt
-            stable-diffusion-model-dl-path: models/ldm/stable-diffusion-v1
-            stable-diffusion-model-dl-name: v1-5-pruned-emaonly.ckpt
     name: ${{ matrix.requirements-file }} on ${{ matrix.python-version }}
     runs-on: ${{ matrix.os }}
     defaults:
@@ -43,6 +39,8 @@ jobs:
         shell: ${{ matrix.default-shell }}
     env:
       INVOKEAI_ROOT: '${{ github.workspace }}/invokeai'
+      PYTHONUNBUFFERED: 1
+      HAVE_SECRETS: ${{ secrets.HUGGINGFACE_TOKEN != '' }}
     steps:
       - name: Checkout sources
         id: checkout-sources
@@ -53,6 +51,19 @@ jobs:
           mkdir -p ${{ env.INVOKEAI_ROOT }}/configs
           cp configs/models.yaml.example ${{ env.INVOKEAI_ROOT }}/configs/models.yaml
 
+      - name: Use Cached Stable Diffusion Model
+        id: cache-sd-model
+        uses: actions/cache@v3
+        env:
+          cache-name: huggingface-${{ matrix.stable-diffusion-model }}
+        with:
+          path: ~/.cache/huggingface
+          key: ${{ env.cache-name }}
+
+      - name: Check model availability
+        if: steps.cache-sd-model.outputs.cache-hit != true && env.HAVE_SECRETS != 'true'
+        run: echo -e '\a ⛔ GitHub model cache not found, and no HUGGINGFACE_TOKEN is available. Will not be able to load Stable Diffusion.' ; exit 1
+
       - name: set test prompt to main branch validation
         if: ${{ github.ref == 'refs/heads/main' }}
         run: echo "TEST_PROMPTS=tests/preflight_prompts.txt" >> $GITHUB_ENV
@@ -81,29 +92,16 @@ jobs:
       - name: install requirements
         run: ${{ env.pythonLocation }}/bin/pip install -r '${{ matrix.requirements-file }}'
 
-      - name: Use Cached Stable Diffusion Model
-        id: cache-sd-model
-        uses: actions/cache@v3
-        env:
-          cache-name: cache-${{ matrix.stable-diffusion-model }}
-        with:
-          path: ${{ env.INVOKEAI_ROOT }}/${{ matrix.stable-diffusion-model-dl-path }}
-          key: ${{ env.cache-name }}
-
-      - name: Download ${{ matrix.stable-diffusion-model }}
-        id: download-stable-diffusion-model
-        if: ${{ steps.cache-sd-model.outputs.cache-hit != 'true' }}
-        run: |
-          mkdir -p "${{ env.INVOKEAI_ROOT }}/${{ matrix.stable-diffusion-model-dl-path }}"
-          curl \
-            -H "Authorization: Bearer ${{ secrets.HUGGINGFACE_TOKEN }}" \
-            -o "${{ env.INVOKEAI_ROOT }}/${{ matrix.stable-diffusion-model-dl-path }}/${{ matrix.stable-diffusion-model-dl-name }}" \
-            -L ${{ matrix.stable-diffusion-model-url }}
-
       - name: run configure_invokeai.py
         id: run-preload-models
         run: |
-          ${{ env.pythonLocation }}/bin/python scripts/configure_invokeai.py --no-interactive --yes
+          if [ "${HAVE_SECRETS}" == true ] ; then
+            mkdir -p ~/.huggingface
+            echo -n '${{ secrets.HUGGINGFACE_TOKEN }}' > ~/.huggingface/token
+          fi
+          ${{ env.pythonLocation }}/bin/python scripts/configure_invokeai.py \
+              --no-interactive --yes \
+              --full-precision  # can't use fp16 weights without a GPU
 
       - name: cat ~/.invokeai
         id: cat-invokeai
@@ -111,6 +109,11 @@ jobs:
 
       - name: Run the tests
         id: run-tests
+        env:
+          # Set offline mode to make sure configure preloaded successfully.
+          HF_HUB_OFFLINE: 1
+          HF_DATASETS_OFFLINE: 1
+          TRANSFORMERS_OFFLINE: 1
         run: |
           time ${{ env.pythonLocation }}/bin/python scripts/invoke.py \
             --no-patchmatch \

From 716ca8f8b99ca4c6903e51d43e500152480193ea Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Fri, 25 Nov 2022 13:31:56 -0800
Subject: [PATCH 025/199] dev: upgrade to diffusers 0.9 (from 0.8.1)

---
 environments-and-requirements/environment-lin-amd.yml  | 2 +-
 environments-and-requirements/environment-lin-cuda.yml | 2 +-
 environments-and-requirements/environment-mac.yml      | 2 +-
 environments-and-requirements/environment-win-cuda.yml | 2 +-
 environments-and-requirements/requirements-base.txt    | 2 +-
 5 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/environments-and-requirements/environment-lin-amd.yml b/environments-and-requirements/environment-lin-amd.yml
index 8f9beb44418..8d9336e0ba9 100644
--- a/environments-and-requirements/environment-lin-amd.yml
+++ b/environments-and-requirements/environment-lin-amd.yml
@@ -11,7 +11,7 @@ dependencies:
     - --extra-index-url  https://download.pytorch.org/whl/rocm5.2/
     - albumentations==0.4.3
     - dependency_injector==4.40.0
-    - diffusers~=0.8
+    - diffusers~=0.9
     - einops==0.3.0
     - eventlet
     - flask==2.1.3
diff --git a/environments-and-requirements/environment-lin-cuda.yml b/environments-and-requirements/environment-lin-cuda.yml
index 9a6f3da25fa..83ff8b5bb02 100644
--- a/environments-and-requirements/environment-lin-cuda.yml
+++ b/environments-and-requirements/environment-lin-cuda.yml
@@ -15,7 +15,7 @@ dependencies:
     - accelerate~=0.13
     - albumentations==0.4.3
     - dependency_injector==4.40.0
-    - diffusers~=0.8
+    - diffusers~=0.9
     - einops==0.3.0
     - eventlet
     - flask==2.1.3
diff --git a/environments-and-requirements/environment-mac.yml b/environments-and-requirements/environment-mac.yml
index 3409ae3c117..0584a82392a 100644
--- a/environments-and-requirements/environment-mac.yml
+++ b/environments-and-requirements/environment-mac.yml
@@ -22,7 +22,7 @@ dependencies:
 
   - albumentations=1.2
   - coloredlogs=15.0
-  - diffusers~=0.8
+  - diffusers~=0.9
   - einops=0.3
   - eventlet
   - grpcio=1.46
diff --git a/environments-and-requirements/environment-win-cuda.yml b/environments-and-requirements/environment-win-cuda.yml
index e9d293cee71..4a2d47dcd67 100644
--- a/environments-and-requirements/environment-win-cuda.yml
+++ b/environments-and-requirements/environment-win-cuda.yml
@@ -15,7 +15,7 @@ dependencies:
     - albumentations==0.4.3
     - basicsr==1.4.1
     - dependency_injector==4.40.0
-    - diffusers~=0.8
+    - diffusers~=0.9
     - einops==0.3.0
     - eventlet
     - flask==2.1.3
diff --git a/environments-and-requirements/requirements-base.txt b/environments-and-requirements/requirements-base.txt
index f3284474eb0..f125544f535 100644
--- a/environments-and-requirements/requirements-base.txt
+++ b/environments-and-requirements/requirements-base.txt
@@ -1,7 +1,7 @@
 # pip will resolve the version which matches torch
 albumentations
 dependency_injector==4.40.0
-diffusers[torch]~=0.8
+diffusers[torch]~=0.9
 einops
 eventlet
 facexlib

From 56153c2ebf834087656d0e80de8cf6e789eb74a6 Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Fri, 25 Nov 2022 13:52:29 -0800
Subject: [PATCH 026/199] lint: correct annotations for Python 3.9.

---
 ldm/invoke/generator/base.py               | 2 ++
 ldm/invoke/generator/diffusers_pipeline.py | 2 ++
 ldm/invoke/model_cache.py                  | 5 +++++
 3 files changed, 9 insertions(+)

diff --git a/ldm/invoke/generator/base.py b/ldm/invoke/generator/base.py
index c8b79f24e14..7325e1334e0 100644
--- a/ldm/invoke/generator/base.py
+++ b/ldm/invoke/generator/base.py
@@ -2,6 +2,8 @@
 Base class for ldm.invoke.generator.*
 including img2img, txt2img, and inpaint
 '''
+from __future__ import annotations
+
 import os
 import os.path as osp
 import random
diff --git a/ldm/invoke/generator/diffusers_pipeline.py b/ldm/invoke/generator/diffusers_pipeline.py
index 5d6b66ccf01..2d3f694687a 100644
--- a/ldm/invoke/generator/diffusers_pipeline.py
+++ b/ldm/invoke/generator/diffusers_pipeline.py
@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import secrets
 import warnings
 from dataclasses import dataclass
diff --git a/ldm/invoke/model_cache.py b/ldm/invoke/model_cache.py
index 199ef4307d3..94eb8e9cea1 100644
--- a/ldm/invoke/model_cache.py
+++ b/ldm/invoke/model_cache.py
@@ -4,15 +4,20 @@
 below a preset minimum, the least recently used model will be
 cleared and loaded from disk when next needed.
 '''
+from __future__ import annotations
+
+import contextlib
 import gc
 import hashlib
 import io
 import os
 import sys
+import textwrap
 import time
 import traceback
 import warnings
 from pathlib import Path
+from typing import Union
 
 import torch
 import transformers

From 09728dd1e082a559845b5b72f3b3d7f1e2d380fe Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Fri, 25 Nov 2022 14:11:19 -0800
Subject: [PATCH 027/199] lint: correct AttributeError.name reference for
 Python 3.9.

---
 ldm/models/diffusion/cross_attention_control.py | 12 ++++++++++--
 1 file changed, 10 insertions(+), 2 deletions(-)

diff --git a/ldm/models/diffusion/cross_attention_control.py b/ldm/models/diffusion/cross_attention_control.py
index 3284f990cef..08145b1e76e 100644
--- a/ldm/models/diffusion/cross_attention_control.py
+++ b/ldm/models/diffusion/cross_attention_control.py
@@ -4,6 +4,7 @@
 
 import torch
 
+
 # adapted from bloc97's CrossAttentionControl colab
 # https://github.com/bloc97/CrossAttentionControl
 
@@ -255,7 +256,7 @@ def attention_slice_wrangler(module, suggested_attention_slice:torch.Tensor, dim
                     lambda module: context.get_slicing_strategy(identifier)
                 )
             except AttributeError as e:
-                if e.name == 'set_attention_slice_wrangler':
+                if is_attribute_error_about(e, 'set_attention_slice_wrangler'):
                     warnings.warn(f"TODO: implement for {type(module)}")  # TODO
                 else:
                     raise
@@ -270,7 +271,14 @@ def remove_attention_function(unet):
                 module.set_attention_slice_wrangler(None)
                 module.set_slicing_strategy_getter(None)
             except AttributeError as e:
-                if e.name == 'set_attention_slice_wrangler':
+                if is_attribute_error_about(e, 'set_attention_slice_wrangler'):
                     warnings.warn(f"TODO: implement for {type(module)}")  # TODO
                 else:
                     raise
+
+
+def is_attribute_error_about(error: AttributeError, attribute: str):
+    if hasattr(error, 'name'):  # Python 3.10
+        return error.name == attribute
+    else:  # Python 3.9
+        return attribute in str(error)

From ea073980b0d3ed14e0db26d3b4a68bb85a554b92 Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Fri, 25 Nov 2022 14:24:00 -0800
Subject: [PATCH 028/199] CI: prefer diffusers-1.4 because it no longer
 requires a token

The RunwayML models still do.
---
 .github/workflows/test-invoke-conda.yml | 6 +-----
 .github/workflows/test-invoke-pip.yml   | 6 +-----
 configs/models.yaml.example             | 6 +++++-
 3 files changed, 7 insertions(+), 11 deletions(-)

diff --git a/.github/workflows/test-invoke-conda.yml b/.github/workflows/test-invoke-conda.yml
index e1c5928b2ea..fd2c9c10e98 100644
--- a/.github/workflows/test-invoke-conda.yml
+++ b/.github/workflows/test-invoke-conda.yml
@@ -14,7 +14,7 @@ jobs:
     strategy:
       matrix:
         stable-diffusion-model:
-          - 'diffusers-1.5'
+          - diffusers-1.4
         environment-yaml:
           - environment-lin-amd.yml
           - environment-lin-cuda.yml
@@ -61,10 +61,6 @@ jobs:
           path: ~/.cache/huggingface
           key: ${{ env.cache-name }}
 
-      - name: Check model availability
-        if: steps.cache-sd-model.outputs.cache-hit != true && env.HAVE_SECRETS != 'true'
-        run: echo -e '\a ⛔ GitHub model cache not found, and no HUGGINGFACE_TOKEN is available. Will not be able to load Stable Diffusion.' ; exit 1
-
       - name: Use cached conda packages
         id: use-cached-conda-packages
         uses: actions/cache@v3
diff --git a/.github/workflows/test-invoke-pip.yml b/.github/workflows/test-invoke-pip.yml
index baaa6f92a82..1803743e289 100644
--- a/.github/workflows/test-invoke-pip.yml
+++ b/.github/workflows/test-invoke-pip.yml
@@ -14,7 +14,7 @@ jobs:
     strategy:
       matrix:
         stable-diffusion-model:
-          - diffusers-1.5
+          - diffusers-1.4
         requirements-file:
           - requirements-lin-cuda.txt
           - requirements-lin-amd.txt
@@ -60,10 +60,6 @@ jobs:
           path: ~/.cache/huggingface
           key: ${{ env.cache-name }}
 
-      - name: Check model availability
-        if: steps.cache-sd-model.outputs.cache-hit != true && env.HAVE_SECRETS != 'true'
-        run: echo -e '\a ⛔ GitHub model cache not found, and no HUGGINGFACE_TOKEN is available. Will not be able to load Stable Diffusion.' ; exit 1
-
       - name: set test prompt to main branch validation
         if: ${{ github.ref == 'refs/heads/main' }}
         run: echo "TEST_PROMPTS=tests/preflight_prompts.txt" >> $GITHUB_ENV
diff --git a/configs/models.yaml.example b/configs/models.yaml.example
index 1eb2781f4a8..87bc13645d1 100644
--- a/configs/models.yaml.example
+++ b/configs/models.yaml.example
@@ -5,11 +5,15 @@
 # model requires a model config file, a weights file,
 # and the width and height of the images it
 # was trained on.
+diffusers-1.4:
+  description: Diffusers version of Stable Diffusion version 1.4
+  format: diffusers
+  repo_name: CompVis/stable-diffusion-v1-4
+  default: true
 diffusers-1.5:
   description: Diffusers version of Stable Diffusion version 1.5
   format: diffusers
   repo_name: runwayml/stable-diffusion-v1-5
-  default: true
 stable-diffusion-1.5:
   description: The newest Stable Diffusion version 1.5 weight file (4.27 GB)
   weights: models/ldm/stable-diffusion-v1/v1-5-pruned-emaonly.ckpt

From c21660a6df4683426762b848d77bd65bccf44d6c Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Sat, 26 Nov 2022 10:52:40 -0800
Subject: [PATCH 029/199] build: there's yet another place to update
 requirements?

---
 environments-and-requirements/requirements-base.txt | 2 +-
 installer/requirements.in                           | 3 ++-
 2 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/environments-and-requirements/requirements-base.txt b/environments-and-requirements/requirements-base.txt
index f125544f535..867273928ee 100644
--- a/environments-and-requirements/requirements-base.txt
+++ b/environments-and-requirements/requirements-base.txt
@@ -31,7 +31,7 @@ taming-transformers-rom1504
 test-tube>=0.7.5
 torch-fidelity
 torchmetrics
-transformers==4.21.*
+transformers~=4.24
 picklescan
 git+https://github.com/invoke-ai/GFPGAN@basicsr-1.4.1#egg=gfpgan ; platform_system == 'Windows'
 git+https://github.com/invoke-ai/GFPGAN@basicsr-1.4.2#egg=gfpgan ; platform_system != 'Windows'
diff --git a/installer/requirements.in b/installer/requirements.in
index 86f6be44349..8699ae88615 100644
--- a/installer/requirements.in
+++ b/installer/requirements.in
@@ -3,7 +3,8 @@
 --trusted-host https://download.pytorch.org
 accelerate~=0.14
 albumentations
-diffusers
+diffusers[torch]~=0.9
+einops
 eventlet
 flask_cors
 flask_socketio

From 8ce1ae550ba617635b6cedfeda468a9f10977d5b Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Sat, 26 Nov 2022 10:58:12 -0800
Subject: [PATCH 030/199] configure: try to download models even without token

Models in the CompVis and stabilityai repos no longer require them. (But runwayml still does.)
---
 scripts/configure_invokeai.py | 134 ++++++++++++++++++++++++----------
 1 file changed, 97 insertions(+), 37 deletions(-)

diff --git a/scripts/configure_invokeai.py b/scripts/configure_invokeai.py
index 2bfefaa28c6..7c410e3ead2 100644
--- a/scripts/configure_invokeai.py
+++ b/scripts/configure_invokeai.py
@@ -8,26 +8,28 @@
 #
 print('Loading Python libraries...\n')
 import argparse
-import sys
 import os
 import re
-import warnings
 import shutil
-from urllib import request
-from tqdm import tqdm
-from omegaconf import OmegaConf
-from huggingface_hub import HfFolder, hf_hub_url
+import sys
+import traceback
+import warnings
 from pathlib import Path
+from typing import Dict
+from urllib import request
+
+import requests
+import transformers
+from diffusers import StableDiffusionPipeline, AutoencoderKL
 from getpass_asterisk import getpass_asterisk
+from huggingface_hub import HfFolder, hf_hub_url, whoami as hf_whoami
+from omegaconf import OmegaConf
+from tqdm import tqdm
 from transformers import CLIPTokenizer, CLIPTextModel
+
 from ldm.invoke.globals import Globals
 from ldm.invoke.readline import generic_completer
 
-import traceback
-import requests
-import clip
-import transformers
-import warnings
 warnings.filterwarnings('ignore')
 import torch
 transformers.logging.set_verbosity_error()
@@ -65,7 +67,7 @@ def introduction():
 def postscript():
     print(
         '''\n** Model Installation Successful **\nYou're all set! You may now launch InvokeAI using one of these two commands:
-Web version: 
+Web version:
     python scripts/invoke.py --web  (connect to http://localhost:9090)
 Command-line version:
    python scripts/invoke.py
@@ -127,7 +129,7 @@ def select_datasets(action:str):
 
         if action == 'customized':
             print('''
-Choose the weight file(s) you wish to download. Before downloading you 
+Choose the weight file(s) you wish to download. Before downloading you
 will be given the option to view and change your selections.
 '''
         )
@@ -142,7 +144,7 @@ def select_datasets(action:str):
                 if Datasets[ds]['recommended']:
                     datasets[ds]=counter
                     counter += 1
-                
+
         print('The following weight files will be downloaded:')
         for ds in datasets:
             dflt = '*' if dflt is None else ''
@@ -166,11 +168,11 @@ def recommended_datasets()->dict:
         if Datasets[ds]['recommended']:
             datasets[ds]=True
     return datasets
-    
+
 #-------------------------------Authenticate against Hugging Face
 def authenticate():
     print('''
-To download the Stable Diffusion weight files from the official Hugging Face 
+To download the Stable Diffusion weight files from the official Hugging Face
 repository, you need to read and accept the CreativeML Responsible AI license.
 
 This involves a few easy steps.
@@ -203,18 +205,18 @@ def authenticate():
     access_token = HfFolder.get_token()
     if access_token is not None:
         print('found')
-    
+
     if access_token is None:
         print('not found')
         print('''
 4. Thank you! The last step is to enter your HuggingFace access token so that
    this script is authorized to initiate the download. Go to the access tokens
-   page of your Hugging Face account and create a token by clicking the 
+   page of your Hugging Face account and create a token by clicking the
    "New token" button:
 
    https://huggingface.co/settings/tokens
 
-   (You can enter anything you like in the token creation field marked "Name". 
+   (You can enter anything you like in the token creation field marked "Name".
    "Role" should be "read").
 
    Now copy the token to your clipboard and paste it here: '''
@@ -235,7 +237,7 @@ def migrate_models_ckpt():
     if rename:
         print(f'model.ckpt => {new_name}')
         os.replace(os.path.join(model_path,'model.ckpt'),os.path.join(model_path,new_name))
-            
+
 #---------------------------------------------
 def download_weight_datasets(models:dict, access_token:str):
     migrate_models_ckpt()
@@ -262,9 +264,9 @@ def download_weight_datasets(models:dict, access_token:str):
 
     HfFolder.save_token(access_token)
     keys = ', '.join(successful.keys())
-    print(f'Successfully installed {keys}') 
+    print(f'Successfully installed {keys}')
     return successful
-    
+
 #---------------------------------------------
 def hf_download_with_resume(repo_id:str, model_dir:str, model_name:str, access_token:str=None)->bool:
     model_dest = os.path.join(model_dir, model_name)
@@ -275,7 +277,7 @@ def hf_download_with_resume(repo_id:str, model_dir:str, model_name:str, access_t
     header = {"Authorization": f'Bearer {access_token}'} if access_token else {}
     open_mode = 'wb'
     exist_size = 0
-    
+
     if os.path.exists(model_dest):
         exist_size = os.path.getsize(model_dest)
         header['Range'] = f'bytes={exist_size}-'
@@ -283,7 +285,7 @@ def hf_download_with_resume(repo_id:str, model_dir:str, model_name:str, access_t
 
     resp = requests.get(url, headers=header, stream=True)
     total = int(resp.headers.get('content-length', 0))
-    
+
     if resp.status_code==416:  # "range not satisfiable", which means nothing to return
         print(f'* {model_name}: complete file found. Skipping.')
         return True
@@ -331,12 +333,64 @@ def download_with_progress_bar(model_url:str, model_dest:str, label:str='the'):
         print(f'Error downloading {label} model')
         print(traceback.format_exc())
 
-                             
+
+#---------------------------------------------
+def download_diffusers(models: Dict, full_precision: bool):
+    # This is a minimal implementation until https://github.com/invoke-ai/InvokeAI/pull/1490 lands,
+    # which moves a bunch of stuff.
+    # We can be more complete after we know it won't be all merge conflicts.
+    diffusers_repos = {
+        'CompVis/stable-diffusion-v1-4-original': 'CompVis/stable-diffusion-v1-4',
+        'runwayml/stable-diffusion-v1-5': 'runwayml/stable-diffusion-v1-5',
+        'runwayml/stable-diffusion-inpainting': 'runwayml/stable-diffusion-inpainting',
+        'hakurei/waifu-diffusion-v1-3': 'hakurei/waifu-diffusion'
+    }
+    vae_repos = {
+        'stabilityai/sd-vae-ft-mse-original': 'stabilityai/sd-vae-ft-mse',
+    }
+    precision_args = {}
+    if not full_precision:
+        precision_args.update(revision='fp16')
+
+    for model_name, model in models.items():
+        repo_id = model['repo_id']
+        if repo_id in vae_repos:
+            print(f" * Downloading diffusers VAE {model_name}...")
+            # TODO: can we autodetect when a repo has no fp16 revision?
+            AutoencoderKL.from_pretrained(repo_id)
+        elif repo_id not in diffusers_repos:
+            print(f" * Downloading diffusers {model_name}...")
+            StableDiffusionPipeline.from_pretrained(repo_id, **precision_args)
+        else:
+            warnings.warn(f" ⚠ FIXME: add diffusers repo for {repo_id}")
+            continue
+
+
+def download_diffusers_in_config(config_path: Path, full_precision: bool):
+    # This is a minimal implementation until https://github.com/invoke-ai/InvokeAI/pull/1490 lands,
+    # which moves a bunch of stuff.
+    # We can be more complete after we know it won't be all merge conflicts.
+    if not is_huggingface_authenticated():
+        print("*⚠ No Hugging Face access token; some downloads may be blocked.")
+
+    precision = 'full' if full_precision else 'float16'
+    cache = ModelCache(OmegaConf.load(config_path), precision=precision,
+                       device_type='cpu', max_loaded_models=1)
+    for model_name in cache.list_models():
+        # TODO: download model without loading it.
+        #   https://github.com/huggingface/diffusers/issues/1301
+        model_config = cache.config[model_name]
+        if model_config.get('format') == 'diffusers':
+            print(f" * Downloading diffusers {model_name}...")
+            cache.get_model(model_name)
+            cache.offload_model(model_name)
+
+
 #---------------------------------------------
 def update_config_file(successfully_downloaded:dict,opt:dict):
     config_file = opt.config_file or Default_config_file
     config_file = os.path.normpath(os.path.join(Globals.root,config_file))
-    
+
     yaml = new_config_file_contents(successfully_downloaded,config_file)
 
     try:
@@ -355,8 +409,8 @@ def update_config_file(successfully_downloaded:dict,opt:dict):
 
     print(f'Successfully created new configuration file {config_file}')
 
-    
-#---------------------------------------------    
+
+#---------------------------------------------
 def new_config_file_contents(successfully_downloaded:dict, config_file:str)->str:
     if os.path.exists(config_file):
         conf = OmegaConf.load(config_file)
@@ -366,19 +420,19 @@ def new_config_file_contents(successfully_downloaded:dict, config_file:str)->str
     # find the VAE file, if there is one
     vaes = {}
     default_selected = False
-    
+
     for model in successfully_downloaded:
         a = Datasets[model]['config'].split('/')
         if a[0] != 'VAE':
             continue
         vae_target = a[1] if len(a)>1 else 'default'
         vaes[vae_target] = Datasets[model]['file']
-    
+
     for model in successfully_downloaded:
         if Datasets[model]['config'].startswith('VAE'): # skip VAE entries
             continue
         stanza = conf[model] if model in conf else { }
-        
+
         stanza['description'] = Datasets[model]['description']
         stanza['weights'] = os.path.join(Model_dir,Weights_dir,Datasets[model]['file'])
         stanza['config'] = os.path.normpath(os.path.join(SD_Configs, Datasets[model]['config']))
@@ -397,14 +451,14 @@ def new_config_file_contents(successfully_downloaded:dict, config_file:str)->str
             default_selected = True
         conf[model] = stanza
     return OmegaConf.to_yaml(conf)
-    
+
 #---------------------------------------------
 # this will preload the Bert tokenizer fles
 def download_bert():
     print('Installing bert tokenizer (ignore deprecation errors)...', end='',file=sys.stderr)
     with warnings.catch_warnings():
         warnings.filterwarnings('ignore', category=DeprecationWarning)
-        from transformers import BertTokenizerFast, AutoFeatureExtractor
+        from transformers import BertTokenizerFast
         download_from_hf(BertTokenizerFast,'bert-base-uncased')
         print('...success',file=sys.stderr)
 
@@ -467,7 +521,7 @@ def download_clipseg():
         model_url = 'https://owncloud.gwdg.de/index.php/s/ioHbRzFx6th32hn/download'
         model_dest = os.path.join(Globals.root,'models/clipseg/clipseg_weights')
         weights_zip = 'models/clipseg/weights.zip'
-        
+
         if not os.path.exists(model_dest):
             os.makedirs(os.path.dirname(model_dest), exist_ok=True)
         if not os.path.exists(f'{model_dest}/rd64-uni-refined.pth'):
@@ -586,7 +640,7 @@ def select_outputs(root:str,yes_to_all:bool=False):
 #-------------------------------------
 def initialize_rootdir(root:str,yes_to_all:bool=False):
     assert os.path.exists('./configs'),'Run this script from within the InvokeAI source code directory, "InvokeAI" or the runtime directory "invokeai".'
-    
+
     print(f'** INITIALIZING INVOKEAI RUNTIME DIRECTORY **')
     root_selected = False
     while not root_selected:
@@ -670,7 +724,7 @@ def initialize_rootdir(root:str,yes_to_all:bool=False):
 # -Ak_euler_a -C10.0
 #
 ''')
-    
+
 #-------------------------------------
 class ProgressBar():
     def __init__(self,model_name='file'):
@@ -727,6 +781,12 @@ def main():
         if opt.interactive:
             print('** DOWNLOADING DIFFUSION WEIGHTS **')
             download_weights(opt)
+        else:
+            config_path = Path(opt.config_file or Default_config_file)
+            if config_path.exists():
+                download_diffusers_in_config(config_path, full_precision=opt.full_precision)
+            else:
+                print("*⚠ No config file found; downloading no weights.")
         print('\n** DOWNLOADING SUPPORT MODELS **')
         download_bert()
         download_clip()
@@ -741,7 +801,7 @@ def main():
     except Exception as e:
         print(f'\nA problem occurred during initialization.\nThe error was: "{str(e)}"')
         print(traceback.format_exc())
-    
+
 #-------------------------------------
 if __name__ == '__main__':
     main()

From 3898336509f40e56f852776d5928efa99dccb963 Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Sun, 27 Nov 2022 08:27:03 -0800
Subject: [PATCH 031/199] configure: add troubleshooting info for
 config-not-found

---
 scripts/configure_invokeai.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/configure_invokeai.py b/scripts/configure_invokeai.py
index 7c410e3ead2..2d8e83720ef 100644
--- a/scripts/configure_invokeai.py
+++ b/scripts/configure_invokeai.py
@@ -786,7 +786,7 @@ def main():
             if config_path.exists():
                 download_diffusers_in_config(config_path, full_precision=opt.full_precision)
             else:
-                print("*⚠ No config file found; downloading no weights.")
+                print(f"*⚠ No config file found; downloading no weights. Looked in {config_path}")
         print('\n** DOWNLOADING SUPPORT MODELS **')
         download_bert()
         download_clip()

From 73ddde7d3f78e3c3b92449ea74ad05dcfa138ec0 Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Sun, 27 Nov 2022 09:09:38 -0800
Subject: [PATCH 032/199] fix(configure): prepend root to config path

---
 scripts/configure_invokeai.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/configure_invokeai.py b/scripts/configure_invokeai.py
index 2d8e83720ef..fd593bd5840 100644
--- a/scripts/configure_invokeai.py
+++ b/scripts/configure_invokeai.py
@@ -782,7 +782,7 @@ def main():
             print('** DOWNLOADING DIFFUSION WEIGHTS **')
             download_weights(opt)
         else:
-            config_path = Path(opt.config_file or Default_config_file)
+            config_path = Path(Globals.root, opt.config_file or Default_config_file)
             if config_path.exists():
                 download_diffusers_in_config(config_path, full_precision=opt.full_precision)
             else:

From 35c4d263e6b98744519d2e35951571c64229809c Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Sun, 27 Nov 2022 10:14:40 -0800
Subject: [PATCH 033/199] fix(configure): remove second `default: true` from
 models example

---
 configs/models.yaml.example | 1 -
 1 file changed, 1 deletion(-)

diff --git a/configs/models.yaml.example b/configs/models.yaml.example
index 87bc13645d1..dbb62f2e32b 100644
--- a/configs/models.yaml.example
+++ b/configs/models.yaml.example
@@ -21,7 +21,6 @@ stable-diffusion-1.5:
   width: 512
   height: 512
   vae: ./models/ldm/stable-diffusion-v1/vae-ft-mse-840000-ema-pruned.ckpt
-  default: true
 stable-diffusion-1.4:
   description: Stable Diffusion inference model version 1.4
   config: configs/stable-diffusion/v1-inference.yaml

From f9dcc9a9b439e7a79db353a2c6dff23e1e3aeb3b Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Tue, 29 Nov 2022 13:23:50 -0800
Subject: [PATCH 034/199] CI: simplify test-on-push logic now that we don't
 need secrets

The "test on push but only in forks" logic was only necessary when tests didn't work for PRs-from-forks.
---
 .github/workflows/test-invoke-conda.yml | 16 +++++++---------
 .github/workflows/test-invoke-pip.yml   | 15 ++++++---------
 2 files changed, 13 insertions(+), 18 deletions(-)

diff --git a/.github/workflows/test-invoke-conda.yml b/.github/workflows/test-invoke-conda.yml
index fd2c9c10e98..5812b63fccd 100644
--- a/.github/workflows/test-invoke-conda.yml
+++ b/.github/workflows/test-invoke-conda.yml
@@ -1,16 +1,14 @@
 name: Test invoke.py
-on: [push, pull_request]
+on:
+  push:
+    branches:
+      - 'main'
+      - 'development'
+      - 'fix-gh-actions-fork'
+  pull_request:
 
 jobs:
   matrix:
-    # Run on:
-    # - pull requests
-    # - pushes to forks (will run in the forked project with that fork's secrets)
-    # - pushes to branches that are *not* pull requests
-    if: |
-      github.event_name == 'pull_request'
-      || github.repository != 'invoke-ai/InvokeAI'
-      || github.ref_protected
     strategy:
       matrix:
         stable-diffusion-model:
diff --git a/.github/workflows/test-invoke-pip.yml b/.github/workflows/test-invoke-pip.yml
index 1803743e289..178c3a72022 100644
--- a/.github/workflows/test-invoke-pip.yml
+++ b/.github/workflows/test-invoke-pip.yml
@@ -1,16 +1,13 @@
 name: Test invoke.py pip
-on: [push, pull_request]
+on:
+  push:
+    branches:
+      - 'main'
+      - 'development'
+  pull_request:
 
 jobs:
   matrix:
-    # Run on:
-    # - pull requests
-    # - pushes to forks (will run in the forked project with that fork's secrets)
-    # - pushes to branches that are *not* pull requests
-    if: |
-      github.event_name == 'pull_request'
-      || github.repository != 'invoke-ai/InvokeAI'
-      || github.ref_protected
     strategy:
       matrix:
         stable-diffusion-model:

From ca1f76b7ba04e1ebe0e473d7c178403aee082383 Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Mon, 28 Nov 2022 18:26:52 -0800
Subject: [PATCH 035/199] create an embedding_manager for diffusers

---
 ldm/invoke/generator/diffusers_pipeline.py | 12 ++++++++++++
 1 file changed, 12 insertions(+)

diff --git a/ldm/invoke/generator/diffusers_pipeline.py b/ldm/invoke/generator/diffusers_pipeline.py
index 2d3f694687a..714d07faacf 100644
--- a/ldm/invoke/generator/diffusers_pipeline.py
+++ b/ldm/invoke/generator/diffusers_pipeline.py
@@ -16,6 +16,7 @@
 from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
 
 from ldm.models.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent
+from ldm.modules.embedding_manager import EmbeddingManager
 from ldm.modules.encoders.modules import WeightedFrozenCLIPEmbedder
 
 
@@ -28,6 +29,16 @@ class PipelineIntermediateState:
     predicted_original: Optional[torch.Tensor] = None
 
 
+# copied from configs/stable-diffusion/v1-inference.yaml
+_default_personalization_config_params = dict(
+    placeholder_strings=["*"],
+    initializer_wods=["sculpture"],
+    per_image_tokens=False,
+    num_vectors_per_token=8,
+    progressive_words=False
+)
+
+
 class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
     r"""
     Pipeline for text-to-image generation using Stable Diffusion.
@@ -89,6 +100,7 @@ def __init__(
             transformer=self.text_encoder
         )
         self.invokeai_diffuser = InvokeAIDiffuserComponent(self.unet, self._unet_forward)
+        self.embedding_manager = EmbeddingManager(self.clip_embedder, **_default_personalization_config_params)
 
     def image_from_embeddings(self, latents: torch.Tensor, num_inference_steps: int,
                               text_embeddings: torch.Tensor, unconditioned_embeddings: torch.Tensor,

From 8157bff4bc5ddf444182e066f950f67afaad0ebc Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Tue, 29 Nov 2022 19:02:45 -0800
Subject: [PATCH 036/199] internal: avoid importing diffusers DummyObject

see https://github.com/huggingface/diffusers/issues/1479
---
 ldm/generate.py                            | 53 +++++++++++-----------
 ldm/invoke/generator/diffusers_pipeline.py |  5 +-
 2 files changed, 29 insertions(+), 29 deletions(-)

diff --git a/ldm/generate.py b/ldm/generate.py
index d99954c2497..63d9a7a6efe 100644
--- a/ldm/generate.py
+++ b/ldm/generate.py
@@ -1,48 +1,47 @@
 # Copyright (c) 2022 Lincoln D. Stein (https://github.com/lstein)
-import pyparsing
 # Derived from source code carrying the following copyrights
 # Copyright (c) 2022 Machine Vision and Learning Group, LMU Munich
 # Copyright (c) 2022 Robin Rombach and Patrick Esser and contributors
 
-import torch
-import numpy as np
-import random
+import gc
 import os
-import time
+import random
 import re
 import sys
+import time
 import traceback
-import transformers
-import io
-import gc
-import hashlib
+
 import cv2
+import numpy as np
 import skimage
-from diffusers import DiffusionPipeline, DDIMScheduler, LMSDiscreteScheduler, EulerDiscreteScheduler, \
-    EulerAncestralDiscreteScheduler, PNDMScheduler, IPNDMScheduler
-
-from omegaconf import OmegaConf
-from ldm.invoke.generator.base import downsampling
+import torch
+import transformers
 from PIL import Image, ImageOps
-from torch import nn
+from diffusers.pipeline_utils import DiffusionPipeline
+from diffusers.schedulers.scheduling_ddim import DDIMScheduler
+from diffusers.schedulers.scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
+from diffusers.schedulers.scheduling_euler_discrete import EulerDiscreteScheduler
+from diffusers.schedulers.scheduling_ipndm import IPNDMScheduler
+from diffusers.schedulers.scheduling_lms_discrete import LMSDiscreteScheduler
+from diffusers.schedulers.scheduling_pndm import PNDMScheduler
+from omegaconf import OmegaConf
 from pytorch_lightning import seed_everything, logging
 
-from ldm.invoke.prompt_parser import PromptParser
-from ldm.util import instantiate_from_config
-from ldm.invoke.globals import Globals
-from ldm.models.diffusion.ddim import DDIMSampler
-from ldm.models.diffusion.plms import PLMSSampler
-from ldm.models.diffusion.ksampler import KSampler
-from ldm.invoke.pngwriter import PngWriter
 from ldm.invoke.args import metadata_from_png
-from ldm.invoke.image_util import InitImageResizer
-from ldm.invoke.devices import choose_torch_device, choose_precision
+from ldm.invoke.concepts_lib import Concepts
 from ldm.invoke.conditioning import get_uc_and_c_and_ec
+from ldm.invoke.devices import choose_torch_device, choose_precision
+from ldm.invoke.globals import Globals
+from ldm.invoke.image_util import InitImageResizer
 from ldm.invoke.model_cache import ModelCache
+from ldm.invoke.pngwriter import PngWriter
 from ldm.invoke.seamless import configure_model_padding
-from ldm.invoke.txt2mask import Txt2Mask, SegmentedGrayscale
-from ldm.invoke.concepts_lib import Concepts
-    
+from ldm.invoke.txt2mask import Txt2Mask
+from ldm.models.diffusion.ddim import DDIMSampler
+from ldm.models.diffusion.ksampler import KSampler
+from ldm.models.diffusion.plms import PLMSSampler
+
+
 def fix_func(orig):
     if hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
         def new_func(*args, **kw):
diff --git a/ldm/invoke/generator/diffusers_pipeline.py b/ldm/invoke/generator/diffusers_pipeline.py
index 714d07faacf..e9d1d052ffc 100644
--- a/ldm/invoke/generator/diffusers_pipeline.py
+++ b/ldm/invoke/generator/diffusers_pipeline.py
@@ -7,10 +7,11 @@
 
 import PIL.Image
 import torch
-from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline
 from diffusers.models import AutoencoderKL, UNet2DConditionModel
 from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
-from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img import preprocess
+from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipeline
+from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img import preprocess, \
+    StableDiffusionImg2ImgPipeline
 from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
 from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
 from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer

From 7c558d5056a1fc2e1a7a06757c0e91f1dfb677e9 Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Tue, 29 Nov 2022 19:07:13 -0800
Subject: [PATCH 037/199] =?UTF-8?q?fix=20"config=20attributes=E2=80=A6not?=
 =?UTF-8?q?=20expected"=20diffusers=20warnings.?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 ldm/invoke/generator/diffusers_pipeline.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/ldm/invoke/generator/diffusers_pipeline.py b/ldm/invoke/generator/diffusers_pipeline.py
index e9d1d052ffc..0a689ece7b3 100644
--- a/ldm/invoke/generator/diffusers_pipeline.py
+++ b/ldm/invoke/generator/diffusers_pipeline.py
@@ -83,8 +83,10 @@ def __init__(
         scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
         safety_checker: Optional[StableDiffusionSafetyChecker],
         feature_extractor: Optional[CLIPFeatureExtractor],
+        requires_safety_checker: bool = False
     ):
-        super().__init__(vae, text_encoder, tokenizer, unet, scheduler, safety_checker, feature_extractor)
+        super().__init__(vae, text_encoder, tokenizer, unet, scheduler,
+                         safety_checker, feature_extractor, requires_safety_checker)
 
         self.register_modules(
             vae=vae,

From e9a0f07033dd6d1b3b55e808b755d5df6ac385fb Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Tue, 29 Nov 2022 19:35:13 -0800
Subject: [PATCH 038/199] fix deprecated scheduler construction

---
 ldm/generate.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ldm/generate.py b/ldm/generate.py
index 63d9a7a6efe..04cdaea89d5 100644
--- a/ldm/generate.py
+++ b/ldm/generate.py
@@ -1023,7 +1023,7 @@ def _set_scheduler(self):
         if self.sampler_name in scheduler_map:
             sampler_class = scheduler_map[self.sampler_name]
             msg = f'>> Setting Sampler to {self.sampler_name} ({sampler_class.__name__})'
-            self.sampler = sampler_class.from_config(
+            self.sampler = sampler_class.from_pretrained(
                 self.model_cache.model_name_or_path(self.model_name),
                 subfolder="scheduler"
             )

From adaa1c7c3e4994834e276b87e98d06e3f675b161 Mon Sep 17 00:00:00 2001
From: Damian Stewart <d@damianstewart.com>
Date: Wed, 30 Nov 2022 22:05:11 +0100
Subject: [PATCH 039/199] work around an apparent MPS torch bug that causes
 conditioning to have no effect

---
 ldm/models/diffusion/shared_invokeai_diffusion.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/ldm/models/diffusion/shared_invokeai_diffusion.py b/ldm/models/diffusion/shared_invokeai_diffusion.py
index d6ec1ea44bf..538410045fe 100644
--- a/ldm/models/diffusion/shared_invokeai_diffusion.py
+++ b/ldm/models/diffusion/shared_invokeai_diffusion.py
@@ -102,8 +102,11 @@ def apply_standard_conditioning(self, x, sigma, unconditioning, conditioning):
         x_twice = torch.cat([x] * 2)
         sigma_twice = torch.cat([sigma] * 2)
         both_conditionings = torch.cat([unconditioning, conditioning])
-        unconditioned_next_x, conditioned_next_x = self.model_forward_callback(x_twice, sigma_twice,
-                                                                               both_conditionings).chunk(2)
+        both_results = self.model_forward_callback(x_twice, sigma_twice, both_conditionings)
+        unconditioned_next_x, conditioned_next_x = both_results.chunk(2)
+        if conditioned_next_x.device.type == 'mps':
+            # prevent a result filled with zeros. seems to be a torch bug.
+            conditioned_next_x = conditioned_next_x.clone()
         return unconditioned_next_x, conditioned_next_x
 
 

From 494936a8d24a09cb278a8e4520db3238b0e63125 Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Wed, 30 Nov 2022 15:25:47 -0800
Subject: [PATCH 040/199] =?UTF-8?q?=F0=9F=9A=A7=20post-rebase=20repair?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .github/workflows/test-invoke-conda.yml       |  4 +-
 backend/invoke_ai_web_server.py               | 42 +++++++++--------
 .../environment-lin-cuda.yml                  |  9 ----
 ldm/generate.py                               | 32 ++++++-------
 ldm/invoke/generator/base.py                  |  7 ++-
 ldm/invoke/model_cache.py                     | 45 ++++++++++++++-----
 .../diffusion/cross_attention_control.py      | 44 +++++++++---------
 scripts/configure_invokeai.py                 | 25 +++++++++++
 8 files changed, 122 insertions(+), 86 deletions(-)

diff --git a/.github/workflows/test-invoke-conda.yml b/.github/workflows/test-invoke-conda.yml
index 5812b63fccd..f37512d2bbc 100644
--- a/.github/workflows/test-invoke-conda.yml
+++ b/.github/workflows/test-invoke-conda.yml
@@ -86,14 +86,14 @@ jobs:
         if: ${{ github.ref != 'refs/heads/main' && github.ref != 'refs/heads/development' }}
         run: echo "TEST_PROMPTS=tests/validate_pr_prompt.txt" >> $GITHUB_ENV
 
-      - name: run preload_models.py
+      - name: run configure_invokeai.py
         id: run-preload-models
         run: |
           if [ "${HAVE_SECRETS}" == true ] ; then
             mkdir -p ~/.huggingface
             echo -n '${{ secrets.HUGGINGFACE_TOKEN }}' > ~/.huggingface/token
           fi
-          python scripts/preload_models.py \
+          python scripts/configure_invokeai.py \
             --no-interactive --yes \
             --full-precision  # can't use fp16 weights without a GPU
 
diff --git a/backend/invoke_ai_web_server.py b/backend/invoke_ai_web_server.py
index d525cf87f89..a66e12a1a16 100644
--- a/backend/invoke_ai_web_server.py
+++ b/backend/invoke_ai_web_server.py
@@ -1,33 +1,31 @@
-import eventlet
+import base64
 import glob
+import io
+import json
+import math
+import mimetypes
 import os
 import shutil
-import mimetypes
 import traceback
-import math
-import io
-import base64
-import os
-import json
+from threading import Event
+from uuid import uuid4
 
-from werkzeug.utils import secure_filename
+import eventlet
+from PIL import Image
+from PIL.Image import Image as ImageType
 from flask import Flask, redirect, send_from_directory, request, make_response
 from flask_socketio import SocketIO
-from PIL import Image, ImageOps
-from PIL.Image import Image as ImageType
-from uuid import uuid4
-from threading import Event
+from werkzeug.utils import secure_filename
 
+from backend.modules.get_canvas_generation_mode import (
+    get_canvas_generation_mode,
+)
+from backend.modules.parameters import parameters_to_command
 from ldm.invoke.args import Args, APP_ID, APP_VERSION, calculate_init_img_hash
 from ldm.invoke.generator.diffusers_pipeline import PipelineIntermediateState
+from ldm.invoke.generator.inpaint import infill_methods
 from ldm.invoke.pngwriter import PngWriter, retrieve_metadata
 from ldm.invoke.prompt_parser import split_weighted_subprompts
-from ldm.invoke.generator.inpaint import infill_methods
-
-from backend.modules.parameters import parameters_to_command
-from backend.modules.get_canvas_generation_mode import (
-    get_canvas_generation_mode,
-)
 
 # Loading Arguments
 opt = Args()
@@ -251,7 +249,7 @@ def find_frontend(self):
                 return candidate
         assert "Frontend files cannot be found. Cannot continue"
 
-                                        
+
     def setup_app(self):
         self.result_url = "outputs/"
         self.init_image_url = "outputs/init-images/"
@@ -776,10 +774,10 @@ def generate_images(
                 ).convert("RGBA")
 
                 """
-                The outpaint image and mask are pre-cropped by the UI, so the bounding box we pass 
+                The outpaint image and mask are pre-cropped by the UI, so the bounding box we pass
                 to the generator should be:
                     {
-                        "x": 0, 
+                        "x": 0,
                         "y": 0,
                         "width": original_bounding_box["width"],
                         "height": original_bounding_box["height"]
@@ -799,7 +797,7 @@ def generate_images(
                 )
 
                 """
-                Apply the mask to the init image, creating a "mask" image with 
+                Apply the mask to the init image, creating a "mask" image with
                 transparency where inpainting should occur. This is the kind of
                 mask that prompt2image() needs.
                 """
diff --git a/environments-and-requirements/environment-lin-cuda.yml b/environments-and-requirements/environment-lin-cuda.yml
index 83ff8b5bb02..4397f95371c 100644
--- a/environments-and-requirements/environment-lin-cuda.yml
+++ b/environments-and-requirements/environment-lin-cuda.yml
@@ -40,15 +40,6 @@ dependencies:
     - torch-fidelity==0.3.0
     - torchmetrics==0.7.0
     - transformers==4.21.3
-    - diffusers~=0.7
-    - torchmetrics==0.7.0
-    - flask==2.1.3
-    - flask_socketio==5.3.0
-    - flask_cors==3.0.10
-    - dependency_injector==4.40.0
-    - eventlet
-    - getpass_asterisk
-    - kornia==0.6.0
     - git+https://github.com/openai/CLIP.git@main#egg=clip
     - git+https://github.com/Birch-san/k-diffusion.git@mps#egg=k-diffusion
     - git+https://github.com/invoke-ai/clipseg.git@relaxed-python-requirement#egg=clipseg
diff --git a/ldm/generate.py b/ldm/generate.py
index 04cdaea89d5..375b72b362a 100644
--- a/ldm/generate.py
+++ b/ldm/generate.py
@@ -236,7 +236,7 @@ def __init__(
             except Exception:
                 print('** An error was encountered while installing the safety checker:')
                 print(traceback.format_exc())
-                
+
     def prompt2png(self, prompt, outdir, **kwargs):
         """
         Takes a prompt and an output directory, writes out the requested number
@@ -330,7 +330,7 @@ def prompt2image(
             infill_method = infill_methods[0], # The infill method to use
             force_outpaint: bool = False,
             enable_image_debugging = False,
-            
+
             **args,
     ):   # eat up additional cruft
         """
@@ -373,7 +373,7 @@ def prompt2image(
             def process_image(image,seed):
                 image.save(f{'images/seed.png'})
 
-        The code used to save images to a directory can be found in ldm/invoke/pngwriter.py. 
+        The code used to save images to a directory can be found in ldm/invoke/pngwriter.py.
         It contains code to create the requested output directory, select a unique informative
         name for each image, and write the prompt into the PNG metadata.
         """
@@ -593,7 +593,7 @@ def apply_postprocessor(
         seed = opt.seed or args.seed
         if seed is None or seed < 0:
             seed = random.randrange(0, np.iinfo(np.uint32).max)
-        
+
         prompt = opt.prompt or args.prompt or ''
         print(f'>> using seed {seed} and prompt "{prompt}" for {image_path}')
 
@@ -645,7 +645,7 @@ def apply_postprocessor(
 
             opt.seed = seed
             opt.prompt = prompt
-            
+
             if len(extend_instructions) > 0:
                 restorer = Outcrop(image,self,)
                 return restorer.process (
@@ -687,7 +687,7 @@ def apply_postprocessor(
                 image_callback = callback,
                 prefix         = prefix
             )
-                
+
         elif tool is None:
             print(f'* please provide at least one postprocessing option, such as -G or -U')
             return None
@@ -710,13 +710,13 @@ def select_generator(
 
         if embiggen is not None:
             return self._make_embiggen()
-            
+
         if inpainting_model_in_use:
             return self._make_omnibus()
 
         if ((init_image is not None) and (mask_image is not None)) or force_outpaint:
             return self._make_inpaint()
-        
+
         if init_image is not None:
             return self._make_img2img()
 
@@ -747,7 +747,7 @@ def _make_images(
         if self._has_transparency(image):
             self._transparency_check_and_warning(image, mask, force_outpaint)
             init_mask = self._create_init_mask(image, width, height, fit=fit)
-            
+
         if (image.width * image.height) > (self.width * self.height) and self.size_matters:
             print(">> This input is larger than your defaults. If you run out of memory, please use a smaller image.")
             self.size_matters = False
@@ -763,7 +763,7 @@ def _make_images(
 
         if invert_mask:
             init_mask = ImageOps.invert(init_mask)
-            
+
         return init_image,init_mask
 
     # lots o' repeated code here! Turn into a make_func()
@@ -822,7 +822,7 @@ def load_model(self):
         self.set_model(self.model_name)
 
     def set_model(self,model_name):
-        """ 
+        """
         Given the name of a model defined in models.yaml, will load and initialize it
         and return the model object. Previously-used models will be cached.
         """
@@ -834,7 +834,7 @@ def set_model(self,model_name):
         if not cache.valid_model(model_name):
             print(f'** "{model_name}" is not a known model name. Please check your models.yaml file')
             return self.model
-        
+
         cache.print_vram_usage()
 
         # have to get rid of all references to model in order
@@ -843,7 +843,7 @@ def set_model(self,model_name):
         self.sampler = None
         self.generators = {}
         gc.collect()
-        
+
         model_data = cache.get_model(model_name)
         if model_data is None:  # restore previous
             model_data = cache.get_model(self.model_name)
@@ -856,7 +856,7 @@ def set_model(self,model_name):
 
         # uncache generators so they pick up new models
         self.generators = {}
-        
+
         seed_everything(random.randrange(0, np.iinfo(np.uint32).max))
         if self.embedding_path is not None:
             self.model.embedding_manager.load(
@@ -905,7 +905,7 @@ def upscale_and_reconstruct(self,
                                 image_callback = None,
                                 prefix = None,
     ):
-            
+
         for r in image_list:
             image, seed = r
             try:
@@ -915,7 +915,7 @@ def upscale_and_reconstruct(self,
                             if self.gfpgan is None:
                                 print('>> GFPGAN not found. Face restoration is disabled.')
                             else:
-                              image = self.gfpgan.process(image, strength, seed)                              
+                              image = self.gfpgan.process(image, strength, seed)
                         if facetool == 'codeformer':
                             if self.codeformer is None:
                                 print('>> CodeFormer not found. Face restoration is disabled.')
diff --git a/ldm/invoke/generator/base.py b/ldm/invoke/generator/base.py
index 7325e1334e0..da2d2900ca9 100644
--- a/ldm/invoke/generator/base.py
+++ b/ldm/invoke/generator/base.py
@@ -9,9 +9,10 @@
 import random
 import traceback
 
+import cv2
 import numpy as np
 import torch
-from PIL import Image, ImageFilter
+from PIL import Image, ImageFilter, ImageChops
 from diffusers import DiffusionPipeline
 from einops import rearrange
 from pytorch_lightning import seed_everything
@@ -169,7 +170,7 @@ def repaste_and_color_correct(self, result: Image.Image, init_image: Image.Image
         # Blur the mask out (into init image) by specified amount
         if mask_blur_radius > 0:
             nm = np.asarray(pil_init_mask, dtype=np.uint8)
-            nmd = cv.erode(nm, kernel=np.ones((3,3), dtype=np.uint8), iterations=int(mask_blur_radius / 2))
+            nmd = cv2.erode(nm, kernel=np.ones((3,3), dtype=np.uint8), iterations=int(mask_blur_radius / 2))
             pmd = Image.fromarray(nmd, mode='L')
             blurred_init_mask = pmd.filter(ImageFilter.BoxBlur(mask_blur_radius))
         else:
@@ -181,8 +182,6 @@ def repaste_and_color_correct(self, result: Image.Image, init_image: Image.Image
         matched_result.paste(init_image, (0,0), mask = multiplied_blurred_init_mask)
         return matched_result
 
-
-
     def sample_to_lowres_estimated_image(self,samples):
         # origingally adapted from code by @erucipe and @keturn here:
         # https://discuss.huggingface.co/t/decoding-latents-to-rgb-without-upscaling/23204/7
diff --git a/ldm/invoke/model_cache.py b/ldm/invoke/model_cache.py
index 94eb8e9cea1..4efa7aca0d2 100644
--- a/ldm/invoke/model_cache.py
+++ b/ldm/invoke/model_cache.py
@@ -21,9 +21,6 @@
 
 import torch
 import transformers
-import textwrap
-import contextlib
-from typing import Union
 from omegaconf import OmegaConf
 from omegaconf.errors import ConfigAttributeError
 from picklescan.scanner import scan_file_path
@@ -99,7 +96,7 @@ def get_model(self, model_name:str):
                 assert self.current_model,'** FATAL: no current model to restore to'
                 print(f'** restoring {self.current_model}')
                 self.get_model(self.current_model)
-                return None
+                return
 
         self.current_model = model_name
         self._push_newest_model(model_name)
@@ -219,7 +216,7 @@ def _load_model(self, model_name:str):
         if model_format == 'ckpt':
             weights = mconfig.weights
             print(f'>> Loading {model_name} from {weights}')
-            model, width, height, model_hash = self._load_ckpt_model(mconfig)
+            model, width, height, model_hash = self._load_ckpt_model(model_name, mconfig)
         elif model_format == 'diffusers':
             model, width, height, model_hash = self._load_diffusers_model(mconfig)
         else:
@@ -237,10 +234,10 @@ def _load_model(self, model_name:str):
             )
         return model, width, height, model_hash
 
-    def _load_ckpt_model(self, mconfig):
+    def _load_ckpt_model(self, model_name, mconfig):
         config = mconfig.config
         weights = mconfig.weights
-        vae = mconfig.get('vae', None)
+        vae = mconfig.get('vae')
         width = mconfig.width
         height = mconfig.height
 
@@ -249,10 +246,22 @@ def _load_ckpt_model(self, mconfig):
         if not os.path.isabs(weights):
             weights = os.path.normpath(os.path.join(Globals.root,weights))
         # scan model
-        self._scan_model(model_name, weights)
+        self.scan_model(model_name, weights)
+
+        print(f'>> Loading {model_name} from {weights}')
+
+        # for usage statistics
+        if self._has_cuda():
+            torch.cuda.reset_peak_memory_stats()
+            torch.cuda.empty_cache()
+
+        tic = time.time()
 
-        c = OmegaConf.load(config)
-        with open(weights, 'rb') as f:
+        # this does the work
+        if not os.path.isabs(config):
+            config = os.path.join(Globals.root,config)
+        omega_config = OmegaConf.load(config)
+        with open(weights,'rb') as f:
             weight_bytes = f.read()
         model_hash = self._cached_sha256(weights, weight_bytes)
         sd = torch.load(io.BytesIO(weight_bytes), map_location='cpu')
@@ -289,6 +298,18 @@ def _load_ckpt_model(self, mconfig):
             if isinstance(module, (torch.nn.Conv2d, torch.nn.ConvTranspose2d)):
                 module._orig_padding_mode = module.padding_mode
 
+        # usage statistics
+        toc = time.time()
+        print(f'>> Model loaded in', '%4.2fs' % (toc - tic))
+
+        if self._has_cuda():
+            print(
+                '>> Max VRAM used to load the model:',
+                '%4.2fG' % (torch.cuda.max_memory_allocated() / 1e9),
+                '\n>> Current VRAM usage:'
+                '%4.2fG' % (torch.cuda.memory_allocated() / 1e9),
+            )
+
         return model, width, height, model_hash
 
     def _load_diffusers_model(self, mconfig):
@@ -308,6 +329,8 @@ def _load_diffusers_model(self, mconfig):
 
         print(f'>> Loading diffusers model from {name_or_path}')
 
+        # TODO: scan weights maybe?
+
         if self.precision == 'float16':
             print('   | Using faster float16 precision')
             pipeline_args.update(revision="fp16", torch_dtype=torch.float16)
@@ -342,7 +365,7 @@ def model_name_or_path(self, model_name:str) -> str | Path:
         else:
             raise ValueError("Model config must specify either repo_name or path.")
 
-    def offload_model(self, model_name:str):
+    def offload_model(self, model_name:str) -> None:
         '''
         Offload the indicated model to CPU. Will call
         _make_cache_room() to free space if needed.
diff --git a/ldm/models/diffusion/cross_attention_control.py b/ldm/models/diffusion/cross_attention_control.py
index 08145b1e76e..ec7c3c215cc 100644
--- a/ldm/models/diffusion/cross_attention_control.py
+++ b/ldm/models/diffusion/cross_attention_control.py
@@ -248,33 +248,33 @@ def attention_slice_wrangler(module, suggested_attention_slice:torch.Tensor, dim
 
     cross_attention_modules = [(name, module) for (name, module) in unet.named_modules()
                                    if type(module).__name__ == "CrossAttention"]
-        for identifier, module in cross_attention_modules:
-            module.identifier = identifier
-            try:
-                module.set_attention_slice_wrangler(attention_slice_wrangler)
-                module.set_slicing_strategy_getter(
-                    lambda module: context.get_slicing_strategy(identifier)
-                )
-            except AttributeError as e:
-                if is_attribute_error_about(e, 'set_attention_slice_wrangler'):
-                    warnings.warn(f"TODO: implement for {type(module)}")  # TODO
-                else:
-                    raise
+    for identifier, module in cross_attention_modules:
+        module.identifier = identifier
+        try:
+            module.set_attention_slice_wrangler(attention_slice_wrangler)
+            module.set_slicing_strategy_getter(
+                lambda module: context.get_slicing_strategy(identifier)
+            )
+        except AttributeError as e:
+            if is_attribute_error_about(e, 'set_attention_slice_wrangler'):
+                warnings.warn(f"TODO: implement for {type(module)}")  # TODO
+            else:
+                raise
 
 
 def remove_attention_function(unet):
     cross_attention_modules = [module for (_, module) in unet.named_modules()
                                    if type(module).__name__ == "CrossAttention"]
-        for module in cross_attention_modules:
-            try:
-                # clear wrangler callback
-                module.set_attention_slice_wrangler(None)
-                module.set_slicing_strategy_getter(None)
-            except AttributeError as e:
-                if is_attribute_error_about(e, 'set_attention_slice_wrangler'):
-                    warnings.warn(f"TODO: implement for {type(module)}")  # TODO
-                else:
-                    raise
+    for module in cross_attention_modules:
+        try:
+            # clear wrangler callback
+            module.set_attention_slice_wrangler(None)
+            module.set_slicing_strategy_getter(None)
+        except AttributeError as e:
+            if is_attribute_error_about(e, 'set_attention_slice_wrangler'):
+                warnings.warn(f"TODO: implement for {type(module)}")  # TODO
+            else:
+                raise
 
 
 def is_attribute_error_about(error: AttributeError, attribute: str):
diff --git a/scripts/configure_invokeai.py b/scripts/configure_invokeai.py
index fd593bd5840..af3489d737a 100644
--- a/scripts/configure_invokeai.py
+++ b/scripts/configure_invokeai.py
@@ -34,6 +34,12 @@
 import torch
 transformers.logging.set_verbosity_error()
 
+try:
+    from ldm.invoke.model_cache import ModelCache
+except ImportError:
+    sys.path.append('.')
+    from ldm.invoke.model_cache import ModelCache
+
 #--------------------------globals-----------------------
 Model_dir = 'models'
 Weights_dir = 'ldm/stable-diffusion-v1/'
@@ -267,6 +273,19 @@ def download_weight_datasets(models:dict, access_token:str):
     print(f'Successfully installed {keys}')
     return successful
 
+#---------------------------------------------
+def is_huggingface_authenticated():
+    # huggingface_hub 0.10 API isn't great for this, it could be OSError, ValueError,
+    # maybe other things, not all end-user-friendly.
+    # noinspection PyBroadException
+    try:
+        response = hf_whoami()
+        if response.get('id') is not None:
+            return True
+    except Exception:
+        pass
+    return False
+
 #---------------------------------------------
 def hf_download_with_resume(repo_id:str, model_dir:str, model_name:str, access_token:str=None)->bool:
     model_dest = os.path.join(model_dir, model_name)
@@ -749,6 +768,12 @@ def main():
                         action=argparse.BooleanOptionalAction,
                         default=True,
                         help='run in interactive mode (default)')
+    parser.add_argument('--full-precision',
+                        dest='full_precision',
+                        action=argparse.BooleanOptionalAction,
+                        type=bool,
+                        default=False,
+                        help='use 32-bit weights instead of faster 16-bit weights')
     parser.add_argument('--yes','-y',
                         dest='yes_to_all',
                         action='store_true',

From ea1cf83c20910b3be6ec7d5df3bb13e1a4bedfdd Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Wed, 30 Nov 2022 22:05:58 -0800
Subject: [PATCH 041/199] preliminary support for outpainting (no masking yet)

---
 ldm/invoke/generator/diffusers_pipeline.py |  76 +++++++++++-
 ldm/invoke/generator/inpaint.py            | 128 +++++++++------------
 2 files changed, 127 insertions(+), 77 deletions(-)

diff --git a/ldm/invoke/generator/diffusers_pipeline.py b/ldm/invoke/generator/diffusers_pipeline.py
index 0a689ece7b3..989d43546e6 100644
--- a/ldm/invoke/generator/diffusers_pipeline.py
+++ b/ldm/invoke/generator/diffusers_pipeline.py
@@ -6,12 +6,13 @@
 from typing import List, Optional, Union, Callable
 
 import PIL.Image
+import einops
 import torch
+import torchvision.transforms as T
 from diffusers.models import AutoencoderKL, UNet2DConditionModel
 from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
 from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipeline
-from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img import preprocess, \
-    StableDiffusionImg2ImgPipeline
+from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img import StableDiffusionImg2ImgPipeline
 from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
 from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
 from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
@@ -40,6 +41,25 @@ class PipelineIntermediateState:
 )
 
 
+def image_resized_to_grid_as_tensor(image: PIL.Image.Image, normalize: bool=True, multiple_of=8) -> torch.FloatTensor:
+    """
+
+    :param image: input image
+    :param normalize: scale the range to [-1, 1] instead of [0, 1]
+    :param multiple_of: resize the input so both dimensions are a multiple of this
+    """
+    w, h = image.size
+    w, h = map(lambda x: x - x % 8, (w, h))  # resize to integer multiple of 8
+    transformation = T.Compose([
+        T.Resize((h, w), T.InterpolationMode.LANCZOS),
+        T.ToTensor(),
+    ])
+    tensor = transformation(image)
+    if normalize:
+        tensor = tensor * 2.0 - 1.0
+    return tensor
+
+
 class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
     r"""
     Pipeline for text-to-image generation using Stable Diffusion.
@@ -270,12 +290,15 @@ def img2img_from_embeddings(self,
                                 noise_func=None,
                                 **extra_step_kwargs) -> StableDiffusionPipelineOutput:
         device = self.unet.device
-        latents_dtype = text_embeddings.dtype
+        latents_dtype = self.unet.dtype
         batch_size = 1
         num_images_per_prompt = 1
 
         if isinstance(init_image, PIL.Image.Image):
-            init_image = preprocess(init_image.convert('RGB'))
+            init_image = image_resized_to_grid_as_tensor(init_image.convert('RGB'))
+
+        if init_image.dim() == 3:
+            init_image = einops.rearrange(init_image, 'c h w -> 1 c h w')
 
         img2img_pipeline = StableDiffusionImg2ImgPipeline(**self.components)
         img2img_pipeline.scheduler.set_timesteps(num_inference_steps, device=device)
@@ -297,6 +320,51 @@ def img2img_from_embeddings(self,
             raise AssertionError("why was that an empty generator?")
         return result
 
+    def inpaint_from_embeddings(
+            self,
+            init_image: torch.FloatTensor,
+            mask_image: torch.FloatTensor,
+            strength: float,
+            num_inference_steps: int,
+            text_embeddings: torch.Tensor, unconditioned_embeddings: torch.Tensor,
+            guidance_scale: float,
+            *, callback: Callable[[PipelineIntermediateState], None] = None,
+            extra_conditioning_info: InvokeAIDiffuserComponent.ExtraConditioningInfo = None,
+            run_id=None,
+            noise_func=None,
+            **extra_step_kwargs) -> StableDiffusionPipelineOutput:
+        device = self.unet.device
+        latents_dtype = self.unet.dtype
+        batch_size = 1
+        num_images_per_prompt = 1
+
+        if isinstance(init_image, PIL.Image.Image):
+            init_image = image_resized_to_grid_as_tensor(init_image.convert('RGB'))
+
+        if init_image.dim() == 3:
+            init_image = einops.rearrange(init_image, 'c h w -> 1 c h w')
+
+        img2img_pipeline = StableDiffusionImg2ImgPipeline(**self.components)
+        img2img_pipeline.scheduler.set_timesteps(num_inference_steps, device=device)
+        timesteps = img2img_pipeline.get_timesteps(num_inference_steps, strength, device=device)
+
+        # 6. Prepare latent variables
+        latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
+        latents = self.prepare_latents_from_image(init_image, latent_timestep, latents_dtype, device, noise_func)
+
+        result = None
+        for result in self.generate_from_embeddings(
+                latents, text_embeddings, unconditioned_embeddings, guidance_scale,
+                extra_conditioning_info=extra_conditioning_info,
+                timesteps=timesteps,
+                run_id=run_id, **extra_step_kwargs):
+            if callback is not None and isinstance(result, PipelineIntermediateState):
+                callback(result)
+        if result is None:
+            raise AssertionError("why was that an empty generator?")
+        return result
+
+
     def prepare_latents_from_image(self, init_image, timestep, dtype, device, noise_func) -> torch.FloatTensor:
         # can't quite use upstream StableDiffusionImg2ImgPipeline.prepare_latents
         # because we have our own noise function
diff --git a/ldm/invoke/generator/inpaint.py b/ldm/invoke/generator/inpaint.py
index 02bac7c999b..8a16e1e86fe 100644
--- a/ldm/invoke/generator/inpaint.py
+++ b/ldm/invoke/generator/inpaint.py
@@ -1,22 +1,21 @@
 '''
 ldm.invoke.generator.inpaint descends from ldm.invoke.generator
 '''
+from __future__ import annotations
 
 import math
 
 import PIL
-import cv2 as cv
+import cv2
 import numpy as np
 import torch
 from PIL import Image, ImageFilter, ImageOps, ImageChops
 from einops import repeat
 
-from ldm.invoke.devices import choose_autocast
 from ldm.invoke.generator.base import downsampling
+from ldm.invoke.generator.diffusers_pipeline import image_resized_to_grid_as_tensor, StableDiffusionGeneratorPipeline
 from ldm.invoke.generator.img2img import Img2Img
 from ldm.invoke.globals import Globals
-from ldm.models.diffusion.ddim import DDIMSampler
-from ldm.models.diffusion.ksampler import KSampler
 from ldm.util import debug_image
 
 infill_methods: list[str] = list()
@@ -36,6 +35,9 @@
 
 class Inpaint(Img2Img):
     def __init__(self, model, precision):
+        self.inpaint_height = 0
+        self.inpaint_width = 0
+        self.enable_image_debugging = False
         self.init_latent = None
         self.pil_image = None
         self.pil_mask = None
@@ -123,13 +125,13 @@ def mask_edge(self, mask: Image, edge_size: int, edge_blur: int) -> Image:
         npgradient = np.uint8(255 * (1.0 - np.floor(np.abs(0.5 - np.float32(npimg) / 255.0) * 2.0)))
 
         # Detect hard edges
-        npedge = cv.Canny(npimg, threshold1=100, threshold2=200)
+        npedge = cv2.Canny(npimg, threshold1=100, threshold2=200)
 
         # Combine
         npmask = npgradient + npedge
 
         # Expand
-        npmask = cv.dilate(npmask, np.ones((3,3), np.uint8), iterations = int(edge_size / 2))
+        npmask = cv2.dilate(npmask, np.ones((3,3), np.uint8), iterations = int(edge_size / 2))
 
         new_mask = Image.fromarray(npmask)
 
@@ -139,15 +141,8 @@ def mask_edge(self, mask: Image, edge_size: int, edge_blur: int) -> Image:
         return ImageOps.invert(new_mask)
 
 
-    def seam_paint(self,
-        im: Image.Image,
-        seam_size: int,
-        seam_blur: int,
-        prompt,sampler,steps,cfg_scale,ddim_eta,
-        conditioning,strength,
-        noise,
-        step_callback
-    ) -> Image.Image:
+    def seam_paint(self, im: Image.Image, seam_size: int, seam_blur: int, prompt, sampler, steps, cfg_scale, ddim_eta,
+                   conditioning, strength, noise, infill_method, step_callback) -> Image.Image:
         hard_mask = self.pil_image.split()[-1].copy()
         mask = self.mask_edge(hard_mask, seam_size, seam_blur)
 
@@ -165,7 +160,8 @@ def seam_paint(self,
             seam_size = 0,
             step_callback = step_callback,
             inpaint_width = im.width,
-            inpaint_height = im.height
+            inpaint_height = im.height,
+            infill_method = infill_method
         )
 
         seam_noise = self.get_noise(im.width, im.height)
@@ -177,7 +173,10 @@ def seam_paint(self,
 
     @torch.no_grad()
     def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
-                       conditioning,init_image,mask_image,strength,
+                       conditioning,
+                       init_image: PIL.Image.Image | torch.FloatTensor,
+                       mask_image: PIL.Image.Image | torch.FloatTensor,
+                       strength: float,
                        mask_blur_radius: int = 8,
                        # Seam settings - when 0, doesn't fill seam
                        seam_size: int = 0,
@@ -223,7 +222,7 @@ def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
             debug_image(init_filled, "init_filled", debug_status=self.enable_image_debugging)
 
             # Create init tensor
-            init_image = self._image_to_tensor(init_filled.convert('RGB'))
+            init_image = image_resized_to_grid_as_tensor(init_filled.convert('RGB'))
 
         if isinstance(mask_image, PIL.Image.Image):
             self.pil_mask = mask_image.copy()
@@ -244,85 +243,68 @@ def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
                 ),
                 resample=Image.Resampling.NEAREST
             )
-            mask_image = self._image_to_tensor(mask_image,normalize=False)
+            mask_image = image_resized_to_grid_as_tensor(mask_image, normalize=False)
 
         self.mask_blur_radius = mask_blur_radius
 
         # klms samplers not supported yet, so ignore previous sampler
-        if isinstance(sampler,KSampler):
-            print(
-                ">> Using recommended DDIM sampler for inpainting."
-            )
-            sampler = DDIMSampler(self.model, device=self.model.device)
-
-        sampler.make_schedule(
-            ddim_num_steps=steps, ddim_eta=ddim_eta, verbose=False
-        )
+        # if isinstance(sampler,KSampler):
+        #     print(
+        #         ">> Using recommended DDIM sampler for inpainting."
+        #     )
+        #     sampler = DDIMSampler(self.model, device=self.model.device)
 
         mask_image = mask_image[0][0].unsqueeze(0).repeat(4,1,1).unsqueeze(0)
         mask_image = repeat(mask_image, '1 ... -> b ...', b=1)
 
-        scope = choose_autocast(self.precision)
-        with scope(self.model.device.type):
-            self.init_latent = self.model.get_first_stage_encoding(
-                self.model.encode_first_stage(init_image)
-            ) # move to latent space
-
         t_enc   = int(strength * steps)
         # todo: support cross-attention control
         uc, c, _ = conditioning
 
         print(f">> target t_enc is {t_enc} steps")
 
-        @torch.no_grad()
+        # noinspection PyTypeChecker
+        pipeline: StableDiffusionGeneratorPipeline = self.model
+        pipeline.scheduler = sampler
+
         def make_image(x_T):
+            # FIXME: some of this z_enc and inpaint_replace stuff was probably important
             # encode (scaled latent)
-            z_enc = sampler.stochastic_encode(
-                self.init_latent,
-                torch.tensor([t_enc]).to(self.model.device),
-                noise=x_T
-            )
-
-            # to replace masked area with latent noise, weighted by inpaint_replace strength
-            if inpaint_replace > 0.0:
-                print(f'>> inpaint will replace what was under the mask with a strength of {inpaint_replace}')
-                l_noise = self.get_noise(kwargs['width'],kwargs['height'])
-                inverted_mask = 1.0-mask_image  # there will be 1s where the mask is
-                masked_region = (1.0-inpaint_replace) * inverted_mask * z_enc + inpaint_replace * inverted_mask * l_noise
-                z_enc   = z_enc * mask_image + masked_region
-
-            # decode it
-            samples = sampler.decode(
-                z_enc,
-                c,
-                t_enc,
-                img_callback                 = step_callback,
-                unconditional_guidance_scale = cfg_scale,
-                unconditional_conditioning = uc,
-                mask                       = mask_image,
-                init_latent                = self.init_latent
+            # z_enc = sampler.stochastic_encode(
+            #     self.init_latent,
+            #     torch.tensor([t_enc]).to(self.model.device),
+            #     noise=x_T
+            # )
+            #
+            # # to replace masked area with latent noise, weighted by inpaint_replace strength
+            # if inpaint_replace > 0.0:
+            #     print(f'>> inpaint will replace what was under the mask with a strength of {inpaint_replace}')
+            #     l_noise = self.get_noise(kwargs['width'],kwargs['height'])
+            #     inverted_mask = 1.0-mask_image  # there will be 1s where the mask is
+            #     masked_region = (1.0-inpaint_replace) * inverted_mask * z_enc + inpaint_replace * inverted_mask * l_noise
+            #     z_enc   = z_enc * mask_image + masked_region
+
+            pipeline_output = pipeline.inpaint_from_embeddings(
+                init_image=init_image,
+                mask_image=mask_image,
+                strength=strength,
+                num_inference_steps=steps,
+                text_embeddings=c,
+                unconditioned_embeddings=uc,
+                guidance_scale=cfg_scale,
+                noise_func=self.get_noise_like,
+                callback=step_callback,
             )
 
-            result = self.sample_to_image(samples)
+            result = pipeline.numpy_to_pil(pipeline_output.images)[0]
 
             # Seam paint if this is our first pass (seam_size set to 0 during seam painting)
             if seam_size > 0:
                 old_image = self.pil_image or init_image
                 old_mask = self.pil_mask or mask_image
 
-                result = self.seam_paint(
-                    result,
-                    seam_size,
-                    seam_blur,
-                    prompt,
-                    sampler,
-                    seam_steps,
-                    cfg_scale,
-                    ddim_eta,
-                    conditioning,
-                    seam_strength,
-                    x_T,
-                    step_callback)
+                result = self.seam_paint(result, seam_size, seam_blur, prompt, sampler, seam_steps, cfg_scale, ddim_eta,
+                                         conditioning, seam_strength, x_T, infill_method, step_callback)
 
                 # Restore original settings
                 self.get_make_image(prompt,sampler,steps,cfg_scale,ddim_eta,

From 9ea10ece9502ec39f058edde538bca6005305116 Mon Sep 17 00:00:00 2001
From: Damian Stewart <d@damianstewart.com>
Date: Sat, 3 Dec 2022 10:10:37 +0100
Subject: [PATCH 042/199] monkey-patch diffusers.attention and use Invoke
 lowvram code

---
 ldm/invoke/generator/diffusers_cross_attention_enhancements.py | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 ldm/invoke/generator/diffusers_cross_attention_enhancements.py

diff --git a/ldm/invoke/generator/diffusers_cross_attention_enhancements.py b/ldm/invoke/generator/diffusers_cross_attention_enhancements.py
new file mode 100644
index 00000000000..e69de29bb2d

From f48706efeef3359bf0edb423173262c9d3ebc21f Mon Sep 17 00:00:00 2001
From: Damian Stewart <d@damianstewart.com>
Date: Sun, 4 Dec 2022 15:15:39 +0100
Subject: [PATCH 043/199] add always_use_cpu arg to bypass MPS

---
 ldm/invoke/CLI.py                             | 45 ++++++++++---------
 ldm/invoke/args.py                            | 16 ++++---
 ldm/invoke/devices.py                         |  3 ++
 .../diffusers_cross_attention_enhancements.py |  0
 ldm/invoke/globals.py                         |  4 ++
 5 files changed, 41 insertions(+), 27 deletions(-)
 delete mode 100644 ldm/invoke/generator/diffusers_cross_attention_enhancements.py

diff --git a/ldm/invoke/CLI.py b/ldm/invoke/CLI.py
index f05ccbb1ca1..15468891aad 100644
--- a/ldm/invoke/CLI.py
+++ b/ldm/invoke/CLI.py
@@ -27,7 +27,7 @@ def main():
     """Initialize command-line parsers and the diffusion model"""
     global infile
     print('* Initializing, be patient...')
-    
+
     opt  = Args()
     args = opt.parse_args()
     if not args:
@@ -47,7 +47,8 @@ def main():
     # alert - setting globals here
     Globals.root = os.path.expanduser(args.root_dir or os.environ.get('INVOKEAI_ROOT') or os.path.abspath('.'))
     Globals.try_patchmatch = args.patchmatch
-    
+    Globals.always_use_cpu = args.always_use_cpu
+
     print(f'>> InvokeAI runtime directory is "{Globals.root}"')
 
     # loading here to avoid long delays on startup
@@ -339,8 +340,8 @@ def image_writer(image, seed, upscaled=False, first_seed=None, use_prefix=None):
                             filename,
                             tool,
                             formatted_dream_prompt,
-                        )                           
-                        
+                        )
+
                     if (not postprocessed) or opt.save_original:
                         # only append to results if we didn't overwrite an earlier output
                         results.append([path, formatted_dream_prompt])
@@ -430,7 +431,7 @@ def do_command(command:str, gen, opt:Args, completer) -> tuple:
         add_embedding_terms(gen, completer)
         completer.add_history(command)
         operation = None
-        
+
     elif command.startswith('!models'):
         gen.model_cache.print_models()
         completer.add_history(command)
@@ -531,7 +532,7 @@ def add_weights_to_config(model_path:str, gen, opt, completer):
 
     completer.complete_extensions(('.yaml','.yml'))
     completer.linebuffer = 'configs/stable-diffusion/v1-inference.yaml'
-    
+
     done = False
     while not done:
         new_config['config'] = input('Configuration file for this model: ')
@@ -562,7 +563,7 @@ def add_weights_to_config(model_path:str, gen, opt, completer):
                 print('** Please enter a valid integer between 64 and 2048')
 
     make_default = input('Make this the default model? [n] ') in ('y','Y')
-    
+
     if write_config_file(opt.conf, gen, model_name, new_config, make_default=make_default):
         completer.add_model(model_name)
 
@@ -575,14 +576,14 @@ def del_config(model_name:str, gen, opt, completer):
     gen.model_cache.commit(opt.conf)
     print(f'** {model_name} deleted')
     completer.del_model(model_name)
-    
+
 def edit_config(model_name:str, gen, opt, completer):
     config = gen.model_cache.config
-    
+
     if model_name not in config:
         print(f'** Unknown model {model_name}')
         return
-    
+
     print(f'\n>> Editing model {model_name} from configuration file {opt.conf}')
 
     conf = config[model_name]
@@ -595,10 +596,10 @@ def edit_config(model_name:str, gen, opt, completer):
     make_default = input('Make this the default model? [n] ') in ('y','Y')
     completer.complete_extensions(None)
     write_config_file(opt.conf, gen, model_name, new_config, clobber=True, make_default=make_default)
-    
+
 def write_config_file(conf_path, gen, model_name, new_config, clobber=False, make_default=False):
     current_model = gen.model_name
-    
+
     op = 'modify' if clobber else 'import'
     print('\n>> New configuration:')
     if make_default:
@@ -621,7 +622,7 @@ def write_config_file(conf_path, gen, model_name, new_config, clobber=False, mak
         gen.model_cache.set_default_model(model_name)
 
     gen.model_cache.commit(conf_path)
-    
+
     do_switch = input(f'Keep model loaded? [y]')
     if len(do_switch)==0 or do_switch[0] in ('y','Y'):
         pass
@@ -651,7 +652,7 @@ def do_postprocess (gen, opt, callback):
         opt.prompt = opt.new_prompt
     else:
         opt.prompt = None
-        
+
     if os.path.dirname(file_path) == '': #basename given
         file_path = os.path.join(opt.outdir,file_path)
 
@@ -716,7 +717,7 @@ def add_postprocessing_to_metadata(opt,original_file,new_file,tool,command):
     )
     meta['image']['postprocessing'] = pp
     write_metadata(new_file,meta)
-    
+
 def prepare_image_metadata(
         opt,
         prefix,
@@ -794,21 +795,21 @@ def invoke_ai_web_server_loop(gen, gfpgan, codeformer, esrgan):
     os.chdir(
         os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
     )
-    
+
     invoke_ai_web_server = InvokeAIWebServer(generate=gen, gfpgan=gfpgan, codeformer=codeformer, esrgan=esrgan)
 
     try:
         invoke_ai_web_server.run()
     except KeyboardInterrupt:
         pass
-    
+
 def add_embedding_terms(gen,completer):
     '''
     Called after setting the model, updates the autocompleter with
     any terms loaded by the embedding manager.
     '''
     completer.add_embedding_terms(gen.model.embedding_manager.list_terms())
-    
+
 def split_variations(variations_string) -> list:
     # shotgun parsing, woo
     parts = []
@@ -865,7 +866,7 @@ def callback(img, step):
             image = gen.sample_to_image(img)
             image.save(filename,'PNG')
     return callback
-    
+
 def retrieve_dream_command(opt,command,completer):
     '''
     Given a full or partial path to a previously-generated image file,
@@ -873,7 +874,7 @@ def retrieve_dream_command(opt,command,completer):
     and pop it into the readline buffer (linux, Mac), or print out a comment
     for cut-and-paste (windows)
 
-    Given a wildcard path to a folder with image png files, 
+    Given a wildcard path to a folder with image png files,
     will retrieve and format the dream command used to generate the images,
     and save them to a file commands.txt for further processing
     '''
@@ -909,7 +910,7 @@ def write_commands(opt, file_path:str, outfilepath:str):
     except ValueError:
         print(f'## "{basename}": unacceptable pattern')
         return
- 
+
     commands = []
     cmd = None
     for path in paths:
@@ -938,7 +939,7 @@ def emergency_model_reconfigure():
     print('   After reconfiguration is done, please relaunch invoke.py.                      ')
     print('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!')
     print('configure_invokeai is launching....\n')
-    
+
     sys.argv = ['configure_invokeai','--interactive']
     import configure_invokeai
     configure_invokeai.main()
diff --git a/ldm/invoke/args.py b/ldm/invoke/args.py
index f1b5e9029d8..66be322e2ee 100644
--- a/ldm/invoke/args.py
+++ b/ldm/invoke/args.py
@@ -337,7 +337,7 @@ def __getattribute__(self,name):
 
         if not hasattr(cmd_switches,name) and not hasattr(arg_switches,name):
             raise AttributeError
-        
+
         value_arg,value_cmd = (None,None)
         try:
             value_cmd = getattr(cmd_switches,name)
@@ -393,7 +393,7 @@ def _create_arg_parser(self):
             description=
             """
             Generate images using Stable Diffusion.
-            Use --web to launch the web interface. 
+            Use --web to launch the web interface.
             Use --from_file to load prompts from a file path or standard input ("-").
             Otherwise you will be dropped into an interactive command prompt (type -h for help.)
             Other command-line arguments are defaults that can usually be overridden
@@ -455,6 +455,12 @@ def _create_arg_parser(self):
             action='store_true',
             help='Force free gpu memory before final decoding',
         )
+        model_group.add_argument(
+            "--always_use_cpu",
+            dest="always_use_cpu",
+            action="store_true",
+            help="Force use of CPU even if GPU is available"
+        )
         model_group.add_argument(
             '--precision',
             dest='precision',
@@ -1036,7 +1042,7 @@ def metadata_dumps(opt,
     Given an Args object, returns a dict containing the keys and
     structure of the proposed stable diffusion metadata standard
     https://github.com/lstein/stable-diffusion/discussions/392
-    This is intended to be turned into JSON and stored in the 
+    This is intended to be turned into JSON and stored in the
     "sd
     '''
 
@@ -1119,7 +1125,7 @@ def args_from_png(png_file_path) -> list[Args]:
         meta = ldm.invoke.pngwriter.retrieve_metadata(png_file_path)
     except AttributeError:
         return [legacy_metadata_load({},png_file_path)]
-    
+
     try:
         return metadata_loads(meta)
     except:
@@ -1218,4 +1224,4 @@ def legacy_metadata_load(meta,pathname) -> Args:
             opt.prompt = ''
             opt.seed = 0
     return opt
-            
+
diff --git a/ldm/invoke/devices.py b/ldm/invoke/devices.py
index 424ae5a6d30..0fc749c4a44 100644
--- a/ldm/invoke/devices.py
+++ b/ldm/invoke/devices.py
@@ -1,9 +1,12 @@
 import torch
 from torch import autocast
 from contextlib import nullcontext
+from ldm.invoke.globals import Globals
 
 def choose_torch_device() -> str:
     '''Convenience routine for guessing which GPU device to run model on'''
+    if Globals.always_use_cpu:
+        return "cpu"
     if torch.cuda.is_available():
         return 'cuda'
     if hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
diff --git a/ldm/invoke/generator/diffusers_cross_attention_enhancements.py b/ldm/invoke/generator/diffusers_cross_attention_enhancements.py
deleted file mode 100644
index e69de29bb2d..00000000000
diff --git a/ldm/invoke/globals.py b/ldm/invoke/globals.py
index b29e9aa35d3..a875c866b77 100644
--- a/ldm/invoke/globals.py
+++ b/ldm/invoke/globals.py
@@ -8,6 +8,7 @@
   - root           - the root directory under which "models" and "outputs" can be found
   - initfile       - path to the initialization file
   - try_patchmatch - option to globally disable loading of 'patchmatch' module
+  - always_use_cpu - force use of CPU even if GPU is available
 '''
 
 import os
@@ -24,3 +25,6 @@
 # Awkward workaround to disable attempted loading of pypatchmatch
 # which is causing CI tests to error out.
 Globals.try_patchmatch = True
+
+# Use CPU even if GPU is available (main use case is for debugging MPS issues)
+Globals.always_use_cpu = False

From ff42027a002a2fad81b04d8538053ba1c7fcd3c0 Mon Sep 17 00:00:00 2001
From: Damian Stewart <d@damianstewart.com>
Date: Sun, 4 Dec 2022 16:07:54 +0100
Subject: [PATCH 044/199] add cross-attention control support to diffusers
 (fails on MPS)

For unknown reasons MPS produces garbage output with .swap(). Use
--always_use_cpu arg to invoke.py for now to test this code on MPS.
---
 ldm/invoke/generator/diffusers_pipeline.py    |   8 +
 .../diffusion/cross_attention_control.py      | 184 ++++++++++++++++--
 ldm/modules/attention.py                      |   2 +
 3 files changed, 182 insertions(+), 12 deletions(-)

diff --git a/ldm/invoke/generator/diffusers_pipeline.py b/ldm/invoke/generator/diffusers_pipeline.py
index 989d43546e6..174a57fd859 100644
--- a/ldm/invoke/generator/diffusers_pipeline.py
+++ b/ldm/invoke/generator/diffusers_pipeline.py
@@ -9,6 +9,14 @@
 import einops
 import torch
 import torchvision.transforms as T
+
+from ldm.models.diffusion.cross_attention_control import InvokeAICrossAttention
+
+from diffusers.models import attention
+# monkeypatch diffusers CrossAttention 🙈
+# this is to make prompt2prompt and (future) attention maps work
+attention.CrossAttention = InvokeAICrossAttention
+
 from diffusers.models import AutoencoderKL, UNet2DConditionModel
 from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
 from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipeline
diff --git a/ldm/models/diffusion/cross_attention_control.py b/ldm/models/diffusion/cross_attention_control.py
index ec7c3c215cc..ec4c3447166 100644
--- a/ldm/models/diffusion/cross_attention_control.py
+++ b/ldm/models/diffusion/cross_attention_control.py
@@ -1,8 +1,11 @@
 import enum
-import warnings
-from typing import Optional
+import math
+from typing import Optional, Callable
 
+import psutil
 import torch
+import diffusers
+from torch import nn
 
 
 # adapted from bloc97's CrossAttentionControl colab
@@ -66,8 +69,12 @@ def __init__(self, arguments: Arguments, step_count: int):
 
     def register_cross_attention_modules(self, model):
         for name,module in get_attention_modules(model, CrossAttentionType.SELF):
+            if name in self.self_cross_attention_module_identifiers:
+                assert False, f"name {name} cannot appear more than once"
             self.self_cross_attention_module_identifiers.append(name)
         for name,module in get_attention_modules(model, CrossAttentionType.TOKENS):
+            if name in self.tokens_cross_attention_module_identifiers:
+                assert False, f"name {name} cannot appear more than once"
             self.tokens_cross_attention_module_identifiers.append(name)
 
     def request_save_attention_maps(self, cross_attention_type: CrossAttentionType):
@@ -189,7 +196,7 @@ def setup_cross_attention_control(model, context: Context):
     # mask=1 means use base prompt attention, mask=0 means use edited prompt attention
     mask = torch.zeros(max_length)
     indices_target = torch.arange(max_length, dtype=torch.long)
-    indices = torch.zeros(max_length, dtype=torch.long)
+    indices = torch.arange(max_length, dtype=torch.long)
     for name, a0, a1, b0, b1 in context.arguments.edit_opcodes:
         if b0 < max_length:
             if name == "equal":# or (name == "replace" and a1 - a0 == b1 - b0):
@@ -204,9 +211,22 @@ def setup_cross_attention_control(model, context: Context):
 
 
 def get_attention_modules(model, which: CrossAttentionType):
+    # cross_attention_class: type = ldm.modules.attention.CrossAttention
+    cross_attention_class: type = InvokeAICrossAttention
     which_attn = "attn1" if which is CrossAttentionType.SELF else "attn2"
-    return [(name,module) for name, module in model.named_modules() if
-                  type(module).__name__ == "CrossAttention" and which_attn in name]
+    attention_module_tuples = [(name,module) for name, module in model.named_modules() if
+                isinstance(module, cross_attention_class) and which_attn in name]
+    cross_attention_modules_in_model_count = len(attention_module_tuples)
+    expected_count = 16
+    if cross_attention_modules_in_model_count != expected_count:
+        # non-fatal error but .swap() won't work.
+        print(f"Error! CrossAttentionControl found an unexpected number of InvokeAICrossAttention modules in the model " +
+              f"(expected {expected_count}, found {cross_attention_modules_in_model_count}). Either monkey-patching failed " +
+              f"or some assumption has changed about the structure of the model itself. Please fix the monkey-patching, " +
+              f"and/or update the {expected_count} above to an appropriate number, and/or find and inform someone who knows " +
+              f"what it means. This error is non-fatal, but it is likely that .swap() and attention map display will not " +
+              f"work properly until it is fixed.")
+    return attention_module_tuples
 
 
 def inject_attention_function(unet, context: Context):
@@ -246,8 +266,7 @@ def attention_slice_wrangler(module, suggested_attention_slice:torch.Tensor, dim
 
         return attention_slice
 
-    cross_attention_modules = [(name, module) for (name, module) in unet.named_modules()
-                                   if type(module).__name__ == "CrossAttention"]
+    cross_attention_modules = get_attention_modules(unet, CrossAttentionType.TOKENS) + get_attention_modules(unet, CrossAttentionType.SELF)
     for identifier, module in cross_attention_modules:
         module.identifier = identifier
         try:
@@ -257,22 +276,21 @@ def attention_slice_wrangler(module, suggested_attention_slice:torch.Tensor, dim
             )
         except AttributeError as e:
             if is_attribute_error_about(e, 'set_attention_slice_wrangler'):
-                warnings.warn(f"TODO: implement for {type(module)}")  # TODO
+                print(f"TODO: implement set_attention_slice_wrangler for {type(module)}")  # TODO
             else:
                 raise
 
 
 def remove_attention_function(unet):
-    cross_attention_modules = [module for (_, module) in unet.named_modules()
-                                   if type(module).__name__ == "CrossAttention"]
-    for module in cross_attention_modules:
+    cross_attention_modules = get_attention_modules(unet, CrossAttentionType.TOKENS) + get_attention_modules(unet, CrossAttentionType.SELF)
+    for identifier, module in cross_attention_modules:
         try:
             # clear wrangler callback
             module.set_attention_slice_wrangler(None)
             module.set_slicing_strategy_getter(None)
         except AttributeError as e:
             if is_attribute_error_about(e, 'set_attention_slice_wrangler'):
-                warnings.warn(f"TODO: implement for {type(module)}")  # TODO
+                print(f"TODO: implement set_attention_slice_wrangler for {type(module)}")
             else:
                 raise
 
@@ -282,3 +300,145 @@ def is_attribute_error_about(error: AttributeError, attribute: str):
         return error.name == attribute
     else:  # Python 3.9
         return attribute in str(error)
+
+
+
+def get_mem_free_total(device):
+    #only on cuda
+    if not torch.cuda.is_available():
+        return None
+    stats = torch.cuda.memory_stats(device)
+    mem_active = stats['active_bytes.all.current']
+    mem_reserved = stats['reserved_bytes.all.current']
+    mem_free_cuda, _ = torch.cuda.mem_get_info(device)
+    mem_free_torch = mem_reserved - mem_active
+    mem_free_total = mem_free_cuda + mem_free_torch
+    return mem_free_total
+
+class InvokeAICrossAttention(diffusers.models.attention.CrossAttention):
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self.mem_total_gb = psutil.virtual_memory().total // (1 << 30)
+
+        self.attention_slice_wrangler = None
+        self.slicing_strategy_getter = None
+
+    def set_attention_slice_wrangler(self, wrangler: Optional[Callable[[nn.Module, torch.Tensor, int, int, int], torch.Tensor]]):
+        '''
+        Set custom attention calculator to be called when attention is calculated
+        :param wrangler: Callback, with args (module, suggested_attention_slice, dim, offset, slice_size),
+        which returns either the suggested_attention_slice or an adjusted equivalent.
+            `module` is the current CrossAttention module for which the callback is being invoked.
+            `suggested_attention_slice` is the default-calculated attention slice
+            `dim` is -1 if the attenion map has not been sliced, or 0 or 1 for dimension-0 or dimension-1 slicing.
+                If `dim` is >= 0, `offset` and `slice_size` specify the slice start and length.
+
+        Pass None to use the default attention calculation.
+        :return:
+        '''
+        self.attention_slice_wrangler = wrangler
+
+    def set_slicing_strategy_getter(self, getter: Optional[Callable[[nn.Module], tuple[int,int]]]):
+        self.slicing_strategy_getter = getter
+
+    def _attention(self, query, key, value):
+        #default_result = super()._attention(query,  key, value)
+        damian_result = self.get_attention_mem_efficient(query, key, value)
+
+        hidden_states = self.reshape_batch_dim_to_heads(damian_result)
+        return hidden_states
+
+    def einsum_lowest_level(self, query, key, value, dim, offset, slice_size):
+        # calculate attention scores
+        #attention_scores = torch.einsum('b i d, b j d -> b i j', q, k) * self.scale
+        if dim is not None:
+            print(f"sliced dim {dim}, offset {offset}, slice_size {slice_size}")
+        attention_scores = torch.baddbmm(
+            torch.empty(query.shape[0], query.shape[1], key.shape[1], dtype=query.dtype, device=query.device),
+            query,
+            key.transpose(-1, -2),
+            beta=0,
+            alpha=self.scale,
+        )
+
+        # calculate attention slice by taking the best scores for each latent pixel
+        default_attention_slice = attention_scores.softmax(dim=-1, dtype=attention_scores.dtype)
+        attention_slice_wrangler = self.attention_slice_wrangler
+        if attention_slice_wrangler is not None:
+            attention_slice = attention_slice_wrangler(self, default_attention_slice, dim, offset, slice_size)
+        else:
+            attention_slice = default_attention_slice
+
+        #return torch.einsum('b i j, b j d -> b i d', attention_slice, v)
+        hidden_states = torch.bmm(attention_slice, value)
+        return hidden_states
+
+
+    def einsum_op_slice_dim0(self, q, k, v, slice_size):
+        r = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
+        for i in range(0, q.shape[0], slice_size):
+            end = i + slice_size
+            r[i:end] = self.einsum_lowest_level(q[i:end], k[i:end], v[i:end], dim=0, offset=i, slice_size=slice_size)
+        return r
+
+    def einsum_op_slice_dim1(self, q, k, v, slice_size):
+        r = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
+        for i in range(0, q.shape[1], slice_size):
+            end = i + slice_size
+            r[:, i:end] = self.einsum_lowest_level(q[:, i:end], k, v, dim=1, offset=i, slice_size=slice_size)
+        return r
+
+    def einsum_op_mps_v1(self, q, k, v):
+        if q.shape[1] <= 4096: # (512x512) max q.shape[1]: 4096
+            return self.einsum_lowest_level(q, k, v, None, None, None)
+        else:
+            slice_size = math.floor(2**30 / (q.shape[0] * q.shape[1]))
+            return self.einsum_op_slice_dim1(q, k, v, slice_size)
+
+    def einsum_op_mps_v2(self, q, k, v):
+        if self.mem_total_gb > 8 and q.shape[1] <= 4096:
+            return self.einsum_lowest_level(q, k, v, None, None, None)
+        else:
+            return self.einsum_op_slice_dim0(q, k, v, 1)
+
+    def einsum_op_tensor_mem(self, q, k, v, max_tensor_mb):
+        size_mb = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size() // (1 << 20)
+        if size_mb <= max_tensor_mb:
+            return self.einsum_lowest_level(q, k, v, None, None, None)
+        div = 1 << int((size_mb - 1) / max_tensor_mb).bit_length()
+        if div <= q.shape[0]:
+            return self.einsum_op_slice_dim0(q, k, v, q.shape[0] // div)
+        return self.einsum_op_slice_dim1(q, k, v, max(q.shape[1] // div, 1))
+
+    def einsum_op_cuda(self, q, k, v):
+        # check if we already have a slicing strategy (this should only happen during cross-attention controlled generation)
+        slicing_strategy_getter = self.slicing_strategy_getter
+        if slicing_strategy_getter is not None:
+            (dim, slice_size) = slicing_strategy_getter(self)
+            if dim is not None:
+                # print("using saved slicing strategy with dim", dim, "slice size", slice_size)
+                if dim == 0:
+                    return self.einsum_op_slice_dim0(q, k, v, slice_size)
+                elif dim == 1:
+                    return self.einsum_op_slice_dim1(q, k, v, slice_size)
+
+        # fallback for when there is no saved strategy, or saved strategy does not slice
+        mem_free_total = get_mem_free_total(q.device)
+        # Divide factor of safety as there's copying and fragmentation
+        return self.einsum_op_tensor_mem(q, k, v, mem_free_total / 3.3 / (1 << 20))
+
+
+    def get_attention_mem_efficient(self, q, k, v):
+        if q.device.type == 'cuda':
+            #print("in get_attention_mem_efficient with q shape", q.shape, ", k shape", k.shape, ", free memory is", get_mem_free_total(q.device))
+            return self.einsum_op_cuda(q, k, v)
+
+        if q.device.type == 'mps' or q.device.type == 'cpu':
+            if self.mem_total_gb >= 32:
+                return self.einsum_op_mps_v1(q, k, v)
+            return self.einsum_op_mps_v2(q, k, v)
+
+        # Smaller slices are faster due to L2/L3/SLC caches.
+        # Tested on i7 with 8MB L3 cache.
+        return self.einsum_op_tensor_mem(q, k, v, 32)
diff --git a/ldm/modules/attention.py b/ldm/modules/attention.py
index 94bb8a2916e..94922270a46 100644
--- a/ldm/modules/attention.py
+++ b/ldm/modules/attention.py
@@ -165,7 +165,9 @@ def get_mem_free_total(device):
 
 
 class CrossAttention(nn.Module):
+
     def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.):
+        print(f"Warning! ldm.modules.attention.CrossAttention is no longer being maintained. Please use InvokeAICrossAttention instead.")
         super().__init__()
         inner_dim = dim_head * heads
         context_dim = default(context_dim, query_dim)

From 875312080d01463171d12a0c7d0ae6206789edb6 Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Sun, 4 Dec 2022 20:12:04 -0800
Subject: [PATCH 045/199] diffusers support for the inpainting model

---
 ldm/invoke/generator/diffusers_pipeline.py | 78 ++++++++++++++++------
 ldm/invoke/generator/inpaint.py            | 47 ++++---------
 2 files changed, 70 insertions(+), 55 deletions(-)

diff --git a/ldm/invoke/generator/diffusers_pipeline.py b/ldm/invoke/generator/diffusers_pipeline.py
index 174a57fd859..430e051ca6f 100644
--- a/ldm/invoke/generator/diffusers_pipeline.py
+++ b/ldm/invoke/generator/diffusers_pipeline.py
@@ -9,10 +9,10 @@
 import einops
 import torch
 import torchvision.transforms as T
+from diffusers.models import attention
 
 from ldm.models.diffusion.cross_attention_control import InvokeAICrossAttention
 
-from diffusers.models import attention
 # monkeypatch diffusers CrossAttention 🙈
 # this is to make prompt2prompt and (future) attention maps work
 attention.CrossAttention = InvokeAICrossAttention
@@ -23,6 +23,7 @@
 from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img import StableDiffusionImg2ImgPipeline
 from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
 from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
+from torchvision.transforms.functional import resize as tv_resize
 from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
 
 from ldm.models.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent
@@ -49,6 +50,21 @@ class PipelineIntermediateState:
 )
 
 
+@dataclass
+class AddsMaskLatents:
+    forward: Callable[[torch.Tensor, torch.Tensor, torch.Tensor], torch.Tensor]
+    mask: torch.FloatTensor
+    mask_latents: torch.FloatTensor
+
+    def __call__(self, latents: torch.FloatTensor, t: torch.Tensor, text_embeddings: torch.FloatTensor) -> torch.Tensor:
+        batch_size = latents.size(0)
+        mask = einops.repeat(self.mask, 'b c h w -> (repeat b) c h w', repeat=batch_size)
+        mask_latents = einops.repeat(self.mask_latents, 'b c h w -> (repeat b) c h w', repeat=batch_size)
+        model_input, _ = einops.pack([latents, mask, mask_latents], 'b * h w')
+        # model_input = torch.cat([latents, mask, mask_latents], dim=1)
+        return self.forward(model_input, t, text_embeddings)
+
+
 def image_resized_to_grid_as_tensor(image: PIL.Image.Image, normalize: bool=True, multiple_of=8) -> torch.FloatTensor:
     """
 
@@ -57,7 +73,7 @@ def image_resized_to_grid_as_tensor(image: PIL.Image.Image, normalize: bool=True
     :param multiple_of: resize the input so both dimensions are a multiple of this
     """
     w, h = image.size
-    w, h = map(lambda x: x - x % 8, (w, h))  # resize to integer multiple of 8
+    w, h = map(lambda x: x - x % multiple_of, (w, h))  # resize to integer multiple of 8
     transformation = T.Compose([
         T.Resize((h, w), T.InterpolationMode.LANCZOS),
         T.ToTensor(),
@@ -68,6 +84,10 @@ def image_resized_to_grid_as_tensor(image: PIL.Image.Image, normalize: bool=True
     return tensor
 
 
+def is_inpainting_model(unet: UNet2DConditionModel):
+    return unet.conv_in.in_channels == 9
+
+
 class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
     r"""
     Pipeline for text-to-image generation using Stable Diffusion.
@@ -314,7 +334,7 @@ def img2img_from_embeddings(self,
         latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
 
         # 6. Prepare latent variables
-        latents = self.prepare_latents_from_image(init_image, latent_timestep, latents_dtype, device, noise_func)
+        latents, _ = self.prepare_latents_from_image(init_image, latent_timestep, latents_dtype, device, noise_func)
 
         result = None
         for result in self.generate_from_embeddings(
@@ -331,7 +351,7 @@ def img2img_from_embeddings(self,
     def inpaint_from_embeddings(
             self,
             init_image: torch.FloatTensor,
-            mask_image: torch.FloatTensor,
+            mask: torch.FloatTensor,
             strength: float,
             num_inference_steps: int,
             text_embeddings: torch.Tensor, unconditioned_embeddings: torch.Tensor,
@@ -349,8 +369,10 @@ def inpaint_from_embeddings(
         if isinstance(init_image, PIL.Image.Image):
             init_image = image_resized_to_grid_as_tensor(init_image.convert('RGB'))
 
+        init_image = init_image.to(device=device, dtype=latents_dtype)
+
         if init_image.dim() == 3:
-            init_image = einops.rearrange(init_image, 'c h w -> 1 c h w')
+            init_image = init_image.unsqueeze(0)
 
         img2img_pipeline = StableDiffusionImg2ImgPipeline(**self.components)
         img2img_pipeline.scheduler.set_timesteps(num_inference_steps, device=device)
@@ -358,22 +380,38 @@ def inpaint_from_embeddings(
 
         # 6. Prepare latent variables
         latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
-        latents = self.prepare_latents_from_image(init_image, latent_timestep, latents_dtype, device, noise_func)
+        latents, init_image_latents = self.prepare_latents_from_image(init_image, latent_timestep, latents_dtype, device, noise_func)
 
-        result = None
-        for result in self.generate_from_embeddings(
-                latents, text_embeddings, unconditioned_embeddings, guidance_scale,
-                extra_conditioning_info=extra_conditioning_info,
-                timesteps=timesteps,
-                run_id=run_id, **extra_step_kwargs):
-            if callback is not None and isinstance(result, PipelineIntermediateState):
-                callback(result)
-        if result is None:
-            raise AssertionError("why was that an empty generator?")
-        return result
+        if is_inpainting_model(self.unet):
+            if mask.dim() == 3:
+                mask = mask.unsqueeze(0)
+            mask = tv_resize(mask, latents.shape[-2:], T.InterpolationMode.BILINEAR)\
+                .to(device=device, dtype=latents_dtype)
+
+            self.invokeai_diffuser.model_forward_callback = \
+                AddsMaskLatents(self._unet_forward, mask, init_image_latents)
+        else:
+            # FIXME: need to add guidance that applies mask
+            pass
 
+        result = None
 
-    def prepare_latents_from_image(self, init_image, timestep, dtype, device, noise_func) -> torch.FloatTensor:
+        try:
+            for result in self.generate_from_embeddings(
+                    latents, text_embeddings, unconditioned_embeddings, guidance_scale,
+                    extra_conditioning_info=extra_conditioning_info,
+                    timesteps=timesteps,
+                    run_id=run_id, **extra_step_kwargs):
+                if callback is not None and isinstance(result, PipelineIntermediateState):
+                    callback(result)
+            if result is None:
+                raise AssertionError("why was that an empty generator?")
+            return result
+        finally:
+            self.invokeai_diffuser.model_forward_callback = self._unet_forward
+
+
+    def prepare_latents_from_image(self, init_image, timestep, dtype, device, noise_func) -> (torch.FloatTensor, torch.FloatTensor):
         # can't quite use upstream StableDiffusionImg2ImgPipeline.prepare_latents
         # because we have our own noise function
         init_image = init_image.to(device=device, dtype=dtype)
@@ -383,8 +421,8 @@ def prepare_latents_from_image(self, init_image, timestep, dtype, device, noise_
         init_latents = 0.18215 * init_latents
 
         noise = noise_func(init_latents)
-
-        return self.scheduler.add_noise(init_latents, noise, timestep)
+        noised_latents = self.scheduler.add_noise(init_latents, noise, timestep)
+        return noised_latents, init_latents
 
     def check_for_safety(self, output, dtype):
         with torch.inference_mode():
diff --git a/ldm/invoke/generator/inpaint.py b/ldm/invoke/generator/inpaint.py
index 25c776de555..976121d720f 100644
--- a/ldm/invoke/generator/inpaint.py
+++ b/ldm/invoke/generator/inpaint.py
@@ -10,9 +10,7 @@
 import numpy as np
 import torch
 from PIL import Image, ImageFilter, ImageOps, ImageChops
-from einops import repeat
 
-from ldm.invoke.generator.base import downsampling
 from ldm.invoke.generator.diffusers_pipeline import image_resized_to_grid_as_tensor, StableDiffusionGeneratorPipeline
 from ldm.invoke.generator.img2img import Img2Img
 from ldm.invoke.globals import Globals
@@ -154,7 +152,7 @@ def seam_paint(self, im: Image.Image, seam_size: int, seam_blur: int, prompt, sa
             ddim_eta,
             conditioning,
             init_image = im.copy().convert('RGBA'),
-            mask_image = mask.convert('RGB'), # Code currently requires an RGB mask
+            mask_image = mask,
             strength = strength,
             mask_blur_radius = 0,
             seam_size = 0,
@@ -228,7 +226,11 @@ def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
             self.pil_mask = mask_image.copy()
             debug_image(mask_image, "mask_image BEFORE multiply with pil_image", debug_status=self.enable_image_debugging)
 
-            mask_image = ImageChops.multiply(mask_image, self.pil_image.split()[-1].convert('RGB'))
+            init_alpha = self.pil_image.getchannel("A")
+            if mask_image.mode != "L":
+                # FIXME: why do we get passed an RGB image here? We can only use single-channel.
+                mask_image = mask_image.convert("L")
+            mask_image = ImageChops.multiply(mask_image, init_alpha)
             self.pil_mask = mask_image
 
             # Resize if requested for inpainting
@@ -236,57 +238,32 @@ def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
                 mask_image = mask_image.resize((inpaint_width, inpaint_height))
 
             debug_image(mask_image, "mask_image AFTER multiply with pil_image", debug_status=self.enable_image_debugging)
-            mask_image = mask_image.resize(
-                (
-                    mask_image.width // downsampling,
-                    mask_image.height // downsampling
-                ),
-                resample=Image.Resampling.NEAREST
-            )
-            mask_image = image_resized_to_grid_as_tensor(mask_image, normalize=False)
+            mask: torch.FloatTensor = image_resized_to_grid_as_tensor(mask_image, normalize=False)
+        else:
+            mask: torch.FloatTensor = mask_image
 
         self.mask_blur_radius = mask_blur_radius
 
-        # klms samplers not supported yet, so ignore previous sampler
-        # if isinstance(sampler,KSampler):
-        #     print(
-        #         ">> Using recommended DDIM sampler for inpainting."
-        #     )
-        #     sampler = DDIMSampler(self.model, device=self.model.device)
-
-        mask_image = mask_image[0][0].unsqueeze(0).repeat(4,1,1).unsqueeze(0)
-        mask_image = repeat(mask_image, '1 ... -> b ...', b=1)
-
-        t_enc   = int(strength * steps)
         # todo: support cross-attention control
         uc, c, _ = conditioning
 
-        print(f">> target t_enc is {t_enc} steps")
-
         # noinspection PyTypeChecker
         pipeline: StableDiffusionGeneratorPipeline = self.model
         pipeline.scheduler = sampler
 
         def make_image(x_T):
             # FIXME: some of this z_enc and inpaint_replace stuff was probably important
-            # encode (scaled latent)
-            # z_enc = sampler.stochastic_encode(
-            #     self.init_latent,
-            #     torch.tensor([t_enc]).to(self.model.device),
-            #     noise=x_T
-            # )
-            #
             # # to replace masked area with latent noise, weighted by inpaint_replace strength
             # if inpaint_replace > 0.0:
             #     print(f'>> inpaint will replace what was under the mask with a strength of {inpaint_replace}')
             #     l_noise = self.get_noise(kwargs['width'],kwargs['height'])
-            #     inverted_mask = 1.0-mask_image  # there will be 1s where the mask is
+            #     inverted_mask = 1.0-mask  # there will be 1s where the mask is
             #     masked_region = (1.0-inpaint_replace) * inverted_mask * z_enc + inpaint_replace * inverted_mask * l_noise
-            #     z_enc   = z_enc * mask_image + masked_region
+            #     z_enc   = z_enc * mask + masked_region
 
             pipeline_output = pipeline.inpaint_from_embeddings(
                 init_image=init_image,
-                mask_image=mask_image,
+                mask=1 - mask,  # expects white means "paint here."
                 strength=strength,
                 num_inference_steps=steps,
                 text_embeddings=c,

From b2664e807ef616896f0785b8c88cd4f4352c501b Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Sun, 4 Dec 2022 20:12:47 -0800
Subject: [PATCH 046/199] fix debug_image to not crash with non-RGB images.

---
 ldm/util.py | 22 +++++++++-------------
 1 file changed, 9 insertions(+), 13 deletions(-)

diff --git a/ldm/util.py b/ldm/util.py
index ae28edb96ad..b653c8e7db4 100644
--- a/ldm/util.py
+++ b/ldm/util.py
@@ -1,17 +1,13 @@
 import importlib
-
-import torch
-import numpy as np
 import math
-from collections import abc
-from einops import rearrange
-from functools import partial
-
 import multiprocessing as mp
-from threading import Thread
+from collections import abc
+from inspect import isfunction
 from queue import Queue
+from threading import Thread
 
-from inspect import isfunction
+import numpy as np
+import torch
 from PIL import Image, ImageDraw, ImageFont
 
 
@@ -221,7 +217,7 @@ def rand_perlin_2d(shape, res, device, fade = lambda t: 6*t**5 - 15*t**4 + 10*t*
     grid = torch.stack(torch.meshgrid(torch.arange(0, res[0], delta[0]), torch.arange(0, res[1], delta[1]), indexing='ij'), dim = -1).to(device) % 1
 
     rand_val = torch.rand(res[0]+1, res[1]+1)
-    
+
     angles = 2*math.pi*rand_val
     gradients = torch.stack((torch.cos(angles), torch.sin(angles)), dim = -1).to(device)
 
@@ -249,8 +245,8 @@ def ask_user(question: str, answers: list):
 def debug_image(debug_image, debug_text, debug_show=True, debug_result=False, debug_status=False ):
     if not debug_status:
         return
-    
-    image_copy = debug_image.copy()
+
+    image_copy = debug_image.copy().convert("RGBA")
     ImageDraw.Draw(image_copy).text(
         (5, 5),
         debug_text,
@@ -261,4 +257,4 @@ def debug_image(debug_image, debug_text, debug_show=True, debug_result=False, de
         image_copy.show()
 
     if debug_result:
-        return image_copy
\ No newline at end of file
+        return image_copy

From f3570d8344f84f8f1567c3ff63fc33a46dcfee59 Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Sun, 4 Dec 2022 23:36:12 -0800
Subject: [PATCH 047/199] inpainting for the normal model [WIP]

This seems to be performing well until the LAST STEP, at which point it dissolves to confetti.
---
 ldm/invoke/generator/diffusers_pipeline.py | 49 ++++++++++++++++++----
 1 file changed, 40 insertions(+), 9 deletions(-)

diff --git a/ldm/invoke/generator/diffusers_pipeline.py b/ldm/invoke/generator/diffusers_pipeline.py
index 430e051ca6f..67a2f2fba6d 100644
--- a/ldm/invoke/generator/diffusers_pipeline.py
+++ b/ldm/invoke/generator/diffusers_pipeline.py
@@ -22,6 +22,7 @@
 from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipeline
 from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img import StableDiffusionImg2ImgPipeline
 from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
+from diffusers.schedulers.scheduling_utils import SchedulerMixin
 from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
 from torchvision.transforms.functional import resize as tv_resize
 from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
@@ -61,10 +62,32 @@ def __call__(self, latents: torch.FloatTensor, t: torch.Tensor, text_embeddings:
         mask = einops.repeat(self.mask, 'b c h w -> (repeat b) c h w', repeat=batch_size)
         mask_latents = einops.repeat(self.mask_latents, 'b c h w -> (repeat b) c h w', repeat=batch_size)
         model_input, _ = einops.pack([latents, mask, mask_latents], 'b * h w')
-        # model_input = torch.cat([latents, mask, mask_latents], dim=1)
         return self.forward(model_input, t, text_embeddings)
 
 
+@dataclass
+class AddsMaskGuidance:
+    forward: Callable[[torch.Tensor, torch.Tensor, torch.Tensor], torch.Tensor]
+    mask: torch.FloatTensor
+    mask_latents: torch.FloatTensor
+    _scheduler: SchedulerMixin
+    _noise_func: Callable
+    _debug: Optional[Callable] = None
+
+    def __call__(self, latents: torch.FloatTensor, t: torch.Tensor, text_embeddings: torch.FloatTensor) -> torch.Tensor:
+        batch_size = latents.size(0)
+        mask = einops.repeat(self.mask, 'b c h w -> (repeat b) c h w', repeat=batch_size)
+        noise = self._noise_func(self.mask_latents)
+        mask_latents = self._scheduler.add_noise(self.mask_latents, noise, t[0])  # .to(dtype=mask_latents.dtype)
+        mask_latents = einops.repeat(mask_latents, 'b c h w -> (repeat b) c h w', repeat=batch_size)
+        # if self._debug:
+        #     self._debug(latents, f"t={t[0]} latents")
+        masked_input = torch.lerp(mask_latents.to(dtype=latents.dtype), latents, mask.to(dtype=latents.dtype))
+        if self._debug:
+            self._debug(masked_input, f"t={t[0]} lerped")
+        return self.forward(masked_input, t, text_embeddings)
+
+
 def image_resized_to_grid_as_tensor(image: PIL.Image.Image, normalize: bool=True, multiple_of=8) -> torch.FloatTensor:
     """
 
@@ -382,17 +405,18 @@ def inpaint_from_embeddings(
         latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
         latents, init_image_latents = self.prepare_latents_from_image(init_image, latent_timestep, latents_dtype, device, noise_func)
 
-        if is_inpainting_model(self.unet):
-            if mask.dim() == 3:
-                mask = mask.unsqueeze(0)
-            mask = tv_resize(mask, latents.shape[-2:], T.InterpolationMode.BILINEAR)\
-                .to(device=device, dtype=latents_dtype)
+        if mask.dim() == 3:
+            mask = mask.unsqueeze(0)
+        mask = tv_resize(mask, latents.shape[-2:], T.InterpolationMode.BILINEAR) \
+            .to(device=device, dtype=latents_dtype)
 
+        if is_inpainting_model(self.unet):
             self.invokeai_diffuser.model_forward_callback = \
                 AddsMaskLatents(self._unet_forward, mask, init_image_latents)
         else:
-            # FIXME: need to add guidance that applies mask
-            pass
+            self.invokeai_diffuser.model_forward_callback = \
+                AddsMaskGuidance(self._unet_forward, mask, init_image_latents,
+                                 self.scheduler, noise_func)  # self.debug_latents)
 
         result = None
 
@@ -417,7 +441,7 @@ def prepare_latents_from_image(self, init_image, timestep, dtype, device, noise_
         init_image = init_image.to(device=device, dtype=dtype)
         with torch.inference_mode():
             init_latent_dist = self.vae.encode(init_image).latent_dist
-            init_latents = init_latent_dist.sample()  # FIXME: uses torch.randn. make reproducible!
+            init_latents = init_latent_dist.sample().to(dtype=dtype)  # FIXME: uses torch.randn. make reproducible!
         init_latents = 0.18215 * init_latents
 
         noise = noise_func(init_latents)
@@ -456,3 +480,10 @@ def _tokenize(self, prompt: Union[str, List[str]]):
     def channels(self) -> int:
         """Compatible with DiffusionWrapper"""
         return self.unet.in_channels
+
+    def debug_latents(self, latents, msg):
+        with torch.inference_mode():
+            from ldm.util import debug_image
+            decoded = self.numpy_to_pil(self.decode_latents(latents))
+            for i, img in enumerate(decoded):
+                debug_image(img, f"latents {msg} {i+1}/{len(decoded)}", debug_status=True)

From c6f31e5f3603a552d0d29a845ed2dec32e57aee6 Mon Sep 17 00:00:00 2001
From: Damian Stewart <null@damianstewart.com>
Date: Sun, 4 Dec 2022 11:41:03 +0100
Subject: [PATCH 048/199] fix off-by-one bug in cross-attention-control (#1774)

prompt token sequences begin with a "beginning-of-sequence" marker <bos> and end with a repeated "end-of-sequence" marker <eos> - to make a default prompt length of <bos> + 75 prompt tokens + <eos>. the .swap() code was failing to take the column for <bos> at index 0 into account. the changes here do that, and also add extra handling for a single <eos> (which may be redundant but which is included for completeness).

based on my understanding and some assumptions about how this all works, the reason .swap() nevertheless seemed to do the right thing, to some extent, is because over multiple steps the conditioning process in Stable Diffusion operates as a feedback loop. a change to token n-1 has flow-on effects to how the [1x4x64x64] latent tensor is modified by all the tokens after it, - and as the next step is processed, all the tokens before it as well. intuitively, a token's conditioning effects "echo" throughout the whole length of the prompt. so even though the token at n-1 was being edited when what the user actually wanted was to edit the token at n, it nevertheless still had some non-negligible effect, in roughly the right direction, often enough that it seemed like it was working properly.
---
 ldm/invoke/conditioning.py | 13 ++++++++++++-
 1 file changed, 12 insertions(+), 1 deletion(-)

diff --git a/ldm/invoke/conditioning.py b/ldm/invoke/conditioning.py
index 54092578a10..328167d7835 100644
--- a/ldm/invoke/conditioning.py
+++ b/ldm/invoke/conditioning.py
@@ -77,8 +77,13 @@ def get_uc_and_c_and_ec(prompt_string_uncleaned, model, log_tokens=False, skip_n
             # for name, a0, a1, b0, b1 in edit_opcodes: only name == 'equal' is currently parsed
             original_token_count = 0
             edited_token_count = 0
-            edit_opcodes = []
             edit_options = []
+            edit_opcodes = []
+            # beginning of sequence
+            edit_opcodes.append(('equal', original_token_count, original_token_count+1, edited_token_count, edited_token_count+1))
+            edit_options.append(None)
+            original_token_count += 1
+            edited_token_count += 1
             for fragment in flattened_prompt.children:
                 if type(fragment) is CrossAttentionControlSubstitute:
                     original_prompt.append(fragment.original)
@@ -105,6 +110,12 @@ def get_uc_and_c_and_ec(prompt_string_uncleaned, model, log_tokens=False, skip_n
                     edit_options.append(None)
                     original_token_count += count
                     edited_token_count += count
+            # end of sequence
+            edit_opcodes.append(('equal', original_token_count, original_token_count+1, edited_token_count, edited_token_count+1))
+            edit_options.append(None)
+            original_token_count += 1
+            edited_token_count += 1
+
             original_embeddings, original_tokens = build_embeddings_and_tokens_for_flattened_prompt(model,
                                                                                                     original_prompt,
                                                                                                     log_tokens=log_tokens,

From 69d42762de89911e4319e94fa7d0d536f38fab9d Mon Sep 17 00:00:00 2001
From: Damian Stewart <d@damianstewart.com>
Date: Mon, 5 Dec 2022 20:00:18 +0100
Subject: [PATCH 049/199] refactor common CrossAttention stuff into a mixin so
 that the old ldm code can still work if necessary

---
 ldm/invoke/generator/diffusers_pipeline.py    |   4 +-
 .../diffusion/cross_attention_control.py      |  45 ++++---
 ldm/modules/attention.py                      | 121 +-----------------
 3 files changed, 37 insertions(+), 133 deletions(-)

diff --git a/ldm/invoke/generator/diffusers_pipeline.py b/ldm/invoke/generator/diffusers_pipeline.py
index 67a2f2fba6d..b0b4ac2a675 100644
--- a/ldm/invoke/generator/diffusers_pipeline.py
+++ b/ldm/invoke/generator/diffusers_pipeline.py
@@ -11,11 +11,11 @@
 import torchvision.transforms as T
 from diffusers.models import attention
 
-from ldm.models.diffusion.cross_attention_control import InvokeAICrossAttention
+from ldm.models.diffusion.cross_attention_control import InvokeAIDiffusersCrossAttention
 
 # monkeypatch diffusers CrossAttention 🙈
 # this is to make prompt2prompt and (future) attention maps work
-attention.CrossAttention = InvokeAICrossAttention
+attention.CrossAttention = InvokeAIDiffusersCrossAttention
 
 from diffusers.models import AutoencoderKL, UNet2DConditionModel
 from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
diff --git a/ldm/models/diffusion/cross_attention_control.py b/ldm/models/diffusion/cross_attention_control.py
index ec4c3447166..06867f445d7 100644
--- a/ldm/models/diffusion/cross_attention_control.py
+++ b/ldm/models/diffusion/cross_attention_control.py
@@ -212,7 +212,7 @@ def setup_cross_attention_control(model, context: Context):
 
 def get_attention_modules(model, which: CrossAttentionType):
     # cross_attention_class: type = ldm.modules.attention.CrossAttention
-    cross_attention_class: type = InvokeAICrossAttention
+    cross_attention_class: type = InvokeAIDiffusersCrossAttention
     which_attn = "attn1" if which is CrossAttentionType.SELF else "attn2"
     attention_module_tuples = [(name,module) for name, module in model.named_modules() if
                 isinstance(module, cross_attention_class) and which_attn in name]
@@ -315,12 +315,16 @@ def get_mem_free_total(device):
     mem_free_total = mem_free_cuda + mem_free_torch
     return mem_free_total
 
-class InvokeAICrossAttention(diffusers.models.attention.CrossAttention):
 
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
-        self.mem_total_gb = psutil.virtual_memory().total // (1 << 30)
 
+class InvokeAICrossAttentionMixin:
+    """
+    Enable InvokeAI-flavoured CrossAttention calculation, which does aggressive low-memory slicing and calls
+    through both to an attention_slice_wrangler and a slicing_strategy_getter for custom attention map wrangling
+    and dymamic slicing strategy selection.
+    """
+    def __init__(self):
+        self.mem_total_gb = psutil.virtual_memory().total // (1 << 30)
         self.attention_slice_wrangler = None
         self.slicing_strategy_getter = None
 
@@ -342,16 +346,9 @@ def set_attention_slice_wrangler(self, wrangler: Optional[Callable[[nn.Module, t
     def set_slicing_strategy_getter(self, getter: Optional[Callable[[nn.Module], tuple[int,int]]]):
         self.slicing_strategy_getter = getter
 
-    def _attention(self, query, key, value):
-        #default_result = super()._attention(query,  key, value)
-        damian_result = self.get_attention_mem_efficient(query, key, value)
-
-        hidden_states = self.reshape_batch_dim_to_heads(damian_result)
-        return hidden_states
-
     def einsum_lowest_level(self, query, key, value, dim, offset, slice_size):
         # calculate attention scores
-        #attention_scores = torch.einsum('b i d, b j d -> b i j', q, k) * self.scale
+        #attention_scores = torch.einsum('b i d, b j d -> b i j', q, k)
         if dim is not None:
             print(f"sliced dim {dim}, offset {offset}, slice_size {slice_size}")
         attention_scores = torch.baddbmm(
@@ -370,11 +367,9 @@ def einsum_lowest_level(self, query, key, value, dim, offset, slice_size):
         else:
             attention_slice = default_attention_slice
 
-        #return torch.einsum('b i j, b j d -> b i d', attention_slice, v)
         hidden_states = torch.bmm(attention_slice, value)
         return hidden_states
 
-
     def einsum_op_slice_dim0(self, q, k, v, slice_size):
         r = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
         for i in range(0, q.shape[0], slice_size):
@@ -424,12 +419,12 @@ def einsum_op_cuda(self, q, k, v):
                     return self.einsum_op_slice_dim1(q, k, v, slice_size)
 
         # fallback for when there is no saved strategy, or saved strategy does not slice
-        mem_free_total = get_mem_free_total(q.device)
+        mem_free_total = self.cached_mem_free_total or get_mem_free_total(q.device)
         # Divide factor of safety as there's copying and fragmentation
         return self.einsum_op_tensor_mem(q, k, v, mem_free_total / 3.3 / (1 << 20))
 
 
-    def get_attention_mem_efficient(self, q, k, v):
+    def get_invokeai_attention_mem_efficient(self, q, k, v):
         if q.device.type == 'cuda':
             #print("in get_attention_mem_efficient with q shape", q.shape, ", k shape", k.shape, ", free memory is", get_mem_free_total(q.device))
             return self.einsum_op_cuda(q, k, v)
@@ -442,3 +437,19 @@ def get_attention_mem_efficient(self, q, k, v):
         # Smaller slices are faster due to L2/L3/SLC caches.
         # Tested on i7 with 8MB L3 cache.
         return self.einsum_op_tensor_mem(q, k, v, 32)
+
+
+class InvokeAIDiffusersCrossAttention(diffusers.models.attention.CrossAttention, InvokeAICrossAttentionMixin):
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        InvokeAICrossAttentionMixin.__init__(self)
+
+    def _attention(self, query, key, value):
+        #default_result = super()._attention(query,  key, value)
+        damian_result = self.get_invokeai_attention_mem_efficient(query, key, value)
+
+        hidden_states = self.reshape_batch_dim_to_heads(damian_result)
+        return hidden_states
+
+
diff --git a/ldm/modules/attention.py b/ldm/modules/attention.py
index 94922270a46..a926f2c3ade 100644
--- a/ldm/modules/attention.py
+++ b/ldm/modules/attention.py
@@ -7,6 +7,7 @@
 from torch import nn, einsum
 from einops import rearrange, repeat
 
+from ldm.models.diffusion.cross_attention_control import InvokeAICrossAttentionMixin
 from ldm.modules.diffusionmodules.util import checkpoint
 
 import psutil
@@ -163,8 +164,7 @@ def get_mem_free_total(device):
     mem_free_total = mem_free_cuda + mem_free_torch
     return mem_free_total
 
-
-class CrossAttention(nn.Module):
+class CrossAttention(nn.Module, InvokeAICrossAttentionMixin):
 
     def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.):
         print(f"Warning! ldm.modules.attention.CrossAttention is no longer being maintained. Please use InvokeAICrossAttention instead.")
@@ -184,117 +184,6 @@ def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.
             nn.Dropout(dropout)
         )
 
-        self.mem_total_gb = psutil.virtual_memory().total // (1 << 30)
-
-        self.cached_mem_free_total = None
-        self.attention_slice_wrangler = None
-        self.slicing_strategy_getter = None
-
-    def set_attention_slice_wrangler(self, wrangler: Optional[Callable[[nn.Module, torch.Tensor, int, int, int], torch.Tensor]]):
-        '''
-        Set custom attention calculator to be called when attention is calculated
-        :param wrangler: Callback, with args (module, suggested_attention_slice, dim, offset, slice_size),
-        which returns either the suggested_attention_slice or an adjusted equivalent.
-            `module` is the current CrossAttention module for which the callback is being invoked.
-            `suggested_attention_slice` is the default-calculated attention slice
-            `dim` is -1 if the attenion map has not been sliced, or 0 or 1 for dimension-0 or dimension-1 slicing.
-                If `dim` is >= 0, `offset` and `slice_size` specify the slice start and length.
-
-        Pass None to use the default attention calculation.
-        :return:
-        '''
-        self.attention_slice_wrangler = wrangler
-
-    def set_slicing_strategy_getter(self, getter: Optional[Callable[[nn.Module], tuple[int,int]]]):
-        self.slicing_strategy_getter = getter
-
-    def cache_free_memory_count(self, device):
-        self.cached_mem_free_total = get_mem_free_total(device)
-        print("free cuda memory: ", self.cached_mem_free_total)
-
-    def clear_cached_free_memory_count(self):
-        self.cached_mem_free_total = None
-
-    def einsum_lowest_level(self, q, k, v, dim, offset, slice_size):
-        # calculate attention scores
-        attention_scores = einsum('b i d, b j d -> b i j', q, k)
-        # calculate attention slice by taking the best scores for each latent pixel
-        default_attention_slice = attention_scores.softmax(dim=-1, dtype=attention_scores.dtype)
-        attention_slice_wrangler = self.attention_slice_wrangler
-        if attention_slice_wrangler is not None:
-            attention_slice = attention_slice_wrangler(self, default_attention_slice, dim, offset, slice_size)
-        else:
-            attention_slice = default_attention_slice
-
-        return einsum('b i j, b j d -> b i d', attention_slice, v)
-
-    def einsum_op_slice_dim0(self, q, k, v, slice_size):
-        r = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
-        for i in range(0, q.shape[0], slice_size):
-            end = i + slice_size
-            r[i:end] = self.einsum_lowest_level(q[i:end], k[i:end], v[i:end], dim=0, offset=i, slice_size=slice_size)
-        return r
-
-    def einsum_op_slice_dim1(self, q, k, v, slice_size):
-        r = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
-        for i in range(0, q.shape[1], slice_size):
-            end = i + slice_size
-            r[:, i:end] = self.einsum_lowest_level(q[:, i:end], k, v, dim=1, offset=i, slice_size=slice_size)
-        return r
-
-    def einsum_op_mps_v1(self, q, k, v):
-        if q.shape[1] <= 4096: # (512x512) max q.shape[1]: 4096
-            return self.einsum_lowest_level(q, k, v, None, None, None)
-        else:
-            slice_size = math.floor(2**30 / (q.shape[0] * q.shape[1]))
-            return self.einsum_op_slice_dim1(q, k, v, slice_size)
-
-    def einsum_op_mps_v2(self, q, k, v):
-        if self.mem_total_gb > 8 and q.shape[1] <= 4096:
-            return self.einsum_lowest_level(q, k, v, None, None, None)
-        else:
-            return self.einsum_op_slice_dim0(q, k, v, 1)
-
-    def einsum_op_tensor_mem(self, q, k, v, max_tensor_mb):
-        size_mb = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size() // (1 << 20)
-        if size_mb <= max_tensor_mb:
-            return self.einsum_lowest_level(q, k, v, None, None, None)
-        div = 1 << int((size_mb - 1) / max_tensor_mb).bit_length()
-        if div <= q.shape[0]:
-            return self.einsum_op_slice_dim0(q, k, v, q.shape[0] // div)
-        return self.einsum_op_slice_dim1(q, k, v, max(q.shape[1] // div, 1))
-
-    def einsum_op_cuda(self, q, k, v):
-        # check if we already have a slicing strategy (this should only happen during cross-attention controlled generation)
-        slicing_strategy_getter = self.slicing_strategy_getter
-        if slicing_strategy_getter is not None:
-            (dim, slice_size) = slicing_strategy_getter(self)
-            if dim is not None:
-                # print("using saved slicing strategy with dim", dim, "slice size", slice_size)
-                if dim == 0:
-                    return self.einsum_op_slice_dim0(q, k, v, slice_size)
-                elif dim == 1:
-                    return self.einsum_op_slice_dim1(q, k, v, slice_size)
-
-        # fallback for when there is no saved strategy, or saved strategy does not slice
-        mem_free_total = self.cached_mem_free_total or get_mem_free_total(q.device)
-        # Divide factor of safety as there's copying and fragmentation
-        return self.einsum_op_tensor_mem(q, k, v, mem_free_total / 3.3 / (1 << 20))
-
-
-    def get_attention_mem_efficient(self, q, k, v):
-        if q.device.type == 'cuda':
-            #print("in get_attention_mem_efficient with q shape", q.shape, ", k shape", k.shape, ", free memory is", get_mem_free_total(q.device))
-            return self.einsum_op_cuda(q, k, v)
-
-        if q.device.type == 'mps':
-            if self.mem_total_gb >= 32:
-                return self.einsum_op_mps_v1(q, k, v)
-            return self.einsum_op_mps_v2(q, k, v)
-
-        # Smaller slices are faster due to L2/L3/SLC caches.
-        # Tested on i7 with 8MB L3 cache.
-        return self.einsum_op_tensor_mem(q, k, v, 32)
 
     def forward(self, x, context=None, mask=None):
         h = self.heads
@@ -307,7 +196,11 @@ def forward(self, x, context=None, mask=None):
 
         q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
 
-        r = self.get_attention_mem_efficient(q, k, v)
+        # prevent scale being applied twice
+        cached_scale = self.scale
+        self.scale = 1
+        r = self.get_invokeai_attention_mem_efficient(q, k, v)
+        self.scale = cached_scale
 
         hidden_states = rearrange(r, '(b h) n d -> b n (h d)', h=h)
         return self.to_out(hidden_states)

From 5c7e6751e0aee1d546956e451dfb1ac188b71e24 Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Mon, 5 Dec 2022 12:36:50 -0800
Subject: [PATCH 050/199] inpainting for the normal model. I think it works
 this time.

---
 ldm/invoke/generator/diffusers_pipeline.py | 95 +++++++++++++++++-----
 1 file changed, 76 insertions(+), 19 deletions(-)

diff --git a/ldm/invoke/generator/diffusers_pipeline.py b/ldm/invoke/generator/diffusers_pipeline.py
index b0b4ac2a675..95de688f578 100644
--- a/ldm/invoke/generator/diffusers_pipeline.py
+++ b/ldm/invoke/generator/diffusers_pipeline.py
@@ -22,8 +22,9 @@
 from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipeline
 from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img import StableDiffusionImg2ImgPipeline
 from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
-from diffusers.schedulers.scheduling_utils import SchedulerMixin
+from diffusers.schedulers.scheduling_utils import SchedulerMixin, SchedulerOutput
 from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
+from diffusers.utils.outputs import BaseOutput
 from torchvision.transforms.functional import resize as tv_resize
 from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
 
@@ -53,39 +54,76 @@ class PipelineIntermediateState:
 
 @dataclass
 class AddsMaskLatents:
+    """Add the channels required for inpainting model input.
+
+    The inpainting model takes the normal latent channels as input, _plus_ a one-channel mask
+    and the latent encoding of the base image.
+
+    This class assumes the same mask and base image should apply to all items in the batch.
+    """
     forward: Callable[[torch.Tensor, torch.Tensor, torch.Tensor], torch.Tensor]
     mask: torch.FloatTensor
-    mask_latents: torch.FloatTensor
+    initial_image_latents: torch.FloatTensor
 
     def __call__(self, latents: torch.FloatTensor, t: torch.Tensor, text_embeddings: torch.FloatTensor) -> torch.Tensor:
+        model_input = self.add_mask_channels(latents)
+        return self.forward(model_input, t, text_embeddings)
+
+    def add_mask_channels(self, latents):
         batch_size = latents.size(0)
+        # duplicate mask and latents for each batch
         mask = einops.repeat(self.mask, 'b c h w -> (repeat b) c h w', repeat=batch_size)
-        mask_latents = einops.repeat(self.mask_latents, 'b c h w -> (repeat b) c h w', repeat=batch_size)
-        model_input, _ = einops.pack([latents, mask, mask_latents], 'b * h w')
-        return self.forward(model_input, t, text_embeddings)
+        image_latents = einops.repeat(self.initial_image_latents, 'b c h w -> (repeat b) c h w', repeat=batch_size)
+        # add mask and image as additional channels
+        model_input, _ = einops.pack([latents, mask, image_latents], 'b * h w')
+        return model_input
+
 
+def are_like_tensors(a: torch.Tensor, b: object) -> bool:
+    return (
+        isinstance(b, torch.Tensor)
+        and (a.size() == b.size())
+    )
 
 @dataclass
 class AddsMaskGuidance:
-    forward: Callable[[torch.Tensor, torch.Tensor, torch.Tensor], torch.Tensor]
     mask: torch.FloatTensor
     mask_latents: torch.FloatTensor
     _scheduler: SchedulerMixin
     _noise_func: Callable
     _debug: Optional[Callable] = None
 
-    def __call__(self, latents: torch.FloatTensor, t: torch.Tensor, text_embeddings: torch.FloatTensor) -> torch.Tensor:
+    def __call__(self, step_output: BaseOutput | SchedulerOutput, t: torch.Tensor, conditioning) -> BaseOutput:
+        output_class = step_output.__class__  # We'll create a new one with masked data.
+
+        # The problem with taking SchedulerOutput instead of the model output is that we're less certain what's in it.
+        # It's reasonable to assume the first thing is prev_sample, but then does it have other things
+        # like pred_original_sample? Should we apply the mask to them too?
+        # But what if there's just some other random field?
+        prev_sample = step_output[0]
+        # Mask anything that has the same shape as prev_sample, return others as-is.
+        return output_class(
+            {k: (self.apply_mask(v, self._t_for_field(k, t))
+                 if are_like_tensors(prev_sample, v) else v)
+            for k, v in step_output.items()}
+        )
+
+    def _t_for_field(self, field_name:str, t):
+        if field_name == "pred_original_sample":
+            return torch.zeros_like(t, dtype=t.dtype)  # it represents t=0
+        return t
+
+    def apply_mask(self, latents: torch.Tensor, t) -> torch.Tensor:
         batch_size = latents.size(0)
         mask = einops.repeat(self.mask, 'b c h w -> (repeat b) c h w', repeat=batch_size)
         noise = self._noise_func(self.mask_latents)
-        mask_latents = self._scheduler.add_noise(self.mask_latents, noise, t[0])  # .to(dtype=mask_latents.dtype)
+        mask_latents = self._scheduler.add_noise(self.mask_latents, noise, t)
+        # TODO: Do we need to also apply scheduler.scale_model_input? Or is add_noise appropriately scaled already?
         mask_latents = einops.repeat(mask_latents, 'b c h w -> (repeat b) c h w', repeat=batch_size)
-        # if self._debug:
-        #     self._debug(latents, f"t={t[0]} latents")
         masked_input = torch.lerp(mask_latents.to(dtype=latents.dtype), latents, mask.to(dtype=latents.dtype))
         if self._debug:
-            self._debug(masked_input, f"t={t[0]} lerped")
-        return self.forward(masked_input, t, text_embeddings)
+            self._debug(masked_input, f"t={t} lerped")
+        return masked_input
 
 
 def image_resized_to_grid_as_tensor(image: PIL.Image.Image, normalize: bool=True, multiple_of=8) -> torch.FloatTensor:
@@ -263,10 +301,14 @@ def generate_from_embeddings(
             run_id: str = None,
             extra_conditioning_info: InvokeAIDiffuserComponent.ExtraConditioningInfo = None,
             timesteps = None,
+            additional_guidance: List[Callable] = None,
             **extra_step_kwargs):
         if run_id is None:
             run_id = secrets.token_urlsafe(self.ID_LENGTH)
 
+        if additional_guidance is None:
+            additional_guidance = []
+
         if extra_conditioning_info is not None and extra_conditioning_info.wants_cross_attention_control:
             self.invokeai_diffuser.setup_cross_attention_control(extra_conditioning_info,
                                                                  step_count=len(self.scheduler.timesteps))
@@ -289,7 +331,8 @@ def generate_from_embeddings(
             batched_t.fill_(t)
             step_output = self.step(batched_t, latents, guidance_scale,
                                     text_embeddings, unconditioned_embeddings,
-                                    i, **extra_step_kwargs)
+                                    i, additional_guidance=additional_guidance,
+                                    **extra_step_kwargs)
             latents = step_output.prev_sample
             predicted_original = getattr(step_output, 'pred_original_sample', None)
             yield PipelineIntermediateState(run_id=run_id, step=i, timestep=int(t), latents=latents,
@@ -306,11 +349,14 @@ def generate_from_embeddings(
     @torch.inference_mode()
     def step(self, t: torch.Tensor, latents: torch.Tensor, guidance_scale: float,
              text_embeddings: torch.Tensor, unconditioned_embeddings: torch.Tensor,
-             step_index:int | None = None,
+             step_index:int | None = None, additional_guidance: List[Callable] = None,
              **extra_step_kwargs):
         # invokeai_diffuser has batched timesteps, but diffusers schedulers expect a single value
         timestep = t[0]
 
+        if additional_guidance is None:
+            additional_guidance = []
+
         # TODO: should this scaling happen here or inside self._unet_forward?
         #     i.e. before or after passing it to InvokeAIDiffuserComponent
         latent_model_input = self.scheduler.scale_model_input(latents, timestep)
@@ -323,7 +369,15 @@ def step(self, t: torch.Tensor, latents: torch.Tensor, guidance_scale: float,
             step_index=step_index)
 
         # compute the previous noisy sample x_t -> x_t-1
-        return self.scheduler.step(noise_pred, timestep, latents, **extra_step_kwargs)
+        step_output = self.scheduler.step(noise_pred, timestep, latents, **extra_step_kwargs)
+
+        # TODO: this additional_guidance extension point feels redundant with InvokeAIDiffusionComponent.
+        #    But the way things are now, scheduler runs _after_ that, so there was
+        #    no way to use it to apply an operation that happens after the last scheduler.step.
+        for guidance in additional_guidance:
+            step_output = guidance(step_output, timestep, (unconditioned_embeddings, text_embeddings))
+
+        return step_output
 
     def _unet_forward(self, latents, t, text_embeddings):
         # predict the noise residual
@@ -401,6 +455,8 @@ def inpaint_from_embeddings(
         img2img_pipeline.scheduler.set_timesteps(num_inference_steps, device=device)
         timesteps = img2img_pipeline.get_timesteps(num_inference_steps, strength, device=device)
 
+        assert img2img_pipeline.scheduler is self.scheduler
+
         # 6. Prepare latent variables
         latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
         latents, init_image_latents = self.prepare_latents_from_image(init_image, latent_timestep, latents_dtype, device, noise_func)
@@ -410,13 +466,14 @@ def inpaint_from_embeddings(
         mask = tv_resize(mask, latents.shape[-2:], T.InterpolationMode.BILINEAR) \
             .to(device=device, dtype=latents_dtype)
 
+        guidance: List[Callable] = []
+
         if is_inpainting_model(self.unet):
+            # TODO: we should probably pass this in so we don't have to try/finally around setting it.
             self.invokeai_diffuser.model_forward_callback = \
                 AddsMaskLatents(self._unet_forward, mask, init_image_latents)
         else:
-            self.invokeai_diffuser.model_forward_callback = \
-                AddsMaskGuidance(self._unet_forward, mask, init_image_latents,
-                                 self.scheduler, noise_func)  # self.debug_latents)
+            guidance.append(AddsMaskGuidance(mask, init_image_latents, self.scheduler, noise_func))
 
         result = None
 
@@ -425,7 +482,7 @@ def inpaint_from_embeddings(
                     latents, text_embeddings, unconditioned_embeddings, guidance_scale,
                     extra_conditioning_info=extra_conditioning_info,
                     timesteps=timesteps,
-                    run_id=run_id, **extra_step_kwargs):
+                    run_id=run_id, additional_guidance=guidance, **extra_step_kwargs):
                 if callback is not None and isinstance(result, PipelineIntermediateState):
                     callback(result)
             if result is None:

From bf6376417a734264dd8bc1095532cf6e273421ce Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Tue, 6 Dec 2022 15:52:49 -0800
Subject: [PATCH 051/199] diffusers: reset num_vectors_per_token

sync with 44a00555718f1df173c60da0ed646cf700e29537
---
 ldm/invoke/generator/diffusers_pipeline.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ldm/invoke/generator/diffusers_pipeline.py b/ldm/invoke/generator/diffusers_pipeline.py
index 95de688f578..22fee100194 100644
--- a/ldm/invoke/generator/diffusers_pipeline.py
+++ b/ldm/invoke/generator/diffusers_pipeline.py
@@ -47,7 +47,7 @@ class PipelineIntermediateState:
     placeholder_strings=["*"],
     initializer_wods=["sculpture"],
     per_image_tokens=False,
-    num_vectors_per_token=8,
+    num_vectors_per_token=1,
     progressive_words=False
 )
 

From 04a5bc938e606c8f807dc449596a831836cb3c59 Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Tue, 6 Dec 2022 19:16:28 -0800
Subject: [PATCH 052/199] diffusers: txt2img2img (hires_fix)

with so much slicing and dicing of pipeline methods to stitch them together
---
 ldm/invoke/generator/diffusers_pipeline.py | 146 ++++++++++++++------
 ldm/invoke/generator/txt2img.py            |   8 --
 ldm/invoke/generator/txt2img2img.py        | 147 +++++++--------------
 3 files changed, 149 insertions(+), 152 deletions(-)

diff --git a/ldm/invoke/generator/diffusers_pipeline.py b/ldm/invoke/generator/diffusers_pipeline.py
index 22fee100194..35db1db3837 100644
--- a/ldm/invoke/generator/diffusers_pipeline.py
+++ b/ldm/invoke/generator/diffusers_pipeline.py
@@ -3,7 +3,7 @@
 import secrets
 import warnings
 from dataclasses import dataclass
-from typing import List, Optional, Union, Callable
+from typing import List, Optional, Union, Callable, Type, TypeVar, Generic, Any, ParamSpec
 
 import PIL.Image
 import einops
@@ -11,11 +11,11 @@
 import torchvision.transforms as T
 from diffusers.models import attention
 
-from ldm.models.diffusion.cross_attention_control import InvokeAIDiffusersCrossAttention
+from ...models.diffusion import cross_attention_control
 
 # monkeypatch diffusers CrossAttention 🙈
 # this is to make prompt2prompt and (future) attention maps work
-attention.CrossAttention = InvokeAIDiffusersCrossAttention
+attention.CrossAttention = cross_attention_control.InvokeAIDiffusersCrossAttention
 
 from diffusers.models import AutoencoderKL, UNet2DConditionModel
 from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
@@ -126,6 +126,10 @@ def apply_mask(self, latents: torch.Tensor, t) -> torch.Tensor:
         return masked_input
 
 
+def trim_to_multiple_of(*args, multiple_of=8):
+    return tuple((x - x % multiple_of) for x in args)
+
+
 def image_resized_to_grid_as_tensor(image: PIL.Image.Image, normalize: bool=True, multiple_of=8) -> torch.FloatTensor:
     """
 
@@ -133,8 +137,7 @@ def image_resized_to_grid_as_tensor(image: PIL.Image.Image, normalize: bool=True
     :param normalize: scale the range to [-1, 1] instead of [0, 1]
     :param multiple_of: resize the input so both dimensions are a multiple of this
     """
-    w, h = image.size
-    w, h = map(lambda x: x - x % multiple_of, (w, h))  # resize to integer multiple of 8
+    w, h = trim_to_multiple_of(*image.size)
     transformation = T.Compose([
         T.Resize((h, w), T.InterpolationMode.LANCZOS),
         T.ToTensor(),
@@ -148,6 +151,26 @@ def image_resized_to_grid_as_tensor(image: PIL.Image.Image, normalize: bool=True
 def is_inpainting_model(unet: UNet2DConditionModel):
     return unet.conv_in.in_channels == 9
 
+CallbackType = TypeVar('CallbackType')
+ReturnType = TypeVar('ReturnType')
+ParamType = ParamSpec('ParamType')
+
+@dataclass(frozen=True)
+class GeneratorToCallbackinator(Generic[ParamType, ReturnType, CallbackType]):
+    generator_method: Callable[ParamType, ReturnType]
+    callback_arg_type: Type[CallbackType]
+
+    def __call__(self, *args: ParamType.args,
+                 callback:Callable[[CallbackType], Any]=None,
+                 **kwargs: ParamType.kwargs) -> ReturnType:
+        result = None
+        for result in self.generator_method(*args, **kwargs):
+            if callback is not None and isinstance(result, self.callback_arg_type):
+                callback(result)
+        if result is None:
+            raise AssertionError("why was that an empty generator?")
+        return result
+
 
 class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
     r"""
@@ -250,6 +273,21 @@ def image_from_embeddings(self, latents: torch.Tensor, num_inference_steps: int,
             raise AssertionError("why was that an empty generator?")
         return result
 
+    def latents_from_embeddings(self, latents: torch.Tensor, num_inference_steps: int,
+                              text_embeddings: torch.Tensor, unconditioned_embeddings: torch.Tensor,
+                              guidance_scale: float,
+                              *, callback: Callable[[PipelineIntermediateState], None]=None,
+                              extra_conditioning_info: InvokeAIDiffuserComponent.ExtraConditioningInfo=None,
+                              run_id=None,
+                              **extra_step_kwargs) -> PipelineIntermediateState:
+        self.scheduler.set_timesteps(num_inference_steps, device=self.unet.device)
+        f = GeneratorToCallbackinator(self.generate_latents_from_embeddings, PipelineIntermediateState)
+        return f(latents, text_embeddings, unconditioned_embeddings, guidance_scale,
+                extra_conditioning_info=extra_conditioning_info,
+                run_id=run_id,
+                callback=callback,
+                **extra_step_kwargs)
+
     def generate(
         self,
         prompt: Union[str, List[str]],
@@ -303,19 +341,42 @@ def generate_from_embeddings(
             timesteps = None,
             additional_guidance: List[Callable] = None,
             **extra_step_kwargs):
+        latents = yield from self.generate_latents_from_embeddings(latents, text_embeddings, unconditioned_embeddings,
+                                                                   guidance_scale, run_id=run_id, extra_conditioning_info=extra_conditioning_info,
+                                                                   timesteps=timesteps, additional_guidance=additional_guidance, **extra_step_kwargs)
+
+        # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
+        torch.cuda.empty_cache()
+
+        with torch.inference_mode():
+            image = self.decode_latents(latents)
+            output = StableDiffusionPipelineOutput(images=image, nsfw_content_detected=[])
+            yield self.check_for_safety(output, dtype=text_embeddings.dtype)
+
+    def generate_latents_from_embeddings(
+        self,
+        latents: torch.Tensor,
+        text_embeddings: torch.Tensor,
+        unconditioned_embeddings: torch.Tensor,
+        guidance_scale: float,
+        *,
+        run_id: str = None,
+        extra_conditioning_info: InvokeAIDiffuserComponent.ExtraConditioningInfo = None,
+        timesteps = None,
+        additional_guidance: List[Callable] = None,
+        **extra_step_kwargs
+    ):
         if run_id is None:
             run_id = secrets.token_urlsafe(self.ID_LENGTH)
-
         if additional_guidance is None:
             additional_guidance = []
-
         if extra_conditioning_info is not None and extra_conditioning_info.wants_cross_attention_control:
             self.invokeai_diffuser.setup_cross_attention_control(extra_conditioning_info,
                                                                  step_count=len(self.scheduler.timesteps))
         else:
             self.invokeai_diffuser.remove_cross_attention_control()
-
         if timesteps is None:
+            # NOTE: Depends on scheduler being already initialized!
             timesteps = self.scheduler.timesteps
 
         # scale the initial noise by the standard deviation required by the scheduler
@@ -326,7 +387,7 @@ def generate_from_embeddings(
         batch_size = latents.shape[0]
         batched_t = torch.full((batch_size,), timesteps[0],
                                dtype=timesteps.dtype, device=self.unet.device)
-        # NOTE: Depends on scheduler being already initialized!
+
         for i, t in enumerate(self.progress_bar(timesteps)):
             batched_t.fill_(t)
             step_output = self.step(batched_t, latents, guidance_scale,
@@ -337,14 +398,7 @@ def generate_from_embeddings(
             predicted_original = getattr(step_output, 'pred_original_sample', None)
             yield PipelineIntermediateState(run_id=run_id, step=i, timestep=int(t), latents=latents,
                                             predicted_original=predicted_original)
-
-        # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
-        torch.cuda.empty_cache()
-
-        with torch.inference_mode():
-            image = self.decode_latents(latents)
-            output = StableDiffusionPipelineOutput(images=image, nsfw_content_detected=[])
-            yield self.check_for_safety(output, dtype=text_embeddings.dtype)
+        return latents
 
     @torch.inference_mode()
     def step(self, t: torch.Tensor, latents: torch.Tensor, guidance_scale: float,
@@ -396,34 +450,38 @@ def img2img_from_embeddings(self,
                                 **extra_step_kwargs) -> StableDiffusionPipelineOutput:
         device = self.unet.device
         latents_dtype = self.unet.dtype
-        batch_size = 1
-        num_images_per_prompt = 1
-
         if isinstance(init_image, PIL.Image.Image):
             init_image = image_resized_to_grid_as_tensor(init_image.convert('RGB'))
 
         if init_image.dim() == 3:
             init_image = einops.rearrange(init_image, 'c h w -> 1 c h w')
 
+        # 6. Prepare latent variables
+        initial_latents = self.non_noised_latents_from_image(init_image, device=device, dtype=latents_dtype)
+
+        result = self.img2img_from_latents_and_embeddings(initial_latents, num_inference_steps, text_embeddings,
+                                                          unconditioned_embeddings, guidance_scale, strength,
+                                                          extra_conditioning_info, noise_func, run_id, callback,
+                                                          **extra_step_kwargs)
+        return result
+
+    def img2img_from_latents_and_embeddings(self, initial_latents, num_inference_steps, text_embeddings,
+                                            unconditioned_embeddings, guidance_scale, strength, extra_conditioning_info,
+                                            noise_func, run_id=None, callback=None, **extra_step_kwargs):
+        device = self.unet.device
+        batch_size = initial_latents.size(0)
         img2img_pipeline = StableDiffusionImg2ImgPipeline(**self.components)
         img2img_pipeline.scheduler.set_timesteps(num_inference_steps, device=device)
         timesteps = img2img_pipeline.get_timesteps(num_inference_steps, strength, device=device)
-        latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
+        latent_timestep = timesteps[:1].repeat(batch_size)
+        latents = self.noise_latents_for_time(initial_latents, latent_timestep, noise_func=noise_func)
 
-        # 6. Prepare latent variables
-        latents, _ = self.prepare_latents_from_image(init_image, latent_timestep, latents_dtype, device, noise_func)
-
-        result = None
-        for result in self.generate_from_embeddings(
-                latents, text_embeddings, unconditioned_embeddings, guidance_scale,
-                extra_conditioning_info=extra_conditioning_info,
-                timesteps=timesteps,
-                run_id=run_id, **extra_step_kwargs):
-            if callback is not None and isinstance(result, PipelineIntermediateState):
-                callback(result)
-        if result is None:
-            raise AssertionError("why was that an empty generator?")
-        return result
+        f = GeneratorToCallbackinator(self.generate_from_embeddings, PipelineIntermediateState)
+        return f(latents, text_embeddings, unconditioned_embeddings, guidance_scale,
+            extra_conditioning_info=extra_conditioning_info,
+            timesteps=timesteps,
+            callback=callback,
+            run_id=run_id, **extra_step_kwargs)
 
     def inpaint_from_embeddings(
             self,
@@ -459,7 +517,10 @@ def inpaint_from_embeddings(
 
         # 6. Prepare latent variables
         latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
-        latents, init_image_latents = self.prepare_latents_from_image(init_image, latent_timestep, latents_dtype, device, noise_func)
+        # can't quite use upstream StableDiffusionImg2ImgPipeline.prepare_latents
+        # because we have our own noise function
+        init_image_latents = self.non_noised_latents_from_image(init_image, device=device, dtype=latents_dtype)
+        latents = self.noise_latents_for_time(init_image_latents, latent_timestep, noise_func=noise_func)
 
         if mask.dim() == 3:
             mask = mask.unsqueeze(0)
@@ -491,19 +552,18 @@ def inpaint_from_embeddings(
         finally:
             self.invokeai_diffuser.model_forward_callback = self._unet_forward
 
-
-    def prepare_latents_from_image(self, init_image, timestep, dtype, device, noise_func) -> (torch.FloatTensor, torch.FloatTensor):
-        # can't quite use upstream StableDiffusionImg2ImgPipeline.prepare_latents
-        # because we have our own noise function
+    def non_noised_latents_from_image(self, init_image, *, device, dtype):
         init_image = init_image.to(device=device, dtype=dtype)
         with torch.inference_mode():
             init_latent_dist = self.vae.encode(init_image).latent_dist
             init_latents = init_latent_dist.sample().to(dtype=dtype)  # FIXME: uses torch.randn. make reproducible!
         init_latents = 0.18215 * init_latents
+        return init_latents
 
-        noise = noise_func(init_latents)
-        noised_latents = self.scheduler.add_noise(init_latents, noise, timestep)
-        return noised_latents, init_latents
+    def noise_latents_for_time(self, latents, timestep, *, noise_func):
+        noise = noise_func(latents)
+        noised_latents = self.scheduler.add_noise(latents, noise, timestep)
+        return noised_latents
 
     def check_for_safety(self, output, dtype):
         with torch.inference_mode():
diff --git a/ldm/invoke/generator/txt2img.py b/ldm/invoke/generator/txt2img.py
index f9af1ac3ed7..36f5219b28e 100644
--- a/ldm/invoke/generator/txt2img.py
+++ b/ldm/invoke/generator/txt2img.py
@@ -29,10 +29,6 @@ def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
         pipeline.scheduler = sampler
 
         def make_image(x_T) -> PIL.Image.Image:
-            # FIXME: restore free_gpu_mem functionality
-            # if self.free_gpu_mem and self.model.model.device != self.model.device:
-            #     self.model.model.to(self.model.device)
-
             pipeline_output = pipeline.image_from_embeddings(
                 latents=x_T,
                 num_inference_steps=steps,
@@ -45,10 +41,6 @@ def make_image(x_T) -> PIL.Image.Image:
                 # TODO: threshold = threshold,
             )
 
-            # FIXME: restore free_gpu_mem functionality
-            # if self.free_gpu_mem:
-            #     self.model.model.to("cpu")
-
             return pipeline.numpy_to_pil(pipeline_output.images)[0]
 
         return make_image
diff --git a/ldm/invoke/generator/txt2img2img.py b/ldm/invoke/generator/txt2img2img.py
index 3da42ebb8af..35c6a39ca29 100644
--- a/ldm/invoke/generator/txt2img2img.py
+++ b/ldm/invoke/generator/txt2img2img.py
@@ -3,13 +3,12 @@
 '''
 
 import math
+from typing import Callable, Optional
 
 import torch
-from PIL import Image
 
 from ldm.invoke.generator.base import Generator
-from ldm.invoke.generator.omnibus import Omnibus
-from ldm.models.diffusion.ddim import DDIMSampler
+from ldm.invoke.generator.diffusers_pipeline import trim_to_multiple_of, StableDiffusionGeneratorPipeline
 
 
 class Txt2Img2Img(Generator):
@@ -17,9 +16,9 @@ def __init__(self, model, precision):
         super().__init__(model, precision)
         self.init_latent = None    # for get_noise()
 
-    @torch.no_grad()
-    def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
-                       conditioning,width,height,strength,step_callback=None,**kwargs):
+    def get_make_image(self, prompt:str, sampler, steps:int, cfg_scale:float, ddim_eta,
+                       conditioning, width:int, height:int, strength:float,
+                       step_callback:Optional[Callable]=None, **kwargs):
         """
         Returns a function returning an image derived from the prompt and the initial image
         Return value depends on the seed at the time you call it
@@ -29,125 +28,72 @@ def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
         scale_dim = min(width, height)
         scale = 512 / scale_dim
 
-        init_width = math.ceil(scale * width / 64) * 64
-        init_height = math.ceil(scale * height / 64) * 64
+        init_width, init_height = trim_to_multiple_of(scale * width, scale * height)
 
-        @torch.no_grad()
-        def make_image(x_T):
+        # noinspection PyTypeChecker
+        pipeline: StableDiffusionGeneratorPipeline = self.model
+        pipeline.scheduler = sampler
 
-            shape = [
-                self.latent_channels,
-                init_height // self.downsampling_factor,
-                init_width // self.downsampling_factor,
-            ]
+        def make_image(x_T):
 
-            sampler.make_schedule(
-                    ddim_num_steps=steps, ddim_eta=ddim_eta, verbose=False
+            pipeline_output = pipeline.latents_from_embeddings(
+                latents=x_T,
+                num_inference_steps=steps,
+                text_embeddings=c,
+                unconditioned_embeddings=uc,
+                guidance_scale=cfg_scale,
+                callback=step_callback,
+                extra_conditioning_info=extra_conditioning_info,
+                # TODO: eta = ddim_eta,
+                # TODO: threshold = threshold,
             )
 
-            #x = self.get_noise(init_width, init_height)
-            x = x_T
-
-            if self.free_gpu_mem and self.model.model.device != self.model.device:
-                self.model.model.to(self.model.device)
-
-            samples, _ = sampler.sample(
-                batch_size                   = 1,
-                S                            = steps,
-                x_T                          = x,
-                conditioning                 = c,
-                shape                        = shape,
-                verbose                      = False,
-                unconditional_guidance_scale = cfg_scale,
-                unconditional_conditioning   = uc,
-                eta                          = ddim_eta,
-                img_callback                 = step_callback,
-                extra_conditioning_info      = extra_conditioning_info
-            )
+            first_pass_latent_output = pipeline_output.latents
 
             print(
                   f"\n>> Interpolating from {init_width}x{init_height} to {width}x{height} using DDIM sampling"
                  )
 
             # resizing
-            samples = torch.nn.functional.interpolate(
-                samples,
+            resized_latents = torch.nn.functional.interpolate(
+                first_pass_latent_output,
                 size=(height // self.downsampling_factor, width // self.downsampling_factor),
                 mode="bilinear"
             )
 
-            t_enc = int(strength * steps)
-            ddim_sampler = DDIMSampler(self.model, device=self.model.device)
-            ddim_sampler.make_schedule(
-                    ddim_num_steps=steps, ddim_eta=ddim_eta, verbose=False
-            )
-
-            z_enc = ddim_sampler.stochastic_encode(
-                samples,
-                torch.tensor([t_enc]).to(self.model.device),
-                noise=self.get_noise(width,height,False)
-            )
-
-            # decode it
-            samples = ddim_sampler.decode(
-                z_enc,
-                c,
-                t_enc,
-                img_callback = step_callback,
-                unconditional_guidance_scale=cfg_scale,
-                unconditional_conditioning=uc,
+            pipeline_output = pipeline.img2img_from_latents_and_embeddings(
+                resized_latents,
+                num_inference_steps=steps,
+                text_embeddings=c,
+                unconditioned_embeddings=uc,
+                guidance_scale=cfg_scale, strength=strength,
                 extra_conditioning_info=extra_conditioning_info,
-                all_timesteps_count=steps
-            )
+                noise_func=self.get_noise_like,
+                callback=step_callback)
 
-            if self.free_gpu_mem:
-                self.model.model.to("cpu")
+            return pipeline.numpy_to_pil(pipeline_output.images)[0]
 
-            return self.sample_to_image(samples)
+
+        # FIXME: do we really need something entirely different for the inpainting model?
 
         # in the case of the inpainting model being loaded, the trick of
         # providing an interpolated latent doesn't work, so we transiently
         # create a 512x512 PIL image, upscale it, and run the inpainting
         # over it in img2img mode. Because the inpaing model is so conservative
         # it doesn't change the image (much)
-        def inpaint_make_image(x_T):
-            omnibus = Omnibus(self.model,self.precision)
-            result = omnibus.generate(
-                prompt,
-                sampler=sampler,
-                width=init_width,
-                height=init_height,
-                step_callback=step_callback,
-                steps = steps,
-                cfg_scale = cfg_scale,
-                ddim_eta = ddim_eta,
-                conditioning = conditioning,
-                **kwargs
-            )
-            assert result is not None and len(result)>0,'** txt2img failed **'
-            image = result[0][0]
-            interpolated_image = image.resize((width,height),resample=Image.Resampling.LANCZOS)
-            print(kwargs.pop('init_image',None))
-            result = omnibus.generate(
-                prompt,
-                sampler=sampler,
-                init_image=interpolated_image,
-                width=width,
-                height=height,
-                seed=result[0][1],
-                step_callback=step_callback,
-                steps = steps,
-                cfg_scale = cfg_scale,
-                ddim_eta = ddim_eta,
-                conditioning = conditioning,
-                **kwargs
-                )
-            return result[0][0]
-            
-        if sampler.uses_inpainting_model():
-            return inpaint_make_image
+
+        return make_image
+
+    def get_noise_like(self, like: torch.Tensor):
+        device = like.device
+        if device.type == 'mps':
+            x = torch.randn_like(like, device='cpu').to(device)
         else:
-            return make_image
+            x = torch.randn_like(like, device=device)
+        if self.perlin > 0.0:
+            shape = like.shape
+            x = (1-self.perlin)*x + self.perlin*self.get_perlin_noise(shape[3], shape[2])
+        return x
 
     # returns a tensor filled with random numbers from a normal distribution
     def get_noise(self,width,height,scale = True):
@@ -175,4 +121,3 @@ def get_noise(self,width,height,scale = True):
                                 scaled_height // self.downsampling_factor,
                                 scaled_width  // self.downsampling_factor],
                                 device=device)
-

From 821c7df2401deb3101654304d963642d22256f53 Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Wed, 7 Dec 2022 18:20:56 -0800
Subject: [PATCH 053/199] refactor(diffusers): reduce some code duplication
 amongst the different tasks

---
 ldm/invoke/generator/diffusers_pipeline.py | 176 +++++++--------------
 1 file changed, 61 insertions(+), 115 deletions(-)

diff --git a/ldm/invoke/generator/diffusers_pipeline.py b/ldm/invoke/generator/diffusers_pipeline.py
index 35db1db3837..4dbd576c510 100644
--- a/ldm/invoke/generator/diffusers_pipeline.py
+++ b/ldm/invoke/generator/diffusers_pipeline.py
@@ -157,6 +157,8 @@ def is_inpainting_model(unet: UNet2DConditionModel):
 
 @dataclass(frozen=True)
 class GeneratorToCallbackinator(Generic[ParamType, ReturnType, CallbackType]):
+    """Convert a generator to a function with a callback and a return value."""
+
     generator_method: Callable[ParamType, ReturnType]
     callback_arg_type: Type[CallbackType]
 
@@ -261,111 +263,48 @@ def image_from_embeddings(self, latents: torch.Tensor, num_inference_steps: int,
         :param run_id:
         :param extra_step_kwargs:
         """
-        self.scheduler.set_timesteps(num_inference_steps, device=self.unet.device)
-        result = None
-        for result in self.generate_from_embeddings(
-                latents, text_embeddings, unconditioned_embeddings, guidance_scale,
-                extra_conditioning_info=extra_conditioning_info,
-                run_id=run_id, **extra_step_kwargs):
-            if callback is not None and isinstance(result, PipelineIntermediateState):
-                callback(result)
-        if result is None:
-            raise AssertionError("why was that an empty generator?")
-        return result
-
-    def latents_from_embeddings(self, latents: torch.Tensor, num_inference_steps: int,
-                              text_embeddings: torch.Tensor, unconditioned_embeddings: torch.Tensor,
-                              guidance_scale: float,
-                              *, callback: Callable[[PipelineIntermediateState], None]=None,
-                              extra_conditioning_info: InvokeAIDiffuserComponent.ExtraConditioningInfo=None,
-                              run_id=None,
-                              **extra_step_kwargs) -> PipelineIntermediateState:
-        self.scheduler.set_timesteps(num_inference_steps, device=self.unet.device)
-        f = GeneratorToCallbackinator(self.generate_latents_from_embeddings, PipelineIntermediateState)
-        return f(latents, text_embeddings, unconditioned_embeddings, guidance_scale,
-                extra_conditioning_info=extra_conditioning_info,
-                run_id=run_id,
-                callback=callback,
-                **extra_step_kwargs)
-
-    def generate(
-        self,
-        prompt: Union[str, List[str]],
-        *,
-        opposing_prompt: Union[str, List[str]] = None,
-        height: Optional[int] = 512,
-        width: Optional[int] = 512,
-        num_inference_steps: Optional[int] = 50,
-        guidance_scale: Optional[float] = 7.5,
-        generator: Optional[torch.Generator] = None,
-        latents: Optional[torch.FloatTensor] = None,
-        run_id: str = None,
-        **extra_step_kwargs,
-    ):
-        if isinstance(prompt, str):
-            batch_size = 1
-        else:
-            batch_size = len(prompt)
-
-        if height % 8 != 0 or width % 8 != 0:
-            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
-
-        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
-        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
-        # corresponds to doing no classifier free guidance.
-        do_classifier_free_guidance = guidance_scale > 1.0
-
-        combined_embeddings = self._encode_prompt(prompt, device=self._execution_device, num_images_per_prompt=1,
-                            do_classifier_free_guidance=do_classifier_free_guidance,
-                            negative_prompt=opposing_prompt)
-        text_embeddings, unconditioned_embeddings = combined_embeddings.chunk(2)
-        self.scheduler.set_timesteps(num_inference_steps)
-        latents = self.prepare_latents(batch_size=batch_size, num_channels_latents=self.unet.in_channels,
-                                       height=height, width=width,
-                                       dtype=self.unet.dtype, device=self._execution_device,
-                                       generator=generator,
-                                       latents=latents)
-
-        yield from self.generate_from_embeddings(latents, text_embeddings, unconditioned_embeddings,
-                                                 guidance_scale, run_id=run_id, **extra_step_kwargs)
-
-    def generate_from_embeddings(
-            self,
-            latents: torch.Tensor,
-            text_embeddings: torch.Tensor,
-            unconditioned_embeddings: torch.Tensor,
-            guidance_scale: float,
-            *,
-            run_id: str = None,
-            extra_conditioning_info: InvokeAIDiffuserComponent.ExtraConditioningInfo = None,
-            timesteps = None,
-            additional_guidance: List[Callable] = None,
-            **extra_step_kwargs):
-        latents = yield from self.generate_latents_from_embeddings(latents, text_embeddings, unconditioned_embeddings,
-                                                                   guidance_scale, run_id=run_id, extra_conditioning_info=extra_conditioning_info,
-                                                                   timesteps=timesteps, additional_guidance=additional_guidance, **extra_step_kwargs)
-
+        result_latents = self.latents_from_embeddings(
+            latents, num_inference_steps, text_embeddings, unconditioned_embeddings, guidance_scale,
+            extra_conditioning_info=extra_conditioning_info,
+            run_id=run_id, callback=callback, **extra_step_kwargs
+        )
         # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
         torch.cuda.empty_cache()
 
         with torch.inference_mode():
-            image = self.decode_latents(latents)
+            image = self.decode_latents(result_latents)
             output = StableDiffusionPipelineOutput(images=image, nsfw_content_detected=[])
-            yield self.check_for_safety(output, dtype=text_embeddings.dtype)
+            return self.check_for_safety(output, dtype=text_embeddings.dtype)
 
-    def generate_latents_from_embeddings(
-        self,
-        latents: torch.Tensor,
-        text_embeddings: torch.Tensor,
-        unconditioned_embeddings: torch.Tensor,
+    def latents_from_embeddings(
+        self, latents: torch.Tensor, num_inference_steps: int,
+        text_embeddings: torch.Tensor, unconditioned_embeddings: torch.Tensor,
         guidance_scale: float,
         *,
-        run_id: str = None,
-        extra_conditioning_info: InvokeAIDiffuserComponent.ExtraConditioningInfo = None,
         timesteps = None,
+        extra_conditioning_info: InvokeAIDiffuserComponent.ExtraConditioningInfo = None,
         additional_guidance: List[Callable] = None,
+        run_id=None,
+        callback: Callable[[PipelineIntermediateState], None]=None,
         **extra_step_kwargs
-    ):
+    ) -> torch.Tensor:
+        if timesteps is None:
+            self.scheduler.set_timesteps(num_inference_steps, device=self.unet.device)
+            timesteps = self.scheduler.timesteps
+        infer_latents_from_embeddings = GeneratorToCallbackinator(self.generate_latents_from_embeddings, PipelineIntermediateState)
+        return infer_latents_from_embeddings(
+            latents, timesteps, text_embeddings, unconditioned_embeddings, guidance_scale,
+            extra_conditioning_info=extra_conditioning_info,
+            additional_guidance=additional_guidance,
+            run_id=run_id,
+            callback=callback,
+            **extra_step_kwargs).latents
+
+    def generate_latents_from_embeddings(self, latents: torch.Tensor, timesteps, text_embeddings: torch.Tensor,
+                                         unconditioned_embeddings: torch.Tensor, guidance_scale: float, *,
+                                         run_id: str = None,
+                                         extra_conditioning_info: InvokeAIDiffuserComponent.ExtraConditioningInfo = None,
+                                         additional_guidance: List[Callable] = None, **extra_step_kwargs):
         if run_id is None:
             run_id = secrets.token_urlsafe(self.ID_LENGTH)
         if additional_guidance is None:
@@ -375,9 +314,6 @@ def generate_latents_from_embeddings(
                                                                  step_count=len(self.scheduler.timesteps))
         else:
             self.invokeai_diffuser.remove_cross_attention_control()
-        if timesteps is None:
-            # NOTE: Depends on scheduler being already initialized!
-            timesteps = self.scheduler.timesteps
 
         # scale the initial noise by the standard deviation required by the scheduler
         latents *= self.scheduler.init_noise_sigma
@@ -448,8 +384,6 @@ def img2img_from_embeddings(self,
                                 run_id=None,
                                 noise_func=None,
                                 **extra_step_kwargs) -> StableDiffusionPipelineOutput:
-        device = self.unet.device
-        latents_dtype = self.unet.dtype
         if isinstance(init_image, PIL.Image.Image):
             init_image = image_resized_to_grid_as_tensor(init_image.convert('RGB'))
 
@@ -457,13 +391,14 @@ def img2img_from_embeddings(self,
             init_image = einops.rearrange(init_image, 'c h w -> 1 c h w')
 
         # 6. Prepare latent variables
+        device = self.unet.device
+        latents_dtype = self.unet.dtype
         initial_latents = self.non_noised_latents_from_image(init_image, device=device, dtype=latents_dtype)
 
-        result = self.img2img_from_latents_and_embeddings(initial_latents, num_inference_steps, text_embeddings,
+        return self.img2img_from_latents_and_embeddings(initial_latents, num_inference_steps, text_embeddings,
                                                           unconditioned_embeddings, guidance_scale, strength,
                                                           extra_conditioning_info, noise_func, run_id, callback,
                                                           **extra_step_kwargs)
-        return result
 
     def img2img_from_latents_and_embeddings(self, initial_latents, num_inference_steps, text_embeddings,
                                             unconditioned_embeddings, guidance_scale, strength, extra_conditioning_info,
@@ -476,13 +411,21 @@ def img2img_from_latents_and_embeddings(self, initial_latents, num_inference_ste
         latent_timestep = timesteps[:1].repeat(batch_size)
         latents = self.noise_latents_for_time(initial_latents, latent_timestep, noise_func=noise_func)
 
-        f = GeneratorToCallbackinator(self.generate_from_embeddings, PipelineIntermediateState)
-        return f(latents, text_embeddings, unconditioned_embeddings, guidance_scale,
+        result_latents = self.latents_from_embeddings(
+            latents, num_inference_steps, text_embeddings, unconditioned_embeddings, guidance_scale,
             extra_conditioning_info=extra_conditioning_info,
             timesteps=timesteps,
             callback=callback,
             run_id=run_id, **extra_step_kwargs)
 
+        # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
+        torch.cuda.empty_cache()
+
+        with torch.inference_mode():
+            image = self.decode_latents(result_latents)
+            output = StableDiffusionPipelineOutput(images=image, nsfw_content_detected=[])
+            return self.check_for_safety(output, dtype=text_embeddings.dtype)
+
     def inpaint_from_embeddings(
             self,
             init_image: torch.FloatTensor,
@@ -536,22 +479,25 @@ def inpaint_from_embeddings(
         else:
             guidance.append(AddsMaskGuidance(mask, init_image_latents, self.scheduler, noise_func))
 
-        result = None
-
         try:
-            for result in self.generate_from_embeddings(
-                    latents, text_embeddings, unconditioned_embeddings, guidance_scale,
-                    extra_conditioning_info=extra_conditioning_info,
-                    timesteps=timesteps,
-                    run_id=run_id, additional_guidance=guidance, **extra_step_kwargs):
-                if callback is not None and isinstance(result, PipelineIntermediateState):
-                    callback(result)
-            if result is None:
-                raise AssertionError("why was that an empty generator?")
-            return result
+            result_latents = self.latents_from_embeddings(
+                latents, num_inference_steps, text_embeddings, unconditioned_embeddings, guidance_scale,
+                extra_conditioning_info=extra_conditioning_info,
+                timesteps=timesteps,
+                run_id=run_id, additional_guidance=guidance,
+                callback=callback,
+                **extra_step_kwargs)
         finally:
             self.invokeai_diffuser.model_forward_callback = self._unet_forward
 
+        # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
+        torch.cuda.empty_cache()
+
+        with torch.inference_mode():
+            image = self.decode_latents(result_latents)
+            output = StableDiffusionPipelineOutput(images=image, nsfw_content_detected=[])
+            return self.check_for_safety(output, dtype=text_embeddings.dtype)
+
     def non_noised_latents_from_image(self, init_image, *, device, dtype):
         init_image = init_image.to(device=device, dtype=dtype)
         with torch.inference_mode():

From 9bcb3b1bf75620b31f45da94bfb38766bfa6902b Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Wed, 7 Dec 2022 18:26:08 -0800
Subject: [PATCH 054/199] fixup! refactor(diffusers): reduce some code
 duplication amongst the different tasks

---
 ldm/invoke/generator/txt2img2img.py | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/ldm/invoke/generator/txt2img2img.py b/ldm/invoke/generator/txt2img2img.py
index 35c6a39ca29..29a7106246f 100644
--- a/ldm/invoke/generator/txt2img2img.py
+++ b/ldm/invoke/generator/txt2img2img.py
@@ -36,7 +36,7 @@ def get_make_image(self, prompt:str, sampler, steps:int, cfg_scale:float, ddim_e
 
         def make_image(x_T):
 
-            pipeline_output = pipeline.latents_from_embeddings(
+            first_pass_latent_output = pipeline.latents_from_embeddings(
                 latents=x_T,
                 num_inference_steps=steps,
                 text_embeddings=c,
@@ -48,8 +48,6 @@ def make_image(x_T):
                 # TODO: threshold = threshold,
             )
 
-            first_pass_latent_output = pipeline_output.latents
-
             print(
                   f"\n>> Interpolating from {init_width}x{init_height} to {width}x{height} using DDIM sampling"
                  )

From 30a8d4c2b38eb2de6b2d51400c430656d012d6d0 Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Wed, 7 Dec 2022 19:00:23 -0800
Subject: [PATCH 055/199] diffusers: enable DPMSolver++ scheduler

---
 ldm/generate.py | 15 +++------------
 1 file changed, 3 insertions(+), 12 deletions(-)

diff --git a/ldm/generate.py b/ldm/generate.py
index 375b72b362a..aefe58c8b74 100644
--- a/ldm/generate.py
+++ b/ldm/generate.py
@@ -19,6 +19,7 @@
 from PIL import Image, ImageOps
 from diffusers.pipeline_utils import DiffusionPipeline
 from diffusers.schedulers.scheduling_ddim import DDIMScheduler
+from diffusers.schedulers.scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
 from diffusers.schedulers.scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
 from diffusers.schedulers.scheduling_euler_discrete import EulerDiscreteScheduler
 from diffusers.schedulers.scheduling_ipndm import IPNDMScheduler
@@ -1005,19 +1006,14 @@ def _set_sampler_legacy(self):
     def _set_scheduler(self):
         default = self.model.scheduler
 
-        higher_order_samplers = [
-            'k_dpm_2',
-            'k_dpm_2_a',
-            'k_heun',
-            'plms',   # Its first step is like Heun
-        ]
         scheduler_map = dict(
             ddim=DDIMScheduler,
             ipndm=IPNDMScheduler,
             k_euler=EulerDiscreteScheduler,
             k_euler_a=EulerAncestralDiscreteScheduler,
             k_lms=LMSDiscreteScheduler,
-            pndm=PNDMScheduler,
+            plms=PNDMScheduler,
+            k_dpmpp_2=DPMSolverMultistepScheduler,
         )
 
         if self.sampler_name in scheduler_map:
@@ -1027,11 +1023,6 @@ def _set_scheduler(self):
                 self.model_cache.model_name_or_path(self.model_name),
                 subfolder="scheduler"
             )
-        elif self.sampler_name in higher_order_samplers:
-            msg = (f'>> Unsupported Sampler: {self.sampler_name} '
-                  f'— diffusers does not yet support higher-order samplers, '
-                  f'Defaulting to {default}')
-            self.sampler = default
         else:
             msg = (f'>> Unsupported Sampler: {self.sampler_name} '
                   f'Defaulting to {default}')

From 9199d698f8200783c577bbbd83acf6d14fef029a Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Thu, 8 Dec 2022 13:02:47 -0800
Subject: [PATCH 056/199] diffusers: upgrade to diffusers 0.10, add Heun
 scheduler

---
 binary_installer/requirements.in           | 2 +-
 ldm/generate.py                            | 8 +++++++-
 ldm/invoke/generator/diffusers_pipeline.py | 4 ++--
 3 files changed, 10 insertions(+), 4 deletions(-)

diff --git a/binary_installer/requirements.in b/binary_installer/requirements.in
index cada6ec51ea..95ef3f5185a 100644
--- a/binary_installer/requirements.in
+++ b/binary_installer/requirements.in
@@ -4,7 +4,7 @@
 --trusted-host https://download.pytorch.org
 accelerate~=0.14
 albumentations
-diffusers[torch]~=0.9
+diffusers[torch]~=0.10
 einops
 eventlet
 flask_cors
diff --git a/ldm/generate.py b/ldm/generate.py
index aefe58c8b74..82fe52f4aef 100644
--- a/ldm/generate.py
+++ b/ldm/generate.py
@@ -17,6 +17,7 @@
 import torch
 import transformers
 from PIL import Image, ImageOps
+from diffusers import HeunDiscreteScheduler
 from diffusers.pipeline_utils import DiffusionPipeline
 from diffusers.schedulers.scheduling_ddim import DDIMScheduler
 from diffusers.schedulers.scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
@@ -1008,12 +1009,17 @@ def _set_scheduler(self):
 
         scheduler_map = dict(
             ddim=DDIMScheduler,
+            dpmpp_2=DPMSolverMultistepScheduler,
             ipndm=IPNDMScheduler,
+            # DPMSolverMultistepScheduler is technically not `k_` anything, as it is neither
+            # the k-diffusers implementation nor included in EDM (Karras 2022), but we can
+            # provide an alias for compatibility.
+            k_dpmpp_2=DPMSolverMultistepScheduler,
             k_euler=EulerDiscreteScheduler,
             k_euler_a=EulerAncestralDiscreteScheduler,
+            k_heun=HeunDiscreteScheduler,
             k_lms=LMSDiscreteScheduler,
             plms=PNDMScheduler,
-            k_dpmpp_2=DPMSolverMultistepScheduler,
         )
 
         if self.sampler_name in scheduler_map:
diff --git a/ldm/invoke/generator/diffusers_pipeline.py b/ldm/invoke/generator/diffusers_pipeline.py
index 4dbd576c510..8f2b4901b87 100644
--- a/ldm/invoke/generator/diffusers_pipeline.py
+++ b/ldm/invoke/generator/diffusers_pipeline.py
@@ -407,7 +407,7 @@ def img2img_from_latents_and_embeddings(self, initial_latents, num_inference_ste
         batch_size = initial_latents.size(0)
         img2img_pipeline = StableDiffusionImg2ImgPipeline(**self.components)
         img2img_pipeline.scheduler.set_timesteps(num_inference_steps, device=device)
-        timesteps = img2img_pipeline.get_timesteps(num_inference_steps, strength, device=device)
+        timesteps, _ = img2img_pipeline.get_timesteps(num_inference_steps, strength, device=device)
         latent_timestep = timesteps[:1].repeat(batch_size)
         latents = self.noise_latents_for_time(initial_latents, latent_timestep, noise_func=noise_func)
 
@@ -454,7 +454,7 @@ def inpaint_from_embeddings(
 
         img2img_pipeline = StableDiffusionImg2ImgPipeline(**self.components)
         img2img_pipeline.scheduler.set_timesteps(num_inference_steps, device=device)
-        timesteps = img2img_pipeline.get_timesteps(num_inference_steps, strength, device=device)
+        timesteps, _ = img2img_pipeline.get_timesteps(num_inference_steps, strength, device=device)
 
         assert img2img_pipeline.scheduler is self.scheduler
 

From c28f56daec5b2f8b48756b7be58ab4e658cfae5d Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Fri, 9 Dec 2022 10:23:39 -0800
Subject: [PATCH 057/199] diffusers(ModelCache): stopgap to make from_cpu
 compatible with diffusers

---
 ldm/invoke/model_cache.py | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/ldm/invoke/model_cache.py b/ldm/invoke/model_cache.py
index 7b7b840b021..ee32ba12e5d 100644
--- a/ldm/invoke/model_cache.py
+++ b/ldm/invoke/model_cache.py
@@ -464,9 +464,12 @@ def _model_to_cpu(self,model):
     def _model_from_cpu(self,model):
         if self.device != 'cpu':
             model.to(self.device)
-            model.first_stage_model.to(self.device)
-            model.cond_stage_model.to(self.device)
-            model.cond_stage_model.device = self.device
+            try:
+                model.first_stage_model.to(self.device)
+                model.cond_stage_model.to(self.device)
+                model.cond_stage_model.device = self.device
+            except AttributeError as e:
+                warnings.warn(f"TODO: clean up legacy model-management: {e}")
         return model
 
     def _pop_oldest_model(self):

From 8e2da9a97efee37058e85caac5be60a8b92bbc11 Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Fri, 9 Dec 2022 10:37:55 -0800
Subject: [PATCH 058/199] CI: default to diffusers-1.5 now that runwayml token
 requirement is gone

---
 .github/workflows/test-invoke-conda.yml | 2 +-
 .github/workflows/test-invoke-pip.yml   | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/test-invoke-conda.yml b/.github/workflows/test-invoke-conda.yml
index d53635d0cc7..383729ae249 100644
--- a/.github/workflows/test-invoke-conda.yml
+++ b/.github/workflows/test-invoke-conda.yml
@@ -11,7 +11,7 @@ jobs:
     strategy:
       matrix:
         stable-diffusion-model:
-          - diffusers-1.4
+          - diffusers-1.5
         environment-yaml:
           - environment-lin-amd.yml
           - environment-lin-cuda.yml
diff --git a/.github/workflows/test-invoke-pip.yml b/.github/workflows/test-invoke-pip.yml
index 67907eaf6df..f00ff0328b6 100644
--- a/.github/workflows/test-invoke-pip.yml
+++ b/.github/workflows/test-invoke-pip.yml
@@ -11,7 +11,7 @@ jobs:
     strategy:
       matrix:
         stable-diffusion-model:
-          - diffusers-1.4
+          - diffusers-1.5
         requirements-file:
           - requirements-lin-cuda.txt
           - requirements-lin-amd.txt

From 811dc23efb00c06df117b7f02a04afce0e4415bb Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Fri, 9 Dec 2022 10:45:16 -0800
Subject: [PATCH 059/199] diffusers: update to 0.10 (and transformers to 4.25)

---
 binary_installer/requirements.in                          | 2 +-
 environments-and-requirements/environment-lin-aarch64.yml | 2 +-
 environments-and-requirements/environment-lin-amd.yml     | 4 ++--
 environments-and-requirements/environment-lin-cuda.yml    | 4 ++--
 environments-and-requirements/environment-mac.yml         | 4 ++--
 environments-and-requirements/environment-win-cuda.yml    | 4 ++--
 environments-and-requirements/requirements-base.txt       | 4 ++--
 7 files changed, 12 insertions(+), 12 deletions(-)

diff --git a/binary_installer/requirements.in b/binary_installer/requirements.in
index 95ef3f5185a..affaa813650 100644
--- a/binary_installer/requirements.in
+++ b/binary_installer/requirements.in
@@ -2,7 +2,7 @@
 --extra-index-url https://download.pytorch.org/whl/torch_stable.html
 --extra-index-url https://download.pytorch.org/whl/cu116
 --trusted-host https://download.pytorch.org
-accelerate~=0.14
+accelerate~=0.15
 albumentations
 diffusers[torch]~=0.10
 einops
diff --git a/environments-and-requirements/environment-lin-aarch64.yml b/environments-and-requirements/environment-lin-aarch64.yml
index 5f71828453a..ae798d777c1 100644
--- a/environments-and-requirements/environment-lin-aarch64.yml
+++ b/environments-and-requirements/environment-lin-aarch64.yml
@@ -28,7 +28,7 @@ dependencies:
   - torch-fidelity=0.3.0
   - torchmetrics=0.7.0
   - torchvision
-  - transformers=4.21.3
+  - transformers~=4.25
   - pip:
       - dependency_injector==4.40.0
       - getpass_asterisk
diff --git a/environments-and-requirements/environment-lin-amd.yml b/environments-and-requirements/environment-lin-amd.yml
index af251956a07..2eb4b1b1480 100644
--- a/environments-and-requirements/environment-lin-amd.yml
+++ b/environments-and-requirements/environment-lin-amd.yml
@@ -11,7 +11,7 @@ dependencies:
     - --extra-index-url  https://download.pytorch.org/whl/rocm5.2/
     - albumentations==0.4.3
     - dependency_injector==4.40.0
-    - diffusers~=0.9
+    - diffusers~=0.10
     - einops==0.3.0
     - eventlet
     - flask==2.1.3
@@ -39,7 +39,7 @@ dependencies:
     - torchaudio
     - torchmetrics==0.7.0
     - torchvision
-    - transformers==4.21.3
+    - transformers~=4.25
     - git+https://github.com/openai/CLIP.git@main#egg=clip
     - git+https://github.com/Birch-san/k-diffusion.git@mps#egg=k-diffusion
     - git+https://github.com/invoke-ai/clipseg.git@relaxed-python-requirement#egg=clipseg
diff --git a/environments-and-requirements/environment-lin-cuda.yml b/environments-and-requirements/environment-lin-cuda.yml
index 43dc46b5c63..0d51656cb0b 100644
--- a/environments-and-requirements/environment-lin-cuda.yml
+++ b/environments-and-requirements/environment-lin-cuda.yml
@@ -15,7 +15,7 @@ dependencies:
     - accelerate~=0.13
     - albumentations==0.4.3
     - dependency_injector==4.40.0
-    - diffusers~=0.9
+    - diffusers~=0.10
     - einops==0.3.0
     - eventlet
     - flask==2.1.3
@@ -39,7 +39,7 @@ dependencies:
     - test-tube>=0.7.5
     - torch-fidelity==0.3.0
     - torchmetrics==0.7.0
-    - transformers==4.21.3
+    - transformers=~4.25
     - git+https://github.com/openai/CLIP.git@main#egg=clip
     - git+https://github.com/Birch-san/k-diffusion.git@mps#egg=k-diffusion
     - git+https://github.com/invoke-ai/clipseg.git@relaxed-python-requirement#egg=clipseg
diff --git a/environments-and-requirements/environment-mac.yml b/environments-and-requirements/environment-mac.yml
index cdf7c2ebf76..dbb6c6717c2 100644
--- a/environments-and-requirements/environment-mac.yml
+++ b/environments-and-requirements/environment-mac.yml
@@ -22,7 +22,7 @@ dependencies:
 
   - albumentations=1.2
   - coloredlogs=15.0
-  - diffusers~=0.9
+  - diffusers~=0.10
   - einops=0.3
   - eventlet
   - grpcio=1.46
@@ -49,7 +49,7 @@ dependencies:
   - sympy=1.10
   - send2trash=1.8
   - tensorboard=2.10
-  - transformers=4.23
+  - transformers=~4.25
   - pip:
       - getpass_asterisk
       - picklescan
diff --git a/environments-and-requirements/environment-win-cuda.yml b/environments-and-requirements/environment-win-cuda.yml
index 3409ff20bf8..8b733796cd3 100644
--- a/environments-and-requirements/environment-win-cuda.yml
+++ b/environments-and-requirements/environment-win-cuda.yml
@@ -15,7 +15,7 @@ dependencies:
     - albumentations==0.4.3
     - basicsr==1.4.1
     - dependency_injector==4.40.0
-    - diffusers~=0.9
+    - diffusers~=0.10
     - einops==0.3.0
     - eventlet
     - flask==2.1.3
@@ -39,7 +39,7 @@ dependencies:
     - test-tube>=0.7.5
     - torch-fidelity==0.3.0
     - torchmetrics==0.7.0
-    - transformers==4.21.3
+    - transformers~=4.25
     - git+https://github.com/openai/CLIP.git@main#egg=clip
     - git+https://github.com/Birch-san/k-diffusion.git@mps#egg=k_diffusion
     - git+https://github.com/invoke-ai/clipseg.git@relaxed-python-requirement#egg=clipseg
diff --git a/environments-and-requirements/requirements-base.txt b/environments-and-requirements/requirements-base.txt
index 370eb2ded15..44848e5500d 100644
--- a/environments-and-requirements/requirements-base.txt
+++ b/environments-and-requirements/requirements-base.txt
@@ -1,7 +1,7 @@
 # pip will resolve the version which matches torch
 albumentations
 dependency_injector==4.40.0
-diffusers[torch]~=0.9
+diffusers[torch]~=0.10
 einops
 eventlet
 facexlib
@@ -31,7 +31,7 @@ taming-transformers-rom1504
 test-tube>=0.7.5
 torch-fidelity
 torchmetrics
-transformers~=4.24
+transformers~=4.25
 picklescan
 # git+https://github.com/invoke-ai/GFPGAN@basicsr-1.4.1#egg=gfpgan ; platform_system == 'Windows'
 git+https://github.com/invoke-ai/GFPGAN@basicsr-1.4.2#egg=gfpgan ; platform_system != 'Windows'

From 1a678360124c82327fae26034d0f6376dac00457 Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Sat, 10 Dec 2022 08:26:24 -0800
Subject: [PATCH 060/199] diffusers: use xformers when available

diffusers no longer auto-enables this as of 0.10.2.
---
 ldm/invoke/generator/diffusers_pipeline.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/ldm/invoke/generator/diffusers_pipeline.py b/ldm/invoke/generator/diffusers_pipeline.py
index 8f2b4901b87..e1a4b120c79 100644
--- a/ldm/invoke/generator/diffusers_pipeline.py
+++ b/ldm/invoke/generator/diffusers_pipeline.py
@@ -10,6 +10,7 @@
 import torch
 import torchvision.transforms as T
 from diffusers.models import attention
+from diffusers.utils.import_utils import is_xformers_available
 
 from ...models.diffusion import cross_attention_control
 
@@ -239,6 +240,9 @@ def __init__(
         self.invokeai_diffuser = InvokeAIDiffuserComponent(self.unet, self._unet_forward)
         self.embedding_manager = EmbeddingManager(self.clip_embedder, **_default_personalization_config_params)
 
+        if is_xformers_available():
+            self.enable_xformers_memory_efficient_attention()
+
     def image_from_embeddings(self, latents: torch.Tensor, num_inference_steps: int,
                               text_embeddings: torch.Tensor, unconditioned_embeddings: torch.Tensor,
                               guidance_scale: float,

From 50c48cffc7c0ebba90f544ab08400d83f60b99dd Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Sat, 10 Dec 2022 08:27:46 -0800
Subject: [PATCH 061/199] diffusers: make masked img2img behave better with
 multi-step schedulers

re-randomizing the noise each step was confusing them.
---
 ldm/invoke/generator/diffusers_pipeline.py | 21 ++++++++++-----------
 1 file changed, 10 insertions(+), 11 deletions(-)

diff --git a/ldm/invoke/generator/diffusers_pipeline.py b/ldm/invoke/generator/diffusers_pipeline.py
index e1a4b120c79..e3bb47cec4a 100644
--- a/ldm/invoke/generator/diffusers_pipeline.py
+++ b/ldm/invoke/generator/diffusers_pipeline.py
@@ -91,7 +91,7 @@ class AddsMaskGuidance:
     mask: torch.FloatTensor
     mask_latents: torch.FloatTensor
     _scheduler: SchedulerMixin
-    _noise_func: Callable
+    noise: torch.Tensor
     _debug: Optional[Callable] = None
 
     def __call__(self, step_output: BaseOutput | SchedulerOutput, t: torch.Tensor, conditioning) -> BaseOutput:
@@ -117,8 +117,9 @@ def _t_for_field(self, field_name:str, t):
     def apply_mask(self, latents: torch.Tensor, t) -> torch.Tensor:
         batch_size = latents.size(0)
         mask = einops.repeat(self.mask, 'b c h w -> (repeat b) c h w', repeat=batch_size)
-        noise = self._noise_func(self.mask_latents)
-        mask_latents = self._scheduler.add_noise(self.mask_latents, noise, t)
+        # Noise shouldn't be re-randomized between steps here. The multistep schedulers
+        # get very confused about what is happening from step to step when we do that.
+        mask_latents = self._scheduler.add_noise(self.mask_latents, self.noise, t)
         # TODO: Do we need to also apply scheduler.scale_model_input? Or is add_noise appropriately scaled already?
         mask_latents = einops.repeat(mask_latents, 'b c h w -> (repeat b) c h w', repeat=batch_size)
         masked_input = torch.lerp(mask_latents.to(dtype=latents.dtype), latents, mask.to(dtype=latents.dtype))
@@ -413,7 +414,9 @@ def img2img_from_latents_and_embeddings(self, initial_latents, num_inference_ste
         img2img_pipeline.scheduler.set_timesteps(num_inference_steps, device=device)
         timesteps, _ = img2img_pipeline.get_timesteps(num_inference_steps, strength, device=device)
         latent_timestep = timesteps[:1].repeat(batch_size)
-        latents = self.noise_latents_for_time(initial_latents, latent_timestep, noise_func=noise_func)
+        noise = noise_func(initial_latents)
+        noised_latents = self.scheduler.add_noise(initial_latents, noise, latent_timestep)
+        latents = noised_latents
 
         result_latents = self.latents_from_embeddings(
             latents, num_inference_steps, text_embeddings, unconditioned_embeddings, guidance_scale,
@@ -467,7 +470,8 @@ def inpaint_from_embeddings(
         # can't quite use upstream StableDiffusionImg2ImgPipeline.prepare_latents
         # because we have our own noise function
         init_image_latents = self.non_noised_latents_from_image(init_image, device=device, dtype=latents_dtype)
-        latents = self.noise_latents_for_time(init_image_latents, latent_timestep, noise_func=noise_func)
+        noise = noise_func(init_image_latents)
+        latents = self.scheduler.add_noise(init_image_latents, noise, latent_timestep)
 
         if mask.dim() == 3:
             mask = mask.unsqueeze(0)
@@ -481,7 +485,7 @@ def inpaint_from_embeddings(
             self.invokeai_diffuser.model_forward_callback = \
                 AddsMaskLatents(self._unet_forward, mask, init_image_latents)
         else:
-            guidance.append(AddsMaskGuidance(mask, init_image_latents, self.scheduler, noise_func))
+            guidance.append(AddsMaskGuidance(mask, init_image_latents, self.scheduler, noise))
 
         try:
             result_latents = self.latents_from_embeddings(
@@ -510,11 +514,6 @@ def non_noised_latents_from_image(self, init_image, *, device, dtype):
         init_latents = 0.18215 * init_latents
         return init_latents
 
-    def noise_latents_for_time(self, latents, timestep, *, noise_func):
-        noise = noise_func(latents)
-        noised_latents = self.scheduler.add_noise(latents, noise, timestep)
-        return noised_latents
-
     def check_for_safety(self, output, dtype):
         with torch.inference_mode():
             screened_images, has_nsfw_concept = self.run_safety_checker(

From 66d32b79b736ec8686523ea7b9aacba924bf0673 Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Sat, 10 Dec 2022 08:29:12 -0800
Subject: [PATCH 062/199] diffusers: work more better with more models.

fixed relative path problem with local models.

fixed models on hub not always having a `fp16` branch.
---
 ldm/invoke/model_cache.py | 20 ++++++++++++++++++--
 1 file changed, 18 insertions(+), 2 deletions(-)

diff --git a/ldm/invoke/model_cache.py b/ldm/invoke/model_cache.py
index ee32ba12e5d..f57dc6de198 100644
--- a/ldm/invoke/model_cache.py
+++ b/ldm/invoke/model_cache.py
@@ -21,6 +21,8 @@
 
 import torch
 import transformers
+from huggingface_hub import hf_hub_download
+from huggingface_hub.utils import RevisionNotFoundError
 from omegaconf import OmegaConf
 from omegaconf.errors import ConfigAttributeError
 from picklescan.scanner import scan_file_path
@@ -323,6 +325,8 @@ def _load_diffusers_model(self, mconfig):
             # model_hash = huggingface_hub.get_hf_file_metadata(url).commit_hash
         elif 'path' in mconfig:
             name_or_path = Path(mconfig['path'])
+            if not name_or_path.is_absolute():
+                name_or_path = Path(Globals.root, name_or_path).resolve()
             # FIXME: What should the model_hash be? A hash of the unet weights? Of all files of all
             #     the submodels hashed together? The commit ID from the repo?
             model_hash = "FIXME TOO"
@@ -335,7 +339,16 @@ def _load_diffusers_model(self, mconfig):
 
         if self.precision == 'float16':
             print('   | Using faster float16 precision')
-            pipeline_args.update(revision="fp16", torch_dtype=torch.float16)
+
+            if not isinstance(name_or_path, Path):
+                try:
+                    hf_hub_download(name_or_path, "model_index.json", revision="fp16")
+                except RevisionNotFoundError as e:
+                    pass
+                else:
+                    pipeline_args.update(revision="fp16")
+
+            pipeline_args.update(torch_dtype=torch.float16)
         else:
             # TODO: more accurately, "using the model's default precision."
             #     How do we find out what that is?
@@ -363,7 +376,10 @@ def model_name_or_path(self, model_name:str) -> str | Path:
         if 'repo_name' in mconfig:
             return mconfig['repo_name']
         elif 'path' in mconfig:
-            return Path(mconfig['path'])
+            path = Path(mconfig['path'])
+            if not path.is_absolute():
+                path = Path(Globals.root, path).resolve()
+            return path
         else:
             raise ValueError("Model config must specify either repo_name or path.")
 

From cd358c40ad89d93106f951e62b32d1d546c71526 Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Sat, 10 Dec 2022 19:48:15 -0800
Subject: [PATCH 063/199] diffusers: stopgap fix for attention_maps_callback
 crash after recent merge

---
 ldm/invoke/generator/txt2img.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/ldm/invoke/generator/txt2img.py b/ldm/invoke/generator/txt2img.py
index b03c4b99fc0..ef3d35dbca7 100644
--- a/ldm/invoke/generator/txt2img.py
+++ b/ldm/invoke/generator/txt2img.py
@@ -40,7 +40,9 @@ def make_image(x_T) -> PIL.Image.Image:
                 extra_conditioning_info=extra_conditioning_info,
                 # TODO: eta = ddim_eta,
                 # TODO: threshold = threshold,
-                attention_maps_callback      = attention_maps_callback,
+                # FIXME: Attention Maps Callback merged from main, but not hooked up
+                #     in diffusers branch yet. - keturn
+                # attention_maps_callback      = attention_maps_callback,
             )
 
             return pipeline.numpy_to_pil(pipeline_output.images)[0]

From a31ac57901a94e16acd112f1fe6b5f225e8bd050 Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Sat, 10 Dec 2022 21:12:42 -0800
Subject: [PATCH 064/199] fixup import merge conflicts

correction for 061c5369a2247c6c92cd69606bcf54c4f1962a0b
---
 backend/invoke_ai_web_server.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/backend/invoke_ai_web_server.py b/backend/invoke_ai_web_server.py
index 426ea0000be..2f3c547a9fc 100644
--- a/backend/invoke_ai_web_server.py
+++ b/backend/invoke_ai_web_server.py
@@ -23,10 +23,11 @@
 from backend.modules.parameters import parameters_to_command
 from ldm.generate import Generate
 from ldm.invoke.args import Args, APP_ID, APP_VERSION, calculate_init_img_hash
+from ldm.invoke.conditioning import get_tokens_for_prompt, get_prompt_structure
 from ldm.invoke.generator.diffusers_pipeline import PipelineIntermediateState
 from ldm.invoke.generator.inpaint import infill_methods
 from ldm.invoke.pngwriter import PngWriter, retrieve_metadata
-from ldm.invoke.prompt_parser import split_weighted_subprompts
+from ldm.invoke.prompt_parser import split_weighted_subprompts, Blend
 
 # Loading Arguments
 opt = Args()

From 4fa26e82e83ddf40b0fa150f7410e3df9badca0d Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Sat, 10 Dec 2022 21:14:10 -0800
Subject: [PATCH 065/199] test: add tests/inpainting inputs for masked img2img

---
 tests/inpainting/coyote-inpainting.prompt |  14 ++++++++++
 tests/inpainting/coyote-input.webp        | Bin 0 -> 36320 bytes
 tests/inpainting/coyote-mask.webp         | Bin 0 -> 1576 bytes
 tests/inpainting/original.json            |  30 ++++++++++++++++++++++
 4 files changed, 44 insertions(+)
 create mode 100644 tests/inpainting/coyote-inpainting.prompt
 create mode 100644 tests/inpainting/coyote-input.webp
 create mode 100644 tests/inpainting/coyote-mask.webp
 create mode 100644 tests/inpainting/original.json

diff --git a/tests/inpainting/coyote-inpainting.prompt b/tests/inpainting/coyote-inpainting.prompt
new file mode 100644
index 00000000000..7f979c55e7c
--- /dev/null
+++ b/tests/inpainting/coyote-inpainting.prompt
@@ -0,0 +1,14 @@
+# 🌻 🌻 🌻 sunflowers 🌻 🌻 🌻
+a coyote, deep palette knife oil painting, sunflowers, plants, desert landscape, award winning -s 50 -S 1234554321 -W 512 -H 512 -C 7.5 -I tests/inpainting/coyote-input.webp -A k_lms -M tests/inpainting/coyote-mask.webp -f 0.2
+a coyote, deep palette knife oil painting, sunflowers, plants, desert landscape, award winning -s 50 -S 1234554321 -W 512 -H 512 -C 7.5 -I tests/inpainting/coyote-input.webp -A k_lms -M tests/inpainting/coyote-mask.webp -f 0.4
+a coyote, deep palette knife oil painting, sunflowers, plants, desert landscape, award winning -s 50 -S 1234554321 -W 512 -H 512 -C 7.5 -I tests/inpainting/coyote-input.webp -A k_lms -M tests/inpainting/coyote-mask.webp -f 0.6
+a coyote, deep palette knife oil painting, sunflowers, plants, desert landscape, award winning -s 50 -S 1234554321 -W 512 -H 512 -C 7.5 -I tests/inpainting/coyote-input.webp -A k_lms -M tests/inpainting/coyote-mask.webp -f 0.8
+a coyote, deep palette knife oil painting, sunflowers, plants, desert landscape, award winning -s 50 -S 1234554321 -W 512 -H 512 -C 7.5 -I tests/inpainting/coyote-input.webp -A k_lms -M tests/inpainting/coyote-mask.webp -f 0.99
+
+# 🌹 🌹 🌹 roses 🌹 🌹 🌹
+a coyote, deep palette knife oil painting, red roses, plants, desert landscape, award winning -s 50 -S 1234554321 -W 512 -H 512 -C 7.5 -I tests/inpainting/coyote-input.webp -A k_lms -M tests/inpainting/coyote-mask.webp -f 0.2
+a coyote, deep palette knife oil painting, red roses, plants, desert landscape, award winning -s 50 -S 1234554321 -W 512 -H 512 -C 7.5 -I tests/inpainting/coyote-input.webp -A k_lms -M tests/inpainting/coyote-mask.webp -f 0.4
+a coyote, deep palette knife oil painting, red roses, plants, desert landscape, award winning -s 50 -S 1234554321 -W 512 -H 512 -C 7.5 -I tests/inpainting/coyote-input.webp -A k_lms -M tests/inpainting/coyote-mask.webp -f 0.6
+a coyote, deep palette knife oil painting, red roses, plants, desert landscape, award winning -s 50 -S 1234554321 -W 512 -H 512 -C 7.5 -I tests/inpainting/coyote-input.webp -A k_lms -M tests/inpainting/coyote-mask.webp -f 0.8
+a coyote, deep palette knife oil painting, red roses, plants, desert landscape, award winning -s 50 -S 1234554321 -W 512 -H 512 -C 7.5 -I tests/inpainting/coyote-input.webp -A k_lms -M tests/inpainting/coyote-mask.webp -f 0.99
+
diff --git a/tests/inpainting/coyote-input.webp b/tests/inpainting/coyote-input.webp
new file mode 100644
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diff --git a/tests/inpainting/coyote-mask.webp b/tests/inpainting/coyote-mask.webp
new file mode 100644
index 0000000000000000000000000000000000000000..1716bd5f187160d8d33e35356a0f437b464e15fa
GIT binary patch
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HcmV?d00001

diff --git a/tests/inpainting/original.json b/tests/inpainting/original.json
new file mode 100644
index 00000000000..f057f6d0dc5
--- /dev/null
+++ b/tests/inpainting/original.json
@@ -0,0 +1,30 @@
+{
+  "model": "stable diffusion",
+  "model_id": null,
+  "model_hash": "cc6cb27103417325ff94f52b7a5d2dde45a7515b25c255d8e396c90014281516",
+  "app_id": "invoke-ai/InvokeAI",
+  "app_version": "v2.2.3",
+  "image": {
+    "height": 512,
+    "steps": 50,
+    "facetool": "gfpgan",
+    "facetool_strength": 0,
+    "seed": 1948097268,
+    "perlin": 0,
+    "init_mask": null,
+    "width": 512,
+    "upscale": null,
+    "cfg_scale": 7.5,
+    "prompt": [
+      {
+        "prompt": "a coyote, deep palette knife oil painting, red aloe, plants, desert landscape, award winning",
+        "weight": 1
+      }
+    ],
+    "threshold": 0,
+    "postprocessing": null,
+    "sampler": "k_lms",
+    "variations": [],
+    "type": "txt2img"
+  }
+}

From 520c17ab86442c4042e8bfd5a2e834429a0fd9a6 Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Sat, 10 Dec 2022 21:19:32 -0800
Subject: [PATCH 066/199] diffusers(AddsMaskedGuidance): partial fix for
 k-schedulers

Prevents them from crashing, but results are still hot garbage.
---
 ldm/invoke/generator/diffusers_pipeline.py | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/ldm/invoke/generator/diffusers_pipeline.py b/ldm/invoke/generator/diffusers_pipeline.py
index e3bb47cec4a..85689f09277 100644
--- a/ldm/invoke/generator/diffusers_pipeline.py
+++ b/ldm/invoke/generator/diffusers_pipeline.py
@@ -90,7 +90,7 @@ def are_like_tensors(a: torch.Tensor, b: object) -> bool:
 class AddsMaskGuidance:
     mask: torch.FloatTensor
     mask_latents: torch.FloatTensor
-    _scheduler: SchedulerMixin
+    scheduler: SchedulerMixin
     noise: torch.Tensor
     _debug: Optional[Callable] = None
 
@@ -117,10 +117,15 @@ def _t_for_field(self, field_name:str, t):
     def apply_mask(self, latents: torch.Tensor, t) -> torch.Tensor:
         batch_size = latents.size(0)
         mask = einops.repeat(self.mask, 'b c h w -> (repeat b) c h w', repeat=batch_size)
+        if t.dim() == 0:
+            # some schedulers expect t to be one-dimensional.
+            # TODO: file diffusers bug about inconsistency?
+            t = einops.repeat(t, '-> batch', batch=batch_size)
         # Noise shouldn't be re-randomized between steps here. The multistep schedulers
         # get very confused about what is happening from step to step when we do that.
-        mask_latents = self._scheduler.add_noise(self.mask_latents, self.noise, t)
+        mask_latents = self.scheduler.add_noise(self.mask_latents, self.noise, t)
         # TODO: Do we need to also apply scheduler.scale_model_input? Or is add_noise appropriately scaled already?
+        # mask_latents = self.scheduler.scale_model_input(mask_latents, t)
         mask_latents = einops.repeat(mask_latents, 'b c h w -> (repeat b) c h w', repeat=batch_size)
         masked_input = torch.lerp(mask_latents.to(dtype=latents.dtype), latents, mask.to(dtype=latents.dtype))
         if self._debug:

From 0b48f2e801f6b6bfee7c9f107b26116523882502 Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Mon, 12 Dec 2022 16:17:10 -0800
Subject: [PATCH 067/199] fix --safety_checker arg parsing

and add note to diffusers loader about where safety checker gets called
---
 ldm/invoke/args.py        | 18 +++++++++---------
 ldm/invoke/model_cache.py |  7 ++++++-
 2 files changed, 15 insertions(+), 10 deletions(-)

diff --git a/ldm/invoke/args.py b/ldm/invoke/args.py
index aa9b94ffaf5..c18a5d35344 100644
--- a/ldm/invoke/args.py
+++ b/ldm/invoke/args.py
@@ -81,18 +81,18 @@
 """
 
 import argparse
-from argparse import Namespace, RawTextHelpFormatter
-import pydoc
-import json
+import base64
+import copy
+import functools
 import hashlib
+import json
 import os
+import pydoc
 import re
-import sys
 import shlex
-import copy
-import base64
-import functools
-import warnings
+import sys
+from argparse import Namespace
+
 import ldm.invoke.pngwriter
 from ldm.invoke.globals import Globals
 from ldm.invoke.prompt_parser import split_weighted_subprompts
@@ -477,7 +477,7 @@ def _create_arg_parser(self):
             default='auto',
         )
         model_group.add_argument(
-            '--nsfw_checker'
+            '--nsfw_checker',
             '--safety_checker',
             action=argparse.BooleanOptionalAction,
             dest='safety_checker',
diff --git a/ldm/invoke/model_cache.py b/ldm/invoke/model_cache.py
index f57dc6de198..0bfbfe32be7 100644
--- a/ldm/invoke/model_cache.py
+++ b/ldm/invoke/model_cache.py
@@ -356,7 +356,12 @@ def _load_diffusers_model(self, mconfig):
 
         pipeline = StableDiffusionGeneratorPipeline.from_pretrained(
             name_or_path,
-            safety_checker=None,  # TODO
+            # TODO: Safety checker is currently handled at a different stage in the code:
+            #     ldm.invoke.generator.base.Generator.safety_check
+            #     We might want to move that here for consistency with diffusers API, or we might
+            #     want to leave it as a separate processing node. It ends up using the same diffusers
+            #     code either way, so we can table it for now.
+            safety_checker=None,
             # TODO: alternate VAE
             # TODO: local_files_only=True
             **pipeline_args

From 31fd285c82ea0292740edcbb5a2c917f8cab1d86 Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Mon, 12 Dec 2022 16:17:46 -0800
Subject: [PATCH 068/199] generate: fix import error

---
 ldm/generate.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/ldm/generate.py b/ldm/generate.py
index 79a543247ea..2d60a479cdd 100644
--- a/ldm/generate.py
+++ b/ldm/generate.py
@@ -16,6 +16,7 @@
 import skimage
 import torch
 import transformers
+from PIL import Image, ImageOps
 from diffusers import HeunDiscreteScheduler
 from diffusers.pipeline_utils import DiffusionPipeline
 from diffusers.schedulers.scheduling_ddim import DDIMScheduler
@@ -26,9 +27,9 @@
 from diffusers.schedulers.scheduling_lms_discrete import LMSDiscreteScheduler
 from diffusers.schedulers.scheduling_pndm import PNDMScheduler
 from omegaconf import OmegaConf
-from PIL import Image, ImageOps
 from pytorch_lightning import seed_everything, logging
 
+import ldm.invoke.conditioning
 from ldm.invoke.args import metadata_from_png
 from ldm.invoke.concepts_lib import Concepts
 from ldm.invoke.conditioning import get_uc_and_c_and_ec
@@ -616,9 +617,9 @@ def apply_postprocessor(
         # used by multiple postfixers
         # todo: cross-attention control
         uc, c, extra_conditioning_info = get_uc_and_c_and_ec(
-            prompt, model =self.model,
+            prompt, model=self.model,
             skip_normalize_legacy_blend=opt.skip_normalize,
-            log_tokens    =ldm.invoke.conditioning.log_tokenization
+            log_tokens=ldm.invoke.conditioning.log_tokenization
         )
 
         if tool in ('gfpgan','codeformer','upscale'):

From 26eac9d94ba2f3a794316ef14ea9dfd9413a8dee Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Tue, 13 Dec 2022 16:13:44 -0800
Subject: [PATCH 069/199] CI: don't try to read the old init location

---
 .github/workflows/test-invoke-pip.yml | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/.github/workflows/test-invoke-pip.yml b/.github/workflows/test-invoke-pip.yml
index d6cd0ac891d..14658be02b2 100644
--- a/.github/workflows/test-invoke-pip.yml
+++ b/.github/workflows/test-invoke-pip.yml
@@ -113,10 +113,6 @@ jobs:
               --no-interactive --yes \
               --full-precision  # can't use fp16 weights without a GPU
 
-      - name: cat ~/.invokeai
-        id: cat-invokeai
-        run: cat ~/.invokeai
-
       - name: Run the tests
         id: run-tests
         if: matrix.os != 'windows-2022'

From 3607042c9da617b7c6dd7bc224a72cf63040cf7e Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Wed, 14 Dec 2022 09:05:45 -0800
Subject: [PATCH 070/199] diffusers: support loading an alternate VAE

---
 configs/models.yaml.example | 16 +++++++++---
 ldm/invoke/model_cache.py   | 49 ++++++++++++++++++++++++++++++++++---
 2 files changed, 59 insertions(+), 6 deletions(-)

diff --git a/configs/models.yaml.example b/configs/models.yaml.example
index 1eb96638969..5187d29b3ea 100644
--- a/configs/models.yaml.example
+++ b/configs/models.yaml.example
@@ -6,14 +6,24 @@
 # and the width and height of the images it
 # was trained on.
 diffusers-1.4:
-  description: Diffusers version of Stable Diffusion version 1.4
+  description: 🤗🧨 Stable Diffusion v1.4
   format: diffusers
   repo_name: CompVis/stable-diffusion-v1-4
-  default: true
 diffusers-1.5:
-  description: Diffusers version of Stable Diffusion version 1.5
+  description: 🤗🧨 Stable Diffusion v1.5
   format: diffusers
   repo_name: runwayml/stable-diffusion-v1-5
+  default: true
+diffusers-1.5+mse:
+  description: 🤗🧨 Stable Diffusion v1.5 + MSE-finetuned VAE
+  format: diffusers
+  repo_name: runwayml/stable-diffusion-v1-5
+  vae:
+    repo_name: stabilityai/sd-vae-ft-mse
+diffusers-inpainting-1.5:
+  description: 🤗🧨 inpainting for Stable Diffusion v1.5
+  format: diffusers
+  repo_name: runwayml/stable-diffusion-inpainting
 stable-diffusion-1.5:
   description: The newest Stable Diffusion version 1.5 weight file (4.27 GB)
   weights: models/ldm/stable-diffusion-v1/v1-5-pruned-emaonly.ckpt
diff --git a/ldm/invoke/model_cache.py b/ldm/invoke/model_cache.py
index 0bfbfe32be7..e6f1c518f32 100644
--- a/ldm/invoke/model_cache.py
+++ b/ldm/invoke/model_cache.py
@@ -21,6 +21,7 @@
 
 import torch
 import transformers
+from diffusers import AutoencoderKL
 from huggingface_hub import hf_hub_download
 from huggingface_hub.utils import RevisionNotFoundError
 from omegaconf import OmegaConf
@@ -337,14 +338,20 @@ def _load_diffusers_model(self, mconfig):
 
         # TODO: scan weights maybe?
 
+        if 'vae' in mconfig:
+            vae = self._load_vae(mconfig['vae'])
+            pipeline_args.update(vae=vae)
+
         if self.precision == 'float16':
             print('   | Using faster float16 precision')
 
             if not isinstance(name_or_path, Path):
+                # hub has no explicit API for different data types, but the main Stable Diffusion
+                # releases set a precedent for putting float16 weights in a fp16 branch.
                 try:
                     hf_hub_download(name_or_path, "model_index.json", revision="fp16")
-                except RevisionNotFoundError as e:
-                    pass
+                except RevisionNotFoundError:
+                    pass  # no such branch, assume we should use the default.
                 else:
                     pipeline_args.update(revision="fp16")
 
@@ -362,7 +369,6 @@ def _load_diffusers_model(self, mconfig):
             #     want to leave it as a separate processing node. It ends up using the same diffusers
             #     code either way, so we can table it for now.
             safety_checker=None,
-            # TODO: alternate VAE
             # TODO: local_files_only=True
             **pipeline_args
         )
@@ -535,3 +541,40 @@ def _cached_sha256(self,path,data) -> Union[str, bytes]:
         with open(hashpath,'w') as f:
             f.write(hash)
         return hash
+
+    def _load_vae(self, vae_config):
+        vae_args = {}
+
+        if 'repo_name' in vae_config:
+            name_or_path = vae_config['repo_name']
+        elif 'path' in vae_config:
+            name_or_path = Path(vae_config['path'])
+            if not name_or_path.is_absolute():
+                name_or_path = Path(Globals.root, name_or_path).resolve()
+        else:
+            raise ValueError("VAE config must specify either repo_name or path.")
+
+        print(f'>> Loading diffusers VAE from {name_or_path}')
+        if self.precision == 'float16':
+            print('   | Using faster float16 precision')
+
+            if not isinstance(name_or_path, Path):
+                try:
+                    hf_hub_download(name_or_path, "model_index.json", revision="fp16")
+                except RevisionNotFoundError:
+                    pass
+                else:
+                    vae_args.update(revision="fp16")
+
+            vae_args.update(torch_dtype=torch.float16)
+        else:
+            # TODO: more accurately, "using the model's default precision."
+            #     How do we find out what that is?
+            print('   | Using more accurate float32 precision')
+
+        if 'subfolder' in vae_config:
+            vae_args['subfolder'] = vae_config['subfolder']
+
+        # At some point we might need to be able to use different classes here? But for now I think
+        # all Stable Diffusion VAE are AutoencoderKL.
+        return AutoencoderKL.from_pretrained(name_or_path, **vae_args)

From 0605cf46097c801b791f6eff144a184d0ebee885 Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Wed, 14 Dec 2022 09:14:25 -0800
Subject: [PATCH 071/199] CI: remove sh-syntax if-statement so it doesn't crash
 powershell

---
 .github/workflows/test-invoke-conda.yml | 6 ++----
 .github/workflows/test-invoke-pip.yml   | 6 ++----
 2 files changed, 4 insertions(+), 8 deletions(-)

diff --git a/.github/workflows/test-invoke-conda.yml b/.github/workflows/test-invoke-conda.yml
index e3f1523162b..792f1e9d626 100644
--- a/.github/workflows/test-invoke-conda.yml
+++ b/.github/workflows/test-invoke-conda.yml
@@ -99,11 +99,9 @@ jobs:
 
       - name: run configure_invokeai.py
         id: run-preload-models
+        env:
+          HUGGING_FACE_HUB_TOKEN: ${{ secrets.HUGGINGFACE_TOKEN }}
         run: |
-          if [ "${HAVE_SECRETS}" == true ] ; then
-            mkdir -p ~/.huggingface
-            echo -n '${{ secrets.HUGGINGFACE_TOKEN }}' > ~/.huggingface/token
-          fi
           python scripts/configure_invokeai.py \
             --no-interactive --yes \
             --full-precision  # can't use fp16 weights without a GPU
diff --git a/.github/workflows/test-invoke-pip.yml b/.github/workflows/test-invoke-pip.yml
index 14658be02b2..9f0a69b1a42 100644
--- a/.github/workflows/test-invoke-pip.yml
+++ b/.github/workflows/test-invoke-pip.yml
@@ -104,11 +104,9 @@ jobs:
 
       - name: run configure_invokeai.py
         id: run-preload-models
+        env:
+          HUGGING_FACE_HUB_TOKEN: ${{ secrets.HUGGINGFACE_TOKEN }}
         run: |
-          if [ "${HAVE_SECRETS}" == true ] ; then
-            mkdir -p ~/.huggingface
-            echo -n '${{ secrets.HUGGINGFACE_TOKEN }}' > ~/.huggingface/token
-          fi
           python3 scripts/configure_invokeai.py \
               --no-interactive --yes \
               --full-precision  # can't use fp16 weights without a GPU

From 2c6db2e77cdb93180d7109abf21819d4e8c1d88e Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Wed, 14 Dec 2022 10:43:16 -0800
Subject: [PATCH 072/199] CI: fold strings in yaml because backslash is not
 line-continuation in powershell

---
 .github/workflows/test-invoke-conda.yml | 24 ++++++++++++------------
 .github/workflows/test-invoke-pip.yml   | 24 ++++++++++++------------
 2 files changed, 24 insertions(+), 24 deletions(-)

diff --git a/.github/workflows/test-invoke-conda.yml b/.github/workflows/test-invoke-conda.yml
index 792f1e9d626..0f623416a68 100644
--- a/.github/workflows/test-invoke-conda.yml
+++ b/.github/workflows/test-invoke-conda.yml
@@ -101,10 +101,10 @@ jobs:
         id: run-preload-models
         env:
           HUGGING_FACE_HUB_TOKEN: ${{ secrets.HUGGINGFACE_TOKEN }}
-        run: |
-          python scripts/configure_invokeai.py \
-            --no-interactive --yes \
-            --full-precision  # can't use fp16 weights without a GPU
+        run: >
+          python scripts/configure_invokeai.py
+          --no-interactive --yes
+          --full-precision  # can't use fp16 weights without a GPU
 
       - name: cat invokeai.init
         id: cat-invokeai
@@ -118,14 +118,14 @@ jobs:
           HF_HUB_OFFLINE: 1
           HF_DATASETS_OFFLINE: 1
           TRANSFORMERS_OFFLINE: 1
-        run: |
-          time python scripts/invoke.py \
-            --no-patchmatch \
-            --no-nsfw_checker \
-            --model ${{ matrix.stable-diffusion-model }} \
-            --from_file ${{ env.TEST_PROMPTS }} \
-            --root="${{ env.INVOKEAI_ROOT }}" \
-            --outdir="${{ env.INVOKEAI_ROOT }}/outputs"
+        run: >
+          python scripts/invoke.py
+          --no-patchmatch
+          --no-nsfw_checker
+          --model ${{ matrix.stable-diffusion-model }}
+          --from_file ${{ env.TEST_PROMPTS }}
+          --root="${{ env.INVOKEAI_ROOT }}"
+          --outdir="${{ env.INVOKEAI_ROOT }}/outputs"
 
       - name: export conda env
         id: export-conda-env
diff --git a/.github/workflows/test-invoke-pip.yml b/.github/workflows/test-invoke-pip.yml
index 9f0a69b1a42..fb4ff13a15a 100644
--- a/.github/workflows/test-invoke-pip.yml
+++ b/.github/workflows/test-invoke-pip.yml
@@ -106,10 +106,10 @@ jobs:
         id: run-preload-models
         env:
           HUGGING_FACE_HUB_TOKEN: ${{ secrets.HUGGINGFACE_TOKEN }}
-        run: |
-          python3 scripts/configure_invokeai.py \
-              --no-interactive --yes \
-              --full-precision  # can't use fp16 weights without a GPU
+        run: >
+          python3 scripts/configure_invokeai.py
+          --no-interactive --yes
+          --full-precision  # can't use fp16 weights without a GPU
 
       - name: Run the tests
         id: run-tests
@@ -119,14 +119,14 @@ jobs:
           HF_HUB_OFFLINE: 1
           HF_DATASETS_OFFLINE: 1
           TRANSFORMERS_OFFLINE: 1
-        run: |
-          time python3 scripts/invoke.py \
-            --no-patchmatch \
-            --no-nsfw_checker \
-            --model ${{ matrix.stable-diffusion-model }} \
-            --from_file ${{ env.TEST_PROMPTS }} \
-            --root="${{ env.INVOKEAI_ROOT }}" \
-            --outdir="${{ env.INVOKEAI_OUTDIR }}"
+        run: >
+          python3 scripts/invoke.py
+          --no-patchmatch
+          --no-nsfw_checker
+          --model ${{ matrix.stable-diffusion-model }}
+          --from_file ${{ env.TEST_PROMPTS }}
+          --root="${{ env.INVOKEAI_ROOT }}"
+          --outdir="${{ env.INVOKEAI_OUTDIR }}"
 
       - name: Archive results
         id: archive-results

From 23eb80b40421b2bb8f4b6d3dd30490d11c447b36 Mon Sep 17 00:00:00 2001
From: Damian Stewart <d@damianstewart.com>
Date: Wed, 14 Dec 2022 21:04:55 +0100
Subject: [PATCH 073/199] attention maps callback stuff for diffusers

---
 ldm/invoke/generator/base.py                  |   1 -
 ldm/invoke/generator/diffusers_pipeline.py    |  62 ++++++---
 ldm/invoke/generator/img2img.py               |   7 +-
 ldm/invoke/generator/inpaint.py               |   2 +
 ldm/invoke/generator/txt2img.py               |   8 +-
 ldm/invoke/generator/txt2img2img.py           |   2 +-
 .../diffusion/cross_attention_control.py      | 122 ------------------
 ldm/models/diffusion/ksampler.py              |  10 +-
 8 files changed, 63 insertions(+), 151 deletions(-)

diff --git a/ldm/invoke/generator/base.py b/ldm/invoke/generator/base.py
index f207b3dc244..68c5ccdeff5 100644
--- a/ldm/invoke/generator/base.py
+++ b/ldm/invoke/generator/base.py
@@ -63,7 +63,6 @@ def set_variation(self, seed, variation_amount, with_variations):
     def generate(self,prompt,init_image,width,height,sampler, iterations=1,seed=None,
                  image_callback=None, step_callback=None, threshold=0.0, perlin=0.0,
                  safety_checker:dict=None,
-                 attention_maps_callback = None,
                  **kwargs):
         scope = choose_autocast(self.precision)
         self.safety_checker = safety_checker
diff --git a/ldm/invoke/generator/diffusers_pipeline.py b/ldm/invoke/generator/diffusers_pipeline.py
index 85689f09277..410761cd794 100644
--- a/ldm/invoke/generator/diffusers_pipeline.py
+++ b/ldm/invoke/generator/diffusers_pipeline.py
@@ -7,12 +7,14 @@
 
 import PIL.Image
 import einops
+import numpy as np
 import torch
 import torchvision.transforms as T
 from diffusers.models import attention
 from diffusers.utils.import_utils import is_xformers_available
 
 from ...models.diffusion import cross_attention_control
+from ...models.diffusion.cross_attention_map_saving import AttentionMapSaver
 
 # monkeypatch diffusers CrossAttention 🙈
 # this is to make prompt2prompt and (future) attention maps work
@@ -41,6 +43,7 @@ class PipelineIntermediateState:
     timestep: int
     latents: torch.Tensor
     predicted_original: Optional[torch.Tensor] = None
+    attention_map_saver: Optional[AttentionMapSaver] = None
 
 
 # copied from configs/stable-diffusion/v1-inference.yaml
@@ -180,6 +183,17 @@ def __call__(self, *args: ParamType.args,
             raise AssertionError("why was that an empty generator?")
         return result
 
+@dataclass
+class InvokeAIStableDiffusionPipelineOutput(StableDiffusionPipelineOutput):
+    r"""
+    Output class for InvokeAI's Stable Diffusion pipeline.
+
+    Args:
+        attention_map_saver (`AttentionMapSaver`): Object containing attention maps that can be displayed to the user
+         after generation completes. Optional.
+    """
+    attention_map_saver: Optional[AttentionMapSaver]
+
 
 class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
     r"""
@@ -255,7 +269,7 @@ def image_from_embeddings(self, latents: torch.Tensor, num_inference_steps: int,
                               *, callback: Callable[[PipelineIntermediateState], None]=None,
                               extra_conditioning_info: InvokeAIDiffuserComponent.ExtraConditioningInfo=None,
                               run_id=None,
-                              **extra_step_kwargs) -> StableDiffusionPipelineOutput:
+                              **extra_step_kwargs) -> InvokeAIStableDiffusionPipelineOutput:
         r"""
         Function invoked when calling the pipeline for generation.
 
@@ -273,7 +287,7 @@ def image_from_embeddings(self, latents: torch.Tensor, num_inference_steps: int,
         :param run_id:
         :param extra_step_kwargs:
         """
-        result_latents = self.latents_from_embeddings(
+        result_latents, result_attention_map_saver = self.latents_from_embeddings(
             latents, num_inference_steps, text_embeddings, unconditioned_embeddings, guidance_scale,
             extra_conditioning_info=extra_conditioning_info,
             run_id=run_id, callback=callback, **extra_step_kwargs
@@ -283,7 +297,7 @@ def image_from_embeddings(self, latents: torch.Tensor, num_inference_steps: int,
 
         with torch.inference_mode():
             image = self.decode_latents(result_latents)
-            output = StableDiffusionPipelineOutput(images=image, nsfw_content_detected=[])
+            output = InvokeAIStableDiffusionPipelineOutput(images=image, nsfw_content_detected=[], attention_map_saver=result_attention_map_saver)
             return self.check_for_safety(output, dtype=text_embeddings.dtype)
 
     def latents_from_embeddings(
@@ -302,13 +316,14 @@ def latents_from_embeddings(
             self.scheduler.set_timesteps(num_inference_steps, device=self.unet.device)
             timesteps = self.scheduler.timesteps
         infer_latents_from_embeddings = GeneratorToCallbackinator(self.generate_latents_from_embeddings, PipelineIntermediateState)
-        return infer_latents_from_embeddings(
+        result: PipelineIntermediateState = infer_latents_from_embeddings(
             latents, timesteps, text_embeddings, unconditioned_embeddings, guidance_scale,
             extra_conditioning_info=extra_conditioning_info,
             additional_guidance=additional_guidance,
             run_id=run_id,
             callback=callback,
-            **extra_step_kwargs).latents
+            **extra_step_kwargs)
+        return result.latents, result.attention_map_saver
 
     def generate_latents_from_embeddings(self, latents: torch.Tensor, timesteps, text_embeddings: torch.Tensor,
                                          unconditioned_embeddings: torch.Tensor, guidance_scale: float, *,
@@ -334,6 +349,8 @@ def generate_latents_from_embeddings(self, latents: torch.Tensor, timesteps, tex
         batched_t = torch.full((batch_size,), timesteps[0],
                                dtype=timesteps.dtype, device=self.unet.device)
 
+        attention_map_saver: AttentionMapSaver = None
+        self.invokeai_diffuser.remove_attention_map_saving()
         for i, t in enumerate(self.progress_bar(timesteps)):
             batched_t.fill_(t)
             step_output = self.step(batched_t, latents, guidance_scale,
@@ -342,9 +359,18 @@ def generate_latents_from_embeddings(self, latents: torch.Tensor, timesteps, tex
                                     **extra_step_kwargs)
             latents = step_output.prev_sample
             predicted_original = getattr(step_output, 'pred_original_sample', None)
+
+            if i == len(timesteps)-1 and extra_conditioning_info is not None:
+                eos_token_index = extra_conditioning_info.tokens_count_including_eos_bos - 1
+                attention_map_token_ids = range(1, eos_token_index)
+                attention_map_saver = AttentionMapSaver(token_ids=attention_map_token_ids, latents_shape=latents.shape[-2:])
+                self.invokeai_diffuser.setup_attention_map_saving(attention_map_saver)
+
             yield PipelineIntermediateState(run_id=run_id, step=i, timestep=int(t), latents=latents,
-                                            predicted_original=predicted_original)
-        return latents
+                                            predicted_original=predicted_original, attention_map_saver=attention_map_saver)
+
+        self.invokeai_diffuser.remove_attention_map_saving()
+        return latents, attention_map_saver
 
     @torch.inference_mode()
     def step(self, t: torch.Tensor, latents: torch.Tensor, guidance_scale: float,
@@ -393,7 +419,7 @@ def img2img_from_embeddings(self,
                                 extra_conditioning_info: InvokeAIDiffuserComponent.ExtraConditioningInfo = None,
                                 run_id=None,
                                 noise_func=None,
-                                **extra_step_kwargs) -> StableDiffusionPipelineOutput:
+                                **extra_step_kwargs) -> InvokeAIStableDiffusionPipelineOutput:
         if isinstance(init_image, PIL.Image.Image):
             init_image = image_resized_to_grid_as_tensor(init_image.convert('RGB'))
 
@@ -412,7 +438,7 @@ def img2img_from_embeddings(self,
 
     def img2img_from_latents_and_embeddings(self, initial_latents, num_inference_steps, text_embeddings,
                                             unconditioned_embeddings, guidance_scale, strength, extra_conditioning_info,
-                                            noise_func, run_id=None, callback=None, **extra_step_kwargs):
+                                            noise_func, run_id=None, callback=None, **extra_step_kwargs) -> InvokeAIStableDiffusionPipelineOutput:
         device = self.unet.device
         batch_size = initial_latents.size(0)
         img2img_pipeline = StableDiffusionImg2ImgPipeline(**self.components)
@@ -423,7 +449,7 @@ def img2img_from_latents_and_embeddings(self, initial_latents, num_inference_ste
         noised_latents = self.scheduler.add_noise(initial_latents, noise, latent_timestep)
         latents = noised_latents
 
-        result_latents = self.latents_from_embeddings(
+        result_latents, result_attention_maps = self.latents_from_embeddings(
             latents, num_inference_steps, text_embeddings, unconditioned_embeddings, guidance_scale,
             extra_conditioning_info=extra_conditioning_info,
             timesteps=timesteps,
@@ -435,7 +461,7 @@ def img2img_from_latents_and_embeddings(self, initial_latents, num_inference_ste
 
         with torch.inference_mode():
             image = self.decode_latents(result_latents)
-            output = StableDiffusionPipelineOutput(images=image, nsfw_content_detected=[])
+            output = InvokeAIStableDiffusionPipelineOutput(images=image, nsfw_content_detected=[], attention_map_saver=result_attention_maps)
             return self.check_for_safety(output, dtype=text_embeddings.dtype)
 
     def inpaint_from_embeddings(
@@ -450,7 +476,7 @@ def inpaint_from_embeddings(
             extra_conditioning_info: InvokeAIDiffuserComponent.ExtraConditioningInfo = None,
             run_id=None,
             noise_func=None,
-            **extra_step_kwargs) -> StableDiffusionPipelineOutput:
+            **extra_step_kwargs) -> InvokeAIStableDiffusionPipelineOutput:
         device = self.unet.device
         latents_dtype = self.unet.dtype
         batch_size = 1
@@ -493,7 +519,7 @@ def inpaint_from_embeddings(
             guidance.append(AddsMaskGuidance(mask, init_image_latents, self.scheduler, noise))
 
         try:
-            result_latents = self.latents_from_embeddings(
+            result_latents, result_attention_maps = self.latents_from_embeddings(
                 latents, num_inference_steps, text_embeddings, unconditioned_embeddings, guidance_scale,
                 extra_conditioning_info=extra_conditioning_info,
                 timesteps=timesteps,
@@ -508,7 +534,7 @@ def inpaint_from_embeddings(
 
         with torch.inference_mode():
             image = self.decode_latents(result_latents)
-            output = StableDiffusionPipelineOutput(images=image, nsfw_content_detected=[])
+            output = InvokeAIStableDiffusionPipelineOutput(images=image, nsfw_content_detected=[], attention_map_saver=result_attention_maps)
             return self.check_for_safety(output, dtype=text_embeddings.dtype)
 
     def non_noised_latents_from_image(self, init_image, *, device, dtype):
@@ -523,7 +549,13 @@ def check_for_safety(self, output, dtype):
         with torch.inference_mode():
             screened_images, has_nsfw_concept = self.run_safety_checker(
                 output.images, device=self._execution_device, dtype=dtype)
-        return StableDiffusionPipelineOutput(screened_images, has_nsfw_concept)
+        screened_attention_map_saver = None
+        if has_nsfw_concept is None or not has_nsfw_concept:
+            screened_attention_map_saver = output.attention_map_saver
+        return InvokeAIStableDiffusionPipelineOutput(screened_images,
+                                                     has_nsfw_concept,
+                                                     # block the attention maps if NSFW content is detected
+                                                     attention_map_saver=screened_attention_map_saver)
 
     @torch.inference_mode()
     def get_learned_conditioning(self, c: List[List[str]], *, return_tokens=True, fragment_weights=None):
diff --git a/ldm/invoke/generator/img2img.py b/ldm/invoke/generator/img2img.py
index 6ea41fda33c..1a470d1ebf1 100644
--- a/ldm/invoke/generator/img2img.py
+++ b/ldm/invoke/generator/img2img.py
@@ -14,7 +14,9 @@ def __init__(self, model, precision):
         self.init_latent = None    # by get_noise()
 
     def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
-                       conditioning,init_image,strength,step_callback=None,threshold=0.0,perlin=0.0,**kwargs):
+                       conditioning,init_image,strength,step_callback=None,threshold=0.0,perlin=0.0,
+                       attention_maps_callback=None,
+                       **kwargs):
         """
         Returns a function returning an image derived from the prompt and the initial image
         Return value depends on the seed at the time you call it.
@@ -35,7 +37,8 @@ def make_image(x_T):
                 noise_func=self.get_noise_like,
                 callback=step_callback
             )
-
+            if pipeline_output.attention_map_saver is not None and attention_maps_callback is not None:
+                attention_maps_callback(pipeline_output.attention_map_saver)
             return pipeline.numpy_to_pil(pipeline_output.images)[0]
 
         return make_image
diff --git a/ldm/invoke/generator/inpaint.py b/ldm/invoke/generator/inpaint.py
index 976121d720f..79fbd542c17 100644
--- a/ldm/invoke/generator/inpaint.py
+++ b/ldm/invoke/generator/inpaint.py
@@ -273,6 +273,8 @@ def make_image(x_T):
                 callback=step_callback,
             )
 
+            if pipeline_output.attention_map_saver is not None and attention_maps_callback is not None:
+                attention_maps_callback(pipeline_output.attention_map_saver)
             result = pipeline.numpy_to_pil(pipeline_output.images)[0]
 
             # Seam paint if this is our first pass (seam_size set to 0 during seam painting)
diff --git a/ldm/invoke/generator/txt2img.py b/ldm/invoke/generator/txt2img.py
index ef3d35dbca7..6c4c7a3f13a 100644
--- a/ldm/invoke/generator/txt2img.py
+++ b/ldm/invoke/generator/txt2img.py
@@ -37,14 +37,12 @@ def make_image(x_T) -> PIL.Image.Image:
                 unconditioned_embeddings=uc,
                 guidance_scale=cfg_scale,
                 callback=step_callback,
-                extra_conditioning_info=extra_conditioning_info,
+                extra_conditioning_info=extra_conditioning_info
                 # TODO: eta = ddim_eta,
                 # TODO: threshold = threshold,
-                # FIXME: Attention Maps Callback merged from main, but not hooked up
-                #     in diffusers branch yet. - keturn
-                # attention_maps_callback      = attention_maps_callback,
             )
-
+            if pipeline_output.attention_map_saver is not None and attention_maps_callback is not None:
+                attention_maps_callback(pipeline_output.attention_map_saver)
             return pipeline.numpy_to_pil(pipeline_output.images)[0]
 
         return make_image
diff --git a/ldm/invoke/generator/txt2img2img.py b/ldm/invoke/generator/txt2img2img.py
index 29a7106246f..56ebcc5bf43 100644
--- a/ldm/invoke/generator/txt2img2img.py
+++ b/ldm/invoke/generator/txt2img2img.py
@@ -36,7 +36,7 @@ def get_make_image(self, prompt:str, sampler, steps:int, cfg_scale:float, ddim_e
 
         def make_image(x_T):
 
-            first_pass_latent_output = pipeline.latents_from_embeddings(
+            first_pass_latent_output, _ = pipeline.latents_from_embeddings(
                 latents=x_T,
                 num_inference_steps=steps,
                 text_embeddings=c,
diff --git a/ldm/models/diffusion/cross_attention_control.py b/ldm/models/diffusion/cross_attention_control.py
index b32ccecae7e..66c5567ebdb 100644
--- a/ldm/models/diffusion/cross_attention_control.py
+++ b/ldm/models/diffusion/cross_attention_control.py
@@ -442,128 +442,6 @@ def get_mem_free_total(device):
     return mem_free_total
 
 
-class InvokeAICrossAttentionMixin:
-    """
-    Enable InvokeAI-flavoured CrossAttention calculation, which does aggressive low-memory slicing and calls
-    through both to an attention_slice_wrangler and a slicing_strategy_getter for custom attention map wrangling
-    and dymamic slicing strategy selection.
-    """
-    def __init__(self):
-        self.mem_total_gb = psutil.virtual_memory().total // (1 << 30)
-        self.attention_slice_wrangler = None
-        self.slicing_strategy_getter = None
-
-    def set_attention_slice_wrangler(self, wrangler: Optional[Callable[[nn.Module, torch.Tensor, int, int, int], torch.Tensor]]):
-        '''
-        Set custom attention calculator to be called when attention is calculated
-        :param wrangler: Callback, with args (module, suggested_attention_slice, dim, offset, slice_size),
-        which returns either the suggested_attention_slice or an adjusted equivalent.
-            `module` is the current CrossAttention module for which the callback is being invoked.
-            `suggested_attention_slice` is the default-calculated attention slice
-            `dim` is -1 if the attenion map has not been sliced, or 0 or 1 for dimension-0 or dimension-1 slicing.
-                If `dim` is >= 0, `offset` and `slice_size` specify the slice start and length.
-
-        Pass None to use the default attention calculation.
-        :return:
-        '''
-        self.attention_slice_wrangler = wrangler
-
-    def set_slicing_strategy_getter(self, getter: Optional[Callable[[nn.Module], tuple[int,int]]]):
-        self.slicing_strategy_getter = getter
-
-    def einsum_lowest_level(self, query, key, value, dim, offset, slice_size):
-        # calculate attention scores
-        #attention_scores = torch.einsum('b i d, b j d -> b i j', q, k)
-        if dim is not None:
-            print(f"sliced dim {dim}, offset {offset}, slice_size {slice_size}")
-        attention_scores = torch.baddbmm(
-            torch.empty(query.shape[0], query.shape[1], key.shape[1], dtype=query.dtype, device=query.device),
-            query,
-            key.transpose(-1, -2),
-            beta=0,
-            alpha=self.scale,
-        )
-
-        # calculate attention slice by taking the best scores for each latent pixel
-        default_attention_slice = attention_scores.softmax(dim=-1, dtype=attention_scores.dtype)
-        attention_slice_wrangler = self.attention_slice_wrangler
-        if attention_slice_wrangler is not None:
-            attention_slice = attention_slice_wrangler(self, default_attention_slice, dim, offset, slice_size)
-        else:
-            attention_slice = default_attention_slice
-
-        hidden_states = torch.bmm(attention_slice, value)
-        return hidden_states
-
-    def einsum_op_slice_dim0(self, q, k, v, slice_size):
-        r = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
-        for i in range(0, q.shape[0], slice_size):
-            end = i + slice_size
-            r[i:end] = self.einsum_lowest_level(q[i:end], k[i:end], v[i:end], dim=0, offset=i, slice_size=slice_size)
-        return r
-
-    def einsum_op_slice_dim1(self, q, k, v, slice_size):
-        r = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
-        for i in range(0, q.shape[1], slice_size):
-            end = i + slice_size
-            r[:, i:end] = self.einsum_lowest_level(q[:, i:end], k, v, dim=1, offset=i, slice_size=slice_size)
-        return r
-
-    def einsum_op_mps_v1(self, q, k, v):
-        if q.shape[1] <= 4096: # (512x512) max q.shape[1]: 4096
-            return self.einsum_lowest_level(q, k, v, None, None, None)
-        else:
-            slice_size = math.floor(2**30 / (q.shape[0] * q.shape[1]))
-            return self.einsum_op_slice_dim1(q, k, v, slice_size)
-
-    def einsum_op_mps_v2(self, q, k, v):
-        if self.mem_total_gb > 8 and q.shape[1] <= 4096:
-            return self.einsum_lowest_level(q, k, v, None, None, None)
-        else:
-            return self.einsum_op_slice_dim0(q, k, v, 1)
-
-    def einsum_op_tensor_mem(self, q, k, v, max_tensor_mb):
-        size_mb = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size() // (1 << 20)
-        if size_mb <= max_tensor_mb:
-            return self.einsum_lowest_level(q, k, v, None, None, None)
-        div = 1 << int((size_mb - 1) / max_tensor_mb).bit_length()
-        if div <= q.shape[0]:
-            return self.einsum_op_slice_dim0(q, k, v, q.shape[0] // div)
-        return self.einsum_op_slice_dim1(q, k, v, max(q.shape[1] // div, 1))
-
-    def einsum_op_cuda(self, q, k, v):
-        # check if we already have a slicing strategy (this should only happen during cross-attention controlled generation)
-        slicing_strategy_getter = self.slicing_strategy_getter
-        if slicing_strategy_getter is not None:
-            (dim, slice_size) = slicing_strategy_getter(self)
-            if dim is not None:
-                # print("using saved slicing strategy with dim", dim, "slice size", slice_size)
-                if dim == 0:
-                    return self.einsum_op_slice_dim0(q, k, v, slice_size)
-                elif dim == 1:
-                    return self.einsum_op_slice_dim1(q, k, v, slice_size)
-
-        # fallback for when there is no saved strategy, or saved strategy does not slice
-        mem_free_total = self.cached_mem_free_total or get_mem_free_total(q.device)
-        # Divide factor of safety as there's copying and fragmentation
-        return self.einsum_op_tensor_mem(q, k, v, mem_free_total / 3.3 / (1 << 20))
-
-
-    def get_invokeai_attention_mem_efficient(self, q, k, v):
-        if q.device.type == 'cuda':
-            #print("in get_attention_mem_efficient with q shape", q.shape, ", k shape", k.shape, ", free memory is", get_mem_free_total(q.device))
-            return self.einsum_op_cuda(q, k, v)
-
-        if q.device.type == 'mps' or q.device.type == 'cpu':
-            if self.mem_total_gb >= 32:
-                return self.einsum_op_mps_v1(q, k, v)
-            return self.einsum_op_mps_v2(q, k, v)
-
-        # Smaller slices are faster due to L2/L3/SLC caches.
-        # Tested on i7 with 8MB L3 cache.
-        return self.einsum_op_tensor_mem(q, k, v, 32)
-
-
 class InvokeAIDiffusersCrossAttention(diffusers.models.attention.CrossAttention, InvokeAICrossAttentionMixin):
 
     def __init__(self, **kwargs):
diff --git a/ldm/models/diffusion/ksampler.py b/ldm/models/diffusion/ksampler.py
index 336ce1d7a08..0038c481e8b 100644
--- a/ldm/models/diffusion/ksampler.py
+++ b/ldm/models/diffusion/ksampler.py
@@ -209,12 +209,12 @@ def route_callback(k_callback_values):
         model_wrap_cfg.prepare_to_sample(S, extra_conditioning_info=extra_conditioning_info)
 
         # setup attention maps saving. checks for None are because there are multiple code paths to get here.
-        attention_maps_saver = None
+        attention_map_saver = None
         if attention_maps_callback is not None and extra_conditioning_info is not None:
             eos_token_index = extra_conditioning_info.tokens_count_including_eos_bos - 1
             attention_map_token_ids = range(1, eos_token_index)
-            attention_maps_saver = AttentionMapSaver(token_ids = attention_map_token_ids, latents_shape=x.shape[-2:])
-            model_wrap_cfg.invokeai_diffuser.setup_attention_map_saving(attention_maps_saver)
+            attention_map_saver = AttentionMapSaver(token_ids = attention_map_token_ids, latents_shape=x.shape[-2:])
+            model_wrap_cfg.invokeai_diffuser.setup_attention_map_saving(attention_map_saver)
 
         extra_args = {
             'cond': conditioning,
@@ -229,8 +229,8 @@ def route_callback(k_callback_values):
             ),
             None,
         )
-        if attention_maps_saver is not None:
-            attention_maps_callback(attention_maps_saver)
+        if attention_map_saver is not None:
+            attention_maps_callback(attention_map_saver)
         return sampling_result
 
     # this code will support inpainting if and when ksampler API modified or

From 12a88c8410b2c608c78e58b1557d5ab50a5c770e Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Wed, 14 Dec 2022 12:53:04 -0800
Subject: [PATCH 074/199] build: fix syntax error in environment-mac

---
 environments-and-requirements/environment-mac.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/environments-and-requirements/environment-mac.yml b/environments-and-requirements/environment-mac.yml
index dbb6c6717c2..f6d9816843b 100644
--- a/environments-and-requirements/environment-mac.yml
+++ b/environments-and-requirements/environment-mac.yml
@@ -49,7 +49,7 @@ dependencies:
   - sympy=1.10
   - send2trash=1.8
   - tensorboard=2.10
-  - transformers=~4.25
+  - transformers~=4.25
   - pip:
       - getpass_asterisk
       - picklescan

From 6e4dad60c866209001dda3dc55826e6b5a093991 Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Wed, 14 Dec 2022 12:55:31 -0800
Subject: [PATCH 075/199] diffusers: add INITIAL_MODELS with
 diffusers-compatible repos

---
 configs/INITIAL_MODELS.diffusers.yaml | 56 +++++++++++++++++++++++++++
 1 file changed, 56 insertions(+)
 create mode 100644 configs/INITIAL_MODELS.diffusers.yaml

diff --git a/configs/INITIAL_MODELS.diffusers.yaml b/configs/INITIAL_MODELS.diffusers.yaml
new file mode 100644
index 00000000000..73a8d77c578
--- /dev/null
+++ b/configs/INITIAL_MODELS.diffusers.yaml
@@ -0,0 +1,56 @@
+stable-diffusion-1.5:
+   description: The newest Stable Diffusion version 1.5 weight file (4.27 GB)
+   repo_id: runwayml/stable-diffusion-v1-5
+   format: diffusers
+   recommended: true
+   width: 512
+   height: 512
+inpainting-1.5:
+   description: RunwayML SD 1.5 model optimized for inpainting (4.27 GB)
+   repo_id: runwayml/stable-diffusion-inpainting
+   format: diffusers
+   recommended: True
+   width: 512
+   height: 512
+ft-mse-improved-autoencoder-840000:
+   description: StabilityAI improved autoencoder fine-tuned for human faces (recommended; 335 MB)
+   repo_id: stabilityai/sd-vae-ft-mse
+   format: diffusers
+   recommended: True
+   width: 512
+   height: 512
+stable-diffusion-1.4:
+   description: The original Stable Diffusion version 1.4 weight file (4.27 GB)
+   repo_id: CompVis/stable-diffusion-v1-4
+   format: diffusers
+   recommended: False
+   width: 512
+   height: 512
+waifu-diffusion-1.3:
+   description: Stable Diffusion 1.4 fine tuned on anime-styled images (4.27)
+   repo_id: hakurei/waifu-diffusion
+   format: diffusers
+   recommended: False
+   width: 512
+   height: 512
+trinart-2.0:
+   description: An SD model finetuned with ~40,000 assorted high resolution manga/anime-style pictures (2.13 GB)
+   repo_id: naclbit/trinart_stable_diffusion_v2
+   format: diffusers
+   recommended: False
+   width: 512
+   height: 512
+papercut-1.0:
+   description: SD 1.5 fine-tuned for papercut art (use "PaperCut" in your prompts) (2.13 GB)
+   repo_id: Fictiverse/Stable_Diffusion_PaperCut_Model
+   format: diffusers
+   recommended: False
+   width: 512
+   height: 512
+voxel_art-1.0:
+   description: Stable Diffusion trained on voxel art (use "VoxelArt" in your prompts) (4.27 GB)
+   repo_id: Fictiverse/Stable_Diffusion_VoxelArt_Model
+   format: diffusers
+   recommended: False
+   width: 512
+   height: 512

From 87b60582bd5fee1ffadb01ca24c572effcdb1601 Mon Sep 17 00:00:00 2001
From: Damian Stewart <d@damianstewart.com>
Date: Fri, 16 Dec 2022 14:10:16 +0100
Subject: [PATCH 076/199] re-enable the embedding manager; closes #1778

---
 ldm/invoke/generator/diffusers_pipeline.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ldm/invoke/generator/diffusers_pipeline.py b/ldm/invoke/generator/diffusers_pipeline.py
index 410761cd794..2e96903074a 100644
--- a/ldm/invoke/generator/diffusers_pipeline.py
+++ b/ldm/invoke/generator/diffusers_pipeline.py
@@ -562,7 +562,7 @@ def get_learned_conditioning(self, c: List[List[str]], *, return_tokens=True, fr
         """
         Compatibility function for ldm.models.diffusion.ddpm.LatentDiffusion.
         """
-        return self.clip_embedder.encode(c, return_tokens=return_tokens, fragment_weights=fragment_weights)
+        return self.clip_embedder.encode(c, return_tokens=return_tokens, fragment_weights=fragment_weights, embedding_manager=self.embedding_manager)
 
     @property
     def cond_stage_model(self):

From 8bcca8cc2dd3900577a3e4225a37647a84bb087d Mon Sep 17 00:00:00 2001
From: Damian Stewart <d@damianstewart.com>
Date: Sun, 18 Dec 2022 16:06:33 +0100
Subject: [PATCH 077/199] Squashed commit of the following:

commit e4a956abc37fcb5cf188388b76b617bc5c8fda7d
Author: Damian Stewart <d@damianstewart.com>
Date:   Sun Dec 18 15:43:07 2022 +0100

    import new load handling from EmbeddingManager and cleanup

commit c4abe91a5ba0d415b45bf734068385668b7a66e6
Merge: 032e856e 1efc6397
Author: Damian Stewart <d@damianstewart.com>
Date:   Sun Dec 18 15:09:53 2022 +0100

    Merge branch 'feature_textual_inversion_mgr' into dev/diffusers_with_textual_inversion_manager

commit 032e856eefb3bbc39534f5daafd25764bcfcef8b
Merge: 8b4f0fe9 bc515e24
Author: Damian Stewart <d@damianstewart.com>
Date:   Sun Dec 18 15:08:01 2022 +0100

    Merge remote-tracking branch 'upstream/dev/diffusers' into dev/diffusers_with_textual_inversion_manager

commit 1efc6397fc6e61c1aff4b0258b93089d61de5955
Author: Damian Stewart <d@damianstewart.com>
Date:   Sun Dec 18 15:04:28 2022 +0100

    cleanup and add performance notes

commit e400f804ac471a0ca2ba432fd658778b20c7bdab
Author: Damian Stewart <d@damianstewart.com>
Date:   Sun Dec 18 14:45:07 2022 +0100

    fix bug and update unit tests

commit deb9ae0ae1016750e93ce8275734061f7285a231
Author: Damian Stewart <d@damianstewart.com>
Date:   Sun Dec 18 14:28:29 2022 +0100

    textual inversion manager seems to work

commit 162e02505dec777e91a983c4d0fb52e950d25ff0
Merge: cbad4583 12769b3d
Author: Damian Stewart <d@damianstewart.com>
Date:   Sun Dec 18 11:58:03 2022 +0100

    Merge branch 'main' into feature_textual_inversion_mgr

commit cbad45836c6aace6871a90f2621a953f49433131
Author: Damian Stewart <d@damianstewart.com>
Date:   Sun Dec 18 11:54:10 2022 +0100

    use position embeddings

commit 070344c69b0e0db340a183857d0a787b348681d3
Author: Damian Stewart <d@damianstewart.com>
Date:   Sun Dec 18 11:53:47 2022 +0100

    Don't crash CLI on exceptions

commit b035ac8c6772dfd9ba41b8eeb9103181cda028f8
Author: Damian Stewart <d@damianstewart.com>
Date:   Sun Dec 18 11:11:55 2022 +0100

    add missing position_embeddings

commit 12769b3d3562ef71e0f54946b532ad077e10043c
Author: Damian Stewart <d@damianstewart.com>
Date:   Fri Dec 16 13:33:25 2022 +0100

    debugging why it don't work

commit bafb7215eabe1515ca5e8388fd3bb2f3ac5362cf
Author: Damian Stewart <d@damianstewart.com>
Date:   Fri Dec 16 13:21:33 2022 +0100

    debugging why it don't work

commit 664a6e9e146b42d96703f0cc8baf8f5efec04ee1
Author: Damian Stewart <d@damianstewart.com>
Date:   Fri Dec 16 12:48:38 2022 +0100

    use TextualInversionManager in place of embeddings (wip, doesn't work)

commit 8b4f0fe9d6e4e2643b36dfa27864294785d7ba4e
Author: Damian Stewart <d@damianstewart.com>
Date:   Fri Dec 16 12:48:38 2022 +0100

    use TextualInversionManager in place of embeddings (wip, doesn't work)

commit ffbe1ab11163ba712e353d89404e301d0e0c6cdf
Merge: 6e4dad60 023df37e
Author: Damian Stewart <d@damianstewart.com>
Date:   Fri Dec 16 02:37:31 2022 +0100

    Merge branch 'feature_textual_inversion_mgr' into dev/diffusers

commit 023df37efffa67434f77def7fc3c9dfb29f699fd
Author: Damian Stewart <d@damianstewart.com>
Date:   Fri Dec 16 02:36:54 2022 +0100

    cleanup

commit 05fac594eaf79d0058e3c48deee93df603f136c2
Author: Damian Stewart <d@damianstewart.com>
Date:   Fri Dec 16 02:07:49 2022 +0100

    tweak error checking

commit 009f32ed39a7280997c3ffab112adadee0b44279
Author: damian <null@damianstewart.com>
Date:   Thu Dec 15 21:29:47 2022 +0100

    unit tests passing for embeddings with vector length >1

commit beb1b08d9a98112ed2fe073580568e1a18698da3
Author: Damian Stewart <d@damianstewart.com>
Date:   Thu Dec 15 13:39:09 2022 +0100

    more explicit equality tests when overwriting

commit 44d8a5a7c85cdabc9ce3a54fd0769a10597b3ca9
Author: Damian Stewart <d@damianstewart.com>
Date:   Thu Dec 15 13:30:13 2022 +0100

    wip textual inversion manager (unit tests passing for 1v embedding overwriting)

commit 417c2b57d90924a839616bfb66804faab8039e4c
Author: Damian Stewart <d@damianstewart.com>
Date:   Thu Dec 15 12:30:55 2022 +0100

    wip textual inversion manager (unit tests passing for base stuff + padding)

commit 2e80872e3b6f7fd7d8eb8928822bd824b63cb2ff
Author: Damian Stewart <d@damianstewart.com>
Date:   Thu Dec 15 10:57:57 2022 +0100

    wip new TextualInversionManager
---
 ldm/generate.py                            |  28 ++-
 ldm/invoke/CLI.py                          |  11 +-
 ldm/invoke/concepts_lib.py                 |   8 +-
 ldm/invoke/conditioning.py                 |   2 +-
 ldm/invoke/generator/diffusers_pipeline.py |  11 +-
 ldm/invoke/readline.py                     |   4 +-
 ldm/models/diffusion/ddpm.py               |   4 +
 ldm/modules/embedding_manager.py           | 103 +++------
 ldm/modules/encoders/modules.py            | 111 +++++----
 ldm/modules/textual_inversion_manager.py   | 241 ++++++++++++++++++++
 tests/test_textual_inversion.py            | 253 +++++++++++++++++++++
 11 files changed, 646 insertions(+), 130 deletions(-)
 create mode 100644 ldm/modules/textual_inversion_manager.py
 create mode 100644 tests/test_textual_inversion.py

diff --git a/ldm/generate.py b/ldm/generate.py
index 2d60a479cdd..61de50579b3 100644
--- a/ldm/generate.py
+++ b/ldm/generate.py
@@ -31,7 +31,7 @@
 
 import ldm.invoke.conditioning
 from ldm.invoke.args import metadata_from_png
-from ldm.invoke.concepts_lib import Concepts
+from ldm.invoke.concepts_lib import HuggingFaceConceptsLibrary
 from ldm.invoke.conditioning import get_uc_and_c_and_ec
 from ldm.invoke.devices import choose_torch_device, choose_precision
 from ldm.invoke.globals import Globals
@@ -443,7 +443,7 @@ def process_image(image,seed):
             self._set_sampler()
 
         # apply the concepts library to the prompt
-        prompt = self.concept_lib().replace_concepts_with_triggers(prompt, lambda concepts: self.load_concepts(concepts))
+        prompt = self.huggingface_concepts_library.replace_concepts_with_triggers(prompt, lambda concepts: self.load_huggingface_concepts(concepts))
 
         # bit of a hack to change the cached sampler's karras threshold to
         # whatever the user asked for
@@ -550,7 +550,7 @@ def process_image(image,seed):
                 print('**Interrupted** Partial results will be returned.')
             else:
                 raise KeyboardInterrupt
-        except RuntimeError as e:
+        except (RuntimeError, Exception) as e:
             print(traceback.format_exc(), file=sys.stderr)
             print('>> Could not generate image.')
 
@@ -867,19 +867,27 @@ def set_model(self,model_name):
 
         seed_everything(random.randrange(0, np.iinfo(np.uint32).max))
         if self.embedding_path is not None:
-            self.model.embedding_manager.load(
-                self.embedding_path, self.precision == 'float32' or self.precision == 'autocast'
-            )
+            for root, _, files in os.walk(self.embedding_path):
+                # loading textual inversions is slow
+                # see note in TextualInversionManager._get_or_create_token_id_and_assign_embedding()
+                verbose = len(files)>4
+                for name in files:
+                    if verbose:
+                        print(f'>> Loading textual inversion from {name}')
+                    ti_path = os.path.join(root, name)
+                    self.model.textual_inversion_manager.load_textual_inversion(ti_path)
+            print(f'>> Textual inversions available: {", ".join(self.model.textual_inversion_manager.get_all_trigger_strings())}')
 
         self.model_name = model_name
         self._set_sampler()  # requires self.model_name to be set first
         return self.model
 
-    def load_concepts(self,concepts:list[str]):
-        self.model.embedding_manager.load_concepts(concepts, self.precision=='float32' or self.precision=='autocast')
+    def load_huggingface_concepts(self, concepts:list[str]):
+        self.model.textual_inversion_manager.load_huggingface_concepts(concepts)
 
-    def concept_lib(self)->Concepts:
-        return self.model.embedding_manager.concepts_library
+    @property
+    def huggingface_concepts_library(self) -> HuggingFaceConceptsLibrary:
+        return self.model.textual_inversion_manager.hf_concepts_library
 
     def correct_colors(self,
                        image_list,
diff --git a/ldm/invoke/CLI.py b/ldm/invoke/CLI.py
index d6804ed61a3..45687703f6a 100644
--- a/ldm/invoke/CLI.py
+++ b/ldm/invoke/CLI.py
@@ -16,7 +16,7 @@
 from ldm.invoke.pngwriter import PngWriter, retrieve_metadata, write_metadata
 from ldm.invoke.image_util import make_grid
 from ldm.invoke.log import write_log
-from ldm.invoke.concepts_lib import Concepts
+from ldm.invoke.concepts_lib import HuggingFaceConceptsLibrary
 from omegaconf import OmegaConf
 from pathlib import Path
 import pyparsing
@@ -141,6 +141,10 @@ def main():
         main_loop(gen, opt)
     except KeyboardInterrupt:
         print("\ngoodbye!")
+    except Exception:
+        print(">> An error occurred:")
+        traceback.print_exc()
+
 
 # TODO: main_loop() has gotten busy. Needs to be refactored.
 def main_loop(gen, opt):
@@ -318,7 +322,7 @@ def image_writer(image, seed, upscaled=False, first_seed=None, use_prefix=None,
                     if use_prefix is not None:
                         prefix = use_prefix
                     postprocessed = upscaled if upscaled else operation=='postprocess'
-                    opt.prompt = gen.concept_lib().replace_triggers_with_concepts(opt.prompt or prompt_in)  # to avoid the problem of non-unique concept triggers
+                    opt.prompt = gen.huggingface_concepts_library.replace_triggers_with_concepts(opt.prompt or prompt_in)  # to avoid the problem of non-unique concept triggers
                     filename, formatted_dream_prompt = prepare_image_metadata(
                         opt,
                         prefix,
@@ -817,7 +821,8 @@ def add_embedding_terms(gen,completer):
     Called after setting the model, updates the autocompleter with
     any terms loaded by the embedding manager.
     '''
-    completer.add_embedding_terms(gen.model.embedding_manager.list_terms())
+    trigger_strings = gen.model.textual_inversion_manager.get_all_trigger_strings()
+    completer.add_embedding_terms(trigger_strings)
 
 def split_variations(variations_string) -> list:
     # shotgun parsing, woo
diff --git a/ldm/invoke/concepts_lib.py b/ldm/invoke/concepts_lib.py
index e40eda814d3..92bf67ca800 100644
--- a/ldm/invoke/concepts_lib.py
+++ b/ldm/invoke/concepts_lib.py
@@ -12,7 +12,7 @@
 from huggingface_hub import HfFolder, hf_hub_url, ModelSearchArguments, ModelFilter, HfApi
 from ldm.invoke.globals import Globals
 
-class Concepts(object):
+class HuggingFaceConceptsLibrary(object):
     def __init__(self, root=None):
         '''
         Initialize the Concepts object. May optionally pass a root directory.
@@ -119,11 +119,11 @@ def get_concept_file(self, concept_name:str, file_name:str='learned_embeds.bin'
             self.download_concept(concept_name)
         path = os.path.join(self._concept_path(concept_name), file_name)
         return path if os.path.exists(path) else None
-        
+
     def concept_is_downloaded(self, concept_name)->bool:
         concept_directory = self._concept_path(concept_name)
         return os.path.exists(concept_directory)
-        
+
     def download_concept(self,concept_name)->bool:
         repo_id = self._concept_id(concept_name)
         dest = self._concept_path(concept_name)
@@ -136,7 +136,7 @@ def download_concept(self,concept_name)->bool:
 
         os.makedirs(dest, exist_ok=True)
         succeeded = True
-        
+
         bytes = 0
         def tally_download_size(chunk, size, total):
             nonlocal bytes
diff --git a/ldm/invoke/conditioning.py b/ldm/invoke/conditioning.py
index cf6e84ec608..c24e69b27fa 100644
--- a/ldm/invoke/conditioning.py
+++ b/ldm/invoke/conditioning.py
@@ -238,7 +238,7 @@ def _get_embeddings_and_tokens_for_prompt(model, flattened_prompt: FlattenedProm
 
 def _get_tokens_length(model, fragments: list[Fragment]):
     fragment_texts = [x.text for x in fragments]
-    tokens = model.cond_stage_model.get_tokens(fragment_texts, include_start_and_end_markers=False)
+    tokens = model.cond_stage_model.get_token_ids(fragment_texts, include_start_and_end_markers=False)
     return sum([len(x) for x in tokens])
 
 
diff --git a/ldm/invoke/generator/diffusers_pipeline.py b/ldm/invoke/generator/diffusers_pipeline.py
index 2e96903074a..c4be2b82304 100644
--- a/ldm/invoke/generator/diffusers_pipeline.py
+++ b/ldm/invoke/generator/diffusers_pipeline.py
@@ -32,7 +32,7 @@
 from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
 
 from ldm.models.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent
-from ldm.modules.embedding_manager import EmbeddingManager
+from ldm.modules.textual_inversion_manager import TextualInversionManager
 from ldm.modules.encoders.modules import WeightedFrozenCLIPEmbedder
 
 
@@ -238,7 +238,8 @@ def __init__(
         scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
         safety_checker: Optional[StableDiffusionSafetyChecker],
         feature_extractor: Optional[CLIPFeatureExtractor],
-        requires_safety_checker: bool = False
+        requires_safety_checker: bool = False,
+        precision: str = 'full',
     ):
         super().__init__(vae, text_encoder, tokenizer, unet, scheduler,
                          safety_checker, feature_extractor, requires_safety_checker)
@@ -258,7 +259,9 @@ def __init__(
             transformer=self.text_encoder
         )
         self.invokeai_diffuser = InvokeAIDiffuserComponent(self.unet, self._unet_forward)
-        self.embedding_manager = EmbeddingManager(self.clip_embedder, **_default_personalization_config_params)
+        use_full_precision = (precision == 'float32' or precision == 'autocast')
+        self.textual_inversion_manager = TextualInversionManager(self.clip_embedder, use_full_precision)
+        self.clip_embedder.set_textual_inversion_manager(self.textual_inversion_manager)
 
         if is_xformers_available():
             self.enable_xformers_memory_efficient_attention()
@@ -562,7 +565,7 @@ def get_learned_conditioning(self, c: List[List[str]], *, return_tokens=True, fr
         """
         Compatibility function for ldm.models.diffusion.ddpm.LatentDiffusion.
         """
-        return self.clip_embedder.encode(c, return_tokens=return_tokens, fragment_weights=fragment_weights, embedding_manager=self.embedding_manager)
+        return self.clip_embedder.encode(c, return_tokens=return_tokens, fragment_weights=fragment_weights)
 
     @property
     def cond_stage_model(self):
diff --git a/ldm/invoke/readline.py b/ldm/invoke/readline.py
index 9c21180dea5..376e0092960 100644
--- a/ldm/invoke/readline.py
+++ b/ldm/invoke/readline.py
@@ -12,7 +12,7 @@
 import re
 import atexit
 from ldm.invoke.args import Args
-from ldm.invoke.concepts_lib import Concepts
+from ldm.invoke.concepts_lib import HuggingFaceConceptsLibrary
 from ldm.invoke.globals import Globals
 
 # ---------------readline utilities---------------------
@@ -276,7 +276,7 @@ def add_embedding_terms(self, terms:list[str]):
 
     def _concept_completions(self, text, state):
         if self.concepts is None:
-            self.concepts = set(Concepts().list_concepts())
+            self.concepts = set(HuggingFaceConceptsLibrary().list_concepts())
             self.embedding_terms.update(self.concepts)
 
         partial = text[1:]  # this removes the leading '<'
diff --git a/ldm/models/diffusion/ddpm.py b/ldm/models/diffusion/ddpm.py
index e2e38459ff7..d9fa762f0b9 100644
--- a/ldm/models/diffusion/ddpm.py
+++ b/ldm/models/diffusion/ddpm.py
@@ -22,6 +22,7 @@
 from omegaconf import ListConfig
 import urllib
 
+from ldm.modules.textual_inversion_manager import TextualInversionManager
 from ldm.util import (
     log_txt_as_img,
     exists,
@@ -678,6 +679,9 @@ def __init__(
         self.embedding_manager = self.instantiate_embedding_manager(
             personalization_config, self.cond_stage_model
         )
+        self.textual_inversion_manager = TextualInversionManager(self.cond_stage_model, full_precision=True)
+        # this circular component dependency is gross and bad, needs to be rethought
+        self.cond_stage_model.set_textual_inversion_manager(self.textual_inversion_manager)
 
         self.emb_ckpt_counter = 0
 
diff --git a/ldm/modules/embedding_manager.py b/ldm/modules/embedding_manager.py
index af9383bbd6c..36704eb64b9 100644
--- a/ldm/modules/embedding_manager.py
+++ b/ldm/modules/embedding_manager.py
@@ -1,11 +1,12 @@
 import os.path
 from cmath import log
 import torch
+from attr import dataclass
 from torch import nn
 
 import sys
 
-from ldm.invoke.concepts_lib import Concepts
+from ldm.invoke.concepts_lib import HuggingFaceConceptsLibrary
 from ldm.data.personalized import per_img_token_list
 from transformers import CLIPTokenizer
 from functools import partial
@@ -14,35 +15,22 @@
 PROGRESSIVE_SCALE = 2000
 
 
-def get_clip_token_for_string(tokenizer, string):
-    batch_encoding = tokenizer(
-        string,
-        truncation=True,
-        max_length=77,
-        return_length=True,
-        return_overflowing_tokens=False,
-        padding='max_length',
-        return_tensors='pt',
-    )
-    tokens = batch_encoding['input_ids']
-    """ assert (
-        torch.count_nonzero(tokens - 49407) == 2
-    ), f"String '{string}' maps to more than a single token. Please use another string" """
+def get_clip_token_id_for_string(tokenizer: CLIPTokenizer, token_str: str) -> int:
+    token_id = tokenizer.convert_tokens_to_ids(token_str)
+    return token_id
 
-    return tokens[0, 1]
-
-
-def get_bert_token_for_string(tokenizer, string):
+def get_bert_token_id_for_string(tokenizer, string) -> int:
     token = tokenizer(string)
     # assert torch.count_nonzero(token) == 3, f"String '{string}' maps to more than a single token. Please use another string"
-
     token = token[0, 1]
+    return token.item()
 
-    return token
 
+def get_embedding_for_clip_token_id(embedder, token_id):
+    if type(token_id) is not torch.Tensor:
+        token_id = torch.tensor(token_id, dtype=torch.int)
+    return embedder(token_id.unsqueeze(0))[0, 0]
 
-def get_embedding_for_clip_token(embedder, token):
-    return embedder(token.unsqueeze(0))[0, 0]
 
 class EmbeddingManager(nn.Module):
     def __init__(
@@ -58,8 +46,7 @@ def __init__(
         super().__init__()
 
         self.embedder = embedder
-        self.concepts_library=Concepts()
-        self.concepts_loaded = dict()
+        self.concepts_library=HuggingFaceConceptsLibrary()
 
         self.string_to_token_dict = {}
         self.string_to_param_dict = nn.ParameterDict()
@@ -77,11 +64,11 @@ def __init__(
             embedder, 'tokenizer'
         ):   # using Stable Diffusion's CLIP encoder
             self.is_clip = True
-            get_token_for_string = partial(
-                get_clip_token_for_string, embedder.tokenizer
+            get_token_id_for_string = partial(
+                get_clip_token_id_for_string, embedder.tokenizer
             )
-            get_embedding_for_tkn = partial(
-                get_embedding_for_clip_token,
+            get_embedding_for_tkn_id = partial(
+                get_embedding_for_clip_token_id,
                 embedder.transformer.text_model.embeddings,
             )
             # per bug report #572
@@ -89,10 +76,10 @@ def __init__(
             token_dim = 768
         else:   # using LDM's BERT encoder
             self.is_clip = False
-            get_token_for_string = partial(
-                get_bert_token_for_string, embedder.tknz_fn
+            get_token_id_for_string = partial(
+                get_bert_token_id_for_string, embedder.tknz_fn
             )
-            get_embedding_for_tkn = embedder.transformer.token_emb
+            get_embedding_for_tkn_id = embedder.transformer.token_emb
             token_dim = 1280
 
         if per_image_tokens:
@@ -100,15 +87,13 @@ def __init__(
 
         for idx, placeholder_string in enumerate(placeholder_strings):
 
-            token = get_token_for_string(placeholder_string)
+            token_id = get_token_id_for_string(placeholder_string)
 
             if initializer_words and idx < len(initializer_words):
-                init_word_token = get_token_for_string(initializer_words[idx])
+                init_word_token_id = get_token_id_for_string(initializer_words[idx])
 
                 with torch.no_grad():
-                    init_word_embedding = get_embedding_for_tkn(
-                        init_word_token.cpu()
-                    )
+                    init_word_embedding = get_embedding_for_tkn_id(init_word_token_id)
 
                 token_params = torch.nn.Parameter(
                     init_word_embedding.unsqueeze(0).repeat(
@@ -132,7 +117,7 @@ def __init__(
                     )
                 )
 
-            self.string_to_token_dict[placeholder_string] = token
+            self.string_to_token_dict[placeholder_string] = token_id
             self.string_to_param_dict[placeholder_string] = token_params
 
     def forward(
@@ -140,6 +125,8 @@ def forward(
         tokenized_text,
         embedded_text,
     ):
+        # torch.save(embedded_text, '/tmp/embedding-manager-uglysonic-pre-rewrite.pt')
+
         b, n, device = *tokenized_text.shape, tokenized_text.device
 
         for (
@@ -164,7 +151,7 @@ def forward(
             )
 
             placeholder_rows, placeholder_cols = torch.where(
-                tokenized_text == placeholder_token.to(tokenized_text.device)
+                tokenized_text == placeholder_token
             )
 
             if placeholder_rows.nelement() == 0:
@@ -182,9 +169,7 @@ def forward(
                 new_token_row = torch.cat(
                     [
                         tokenized_text[row][:col],
-                        placeholder_token.repeat(num_vectors_for_token).to(
-                            device
-                        ),
+                        torch.tensor([placeholder_token] * num_vectors_for_token, device=device),
                         tokenized_text[row][col + 1 :],
                     ],
                     axis=0,
@@ -212,22 +197,6 @@ def save(self, ckpt_path):
             ckpt_path,
         )
 
-    def load_concepts(self, concepts:list[str], full=True):
-        bin_files = list()
-        for concept_name in concepts:
-            if concept_name in self.concepts_loaded:
-                continue
-            else:
-                bin_file = self.concepts_library.get_concept_model_path(concept_name)
-                if not bin_file:
-                    continue
-                bin_files.append(bin_file)
-                self.concepts_loaded[concept_name]=True
-        self.load(bin_files, full)
-
-    def list_terms(self) -> list[str]:
-        return self.concepts_loaded.keys()
-
     def load(self, ckpt_paths, full=True):
         if len(ckpt_paths) == 0:
             return
@@ -241,7 +210,7 @@ def load(self, ckpt_paths, full=True):
         # both will be stored in this dictionary
         for term in self.string_to_param_dict.keys():
             term = term.strip('<').strip('>')
-            self.concepts_loaded[term] = True  
+            self.concepts_loaded[term] = True
         print(f'>> Current embedding manager terms: {", ".join(self.string_to_param_dict.keys())}')
 
     def _expand_directories(self, paths:list[str]):
@@ -282,14 +251,16 @@ def add_embedding(self, token_str, embedding, full):
         if len(embedding.shape) == 1:
             embedding = embedding.unsqueeze(0)
 
-        num_tokens_added = self.embedder.tokenizer.add_tokens(token_str)
-        current_embeddings = self.embedder.transformer.resize_token_embeddings(None)
-        current_token_count = current_embeddings.num_embeddings
-        new_token_count = current_token_count + num_tokens_added
-        self.embedder.transformer.resize_token_embeddings(new_token_count)
+        existing_token_id = get_clip_token_id_for_string(self.embedder.tokenizer, token_str)
+        if existing_token_id == self.embedder.tokenizer.unk_token_id:
+            num_tokens_added = self.embedder.tokenizer.add_tokens(token_str)
+            current_embeddings = self.embedder.transformer.resize_token_embeddings(None)
+            current_token_count = current_embeddings.num_embeddings
+            new_token_count = current_token_count + num_tokens_added
+            self.embedder.transformer.resize_token_embeddings(new_token_count)
 
-        token = get_clip_token_for_string(self.embedder.tokenizer, token_str)
-        self.string_to_token_dict[token_str] = token
+        token_id = get_clip_token_id_for_string(self.embedder.tokenizer, token_str)
+        self.string_to_token_dict[token_str] = token_id
         self.string_to_param_dict[token_str] = torch.nn.Parameter(embedding)
 
     def parse_embedding(self, embedding_file: str):
diff --git a/ldm/modules/encoders/modules.py b/ldm/modules/encoders/modules.py
index cf460519333..e119bdef5ad 100644
--- a/ldm/modules/encoders/modules.py
+++ b/ldm/modules/encoders/modules.py
@@ -11,6 +11,7 @@
 import kornia
 from ldm.invoke.devices import choose_torch_device
 from ldm.invoke.globals import Globals
+#from ldm.modules.textual_inversion_manager import TextualInversionManager
 
 from ldm.modules.x_transformer import (
     Encoder,
@@ -477,7 +478,12 @@ class WeightedFrozenCLIPEmbedder(FrozenCLIPEmbedder):
     fragment_weights_key = "fragment_weights"
     return_tokens_key = "return_tokens"
 
+    def set_textual_inversion_manager(self, manager): #TextualInversionManager):
+        # TODO all of the weighting and expanding stuff needs be moved out of this class
+        self.textual_inversion_manager = manager
+
     def forward(self, text: list, **kwargs):
+        # TODO all of the weighting and expanding stuff needs be moved out of this class
         '''
 
         :param text: A batch of prompt strings, or, a batch of lists of fragments of prompt strings to which different
@@ -572,19 +578,42 @@ def forward(self, text: list, **kwargs):
         else:
             return batch_z
 
-    def get_tokens(self, fragments: list[str], include_start_and_end_markers: bool = True) -> list[list[int]]:
-        tokens = self.tokenizer(
+    def get_token_ids(self, fragments: list[str], include_start_and_end_markers: bool = True) -> list[list[int]]:
+        """
+        Convert a list of strings like `["a cat", "sitting", "on a mat"]` into a list of lists of token ids like
+        `[[bos, 0, 1, eos], [bos, 2, eos], [bos, 3, 0, 4, eos]]`. bos/eos markers are skipped if
+        `include_start_and_end_markers` is `False`. Each list will be restricted to the maximum permitted length
+        (typically 75 tokens + eos/bos markers).
+
+        :param fragments: The strings to convert.
+        :param include_start_and_end_markers:
+        :return:
+        """
+        # for args documentation see ENCODE_KWARGS_DOCSTRING in tokenization_utils_base.py (in `transformers` lib)
+        token_ids_list = self.tokenizer(
             fragments,
             truncation=True,
             max_length=self.max_length,
             return_overflowing_tokens=False,
             padding='do_not_pad',
-            return_tensors=None,  # just give me a list of ints
+            return_tensors=None,  # just give me lists of ints
         )['input_ids']
-        if include_start_and_end_markers:
-            return tokens
-        else:
-            return [x[1:-1] for x in tokens]
+
+        result = []
+        for token_ids in token_ids_list:
+            # trim eos/bos
+            token_ids = token_ids[1:-1]
+            # pad for textual inversions with vector length >1
+            token_ids = self.textual_inversion_manager.expand_textual_inversion_token_ids(token_ids)
+            # restrict length to max_length-2 (leaving room for bos/eos)
+            token_ids = token_ids[0:self.max_length - 2]
+            # add back eos/bos if requested
+            if include_start_and_end_markers:
+                token_ids = [self.tokenizer.bos_token_id] + token_ids + [self.tokenizer.eos_token_id]
+
+            result.append(token_ids)
+
+        return result
 
 
     @classmethod
@@ -609,56 +638,58 @@ def get_tokens_and_weights(self, fragments: list[str], weights: list[float]) ->
         if len(fragments) == 0 and len(weights) == 0:
             fragments = ['']
             weights = [1]
-        item_encodings = self.tokenizer(
-            fragments,
-            truncation=True,
-            max_length=self.max_length,
-            return_overflowing_tokens=True,
-            padding='do_not_pad',
-            return_tensors=None,  # just give me a list of ints
-        )['input_ids']
-        all_tokens = []
+        per_fragment_token_ids = self.get_token_ids(fragments, include_start_and_end_markers=False)
+        all_token_ids = []
         per_token_weights = []
         #print("all fragments:", fragments, weights)
-        for index, fragment in enumerate(item_encodings):
-            weight = weights[index]
+        for index, fragment in enumerate(per_fragment_token_ids):
+            weight = float(weights[index])
             #print("processing fragment", fragment, weight)
-            fragment_tokens = item_encodings[index]
-            #print("fragment", fragment, "processed to", fragment_tokens)
-            # trim bos and eos markers before appending
-            all_tokens.extend(fragment_tokens[1:-1])
-            per_token_weights.extend([weight] * (len(fragment_tokens) - 2))
-
-        if (len(all_tokens) + 2) > self.max_length:
-            excess_token_count = (len(all_tokens) + 2) - self.max_length
+            this_fragment_token_ids = per_fragment_token_ids[index]
+            #print("fragment", fragment, "processed to", this_fragment_token_ids)
+            # append
+            all_token_ids += this_fragment_token_ids
+            # fill out weights tensor with one float per token
+            per_token_weights += [weight] * len(this_fragment_token_ids)
+
+        # leave room for bos/eos
+        if len(all_token_ids) > self.max_length - 2:
+            excess_token_count = len(all_token_ids) - self.max_length - 2
+            # TODO build nice description string of how the truncation was applied
+            # this should be done by calling self.tokenizer.convert_ids_to_tokens() then passing the result to
+            # self.tokenizer.convert_tokens_to_string() for the token_ids on each side of the truncation limit.
             print(f">> Prompt is {excess_token_count} token(s) too long and has been truncated")
-            all_tokens = all_tokens[:self.max_length - 2]
-            per_token_weights = per_token_weights[:self.max_length - 2]
+            all_token_ids = all_token_ids[0:self.max_length]
+            per_token_weights = per_token_weights[0:self.max_length]
 
         # pad out to a 77-entry array: [eos_token, <prompt tokens>, eos_token, ..., eos_token]
         # (77 = self.max_length)
-        pad_length = self.max_length - 1 - len(all_tokens)
-        all_tokens.insert(0, self.tokenizer.bos_token_id)
-        all_tokens.extend([self.tokenizer.eos_token_id] * pad_length)
-        per_token_weights.insert(0, 1)
-        per_token_weights.extend([1] * pad_length)
+        all_token_ids = [self.tokenizer.bos_token_id] + all_token_ids + [self.tokenizer.eos_token_id]
+        per_token_weights = [1.0] + per_token_weights + [1.0]
+        pad_length = self.max_length - len(all_token_ids)
+        all_token_ids += [self.tokenizer.eos_token_id] * pad_length
+        per_token_weights += [1.0] * pad_length
 
-        all_tokens_tensor = torch.tensor(all_tokens, dtype=torch.long).to(self.device)
+        all_token_ids_tensor = torch.tensor(all_token_ids, dtype=torch.long).to(self.device)
         per_token_weights_tensor = torch.tensor(per_token_weights, dtype=torch.float32).to(self.device)
-        #print(f"assembled all_tokens_tensor with shape {all_tokens_tensor.shape}")
-        return all_tokens_tensor, per_token_weights_tensor
+        #print(f"assembled all_token_ids_tensor with shape {all_token_ids_tensor.shape}")
+        return all_token_ids_tensor, per_token_weights_tensor
 
-    def build_weighted_embedding_tensor(self, tokens: torch.Tensor, per_token_weights: torch.Tensor, weight_delta_from_empty=True, **kwargs) -> torch.Tensor:
+    def build_weighted_embedding_tensor(self, token_ids: torch.Tensor, per_token_weights: torch.Tensor, weight_delta_from_empty=True, **kwargs) -> torch.Tensor:
         '''
         Build a tensor representing the passed-in tokens, each of which has a weight.
-        :param tokens: A tensor of shape (77) containing token ids (integers)
+        :param token_ids: A tensor of shape (77) containing token ids (integers)
         :param per_token_weights: A tensor of shape (77) containing weights (floats)
         :param method: Whether to multiply the whole feature vector for each token or just its distance from an "empty" feature vector
         :param kwargs: passed on to self.transformer()
         :return: A tensor of shape (1, 77, 768) representing the requested weighted embeddings.
         '''
         #print(f"building weighted embedding tensor for {tokens} with weights {per_token_weights}")
-        z = self.transformer(input_ids=tokens.unsqueeze(0), **kwargs)
+        if token_ids.shape != torch.Size([self.max_length]):
+            raise ValueError(f"token_ids has shape {token_ids.shape} - expected [{self.max_length}]")
+
+        z = self.transformer(input_ids=token_ids.unsqueeze(0), **kwargs)
+
         batch_weights_expanded = per_token_weights.reshape(per_token_weights.shape + (1,)).expand(z.shape)
 
         if weight_delta_from_empty:
@@ -672,7 +703,7 @@ def build_weighted_embedding_tensor(self, tokens: torch.Tensor, per_token_weight
             z_delta_from_empty = z - empty_z
             weighted_z = empty_z + (z_delta_from_empty * batch_weights_expanded)
 
-            weighted_z_delta_from_empty = (weighted_z-empty_z)
+            #weighted_z_delta_from_empty = (weighted_z-empty_z)
             #print("weighted z has delta from empty with sum", weighted_z_delta_from_empty.sum().item(), "mean", weighted_z_delta_from_empty.mean().item() )
 
             #print("using empty-delta method, first 5 rows:")
diff --git a/ldm/modules/textual_inversion_manager.py b/ldm/modules/textual_inversion_manager.py
new file mode 100644
index 00000000000..dc19037387f
--- /dev/null
+++ b/ldm/modules/textual_inversion_manager.py
@@ -0,0 +1,241 @@
+import os
+import traceback
+from typing import Union
+
+import torch
+from attr import dataclass
+from picklescan.scanner import scan_file_path
+
+from ldm.invoke.concepts_lib import HuggingFaceConceptsLibrary
+from ldm.modules.embedding_manager import get_clip_token_id_for_string
+from ldm.modules.encoders.modules import FrozenCLIPEmbedder
+
+
+@dataclass
+class TextualInversion:
+    trigger_string: str
+    trigger_token_id: int
+    pad_token_ids: list[int]
+    embedding: torch.Tensor
+
+    @property
+    def embedding_vector_length(self) -> int:
+        return self.embedding.shape[0]
+
+class TextualInversionManager():
+    def __init__(self, clip_embedder: FrozenCLIPEmbedder, full_precision: bool=True):
+        self.clip_embedder = clip_embedder
+        self.full_precision = full_precision
+        self.hf_concepts_library = HuggingFaceConceptsLibrary()
+        default_textual_inversions: list[TextualInversion] = []
+        self.textual_inversions = default_textual_inversions
+
+    def load_huggingface_concepts(self, concepts: list[str]):
+        for concept_name in concepts:
+            if concept_name in self.hf_concepts_library.concepts_loaded:
+                continue
+            bin_file = self.hf_concepts_library.get_concept_model_path(concept_name)
+            if not bin_file:
+                continue
+            self.load_textual_inversion(bin_file)
+            self.hf_concepts_library.concepts_loaded[concept_name]=True
+
+    def get_all_trigger_strings(self) -> list[str]:
+        return [ti.trigger_string for ti in self.textual_inversions]
+
+    def load_textual_inversion(self, ckpt_path):
+        try:
+            scan_result = scan_file_path(ckpt_path)
+            if scan_result.infected_files == 1:
+                print(f'\n### Security Issues Found in Model: {scan_result.issues_count}')
+                print('### For your safety, InvokeAI will not load this embed.')
+                return
+        except Exception:
+            print(f"### WARNING::: Invalid or corrupt embeddings found. Ignoring: {ckpt_path}")
+            return
+
+        embedding_info = self._parse_embedding(ckpt_path)
+        if embedding_info:
+            self._add_textual_inversion(embedding_info['name'], embedding_info['embedding'])
+        else:
+            print(f'>> Failed to load embedding located at {ckpt_path}. Unsupported file.')
+
+    def _add_textual_inversion(self, trigger_str, embedding) -> int:
+        """
+        Add a textual inversion to be recognised.
+        :param trigger_str: The trigger text in the prompt that activates this textual inversion. If unknown to the embedder's tokenizer, will be added.
+        :param embedding: The actual embedding data that will be inserted into the conditioning at the point where the token_str appears.
+        :return: The token id for the added embedding, either existing or newly-added.
+        """
+        if trigger_str in [ti.trigger_string for ti in self.textual_inversions]:
+            print(f">> TextualInversionManager refusing to overwrite already-loaded token '{trigger_str}'")
+            return
+        if not self.full_precision:
+            embedding = embedding.half()
+        if len(embedding.shape) == 1:
+            embedding = embedding.unsqueeze(0)
+        elif len(embedding.shape) > 2:
+            raise ValueError(f"TextualInversionManager cannot add {trigger_str} because the embedding shape {embedding.shape} is incorrect. The embedding must have shape [token_dim] or [V, token_dim] where V is vector length and token_dim is 768 for SD1 or 1280 for SD2.")
+
+        # for embeddings with vector length > 1
+        pad_token_strings = [trigger_str + "-!pad-" + str(pad_index) for pad_index in range(1, embedding.shape[0])]
+
+        try:
+            trigger_token_id = self._get_or_create_token_id_and_assign_embedding(trigger_str, embedding[0])
+            # todo: batched UI for faster loading when vector length >2
+            pad_token_ids = [self._get_or_create_token_id_and_assign_embedding(pad_token_str, embedding[1 + i]) \
+                             for (i, pad_token_str) in enumerate(pad_token_strings)]
+            self.textual_inversions.append(TextualInversion(
+                trigger_string=trigger_str,
+                trigger_token_id=trigger_token_id,
+                pad_token_ids=pad_token_ids,
+                embedding=embedding
+            ))
+            return trigger_token_id
+
+        except ValueError:
+            traceback.print_exc()
+            print(f">> TextualInversionManager was unable to add a textual inversion with trigger string {trigger_str}.")
+            raise
+
+
+    def has_textual_inversion_for_trigger_string(self, trigger_string: str) -> bool:
+        try:
+            ti = self.get_textual_inversion_for_trigger_string(trigger_string)
+            return ti is not None
+        except StopIteration:
+            return False
+
+    def get_textual_inversion_for_trigger_string(self, trigger_string: str) -> TextualInversion:
+        return next(ti for ti in self.textual_inversions if ti.trigger_string == trigger_string)
+
+
+    def get_textual_inversion_for_token_id(self, token_id: int) -> TextualInversion:
+        return next(ti for ti in self.textual_inversions if ti.trigger_token_id == token_id)
+
+    def expand_textual_inversion_token_ids(self, prompt_token_ids: list[int]) -> list[int]:
+        """
+        Insert padding tokens as necessary into the passed-in list of token ids to match any textual inversions it includes.
+
+        :param prompt_token_ids: The prompt as a list of token ids (`int`s). Should not include bos and eos markers.
+        :return: The prompt token ids with any necessary padding to account for textual inversions inserted. May be too
+                long - caller is responsible for prepending/appending eos and bos token ids, and truncating if necessary.
+        """
+        if len(prompt_token_ids) == 0:
+            return prompt_token_ids
+
+        if prompt_token_ids[0] == self.clip_embedder.tokenizer.bos_token_id:
+            raise ValueError("prompt_token_ids must not start with bos_token_id")
+        if prompt_token_ids[-1] == self.clip_embedder.tokenizer.eos_token_id:
+            raise ValueError("prompt_token_ids must not end with eos_token_id")
+        textual_inversion_trigger_token_ids = [ti.trigger_token_id for ti in self.textual_inversions]
+        prompt_token_ids = prompt_token_ids.copy()
+        for i, token_id in reversed(list(enumerate(prompt_token_ids))):
+            if token_id in textual_inversion_trigger_token_ids:
+                textual_inversion = next(ti for ti in self.textual_inversions if ti.trigger_token_id == token_id)
+                for pad_idx in range(0, textual_inversion.embedding_vector_length-1):
+                    prompt_token_ids.insert(i+pad_idx+1, textual_inversion.pad_token_ids[pad_idx])
+
+        return prompt_token_ids
+
+    def _get_or_create_token_id_and_assign_embedding(self, token_str: str, embedding: torch.Tensor):
+        if len(embedding.shape) != 1:
+            raise ValueError("Embedding has incorrect shape - must be [token_dim] where token_dim is 768 for SD1 or 1280 for SD2")
+        existing_token_id = get_clip_token_id_for_string(self.clip_embedder.tokenizer, token_str)
+        if existing_token_id == self.clip_embedder.tokenizer.unk_token_id:
+            num_tokens_added = self.clip_embedder.tokenizer.add_tokens(token_str)
+            current_embeddings = self.clip_embedder.transformer.resize_token_embeddings(None)
+            current_token_count = current_embeddings.num_embeddings
+            new_token_count = current_token_count + num_tokens_added
+            # the following call is slow - todo make batched for better performance with vector length >1
+            self.clip_embedder.transformer.resize_token_embeddings(new_token_count)
+
+        token_id = get_clip_token_id_for_string(self.clip_embedder.tokenizer, token_str)
+        self.clip_embedder.transformer.get_input_embeddings().weight.data[token_id] = embedding
+
+        return token_id
+    def _parse_embedding(self, embedding_file: str):
+        file_type = embedding_file.split('.')[-1]
+        if file_type == 'pt':
+            return self._parse_embedding_pt(embedding_file)
+        elif file_type == 'bin':
+            return self._parse_embedding_bin(embedding_file)
+        else:
+            print(f'>> Not a recognized embedding file: {embedding_file}')
+
+    def _parse_embedding_pt(self, embedding_file):
+        embedding_ckpt = torch.load(embedding_file, map_location='cpu')
+        embedding_info = {}
+
+        # Check if valid embedding file
+        if 'string_to_token' and 'string_to_param' in embedding_ckpt:
+
+            # Catch variants that do not have the expected keys or values.
+            try:
+                embedding_info['name'] = embedding_ckpt['name'] or os.path.basename(os.path.splitext(embedding_file)[0])
+
+                # Check num of embeddings and warn user only the first will be used
+                embedding_info['num_of_embeddings'] = len(embedding_ckpt["string_to_token"])
+                if embedding_info['num_of_embeddings'] > 1:
+                    print('>> More than 1 embedding found. Will use the first one')
+
+                embedding = list(embedding_ckpt['string_to_param'].values())[0]
+            except (AttributeError,KeyError):
+                return self._handle_broken_pt_variants(embedding_ckpt, embedding_file)
+
+            embedding_info['embedding'] = embedding
+            embedding_info['num_vectors_per_token'] = embedding.size()[0]
+            embedding_info['token_dim'] = embedding.size()[1]
+
+            try:
+                embedding_info['trained_steps'] = embedding_ckpt['step']
+                embedding_info['trained_model_name'] = embedding_ckpt['sd_checkpoint_name']
+                embedding_info['trained_model_checksum'] = embedding_ckpt['sd_checkpoint']
+            except AttributeError:
+                print(">> No Training Details Found. Passing ...")
+
+        # .pt files found at https://cyberes.github.io/stable-diffusion-textual-inversion-models/
+        # They are actually .bin files
+        elif len(embedding_ckpt.keys())==1:
+            print('>> Detected .bin file masquerading as .pt file')
+            embedding_info = self._parse_embedding_bin(embedding_file)
+
+        else:
+            print('>> Invalid embedding format')
+            embedding_info = None
+
+        return embedding_info
+
+    def _parse_embedding_bin(self, embedding_file):
+        embedding_ckpt = torch.load(embedding_file, map_location='cpu')
+        embedding_info = {}
+
+        if list(embedding_ckpt.keys()) == 0:
+            print(">> Invalid concepts file")
+            embedding_info = None
+        else:
+            for token in list(embedding_ckpt.keys()):
+                embedding_info['name'] = token or os.path.basename(os.path.splitext(embedding_file)[0])
+                embedding_info['embedding'] = embedding_ckpt[token]
+                embedding_info['num_vectors_per_token'] = 1 # All Concepts seem to default to 1
+                embedding_info['token_dim'] = embedding_info['embedding'].size()[0]
+
+        return embedding_info
+
+    def _handle_broken_pt_variants(self, embedding_ckpt:dict, embedding_file:str)->dict:
+        '''
+        This handles the broken .pt file variants. We only know of one at present.
+        '''
+        embedding_info = {}
+        if isinstance(list(embedding_ckpt['string_to_token'].values())[0],torch.Tensor):
+            print('>> Detected .pt file variant 1') # example at https://github.com/invoke-ai/InvokeAI/issues/1829
+            for token in list(embedding_ckpt['string_to_token'].keys()):
+                embedding_info['name'] = token if token != '*' else os.path.basename(os.path.splitext(embedding_file)[0])
+                embedding_info['embedding'] = embedding_ckpt['string_to_param'].state_dict()[token]
+                embedding_info['num_vectors_per_token'] = embedding_info['embedding'].shape[0]
+                embedding_info['token_dim'] = embedding_info['embedding'].size()[0]
+        else:
+            print('>> Invalid embedding format')
+            embedding_info = None
+
+        return embedding_info
diff --git a/tests/test_textual_inversion.py b/tests/test_textual_inversion.py
new file mode 100644
index 00000000000..01d15a6319f
--- /dev/null
+++ b/tests/test_textual_inversion.py
@@ -0,0 +1,253 @@
+
+import unittest
+from typing import Union
+
+import torch
+
+from ldm.modules.textual_inversion_manager import TextualInversionManager
+
+
+KNOWN_WORDS = ['a', 'b', 'c']
+KNOWN_WORDS_TOKEN_IDS = [0, 1, 2]
+UNKNOWN_WORDS = ['d', 'e', 'f']
+
+class DummyEmbeddingsList(list):
+    def __getattr__(self, name):
+        if name == 'num_embeddings':
+            return len(self)
+        elif name == 'weight':
+            return self
+        elif name == 'data':
+            return self
+
+class DummyTransformer:
+    def __init__(self):
+        self.embeddings = DummyEmbeddingsList([0] * len(KNOWN_WORDS))
+
+    def resize_token_embeddings(self, new_size=None):
+        if new_size is None:
+            return self.embeddings
+        else:
+            while len(self.embeddings) > new_size:
+                self.embeddings.pop(-1)
+            while len(self.embeddings) < new_size:
+                self.embeddings.append(0)
+
+    def get_input_embeddings(self):
+        return self.embeddings
+
+class DummyTokenizer():
+    def __init__(self):
+        self.tokens = KNOWN_WORDS.copy()
+        self.bos_token_id = 49406 # these are what the real CLIPTokenizer has
+        self.eos_token_id = 49407
+        self.pad_token_id = 49407
+        self.unk_token_id = 49407
+
+    def convert_tokens_to_ids(self, token_str):
+        try:
+            return self.tokens.index(token_str)
+        except ValueError:
+            return self.unk_token_id
+
+    def add_tokens(self, token_str):
+        self.tokens.append(token_str)
+        return 1
+
+
+class DummyClipEmbedder:
+    def __init__(self):
+        self.max_length = 77
+        self.transformer = DummyTransformer()
+        self.tokenizer = DummyTokenizer()
+        self.position_embeddings_tensor = torch.randn([77,768], dtype=torch.float32)
+
+    def position_embedding(self, indices: Union[list,torch.Tensor]):
+        if type(indices) is list:
+            indices = torch.tensor(indices, dtype=int)
+        return torch.index_select(self.position_embeddings_tensor, 0, indices)
+
+
+def was_embedding_overwritten_correctly(tim: TextualInversionManager, overwritten_embedding: torch.Tensor, ti_indices: list, ti_embedding: torch.Tensor) -> bool:
+    return torch.allclose(overwritten_embedding[ti_indices], ti_embedding + tim.clip_embedder.position_embedding(ti_indices))
+
+class TextualInversionManagerTestCase(unittest.TestCase):
+
+
+    def test_construction(self):
+        tim = TextualInversionManager(DummyClipEmbedder())
+
+    def test_add_embedding_for_known_token(self):
+        tim = TextualInversionManager(DummyClipEmbedder())
+        test_embedding = torch.randn([1, 768])
+        test_embedding_name = KNOWN_WORDS[0]
+        self.assertFalse(tim.has_textual_inversion_for_trigger_string(test_embedding_name))
+
+        pre_embeddings_count = len(tim.clip_embedder.transformer.resize_token_embeddings(None))
+
+        token_id = tim._add_textual_inversion(test_embedding_name, test_embedding)
+        self.assertEqual(token_id, 0)
+
+
+        # check adding 'test' did not create a new word
+        embeddings_count = len(tim.clip_embedder.transformer.resize_token_embeddings(None))
+        self.assertEqual(pre_embeddings_count, embeddings_count)
+
+        # check it was added
+        self.assertTrue(tim.has_textual_inversion_for_trigger_string(test_embedding_name))
+        textual_inversion = tim.get_textual_inversion_for_trigger_string(test_embedding_name)
+        self.assertIsNotNone(textual_inversion)
+        self.assertTrue(torch.equal(textual_inversion.embedding, test_embedding))
+        self.assertEqual(textual_inversion.trigger_string, test_embedding_name)
+        self.assertEqual(textual_inversion.trigger_token_id, token_id)
+
+    def test_add_embedding_for_unknown_token(self):
+        tim = TextualInversionManager(DummyClipEmbedder())
+        test_embedding_1 = torch.randn([1, 768])
+        test_embedding_name_1 = UNKNOWN_WORDS[0]
+
+        pre_embeddings_count = len(tim.clip_embedder.transformer.resize_token_embeddings(None))
+
+        added_token_id_1 = tim._add_textual_inversion(test_embedding_name_1, test_embedding_1)
+        # new token id should get added on the end
+        self.assertEqual(added_token_id_1, len(KNOWN_WORDS))
+
+        # check adding did create a new word
+        embeddings_count = len(tim.clip_embedder.transformer.resize_token_embeddings(None))
+        self.assertEqual(pre_embeddings_count+1, embeddings_count)
+
+        # check it was added
+        self.assertTrue(tim.has_textual_inversion_for_trigger_string(test_embedding_name_1))
+        textual_inversion = next(ti for ti in tim.textual_inversions if ti.trigger_token_id == added_token_id_1)
+        self.assertIsNotNone(textual_inversion)
+        self.assertTrue(torch.equal(textual_inversion.embedding, test_embedding_1))
+        self.assertEqual(textual_inversion.trigger_string, test_embedding_name_1)
+        self.assertEqual(textual_inversion.trigger_token_id, added_token_id_1)
+
+        # add another one
+        test_embedding_2 = torch.randn([1, 768])
+        test_embedding_name_2 = UNKNOWN_WORDS[1]
+
+        pre_embeddings_count = len(tim.clip_embedder.transformer.resize_token_embeddings(None))
+
+        added_token_id_2 = tim._add_textual_inversion(test_embedding_name_2, test_embedding_2)
+        self.assertEqual(added_token_id_2, len(KNOWN_WORDS)+1)
+
+        # check adding did create a new word
+        embeddings_count = len(tim.clip_embedder.transformer.resize_token_embeddings(None))
+        self.assertEqual(pre_embeddings_count+1, embeddings_count)
+
+        # check it was added
+        self.assertTrue(tim.has_textual_inversion_for_trigger_string(test_embedding_name_2))
+        textual_inversion = next(ti for ti in tim.textual_inversions if ti.trigger_token_id == added_token_id_2)
+        self.assertIsNotNone(textual_inversion)
+        self.assertTrue(torch.equal(textual_inversion.embedding, test_embedding_2))
+        self.assertEqual(textual_inversion.trigger_string, test_embedding_name_2)
+        self.assertEqual(textual_inversion.trigger_token_id, added_token_id_2)
+
+        # check the old one is still there
+        self.assertTrue(tim.has_textual_inversion_for_trigger_string(test_embedding_name_1))
+        textual_inversion = next(ti for ti in tim.textual_inversions if ti.trigger_token_id == added_token_id_1)
+        self.assertIsNotNone(textual_inversion)
+        self.assertTrue(torch.equal(textual_inversion.embedding, test_embedding_1))
+        self.assertEqual(textual_inversion.trigger_string, test_embedding_name_1)
+        self.assertEqual(textual_inversion.trigger_token_id, added_token_id_1)
+
+
+    def test_pad_raises_on_eos_bos(self):
+        tim = TextualInversionManager(DummyClipEmbedder())
+        prompt_token_ids_with_eos_bos = [tim.clip_embedder.tokenizer.bos_token_id] + \
+                                         [KNOWN_WORDS_TOKEN_IDS] + \
+                                         [tim.clip_embedder.tokenizer.eos_token_id]
+        with self.assertRaises(ValueError):
+            expanded_prompt_token_ids = tim.expand_textual_inversion_token_ids(prompt_token_ids=prompt_token_ids_with_eos_bos)
+
+    def test_pad_tokens_list_vector_length_1(self):
+        tim = TextualInversionManager(DummyClipEmbedder())
+        prompt_token_ids = KNOWN_WORDS_TOKEN_IDS.copy()
+
+        expanded_prompt_token_ids = tim.expand_textual_inversion_token_ids(prompt_token_ids=prompt_token_ids)
+        self.assertEqual(prompt_token_ids, expanded_prompt_token_ids)
+
+        test_embedding_1v = torch.randn([1, 768])
+        test_embedding_1v_token = "<inversion-trigger-vector-length-1>"
+        test_embedding_1v_token_id = tim._add_textual_inversion(test_embedding_1v_token, test_embedding_1v)
+        self.assertEqual(test_embedding_1v_token_id, len(KNOWN_WORDS))
+
+        # at the end
+        prompt_token_ids_1v_append = prompt_token_ids + [test_embedding_1v_token_id]
+        expanded_prompt_token_ids = tim.expand_textual_inversion_token_ids(prompt_token_ids=prompt_token_ids_1v_append)
+        self.assertEqual(prompt_token_ids_1v_append, expanded_prompt_token_ids)
+
+        # at the start
+        prompt_token_ids_1v_prepend = [test_embedding_1v_token_id] + prompt_token_ids
+        expanded_prompt_token_ids = tim.expand_textual_inversion_token_ids(prompt_token_ids=prompt_token_ids_1v_prepend)
+        self.assertEqual(prompt_token_ids_1v_prepend, expanded_prompt_token_ids)
+
+        # in the middle
+        prompt_token_ids_1v_insert = prompt_token_ids[0:2] + [test_embedding_1v_token_id] + prompt_token_ids[2:3]
+        expanded_prompt_token_ids = tim.expand_textual_inversion_token_ids(prompt_token_ids=prompt_token_ids_1v_insert)
+        self.assertEqual(prompt_token_ids_1v_insert, expanded_prompt_token_ids)
+
+    def test_pad_tokens_list_vector_length_2(self):
+        tim = TextualInversionManager(DummyClipEmbedder())
+        prompt_token_ids = KNOWN_WORDS_TOKEN_IDS.copy()
+
+        expanded_prompt_token_ids = tim.expand_textual_inversion_token_ids(prompt_token_ids=prompt_token_ids)
+        self.assertEqual(prompt_token_ids, expanded_prompt_token_ids)
+
+        test_embedding_2v = torch.randn([2, 768])
+        test_embedding_2v_token = "<inversion-trigger-vector-length-2>"
+        test_embedding_2v_token_id = tim._add_textual_inversion(test_embedding_2v_token, test_embedding_2v)
+        test_embedding_2v_pad_token_ids = tim.get_textual_inversion_for_token_id(test_embedding_2v_token_id).pad_token_ids
+        self.assertEqual(test_embedding_2v_token_id, len(KNOWN_WORDS))
+
+        # at the end
+        prompt_token_ids_2v_append = prompt_token_ids + [test_embedding_2v_token_id]
+        expanded_prompt_token_ids = tim.expand_textual_inversion_token_ids(prompt_token_ids=prompt_token_ids_2v_append)
+        self.assertNotEqual(prompt_token_ids_2v_append, expanded_prompt_token_ids)
+        self.assertEqual(prompt_token_ids + [test_embedding_2v_token_id] + test_embedding_2v_pad_token_ids, expanded_prompt_token_ids)
+
+        # at the start
+        prompt_token_ids_2v_prepend = [test_embedding_2v_token_id] + prompt_token_ids
+        expanded_prompt_token_ids = tim.expand_textual_inversion_token_ids(prompt_token_ids=prompt_token_ids_2v_prepend)
+        self.assertNotEqual(prompt_token_ids_2v_prepend, expanded_prompt_token_ids)
+        self.assertEqual([test_embedding_2v_token_id] + test_embedding_2v_pad_token_ids + prompt_token_ids, expanded_prompt_token_ids)
+
+        # in the middle
+        prompt_token_ids_2v_insert = prompt_token_ids[0:2] + [test_embedding_2v_token_id] + prompt_token_ids[2:3]
+        expanded_prompt_token_ids = tim.expand_textual_inversion_token_ids(prompt_token_ids=prompt_token_ids_2v_insert)
+        self.assertNotEqual(prompt_token_ids_2v_insert, expanded_prompt_token_ids)
+        self.assertEqual(prompt_token_ids[0:2] + [test_embedding_2v_token_id] + test_embedding_2v_pad_token_ids + prompt_token_ids[2:3], expanded_prompt_token_ids)
+
+    def test_pad_tokens_list_vector_length_8(self):
+        tim = TextualInversionManager(DummyClipEmbedder())
+        prompt_token_ids = KNOWN_WORDS_TOKEN_IDS.copy()
+
+        expanded_prompt_token_ids = tim.expand_textual_inversion_token_ids(prompt_token_ids=prompt_token_ids)
+        self.assertEqual(prompt_token_ids, expanded_prompt_token_ids)
+
+        test_embedding_8v = torch.randn([8, 768])
+        test_embedding_8v_token = "<inversion-trigger-vector-length-8>"
+        test_embedding_8v_token_id = tim._add_textual_inversion(test_embedding_8v_token, test_embedding_8v)
+        test_embedding_8v_pad_token_ids = tim.get_textual_inversion_for_token_id(test_embedding_8v_token_id).pad_token_ids
+        self.assertEqual(test_embedding_8v_token_id, len(KNOWN_WORDS))
+
+        # at the end
+        prompt_token_ids_8v_append = prompt_token_ids + [test_embedding_8v_token_id]
+        expanded_prompt_token_ids = tim.expand_textual_inversion_token_ids(prompt_token_ids=prompt_token_ids_8v_append)
+        self.assertNotEqual(prompt_token_ids_8v_append, expanded_prompt_token_ids)
+        self.assertEqual(prompt_token_ids + [test_embedding_8v_token_id] + test_embedding_8v_pad_token_ids, expanded_prompt_token_ids)
+
+        # at the start
+        prompt_token_ids_8v_prepend = [test_embedding_8v_token_id] + prompt_token_ids
+        expanded_prompt_token_ids = tim.expand_textual_inversion_token_ids(prompt_token_ids=prompt_token_ids_8v_prepend)
+        self.assertNotEqual(prompt_token_ids_8v_prepend, expanded_prompt_token_ids)
+        self.assertEqual([test_embedding_8v_token_id] + test_embedding_8v_pad_token_ids + prompt_token_ids, expanded_prompt_token_ids)
+
+        # in the middle
+        prompt_token_ids_8v_insert = prompt_token_ids[0:2] + [test_embedding_8v_token_id] + prompt_token_ids[2:3]
+        expanded_prompt_token_ids = tim.expand_textual_inversion_token_ids(prompt_token_ids=prompt_token_ids_8v_insert)
+        self.assertNotEqual(prompt_token_ids_8v_insert, expanded_prompt_token_ids)
+        self.assertEqual(prompt_token_ids[0:2] + [test_embedding_8v_token_id] + test_embedding_8v_pad_token_ids + prompt_token_ids[2:3], expanded_prompt_token_ids)
+

From c7585003ffdbbd20974c8236d61630265f5e43f9 Mon Sep 17 00:00:00 2001
From: Damian Stewart <d@damianstewart.com>
Date: Sun, 18 Dec 2022 18:43:54 +0100
Subject: [PATCH 078/199] stop using WeightedFrozenCLIPEmbedder

---
 ldm/invoke/conditioning.py                    |   3 +-
 ldm/invoke/generator/diffusers_pipeline.py    |  25 +-
 ldm/modules/embedding_manager.py              |   9 +-
 ldm/modules/prompt_to_embeddings_converter.py | 235 ++++++++++++++++++
 ldm/modules/textual_inversion_manager.py      |  32 ++-
 tests/test_textual_inversion.py               |  41 +--
 6 files changed, 298 insertions(+), 47 deletions(-)
 create mode 100644 ldm/modules/prompt_to_embeddings_converter.py

diff --git a/ldm/invoke/conditioning.py b/ldm/invoke/conditioning.py
index c24e69b27fa..4930d3c25bf 100644
--- a/ldm/invoke/conditioning.py
+++ b/ldm/invoke/conditioning.py
@@ -16,6 +16,7 @@
 from ..models.diffusion import cross_attention_control
 from ..models.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent
 from ..modules.encoders.modules import WeightedFrozenCLIPEmbedder
+from ..modules.prompt_to_embeddings_converter import WeightedPromptFragmentsToEmbeddingsConverter
 
 
 def get_uc_and_c_and_ec(prompt_string, model, log_tokens=False, skip_normalize_legacy_blend=False):
@@ -216,7 +217,7 @@ def _get_conditioning_for_blend(model, blend: Blend, log_tokens: bool = False):
                                                                   log_display_label=f"(blend part {i + 1}, weight={blend.weights[i]})")
         embeddings_to_blend = this_embedding if embeddings_to_blend is None else torch.cat(
             (embeddings_to_blend, this_embedding))
-    conditioning = WeightedFrozenCLIPEmbedder.apply_embedding_weights(embeddings_to_blend.unsqueeze(0),
+    conditioning = WeightedPromptFragmentsToEmbeddingsConverter.apply_embedding_weights(embeddings_to_blend.unsqueeze(0),
                                                                       blend.weights,
                                                                       normalize=blend.normalize_weights)
     return conditioning
diff --git a/ldm/invoke/generator/diffusers_pipeline.py b/ldm/invoke/generator/diffusers_pipeline.py
index c4be2b82304..5091e6c7611 100644
--- a/ldm/invoke/generator/diffusers_pipeline.py
+++ b/ldm/invoke/generator/diffusers_pipeline.py
@@ -15,6 +15,7 @@
 
 from ...models.diffusion import cross_attention_control
 from ...models.diffusion.cross_attention_map_saving import AttentionMapSaver
+from ...modules.prompt_to_embeddings_converter import WeightedPromptFragmentsToEmbeddingsConverter
 
 # monkeypatch diffusers CrossAttention 🙈
 # this is to make prompt2prompt and (future) attention maps work
@@ -239,7 +240,7 @@ def __init__(
         safety_checker: Optional[StableDiffusionSafetyChecker],
         feature_extractor: Optional[CLIPFeatureExtractor],
         requires_safety_checker: bool = False,
-        precision: str = 'full',
+        precision: str = 'float32',
     ):
         super().__init__(vae, text_encoder, tokenizer, unet, scheduler,
                          safety_checker, feature_extractor, requires_safety_checker)
@@ -253,15 +254,17 @@ def __init__(
             safety_checker=safety_checker,
             feature_extractor=feature_extractor,
         )
+        self.invokeai_diffuser = InvokeAIDiffuserComponent(self.unet, self._unet_forward)
+        use_full_precision = (precision == 'float32' or precision == 'autocast')
+        self.textual_inversion_manager = TextualInversionManager(tokenizer=self.tokenizer,
+                                                                 text_encoder=self.text_encoder,
+                                                                 full_precision=use_full_precision)
         # InvokeAI's interface for text embeddings and whatnot
-        self.clip_embedder = WeightedFrozenCLIPEmbedder(
+        self.prompt_fragments_to_embeddings_converter = WeightedPromptFragmentsToEmbeddingsConverter(
             tokenizer=self.tokenizer,
-            transformer=self.text_encoder
+            text_encoder=self.text_encoder,
+            textual_inversion_manager=self.textual_inversion_manager
         )
-        self.invokeai_diffuser = InvokeAIDiffuserComponent(self.unet, self._unet_forward)
-        use_full_precision = (precision == 'float32' or precision == 'autocast')
-        self.textual_inversion_manager = TextualInversionManager(self.clip_embedder, use_full_precision)
-        self.clip_embedder.set_textual_inversion_manager(self.textual_inversion_manager)
 
         if is_xformers_available():
             self.enable_xformers_memory_efficient_attention()
@@ -565,12 +568,16 @@ def get_learned_conditioning(self, c: List[List[str]], *, return_tokens=True, fr
         """
         Compatibility function for ldm.models.diffusion.ddpm.LatentDiffusion.
         """
-        return self.clip_embedder.encode(c, return_tokens=return_tokens, fragment_weights=fragment_weights)
+        return self.prompt_fragments_to_embeddings_converter.get_embeddings_for_weighted_prompt_fragments(
+            text=c,
+            fragment_weights=fragment_weights,
+            should_return_tokens=return_tokens,
+            device=self.device)
 
     @property
     def cond_stage_model(self):
         warnings.warn("legacy compatibility layer", DeprecationWarning)
-        return self.clip_embedder
+        return self.prompt_fragments_to_embeddings_converter
 
     @torch.inference_mode()
     def _tokenize(self, prompt: Union[str, List[str]]):
diff --git a/ldm/modules/embedding_manager.py b/ldm/modules/embedding_manager.py
index 36704eb64b9..8bbdcd91a4d 100644
--- a/ldm/modules/embedding_manager.py
+++ b/ldm/modules/embedding_manager.py
@@ -19,13 +19,6 @@ def get_clip_token_id_for_string(tokenizer: CLIPTokenizer, token_str: str) -> in
     token_id = tokenizer.convert_tokens_to_ids(token_str)
     return token_id
 
-def get_bert_token_id_for_string(tokenizer, string) -> int:
-    token = tokenizer(string)
-    # assert torch.count_nonzero(token) == 3, f"String '{string}' maps to more than a single token. Please use another string"
-    token = token[0, 1]
-    return token.item()
-
-
 def get_embedding_for_clip_token_id(embedder, token_id):
     if type(token_id) is not torch.Tensor:
         token_id = torch.tensor(token_id, dtype=torch.int)
@@ -289,7 +282,7 @@ def parse_embedding_pt(self, embedding_file):
                     print('>> More than 1 embedding found. Will use the first one')
 
                 embedding = list(embedding_ckpt['string_to_param'].values())[0]
-            except (AttributeError,KeyError): 
+            except (AttributeError,KeyError):
                 return self.handle_broken_pt_variants(embedding_ckpt, embedding_file)
 
             embedding_info['embedding'] = embedding
diff --git a/ldm/modules/prompt_to_embeddings_converter.py b/ldm/modules/prompt_to_embeddings_converter.py
new file mode 100644
index 00000000000..a024a1bc006
--- /dev/null
+++ b/ldm/modules/prompt_to_embeddings_converter.py
@@ -0,0 +1,235 @@
+import math
+
+import torch
+from transformers import CLIPTokenizer, CLIPTextModel
+
+from ldm.modules.textual_inversion_manager import TextualInversionManager
+
+
+class WeightedPromptFragmentsToEmbeddingsConverter():
+
+    def __init__(self,
+                tokenizer: CLIPTokenizer, # converts strings to lists of int token ids
+                text_encoder: CLIPTextModel, # convert a list of int token ids to a tensor of embeddings
+                textual_inversion_manager: TextualInversionManager = None
+                ):
+        self.tokenizer = tokenizer
+        self.text_encoder = text_encoder
+        self.textual_inversion_manager = textual_inversion_manager
+
+    @property
+    def max_length(self):
+        return self.tokenizer.model_max_length
+
+    def get_embeddings_for_weighted_prompt_fragments(self,
+                                                     text: list[str],
+                                                     fragment_weights: list[float],
+                                                     should_return_tokens: bool = False,
+                                                     device='cpu'
+                                                     ) -> torch.Tensor:
+        '''
+
+        :param text: A list of fragments of text to which different weights are to be applied.
+        :param fragment_weights: A batch of lists of weights, one for each entry in `fragments`.
+        :return: A tensor of shape `[1, 77, token_dim]` containing weighted embeddings where token_dim is 768 for SD1
+                    and 1280 for SD2
+        '''
+        if len(text) != len(fragment_weights):
+            raise ValueError(f"lengths of text and fragment_weights lists are not the same ({len(text)} != {len(fragment_weights)})")
+
+        batch_z = None
+        batch_tokens = None
+        for fragments, weights in zip(text, fragment_weights):
+
+            # First, weight tokens in individual fragments by scaling the feature vectors as requested (effectively
+            # applying a multiplier to the CFG scale on a per-token basis).
+            # For tokens weighted<1, intuitively we want SD to become not merely *less* interested in the concept
+            # captured by the fragment but actually *dis*interested in it (a 0.01 interest in "red" is still an active
+            # interest, however small, in redness; what the user probably intends when they attach the number 0.01 to
+            # "red" is to tell SD that it should almost completely *ignore* redness).
+            # To do this, the embedding is lerped away from base_embedding in the direction of an embedding for a prompt
+            # string from which the low-weighted fragment has been simply removed. The closer the weight is to zero, the
+            # closer the resulting embedding is to an embedding for a prompt that simply lacks this fragment.
+
+            # handle weights >=1
+            tokens, per_token_weights = self.get_token_ids_and_expand_weights(fragments, weights, device=device)
+            base_embedding = self.build_weighted_embedding_tensor(tokens, per_token_weights)
+
+            # this is our starting point
+            embeddings = base_embedding.unsqueeze(0)
+            per_embedding_weights = [1.0]
+
+            # now handle weights <1
+            # Do this by building extra embeddings tensors that lack the words being <1 weighted. These will be lerped
+            # with the embeddings tensors that have the words, such that if the weight of a word is 0.5, the resulting
+            # embedding will be exactly half-way between the unweighted prompt and the prompt with the <1 weighted words
+            # removed.
+            # eg for "mountain:1 man:0.5", intuitively the "man" should be "half-gone". therefore, append an embedding
+            # for "mountain" (i.e. without "man") to the already-produced embedding for "mountain man", and weight it
+            # such that the resulting lerped embedding is exactly half-way between "mountain man" and "mountain".
+            for index, fragment_weight in enumerate(weights):
+                if fragment_weight < 1:
+                    fragments_without_this = fragments[:index] + fragments[index+1:]
+                    weights_without_this = weights[:index] + weights[index+1:]
+                    tokens, per_token_weights = self.get_token_ids_and_expand_weights(fragments_without_this, weights_without_this, device=device)
+                    embedding_without_this = self.build_weighted_embedding_tensor(tokens, per_token_weights)
+
+                    embeddings = torch.cat((embeddings, embedding_without_this.unsqueeze(0)), dim=1)
+                    # weight of the embedding *without* this fragment gets *stronger* as its weight approaches 0
+                    # if fragment_weight = 0, basically we want embedding_without_this to completely overwhelm base_embedding
+                    # therefore:
+                    # fragment_weight = 1: we are at base_z => lerp weight 0
+                    # fragment_weight = 0.5: we are halfway between base_z and here => lerp weight 1
+                    # fragment_weight = 0: we're now entirely overriding base_z ==> lerp weight inf
+                    # so let's use tan(), because:
+                    # tan is 0.0 at 0,
+                    #        1.0 at PI/4, and
+                    #        inf at PI/2
+                    # -> tan((1-weight)*PI/2) should give us ideal lerp weights
+                    epsilon = 1e-9
+                    fragment_weight = max(epsilon, fragment_weight) # inf is bad
+                    embedding_lerp_weight = math.tan((1.0 - fragment_weight) * math.pi / 2)
+                    # todo handle negative weight?
+
+                    per_embedding_weights.append(embedding_lerp_weight)
+
+            lerped_embeddings = self.apply_embedding_weights(embeddings, per_embedding_weights, normalize=True).squeeze(0)
+
+            #print(f"assembled tokens for '{fragments}' into tensor of shape {lerped_embeddings.shape}")
+
+            # append to batch
+            batch_z = lerped_embeddings.unsqueeze(0) if batch_z is None else torch.cat([batch_z, lerped_embeddings.unsqueeze(0)], dim=1)
+            batch_tokens = tokens.unsqueeze(0) if batch_tokens is None else torch.cat([batch_tokens, tokens.unsqueeze(0)], dim=1)
+
+        # should have shape (B, 77, 768)
+        #print(f"assembled all tokens into tensor of shape {batch_z.shape}")
+
+        if should_return_tokens:
+            return batch_z, batch_tokens
+        else:
+            return batch_z
+
+    def get_token_ids(self, fragments: list[str], include_start_and_end_markers: bool = True) -> list[list[int]]:
+        """
+        Convert a list of strings like `["a cat", "sitting", "on a mat"]` into a list of lists of token ids like
+        `[[bos, 0, 1, eos], [bos, 2, eos], [bos, 3, 0, 4, eos]]`. bos/eos markers are skipped if
+        `include_start_and_end_markers` is `False`. Each list will be restricted to the maximum permitted length
+        (typically 75 tokens + eos/bos markers).
+
+        :param fragments: The strings to convert.
+        :param include_start_and_end_markers:
+        :return:
+        """
+        # for args documentation see ENCODE_KWARGS_DOCSTRING in tokenization_utils_base.py (in `transformers` lib)
+        token_ids_list = self.tokenizer(
+            fragments,
+            truncation=True,
+            max_length=self.max_length,
+            return_overflowing_tokens=False,
+            padding='do_not_pad',
+            return_tensors=None,  # just give me lists of ints
+        )['input_ids']
+
+        result = []
+        for token_ids in token_ids_list:
+            # trim eos/bos
+            token_ids = token_ids[1:-1]
+            # pad for textual inversions with vector length >1
+            token_ids = self.textual_inversion_manager.expand_textual_inversion_token_ids(token_ids)
+            # restrict length to max_length-2 (leaving room for bos/eos)
+            token_ids = token_ids[0:self.max_length - 2]
+            # add back eos/bos if requested
+            if include_start_and_end_markers:
+                token_ids = [self.tokenizer.bos_token_id] + token_ids + [self.tokenizer.eos_token_id]
+
+            result.append(token_ids)
+
+        return result
+
+
+    @classmethod
+    def apply_embedding_weights(self, embeddings: torch.Tensor, per_embedding_weights: list[float], normalize:bool) -> torch.Tensor:
+        per_embedding_weights = torch.tensor(per_embedding_weights, dtype=embeddings.dtype, device=embeddings.device)
+        if normalize:
+            per_embedding_weights = per_embedding_weights / torch.sum(per_embedding_weights)
+        reshaped_weights = per_embedding_weights.reshape(per_embedding_weights.shape + (1, 1,))
+        #reshaped_weights = per_embedding_weights.reshape(per_embedding_weights.shape + (1,1,)).expand(embeddings.shape)
+        return torch.sum(embeddings * reshaped_weights, dim=1)
+        # lerped embeddings has shape (77, 768)
+
+
+    def get_token_ids_and_expand_weights(self, fragments: list[str], weights: list[float], device: str) -> (torch.Tensor, torch.Tensor):
+        '''
+        Given a list of text fragments and corresponding weights: tokenize each fragment, append the token sequences
+        together and return a padded token sequence starting with the bos marker, ending with the eos marker, and padded
+        or truncated as appropriate to `self.max_length`. Also return a list of weights expanded from the passed-in
+        weights to match each token.
+
+        :param fragments: Text fragments to tokenize and concatenate. May be empty.
+        :param weights: Per-fragment weights (i.e. quasi-CFG scaling). Values from 0 to inf are permitted. In practise with SD1.5
+                        values >1.6 tend to produce garbage output. Must have same length as `fragment`.
+        :return: A tuple of tensors `(token_ids, weights)`. `token_ids` is ints, `weights` is floats, both have shape `[self.max_length]`.
+        '''
+        if len(fragments) != len(weights):
+            raise ValueError(f"lengths of text and fragment_weights lists are not the same ({len(fragments)} != {len(weights)})")
+
+        # empty is meaningful
+        if len(fragments) == 0:
+            fragments = ['']
+            weights = [1.0]
+        per_fragment_token_ids = self.get_token_ids(fragments, include_start_and_end_markers=False)
+        all_token_ids = []
+        per_token_weights = []
+        #print("all fragments:", fragments, weights)
+        for this_fragment_token_ids, weight in zip(per_fragment_token_ids, weights):
+            # append
+            all_token_ids += this_fragment_token_ids
+            # fill out weights tensor with one float per token
+            per_token_weights += [float(weight)] * len(this_fragment_token_ids)
+
+        # leave room for bos/eos
+        if len(all_token_ids) > self.max_length - 2:
+            excess_token_count = len(all_token_ids) - self.max_length - 2
+            # TODO build nice description string of how the truncation was applied
+            # this should be done by calling self.tokenizer.convert_ids_to_tokens() then passing the result to
+            # self.tokenizer.convert_tokens_to_string() for the token_ids on each side of the truncation limit.
+            print(f">> Prompt is {excess_token_count} token(s) too long and has been truncated")
+            all_token_ids = all_token_ids[0:self.max_length]
+            per_token_weights = per_token_weights[0:self.max_length]
+
+        # pad out to a self.max_length-entry array: [eos_token, <prompt tokens>, eos_token, ..., eos_token]
+        # (typically self.max_length == 77)
+        all_token_ids = [self.tokenizer.bos_token_id] + all_token_ids + [self.tokenizer.eos_token_id]
+        per_token_weights = [1.0] + per_token_weights + [1.0]
+        pad_length = self.max_length - len(all_token_ids)
+        all_token_ids += [self.tokenizer.eos_token_id] * pad_length
+        per_token_weights += [1.0] * pad_length
+
+        all_token_ids_tensor = torch.tensor(all_token_ids, dtype=torch.long, device=device)
+        per_token_weights_tensor = torch.tensor(per_token_weights, dtype=torch.float32, device=device)
+        #print(f"assembled all_token_ids_tensor with shape {all_token_ids_tensor.shape}")
+        return all_token_ids_tensor, per_token_weights_tensor
+
+    def build_weighted_embedding_tensor(self, token_ids: torch.Tensor, per_token_weights: torch.Tensor) -> torch.Tensor:
+        '''
+        Build a tensor that embeds the passed-in token IDs and applyies the given per_token weights
+        :param token_ids: A tensor of shape `[self.max_length]` containing token IDs (ints)
+        :param per_token_weights: A tensor of shape `[self.max_length]` containing weights (floats)
+        :return: A tensor of shape `[1, self.max_length, token_dim]` representing the requested weighted embeddings
+        where `token_dim` is 768 for SD1 and 1280 for SD2.
+        '''
+        #print(f"building weighted embedding tensor for {tokens} with weights {per_token_weights}")
+        if token_ids.shape != torch.Size([self.max_length]):
+            raise ValueError(f"token_ids has shape {token_ids.shape} - expected [{self.max_length}]")
+
+        z = self.text_encoder.forward(input_ids=token_ids.unsqueeze(0),
+                                      return_dict=False)[0]
+        empty_token_ids = torch.tensor([self.tokenizer.bos_token_id] +
+                                    [self.tokenizer.pad_token_id] * (self.max_length-2) +
+                                    [self.tokenizer.eos_token_id], dtype=torch.int, device=token_ids.device).unsqueeze(0)
+        empty_z = self.text_encoder(input_ids=empty_token_ids).last_hidden_state
+        batch_weights_expanded = per_token_weights.reshape(per_token_weights.shape + (1,)).expand(z.shape)
+        z_delta_from_empty = z - empty_z
+        weighted_z = empty_z + (z_delta_from_empty * batch_weights_expanded)
+
+        return weighted_z
diff --git a/ldm/modules/textual_inversion_manager.py b/ldm/modules/textual_inversion_manager.py
index dc19037387f..543fa52111e 100644
--- a/ldm/modules/textual_inversion_manager.py
+++ b/ldm/modules/textual_inversion_manager.py
@@ -5,10 +5,9 @@
 import torch
 from attr import dataclass
 from picklescan.scanner import scan_file_path
+from transformers import CLIPTokenizer, CLIPTextModel
 
 from ldm.invoke.concepts_lib import HuggingFaceConceptsLibrary
-from ldm.modules.embedding_manager import get_clip_token_id_for_string
-from ldm.modules.encoders.modules import FrozenCLIPEmbedder
 
 
 @dataclass
@@ -23,8 +22,12 @@ def embedding_vector_length(self) -> int:
         return self.embedding.shape[0]
 
 class TextualInversionManager():
-    def __init__(self, clip_embedder: FrozenCLIPEmbedder, full_precision: bool=True):
-        self.clip_embedder = clip_embedder
+    def __init__(self,
+                 tokenizer: CLIPTokenizer,
+                 text_encoder: CLIPTextModel,
+                 full_precision: bool=True):
+        self.tokenizer = tokenizer
+        self.text_encoder = text_encoder
         self.full_precision = full_precision
         self.hf_concepts_library = HuggingFaceConceptsLibrary()
         default_textual_inversions: list[TextualInversion] = []
@@ -124,9 +127,9 @@ def expand_textual_inversion_token_ids(self, prompt_token_ids: list[int]) -> lis
         if len(prompt_token_ids) == 0:
             return prompt_token_ids
 
-        if prompt_token_ids[0] == self.clip_embedder.tokenizer.bos_token_id:
+        if prompt_token_ids[0] == self.tokenizer.bos_token_id:
             raise ValueError("prompt_token_ids must not start with bos_token_id")
-        if prompt_token_ids[-1] == self.clip_embedder.tokenizer.eos_token_id:
+        if prompt_token_ids[-1] == self.tokenizer.eos_token_id:
             raise ValueError("prompt_token_ids must not end with eos_token_id")
         textual_inversion_trigger_token_ids = [ti.trigger_token_id for ti in self.textual_inversions]
         prompt_token_ids = prompt_token_ids.copy()
@@ -141,19 +144,22 @@ def expand_textual_inversion_token_ids(self, prompt_token_ids: list[int]) -> lis
     def _get_or_create_token_id_and_assign_embedding(self, token_str: str, embedding: torch.Tensor):
         if len(embedding.shape) != 1:
             raise ValueError("Embedding has incorrect shape - must be [token_dim] where token_dim is 768 for SD1 or 1280 for SD2")
-        existing_token_id = get_clip_token_id_for_string(self.clip_embedder.tokenizer, token_str)
-        if existing_token_id == self.clip_embedder.tokenizer.unk_token_id:
-            num_tokens_added = self.clip_embedder.tokenizer.add_tokens(token_str)
-            current_embeddings = self.clip_embedder.transformer.resize_token_embeddings(None)
+        existing_token_id = self.tokenizer.convert_tokens_to_ids(token_str)
+        if existing_token_id == self.tokenizer.unk_token_id:
+            num_tokens_added = self.tokenizer.add_tokens(token_str)
+            current_embeddings = self.text_encoder.resize_token_embeddings(None)
             current_token_count = current_embeddings.num_embeddings
             new_token_count = current_token_count + num_tokens_added
             # the following call is slow - todo make batched for better performance with vector length >1
-            self.clip_embedder.transformer.resize_token_embeddings(new_token_count)
+            self.text_encoder.resize_token_embeddings(new_token_count)
 
-        token_id = get_clip_token_id_for_string(self.clip_embedder.tokenizer, token_str)
-        self.clip_embedder.transformer.get_input_embeddings().weight.data[token_id] = embedding
+        token_id = self.tokenizer.convert_tokens_to_ids(token_str)
+        if token_id == self.tokenizer.unk_token_id:
+            raise RuntimeError(f"Unable to find token id for token '{token_str}'")
+        self.text_encoder.get_input_embeddings().weight.data[token_id] = embedding
 
         return token_id
+
     def _parse_embedding(self, embedding_file: str):
         file_type = embedding_file.split('.')[-1]
         if file_type == 'pt':
diff --git a/tests/test_textual_inversion.py b/tests/test_textual_inversion.py
index 01d15a6319f..6e150f1d21d 100644
--- a/tests/test_textual_inversion.py
+++ b/tests/test_textual_inversion.py
@@ -51,6 +51,8 @@ def convert_tokens_to_ids(self, token_str):
             return self.unk_token_id
 
     def add_tokens(self, token_str):
+        if token_str in self.tokens:
+            return 0
         self.tokens.append(token_str)
         return 1
 
@@ -71,26 +73,33 @@ def position_embedding(self, indices: Union[list,torch.Tensor]):
 def was_embedding_overwritten_correctly(tim: TextualInversionManager, overwritten_embedding: torch.Tensor, ti_indices: list, ti_embedding: torch.Tensor) -> bool:
     return torch.allclose(overwritten_embedding[ti_indices], ti_embedding + tim.clip_embedder.position_embedding(ti_indices))
 
+
+def make_dummy_textual_inversion_manager():
+    return TextualInversionManager(
+        tokenizer=DummyTokenizer(),
+        text_encoder=DummyTransformer()
+    )
+
 class TextualInversionManagerTestCase(unittest.TestCase):
 
 
     def test_construction(self):
-        tim = TextualInversionManager(DummyClipEmbedder())
+        tim = make_dummy_textual_inversion_manager()
 
     def test_add_embedding_for_known_token(self):
-        tim = TextualInversionManager(DummyClipEmbedder())
+        tim = make_dummy_textual_inversion_manager()
         test_embedding = torch.randn([1, 768])
         test_embedding_name = KNOWN_WORDS[0]
         self.assertFalse(tim.has_textual_inversion_for_trigger_string(test_embedding_name))
 
-        pre_embeddings_count = len(tim.clip_embedder.transformer.resize_token_embeddings(None))
+        pre_embeddings_count = len(tim.text_encoder.resize_token_embeddings(None))
 
         token_id = tim._add_textual_inversion(test_embedding_name, test_embedding)
         self.assertEqual(token_id, 0)
 
 
         # check adding 'test' did not create a new word
-        embeddings_count = len(tim.clip_embedder.transformer.resize_token_embeddings(None))
+        embeddings_count = len(tim.text_encoder.resize_token_embeddings(None))
         self.assertEqual(pre_embeddings_count, embeddings_count)
 
         # check it was added
@@ -102,18 +111,18 @@ def test_add_embedding_for_known_token(self):
         self.assertEqual(textual_inversion.trigger_token_id, token_id)
 
     def test_add_embedding_for_unknown_token(self):
-        tim = TextualInversionManager(DummyClipEmbedder())
+        tim = make_dummy_textual_inversion_manager()
         test_embedding_1 = torch.randn([1, 768])
         test_embedding_name_1 = UNKNOWN_WORDS[0]
 
-        pre_embeddings_count = len(tim.clip_embedder.transformer.resize_token_embeddings(None))
+        pre_embeddings_count = len(tim.text_encoder.resize_token_embeddings(None))
 
         added_token_id_1 = tim._add_textual_inversion(test_embedding_name_1, test_embedding_1)
         # new token id should get added on the end
         self.assertEqual(added_token_id_1, len(KNOWN_WORDS))
 
         # check adding did create a new word
-        embeddings_count = len(tim.clip_embedder.transformer.resize_token_embeddings(None))
+        embeddings_count = len(tim.text_encoder.resize_token_embeddings(None))
         self.assertEqual(pre_embeddings_count+1, embeddings_count)
 
         # check it was added
@@ -128,13 +137,13 @@ def test_add_embedding_for_unknown_token(self):
         test_embedding_2 = torch.randn([1, 768])
         test_embedding_name_2 = UNKNOWN_WORDS[1]
 
-        pre_embeddings_count = len(tim.clip_embedder.transformer.resize_token_embeddings(None))
+        pre_embeddings_count = len(tim.text_encoder.resize_token_embeddings(None))
 
         added_token_id_2 = tim._add_textual_inversion(test_embedding_name_2, test_embedding_2)
         self.assertEqual(added_token_id_2, len(KNOWN_WORDS)+1)
 
         # check adding did create a new word
-        embeddings_count = len(tim.clip_embedder.transformer.resize_token_embeddings(None))
+        embeddings_count = len(tim.text_encoder.resize_token_embeddings(None))
         self.assertEqual(pre_embeddings_count+1, embeddings_count)
 
         # check it was added
@@ -155,15 +164,15 @@ def test_add_embedding_for_unknown_token(self):
 
 
     def test_pad_raises_on_eos_bos(self):
-        tim = TextualInversionManager(DummyClipEmbedder())
-        prompt_token_ids_with_eos_bos = [tim.clip_embedder.tokenizer.bos_token_id] + \
+        tim = make_dummy_textual_inversion_manager()
+        prompt_token_ids_with_eos_bos = [tim.tokenizer.bos_token_id] + \
                                          [KNOWN_WORDS_TOKEN_IDS] + \
-                                         [tim.clip_embedder.tokenizer.eos_token_id]
+                                         [tim.tokenizer.eos_token_id]
         with self.assertRaises(ValueError):
-            expanded_prompt_token_ids = tim.expand_textual_inversion_token_ids(prompt_token_ids=prompt_token_ids_with_eos_bos)
+            tim.expand_textual_inversion_token_ids(prompt_token_ids=prompt_token_ids_with_eos_bos)
 
     def test_pad_tokens_list_vector_length_1(self):
-        tim = TextualInversionManager(DummyClipEmbedder())
+        tim = make_dummy_textual_inversion_manager()
         prompt_token_ids = KNOWN_WORDS_TOKEN_IDS.copy()
 
         expanded_prompt_token_ids = tim.expand_textual_inversion_token_ids(prompt_token_ids=prompt_token_ids)
@@ -190,7 +199,7 @@ def test_pad_tokens_list_vector_length_1(self):
         self.assertEqual(prompt_token_ids_1v_insert, expanded_prompt_token_ids)
 
     def test_pad_tokens_list_vector_length_2(self):
-        tim = TextualInversionManager(DummyClipEmbedder())
+        tim = make_dummy_textual_inversion_manager()
         prompt_token_ids = KNOWN_WORDS_TOKEN_IDS.copy()
 
         expanded_prompt_token_ids = tim.expand_textual_inversion_token_ids(prompt_token_ids=prompt_token_ids)
@@ -221,7 +230,7 @@ def test_pad_tokens_list_vector_length_2(self):
         self.assertEqual(prompt_token_ids[0:2] + [test_embedding_2v_token_id] + test_embedding_2v_pad_token_ids + prompt_token_ids[2:3], expanded_prompt_token_ids)
 
     def test_pad_tokens_list_vector_length_8(self):
-        tim = TextualInversionManager(DummyClipEmbedder())
+        tim = make_dummy_textual_inversion_manager()
         prompt_token_ids = KNOWN_WORDS_TOKEN_IDS.copy()
 
         expanded_prompt_token_ids = tim.expand_textual_inversion_token_ids(prompt_token_ids=prompt_token_ids)

From ca95445a0d3cc726c7667569f3f4c8ac9e652dcf Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Wed, 21 Dec 2022 01:56:27 +0000
Subject: [PATCH 079/199] store diffusion models locally

- configure_invokeai.py reconfigured to store diffusion models rather than
  CompVis models
- hugging face caching model is used, but cache is set to ~/invokeai/models/repo_id
- models.yaml does **NOT** use path, just repo_id
- "repo_name" changed to "repo_id" to following hugging face conventions
- Models are loaded with full precision pending further work.
---
 configs/INITIAL_MODELS.yaml                |  32 ++--
 ldm/invoke/generator/diffusers_pipeline.py |   7 +-
 ldm/invoke/model_cache.py                  |  81 +++++----
 scripts/configure_invokeai.py              | 190 ++++-----------------
 4 files changed, 98 insertions(+), 212 deletions(-)

diff --git a/configs/INITIAL_MODELS.yaml b/configs/INITIAL_MODELS.yaml
index 6cfb863df8d..4139c67d58b 100644
--- a/configs/INITIAL_MODELS.yaml
+++ b/configs/INITIAL_MODELS.yaml
@@ -1,72 +1,63 @@
 stable-diffusion-1.5:
    description: The newest Stable Diffusion version 1.5 weight file (4.27 GB)
    repo_id: runwayml/stable-diffusion-v1-5
-   config: v1-inference.yaml
-   file: v1-5-pruned-emaonly.ckpt
-   recommended: true
+   format: diffusers
+   recommended: True
    width: 512
    height: 512
 inpainting-1.5:
    description: RunwayML SD 1.5 model optimized for inpainting (4.27 GB)
    repo_id: runwayml/stable-diffusion-inpainting
-   config: v1-inpainting-inference.yaml
-   file: sd-v1-5-inpainting.ckpt
+   format: diffusers
    recommended: True
    width: 512
    height: 512
 ft-mse-improved-autoencoder-840000:
    description: StabilityAI improved autoencoder fine-tuned for human faces (recommended; 335 MB)
-   repo_id: stabilityai/sd-vae-ft-mse-original
-   config: VAE/default
-   file: vae-ft-mse-840000-ema-pruned.ckpt
+   repo_id: stabilityai/sd-vae-ft-mse
    recommended: True
+   format: vae
    width: 512
    height: 512
 stable-diffusion-1.4:
    description: The original Stable Diffusion version 1.4 weight file (4.27 GB)
    repo_id: CompVis/stable-diffusion-v-1-4-original
-   config: v1-inference.yaml
-   file: sd-v1-4.ckpt
    recommended: False
+   format: diffusers
    width: 512
    height: 512
 waifu-diffusion-1.3:
    description: Stable Diffusion 1.4 fine tuned on anime-styled images (4.27 GB)
    repo_id: hakurei/waifu-diffusion-v1-3
-   config: v1-inference.yaml
-   file: model-epoch09-float32.ckpt
+   format: diffusers
    recommended: False
    width: 512
    height: 512
 trinart-2.0:
    description: An SD model finetuned with ~40,000 assorted high resolution manga/anime-style pictures (2.13 GB)
    repo_id: naclbit/trinart_stable_diffusion_v2
-   config: v1-inference.yaml
-   file: trinart2_step95000.ckpt
+   format: diffusers
    recommended: False
    width: 512
    height: 512
 trinart_characters-1.0:
    description: An SD model finetuned with 19.2M anime/manga style images (2.13 GB)
    repo_id: naclbit/trinart_characters_19.2m_stable_diffusion_v1
-   config: v1-inference.yaml
-   file: trinart_characters_it4_v1.ckpt
+   format: diffusers
    recommended: False
    width: 512
    height: 512
 trinart_vae:
    description: Custom autoencoder for trinart_characters
    repo_id: naclbit/trinart_characters_19.2m_stable_diffusion_v1
-   config: VAE/trinart
-   file: autoencoder_fix_kl-f8-trinart_characters.ckpt
+   format: vae
    recommended: False
    width: 512
    height: 512
 papercut-1.0:
    description: SD 1.5 fine-tuned for papercut art (use "PaperCut" in your prompts) (2.13 GB)
    repo_id: Fictiverse/Stable_Diffusion_PaperCut_Model
-   config: v1-inference.yaml
-   file: PaperCut_v1.ckpt
+   format: diffusers
    recommended: False
    width: 512
    height: 512
@@ -75,6 +66,7 @@ voxel_art-1.0:
    repo_id: Fictiverse/Stable_Diffusion_VoxelArt_Model
    config: v1-inference.yaml
    file: VoxelArt_v1.ckpt
+   format: diffusers
    recommended: False
    width: 512
    height: 512
diff --git a/ldm/invoke/generator/diffusers_pipeline.py b/ldm/invoke/generator/diffusers_pipeline.py
index 5091e6c7611..771184c4824 100644
--- a/ldm/invoke/generator/diffusers_pipeline.py
+++ b/ldm/invoke/generator/diffusers_pipeline.py
@@ -3,7 +3,12 @@
 import secrets
 import warnings
 from dataclasses import dataclass
-from typing import List, Optional, Union, Callable, Type, TypeVar, Generic, Any, ParamSpec
+import sys
+from typing import List, Optional, Union, Callable, Type, TypeVar, Generic, Any
+if sys.version_info < (3, 10):
+    from typing_extensions import ParamSpec
+else:
+    from typing import ParamSpec
 
 import PIL.Image
 import einops
diff --git a/ldm/invoke/model_cache.py b/ldm/invoke/model_cache.py
index e6f1c518f32..1c057ac9058 100644
--- a/ldm/invoke/model_cache.py
+++ b/ldm/invoke/model_cache.py
@@ -320,21 +320,19 @@ def _load_ckpt_model(self, model_name, mconfig):
     def _load_diffusers_model(self, mconfig):
         pipeline_args = {}
 
-        if 'repo_name' in mconfig:
-            name_or_path = mconfig['repo_name']
+        # look for local files first, then use the hugging face cache mechanism
+        if 'repo_id' in mconfig:  # "repo_name" => "repo_id" in order to be consistent with Hugging Face convention
+            name = mconfig['repo_id']
             model_hash = "FIXME"
             # model_hash = huggingface_hub.get_hf_file_metadata(url).commit_hash
-        elif 'path' in mconfig:
-            name_or_path = Path(mconfig['path'])
-            if not name_or_path.is_absolute():
-                name_or_path = Path(Globals.root, name_or_path).resolve()
-            # FIXME: What should the model_hash be? A hash of the unet weights? Of all files of all
-            #     the submodels hashed together? The commit ID from the repo?
-            model_hash = "FIXME TOO"
+        elif 'repo_name' in mconfig:    # to avoid breakage
+            print('** "repo_name" is deprecated in models.yaml. Please use "repo_id"')
+            name = mconfig['repo_id']
+            model_hash = "FIXME"
         else:
-            raise ValueError("Model config must specify either repo_name or path.")
+            raise ValueError("Model config must specify repo_id.")
 
-        print(f'>> Loading diffusers model from {name_or_path}')
+        print(f'>> Loading diffusers model from {name}')
 
         # TODO: scan weights maybe?
 
@@ -342,35 +340,38 @@ def _load_diffusers_model(self, mconfig):
             vae = self._load_vae(mconfig['vae'])
             pipeline_args.update(vae=vae)
 
-        if self.precision == 'float16':
-            print('   | Using faster float16 precision')
-
-            if not isinstance(name_or_path, Path):
-                # hub has no explicit API for different data types, but the main Stable Diffusion
-                # releases set a precedent for putting float16 weights in a fp16 branch.
-                try:
-                    hf_hub_download(name_or_path, "model_index.json", revision="fp16")
-                except RevisionNotFoundError:
-                    pass  # no such branch, assume we should use the default.
-                else:
-                    pipeline_args.update(revision="fp16")
-
-            pipeline_args.update(torch_dtype=torch.float16)
-        else:
-            # TODO: more accurately, "using the model's default precision."
-            #     How do we find out what that is?
-            print('   | Using more accurate float32 precision')
+        cache_dir = os.path.join(Globals.root,'models',name)
+        # FIX: this commented block is causing the cache read to fail in from_pretrained()
+        # if self.precision == 'float16':
+        #     print('   | Using faster float16 precision')
+
+        #     if not isinstance(name, Path):
+        #         # hub has no explicit API for different data types, but the main Stable Diffusion
+        #         # releases set a precedent for putting float16 weights in a fp16 branch.
+        #         try:
+        #             hf_hub_download(name, "model_index.json", revision="fp16", cache_dir=cache_dir)
+        #         except RevisionNotFoundError:
+        #             pass  # no such branch, assume we should use the default.
+        #         else:
+        #             pipeline_args.update(revision="fp16")
+
+        #     pipeline_args.update(torch_dtype=torch.float16)
+        # else:
+        #     # TODO: more accurately, "using the model's default precision."
+        #     #     How do we find out what that is?
+        #     print('   | Using more accurate float32 precision')
 
         pipeline = StableDiffusionGeneratorPipeline.from_pretrained(
-            name_or_path,
+            name,
             # TODO: Safety checker is currently handled at a different stage in the code:
             #     ldm.invoke.generator.base.Generator.safety_check
             #     We might want to move that here for consistency with diffusers API, or we might
             #     want to leave it as a separate processing node. It ends up using the same diffusers
             #     code either way, so we can table it for now.
             safety_checker=None,
-            # TODO: local_files_only=True
-            **pipeline_args
+            cache_dir=cache_dir,
+            local_files_only=True,
+            **pipeline_args,
         )
         pipeline.to(self.device)
 
@@ -384,9 +385,12 @@ def model_name_or_path(self, model_name:str) -> str | Path:
             raise ValueError(f'"{model_name}" is not a known model name. Please check your models.yaml file')
 
         mconfig = self.config[model_name]
-        if 'repo_name' in mconfig:
+        if 'repo_id' in mconfig:
+            return mconfig['repo_id']
+        elif 'repo_name' in mconfig:
             return mconfig['repo_name']
         elif 'path' in mconfig:
+            assert f'there should be no paths in {mconfig}'
             path = Path(mconfig['path'])
             if not path.is_absolute():
                 path = Path(Globals.root, path).resolve()
@@ -545,14 +549,17 @@ def _cached_sha256(self,path,data) -> Union[str, bytes]:
     def _load_vae(self, vae_config):
         vae_args = {}
 
-        if 'repo_name' in vae_config:
-            name_or_path = vae_config['repo_name']
-        elif 'path' in vae_config:
+        if 'path' in vae_config:
             name_or_path = Path(vae_config['path'])
             if not name_or_path.is_absolute():
                 name_or_path = Path(Globals.root, name_or_path).resolve()
+        elif 'repo_id' in vae_config:
+            name_or_path = vae_config['repo_id']
+        elif 'repo_name' in vae_config:
+            print('** "repo_name" is deprecated in models.yaml. Use "repo_id" instead.')
+            name_or_path = vae_config['repo_name']
         else:
-            raise ValueError("VAE config must specify either repo_name or path.")
+            raise ValueError("VAE config must specify either repo_id or path.")
 
         print(f'>> Loading diffusers VAE from {name_or_path}')
         if self.precision == 'float16':
diff --git a/scripts/configure_invokeai.py b/scripts/configure_invokeai.py
index 39b31c8afd9..78995e55e70 100755
--- a/scripts/configure_invokeai.py
+++ b/scripts/configure_invokeai.py
@@ -22,6 +22,7 @@
 import requests
 import transformers
 from diffusers import StableDiffusionPipeline, AutoencoderKL
+from ldm.invoke.generator.diffusers_pipeline import StableDiffusionGeneratorPipeline
 from getpass_asterisk import getpass_asterisk
 from huggingface_hub import HfFolder, hf_hub_url, login as hf_hub_login, whoami as hf_whoami
 from omegaconf import OmegaConf
@@ -247,6 +248,7 @@ def authenticate(yes_to_all=False):
     print("=" * os.get_terminal_size()[0])
     print('Authenticating to Huggingface')
     hf_envvars = [ "HUGGING_FACE_HUB_TOKEN", "HUGGINGFACE_TOKEN" ]
+    token_found = False
     if not (access_token := HfFolder.get_token()):
         print(f"Huggingface token not found in cache.")
 
@@ -264,13 +266,16 @@ def authenticate(yes_to_all=False):
         print(f"Huggingface token found in cache.")
         try:
             HfLogin(access_token)
+            token_found = True
         except ValueError:
             print(f"Login failed due to invalid token found in cache")
 
-    if not yes_to_all:
-        print('''
-You may optionally enter your Huggingface token now. InvokeAI *will* work without it, but some functionality may be limited.
-See https://invoke-ai.github.io/InvokeAI/features/CONCEPTS/#using-a-hugging-face-concept for more information.
+    if not (yes_to_all or token_found):
+        print(''' You may optionally enter your Huggingface token now. InvokeAI
+*will* work without it but you will not be able to automatically
+download some of the Hugging Face style concepts.  See
+https://invoke-ai.github.io/InvokeAI/features/CONCEPTS/#using-a-hugging-face-concept
+for more information.
 
 Visit https://huggingface.co/settings/tokens to generate a token. (Sign up for an account if needed).
 
@@ -325,27 +330,14 @@ def download_weight_datasets(models:dict, access_token:str):
     successful = dict()
     for mod in models.keys():
         repo_id = Datasets[mod]['repo_id']
-        filename = Datasets[mod]['file']
-        dest = os.path.join(Globals.root,Model_dir,Weights_dir)
-        success = hf_download_with_resume(
-            repo_id=repo_id,
-            model_dir=dest,
-            model_name=filename,
-            access_token=access_token
+        model_class = StableDiffusionGeneratorPipeline if Datasets[mod]['format']=='diffusers' else AutoencoderKL
+        path = download_from_hf(
+            model_class,
+            repo_id,
+            safety_checker=None,
         )
-        if success:
-            successful[mod] = True
-    if len(successful) < len(models):
-        print(f'\n\n** There were errors downloading one or more files. **')
-        print('Please double-check your license agreements, and your access token.')
-        HfFolder.delete_token()
-        print('Press any key to try again. Type ^C to quit.\n')
-        input()
-        return None
-
-    HfFolder.save_token(access_token)
-    keys = ', '.join(successful.keys())
-    print(f'Successfully installed {keys}')
+        if path:
+            successful[mod] = path
     return successful
 
 #---------------------------------------------
@@ -361,56 +353,6 @@ def is_huggingface_authenticated():
         pass
     return False
 
-#---------------------------------------------
-def hf_download_with_resume(repo_id:str, model_dir:str, model_name:str, access_token:str=None)->bool:
-    model_dest = os.path.join(model_dir, model_name)
-    os.makedirs(model_dir, exist_ok=True)
-
-    url = hf_hub_url(repo_id, model_name)
-
-    header = {"Authorization": f'Bearer {access_token}'} if access_token else {}
-    open_mode = 'wb'
-    exist_size = 0
-
-    if os.path.exists(model_dest):
-        exist_size = os.path.getsize(model_dest)
-        header['Range'] = f'bytes={exist_size}-'
-        open_mode = 'ab'
-
-    resp = requests.get(url, headers=header, stream=True)
-    total = int(resp.headers.get('content-length', 0))
-
-    if resp.status_code==416:  # "range not satisfiable", which means nothing to return
-        print(f'* {model_name}: complete file found. Skipping.')
-        return True
-    elif resp.status_code != 200:
-        print(f'** An error occurred during downloading {model_name}: {resp.reason}')
-    elif exist_size > 0:
-        print(f'* {model_name}: partial file found. Resuming...')
-    else:
-        print(f'* {model_name}: Downloading...')
-
-    try:
-        if total < 2000:
-            print(f'*** ERROR DOWNLOADING {model_name}: {resp.text}')
-            return False
-
-        with open(model_dest, open_mode) as file, tqdm(
-                desc=model_name,
-                initial=exist_size,
-                total=total+exist_size,
-                unit='iB',
-                unit_scale=True,
-                unit_divisor=1000,
-        ) as bar:
-            for data in resp.iter_content(chunk_size=1024):
-                size = file.write(data)
-                bar.update(size)
-    except Exception as e:
-        print(f'An error occurred while downloading {model_name}: {str(e)}')
-        return False
-    return True
-
 #---------------------------------------------
 def download_with_progress_bar(model_url:str, model_dest:str, label:str='the'):
     try:
@@ -427,59 +369,6 @@ def download_with_progress_bar(model_url:str, model_dest:str, label:str='the'):
         print(f'Error downloading {label} model')
         print(traceback.format_exc())
 
-
-#---------------------------------------------
-def download_diffusers(models: Dict, full_precision: bool):
-    # This is a minimal implementation until https://github.com/invoke-ai/InvokeAI/pull/1490 lands,
-    # which moves a bunch of stuff.
-    # We can be more complete after we know it won't be all merge conflicts.
-    diffusers_repos = {
-        'CompVis/stable-diffusion-v1-4-original': 'CompVis/stable-diffusion-v1-4',
-        'runwayml/stable-diffusion-v1-5': 'runwayml/stable-diffusion-v1-5',
-        'runwayml/stable-diffusion-inpainting': 'runwayml/stable-diffusion-inpainting',
-        'hakurei/waifu-diffusion-v1-3': 'hakurei/waifu-diffusion'
-    }
-    vae_repos = {
-        'stabilityai/sd-vae-ft-mse-original': 'stabilityai/sd-vae-ft-mse',
-    }
-    precision_args = {}
-    if not full_precision:
-        precision_args.update(revision='fp16')
-
-    for model_name, model in models.items():
-        repo_id = model['repo_id']
-        if repo_id in vae_repos:
-            print(f" * Downloading diffusers VAE {model_name}...")
-            # TODO: can we autodetect when a repo has no fp16 revision?
-            AutoencoderKL.from_pretrained(repo_id)
-        elif repo_id not in diffusers_repos:
-            print(f" * Downloading diffusers {model_name}...")
-            StableDiffusionPipeline.from_pretrained(repo_id, **precision_args)
-        else:
-            warnings.warn(f" ⚠ FIXME: add diffusers repo for {repo_id}")
-            continue
-
-
-def download_diffusers_in_config(config_path: Path, full_precision: bool):
-    # This is a minimal implementation until https://github.com/invoke-ai/InvokeAI/pull/1490 lands,
-    # which moves a bunch of stuff.
-    # We can be more complete after we know it won't be all merge conflicts.
-    if not is_huggingface_authenticated():
-        print("*⚠ No Hugging Face access token; some downloads may be blocked.")
-
-    precision = 'full' if full_precision else 'float16'
-    cache = ModelCache(OmegaConf.load(config_path), precision=precision,
-                       device_type='cpu', max_loaded_models=1)
-    for model_name in cache.list_models():
-        # TODO: download model without loading it.
-        #   https://github.com/huggingface/diffusers/issues/1301
-        model_config = cache.config[model_name]
-        if model_config.get('format') == 'diffusers':
-            print(f" * Downloading diffusers {model_name}...")
-            cache.get_model(model_name)
-            cache.offload_model(model_name)
-
-
 #---------------------------------------------
 def update_config_file(successfully_downloaded:dict,opt:dict):
     config_file = opt.config_file or Default_config_file
@@ -516,29 +405,24 @@ def new_config_file_contents(successfully_downloaded:dict, config_file:str)->str
     default_selected = False
 
     for model in successfully_downloaded:
-        a = Datasets[model]['config'].split('/')
-        if a[0] != 'VAE':
-            continue
-        vae_target = a[1] if len(a)>1 else 'default'
-        vaes[vae_target] = Datasets[model]['file']
-
-    for model in successfully_downloaded:
-        if Datasets[model]['config'].startswith('VAE'): # skip VAE entries
+        # TODO: fix VAEs
+        if Datasets[model]['format']=='vae':
             continue
         stanza = conf[model] if model in conf else { }
-
         stanza['description'] = Datasets[model]['description']
-        stanza['weights'] = os.path.join(Model_dir,Weights_dir,Datasets[model]['file'])
-        stanza['config'] = os.path.normpath(os.path.join(SD_Configs, Datasets[model]['config']))
+        stanza['repo_id'] = Datasets[model]['repo_id']
+        stanza['format'] = 'diffusers'
         stanza['width'] = Datasets[model]['width']
         stanza['height'] = Datasets[model]['height']
         stanza.pop('default',None)  # this will be set later
-        if vaes:
-            for target in vaes:
-                if re.search(target, model, flags=re.IGNORECASE):
-                    stanza['vae'] = os.path.normpath(os.path.join(Model_dir,Weights_dir,vaes[target]))
-                else:
-                    stanza['vae'] = os.path.normpath(os.path.join(Model_dir,Weights_dir,vaes['default']))
+
+        # FIX: handle the VAE
+        # if vaes:
+        #     for target in vaes:
+        #         if re.search(target, model, flags=re.IGNORECASE):
+        #             stanza['vae'] = os.path.normpath(os.path.join(Model_dir,Weights_dir,vaes[target]))
+        #         else:
+        #             stanza['vae'] = os.path.normpath(os.path.join(Model_dir,Weights_dir,vaes['default']))
         # BUG - the first stanza is always the default. User should select.
         if not default_selected:
             stanza['default'] = True
@@ -557,12 +441,15 @@ def download_bert():
         print('...success',file=sys.stderr)
 
 #---------------------------------------------
-def download_from_hf(model_class:object, model_name:str):
+def download_from_hf(model_class:object, model_name:str, **kwargs):
     print('',file=sys.stderr)  # to prevent tqdm from overwriting
-    return model_class.from_pretrained(model_name,
-                                       cache_dir=os.path.join(Globals.root,Model_dir,model_name),
-                                       resume_download=True
+    path = os.path.join(Globals.root,Model_dir,model_name)
+    model = model_class.from_pretrained(model_name,
+                                        cache_dir=path,
+                                        resume_download=True,
+                                        **kwargs,
     )
+    return path if model else None
 
 #---------------------------------------------
 def download_clip():
@@ -682,8 +569,8 @@ def download_weights(opt:dict) -> Union[str, None]:
     else:  # 'skip'
         return
 
-
     access_token = authenticate()
+    HfFolder.save_token(access_token)
 
     print('\n** DOWNLOADING WEIGHTS **')
     successfully_downloaded = download_weight_datasets(models, access_token)
@@ -859,11 +746,6 @@ def main():
         else:
             print('** DOWNLOADING DIFFUSION WEIGHTS **')
             errors.add(download_weights(opt))
-            config_path = Path(Globals.root, opt.config_file or Default_config_file)
-            if config_path.exists():
-                download_diffusers_in_config(config_path, full_precision=opt.full_precision)
-            else:
-                print(f"*⚠ No config file found; downloading no weights. Looked in {config_path}")
         print('\n** DOWNLOADING SUPPORT MODELS **')
         download_bert()
         download_clip()

From 296bd013612a4aace159ebda18740f41f842f9d9 Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Wed, 21 Dec 2022 02:10:03 +0000
Subject: [PATCH 080/199] allow non-local files during development

---
 ldm/invoke/model_cache.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ldm/invoke/model_cache.py b/ldm/invoke/model_cache.py
index 1c057ac9058..7cfb6f034ec 100644
--- a/ldm/invoke/model_cache.py
+++ b/ldm/invoke/model_cache.py
@@ -370,7 +370,7 @@ def _load_diffusers_model(self, mconfig):
             #     code either way, so we can table it for now.
             safety_checker=None,
             cache_dir=cache_dir,
-            local_files_only=True,
+#            local_files_only=True,
             **pipeline_args,
         )
         pipeline.to(self.device)

From b29dcc7ab682c6d9474e873351db49ff2be73ad1 Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Wed, 21 Dec 2022 04:36:30 +0000
Subject: [PATCH 081/199] path takes priority over repo_id

---
 ldm/generate.py           |  1 -
 ldm/invoke/model_cache.py | 42 +++++++++++++++++++++------------------
 2 files changed, 23 insertions(+), 20 deletions(-)

diff --git a/ldm/generate.py b/ldm/generate.py
index a0de4a97837..cea0ee05c33 100644
--- a/ldm/generate.py
+++ b/ldm/generate.py
@@ -40,7 +40,6 @@
 from ldm.invoke.pngwriter import PngWriter
 from ldm.invoke.seamless import configure_model_padding
 from ldm.invoke.txt2mask import Txt2Mask, SegmentedGrayscale
-from ldm.invoke.concepts_lib import Concepts
 from ldm.invoke.generator.inpaint import infill_methods
 
 from ldm.models.diffusion.ddim import DDIMSampler
diff --git a/ldm/invoke/model_cache.py b/ldm/invoke/model_cache.py
index 7cfb6f034ec..dcca49eea60 100644
--- a/ldm/invoke/model_cache.py
+++ b/ldm/invoke/model_cache.py
@@ -319,20 +319,24 @@ def _load_ckpt_model(self, model_name, mconfig):
 
     def _load_diffusers_model(self, mconfig):
         pipeline_args = {}
-
-        # look for local files first, then use the hugging face cache mechanism
-        if 'repo_id' in mconfig:  # "repo_name" => "repo_id" in order to be consistent with Hugging Face convention
-            name = mconfig['repo_id']
+        if 'path' in mconfig:
+            name_or_path = Path(mconfig['path'])
+            if not name_or_path.is_absolute():
+                name_or_path = Path(Globals.root, name_or_path).resolve()
+            # FIXME: What should the model_hash be? A hash of the unet weights? Of all files of all
+            #     the submodels hashed together? The commit ID from the repo?
+            model_hash = "FIXME TOO"
+        elif 'repo_id' in mconfig:
+            name_or_path = mconfig['repo_id']
             model_hash = "FIXME"
             # model_hash = huggingface_hub.get_hf_file_metadata(url).commit_hash
-        elif 'repo_name' in mconfig:    # to avoid breakage
-            print('** "repo_name" is deprecated in models.yaml. Please use "repo_id"')
-            name = mconfig['repo_id']
+        elif 'repo_name' in mconfig:
+            name_or_path = mconfig['repo_name']
             model_hash = "FIXME"
+            # model_hash = huggingface_hub.get_hf_file_metadata(url).commit_hash
         else:
-            raise ValueError("Model config must specify repo_id.")
-
-        print(f'>> Loading diffusers model from {name}')
+            raise ValueError("Model config must specify either repo_name or path.")
+        print(f'>> Loading diffusers model from {name_or_path}')
 
         # TODO: scan weights maybe?
 
@@ -340,7 +344,8 @@ def _load_diffusers_model(self, mconfig):
             vae = self._load_vae(mconfig['vae'])
             pipeline_args.update(vae=vae)
 
-        cache_dir = os.path.join(Globals.root,'models',name)
+        # FIX: if name is a path, this is not right
+        cache_dir = os.path.join(Globals.root,'models',name_or_path) if type(name_or_path) == str else None
         # FIX: this commented block is causing the cache read to fail in from_pretrained()
         # if self.precision == 'float16':
         #     print('   | Using faster float16 precision')
@@ -362,7 +367,7 @@ def _load_diffusers_model(self, mconfig):
         #     print('   | Using more accurate float32 precision')
 
         pipeline = StableDiffusionGeneratorPipeline.from_pretrained(
-            name,
+            name_or_path,
             # TODO: Safety checker is currently handled at a different stage in the code:
             #     ldm.invoke.generator.base.Generator.safety_check
             #     We might want to move that here for consistency with diffusers API, or we might
@@ -385,18 +390,17 @@ def model_name_or_path(self, model_name:str) -> str | Path:
             raise ValueError(f'"{model_name}" is not a known model name. Please check your models.yaml file')
 
         mconfig = self.config[model_name]
-        if 'repo_id' in mconfig:
-            return mconfig['repo_id']
-        elif 'repo_name' in mconfig:
-            return mconfig['repo_name']
-        elif 'path' in mconfig:
-            assert f'there should be no paths in {mconfig}'
+        if 'path' in mconfig:
             path = Path(mconfig['path'])
             if not path.is_absolute():
                 path = Path(Globals.root, path).resolve()
             return path
+        elif 'repo_id' in mconfig:
+            return mconfig['repo_id']
+        elif 'repo_name' in mconfig:
+            return mconfig['repo_name']
         else:
-            raise ValueError("Model config must specify either repo_name or path.")
+            raise ValueError("Model config must specify either repo_id or path.")
 
     def offload_model(self, model_name:str) -> None:
         '''

From 0e8072b46f7a1582ed1e7e3351701c591c2f9975 Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Wed, 21 Dec 2022 19:19:18 +0000
Subject: [PATCH 082/199] MVP for model_cache and configure_invokeai

- Feature complete (almost)

- configure_invokeai.py downloads both .ckpt and diffuser models,
  along with their VAEs. Both types of download are controlled by
  a unified INITIAL_MODELS.yaml file.

- model_cache can load both type of model and switches back and forth
  in CPU. No memory leaks detected

TO DO:

  1. I have not yet turned on the LocalOnly flag for diffuser models, so
     the code will check the Hugging Face repo for updates before using the
     locally cached models. This will break firewalled systems. I am thinking
     of putting in a global check for internet connectivity at startup time
     and setting the LocalOnly flag based on this. It would be good to check
     updates if there is connectivity.

  2. I have not gone completely through INITIAL_MODELS.yaml to check which
     models are available as diffusers and which are not. So models like
     PaperCut and VoxelArt may not load properly. The runway and stability
     models are checked, as well as the Trinart models.

  3. Add stanzas for SD 2.0 and 2.1 in INITIAL_MODELS.yaml

REMAINING PROBLEMS NOT DIRECTLY RELATED TO MODEL_CACHE:

  1. When loading a .ckpt file there are lots of messages like this:

     Warning! ldm.modules.attention.CrossAttention is no longer being
     maintained. Please use InvokeAICrossAttention instead.

     I'm not sure how to address this.

  2. The ckpt models ***don't actually run*** due to the lack of special-case
     support for them in the generator objects. For example, here's the hard
     crash you get when you run txt2img against the legacy waifu-diffusion-1.3
     model:
```
     >> An error occurred:
     Traceback (most recent call last):
       File "/data/lstein/InvokeAI/ldm/invoke/CLI.py", line 140, in main
           main_loop(gen, opt)
      File "/data/lstein/InvokeAI/ldm/invoke/CLI.py", line 371, in main_loop
         gen.prompt2image(
      File "/data/lstein/InvokeAI/ldm/generate.py", line 496, in prompt2image
	 results = generator.generate(
      File "/data/lstein/InvokeAI/ldm/invoke/generator/base.py", line 108, in generate
         image = make_image(x_T)
      File "/data/lstein/InvokeAI/ldm/invoke/generator/txt2img.py", line 33, in make_image
         pipeline_output = pipeline.image_from_embeddings(
      File "/home/lstein/invokeai/.venv/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1265, in __getattr__
         raise AttributeError("'{}' object has no attribute '{}'".format(
     AttributeError: 'LatentDiffusion' object has no attribute 'image_from_embeddings'
```

  3. The inpainting diffusion model isn't working. Here's the output of "banana
     sushi" when inpainting-1.5 is loaded:

```
    Traceback (most recent call last):
      File "/data/lstein/InvokeAI/ldm/generate.py", line 496, in prompt2image
        results = generator.generate(
      File "/data/lstein/InvokeAI/ldm/invoke/generator/base.py", line 108, in generate
        image = make_image(x_T)
      File "/data/lstein/InvokeAI/ldm/invoke/generator/txt2img.py", line 33, in make_image
        pipeline_output = pipeline.image_from_embeddings(
      File "/data/lstein/InvokeAI/ldm/invoke/generator/diffusers_pipeline.py", line 301, in image_from_embeddings
        result_latents, result_attention_map_saver = self.latents_from_embeddings(
      File "/data/lstein/InvokeAI/ldm/invoke/generator/diffusers_pipeline.py", line 330, in latents_from_embeddings
        result: PipelineIntermediateState = infer_latents_from_embeddings(
      File "/data/lstein/InvokeAI/ldm/invoke/generator/diffusers_pipeline.py", line 185, in __call__
        for result in self.generator_method(*args, **kwargs):
      File "/data/lstein/InvokeAI/ldm/invoke/generator/diffusers_pipeline.py", line 367, in generate_latents_from_embeddings
        step_output = self.step(batched_t, latents, guidance_scale,
      File "/home/lstein/invokeai/.venv/lib/python3.9/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
        return func(*args, **kwargs)
      File "/data/lstein/InvokeAI/ldm/invoke/generator/diffusers_pipeline.py", line 409, in step
        step_output = self.scheduler.step(noise_pred, timestep, latents, **extra_step_kwargs)
      File "/home/lstein/invokeai/.venv/lib/python3.9/site-packages/diffusers/schedulers/scheduling_lms_discrete.py", line 223, in step
        pred_original_sample = sample - sigma * model_output
    RuntimeError: The size of tensor a (9) must match the size of tensor b (4) at non-singleton dimension 1
```
---
 configs/INITIAL_MODELS.yaml   |  47 +++++----
 ldm/invoke/model_cache.py     | 178 ++++++++++++++++------------------
 ldm/models/diffusion/ddpm.py  |   6 +-
 scripts/configure_invokeai.py | 132 ++++++++++++++++++++-----
 4 files changed, 221 insertions(+), 142 deletions(-)

diff --git a/configs/INITIAL_MODELS.yaml b/configs/INITIAL_MODELS.yaml
index 4139c67d58b..68be4ca6801 100644
--- a/configs/INITIAL_MODELS.yaml
+++ b/configs/INITIAL_MODELS.yaml
@@ -3,6 +3,8 @@ stable-diffusion-1.5:
    repo_id: runwayml/stable-diffusion-v1-5
    format: diffusers
    recommended: True
+   vae:
+     repo_id: stabilityai/sd-vae-ft-mse
    width: 512
    height: 512
 inpainting-1.5:
@@ -12,25 +14,24 @@ inpainting-1.5:
    recommended: True
    width: 512
    height: 512
-ft-mse-improved-autoencoder-840000:
-   description: StabilityAI improved autoencoder fine-tuned for human faces (recommended; 335 MB)
-   repo_id: stabilityai/sd-vae-ft-mse
-   recommended: True
-   format: vae
-   width: 512
-   height: 512
 stable-diffusion-1.4:
    description: The original Stable Diffusion version 1.4 weight file (4.27 GB)
    repo_id: CompVis/stable-diffusion-v-1-4-original
    recommended: False
    format: diffusers
+   vae:
+      repo_id: stabilityai/sd-vae-ft-mse
    width: 512
    height: 512
 waifu-diffusion-1.3:
    description: Stable Diffusion 1.4 fine tuned on anime-styled images (4.27 GB)
    repo_id: hakurei/waifu-diffusion-v1-3
-   format: diffusers
-   recommended: False
+   config: v1-inference.yaml
+   file: model-epoch09-float32.ckpt
+   format: ckpt
+   vae:
+      repo_id: stabilityai/sd-vae-ft-mse-original
+      file: vae-ft-mse-840000-ema-pruned.ckpt
    width: 512
    height: 512
 trinart-2.0:
@@ -38,19 +39,19 @@ trinart-2.0:
    repo_id: naclbit/trinart_stable_diffusion_v2
    format: diffusers
    recommended: False
+   vae:
+     repo_id: stabilityai/sd-vae-ft-mse
    width: 512
    height: 512
-trinart_characters-1.0:
-   description: An SD model finetuned with 19.2M anime/manga style images (2.13 GB)
-   repo_id: naclbit/trinart_characters_19.2m_stable_diffusion_v1
-   format: diffusers
-   recommended: False
-   width: 512
-   height: 512
-trinart_vae:
-   description: Custom autoencoder for trinart_characters
-   repo_id: naclbit/trinart_characters_19.2m_stable_diffusion_v1
-   format: vae
+trinart_characters-2.0:
+   description: An SD model finetuned with 19.2M anime/manga style images (4.27 GB)
+   repo_id: naclbit/trinart_derrida_characters_v2_stable_diffusion
+   config: v1-inference.yaml
+   file: derrida_final.ckpt
+   format: ckpt
+   vae:
+      repo_id: naclbit/trinart_derrida_characters_v2_stable_diffusion
+      file: autoencoder_fix_kl-f8-trinart_characters.ckpt
    recommended: False
    width: 512
    height: 512
@@ -58,6 +59,8 @@ papercut-1.0:
    description: SD 1.5 fine-tuned for papercut art (use "PaperCut" in your prompts) (2.13 GB)
    repo_id: Fictiverse/Stable_Diffusion_PaperCut_Model
    format: diffusers
+   vae:
+     repo_id: stabilityai/sd-vae-ft-mse
    recommended: False
    width: 512
    height: 512
@@ -66,7 +69,9 @@ voxel_art-1.0:
    repo_id: Fictiverse/Stable_Diffusion_VoxelArt_Model
    config: v1-inference.yaml
    file: VoxelArt_v1.ckpt
-   format: diffusers
+   format: ckpt
+   vae:
+     repo_id: stabilityai/sd-vae-ft-mse
    recommended: False
    width: 512
    height: 512
diff --git a/ldm/invoke/model_cache.py b/ldm/invoke/model_cache.py
index dcca49eea60..867ce576caf 100644
--- a/ldm/invoke/model_cache.py
+++ b/ldm/invoke/model_cache.py
@@ -25,6 +25,7 @@
 from huggingface_hub import hf_hub_download
 from huggingface_hub.utils import RevisionNotFoundError
 from omegaconf import OmegaConf
+from omegaconf.dictconfig import DictConfig
 from omegaconf.errors import ConfigAttributeError
 from picklescan.scanner import scan_file_path
 
@@ -319,77 +320,60 @@ def _load_ckpt_model(self, model_name, mconfig):
 
     def _load_diffusers_model(self, mconfig):
         pipeline_args = {}
-        if 'path' in mconfig:
-            name_or_path = Path(mconfig['path'])
-            if not name_or_path.is_absolute():
-                name_or_path = Path(Globals.root, name_or_path).resolve()
-            # FIXME: What should the model_hash be? A hash of the unet weights? Of all files of all
-            #     the submodels hashed together? The commit ID from the repo?
-            model_hash = "FIXME TOO"
-        elif 'repo_id' in mconfig:
-            name_or_path = mconfig['repo_id']
-            model_hash = "FIXME"
-            # model_hash = huggingface_hub.get_hf_file_metadata(url).commit_hash
-        elif 'repo_name' in mconfig:
-            name_or_path = mconfig['repo_name']
-            model_hash = "FIXME"
-            # model_hash = huggingface_hub.get_hf_file_metadata(url).commit_hash
-        else:
-            raise ValueError("Model config must specify either repo_name or path.")
+        name_or_path = self.model_name_or_path(mconfig)
+        model_hash = 'FIXME'
+        using_fp16 = False
         print(f'>> Loading diffusers model from {name_or_path}')
 
         # TODO: scan weights maybe?
-
         if 'vae' in mconfig:
             vae = self._load_vae(mconfig['vae'])
             pipeline_args.update(vae=vae)
 
-        # FIX: if name is a path, this is not right
-        cache_dir = os.path.join(Globals.root,'models',name_or_path) if type(name_or_path) == str else None
-        # FIX: this commented block is causing the cache read to fail in from_pretrained()
-        # if self.precision == 'float16':
-        #     print('   | Using faster float16 precision')
-
-        #     if not isinstance(name, Path):
-        #         # hub has no explicit API for different data types, but the main Stable Diffusion
-        #         # releases set a precedent for putting float16 weights in a fp16 branch.
-        #         try:
-        #             hf_hub_download(name, "model_index.json", revision="fp16", cache_dir=cache_dir)
-        #         except RevisionNotFoundError:
-        #             pass  # no such branch, assume we should use the default.
-        #         else:
-        #             pipeline_args.update(revision="fp16")
-
-        #     pipeline_args.update(torch_dtype=torch.float16)
-        # else:
-        #     # TODO: more accurately, "using the model's default precision."
-        #     #     How do we find out what that is?
-        #     print('   | Using more accurate float32 precision')
-
-        pipeline = StableDiffusionGeneratorPipeline.from_pretrained(
-            name_or_path,
-            # TODO: Safety checker is currently handled at a different stage in the code:
-            #     ldm.invoke.generator.base.Generator.safety_check
-            #     We might want to move that here for consistency with diffusers API, or we might
-            #     want to leave it as a separate processing node. It ends up using the same diffusers
-            #     code either way, so we can table it for now.
-            safety_checker=None,
-            cache_dir=cache_dir,
-#            local_files_only=True,
-            **pipeline_args,
-        )
+        cache_dir = None if isinstance(name_or_path,Path) else os.path.join(Globals.root,'models',name_or_path)
+        if self.precision == 'float16':
+            print('   | Using faster float16 precision')
+            using_fp16 = True
+            pipeline_args.update(revision="fp16")
+            pipeline_args.update(torch_dtype=torch.float16)
+        else:
+            # TODO: more accurately, "using the model's default precision."
+            #     How do we find out what that is?
+            print('   | Using more accurate float32 precision')
+        try:
+            pipeline = StableDiffusionGeneratorPipeline.from_pretrained(
+                name_or_path,
+                safety_checker=None,
+                cache_dir=cache_dir,
+                #  local_files_only=True,
+                **pipeline_args,
+            )
+        except Exception as e:
+            if using_fp16:
+                pipeline_args.pop('revision')
+                pipeline = StableDiffusionGeneratorPipeline.from_pretrained(
+                    name_or_path,
+                    safety_checker=None,
+                    cache_dir=cache_dir,
+                    #  local_files_only=True,
+                    **pipeline_args,
+                )
+
         pipeline.to(self.device)
 
-        width = pipeline.vae.block_out_channels[-1]
+        width = pipeline.vae.block_out_channels[-2]
         height = pipeline.vae.block_out_channels[-1]
 
         return pipeline, width, height, model_hash
 
-    def model_name_or_path(self, model_name:str) -> str | Path:
-        if model_name not in self.config:
+    def model_name_or_path(self, model_name:Union[str,DictConfig]) -> str | Path:
+        if isinstance(model_name,DictConfig):
+            mconfig = model_name
+        elif model_name in self.config:
+            mconfig = self.config[model_name]            
+        else:
             raise ValueError(f'"{model_name}" is not a known model name. Please check your models.yaml file')
 
-        mconfig = self.config[model_name]
         if 'path' in mconfig:
             path = Path(mconfig['path'])
             if not path.is_absolute():
@@ -484,27 +468,36 @@ def _invalidate_cached_model(self,model_name:str) -> None:
         self.models.pop(model_name,None)
 
     def _model_to_cpu(self,model):
-        if self.device != 'cpu':
-            try:
-                model.cond_stage_model.device = 'cpu'
-                model.first_stage_model.to('cpu')
-                model.cond_stage_model.to('cpu')
-                model.model.to('cpu')
-            except AttributeError as e:
-                warnings.warn(f"TODO: clean up legacy model-management: {e}")
-            return model.to('cpu')
-        else:
+        if self.device == 'cpu':
             return model
 
+        # diffusers really really doesn't like us moving a float16 model onto CPU
+        import logging
+        logging.getLogger('diffusers.pipeline_utils').setLevel(logging.CRITICAL)
+        model.cond_stage_model.device = 'cpu'
+        model.to('cpu')
+        logging.getLogger('pipeline_utils').setLevel(logging.INFO)
+
+        for submodel in ('first_stage_model','cond_stage_model','model'):
+            try:
+                getattr(model,submodel).to('cpu')
+            except AttributeError:
+                pass
+        return model
+
     def _model_from_cpu(self,model):
-        if self.device != 'cpu':
-            model.to(self.device)
+        if self.device == 'cpu':
+            return model
+        
+        model.to(self.device)
+        model.cond_stage_model.device = self.device
+
+        for submodel in ('first_stage_model','cond_stage_model','model'):
             try:
-                model.first_stage_model.to(self.device)
-                model.cond_stage_model.to(self.device)
-                model.cond_stage_model.device = self.device
-            except AttributeError as e:
-                warnings.warn(f"TODO: clean up legacy model-management: {e}")
+                getattr(model,submodel).to(self.device)
+            except AttributeError:
+                pass
+
         return model
 
     def _pop_oldest_model(self):
@@ -552,40 +545,31 @@ def _cached_sha256(self,path,data) -> Union[str, bytes]:
 
     def _load_vae(self, vae_config):
         vae_args = {}
-
-        if 'path' in vae_config:
-            name_or_path = Path(vae_config['path'])
-            if not name_or_path.is_absolute():
-                name_or_path = Path(Globals.root, name_or_path).resolve()
-        elif 'repo_id' in vae_config:
-            name_or_path = vae_config['repo_id']
-        elif 'repo_name' in vae_config:
-            print('** "repo_name" is deprecated in models.yaml. Use "repo_id" instead.')
-            name_or_path = vae_config['repo_name']
-        else:
-            raise ValueError("VAE config must specify either repo_id or path.")
+        name_or_path = self.model_name_or_path(vae_config)
+        using_fp16 = False        
 
         print(f'>> Loading diffusers VAE from {name_or_path}')
         if self.precision == 'float16':
             print('   | Using faster float16 precision')
-
-            if not isinstance(name_or_path, Path):
-                try:
-                    hf_hub_download(name_or_path, "model_index.json", revision="fp16")
-                except RevisionNotFoundError:
-                    pass
-                else:
-                    vae_args.update(revision="fp16")
-
+            using_fp16 = True
+            vae_args.update(revision="fp16")
             vae_args.update(torch_dtype=torch.float16)
         else:
             # TODO: more accurately, "using the model's default precision."
             #     How do we find out what that is?
             print('   | Using more accurate float32 precision')
 
+        # At some point we might need to be able to use different classes here? But for now I think
+        # all Stable Diffusion VAE are AutoencoderKL.
+        try:
+            vae = AutoencoderKL.from_pretrained(name_or_path, **vae_args)
+        except Exception as e:
+            if using_fp16:
+                vae_args.pop('revision')
+                vae = AutoencoderKL.from_pretrained(name_or_path, **vae_args)
+
+        # comment by lstein: I don't know what this does
         if 'subfolder' in vae_config:
             vae_args['subfolder'] = vae_config['subfolder']
 
-        # At some point we might need to be able to use different classes here? But for now I think
-        # all Stable Diffusion VAE are AutoencoderKL.
-        return AutoencoderKL.from_pretrained(name_or_path, **vae_args)
+        return vae
diff --git a/ldm/models/diffusion/ddpm.py b/ldm/models/diffusion/ddpm.py
index d9fa762f0b9..7c7ba9f5fdf 100644
--- a/ldm/models/diffusion/ddpm.py
+++ b/ldm/models/diffusion/ddpm.py
@@ -679,7 +679,11 @@ def __init__(
         self.embedding_manager = self.instantiate_embedding_manager(
             personalization_config, self.cond_stage_model
         )
-        self.textual_inversion_manager = TextualInversionManager(self.cond_stage_model, full_precision=True)
+        self.textual_inversion_manager = TextualInversionManager(
+            tokenizer = self.cond_stage_model.tokenizer,
+            text_encoder = self.cond_stage_model.transformer,
+            full_precision = True
+        )
         # this circular component dependency is gross and bad, needs to be rethought
         self.cond_stage_model.set_textual_inversion_manager(self.textual_inversion_manager)
 
diff --git a/scripts/configure_invokeai.py b/scripts/configure_invokeai.py
index 40f24585030..edc1957b12c 100755
--- a/scripts/configure_invokeai.py
+++ b/scripts/configure_invokeai.py
@@ -25,7 +25,9 @@
 from ldm.invoke.generator.diffusers_pipeline import StableDiffusionGeneratorPipeline
 from getpass_asterisk import getpass_asterisk
 from huggingface_hub import HfFolder, hf_hub_url, login as hf_hub_login, whoami as hf_whoami
+from huggingface_hub.utils._errors import RevisionNotFoundError
 from omegaconf import OmegaConf
+from omegaconf.dictconfig import DictConfig
 from tqdm import tqdm
 from transformers import CLIPTokenizer, CLIPTextModel
 
@@ -158,9 +160,10 @@ def select_datasets(action:str):
 '''
         )
             for ds in Datasets.keys():
-                recommended = '(recommended)' if Datasets[ds]['recommended'] else ''
-                print(f'[{counter}] {ds}:\n    {Datasets[ds]["description"]} {recommended}')
-                if yes_or_no('    Download?',default_yes=Datasets[ds]['recommended']):
+                recommended = Datasets[ds].get('recommended',False)
+                r_str = '(recommended)' if recommended else ''
+                print(f'[{counter}] {ds}:\n    {Datasets[ds]["description"]} {r_str}')
+                if yes_or_no('    Download?',default_yes=recommended):
                     datasets[ds]=counter
                     counter += 1
         else:
@@ -329,17 +332,101 @@ def download_weight_datasets(models:dict, access_token:str):
     migrate_models_ckpt()
     successful = dict()
     for mod in models.keys():
-        repo_id = Datasets[mod]['repo_id']
-        model_class = StableDiffusionGeneratorPipeline if Datasets[mod]['format']=='diffusers' else AutoencoderKL
+        print(f'{mod}...',file=sys.stderr,end='')
+        successful[mod] = _download_repo_or_file(Datasets[mod], access_token)
+    return successful
+
+def _download_repo_or_file(mconfig:DictConfig, access_token:str)->Path:
+    path = None
+    if mconfig['format'] == 'ckpt':
+        path = _download_ckpt_weights(mconfig, access_token)
+    else:
+        path = _download_diffusion_weights(mconfig, access_token)
+    return path
+
+def _download_ckpt_weights(mconfig:DictConfig, access_token:str)->Path:
+    repo_id = mconfig['repo_id']
+    filename = mconfig['file']
+    cache_dir = os.path.join(Globals.root, Model_dir, Weights_dir)
+    return hf_download_with_resume(
+        repo_id=repo_id,
+        model_dir=cache_dir,
+        model_name=filename,
+        access_token=access_token
+    )
+
+def _download_diffusion_weights(mconfig:DictConfig, access_token:str):
+    repo_id = mconfig['repo_id']
+    model_class = StableDiffusionGeneratorPipeline if mconfig['format']=='diffusers' else AutoencoderKL
+    path = None
+    try:
         path = download_from_hf(
             model_class,
             repo_id,
             safety_checker=None,
+            revision='fp16',
         )
-        if path:
-            successful[mod] = path
-    return successful
+    except OSError:
+        path = download_from_hf(
+            model_class,
+            repo_id,
+            safety_checker=None,
+        )
+    except Exception as e:
+        print(f'could not download; exception = {type(e)}')
+    return path
+
+#---------------------------------------------
+def hf_download_with_resume(repo_id:str, model_dir:str, model_name:str, access_token:str=None)->Path:
+    model_dest = Path(os.path.join(model_dir, model_name))
+    os.makedirs(model_dir, exist_ok=True)
+
+    url = hf_hub_url(repo_id, model_name)
+
+    header = {"Authorization": f'Bearer {access_token}'} if access_token else {}
+    open_mode = 'wb'
+    exist_size = 0
+
+    if os.path.exists(model_dest):
+        exist_size = os.path.getsize(model_dest)
+        header['Range'] = f'bytes={exist_size}-'
+        open_mode = 'ab'
+
+    resp = requests.get(url, headers=header, stream=True)
+    total = int(resp.headers.get('content-length', 0))
+
+    if resp.status_code==416:  # "range not satisfiable", which means nothing to return
+        print(f'* {model_name}: complete file found. Skipping.')
+        return model_dest
+    elif resp.status_code != 200:
+        print(f'** An error occurred during downloading {model_name}: {resp.reason}')
+    elif exist_size > 0:
+        print(f'* {model_name}: partial file found. Resuming...')
+    else:
+        print(f'* {model_name}: Downloading...')
 
+    try:
+        if total < 2000:
+            print(f'*** ERROR DOWNLOADING {model_name}: {resp.text}')
+            return None
+
+        with open(model_dest, open_mode) as file, tqdm(
+                desc=model_name,
+                initial=exist_size,
+                total=total+exist_size,
+                unit='iB',
+                unit_scale=True,
+                unit_divisor=1000,
+        ) as bar:
+            for data in resp.iter_content(chunk_size=1024):
+                size = file.write(data)
+                bar.update(size)
+    except Exception as e:
+        print(f'An error occurred while downloading {model_name}: {str(e)}')
+        return None
+    return model_dest
+
+# -----------------------------------------------------------------------------------
 #---------------------------------------------
 def is_huggingface_authenticated():
     # huggingface_hub 0.10 API isn't great for this, it could be OSError, ValueError,
@@ -405,24 +492,23 @@ def new_config_file_contents(successfully_downloaded:dict, config_file:str)->str
     default_selected = False
 
     for model in successfully_downloaded:
-        # TODO: fix VAEs
-        if Datasets[model]['format']=='vae':
-            continue
         stanza = conf[model] if model in conf else { }
-        stanza['description'] = Datasets[model]['description']
-        stanza['repo_id'] = Datasets[model]['repo_id']
-        stanza['format'] = 'diffusers'
-        stanza['width'] = Datasets[model]['width']
-        stanza['height'] = Datasets[model]['height']
+        mod = Datasets[model]
+        stanza['description'] = mod['description']
+        stanza['repo_id'] = mod['repo_id']
+        stanza['format'] = mod['format']
+        stanza['width'] = mod['width']
+        stanza['height'] = mod['height']
+        if 'file' in mod:
+            stanza['weights'] = os.path.relpath(successfully_downloaded[model], start=Globals.root)
+            stanza['config'] = os.path.normpath(os.path.join(SD_Configs,mod['config']))
+        if 'vae' in mod:
+            if 'file' in mod['vae']:
+                stanza['vae'] = os.path.normpath(os.path.join(Model_dir, Weights_dir,mod['vae']['file']))
+            else:
+                stanza['vae'] = mod['vae']
         stanza.pop('default',None)  # this will be set later
 
-        # FIX: handle the VAE
-        # if vaes:
-        #     for target in vaes:
-        #         if re.search(target, model, flags=re.IGNORECASE):
-        #             stanza['vae'] = os.path.normpath(os.path.join(Model_dir,Weights_dir,vaes[target]))
-        #         else:
-        #             stanza['vae'] = os.path.normpath(os.path.join(Model_dir,Weights_dir,vaes['default']))
         # BUG - the first stanza is always the default. User should select.
         if not default_selected:
             stanza['default'] = True

From ebbebd6f0827f4824ac3707f1101f05584bd6342 Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Thu, 22 Dec 2022 18:01:47 +0000
Subject: [PATCH 083/199] proper support for float32/float16

- configure script now correctly detects user's preference for
  fp16/32 and downloads the correct diffuser version. If fp16
  version not available, falls back to fp32 version.

- misc code cleanup and simplification in model_cache
---
 configs/INITIAL_MODELS.yaml   |  7 ++-
 ldm/invoke/model_cache.py     | 89 +++++++++++++++++++----------------
 scripts/configure_invokeai.py | 56 ++++++++++++----------
 3 files changed, 86 insertions(+), 66 deletions(-)

diff --git a/configs/INITIAL_MODELS.yaml b/configs/INITIAL_MODELS.yaml
index 68be4ca6801..feea63065ed 100644
--- a/configs/INITIAL_MODELS.yaml
+++ b/configs/INITIAL_MODELS.yaml
@@ -1,5 +1,10 @@
+stable-diffusion-2.1:
+   description: Stable Diffusion version 2.1 diffusers model (5.21 GB)
+   repo_id: stabilityai/stable-diffusion-2-1
+   format: diffusers
+   recommended: True
 stable-diffusion-1.5:
-   description: The newest Stable Diffusion version 1.5 weight file (4.27 GB)
+   description: Stable Diffusion version 1.5 weight file (4.27 GB)
    repo_id: runwayml/stable-diffusion-v1-5
    format: diffusers
    recommended: True
diff --git a/ldm/invoke/model_cache.py b/ldm/invoke/model_cache.py
index 867ce576caf..a67da0ef5e5 100644
--- a/ldm/invoke/model_cache.py
+++ b/ldm/invoke/model_cache.py
@@ -319,45 +319,49 @@ def _load_ckpt_model(self, model_name, mconfig):
         return model, width, height, model_hash
 
     def _load_diffusers_model(self, mconfig):
-        pipeline_args = {}
         name_or_path = self.model_name_or_path(mconfig)
         model_hash = 'FIXME'
-        using_fp16 = False
+        using_fp16 = self.precision == 'float16'
+
         print(f'>> Loading diffusers model from {name_or_path}')
+        if using_fp16:
+            print(f'  | Using faster float16 precision')
+        else:
+            print(f'  | Using more accurate float32 precision')
 
         # TODO: scan weights maybe?
+        pipeline_args = dict(
+            safety_checker=None,
+            #local_files_only=True
+        )
         if 'vae' in mconfig:
             vae = self._load_vae(mconfig['vae'])
             pipeline_args.update(vae=vae)
-
-        cache_dir = None if isinstance(name_or_path,Path) else os.path.join(Globals.root,'models',name_or_path)
-        if self.precision == 'float16':
-            print('   | Using faster float16 precision')
-            using_fp16 = True
-            pipeline_args.update(revision="fp16")
+        if not isinstance(name_or_path,Path):
+            pipeline_args.update(cache_dir=os.path.join(Globals.root,'models',name_or_path))
+        if using_fp16:
             pipeline_args.update(torch_dtype=torch.float16)
+            fp_args_list = [{'revision':'fp16'},{}]
         else:
-            # TODO: more accurately, "using the model's default precision."
-            #     How do we find out what that is?
-            print('   | Using more accurate float32 precision')
-        try:
-            pipeline = StableDiffusionGeneratorPipeline.from_pretrained(
-                name_or_path,
-                safety_checker=None,
-                cache_dir=cache_dir,
-                #  local_files_only=True,
-                **pipeline_args,
-            )
-        except Exception as e:
-            if using_fp16:
-                pipeline_args.pop('revision')
+            fp_args_list = [{}]            
+
+        pipeline = None
+        for fp_args in fp_args_list:
+            try:
                 pipeline = StableDiffusionGeneratorPipeline.from_pretrained(
                     name_or_path,
-                    safety_checker=None,
-                    cache_dir=cache_dir,
-                    #  local_files_only=True,
                     **pipeline_args,
+                    **fp_args,
                 )
+            except OSError as e:
+                if str(e).startswith('fp16 is not a valid'):
+                    print(f'Could not fetch half-precision version of model {repo_id}; fetching full-precision instead')
+                else:
+                    print(f'An unexpected error occurred while downloading the model: {e})')
+            if pipeline:
+                break
+
+            assert pipeline is not None, OSError(f'"{model_name}" could not be loaded')        
 
         pipeline.to(self.device)
 
@@ -546,27 +550,30 @@ def _cached_sha256(self,path,data) -> Union[str, bytes]:
     def _load_vae(self, vae_config):
         vae_args = {}
         name_or_path = self.model_name_or_path(vae_config)
-        using_fp16 = False        
+        using_fp16 = self.precision == 'float16'
 
         print(f'>> Loading diffusers VAE from {name_or_path}')
-        if self.precision == 'float16':
-            print('   | Using faster float16 precision')
-            using_fp16 = True
-            vae_args.update(revision="fp16")
+        if using_fp16:
+            print(f'  | Using faster float16 precision')
             vae_args.update(torch_dtype=torch.float16)
+            fp_args_list = [{'revision':'fp16'},{}]
         else:
-            # TODO: more accurately, "using the model's default precision."
-            #     How do we find out what that is?
-            print('   | Using more accurate float32 precision')
+            print(f'  | Using more accurate float32 precision')
+            fp_args_list = [{}]
 
-        # At some point we might need to be able to use different classes here? But for now I think
-        # all Stable Diffusion VAE are AutoencoderKL.
-        try:
-            vae = AutoencoderKL.from_pretrained(name_or_path, **vae_args)
-        except Exception as e:
-            if using_fp16:
-                vae_args.pop('revision')
-                vae = AutoencoderKL.from_pretrained(name_or_path, **vae_args)
+        vae = None
+        for fp_args in fp_args_list:
+            # At some point we might need to be able to use different classes here? But for now I think
+            # all Stable Diffusion VAE are AutoencoderKL.
+            try:
+                vae = AutoencoderKL.from_pretrained(name_or_path, **vae_args, **fp_args)
+            except OSError as e:
+                if str(e).startswith('fp16 is not a valid'):
+                    print(f'Could not fetch half-precision version of model {repo_id}; fetching full-precision instead')
+                else:
+                    print(f'An unexpected error occurred while downloading the model: {e})')
+            if vae:
+                break
 
         # comment by lstein: I don't know what this does
         if 'subfolder' in vae_config:
diff --git a/scripts/configure_invokeai.py b/scripts/configure_invokeai.py
index edc1957b12c..c26359cc357 100755
--- a/scripts/configure_invokeai.py
+++ b/scripts/configure_invokeai.py
@@ -23,6 +23,7 @@
 import transformers
 from diffusers import StableDiffusionPipeline, AutoencoderKL
 from ldm.invoke.generator.diffusers_pipeline import StableDiffusionGeneratorPipeline
+from ldm.invoke.devices import choose_precision, choose_torch_device
 from getpass_asterisk import getpass_asterisk
 from huggingface_hub import HfFolder, hf_hub_url, login as hf_hub_login, whoami as hf_whoami
 from huggingface_hub.utils._errors import RevisionNotFoundError
@@ -328,20 +329,20 @@ def migrate_models_ckpt():
         os.replace(os.path.join(model_path,'model.ckpt'),os.path.join(model_path,new_name))
 
 #---------------------------------------------
-def download_weight_datasets(models:dict, access_token:str):
+def download_weight_datasets(models:dict, access_token:str, precision:str='float32'):
     migrate_models_ckpt()
     successful = dict()
     for mod in models.keys():
         print(f'{mod}...',file=sys.stderr,end='')
-        successful[mod] = _download_repo_or_file(Datasets[mod], access_token)
+        successful[mod] = _download_repo_or_file(Datasets[mod], access_token, precision=precision)
     return successful
 
-def _download_repo_or_file(mconfig:DictConfig, access_token:str)->Path:
+def _download_repo_or_file(mconfig:DictConfig, access_token:str, precision:str='float32')->Path:
     path = None
     if mconfig['format'] == 'ckpt':
         path = _download_ckpt_weights(mconfig, access_token)
     else:
-        path = _download_diffusion_weights(mconfig, access_token)
+        path = _download_diffusion_weights(mconfig, access_token, precision=precision)
     return path
 
 def _download_ckpt_weights(mconfig:DictConfig, access_token:str)->Path:
@@ -355,25 +356,26 @@ def _download_ckpt_weights(mconfig:DictConfig, access_token:str)->Path:
         access_token=access_token
     )
 
-def _download_diffusion_weights(mconfig:DictConfig, access_token:str):
+def _download_diffusion_weights(mconfig:DictConfig, access_token:str, precision:str='float32'):
     repo_id = mconfig['repo_id']
     model_class = StableDiffusionGeneratorPipeline if mconfig['format']=='diffusers' else AutoencoderKL
+    extra_arg_list = [{'revision':'fp16'},{}] if precision=='float16' else [{}]
     path = None
-    try:
-        path = download_from_hf(
-            model_class,
-            repo_id,
-            safety_checker=None,
-            revision='fp16',
-        )
-    except OSError:
-        path = download_from_hf(
-            model_class,
-            repo_id,
-            safety_checker=None,
-        )
-    except Exception as e:
-        print(f'could not download; exception = {type(e)}')
+    for extra_args in extra_arg_list:
+        try:
+            path = download_from_hf(
+                model_class,
+                repo_id,
+                safety_checker=None,
+                **extra_args,
+            )
+        except OSError as e:
+            if str(e).startswith('fp16 is not a valid'):
+                print(f'Could not fetch half-precision version of model {repo_id}; fetching full-precision instead')
+            else:
+                print(f'An unexpected error occurred while downloading the model: {e})')
+        if path:
+            break
     return path
 
 #---------------------------------------------
@@ -497,8 +499,12 @@ def new_config_file_contents(successfully_downloaded:dict, config_file:str)->str
         stanza['description'] = mod['description']
         stanza['repo_id'] = mod['repo_id']
         stanza['format'] = mod['format']
-        stanza['width'] = mod['width']
-        stanza['height'] = mod['height']
+        # diffusers don't need width and height (probably .ckpt doesn't either)
+        # so we no longer require these in INITIAL_MODELS.yaml
+        if 'width' in mod:
+            stanza['width'] = mod['width']
+        if 'height' in mod:
+            stanza['height'] = mod['height']
         if 'file' in mod:
             stanza['weights'] = os.path.relpath(successfully_downloaded[model], start=Globals.root)
             stanza['config'] = os.path.normpath(os.path.join(SD_Configs,mod['config']))
@@ -633,11 +639,13 @@ def download_safety_checker():
 #-------------------------------------
 def download_weights(opt:dict) -> Union[str, None]:
 
+    precision = 'float32' if opt.full_precision else choose_precision(torch.device(choose_torch_device()))
+
     if opt.yes_to_all:
         models = recommended_datasets()
         access_token = authenticate(opt.yes_to_all)
         if len(models)>0:
-            successfully_downloaded = download_weight_datasets(models, access_token)
+            successfully_downloaded = download_weight_datasets(models, access_token, precision=precision)
             update_config_file(successfully_downloaded,opt)
             return
 
@@ -659,7 +667,7 @@ def download_weights(opt:dict) -> Union[str, None]:
     HfFolder.save_token(access_token)
 
     print('\n** DOWNLOADING WEIGHTS **')
-    successfully_downloaded = download_weight_datasets(models, access_token)
+    successfully_downloaded = download_weight_datasets(models, access_token, precision=precision)
 
     update_config_file(successfully_downloaded,opt)
     if len(successfully_downloaded) < len(models):

From c73112d12a347c0a6bba3ec0885650513c896a7b Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Thu, 22 Dec 2022 20:03:29 +0000
Subject: [PATCH 084/199] add on-the-fly conversion of .ckpt to diffusers
 models

1. On-the-fly conversion code can be found in the file ldm/invoke/ckpt_to_diffusers.py.

2. A new !optimize command has been added to the CLI. Should be ported to Web GUI.

User experience on the CLI is this:

```
invoke> !optimize /home/lstein/invokeai/models/ldm/stable-diffusion-v1/sd-v1-4.ckpt
INFO: Converting legacy weights file /home/lstein/invokeai/models/ldm/stable-diffusion-v1/sd-v1-4.ckpt to optimized diffuser model.
      This operation will take 30-60s to complete.
Success. Optimized model is now located at /home/lstein/tmp/invokeai/models/optimized-ckpts/sd-v1-4
Writing new config file entry for sd-v1-4...

>> New configuration:
sd-v1-4:
  description: Optimized version of sd-v1-4
  format: diffusers
  path: /home/lstein/tmp/invokeai/models/optimized-ckpts/sd-v1-4

OK to import [n]? y
>> Verifying that new model loads...
>> Current VRAM usage:  2.60G
>> Offloading stable-diffusion-2.1 to CPU
>> Loading diffusers model from /home/lstein/tmp/invokeai/models/optimized-ckpts/sd-v1-4
  | Using faster float16 precision
You have disabled the safety checker for <class 'ldm.invoke.generator.diffusers_pipeline.StableDiffusionGeneratorPipeline'> by passing `safety_checker=None`. Ensure that you abide to the conditions of the Stable Diffusion \
license and do not expose unfiltered results in services or applications open to the public. Both the diffusers team and Hugging Face strongly recommend to keep the safety filter enabled in all public facing circumstances,\
 disabling it only for use-cases that involve analyzing network behavior or auditing its results. For more information, please have a look at https://github.com/huggingface/diffusers/pull/254 .
  | training width x height = (512 x 512)
>> Model loaded in 3.48s
>> Max VRAM used to load the model: 2.17G
>> Current VRAM usage:2.17G
>> Textual inversions available:
>> Setting Sampler to k_lms (LMSDiscreteScheduler)
Keep model loaded? [y]
```
---
 configs/stable-diffusion/v2-inference-v.yaml |  68 ++
 ldm/invoke/CLI.py                            |  50 +-
 ldm/invoke/args.py                           |  11 +-
 ldm/invoke/ckpt_to_diffuser.py               | 943 +++++++++++++++++++
 ldm/invoke/model_cache.py                    |  16 +-
 ldm/invoke/readline.py                       |   4 +-
 6 files changed, 1079 insertions(+), 13 deletions(-)
 create mode 100644 configs/stable-diffusion/v2-inference-v.yaml
 create mode 100644 ldm/invoke/ckpt_to_diffuser.py

diff --git a/configs/stable-diffusion/v2-inference-v.yaml b/configs/stable-diffusion/v2-inference-v.yaml
new file mode 100644
index 00000000000..8ec8dfbfefe
--- /dev/null
+++ b/configs/stable-diffusion/v2-inference-v.yaml
@@ -0,0 +1,68 @@
+model:
+  base_learning_rate: 1.0e-4
+  target: ldm.models.diffusion.ddpm.LatentDiffusion
+  params:
+    parameterization: "v"
+    linear_start: 0.00085
+    linear_end: 0.0120
+    num_timesteps_cond: 1
+    log_every_t: 200
+    timesteps: 1000
+    first_stage_key: "jpg"
+    cond_stage_key: "txt"
+    image_size: 64
+    channels: 4
+    cond_stage_trainable: false
+    conditioning_key: crossattn
+    monitor: val/loss_simple_ema
+    scale_factor: 0.18215
+    use_ema: False # we set this to false because this is an inference only config
+
+    unet_config:
+      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+      params:
+        use_checkpoint: True
+        use_fp16: True
+        image_size: 32 # unused
+        in_channels: 4
+        out_channels: 4
+        model_channels: 320
+        attention_resolutions: [ 4, 2, 1 ]
+        num_res_blocks: 2
+        channel_mult: [ 1, 2, 4, 4 ]
+        num_head_channels: 64 # need to fix for flash-attn
+        use_spatial_transformer: True
+        use_linear_in_transformer: True
+        transformer_depth: 1
+        context_dim: 1024
+        legacy: False
+
+    first_stage_config:
+      target: ldm.models.autoencoder.AutoencoderKL
+      params:
+        embed_dim: 4
+        monitor: val/rec_loss
+        ddconfig:
+          #attn_type: "vanilla-xformers"
+          double_z: true
+          z_channels: 4
+          resolution: 256
+          in_channels: 3
+          out_ch: 3
+          ch: 128
+          ch_mult:
+          - 1
+          - 2
+          - 4
+          - 4
+          num_res_blocks: 2
+          attn_resolutions: []
+          dropout: 0.0
+        lossconfig:
+          target: torch.nn.Identity
+
+    cond_stage_config:
+      target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder
+      params:
+        freeze: True
+        layer: "penultimate"
diff --git a/ldm/invoke/CLI.py b/ldm/invoke/CLI.py
index 09dc2193aca..c49349de322 100644
--- a/ldm/invoke/CLI.py
+++ b/ldm/invoke/CLI.py
@@ -460,6 +460,17 @@ def do_command(command:str, gen, opt:Args, completer) -> tuple:
         completer.add_history(command)
         operation = None
 
+    elif command.startswith('!optimize'):
+        path = shlex.split(command)
+        if len(path) < 2:
+            print('** please provide a path to a .ckpt file')
+        elif not os.path.exists(path[1]):
+            print(f'** {path[1]}: file not found')
+        else:
+            optimize_model(path[1], gen, opt, completer)
+        completer.add_history(command)
+        operation = None
+
     elif command.startswith('!edit'):
         path = shlex.split(command)
         if len(path) < 2:
@@ -532,6 +543,7 @@ def add_weights_to_config(model_path:str, gen, opt, completer):
 
     new_config = {}
     new_config['weights'] = model_path
+    new_config['format'] = 'ckpt'
 
     done = False
     while not done:
@@ -579,6 +591,36 @@ def add_weights_to_config(model_path:str, gen, opt, completer):
     if write_config_file(opt.conf, gen, model_name, new_config, make_default=make_default):
         completer.add_model(model_name)
 
+def optimize_model(model_path:str, gen, opt, completer):
+    from ldm.invoke.ckpt_to_diffuser import convert_ckpt_to_diffuser
+    import transformers
+    basename = os.path.basename(os.path.splitext(model_path)[0])
+    dump_path = os.path.join(Globals.root, 'models','optimized-ckpts',basename)
+    if os.path.exists(dump_path):
+        print(f'ERROR: The path {dump_path} already exists. Please move or remove it and try again.')
+        return
+    
+    print(f'INFO: Converting legacy weights file {model_path} to optimized diffuser model.')
+    print(f'      This operation will take 30-60s to complete.')
+    try:
+        verbosity =transformers.logging.get_verbosity()
+        transformers.logging.set_verbosity_error()
+        convert_ckpt_to_diffuser(model_path, dump_path)
+        transformers.logging.set_verbosity(verbosity)
+        print(f'Success. Optimized model is now located at {dump_path}')
+        print(f'Writing new config file entry for {basename}...')
+        model_name = basename
+        new_config = dict(
+            path=dump_path,
+            description=f'Optimized version of {basename}',
+            format='diffusers',
+        )
+        if write_config_file(opt.conf, gen, model_name, new_config):
+            completer.add_model(model_name)
+    except Exception as e:
+        print(f'** Conversion failed: {str(e)}')
+        traceback.print_exc()
+
 def del_config(model_name:str, gen, opt, completer):
     current_model = gen.model_name
     if model_name == current_model:
@@ -601,7 +643,7 @@ def edit_config(model_name:str, gen, opt, completer):
     conf = config[model_name]
     new_config = {}
     completer.complete_extensions(('.yaml','.yml','.ckpt','.vae.pt'))
-    for field in ('description', 'weights', 'vae', 'config', 'width','height'):
+    for field in ('description', 'weights', 'vae', 'config', 'width', 'height', 'format'):
         completer.linebuffer = str(conf[field]) if field in conf else ''
         new_value = input(f'{field}: ')
         new_config[field] = int(new_value) if field in ('width','height') else new_value
@@ -625,8 +667,12 @@ def write_config_file(conf_path, gen, model_name, new_config, clobber=False, mak
         gen.model_cache.add_model(model_name, new_config, clobber)
         assert gen.set_model(model_name) is not None, 'model failed to load'
     except AssertionError as e:
+        traceback.print_exc()
         print(f'** aborting **')
-        gen.model_cache.del_model(model_name)
+        try:
+            gen.model_cache.del_model(model_name)
+        except Exception:
+            pass
         return False
 
     if make_default:
diff --git a/ldm/invoke/args.py b/ldm/invoke/args.py
index 7fa6e7a22c6..c7417900b69 100644
--- a/ldm/invoke/args.py
+++ b/ldm/invoke/args.py
@@ -724,11 +724,12 @@ def _create_dream_cmd_parser(self):
             !NN retrieves the NNth command from the history
 
             *Model manipulation*
-            !models                                 -- list models in configs/models.yaml
-            !switch <model_name>                    -- switch to model named <model_name>
-            !import_model path/to/weights/file.ckpt -- adds a model to your config
-            !edit_model <model_name>                -- edit a model's description
-            !del_model <model_name>                 -- delete a model
+            !models                                   -- list models in configs/models.yaml
+            !switch <model_name>                      -- switch to model named <model_name>
+            !import_model path/to/weights/file.ckpt   -- adds a .ckpt model to your config
+            !optimize_model path/to/weights/file.ckpt -- converts a .ckpt file model a diffusers model
+            !edit_model <model_name>                  -- edit a model's description
+            !del_model <model_name>                   -- delete a model
             """
         )
         render_group     = parser.add_argument_group('General rendering')
diff --git a/ldm/invoke/ckpt_to_diffuser.py b/ldm/invoke/ckpt_to_diffuser.py
new file mode 100644
index 00000000000..15702ccb35b
--- /dev/null
+++ b/ldm/invoke/ckpt_to_diffuser.py
@@ -0,0 +1,943 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# Adapted for use as a module by Lincoln Stein <lstein@gmail.com>
+""" Conversion script for the LDM checkpoints. """
+
+import os
+import re
+import torch
+from ldm.invoke.globals import Globals
+
+try:
+    from omegaconf import OmegaConf
+except ImportError:
+    raise ImportError(
+        "OmegaConf is required to convert the LDM checkpoints. Please install it with `pip install OmegaConf`."
+    )
+
+from diffusers import (
+    AutoencoderKL,
+    DDIMScheduler,
+    DPMSolverMultistepScheduler,
+    EulerAncestralDiscreteScheduler,
+    EulerDiscreteScheduler,
+    HeunDiscreteScheduler,
+    LDMTextToImagePipeline,
+    LMSDiscreteScheduler,
+    PNDMScheduler,
+    StableDiffusionPipeline,
+    UNet2DConditionModel,
+)
+from diffusers.pipelines.latent_diffusion.pipeline_latent_diffusion import LDMBertConfig, LDMBertModel
+from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder, PaintByExamplePipeline
+from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
+from transformers import AutoFeatureExtractor, BertTokenizerFast, CLIPTextModel, CLIPTokenizer, CLIPVisionConfig
+
+def shave_segments(path, n_shave_prefix_segments=1):
+    """
+    Removes segments. Positive values shave the first segments, negative shave the last segments.
+    """
+    if n_shave_prefix_segments >= 0:
+        return ".".join(path.split(".")[n_shave_prefix_segments:])
+    else:
+        return ".".join(path.split(".")[:n_shave_prefix_segments])
+
+
+def renew_resnet_paths(old_list, n_shave_prefix_segments=0):
+    """
+    Updates paths inside resnets to the new naming scheme (local renaming)
+    """
+    mapping = []
+    for old_item in old_list:
+        new_item = old_item.replace("in_layers.0", "norm1")
+        new_item = new_item.replace("in_layers.2", "conv1")
+
+        new_item = new_item.replace("out_layers.0", "norm2")
+        new_item = new_item.replace("out_layers.3", "conv2")
+
+        new_item = new_item.replace("emb_layers.1", "time_emb_proj")
+        new_item = new_item.replace("skip_connection", "conv_shortcut")
+
+        new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
+
+        mapping.append({"old": old_item, "new": new_item})
+
+    return mapping
+
+
+def renew_vae_resnet_paths(old_list, n_shave_prefix_segments=0):
+    """
+    Updates paths inside resnets to the new naming scheme (local renaming)
+    """
+    mapping = []
+    for old_item in old_list:
+        new_item = old_item
+
+        new_item = new_item.replace("nin_shortcut", "conv_shortcut")
+        new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
+
+        mapping.append({"old": old_item, "new": new_item})
+
+    return mapping
+
+
+def renew_attention_paths(old_list, n_shave_prefix_segments=0):
+    """
+    Updates paths inside attentions to the new naming scheme (local renaming)
+    """
+    mapping = []
+    for old_item in old_list:
+        new_item = old_item
+
+        #         new_item = new_item.replace('norm.weight', 'group_norm.weight')
+        #         new_item = new_item.replace('norm.bias', 'group_norm.bias')
+
+        #         new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
+        #         new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')
+
+        #         new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
+
+        mapping.append({"old": old_item, "new": new_item})
+
+    return mapping
+
+
+def renew_vae_attention_paths(old_list, n_shave_prefix_segments=0):
+    """
+    Updates paths inside attentions to the new naming scheme (local renaming)
+    """
+    mapping = []
+    for old_item in old_list:
+        new_item = old_item
+
+        new_item = new_item.replace("norm.weight", "group_norm.weight")
+        new_item = new_item.replace("norm.bias", "group_norm.bias")
+
+        new_item = new_item.replace("q.weight", "query.weight")
+        new_item = new_item.replace("q.bias", "query.bias")
+
+        new_item = new_item.replace("k.weight", "key.weight")
+        new_item = new_item.replace("k.bias", "key.bias")
+
+        new_item = new_item.replace("v.weight", "value.weight")
+        new_item = new_item.replace("v.bias", "value.bias")
+
+        new_item = new_item.replace("proj_out.weight", "proj_attn.weight")
+        new_item = new_item.replace("proj_out.bias", "proj_attn.bias")
+
+        new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)
+
+        mapping.append({"old": old_item, "new": new_item})
+
+    return mapping
+
+
+def assign_to_checkpoint(
+    paths, checkpoint, old_checkpoint, attention_paths_to_split=None, additional_replacements=None, config=None
+):
+    """
+    This does the final conversion step: take locally converted weights and apply a global renaming
+    to them. It splits attention layers, and takes into account additional replacements
+    that may arise.
+
+    Assigns the weights to the new checkpoint.
+    """
+    assert isinstance(paths, list), "Paths should be a list of dicts containing 'old' and 'new' keys."
+
+    # Splits the attention layers into three variables.
+    if attention_paths_to_split is not None:
+        for path, path_map in attention_paths_to_split.items():
+            old_tensor = old_checkpoint[path]
+            channels = old_tensor.shape[0] // 3
+
+            target_shape = (-1, channels) if len(old_tensor.shape) == 3 else (-1)
+
+            num_heads = old_tensor.shape[0] // config["num_head_channels"] // 3
+
+            old_tensor = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:])
+            query, key, value = old_tensor.split(channels // num_heads, dim=1)
+
+            checkpoint[path_map["query"]] = query.reshape(target_shape)
+            checkpoint[path_map["key"]] = key.reshape(target_shape)
+            checkpoint[path_map["value"]] = value.reshape(target_shape)
+
+    for path in paths:
+        new_path = path["new"]
+
+        # These have already been assigned
+        if attention_paths_to_split is not None and new_path in attention_paths_to_split:
+            continue
+
+        # Global renaming happens here
+        new_path = new_path.replace("middle_block.0", "mid_block.resnets.0")
+        new_path = new_path.replace("middle_block.1", "mid_block.attentions.0")
+        new_path = new_path.replace("middle_block.2", "mid_block.resnets.1")
+
+        if additional_replacements is not None:
+            for replacement in additional_replacements:
+                new_path = new_path.replace(replacement["old"], replacement["new"])
+
+        # proj_attn.weight has to be converted from conv 1D to linear
+        if "proj_attn.weight" in new_path:
+            checkpoint[new_path] = old_checkpoint[path["old"]][:, :, 0]
+        else:
+            checkpoint[new_path] = old_checkpoint[path["old"]]
+
+
+def conv_attn_to_linear(checkpoint):
+    keys = list(checkpoint.keys())
+    attn_keys = ["query.weight", "key.weight", "value.weight"]
+    for key in keys:
+        if ".".join(key.split(".")[-2:]) in attn_keys:
+            if checkpoint[key].ndim > 2:
+                checkpoint[key] = checkpoint[key][:, :, 0, 0]
+        elif "proj_attn.weight" in key:
+            if checkpoint[key].ndim > 2:
+                checkpoint[key] = checkpoint[key][:, :, 0]
+
+
+def create_unet_diffusers_config(original_config, image_size: int):
+    """
+    Creates a config for the diffusers based on the config of the LDM model.
+    """
+    unet_params = original_config.model.params.unet_config.params
+    vae_params = original_config.model.params.first_stage_config.params.ddconfig
+
+    block_out_channels = [unet_params.model_channels * mult for mult in unet_params.channel_mult]
+
+    down_block_types = []
+    resolution = 1
+    for i in range(len(block_out_channels)):
+        block_type = "CrossAttnDownBlock2D" if resolution in unet_params.attention_resolutions else "DownBlock2D"
+        down_block_types.append(block_type)
+        if i != len(block_out_channels) - 1:
+            resolution *= 2
+
+    up_block_types = []
+    for i in range(len(block_out_channels)):
+        block_type = "CrossAttnUpBlock2D" if resolution in unet_params.attention_resolutions else "UpBlock2D"
+        up_block_types.append(block_type)
+        resolution //= 2
+
+    vae_scale_factor = 2 ** (len(vae_params.ch_mult) - 1)
+
+    head_dim = unet_params.num_heads if "num_heads" in unet_params else None
+    use_linear_projection = (
+        unet_params.use_linear_in_transformer if "use_linear_in_transformer" in unet_params else False
+    )
+    if use_linear_projection:
+        # stable diffusion 2-base-512 and 2-768
+        if head_dim is None:
+            head_dim = [5, 10, 20, 20]
+
+    config = dict(
+        sample_size=image_size // vae_scale_factor,
+        in_channels=unet_params.in_channels,
+        out_channels=unet_params.out_channels,
+        down_block_types=tuple(down_block_types),
+        up_block_types=tuple(up_block_types),
+        block_out_channels=tuple(block_out_channels),
+        layers_per_block=unet_params.num_res_blocks,
+        cross_attention_dim=unet_params.context_dim,
+        attention_head_dim=head_dim,
+        use_linear_projection=use_linear_projection,
+    )
+
+    return config
+
+
+def create_vae_diffusers_config(original_config, image_size: int):
+    """
+    Creates a config for the diffusers based on the config of the LDM model.
+    """
+    vae_params = original_config.model.params.first_stage_config.params.ddconfig
+    _ = original_config.model.params.first_stage_config.params.embed_dim
+
+    block_out_channels = [vae_params.ch * mult for mult in vae_params.ch_mult]
+    down_block_types = ["DownEncoderBlock2D"] * len(block_out_channels)
+    up_block_types = ["UpDecoderBlock2D"] * len(block_out_channels)
+
+    config = dict(
+        sample_size=image_size,
+        in_channels=vae_params.in_channels,
+        out_channels=vae_params.out_ch,
+        down_block_types=tuple(down_block_types),
+        up_block_types=tuple(up_block_types),
+        block_out_channels=tuple(block_out_channels),
+        latent_channels=vae_params.z_channels,
+        layers_per_block=vae_params.num_res_blocks,
+    )
+    return config
+
+
+def create_diffusers_schedular(original_config):
+    schedular = DDIMScheduler(
+        num_train_timesteps=original_config.model.params.timesteps,
+        beta_start=original_config.model.params.linear_start,
+        beta_end=original_config.model.params.linear_end,
+        beta_schedule="scaled_linear",
+    )
+    return schedular
+
+
+def create_ldm_bert_config(original_config):
+    bert_params = original_config.model.params.cond_stage_config.params
+    config = LDMBertConfig(
+        d_model=bert_params.n_embed,
+        encoder_layers=bert_params.n_layer,
+        encoder_ffn_dim=bert_params.n_embed * 4,
+    )
+    return config
+
+
+def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False):
+    """
+    Takes a state dict and a config, and returns a converted checkpoint.
+    """
+
+    # extract state_dict for UNet
+    unet_state_dict = {}
+    keys = list(checkpoint.keys())
+
+    unet_key = "model.diffusion_model."
+    # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
+    if sum(k.startswith("model_ema") for k in keys) > 100:
+        print(f"Checkpoint {path} has both EMA and non-EMA weights.")
+        if extract_ema:
+            print(
+                "In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA"
+                " weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag."
+            )
+            for key in keys:
+                if key.startswith("model.diffusion_model"):
+                    flat_ema_key = "model_ema." + "".join(key.split(".")[1:])
+                    unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(flat_ema_key)
+        else:
+            print(
+                "In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA"
+                " weights (usually better for inference), please make sure to add the `--extract_ema` flag."
+            )
+
+    for key in keys:
+        if key.startswith(unet_key):
+            unet_state_dict[key.replace(unet_key, "")] = checkpoint.pop(key)
+
+    new_checkpoint = {}
+
+    new_checkpoint["time_embedding.linear_1.weight"] = unet_state_dict["time_embed.0.weight"]
+    new_checkpoint["time_embedding.linear_1.bias"] = unet_state_dict["time_embed.0.bias"]
+    new_checkpoint["time_embedding.linear_2.weight"] = unet_state_dict["time_embed.2.weight"]
+    new_checkpoint["time_embedding.linear_2.bias"] = unet_state_dict["time_embed.2.bias"]
+
+    new_checkpoint["conv_in.weight"] = unet_state_dict["input_blocks.0.0.weight"]
+    new_checkpoint["conv_in.bias"] = unet_state_dict["input_blocks.0.0.bias"]
+
+    new_checkpoint["conv_norm_out.weight"] = unet_state_dict["out.0.weight"]
+    new_checkpoint["conv_norm_out.bias"] = unet_state_dict["out.0.bias"]
+    new_checkpoint["conv_out.weight"] = unet_state_dict["out.2.weight"]
+    new_checkpoint["conv_out.bias"] = unet_state_dict["out.2.bias"]
+
+    # Retrieves the keys for the input blocks only
+    num_input_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "input_blocks" in layer})
+    input_blocks = {
+        layer_id: [key for key in unet_state_dict if f"input_blocks.{layer_id}" in key]
+        for layer_id in range(num_input_blocks)
+    }
+
+    # Retrieves the keys for the middle blocks only
+    num_middle_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "middle_block" in layer})
+    middle_blocks = {
+        layer_id: [key for key in unet_state_dict if f"middle_block.{layer_id}" in key]
+        for layer_id in range(num_middle_blocks)
+    }
+
+    # Retrieves the keys for the output blocks only
+    num_output_blocks = len({".".join(layer.split(".")[:2]) for layer in unet_state_dict if "output_blocks" in layer})
+    output_blocks = {
+        layer_id: [key for key in unet_state_dict if f"output_blocks.{layer_id}" in key]
+        for layer_id in range(num_output_blocks)
+    }
+
+    for i in range(1, num_input_blocks):
+        block_id = (i - 1) // (config["layers_per_block"] + 1)
+        layer_in_block_id = (i - 1) % (config["layers_per_block"] + 1)
+
+        resnets = [
+            key for key in input_blocks[i] if f"input_blocks.{i}.0" in key and f"input_blocks.{i}.0.op" not in key
+        ]
+        attentions = [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key]
+
+        if f"input_blocks.{i}.0.op.weight" in unet_state_dict:
+            new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.weight"] = unet_state_dict.pop(
+                f"input_blocks.{i}.0.op.weight"
+            )
+            new_checkpoint[f"down_blocks.{block_id}.downsamplers.0.conv.bias"] = unet_state_dict.pop(
+                f"input_blocks.{i}.0.op.bias"
+            )
+
+        paths = renew_resnet_paths(resnets)
+        meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
+        assign_to_checkpoint(
+            paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
+        )
+
+        if len(attentions):
+            paths = renew_attention_paths(attentions)
+            meta_path = {"old": f"input_blocks.{i}.1", "new": f"down_blocks.{block_id}.attentions.{layer_in_block_id}"}
+            assign_to_checkpoint(
+                paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
+            )
+
+    resnet_0 = middle_blocks[0]
+    attentions = middle_blocks[1]
+    resnet_1 = middle_blocks[2]
+
+    resnet_0_paths = renew_resnet_paths(resnet_0)
+    assign_to_checkpoint(resnet_0_paths, new_checkpoint, unet_state_dict, config=config)
+
+    resnet_1_paths = renew_resnet_paths(resnet_1)
+    assign_to_checkpoint(resnet_1_paths, new_checkpoint, unet_state_dict, config=config)
+
+    attentions_paths = renew_attention_paths(attentions)
+    meta_path = {"old": "middle_block.1", "new": "mid_block.attentions.0"}
+    assign_to_checkpoint(
+        attentions_paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
+    )
+
+    for i in range(num_output_blocks):
+        block_id = i // (config["layers_per_block"] + 1)
+        layer_in_block_id = i % (config["layers_per_block"] + 1)
+        output_block_layers = [shave_segments(name, 2) for name in output_blocks[i]]
+        output_block_list = {}
+
+        for layer in output_block_layers:
+            layer_id, layer_name = layer.split(".")[0], shave_segments(layer, 1)
+            if layer_id in output_block_list:
+                output_block_list[layer_id].append(layer_name)
+            else:
+                output_block_list[layer_id] = [layer_name]
+
+        if len(output_block_list) > 1:
+            resnets = [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key]
+            attentions = [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key]
+
+            resnet_0_paths = renew_resnet_paths(resnets)
+            paths = renew_resnet_paths(resnets)
+
+            meta_path = {"old": f"output_blocks.{i}.0", "new": f"up_blocks.{block_id}.resnets.{layer_in_block_id}"}
+            assign_to_checkpoint(
+                paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
+            )
+
+            if ["conv.weight", "conv.bias"] in output_block_list.values():
+                index = list(output_block_list.values()).index(["conv.weight", "conv.bias"])
+                new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[
+                    f"output_blocks.{i}.{index}.conv.weight"
+                ]
+                new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.bias"] = unet_state_dict[
+                    f"output_blocks.{i}.{index}.conv.bias"
+                ]
+
+                # Clear attentions as they have been attributed above.
+                if len(attentions) == 2:
+                    attentions = []
+
+            if len(attentions):
+                paths = renew_attention_paths(attentions)
+                meta_path = {
+                    "old": f"output_blocks.{i}.1",
+                    "new": f"up_blocks.{block_id}.attentions.{layer_in_block_id}",
+                }
+                assign_to_checkpoint(
+                    paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
+                )
+        else:
+            resnet_0_paths = renew_resnet_paths(output_block_layers, n_shave_prefix_segments=1)
+            for path in resnet_0_paths:
+                old_path = ".".join(["output_blocks", str(i), path["old"]])
+                new_path = ".".join(["up_blocks", str(block_id), "resnets", str(layer_in_block_id), path["new"]])
+
+                new_checkpoint[new_path] = unet_state_dict[old_path]
+
+    return new_checkpoint
+
+
+def convert_ldm_vae_checkpoint(checkpoint, config):
+    # extract state dict for VAE
+    vae_state_dict = {}
+    vae_key = "first_stage_model."
+    keys = list(checkpoint.keys())
+    for key in keys:
+        if key.startswith(vae_key):
+            vae_state_dict[key.replace(vae_key, "")] = checkpoint.get(key)
+
+    new_checkpoint = {}
+
+    new_checkpoint["encoder.conv_in.weight"] = vae_state_dict["encoder.conv_in.weight"]
+    new_checkpoint["encoder.conv_in.bias"] = vae_state_dict["encoder.conv_in.bias"]
+    new_checkpoint["encoder.conv_out.weight"] = vae_state_dict["encoder.conv_out.weight"]
+    new_checkpoint["encoder.conv_out.bias"] = vae_state_dict["encoder.conv_out.bias"]
+    new_checkpoint["encoder.conv_norm_out.weight"] = vae_state_dict["encoder.norm_out.weight"]
+    new_checkpoint["encoder.conv_norm_out.bias"] = vae_state_dict["encoder.norm_out.bias"]
+
+    new_checkpoint["decoder.conv_in.weight"] = vae_state_dict["decoder.conv_in.weight"]
+    new_checkpoint["decoder.conv_in.bias"] = vae_state_dict["decoder.conv_in.bias"]
+    new_checkpoint["decoder.conv_out.weight"] = vae_state_dict["decoder.conv_out.weight"]
+    new_checkpoint["decoder.conv_out.bias"] = vae_state_dict["decoder.conv_out.bias"]
+    new_checkpoint["decoder.conv_norm_out.weight"] = vae_state_dict["decoder.norm_out.weight"]
+    new_checkpoint["decoder.conv_norm_out.bias"] = vae_state_dict["decoder.norm_out.bias"]
+
+    new_checkpoint["quant_conv.weight"] = vae_state_dict["quant_conv.weight"]
+    new_checkpoint["quant_conv.bias"] = vae_state_dict["quant_conv.bias"]
+    new_checkpoint["post_quant_conv.weight"] = vae_state_dict["post_quant_conv.weight"]
+    new_checkpoint["post_quant_conv.bias"] = vae_state_dict["post_quant_conv.bias"]
+
+    # Retrieves the keys for the encoder down blocks only
+    num_down_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "encoder.down" in layer})
+    down_blocks = {
+        layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks)
+    }
+
+    # Retrieves the keys for the decoder up blocks only
+    num_up_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "decoder.up" in layer})
+    up_blocks = {
+        layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks)
+    }
+
+    for i in range(num_down_blocks):
+        resnets = [key for key in down_blocks[i] if f"down.{i}" in key and f"down.{i}.downsample" not in key]
+
+        if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict:
+            new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = vae_state_dict.pop(
+                f"encoder.down.{i}.downsample.conv.weight"
+            )
+            new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"] = vae_state_dict.pop(
+                f"encoder.down.{i}.downsample.conv.bias"
+            )
+
+        paths = renew_vae_resnet_paths(resnets)
+        meta_path = {"old": f"down.{i}.block", "new": f"down_blocks.{i}.resnets"}
+        assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
+
+    mid_resnets = [key for key in vae_state_dict if "encoder.mid.block" in key]
+    num_mid_res_blocks = 2
+    for i in range(1, num_mid_res_blocks + 1):
+        resnets = [key for key in mid_resnets if f"encoder.mid.block_{i}" in key]
+
+        paths = renew_vae_resnet_paths(resnets)
+        meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"}
+        assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
+
+    mid_attentions = [key for key in vae_state_dict if "encoder.mid.attn" in key]
+    paths = renew_vae_attention_paths(mid_attentions)
+    meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
+    assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
+    conv_attn_to_linear(new_checkpoint)
+
+    for i in range(num_up_blocks):
+        block_id = num_up_blocks - 1 - i
+        resnets = [
+            key for key in up_blocks[block_id] if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key
+        ]
+
+        if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict:
+            new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"] = vae_state_dict[
+                f"decoder.up.{block_id}.upsample.conv.weight"
+            ]
+            new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"] = vae_state_dict[
+                f"decoder.up.{block_id}.upsample.conv.bias"
+            ]
+
+        paths = renew_vae_resnet_paths(resnets)
+        meta_path = {"old": f"up.{block_id}.block", "new": f"up_blocks.{i}.resnets"}
+        assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
+
+    mid_resnets = [key for key in vae_state_dict if "decoder.mid.block" in key]
+    num_mid_res_blocks = 2
+    for i in range(1, num_mid_res_blocks + 1):
+        resnets = [key for key in mid_resnets if f"decoder.mid.block_{i}" in key]
+
+        paths = renew_vae_resnet_paths(resnets)
+        meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"}
+        assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
+
+    mid_attentions = [key for key in vae_state_dict if "decoder.mid.attn" in key]
+    paths = renew_vae_attention_paths(mid_attentions)
+    meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
+    assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
+    conv_attn_to_linear(new_checkpoint)
+    return new_checkpoint
+
+
+def convert_ldm_bert_checkpoint(checkpoint, config):
+    def _copy_attn_layer(hf_attn_layer, pt_attn_layer):
+        hf_attn_layer.q_proj.weight.data = pt_attn_layer.to_q.weight
+        hf_attn_layer.k_proj.weight.data = pt_attn_layer.to_k.weight
+        hf_attn_layer.v_proj.weight.data = pt_attn_layer.to_v.weight
+
+        hf_attn_layer.out_proj.weight = pt_attn_layer.to_out.weight
+        hf_attn_layer.out_proj.bias = pt_attn_layer.to_out.bias
+
+    def _copy_linear(hf_linear, pt_linear):
+        hf_linear.weight = pt_linear.weight
+        hf_linear.bias = pt_linear.bias
+
+    def _copy_layer(hf_layer, pt_layer):
+        # copy layer norms
+        _copy_linear(hf_layer.self_attn_layer_norm, pt_layer[0][0])
+        _copy_linear(hf_layer.final_layer_norm, pt_layer[1][0])
+
+        # copy attn
+        _copy_attn_layer(hf_layer.self_attn, pt_layer[0][1])
+
+        # copy MLP
+        pt_mlp = pt_layer[1][1]
+        _copy_linear(hf_layer.fc1, pt_mlp.net[0][0])
+        _copy_linear(hf_layer.fc2, pt_mlp.net[2])
+
+    def _copy_layers(hf_layers, pt_layers):
+        for i, hf_layer in enumerate(hf_layers):
+            if i != 0:
+                i += i
+            pt_layer = pt_layers[i : i + 2]
+            _copy_layer(hf_layer, pt_layer)
+
+    hf_model = LDMBertModel(config).eval()
+
+    # copy  embeds
+    hf_model.model.embed_tokens.weight = checkpoint.transformer.token_emb.weight
+    hf_model.model.embed_positions.weight.data = checkpoint.transformer.pos_emb.emb.weight
+
+    # copy layer norm
+    _copy_linear(hf_model.model.layer_norm, checkpoint.transformer.norm)
+
+    # copy hidden layers
+    _copy_layers(hf_model.model.layers, checkpoint.transformer.attn_layers.layers)
+
+    _copy_linear(hf_model.to_logits, checkpoint.transformer.to_logits)
+
+    return hf_model
+
+
+def convert_ldm_clip_checkpoint(checkpoint):
+    text_model = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14")
+
+    keys = list(checkpoint.keys())
+
+    text_model_dict = {}
+
+    for key in keys:
+        if key.startswith("cond_stage_model.transformer"):
+            text_model_dict[key[len("cond_stage_model.transformer.") :]] = checkpoint[key]
+
+    text_model.load_state_dict(text_model_dict)
+
+    return text_model
+
+
+textenc_conversion_lst = [
+    ("cond_stage_model.model.positional_embedding", "text_model.embeddings.position_embedding.weight"),
+    ("cond_stage_model.model.token_embedding.weight", "text_model.embeddings.token_embedding.weight"),
+    ("cond_stage_model.model.ln_final.weight", "text_model.final_layer_norm.weight"),
+    ("cond_stage_model.model.ln_final.bias", "text_model.final_layer_norm.bias"),
+]
+textenc_conversion_map = {x[0]: x[1] for x in textenc_conversion_lst}
+
+textenc_transformer_conversion_lst = [
+    # (stable-diffusion, HF Diffusers)
+    ("resblocks.", "text_model.encoder.layers."),
+    ("ln_1", "layer_norm1"),
+    ("ln_2", "layer_norm2"),
+    (".c_fc.", ".fc1."),
+    (".c_proj.", ".fc2."),
+    (".attn", ".self_attn"),
+    ("ln_final.", "transformer.text_model.final_layer_norm."),
+    ("token_embedding.weight", "transformer.text_model.embeddings.token_embedding.weight"),
+    ("positional_embedding", "transformer.text_model.embeddings.position_embedding.weight"),
+]
+protected = {re.escape(x[0]): x[1] for x in textenc_transformer_conversion_lst}
+textenc_pattern = re.compile("|".join(protected.keys()))
+
+
+def convert_paint_by_example_checkpoint(checkpoint):
+    config = CLIPVisionConfig.from_pretrained("openai/clip-vit-large-patch14")
+    model = PaintByExampleImageEncoder(config)
+
+    keys = list(checkpoint.keys())
+
+    text_model_dict = {}
+
+    for key in keys:
+        if key.startswith("cond_stage_model.transformer"):
+            text_model_dict[key[len("cond_stage_model.transformer.") :]] = checkpoint[key]
+
+    # load clip vision
+    model.model.load_state_dict(text_model_dict)
+
+    # load mapper
+    keys_mapper = {
+        k[len("cond_stage_model.mapper.res") :]: v
+        for k, v in checkpoint.items()
+        if k.startswith("cond_stage_model.mapper")
+    }
+
+    MAPPING = {
+        "attn.c_qkv": ["attn1.to_q", "attn1.to_k", "attn1.to_v"],
+        "attn.c_proj": ["attn1.to_out.0"],
+        "ln_1": ["norm1"],
+        "ln_2": ["norm3"],
+        "mlp.c_fc": ["ff.net.0.proj"],
+        "mlp.c_proj": ["ff.net.2"],
+    }
+
+    mapped_weights = {}
+    for key, value in keys_mapper.items():
+        prefix = key[: len("blocks.i")]
+        suffix = key.split(prefix)[-1].split(".")[-1]
+        name = key.split(prefix)[-1].split(suffix)[0][1:-1]
+        mapped_names = MAPPING[name]
+
+        num_splits = len(mapped_names)
+        for i, mapped_name in enumerate(mapped_names):
+            new_name = ".".join([prefix, mapped_name, suffix])
+            shape = value.shape[0] // num_splits
+            mapped_weights[new_name] = value[i * shape : (i + 1) * shape]
+
+    model.mapper.load_state_dict(mapped_weights)
+
+    # load final layer norm
+    model.final_layer_norm.load_state_dict(
+        {
+            "bias": checkpoint["cond_stage_model.final_ln.bias"],
+            "weight": checkpoint["cond_stage_model.final_ln.weight"],
+        }
+    )
+
+    # load final proj
+    model.proj_out.load_state_dict(
+        {
+            "bias": checkpoint["proj_out.bias"],
+            "weight": checkpoint["proj_out.weight"],
+        }
+    )
+
+    # load uncond vector
+    model.uncond_vector.data = torch.nn.Parameter(checkpoint["learnable_vector"])
+    return model
+
+
+def convert_open_clip_checkpoint(checkpoint):
+    text_model = CLIPTextModel.from_pretrained("stabilityai/stable-diffusion-2", subfolder="text_encoder")
+
+    keys = list(checkpoint.keys())
+
+    text_model_dict = {}
+
+    d_model = int(checkpoint["cond_stage_model.model.text_projection"].shape[0])
+
+    text_model_dict["text_model.embeddings.position_ids"] = text_model.text_model.embeddings.get_buffer("position_ids")
+
+    for key in keys:
+        if "resblocks.23" in key:  # Diffusers drops the final layer and only uses the penultimate layer
+            continue
+        if key in textenc_conversion_map:
+            text_model_dict[textenc_conversion_map[key]] = checkpoint[key]
+        if key.startswith("cond_stage_model.model.transformer."):
+            new_key = key[len("cond_stage_model.model.transformer.") :]
+            if new_key.endswith(".in_proj_weight"):
+                new_key = new_key[: -len(".in_proj_weight")]
+                new_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], new_key)
+                text_model_dict[new_key + ".q_proj.weight"] = checkpoint[key][:d_model, :]
+                text_model_dict[new_key + ".k_proj.weight"] = checkpoint[key][d_model : d_model * 2, :]
+                text_model_dict[new_key + ".v_proj.weight"] = checkpoint[key][d_model * 2 :, :]
+            elif new_key.endswith(".in_proj_bias"):
+                new_key = new_key[: -len(".in_proj_bias")]
+                new_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], new_key)
+                text_model_dict[new_key + ".q_proj.bias"] = checkpoint[key][:d_model]
+                text_model_dict[new_key + ".k_proj.bias"] = checkpoint[key][d_model : d_model * 2]
+                text_model_dict[new_key + ".v_proj.bias"] = checkpoint[key][d_model * 2 :]
+            else:
+                new_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], new_key)
+
+                text_model_dict[new_key] = checkpoint[key]
+
+    text_model.load_state_dict(text_model_dict)
+
+    return text_model
+
+def convert_ckpt_to_diffuser(checkpoint_path:str,
+                             dump_path:str,
+                             original_config_file:str=None,
+                             num_in_channels:int=None,
+                             scheduler_type:str='pndm',
+                             pipeline_type:str=None,
+                             image_size:int=None,
+                             prediction_type:str=None,
+                             extract_ema:bool=False,
+                             upcast_attn:bool=False,
+                             ):
+
+    checkpoint = torch.load(checkpoint_path)
+
+    # Sometimes models don't have the global_step item
+    if "global_step" in checkpoint:
+        global_step = checkpoint["global_step"]
+    else:
+        print("global_step key not found in model")
+        global_step = None
+    checkpoint = checkpoint["state_dict"]
+
+    upcast_attention = False
+    if original_config_file is None:
+        key_name = "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight"
+
+        if key_name in checkpoint and checkpoint[key_name].shape[-1] == 1024:
+            original_config_file = os.path.join(Globals.root,'configs','stable-diffusion','v2-inference-v.yaml')
+
+            if global_step == 110000:
+                # v2.1 needs to upcast attention
+                upcast_attention = True
+        else:
+            original_config_file = os.path.join(Globals.root,'configs','stable-diffusion','v1-inference.yaml')
+
+    original_config = OmegaConf.load(original_config_file)
+
+    if num_in_channels is not None:
+        original_config["model"]["params"]["unet_config"]["params"]["in_channels"] = num_in_channels
+
+    if (
+        "parameterization" in original_config["model"]["params"]
+        and original_config["model"]["params"]["parameterization"] == "v"
+    ):
+        if prediction_type is None:
+            # NOTE: For stable diffusion 2 base it is recommended to pass `prediction_type=="epsilon"`
+            # as it relies on a brittle global step parameter here
+            prediction_type = "epsilon" if global_step == 875000 else "v_prediction"
+        if image_size is None:
+            # NOTE: For stable diffusion 2 base one has to pass `image_size==512`
+            # as it relies on a brittle global step parameter here
+            image_size = 512 if global_step == 875000 else 768
+    else:
+        if prediction_type is None:
+            prediction_type = "epsilon"
+        if image_size is None:
+            image_size = 512
+
+    num_train_timesteps = original_config.model.params.timesteps
+    beta_start = original_config.model.params.linear_start
+    beta_end = original_config.model.params.linear_end
+
+    scheduler = DDIMScheduler(
+        beta_end=beta_end,
+        beta_schedule="scaled_linear",
+        beta_start=beta_start,
+        num_train_timesteps=num_train_timesteps,
+        steps_offset=1,
+        clip_sample=False,
+        set_alpha_to_one=False,
+        prediction_type=prediction_type,
+    )
+    # make sure scheduler works correctly with DDIM
+    scheduler.register_to_config(clip_sample=False)
+
+    if scheduler_type == "pndm":
+        config = dict(scheduler.config)
+        config["skip_prk_steps"] = True
+        scheduler = PNDMScheduler.from_config(config)
+    elif scheduler_type == "lms":
+        scheduler = LMSDiscreteScheduler.from_config(scheduler.config)
+    elif scheduler_type == "heun":
+        scheduler = HeunDiscreteScheduler.from_config(scheduler.config)
+    elif scheduler_type == "euler":
+        scheduler = EulerDiscreteScheduler.from_config(scheduler.config)
+    elif scheduler_type == "euler-ancestral":
+        scheduler = EulerAncestralDiscreteScheduler.from_config(scheduler.config)
+    elif scheduler_type == "dpm":
+        scheduler = DPMSolverMultistepScheduler.from_config(scheduler.config)
+    elif scheduler_type == "ddim":
+        scheduler = scheduler
+    else:
+        raise ValueError(f"Scheduler of type {scheduler_type} doesn't exist!")
+
+    # Convert the UNet2DConditionModel model.
+    unet_config = create_unet_diffusers_config(original_config, image_size=image_size)
+    unet_config["upcast_attention"] = upcast_attention
+    unet = UNet2DConditionModel(**unet_config)
+
+    converted_unet_checkpoint = convert_ldm_unet_checkpoint(
+        checkpoint, unet_config, path=checkpoint_path, extract_ema=extract_ema
+    )
+
+    unet.load_state_dict(converted_unet_checkpoint)
+
+    # Convert the VAE model.
+    vae_config = create_vae_diffusers_config(original_config, image_size=image_size)
+    converted_vae_checkpoint = convert_ldm_vae_checkpoint(checkpoint, vae_config)
+
+    vae = AutoencoderKL(**vae_config)
+    vae.load_state_dict(converted_vae_checkpoint)
+
+    # Convert the text model.
+    model_type = pipeline_type
+    if model_type is None:
+        model_type = original_config.model.params.cond_stage_config.target.split(".")[-1]
+
+    if model_type == "FrozenOpenCLIPEmbedder":
+        text_model = convert_open_clip_checkpoint(checkpoint)
+        tokenizer = CLIPTokenizer.from_pretrained("stabilityai/stable-diffusion-2", subfolder="tokenizer")
+        pipe = StableDiffusionPipeline(
+            vae=vae,
+            text_encoder=text_model,
+            tokenizer=tokenizer,
+            unet=unet,
+            scheduler=scheduler,
+            safety_checker=None,
+            feature_extractor=None,
+            requires_safety_checker=False,
+        )
+    elif model_type == "PaintByExample":
+        vision_model = convert_paint_by_example_checkpoint(checkpoint)
+        tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
+        feature_extractor = AutoFeatureExtractor.from_pretrained("CompVis/stable-diffusion-safety-checker")
+        pipe = PaintByExamplePipeline(
+            vae=vae,
+            image_encoder=vision_model,
+            unet=unet,
+            scheduler=scheduler,
+            safety_checker=None,
+            feature_extractor=feature_extractor,
+        )
+    elif model_type in ['FrozenCLIPEmbedder','WeightedFrozenCLIPEmbedder']:
+        text_model = convert_ldm_clip_checkpoint(checkpoint)
+        tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
+        safety_checker = StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker")
+        feature_extractor = AutoFeatureExtractor.from_pretrained("CompVis/stable-diffusion-safety-checker")
+        pipe = StableDiffusionPipeline(
+            vae=vae,
+            text_encoder=text_model,
+            tokenizer=tokenizer,
+            unet=unet,
+            scheduler=scheduler,
+            safety_checker=safety_checker,
+            feature_extractor=feature_extractor,
+        )
+    else:
+        text_config = create_ldm_bert_config(original_config)
+        text_model = convert_ldm_bert_checkpoint(checkpoint, text_config)
+        tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased")
+        pipe = LDMTextToImagePipeline(vqvae=vae, bert=text_model, tokenizer=tokenizer, unet=unet, scheduler=scheduler)
+
+    pipe.save_pretrained(dump_path)
diff --git a/ldm/invoke/model_cache.py b/ldm/invoke/model_cache.py
index a67da0ef5e5..bdeb85082d3 100644
--- a/ldm/invoke/model_cache.py
+++ b/ldm/invoke/model_cache.py
@@ -180,7 +180,7 @@ def del_model(self, model_name:str) -> None:
         if model_name in self.stack:
             self.stack.remove(model_name)
 
-    def add_model(self, model_name:str, model_attributes:dict, clobber=False) -> None:
+    def add_model(self, model_name:str, model_attributes:dict, clobber:bool=False) -> None:
         '''
         Update the named model with a dictionary of attributes. Will fail with an
         assertion error if the name already exists. Pass clobber=True to overwrite.
@@ -189,8 +189,14 @@ def add_model(self, model_name:str, model_attributes:dict, clobber=False) -> Non
         attributes are incorrect or the model name is missing.
         '''
         omega = self.config
-        for field in ('description','weights','height','width','config'):
-            assert field in model_attributes, f'required field {field} is missing'
+        assert 'format' in model_attributes, f'missing required field "format"'
+        if model_attributes['format']=='diffusers':
+            assert 'description' in model_attributes, 'required field "description" is missing'
+            assert 'path' in model_attributes or 'repo_id' in model_attributes,'model must have either the "path" or "repo_id" fields defined'
+        else:
+            for field in ('description','weights','height','width','config'):
+                assert field in model_attributes, f'required field {field} is missing'
+
         assert (clobber or model_name not in omega), f'attempt to overwrite existing model definition "{model_name}"'
 
         config = omega[model_name] if model_name in omega else {}
@@ -368,6 +374,8 @@ def _load_diffusers_model(self, mconfig):
         width = pipeline.vae.block_out_channels[-2]
         height = pipeline.vae.block_out_channels[-1]
 
+        print(f'  | training width x height = ({width} x {height})')
+
         return pipeline, width, height, model_hash
 
     def model_name_or_path(self, model_name:Union[str,DictConfig]) -> str | Path:
@@ -569,7 +577,7 @@ def _load_vae(self, vae_config):
                 vae = AutoencoderKL.from_pretrained(name_or_path, **vae_args, **fp_args)
             except OSError as e:
                 if str(e).startswith('fp16 is not a valid'):
-                    print(f'Could not fetch half-precision version of model {repo_id}; fetching full-precision instead')
+                    print(f'Could not fetch half-precision version of model {name_or_path}; fetching full-precision instead')
                 else:
                     print(f'An unexpected error occurred while downloading the model: {e})')
             if vae:
diff --git a/ldm/invoke/readline.py b/ldm/invoke/readline.py
index 376e0092960..d54d96409b3 100644
--- a/ldm/invoke/readline.py
+++ b/ldm/invoke/readline.py
@@ -59,7 +59,7 @@
     '--png_compression','-z',
     '--text_mask','-tm',
     '!fix','!fetch','!replay','!history','!search','!clear',
-    '!models','!switch','!import_model','!edit_model','!del_model',
+    '!models','!switch','!import_model','!optimize_model','!edit_model','!del_model',
     '!mask',
     )
 MODEL_COMMANDS = (
@@ -68,7 +68,7 @@
     '!del_model',
     )
 WEIGHT_COMMANDS = (
-    '!import_model',
+    '!import_model','!optimize_model',
     )
 IMG_PATH_COMMANDS = (
     '--outdir[=\s]',

From b71b789186c63c45f8f24bbe7b2527b39e3a0e31 Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Thu, 22 Dec 2022 15:48:30 -0500
Subject: [PATCH 085/199] add parallel set of generator files for ckpt legacy
 generation

---
 ldm/generate.py                          |  66 ++-
 ldm/invoke/ckpt_generator/__init__.py    |   4 +
 ldm/invoke/ckpt_generator/base.py        | 338 +++++++++++++++
 ldm/invoke/ckpt_generator/embiggen.py    | 501 +++++++++++++++++++++++
 ldm/invoke/ckpt_generator/img2img.py     |  97 +++++
 ldm/invoke/ckpt_generator/inpaint.py     | 358 ++++++++++++++++
 ldm/invoke/ckpt_generator/omnibus.py     | 175 ++++++++
 ldm/invoke/ckpt_generator/txt2img.py     |  88 ++++
 ldm/invoke/ckpt_generator/txt2img2img.py | 182 ++++++++
 ldm/invoke/model_cache.py                |  15 +
 10 files changed, 1783 insertions(+), 41 deletions(-)
 create mode 100644 ldm/invoke/ckpt_generator/__init__.py
 create mode 100644 ldm/invoke/ckpt_generator/base.py
 create mode 100644 ldm/invoke/ckpt_generator/embiggen.py
 create mode 100644 ldm/invoke/ckpt_generator/img2img.py
 create mode 100644 ldm/invoke/ckpt_generator/inpaint.py
 create mode 100644 ldm/invoke/ckpt_generator/omnibus.py
 create mode 100644 ldm/invoke/ckpt_generator/txt2img.py
 create mode 100644 ldm/invoke/ckpt_generator/txt2img2img.py

diff --git a/ldm/generate.py b/ldm/generate.py
index cea0ee05c33..5f246ee84fa 100644
--- a/ldm/generate.py
+++ b/ldm/generate.py
@@ -10,6 +10,7 @@
 import sys
 import time
 import traceback
+import importlib
 
 import cv2
 import numpy as np
@@ -179,7 +180,6 @@ def __init__(
         self.sampler        = None
         self.device         = None
         self.session_peakmem = None
-        self.generators     = {}
         self.base_generator = None
         self.seed           = None
         self.outdir = outdir
@@ -772,55 +772,36 @@ def _make_images(
 
         return init_image,init_mask
 
-    # lots o' repeated code here! Turn into a make_func()
     def _make_base(self):
-        if not self.generators.get('base'):
-            from ldm.invoke.generator import Generator
-            self.generators['base'] = Generator(self.model, self.precision)
-        return self.generators['base']
+        return self._load_generator('generator','Generator')
+
+    def _make_txt2img(self):
+        return self._load_generator('txt2img','Txt2Img')
 
     def _make_img2img(self):
-        if not self.generators.get('img2img'):
-            from ldm.invoke.generator.img2img import Img2Img
-            self.generators['img2img'] = Img2Img(self.model, self.precision)
-            self.generators['img2img'].free_gpu_mem = self.free_gpu_mem
-        return self.generators['img2img']
+        return self._load_generator('img2img','Img2Img')
 
     def _make_embiggen(self):
-        if not self.generators.get('embiggen'):
-            from ldm.invoke.generator.embiggen import Embiggen
-            self.generators['embiggen'] = Embiggen(self.model, self.precision)
-        return self.generators['embiggen']
-
-    def _make_txt2img(self):
-        if not self.generators.get('txt2img'):
-            from ldm.invoke.generator.txt2img import Txt2Img
-            self.generators['txt2img'] = Txt2Img(self.model, self.precision)
-            self.generators['txt2img'].free_gpu_mem = self.free_gpu_mem
-        return self.generators['txt2img']
-
+        return self._load_generator('embiggen','Embiggen')
+   
     def _make_txt2img2img(self):
-        if not self.generators.get('txt2img2'):
-            from ldm.invoke.generator.txt2img2img import Txt2Img2Img
-            self.generators['txt2img2'] = Txt2Img2Img(self.model, self.precision)
-            self.generators['txt2img2'].free_gpu_mem = self.free_gpu_mem
-        return self.generators['txt2img2']
+        return self._load_generator('txt2img2img','Txt2Img2Img')
 
     def _make_inpaint(self):
-        if not self.generators.get('inpaint'):
-            from ldm.invoke.generator.inpaint import Inpaint
-            self.generators['inpaint'] = Inpaint(self.model, self.precision)
-            self.generators['inpaint'].free_gpu_mem = self.free_gpu_mem
-        return self.generators['inpaint']
-
-    # "omnibus" supports the runwayML custom inpainting model, which does
-    # txt2img, img2img and inpainting using slight variations on the same code
+        return self._load_generator('inpaint','Inpaint')
+
     def _make_omnibus(self):
-        if not self.generators.get('omnibus'):
-            from ldm.invoke.generator.omnibus import Omnibus
-            self.generators['omnibus'] = Omnibus(self.model, self.precision)
-            self.generators['omnibus'].free_gpu_mem = self.free_gpu_mem
-        return self.generators['omnibus']
+        return self._load_generator('omnibus','Omnibus')
+
+    def _load_generator(self, module, class_name):
+        if self.is_legacy_model(self.model_name):
+            mn = f'ldm.invoke.ckpt_{module}'
+            cn = f'Ckpt{class_name}'
+        else:
+            mn = f'ldm.invoke.{module}'
+            cn = class_name
+        importlib.import_module(mn)
+        return cn(self.model, self.precision)
 
     def load_model(self):
         '''
@@ -981,6 +962,9 @@ def sample_to_image(self, samples):
     def sample_to_lowres_estimated_image(self, samples):
         return self._make_base().sample_to_lowres_estimated_image(samples)
 
+    def is_legacy_model(model_name)->bool:
+        return self.model_cache.is_legacy(model_name)
+
     def _set_sampler(self):
         if isinstance(self.model, DiffusionPipeline):
             return self._set_scheduler()
diff --git a/ldm/invoke/ckpt_generator/__init__.py b/ldm/invoke/ckpt_generator/__init__.py
new file mode 100644
index 00000000000..2fa5573c84d
--- /dev/null
+++ b/ldm/invoke/ckpt_generator/__init__.py
@@ -0,0 +1,4 @@
+'''
+Initialization file for the ldm.invoke.generator package
+'''
+from .base import Generator
diff --git a/ldm/invoke/ckpt_generator/base.py b/ldm/invoke/ckpt_generator/base.py
new file mode 100644
index 00000000000..c73bb50c9bf
--- /dev/null
+++ b/ldm/invoke/ckpt_generator/base.py
@@ -0,0 +1,338 @@
+'''
+Base class for ldm.invoke.ckpt_generator.*
+including img2img, txt2img, and inpaint
+
+THESE MODULES ARE TRANSITIONAL AND WILL BE REMOVED AT A FUTURE DATE
+WHEN LEGACY CKPT MODEL SUPPORT IS DISCONTINUED.
+'''
+import torch
+import numpy as  np
+import random
+import os
+import os.path as osp
+import traceback
+from tqdm import tqdm, trange
+from PIL import Image, ImageFilter, ImageChops
+import cv2 as cv
+from einops import rearrange, repeat
+from pytorch_lightning import seed_everything
+from ldm.invoke.devices import choose_autocast
+from ldm.models.diffusion.cross_attention_map_saving import AttentionMapSaver
+from ldm.util import rand_perlin_2d
+
+downsampling = 8
+CAUTION_IMG = 'assets/caution.png'
+
+class CkptGenerator():
+    def __init__(self, model, precision):
+        self.model = model
+        self.precision = precision
+        self.seed = None
+        self.latent_channels = model.channels
+        self.downsampling_factor = downsampling   # BUG: should come from model or config
+        self.safety_checker = None
+        self.perlin = 0.0
+        self.threshold = 0
+        self.variation_amount = 0
+        self.with_variations = []
+        self.use_mps_noise = False
+        self.free_gpu_mem = None
+        self.caution_img = None
+
+    # this is going to be overridden in img2img.py, txt2img.py and inpaint.py
+    def get_make_image(self,prompt,**kwargs):
+        """
+        Returns a function returning an image derived from the prompt and the initial image
+        Return value depends on the seed at the time you call it
+        """
+        raise NotImplementedError("image_iterator() must be implemented in a descendent class")
+
+    def set_variation(self, seed, variation_amount, with_variations):
+        self.seed             = seed
+        self.variation_amount = variation_amount
+        self.with_variations  = with_variations
+
+    def generate(self,prompt,init_image,width,height,sampler, iterations=1,seed=None,
+                 image_callback=None, step_callback=None, threshold=0.0, perlin=0.0,
+                 safety_checker:dict=None,
+                 attention_maps_callback = None,
+                 **kwargs):
+        scope = choose_autocast(self.precision)
+        self.safety_checker = safety_checker
+        attention_maps_images = []
+        attention_maps_callback = lambda saver: attention_maps_images.append(saver.get_stacked_maps_image())
+        make_image = self.get_make_image(
+            prompt,
+            sampler = sampler,
+            init_image    = init_image,
+            width         = width,
+            height        = height,
+            step_callback = step_callback,
+            threshold     = threshold,
+            perlin        = perlin,
+            attention_maps_callback = attention_maps_callback,
+            **kwargs
+        )
+        results             = []
+        seed                = seed if seed is not None and seed >= 0 else self.new_seed()
+        first_seed          = seed
+        seed, initial_noise = self.generate_initial_noise(seed, width, height)
+
+        # There used to be an additional self.model.ema_scope() here, but it breaks
+        # the inpaint-1.5 model. Not sure what it did.... ?
+        with scope(self.model.device.type):
+            for n in trange(iterations, desc='Generating'):
+                x_T = None
+                if self.variation_amount > 0:
+                    seed_everything(seed)
+                    target_noise = self.get_noise(width,height)
+                    x_T = self.slerp(self.variation_amount, initial_noise, target_noise)
+                elif initial_noise is not None:
+                    # i.e. we specified particular variations
+                    x_T = initial_noise
+                else:
+                    seed_everything(seed)
+                    try:
+                        x_T = self.get_noise(width,height)
+                    except:
+                        print('** An error occurred while getting initial noise **')
+                        print(traceback.format_exc())
+
+                image = make_image(x_T)
+
+                if self.safety_checker is not None:
+                    image = self.safety_check(image)
+
+                results.append([image, seed])
+
+                if image_callback is not None:
+                    attention_maps_image = None if len(attention_maps_images)==0 else attention_maps_images[-1]
+                    image_callback(image, seed, first_seed=first_seed, attention_maps_image=attention_maps_image)
+
+                seed = self.new_seed()
+
+        return results
+
+    def sample_to_image(self,samples)->Image.Image:
+        """
+        Given samples returned from a sampler, converts
+        it into a PIL Image
+        """
+        x_samples = self.model.decode_first_stage(samples)
+        x_samples = torch.clamp((x_samples + 1.0) / 2.0, min=0.0, max=1.0)
+        if len(x_samples) != 1:
+            raise Exception(
+                f'>> expected to get a single image, but got {len(x_samples)}')
+        x_sample = 255.0 * rearrange(
+            x_samples[0].cpu().numpy(), 'c h w -> h w c'
+        )
+        return Image.fromarray(x_sample.astype(np.uint8))
+
+        # write an approximate RGB image from latent samples for a single step to PNG
+
+    def repaste_and_color_correct(self, result: Image.Image, init_image: Image.Image, init_mask: Image.Image, mask_blur_radius: int = 8) -> Image.Image:
+        if init_image is None or init_mask is None:
+            return result
+
+        # Get the original alpha channel of the mask if there is one.
+        # Otherwise it is some other black/white image format ('1', 'L' or 'RGB')
+        pil_init_mask = init_mask.getchannel('A') if init_mask.mode == 'RGBA' else init_mask.convert('L')
+        pil_init_image = init_image.convert('RGBA') # Add an alpha channel if one doesn't exist
+
+        # Build an image with only visible pixels from source to use as reference for color-matching.
+        init_rgb_pixels = np.asarray(init_image.convert('RGB'), dtype=np.uint8)
+        init_a_pixels = np.asarray(pil_init_image.getchannel('A'), dtype=np.uint8)
+        init_mask_pixels = np.asarray(pil_init_mask, dtype=np.uint8)
+
+        # Get numpy version of result
+        np_image = np.asarray(result, dtype=np.uint8)
+
+        # Mask and calculate mean and standard deviation
+        mask_pixels = init_a_pixels * init_mask_pixels > 0
+        np_init_rgb_pixels_masked = init_rgb_pixels[mask_pixels, :]
+        np_image_masked = np_image[mask_pixels, :]
+
+        if np_init_rgb_pixels_masked.size > 0:
+            init_means = np_init_rgb_pixels_masked.mean(axis=0)
+            init_std = np_init_rgb_pixels_masked.std(axis=0)
+            gen_means = np_image_masked.mean(axis=0)
+            gen_std = np_image_masked.std(axis=0)
+
+            # Color correct
+            np_matched_result = np_image.copy()
+            np_matched_result[:,:,:] = (((np_matched_result[:,:,:].astype(np.float32) - gen_means[None,None,:]) / gen_std[None,None,:]) * init_std[None,None,:] + init_means[None,None,:]).clip(0, 255).astype(np.uint8)
+            matched_result = Image.fromarray(np_matched_result, mode='RGB')
+        else:
+            matched_result = Image.fromarray(np_image, mode='RGB')
+
+        # Blur the mask out (into init image) by specified amount
+        if mask_blur_radius > 0:
+            nm = np.asarray(pil_init_mask, dtype=np.uint8)
+            nmd = cv.erode(nm, kernel=np.ones((3,3), dtype=np.uint8), iterations=int(mask_blur_radius / 2))
+            pmd = Image.fromarray(nmd, mode='L')
+            blurred_init_mask = pmd.filter(ImageFilter.BoxBlur(mask_blur_radius))
+        else:
+            blurred_init_mask = pil_init_mask
+
+        multiplied_blurred_init_mask = ImageChops.multiply(blurred_init_mask, self.pil_image.split()[-1])
+
+        # Paste original on color-corrected generation (using blurred mask)
+        matched_result.paste(init_image, (0,0), mask = multiplied_blurred_init_mask)
+        return matched_result
+
+
+
+    def sample_to_lowres_estimated_image(self,samples):
+        # origingally adapted from code by @erucipe and @keturn here:
+        # https://discuss.huggingface.co/t/decoding-latents-to-rgb-without-upscaling/23204/7
+
+        # these updated numbers for v1.5 are from @torridgristle
+        v1_5_latent_rgb_factors = torch.tensor([
+            #    R        G        B
+            [ 0.3444,  0.1385,  0.0670], # L1
+            [ 0.1247,  0.4027,  0.1494], # L2
+            [-0.3192,  0.2513,  0.2103], # L3
+            [-0.1307, -0.1874, -0.7445]  # L4
+        ], dtype=samples.dtype, device=samples.device)
+
+        latent_image = samples[0].permute(1, 2, 0) @ v1_5_latent_rgb_factors
+        latents_ubyte = (((latent_image + 1) / 2)
+                         .clamp(0, 1)  # change scale from -1..1 to 0..1
+                         .mul(0xFF)  # to 0..255
+                         .byte()).cpu()
+
+        return Image.fromarray(latents_ubyte.numpy())
+
+    def generate_initial_noise(self, seed, width, height):
+        initial_noise = None
+        if self.variation_amount > 0 or len(self.with_variations) > 0:
+            # use fixed initial noise plus random noise per iteration
+            seed_everything(seed)
+            initial_noise = self.get_noise(width,height)
+            for v_seed, v_weight in self.with_variations:
+                seed = v_seed
+                seed_everything(seed)
+                next_noise = self.get_noise(width,height)
+                initial_noise = self.slerp(v_weight, initial_noise, next_noise)
+            if self.variation_amount > 0:
+                random.seed() # reset RNG to an actually random state, so we can get a random seed for variations
+                seed = random.randrange(0,np.iinfo(np.uint32).max)
+            return (seed, initial_noise)
+        else:
+            return (seed, None)
+
+    # returns a tensor filled with random numbers from a normal distribution
+    def get_noise(self,width,height):
+        """
+        Returns a tensor filled with random numbers, either form a normal distribution
+        (txt2img) or from the latent image (img2img, inpaint)
+        """
+        raise NotImplementedError("get_noise() must be implemented in a descendent class")
+
+    def get_perlin_noise(self,width,height):
+        fixdevice = 'cpu' if (self.model.device.type == 'mps') else self.model.device
+        return torch.stack([rand_perlin_2d((height, width), (8, 8), device = self.model.device).to(fixdevice) for _ in range(self.latent_channels)], dim=0).to(self.model.device)
+
+    def new_seed(self):
+        self.seed = random.randrange(0, np.iinfo(np.uint32).max)
+        return self.seed
+
+    def slerp(self, t, v0, v1, DOT_THRESHOLD=0.9995):
+        '''
+        Spherical linear interpolation
+        Args:
+            t (float/np.ndarray): Float value between 0.0 and 1.0
+            v0 (np.ndarray): Starting vector
+            v1 (np.ndarray): Final vector
+            DOT_THRESHOLD (float): Threshold for considering the two vectors as
+                                colineal. Not recommended to alter this.
+        Returns:
+            v2 (np.ndarray): Interpolation vector between v0 and v1
+        '''
+        inputs_are_torch = False
+        if not isinstance(v0, np.ndarray):
+            inputs_are_torch = True
+            v0 = v0.detach().cpu().numpy()
+        if not isinstance(v1, np.ndarray):
+            inputs_are_torch = True
+            v1 = v1.detach().cpu().numpy()
+
+        dot = np.sum(v0 * v1 / (np.linalg.norm(v0) * np.linalg.norm(v1)))
+        if np.abs(dot) > DOT_THRESHOLD:
+            v2 = (1 - t) * v0 + t * v1
+        else:
+            theta_0 = np.arccos(dot)
+            sin_theta_0 = np.sin(theta_0)
+            theta_t = theta_0 * t
+            sin_theta_t = np.sin(theta_t)
+            s0 = np.sin(theta_0 - theta_t) / sin_theta_0
+            s1 = sin_theta_t / sin_theta_0
+            v2 = s0 * v0 + s1 * v1
+
+        if inputs_are_torch:
+            v2 = torch.from_numpy(v2).to(self.model.device)
+
+        return v2
+
+    def safety_check(self,image:Image.Image):
+        '''
+        If the CompViz safety checker flags an NSFW image, we
+        blur it out.
+        '''
+        import diffusers
+
+        checker = self.safety_checker['checker']
+        extractor = self.safety_checker['extractor']
+        features = extractor([image], return_tensors="pt")
+        features.to(self.model.device)
+
+        # unfortunately checker requires the numpy version, so we have to convert back
+        x_image = np.array(image).astype(np.float32) / 255.0
+        x_image = x_image[None].transpose(0, 3, 1, 2)
+
+        diffusers.logging.set_verbosity_error()
+        checked_image, has_nsfw_concept = checker(images=x_image, clip_input=features.pixel_values)
+        if has_nsfw_concept[0]:
+            print('** An image with potential non-safe content has been detected. A blurred image will be returned. **')
+            return self.blur(image)
+        else:
+            return image
+
+    def blur(self,input):
+        blurry = input.filter(filter=ImageFilter.GaussianBlur(radius=32))
+        try:
+            caution = self.get_caution_img()
+            if caution:
+                blurry.paste(caution,(0,0),caution)
+        except FileNotFoundError:
+            pass
+        return blurry
+
+    def get_caution_img(self):
+        path = None
+        if self.caution_img:
+            return self.caution_img
+        # Find the caution image. If we are installed in the package directory it will
+        # be six levels up. If we are in the repo directory it will be three levels up.
+        for dots in ('../../..','../../../../../..'):
+            caution_path = osp.join(osp.dirname(__file__),dots,CAUTION_IMG)
+            if osp.exists(caution_path):
+                path = caution_path
+                break
+        if not path:
+            return
+        caution = Image.open(path)
+        self.caution_img = caution.resize((caution.width // 2, caution.height //2))
+        return self.caution_img
+
+    # this is a handy routine for debugging use. Given a generated sample,
+    # convert it into a PNG image and store it at the indicated path
+    def save_sample(self, sample, filepath):
+        image = self.sample_to_image(sample)
+        dirname = os.path.dirname(filepath) or '.'
+        if not os.path.exists(dirname):
+            print(f'** creating directory {dirname}')
+            os.makedirs(dirname, exist_ok=True)
+        image.save(filepath,'PNG')
+
+
diff --git a/ldm/invoke/ckpt_generator/embiggen.py b/ldm/invoke/ckpt_generator/embiggen.py
new file mode 100644
index 00000000000..0b43d3d19b3
--- /dev/null
+++ b/ldm/invoke/ckpt_generator/embiggen.py
@@ -0,0 +1,501 @@
+'''
+ldm.invoke.ckpt_generator.embiggen descends from ldm.invoke.ckpt_generator
+and generates with ldm.invoke.ckpt_generator.img2img
+'''
+
+import torch
+import numpy as  np
+from tqdm import trange
+from PIL               import Image
+from ldm.invoke.ckpt_generator.base      import CkptGenerator
+from ldm.invoke.ckpt_generator.img2img   import CkptImg2Img
+from ldm.invoke.devices import choose_autocast
+from ldm.models.diffusion.ddim     import DDIMSampler
+
+class CkptEmbiggen(CkptGenerator):
+    def __init__(self, model, precision):
+        super().__init__(model, precision)
+        self.init_latent         = None
+
+    # Replace generate because Embiggen doesn't need/use most of what it does normallly
+    def generate(self,prompt,iterations=1,seed=None,
+                 image_callback=None, step_callback=None,
+                 **kwargs):
+
+        scope      = choose_autocast(self.precision)
+        make_image = self.get_make_image(
+            prompt,
+            step_callback = step_callback,
+            **kwargs
+        )
+        results             = []
+        seed                = seed if seed else self.new_seed()
+
+        # Noise will be generated by the Img2Img generator when called
+        with scope(self.model.device.type), self.model.ema_scope():
+            for n in trange(iterations, desc='Generating'):
+                # make_image will call Img2Img which will do the equivalent of get_noise itself
+                image = make_image()
+                results.append([image, seed])
+                if image_callback is not None:
+                    image_callback(image, seed, prompt_in=prompt)
+                seed = self.new_seed()
+        return results
+
+    @torch.no_grad()
+    def get_make_image(
+        self,
+        prompt,
+        sampler,
+        steps,
+        cfg_scale,
+        ddim_eta,
+        conditioning,
+        init_img,
+        strength,
+        width,
+        height,
+        embiggen,
+        embiggen_tiles,
+        step_callback=None,
+        **kwargs
+    ):
+        """
+        Returns a function returning an image derived from the prompt and multi-stage twice-baked potato layering over the img2img on the initial image
+        Return value depends on the seed at the time you call it
+        """
+        assert not sampler.uses_inpainting_model(), "--embiggen is not supported by inpainting models"
+
+        # Construct embiggen arg array, and sanity check arguments
+        if embiggen == None:  # embiggen can also be called with just embiggen_tiles
+            embiggen = [1.0]  # If not specified, assume no scaling
+        elif embiggen[0] < 0:
+            embiggen[0] = 1.0
+            print(
+                '>> Embiggen scaling factor cannot be negative, fell back to the default of 1.0 !')
+        if len(embiggen) < 2:
+            embiggen.append(0.75)
+        elif embiggen[1] > 1.0 or embiggen[1] < 0:
+            embiggen[1] = 0.75
+            print('>> Embiggen upscaling strength for ESRGAN must be between 0 and 1, fell back to the default of 0.75 !')
+        if len(embiggen) < 3:
+            embiggen.append(0.25)
+        elif embiggen[2] < 0:
+            embiggen[2] = 0.25
+            print('>> Overlap size for Embiggen must be a positive ratio between 0 and 1 OR a number of pixels, fell back to the default of 0.25 !')
+
+        # Convert tiles from their user-freindly count-from-one to count-from-zero, because we need to do modulo math
+        # and then sort them, because... people.
+        if embiggen_tiles:
+            embiggen_tiles = list(map(lambda n: n-1, embiggen_tiles))
+            embiggen_tiles.sort()
+
+        if strength >= 0.5:
+            print(f'* WARNING: Embiggen may produce mirror motifs if the strength (-f) is too high (currently {strength}). Try values between 0.35-0.45.')
+
+        # Prep img2img generator, since we wrap over it
+        gen_img2img = CkptImg2Img(self.model,self.precision)
+
+        # Open original init image (not a tensor) to manipulate
+        initsuperimage = Image.open(init_img)
+
+        with Image.open(init_img) as img:
+            initsuperimage = img.convert('RGB')
+
+        # Size of the target super init image in pixels
+        initsuperwidth, initsuperheight = initsuperimage.size
+
+        # Increase by scaling factor if not already resized, using ESRGAN as able
+        if embiggen[0] != 1.0:
+            initsuperwidth = round(initsuperwidth*embiggen[0])
+            initsuperheight = round(initsuperheight*embiggen[0])
+            if embiggen[1] > 0:  # No point in ESRGAN upscaling if strength is set zero
+                from ldm.invoke.restoration.realesrgan import ESRGAN
+                esrgan = ESRGAN()
+                print(
+                    f'>> ESRGAN upscaling init image prior to cutting with Embiggen with strength {embiggen[1]}')
+                if embiggen[0] > 2:
+                    initsuperimage = esrgan.process(
+                        initsuperimage,
+                        embiggen[1],  # upscale strength
+                        self.seed,
+                        4,  # upscale scale
+                    )
+                else:
+                    initsuperimage = esrgan.process(
+                        initsuperimage,
+                        embiggen[1],  # upscale strength
+                        self.seed,
+                        2,  # upscale scale
+                    )
+            # We could keep recursively re-running ESRGAN for a requested embiggen[0] larger than 4x
+            #   but from personal experiance it doesn't greatly improve anything after 4x
+            # Resize to target scaling factor resolution
+            initsuperimage = initsuperimage.resize(
+                (initsuperwidth, initsuperheight), Image.Resampling.LANCZOS)
+
+        # Use width and height as tile widths and height
+        # Determine buffer size in pixels
+        if embiggen[2] < 1:
+            if embiggen[2] < 0:
+                embiggen[2] = 0
+            overlap_size_x = round(embiggen[2] * width)
+            overlap_size_y = round(embiggen[2] * height)
+        else:
+            overlap_size_x = round(embiggen[2])
+            overlap_size_y = round(embiggen[2])
+
+        # With overall image width and height known, determine how many tiles we need
+        def ceildiv(a, b):
+            return -1 * (-a // b)
+
+        # X and Y needs to be determined independantly (we may have savings on one based on the buffer pixel count)
+        # (initsuperwidth - width) is the area remaining to the right that we need to layers tiles to fill
+        # (width - overlap_size_x) is how much new we can fill with a single tile
+        emb_tiles_x = 1
+        emb_tiles_y = 1
+        if (initsuperwidth - width) > 0:
+            emb_tiles_x = ceildiv(initsuperwidth - width,
+                                  width - overlap_size_x) + 1
+        if (initsuperheight - height) > 0:
+            emb_tiles_y = ceildiv(initsuperheight - height,
+                                  height - overlap_size_y) + 1
+        # Sanity
+        assert emb_tiles_x > 1 or emb_tiles_y > 1, f'ERROR: Based on the requested dimensions of {initsuperwidth}x{initsuperheight} and tiles of {width}x{height} you don\'t need to Embiggen! Check your arguments.'
+
+        # Prep alpha layers --------------
+        # https://stackoverflow.com/questions/69321734/how-to-create-different-transparency-like-gradient-with-python-pil
+        # agradientL is Left-side transparent
+        agradientL = Image.linear_gradient('L').rotate(
+            90).resize((overlap_size_x, height))
+        # agradientT is Top-side transparent
+        agradientT = Image.linear_gradient('L').resize((width, overlap_size_y))
+        # radial corner is the left-top corner, made full circle then cut to just the left-top quadrant
+        agradientC = Image.new('L', (256, 256))
+        for y in range(256):
+            for x in range(256):
+                # Find distance to lower right corner (numpy takes arrays)
+                distanceToLR = np.sqrt([(255 - x) ** 2 + (255 - y) ** 2])[0]
+                # Clamp values to max 255
+                if distanceToLR > 255:
+                    distanceToLR = 255
+                #Place the pixel as invert of distance
+                agradientC.putpixel((x, y), round(255 - distanceToLR))
+
+        # Create alternative asymmetric diagonal corner to use on "tailing" intersections to prevent hard edges
+        # Fits for a left-fading gradient on the bottom side and full opacity on the right side.
+        agradientAsymC = Image.new('L', (256, 256))
+        for y in range(256):
+            for x in range(256):
+                value = round(max(0, x-(255-y)) * (255 / max(1,y)))
+                #Clamp values
+                value = max(0, value)
+                value = min(255, value)
+                agradientAsymC.putpixel((x, y), value)
+
+        # Create alpha layers default fully white
+        alphaLayerL = Image.new("L", (width, height), 255)
+        alphaLayerT = Image.new("L", (width, height), 255)
+        alphaLayerLTC = Image.new("L", (width, height), 255)
+        # Paste gradients into alpha layers
+        alphaLayerL.paste(agradientL, (0, 0))
+        alphaLayerT.paste(agradientT, (0, 0))
+        alphaLayerLTC.paste(agradientL, (0, 0))
+        alphaLayerLTC.paste(agradientT, (0, 0))
+        alphaLayerLTC.paste(agradientC.resize((overlap_size_x, overlap_size_y)), (0, 0))
+        # make masks with an asymmetric upper-right corner so when the curved transparent corner of the next tile
+        # to its right is placed it doesn't reveal a hard trailing semi-transparent edge in the overlapping space
+        alphaLayerTaC = alphaLayerT.copy()
+        alphaLayerTaC.paste(agradientAsymC.rotate(270).resize((overlap_size_x, overlap_size_y)), (width - overlap_size_x, 0))
+        alphaLayerLTaC = alphaLayerLTC.copy()
+        alphaLayerLTaC.paste(agradientAsymC.rotate(270).resize((overlap_size_x, overlap_size_y)), (width - overlap_size_x, 0))
+
+        if embiggen_tiles:
+            # Individual unconnected sides
+            alphaLayerR = Image.new("L", (width, height), 255)
+            alphaLayerR.paste(agradientL.rotate(
+                180), (width - overlap_size_x, 0))
+            alphaLayerB = Image.new("L", (width, height), 255)
+            alphaLayerB.paste(agradientT.rotate(
+                180), (0, height - overlap_size_y))
+            alphaLayerTB = Image.new("L", (width, height), 255)
+            alphaLayerTB.paste(agradientT, (0, 0))
+            alphaLayerTB.paste(agradientT.rotate(
+                180), (0, height - overlap_size_y))
+            alphaLayerLR = Image.new("L", (width, height), 255)
+            alphaLayerLR.paste(agradientL, (0, 0))
+            alphaLayerLR.paste(agradientL.rotate(
+                180), (width - overlap_size_x, 0))
+
+            # Sides and corner Layers
+            alphaLayerRBC = Image.new("L", (width, height), 255)
+            alphaLayerRBC.paste(agradientL.rotate(
+                180), (width - overlap_size_x, 0))
+            alphaLayerRBC.paste(agradientT.rotate(
+                180), (0, height - overlap_size_y))
+            alphaLayerRBC.paste(agradientC.rotate(180).resize(
+                (overlap_size_x, overlap_size_y)), (width - overlap_size_x, height - overlap_size_y))
+            alphaLayerLBC = Image.new("L", (width, height), 255)
+            alphaLayerLBC.paste(agradientL, (0, 0))
+            alphaLayerLBC.paste(agradientT.rotate(
+                180), (0, height - overlap_size_y))
+            alphaLayerLBC.paste(agradientC.rotate(90).resize(
+                (overlap_size_x, overlap_size_y)), (0, height - overlap_size_y))
+            alphaLayerRTC = Image.new("L", (width, height), 255)
+            alphaLayerRTC.paste(agradientL.rotate(
+                180), (width - overlap_size_x, 0))
+            alphaLayerRTC.paste(agradientT, (0, 0))
+            alphaLayerRTC.paste(agradientC.rotate(270).resize(
+                (overlap_size_x, overlap_size_y)), (width - overlap_size_x, 0))
+
+            # All but X layers
+            alphaLayerABT = Image.new("L", (width, height), 255)
+            alphaLayerABT.paste(alphaLayerLBC, (0, 0))
+            alphaLayerABT.paste(agradientL.rotate(
+                180), (width - overlap_size_x, 0))
+            alphaLayerABT.paste(agradientC.rotate(180).resize(
+                (overlap_size_x, overlap_size_y)), (width - overlap_size_x, height - overlap_size_y))
+            alphaLayerABL = Image.new("L", (width, height), 255)
+            alphaLayerABL.paste(alphaLayerRTC, (0, 0))
+            alphaLayerABL.paste(agradientT.rotate(
+                180), (0, height - overlap_size_y))
+            alphaLayerABL.paste(agradientC.rotate(180).resize(
+                (overlap_size_x, overlap_size_y)), (width - overlap_size_x, height - overlap_size_y))
+            alphaLayerABR = Image.new("L", (width, height), 255)
+            alphaLayerABR.paste(alphaLayerLBC, (0, 0))
+            alphaLayerABR.paste(agradientT, (0, 0))
+            alphaLayerABR.paste(agradientC.resize(
+                (overlap_size_x, overlap_size_y)), (0, 0))
+            alphaLayerABB = Image.new("L", (width, height), 255)
+            alphaLayerABB.paste(alphaLayerRTC, (0, 0))
+            alphaLayerABB.paste(agradientL, (0, 0))
+            alphaLayerABB.paste(agradientC.resize(
+                (overlap_size_x, overlap_size_y)), (0, 0))
+
+            # All-around layer
+            alphaLayerAA = Image.new("L", (width, height), 255)
+            alphaLayerAA.paste(alphaLayerABT, (0, 0))
+            alphaLayerAA.paste(agradientT, (0, 0))
+            alphaLayerAA.paste(agradientC.resize(
+                (overlap_size_x, overlap_size_y)), (0, 0))
+            alphaLayerAA.paste(agradientC.rotate(270).resize(
+                (overlap_size_x, overlap_size_y)), (width - overlap_size_x, 0))
+
+        # Clean up temporary gradients
+        del agradientL
+        del agradientT
+        del agradientC
+
+        def make_image():
+            # Make main tiles -------------------------------------------------
+            if embiggen_tiles:
+                print(f'>> Making {len(embiggen_tiles)} Embiggen tiles...')
+            else:
+                print(
+                    f'>> Making {(emb_tiles_x * emb_tiles_y)} Embiggen tiles ({emb_tiles_x}x{emb_tiles_y})...')
+
+            emb_tile_store = []
+            # Although we could use the same seed for every tile for determinism, at higher strengths this may
+            # produce duplicated structures for each tile and make the tiling effect more obvious
+            # instead track and iterate a local seed we pass to Img2Img
+            seed = self.seed
+            seedintlimit = np.iinfo(np.uint32).max - 1 # only retreive this one from numpy
+
+            for tile in range(emb_tiles_x * emb_tiles_y):
+                # Don't iterate on first tile
+                if tile != 0:
+                    if seed < seedintlimit:
+                        seed += 1
+                    else:
+                        seed = 0
+
+                # Determine if this is a re-run and replace
+                if embiggen_tiles and not tile in embiggen_tiles:
+                    continue
+                # Get row and column entries
+                emb_row_i = tile // emb_tiles_x
+                emb_column_i = tile % emb_tiles_x
+                # Determine bounds to cut up the init image
+                # Determine upper-left point
+                if emb_column_i + 1 == emb_tiles_x:
+                    left = initsuperwidth - width
+                else:
+                    left = round(emb_column_i * (width - overlap_size_x))
+                if emb_row_i + 1 == emb_tiles_y:
+                    top = initsuperheight - height
+                else:
+                    top = round(emb_row_i * (height - overlap_size_y))
+                right = left + width
+                bottom = top + height
+
+                # Cropped image of above dimension (does not modify the original)
+                newinitimage = initsuperimage.crop((left, top, right, bottom))
+                # DEBUG:
+                # newinitimagepath = init_img[0:-4] + f'_emb_Ti{tile}.png'
+                # newinitimage.save(newinitimagepath)
+
+                if embiggen_tiles:
+                    print(
+                        f'Making tile #{tile + 1} ({embiggen_tiles.index(tile) + 1} of {len(embiggen_tiles)} requested)')
+                else:
+                    print(
+                        f'Starting {tile + 1} of {(emb_tiles_x * emb_tiles_y)} tiles')
+
+                # create a torch tensor from an Image
+                newinitimage = np.array(
+                    newinitimage).astype(np.float32) / 255.0
+                newinitimage = newinitimage[None].transpose(0, 3, 1, 2)
+                newinitimage = torch.from_numpy(newinitimage)
+                newinitimage = 2.0 * newinitimage - 1.0
+                newinitimage = newinitimage.to(self.model.device)
+
+                tile_results = gen_img2img.generate(
+                    prompt,
+                    iterations     = 1,
+                    seed           = seed,
+                    sampler        = DDIMSampler(self.model, device=self.model.device),
+                    steps          = steps,
+                    cfg_scale      = cfg_scale,
+                    conditioning   = conditioning,
+                    ddim_eta       = ddim_eta,
+                    image_callback = None,  # called only after the final image is generated
+                    step_callback  = step_callback,   # called after each intermediate image is generated
+                    width          = width,
+                    height         = height,
+                    init_image     = newinitimage,    # notice that init_image is different from init_img
+                    mask_image     = None,
+                    strength       = strength,
+                )
+
+                emb_tile_store.append(tile_results[0][0])
+                # DEBUG (but, also has other uses), worth saving if you want tiles without a transparency overlap to manually composite
+                # emb_tile_store[-1].save(init_img[0:-4] + f'_emb_To{tile}.png')
+                del newinitimage
+
+            # Sanity check we have them all
+            if len(emb_tile_store) == (emb_tiles_x * emb_tiles_y) or (embiggen_tiles != [] and len(emb_tile_store) == len(embiggen_tiles)):
+                outputsuperimage = Image.new(
+                    "RGBA", (initsuperwidth, initsuperheight))
+                if embiggen_tiles:
+                    outputsuperimage.alpha_composite(
+                        initsuperimage.convert('RGBA'), (0, 0))
+                for tile in range(emb_tiles_x * emb_tiles_y):
+                    if embiggen_tiles:
+                        if tile in embiggen_tiles:
+                            intileimage = emb_tile_store.pop(0)
+                        else:
+                            continue
+                    else:
+                        intileimage = emb_tile_store[tile]
+                    intileimage = intileimage.convert('RGBA')
+                    # Get row and column entries
+                    emb_row_i = tile // emb_tiles_x
+                    emb_column_i = tile % emb_tiles_x
+                    if emb_row_i == 0 and emb_column_i == 0 and not embiggen_tiles:
+                        left = 0
+                        top = 0
+                    else:
+                        # Determine upper-left point
+                        if emb_column_i + 1 == emb_tiles_x:
+                            left = initsuperwidth - width
+                        else:
+                            left = round(emb_column_i *
+                                         (width - overlap_size_x))
+                        if emb_row_i + 1 == emb_tiles_y:
+                            top = initsuperheight - height
+                        else:
+                            top = round(emb_row_i * (height - overlap_size_y))
+                        # Handle gradients for various conditions
+                        # Handle emb_rerun case
+                        if embiggen_tiles:
+                            # top of image
+                            if emb_row_i == 0:
+                                if emb_column_i == 0:
+                                    if (tile+1) in embiggen_tiles:  # Look-ahead right
+                                        if (tile+emb_tiles_x) not in embiggen_tiles:  # Look-ahead down
+                                            intileimage.putalpha(alphaLayerB)
+                                        # Otherwise do nothing on this tile
+                                    elif (tile+emb_tiles_x) in embiggen_tiles:  # Look-ahead down only
+                                        intileimage.putalpha(alphaLayerR)
+                                    else:
+                                        intileimage.putalpha(alphaLayerRBC)
+                                elif emb_column_i == emb_tiles_x - 1:
+                                    if (tile+emb_tiles_x) in embiggen_tiles:  # Look-ahead down
+                                        intileimage.putalpha(alphaLayerL)
+                                    else:
+                                        intileimage.putalpha(alphaLayerLBC)
+                                else:
+                                    if (tile+1) in embiggen_tiles:  # Look-ahead right
+                                        if (tile+emb_tiles_x) in embiggen_tiles:  # Look-ahead down
+                                            intileimage.putalpha(alphaLayerL)
+                                        else:
+                                            intileimage.putalpha(alphaLayerLBC)
+                                    elif (tile+emb_tiles_x) in embiggen_tiles:  # Look-ahead down only
+                                        intileimage.putalpha(alphaLayerLR)
+                                    else:
+                                        intileimage.putalpha(alphaLayerABT)
+                            # bottom of image
+                            elif emb_row_i == emb_tiles_y - 1:
+                                if emb_column_i == 0:
+                                    if (tile+1) in embiggen_tiles: # Look-ahead right
+                                        intileimage.putalpha(alphaLayerTaC)
+                                    else:
+                                        intileimage.putalpha(alphaLayerRTC)
+                                elif emb_column_i == emb_tiles_x - 1:
+                                    # No tiles to look ahead to
+                                    intileimage.putalpha(alphaLayerLTC)
+                                else:
+                                    if (tile+1) in embiggen_tiles: # Look-ahead right
+                                        intileimage.putalpha(alphaLayerLTaC)
+                                    else:
+                                        intileimage.putalpha(alphaLayerABB)
+                            # vertical middle of image
+                            else:
+                                if emb_column_i == 0:
+                                    if (tile+1) in embiggen_tiles: # Look-ahead right
+                                        if (tile+emb_tiles_x) in embiggen_tiles: # Look-ahead down
+                                            intileimage.putalpha(alphaLayerTaC)
+                                        else:
+                                            intileimage.putalpha(alphaLayerTB)
+                                    elif (tile+emb_tiles_x) in embiggen_tiles:  # Look-ahead down only
+                                        intileimage.putalpha(alphaLayerRTC)
+                                    else:
+                                        intileimage.putalpha(alphaLayerABL)
+                                elif emb_column_i == emb_tiles_x - 1:
+                                    if (tile+emb_tiles_x) in embiggen_tiles:  # Look-ahead down
+                                        intileimage.putalpha(alphaLayerLTC)
+                                    else:
+                                        intileimage.putalpha(alphaLayerABR)
+                                else:
+                                    if (tile+1) in embiggen_tiles: # Look-ahead right
+                                        if (tile+emb_tiles_x) in embiggen_tiles: # Look-ahead down
+                                            intileimage.putalpha(alphaLayerLTaC)
+                                        else:
+                                            intileimage.putalpha(alphaLayerABR)
+                                    elif (tile+emb_tiles_x) in embiggen_tiles:  # Look-ahead down only
+                                        intileimage.putalpha(alphaLayerABB)
+                                    else:
+                                        intileimage.putalpha(alphaLayerAA)
+                        # Handle normal tiling case (much simpler - since we tile left to right, top to bottom)
+                        else:
+                            if emb_row_i == 0 and emb_column_i >= 1:
+                                intileimage.putalpha(alphaLayerL)
+                            elif emb_row_i >= 1 and emb_column_i == 0:
+                                if emb_column_i + 1 == emb_tiles_x: # If we don't have anything that can be placed to the right
+                                    intileimage.putalpha(alphaLayerT)
+                                else:
+                                    intileimage.putalpha(alphaLayerTaC)
+                            else:
+                                if emb_column_i + 1 == emb_tiles_x: # If we don't have anything that can be placed to the right
+                                    intileimage.putalpha(alphaLayerLTC)
+                                else:
+                                    intileimage.putalpha(alphaLayerLTaC)
+                    # Layer tile onto final image
+                    outputsuperimage.alpha_composite(intileimage, (left, top))
+            else:
+                print(f'Error: could not find all Embiggen output tiles in memory? Something must have gone wrong with img2img generation.')
+
+            # after internal loops and patching up return Embiggen image
+            return outputsuperimage
+        # end of function declaration
+        return make_image
diff --git a/ldm/invoke/ckpt_generator/img2img.py b/ldm/invoke/ckpt_generator/img2img.py
new file mode 100644
index 00000000000..e1f12b542e6
--- /dev/null
+++ b/ldm/invoke/ckpt_generator/img2img.py
@@ -0,0 +1,97 @@
+'''
+ldm.invoke.ckpt_generator.img2img descends from ldm.invoke.generator
+'''
+
+import torch
+import numpy as  np
+import PIL
+from torch import Tensor
+from PIL import Image
+from ldm.invoke.devices import choose_autocast
+from ldm.invoke.ckpt_generator.base import CkptGenerator
+from ldm.models.diffusion.ddim import DDIMSampler
+from ldm.models.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent
+
+class CkptImg2Img(CkptGenerator):
+    def __init__(self, model, precision):
+        super().__init__(model, precision)
+        self.init_latent = None    # by get_noise()
+
+    def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
+                       conditioning,init_image,strength,step_callback=None,threshold=0.0,perlin=0.0,**kwargs):
+        """
+        Returns a function returning an image derived from the prompt and the initial image
+        Return value depends on the seed at the time you call it.
+        """
+        self.perlin = perlin
+
+        sampler.make_schedule(
+            ddim_num_steps=steps, ddim_eta=ddim_eta, verbose=False
+        )
+
+        if isinstance(init_image, PIL.Image.Image):
+            init_image = self._image_to_tensor(init_image.convert('RGB'))
+
+        scope = choose_autocast(self.precision)
+        with scope(self.model.device.type):
+            self.init_latent = self.model.get_first_stage_encoding(
+                self.model.encode_first_stage(init_image)
+            ) # move to latent space
+
+        t_enc = int(strength * steps)
+        uc, c, extra_conditioning_info   = conditioning
+
+        def make_image(x_T):
+            # encode (scaled latent)
+            z_enc = sampler.stochastic_encode(
+                self.init_latent,
+                torch.tensor([t_enc - 1]).to(self.model.device),
+                noise=x_T
+            )
+
+            if self.free_gpu_mem and self.model.model.device != self.model.device:
+                self.model.model.to(self.model.device)
+
+            # decode it
+            samples = sampler.decode(
+                z_enc,
+                c,
+                t_enc,
+                img_callback = step_callback,
+                unconditional_guidance_scale=cfg_scale,
+                unconditional_conditioning=uc,
+                init_latent = self.init_latent, # changes how noising is performed in ksampler
+                extra_conditioning_info = extra_conditioning_info,
+                all_timesteps_count = steps
+            )
+
+            if self.free_gpu_mem:
+                self.model.model.to("cpu")
+
+            return self.sample_to_image(samples)
+
+        return make_image
+
+    def get_noise(self,width,height):
+        device      = self.model.device
+        init_latent = self.init_latent
+        assert init_latent is not None,'call to get_noise() when init_latent not set'
+        if device.type == 'mps':
+            x = torch.randn_like(init_latent, device='cpu').to(device)
+        else:
+            x = torch.randn_like(init_latent, device=device)
+        if self.perlin > 0.0:
+            shape = init_latent.shape
+            x = (1-self.perlin)*x + self.perlin*self.get_perlin_noise(shape[3], shape[2])
+        return x
+
+    def _image_to_tensor(self, image:Image, normalize:bool=True)->Tensor:
+        image = np.array(image).astype(np.float32) / 255.0
+        if len(image.shape) == 2:  # 'L' image, as in a mask
+            image = image[None,None]
+        else:                      # 'RGB' image
+            image = image[None].transpose(0, 3, 1, 2)
+        image = torch.from_numpy(image)
+        if normalize:
+            image = 2.0 * image - 1.0
+        return image.to(self.model.device)
diff --git a/ldm/invoke/ckpt_generator/inpaint.py b/ldm/invoke/ckpt_generator/inpaint.py
new file mode 100644
index 00000000000..ac99430a12d
--- /dev/null
+++ b/ldm/invoke/ckpt_generator/inpaint.py
@@ -0,0 +1,358 @@
+'''
+ldm.invoke.ckpt_generator.inpaint descends from ldm.invoke.ckpt_generator
+'''
+
+import math
+import torch
+import torchvision.transforms as T
+import numpy as  np
+import cv2 as cv
+import PIL
+from PIL import Image, ImageFilter, ImageOps, ImageChops
+from skimage.exposure.histogram_matching import match_histograms
+from einops import rearrange, repeat
+from ldm.invoke.devices             import choose_autocast
+from ldm.invoke.ckpt_generator.img2img   import CkptImg2Img
+from ldm.models.diffusion.ddim     import DDIMSampler
+from ldm.models.diffusion.ksampler import KSampler
+from ldm.invoke.generator.base import downsampling
+from ldm.util import debug_image
+from ldm.invoke.patchmatch import PatchMatch 
+from ldm.invoke.globals import Globals
+
+def infill_methods()->list[str]:
+    methods = list()
+    if PatchMatch.patchmatch_available():
+        methods.append('patchmatch')
+    methods.append('tile')
+    return methods
+
+class Inpaint(CkptImg2Img):
+    def __init__(self, model, precision):
+        self.init_latent = None
+        self.pil_image = None
+        self.pil_mask = None
+        self.mask_blur_radius = 0
+        self.infill_method = None
+        super().__init__(model, precision)
+
+    # Outpaint support code
+    def get_tile_images(self, image: np.ndarray, width=8, height=8):
+        _nrows, _ncols, depth = image.shape
+        _strides = image.strides
+
+        nrows, _m = divmod(_nrows, height)
+        ncols, _n = divmod(_ncols, width)
+        if _m != 0 or _n != 0:
+            return None
+
+        return np.lib.stride_tricks.as_strided(
+            np.ravel(image),
+            shape=(nrows, ncols, height, width, depth),
+            strides=(height * _strides[0], width * _strides[1], *_strides),
+            writeable=False
+        )
+
+    def infill_patchmatch(self, im: Image.Image) -> Image:
+        if im.mode != 'RGBA':
+            return im
+
+        # Skip patchmatch if patchmatch isn't available
+        if not PatchMatch.patchmatch_available():
+            return im
+
+        # Patchmatch (note, we may want to expose patch_size? Increasing it significantly impacts performance though)
+        im_patched_np = PatchMatch.inpaint(im.convert('RGB'), ImageOps.invert(im.split()[-1]), patch_size = 3)
+        im_patched = Image.fromarray(im_patched_np, mode = 'RGB')
+        return im_patched
+
+    def tile_fill_missing(self, im: Image.Image, tile_size: int = 16, seed: int = None) -> Image:
+        # Only fill if there's an alpha layer
+        if im.mode != 'RGBA':
+            return im
+
+        a = np.asarray(im, dtype=np.uint8)
+
+        tile_size = (tile_size, tile_size)
+
+        # Get the image as tiles of a specified size
+        tiles = self.get_tile_images(a,*tile_size).copy()
+
+        # Get the mask as tiles
+        tiles_mask = tiles[:,:,:,:,3]
+
+        # Find any mask tiles with any fully transparent pixels (we will be replacing these later)
+        tmask_shape = tiles_mask.shape
+        tiles_mask = tiles_mask.reshape(math.prod(tiles_mask.shape))
+        n,ny = (math.prod(tmask_shape[0:2])), math.prod(tmask_shape[2:])
+        tiles_mask = (tiles_mask > 0)
+        tiles_mask = tiles_mask.reshape((n,ny)).all(axis = 1)
+
+        # Get RGB tiles in single array and filter by the mask
+        tshape = tiles.shape
+        tiles_all = tiles.reshape((math.prod(tiles.shape[0:2]), * tiles.shape[2:]))
+        filtered_tiles = tiles_all[tiles_mask]
+
+        if len(filtered_tiles) == 0:
+            return im
+
+        # Find all invalid tiles and replace with a random valid tile
+        replace_count = (tiles_mask == False).sum()
+        rng = np.random.default_rng(seed = seed)
+        tiles_all[np.logical_not(tiles_mask)] = filtered_tiles[rng.choice(filtered_tiles.shape[0], replace_count),:,:,:]
+
+        # Convert back to an image
+        tiles_all = tiles_all.reshape(tshape)
+        tiles_all = tiles_all.swapaxes(1,2)
+        st = tiles_all.reshape((math.prod(tiles_all.shape[0:2]), math.prod(tiles_all.shape[2:4]), tiles_all.shape[4]))
+        si = Image.fromarray(st, mode='RGBA')
+
+        return si
+
+
+    def mask_edge(self, mask: Image, edge_size: int, edge_blur: int) -> Image:
+        npimg = np.asarray(mask, dtype=np.uint8)
+
+        # Detect any partially transparent regions
+        npgradient = np.uint8(255 * (1.0 - np.floor(np.abs(0.5 - np.float32(npimg) / 255.0) * 2.0)))
+
+        # Detect hard edges
+        npedge = cv.Canny(npimg, threshold1=100, threshold2=200)
+
+        # Combine
+        npmask = npgradient + npedge
+
+        # Expand
+        npmask = cv.dilate(npmask, np.ones((3,3), np.uint8), iterations = int(edge_size / 2))
+
+        new_mask = Image.fromarray(npmask)
+
+        if edge_blur > 0:
+            new_mask = new_mask.filter(ImageFilter.BoxBlur(edge_blur))
+
+        return ImageOps.invert(new_mask)
+
+
+    def seam_paint(self,
+        im: Image.Image,
+        seam_size: int,
+        seam_blur: int,
+        prompt,sampler,steps,cfg_scale,ddim_eta,
+        conditioning,strength,
+        noise,
+        step_callback
+    ) -> Image.Image:
+        hard_mask = self.pil_image.split()[-1].copy()
+        mask = self.mask_edge(hard_mask, seam_size, seam_blur)
+
+        make_image = self.get_make_image(
+            prompt,
+            sampler,
+            steps,
+            cfg_scale,
+            ddim_eta,
+            conditioning,
+            init_image = im.copy().convert('RGBA'),
+            mask_image = mask.convert('RGB'), # Code currently requires an RGB mask
+            strength = strength,
+            mask_blur_radius = 0,
+            seam_size = 0,
+            step_callback = step_callback,
+            inpaint_width = im.width,
+            inpaint_height = im.height
+        )
+
+        seam_noise = self.get_noise(im.width, im.height)
+
+        result = make_image(seam_noise)
+
+        return result
+
+
+    @torch.no_grad()
+    def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
+                       conditioning,init_image,mask_image,strength,
+                       mask_blur_radius: int = 8,
+                       # Seam settings - when 0, doesn't fill seam
+                       seam_size: int = 0,
+                       seam_blur: int = 0,
+                       seam_strength: float = 0.7,
+                       seam_steps: int = 10,
+                       tile_size: int = 32,
+                       step_callback=None,
+                       inpaint_replace=False, enable_image_debugging=False,
+                       infill_method = None,
+                       inpaint_width=None,
+                       inpaint_height=None,
+                       **kwargs):
+        """
+        Returns a function returning an image derived from the prompt and
+        the initial image + mask.  Return value depends on the seed at
+        the time you call it.  kwargs are 'init_latent' and 'strength'
+        """
+
+        self.enable_image_debugging = enable_image_debugging
+        self.infill_method = infill_method or infill_methods()[0], # The infill method to use
+        
+        self.inpaint_width = inpaint_width
+        self.inpaint_height = inpaint_height
+
+        if isinstance(init_image, PIL.Image.Image):
+            self.pil_image = init_image.copy()
+
+            # Do infill
+            if infill_method == 'patchmatch' and PatchMatch.patchmatch_available():
+                init_filled = self.infill_patchmatch(self.pil_image.copy())
+            else: # if infill_method == 'tile': # Only two methods right now, so always use 'tile' if not patchmatch
+                init_filled = self.tile_fill_missing(
+                    self.pil_image.copy(),
+                    seed = self.seed,
+                    tile_size = tile_size
+                )
+            init_filled.paste(init_image, (0,0), init_image.split()[-1])
+
+            # Resize if requested for inpainting
+            if inpaint_width and inpaint_height:
+                init_filled = init_filled.resize((inpaint_width, inpaint_height))
+
+            debug_image(init_filled, "init_filled", debug_status=self.enable_image_debugging)
+
+            # Create init tensor
+            init_image = self._image_to_tensor(init_filled.convert('RGB'))
+
+        if isinstance(mask_image, PIL.Image.Image):
+            self.pil_mask = mask_image.copy()
+            debug_image(mask_image, "mask_image BEFORE multiply with pil_image", debug_status=self.enable_image_debugging)
+
+            mask_image = ImageChops.multiply(mask_image, self.pil_image.split()[-1].convert('RGB'))
+            self.pil_mask = mask_image
+
+            # Resize if requested for inpainting
+            if inpaint_width and inpaint_height:
+                mask_image = mask_image.resize((inpaint_width, inpaint_height))
+
+            debug_image(mask_image, "mask_image AFTER multiply with pil_image", debug_status=self.enable_image_debugging)
+            mask_image = mask_image.resize(
+                (
+                    mask_image.width // downsampling,
+                    mask_image.height // downsampling
+                ),
+                resample=Image.Resampling.NEAREST
+            )
+            mask_image = self._image_to_tensor(mask_image,normalize=False)
+
+        self.mask_blur_radius = mask_blur_radius
+
+        # klms samplers not supported yet, so ignore previous sampler
+        if isinstance(sampler,KSampler):
+            print(
+                f">> Using recommended DDIM sampler for inpainting."
+            )
+            sampler = DDIMSampler(self.model, device=self.model.device)
+
+        sampler.make_schedule(
+            ddim_num_steps=steps, ddim_eta=ddim_eta, verbose=False
+        )
+
+        mask_image = mask_image[0][0].unsqueeze(0).repeat(4,1,1).unsqueeze(0)
+        mask_image = repeat(mask_image, '1 ... -> b ...', b=1)
+
+        scope = choose_autocast(self.precision)
+        with scope(self.model.device.type):
+            self.init_latent = self.model.get_first_stage_encoding(
+                self.model.encode_first_stage(init_image)
+            ) # move to latent space
+
+        t_enc   = int(strength * steps)
+        # todo: support cross-attention control
+        uc, c, _ = conditioning
+
+        print(f">> target t_enc is {t_enc} steps")
+
+        @torch.no_grad()
+        def make_image(x_T):
+            # encode (scaled latent)
+            z_enc = sampler.stochastic_encode(
+                self.init_latent,
+                torch.tensor([t_enc - 1]).to(self.model.device),
+                noise=x_T
+            )
+
+            # to replace masked area with latent noise, weighted by inpaint_replace strength
+            if inpaint_replace > 0.0:
+                print(f'>> inpaint will replace what was under the mask with a strength of {inpaint_replace}')
+                l_noise = self.get_noise(kwargs['width'],kwargs['height'])
+                inverted_mask = 1.0-mask_image  # there will be 1s where the mask is
+                masked_region = (1.0-inpaint_replace) * inverted_mask * z_enc + inpaint_replace * inverted_mask * l_noise
+                z_enc   = z_enc * mask_image + masked_region
+
+            if self.free_gpu_mem and self.model.model.device != self.model.device:
+                self.model.model.to(self.model.device)
+
+            # decode it
+            samples = sampler.decode(
+                z_enc,
+                c,
+                t_enc,
+                img_callback                 = step_callback,
+                unconditional_guidance_scale = cfg_scale,
+                unconditional_conditioning = uc,
+                mask                       = mask_image,
+                init_latent                = self.init_latent
+            )
+
+            result = self.sample_to_image(samples)
+
+            # Seam paint if this is our first pass (seam_size set to 0 during seam painting)
+            if seam_size > 0:
+                old_image = self.pil_image or init_image
+                old_mask = self.pil_mask or mask_image
+
+                result = self.seam_paint(
+                    result,
+                    seam_size,
+                    seam_blur,
+                    prompt,
+                    sampler,
+                    seam_steps,
+                    cfg_scale,
+                    ddim_eta,
+                    conditioning,
+                    seam_strength,
+                    x_T,
+                    step_callback)
+
+                # Restore original settings
+                self.get_make_image(prompt,sampler,steps,cfg_scale,ddim_eta,
+                       conditioning,
+                       old_image,
+                       old_mask,
+                       strength,
+                       mask_blur_radius, seam_size, seam_blur, seam_strength,
+                       seam_steps, tile_size, step_callback,
+                       inpaint_replace, enable_image_debugging,
+                       inpaint_width = inpaint_width,
+                       inpaint_height = inpaint_height,
+                       infill_method = infill_method,
+                       **kwargs)
+
+            return result
+
+        return make_image
+
+
+    def sample_to_image(self, samples)->Image.Image:
+        gen_result = super().sample_to_image(samples).convert('RGB')
+        debug_image(gen_result, "gen_result", debug_status=self.enable_image_debugging)
+
+        # Resize if necessary
+        if self.inpaint_width and self.inpaint_height:
+            gen_result = gen_result.resize(self.pil_image.size)
+
+        if self.pil_image is None or self.pil_mask is None:
+            return gen_result
+
+        corrected_result = super().repaste_and_color_correct(gen_result, self.pil_image, self.pil_mask, self.mask_blur_radius)
+        debug_image(corrected_result, "corrected_result", debug_status=self.enable_image_debugging)
+
+        return corrected_result
diff --git a/ldm/invoke/ckpt_generator/omnibus.py b/ldm/invoke/ckpt_generator/omnibus.py
new file mode 100644
index 00000000000..b728e6b4488
--- /dev/null
+++ b/ldm/invoke/ckpt_generator/omnibus.py
@@ -0,0 +1,175 @@
+"""omnibus module to be used with the runwayml 9-channel custom inpainting model"""
+
+import torch
+import numpy as  np
+from einops import repeat
+from PIL import Image, ImageOps, ImageChops
+from ldm.invoke.devices import choose_autocast
+from ldm.invoke.ckpt_generator.base import downsampling
+from ldm.invoke.ckpt_generator.img2img import CkptImg2Img
+from ldm.invoke.ckpt_generator.txt2img import CkptTxt2Img
+
+class CkptOmnibus(CkptImg2Img,CkptTxt2Img):
+    def __init__(self, model, precision):
+        super().__init__(model, precision)
+        self.pil_mask = None
+        self.pil_image = None
+
+    def get_make_image(
+            self,
+            prompt,
+            sampler,
+            steps,
+            cfg_scale,
+            ddim_eta,
+            conditioning,
+            width,
+            height,
+            init_image = None,
+            mask_image = None,
+            strength = None,
+            step_callback=None,
+            threshold=0.0,
+            perlin=0.0,
+            mask_blur_radius: int = 8,
+            **kwargs):
+        """
+        Returns a function returning an image derived from the prompt and the initial image
+        Return value depends on the seed at the time you call it.
+        """
+        self.perlin = perlin
+        num_samples = 1
+
+        sampler.make_schedule(
+            ddim_num_steps=steps, ddim_eta=ddim_eta, verbose=False
+        )
+
+        if isinstance(init_image, Image.Image):
+            self.pil_image = init_image
+            if init_image.mode != 'RGB':
+                init_image = init_image.convert('RGB')
+            init_image = self._image_to_tensor(init_image)
+
+        if isinstance(mask_image, Image.Image):
+            self.pil_mask = mask_image
+
+            mask_image = ImageChops.multiply(mask_image.convert('L'), self.pil_image.split()[-1])
+            mask_image = self._image_to_tensor(ImageOps.invert(mask_image), normalize=False)
+
+        self.mask_blur_radius = mask_blur_radius
+
+        t_enc = steps
+
+        if init_image is not None and mask_image is not None: # inpainting
+            masked_image = init_image * (1 - mask_image)  # masked image is the image masked by mask - masked regions zero
+
+        elif init_image is not None: # img2img
+            scope = choose_autocast(self.precision)
+
+            with scope(self.model.device.type):
+                self.init_latent = self.model.get_first_stage_encoding(
+                    self.model.encode_first_stage(init_image)
+                ) # move to latent space
+
+            # create a completely black mask  (1s)
+            mask_image = torch.ones(1, 1, init_image.shape[2], init_image.shape[3], device=self.model.device)
+            # and the masked image is just a copy of the original
+            masked_image = init_image
+
+        else: # txt2img
+            init_image = torch.zeros(1, 3, height, width, device=self.model.device)
+            mask_image = torch.ones(1, 1, height, width, device=self.model.device)
+            masked_image = init_image
+
+        self.init_latent = init_image
+        height = init_image.shape[2]
+        width = init_image.shape[3]
+        model = self.model
+
+        def make_image(x_T):
+            with torch.no_grad():
+                scope = choose_autocast(self.precision)
+                with scope(self.model.device.type):
+
+                    batch = self.make_batch_sd(
+                        init_image,
+                        mask_image,
+                        masked_image,
+                        prompt=prompt,
+                        device=model.device,
+                        num_samples=num_samples,
+                    )
+
+                    c = model.cond_stage_model.encode(batch["txt"])
+                    c_cat = list()
+                    for ck in model.concat_keys:
+                        cc = batch[ck].float()
+                        if ck != model.masked_image_key:
+                            bchw = [num_samples, 4, height//8, width//8]
+                            cc = torch.nn.functional.interpolate(cc, size=bchw[-2:])
+                        else:
+                            cc = model.get_first_stage_encoding(model.encode_first_stage(cc))
+                        c_cat.append(cc)
+                    c_cat = torch.cat(c_cat, dim=1)
+
+                    # cond
+                    cond={"c_concat": [c_cat], "c_crossattn": [c]}
+
+                    # uncond cond
+                    uc_cross = model.get_unconditional_conditioning(num_samples, "")
+                    uc_full = {"c_concat": [c_cat], "c_crossattn": [uc_cross]}
+                    shape = [model.channels, height//8, width//8]
+
+                    samples, _ = sampler.sample(
+                        batch_size = 1,
+                        S = steps,
+                        x_T = x_T,
+                        conditioning = cond,
+                        shape = shape,
+                        verbose = False,
+                        unconditional_guidance_scale = cfg_scale,
+                        unconditional_conditioning = uc_full,
+                        eta = 1.0,
+                        img_callback = step_callback,
+                        threshold = threshold,
+                    )
+                    if self.free_gpu_mem:
+                        self.model.model.to("cpu")
+            return self.sample_to_image(samples)
+
+        return make_image
+
+    def make_batch_sd(
+            self,
+            image,
+            mask,
+            masked_image,
+            prompt,
+            device,
+            num_samples=1):
+        batch = {
+                "image": repeat(image.to(device=device), "1 ... -> n ...", n=num_samples),
+                "txt": num_samples * [prompt],
+                "mask": repeat(mask.to(device=device), "1 ... -> n ...", n=num_samples),
+                "masked_image": repeat(masked_image.to(device=device), "1 ... -> n ...", n=num_samples),
+                }
+        return batch
+
+    def get_noise(self, width:int, height:int):
+        if self.init_latent is not None:
+            height = self.init_latent.shape[2]
+            width = self.init_latent.shape[3]
+        return Txt2Img.get_noise(self,width,height)
+
+
+    def sample_to_image(self, samples)->Image.Image:
+        gen_result = super().sample_to_image(samples).convert('RGB')
+
+        if self.pil_image is None or self.pil_mask is None:
+            return gen_result
+        if self.pil_image.size != self.pil_mask.size:
+            return gen_result
+
+        corrected_result = super(CkptImg2Img, self).repaste_and_color_correct(gen_result, self.pil_image, self.pil_mask, self.mask_blur_radius)
+
+        return corrected_result
diff --git a/ldm/invoke/ckpt_generator/txt2img.py b/ldm/invoke/ckpt_generator/txt2img.py
new file mode 100644
index 00000000000..48b83be2edb
--- /dev/null
+++ b/ldm/invoke/ckpt_generator/txt2img.py
@@ -0,0 +1,88 @@
+'''
+ldm.invoke.ckpt_generator.txt2img inherits from ldm.invoke.ckpt_generator
+'''
+
+import torch
+import numpy as  np
+from ldm.invoke.ckpt_generator.base import CkptGenerator
+from ldm.models.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent
+import gc
+
+
+class CkptTxt2Img(CkptGenerator):
+    def __init__(self, model, precision):
+        super().__init__(model, precision)
+
+    @torch.no_grad()
+    def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
+                       conditioning,width,height,step_callback=None,threshold=0.0,perlin=0.0,
+                       attention_maps_callback=None,
+                       **kwargs):
+        """
+        Returns a function returning an image derived from the prompt and the initial image
+        Return value depends on the seed at the time you call it
+        kwargs are 'width' and 'height'
+        """
+        self.perlin = perlin
+        uc, c, extra_conditioning_info   = conditioning
+
+        @torch.no_grad()
+        def make_image(x_T):
+            shape = [
+                self.latent_channels,
+                height // self.downsampling_factor,
+                width  // self.downsampling_factor,
+            ]
+
+            if self.free_gpu_mem and self.model.model.device != self.model.device:
+                self.model.model.to(self.model.device)
+
+            sampler.make_schedule(ddim_num_steps=steps, ddim_eta=ddim_eta, verbose=False)
+
+            samples, _ = sampler.sample(
+                batch_size                   = 1,
+                S                            = steps,
+                x_T                          = x_T,
+                conditioning                 = c,
+                shape                        = shape,
+                verbose                      = False,
+                unconditional_guidance_scale = cfg_scale,
+                unconditional_conditioning   = uc,
+                extra_conditioning_info      = extra_conditioning_info,
+                eta                          = ddim_eta,
+                img_callback                 = step_callback,
+                threshold                    = threshold,
+                attention_maps_callback      = attention_maps_callback,
+            )
+
+            if self.free_gpu_mem:
+                self.model.model.to('cpu')
+                self.model.cond_stage_model.device = 'cpu'
+                self.model.cond_stage_model.to('cpu')
+                gc.collect()
+                torch.cuda.empty_cache()
+
+            return self.sample_to_image(samples)
+
+        return make_image
+
+
+    # returns a tensor filled with random numbers from a normal distribution
+    def get_noise(self,width,height):
+        device         = self.model.device
+        if self.use_mps_noise or device.type == 'mps':
+            x = torch.randn([1,
+                                self.latent_channels,
+                                height // self.downsampling_factor,
+                                width  // self.downsampling_factor],
+                               device='cpu').to(device)
+        else:
+            x = torch.randn([1,
+                                self.latent_channels,
+                                height // self.downsampling_factor,
+                                width  // self.downsampling_factor],
+                               device=device)
+        if self.perlin > 0.0:
+            x = (1-self.perlin)*x + self.perlin*self.get_perlin_noise(width  // self.downsampling_factor, height // self.downsampling_factor)
+        return x
+
diff --git a/ldm/invoke/ckpt_generator/txt2img2img.py b/ldm/invoke/ckpt_generator/txt2img2img.py
new file mode 100644
index 00000000000..8c650f9ebda
--- /dev/null
+++ b/ldm/invoke/ckpt_generator/txt2img2img.py
@@ -0,0 +1,182 @@
+'''
+ldm.invoke.ckpt_generator.txt2img inherits from ldm.invoke.ckpt_generator
+'''
+
+import torch
+import numpy as  np
+import math
+import gc
+from ldm.invoke.ckpt_generator.base import CkptGenerator
+from ldm.invoke.ckpt_generator.omnibus import CkptOmnibus
+from ldm.models.diffusion.ddim import DDIMSampler
+from ldm.models.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent
+from PIL import Image
+
+class CkptTxt2Img2Img(Generator):
+    def __init__(self, model, precision):
+        super().__init__(model, precision)
+        self.init_latent = None    # for get_noise()
+
+    @torch.no_grad()
+    def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
+                       conditioning,width,height,strength,step_callback=None,**kwargs):
+        """
+        Returns a function returning an image derived from the prompt and the initial image
+        Return value depends on the seed at the time you call it
+        kwargs are 'width' and 'height'
+        """
+        uc, c, extra_conditioning_info = conditioning
+        scale_dim = min(width, height)
+        scale = 512 / scale_dim
+
+        init_width = math.ceil(scale * width / 64) * 64
+        init_height = math.ceil(scale * height / 64) * 64
+
+        @torch.no_grad()
+        def make_image(x_T):
+
+            shape = [
+                self.latent_channels,
+                init_height // self.downsampling_factor,
+                init_width // self.downsampling_factor,
+            ]
+
+            sampler.make_schedule(
+                    ddim_num_steps=steps, ddim_eta=ddim_eta, verbose=False
+            )
+
+            #x = self.get_noise(init_width, init_height)
+            x = x_T
+
+            if self.free_gpu_mem and self.model.model.device != self.model.device:
+                self.model.model.to(self.model.device)
+
+            samples, _ = sampler.sample(
+                batch_size                   = 1,
+                S                            = steps,
+                x_T                          = x,
+                conditioning                 = c,
+                shape                        = shape,
+                verbose                      = False,
+                unconditional_guidance_scale = cfg_scale,
+                unconditional_conditioning   = uc,
+                eta                          = ddim_eta,
+                img_callback                 = step_callback,
+                extra_conditioning_info      = extra_conditioning_info
+            )
+
+            print(
+                  f"\n>> Interpolating from {init_width}x{init_height} to {width}x{height} using DDIM sampling"
+                 )
+
+            # resizing
+            samples = torch.nn.functional.interpolate(
+                samples,
+                size=(height // self.downsampling_factor, width // self.downsampling_factor),
+                mode="bilinear"
+            )
+
+            t_enc = int(strength * steps)
+            ddim_sampler = DDIMSampler(self.model, device=self.model.device)
+            ddim_sampler.make_schedule(
+                    ddim_num_steps=steps, ddim_eta=ddim_eta, verbose=False
+            )
+
+            z_enc = ddim_sampler.stochastic_encode(
+                samples,
+                torch.tensor([t_enc-1]).to(self.model.device),
+                noise=self.get_noise(width,height,False)
+            )
+
+            # decode it
+            samples = ddim_sampler.decode(
+                z_enc,
+                c,
+                t_enc,
+                img_callback = step_callback,
+                unconditional_guidance_scale=cfg_scale,
+                unconditional_conditioning=uc,
+                extra_conditioning_info=extra_conditioning_info,
+                all_timesteps_count=steps
+            )
+
+            if self.free_gpu_mem:
+                self.model.model.to('cpu')
+                self.model.cond_stage_model.device = 'cpu'
+                self.model.cond_stage_model.to('cpu')
+                gc.collect()
+                torch.cuda.empty_cache()
+
+            return self.sample_to_image(samples)
+
+        # in the case of the inpainting model being loaded, the trick of
+        # providing an interpolated latent doesn't work, so we transiently
+        # create a 512x512 PIL image, upscale it, and run the inpainting
+        # over it in img2img mode. Because the inpaing model is so conservative
+        # it doesn't change the image (much)
+        def inpaint_make_image(x_T):
+            omnibus = CkptOmnibus(self.model,self.precision)
+            result = omnibus.generate(
+                prompt,
+                sampler=sampler,
+                width=init_width,
+                height=init_height,
+                step_callback=step_callback,
+                steps = steps,
+                cfg_scale = cfg_scale,
+                ddim_eta = ddim_eta,
+                conditioning = conditioning,
+                **kwargs
+            )
+            assert result is not None and len(result)>0,'** txt2img failed **'
+            image = result[0][0]
+            interpolated_image = image.resize((width,height),resample=Image.Resampling.LANCZOS)
+            print(kwargs.pop('init_image',None))
+            result = omnibus.generate(
+                prompt,
+                sampler=sampler,
+                init_image=interpolated_image,
+                width=width,
+                height=height,
+                seed=result[0][1],
+                step_callback=step_callback,
+                steps = steps,
+                cfg_scale = cfg_scale,
+                ddim_eta = ddim_eta,
+                conditioning = conditioning,
+                **kwargs
+                )
+            return result[0][0]
+
+        if sampler.uses_inpainting_model():
+            return inpaint_make_image
+        else:
+            return make_image
+
+    # returns a tensor filled with random numbers from a normal distribution
+    def get_noise(self,width,height,scale = True):
+        # print(f"Get noise: {width}x{height}")
+        if scale:
+            trained_square = 512 * 512
+            actual_square = width * height
+            scale = math.sqrt(trained_square / actual_square)
+            scaled_width = math.ceil(scale * width / 64) * 64
+            scaled_height = math.ceil(scale * height / 64) * 64
+        else:
+            scaled_width = width
+            scaled_height = height
+
+        device      = self.model.device
+        if self.use_mps_noise or device.type == 'mps':
+            return torch.randn([1,
+                                self.latent_channels,
+                                scaled_height // self.downsampling_factor,
+                                scaled_width  // self.downsampling_factor],
+                                device='cpu').to(device)
+        else:
+            return torch.randn([1,
+                                self.latent_channels,
+                                scaled_height // self.downsampling_factor,
+                                scaled_width  // self.downsampling_factor],
+                                device=device)
+
diff --git a/ldm/invoke/model_cache.py b/ldm/invoke/model_cache.py
index bdeb85082d3..859dc360ab5 100644
--- a/ldm/invoke/model_cache.py
+++ b/ldm/invoke/model_cache.py
@@ -132,6 +132,21 @@ def set_default_model(self,model_name:str) -> None:
             config[model].pop('default',None)
         config[model_name]['default'] = True
 
+    def model_info(self, model_name:str)->dict:
+        '''
+        Given a model name returns the config object describing it.
+        '''
+        if model_name not in self.config:
+            return None
+        return self.config[model_name]
+
+    def is_legacy(self,model_name:str)->bool:
+        '''
+        Return true if this is a legacy (.ckpt) model
+        '''
+        info = self.model_info(model_name)
+        return info['format']=='ckpt' if info else False
+        
     def list_models(self) -> dict:
         '''
         Return a dict of models in the format:

From 10b2870dafe794d23a49b6caccdf6b85d9daa3fd Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Thu, 22 Dec 2022 21:04:51 +0000
Subject: [PATCH 086/199] generation using legacy ckpt models now working

---
 ldm/generate.py                       | 21 +++++++++++----------
 ldm/invoke/ckpt_generator/__init__.py |  2 +-
 2 files changed, 12 insertions(+), 11 deletions(-)

diff --git a/ldm/generate.py b/ldm/generate.py
index 5f246ee84fa..3ec72614d32 100644
--- a/ldm/generate.py
+++ b/ldm/generate.py
@@ -773,19 +773,19 @@ def _make_images(
         return init_image,init_mask
 
     def _make_base(self):
-        return self._load_generator('generator','Generator')
+        return self._load_generator('','Generator')
 
     def _make_txt2img(self):
-        return self._load_generator('txt2img','Txt2Img')
+        return self._load_generator('.txt2img','Txt2Img')
 
     def _make_img2img(self):
-        return self._load_generator('img2img','Img2Img')
+        return self._load_generator('.img2img','Img2Img')
 
     def _make_embiggen(self):
-        return self._load_generator('embiggen','Embiggen')
+        return self._load_generator('.embiggen','Embiggen')
    
     def _make_txt2img2img(self):
-        return self._load_generator('txt2img2img','Txt2Img2Img')
+        return self._load_generator('.txt2img2img','Txt2Img2Img')
 
     def _make_inpaint(self):
         return self._load_generator('inpaint','Inpaint')
@@ -795,13 +795,14 @@ def _make_omnibus(self):
 
     def _load_generator(self, module, class_name):
         if self.is_legacy_model(self.model_name):
-            mn = f'ldm.invoke.ckpt_{module}'
+            mn = f'ldm.invoke.ckpt_generator{module}'
             cn = f'Ckpt{class_name}'
         else:
-            mn = f'ldm.invoke.{module}'
+            mn = f'ldm.invoke.generator{module}'
             cn = class_name
-        importlib.import_module(mn)
-        return cn(self.model, self.precision)
+        module = importlib.import_module(mn)
+        constructor = getattr(module,cn)
+        return constructor(self.model, self.precision)
 
     def load_model(self):
         '''
@@ -962,7 +963,7 @@ def sample_to_image(self, samples):
     def sample_to_lowres_estimated_image(self, samples):
         return self._make_base().sample_to_lowres_estimated_image(samples)
 
-    def is_legacy_model(model_name)->bool:
+    def is_legacy_model(self,model_name)->bool:
         return self.model_cache.is_legacy(model_name)
 
     def _set_sampler(self):
diff --git a/ldm/invoke/ckpt_generator/__init__.py b/ldm/invoke/ckpt_generator/__init__.py
index 2fa5573c84d..d25e192149c 100644
--- a/ldm/invoke/ckpt_generator/__init__.py
+++ b/ldm/invoke/ckpt_generator/__init__.py
@@ -1,4 +1,4 @@
 '''
 Initialization file for the ldm.invoke.generator package
 '''
-from .base import Generator
+from .base import CkptGenerator

From a4f3d8a11473e24c3f56a4aadd0d860a6f1144de Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Thu, 22 Dec 2022 20:42:19 -0800
Subject: [PATCH 087/199] diffusers: fix missing attention_maps_callback

fix for 23eb80b40421b2bb8f4b6d3dd30490d11c447b36
---
 ldm/invoke/generator/inpaint.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/ldm/invoke/generator/inpaint.py b/ldm/invoke/generator/inpaint.py
index a55276d66c3..bbee1e4b0ae 100644
--- a/ldm/invoke/generator/inpaint.py
+++ b/ldm/invoke/generator/inpaint.py
@@ -16,6 +16,7 @@
 from ldm.invoke.patchmatch import PatchMatch
 from ldm.util import debug_image
 
+
 def infill_methods()->list[str]:
     methods = list()
     if PatchMatch.patchmatch_available():
@@ -180,6 +181,7 @@ def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
                        infill_method = None,
                        inpaint_width=None,
                        inpaint_height=None,
+                       attention_maps_callback=None,
                        **kwargs):
         """
         Returns a function returning an image derived from the prompt and

From 3a61258d84c7412fa7d1f2076f8d673343641a2f Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Fri, 23 Dec 2022 16:58:53 +0000
Subject: [PATCH 088/199] associate legacy CrossAttention with .ckpt models

---
 ldm/invoke/model_cache.py                       | 8 ++++----
 ldm/models/diffusion/cross_attention_control.py | 4 +++-
 ldm/modules/attention.py                        | 1 -
 3 files changed, 7 insertions(+), 6 deletions(-)

diff --git a/ldm/invoke/model_cache.py b/ldm/invoke/model_cache.py
index 859dc360ab5..9f01ab9baff 100644
--- a/ldm/invoke/model_cache.py
+++ b/ldm/invoke/model_cache.py
@@ -389,7 +389,7 @@ def _load_diffusers_model(self, mconfig):
         width = pipeline.vae.block_out_channels[-2]
         height = pipeline.vae.block_out_channels[-1]
 
-        print(f'  | training width x height = ({width} x {height})')
+        print(f'  | default image dimensions = {width} x {height}')
 
         return pipeline, width, height, model_hash
 
@@ -575,7 +575,7 @@ def _load_vae(self, vae_config):
         name_or_path = self.model_name_or_path(vae_config)
         using_fp16 = self.precision == 'float16'
 
-        print(f'>> Loading diffusers VAE from {name_or_path}')
+        print(f'  | Loading diffusers VAE from {name_or_path}')
         if using_fp16:
             print(f'  | Using faster float16 precision')
             vae_args.update(torch_dtype=torch.float16)
@@ -592,9 +592,9 @@ def _load_vae(self, vae_config):
                 vae = AutoencoderKL.from_pretrained(name_or_path, **vae_args, **fp_args)
             except OSError as e:
                 if str(e).startswith('fp16 is not a valid'):
-                    print(f'Could not fetch half-precision version of model {name_or_path}; fetching full-precision instead')
+                    print(f'  | Half-precision version of model not available; fetching full-precision instead')
                 else:
-                    print(f'An unexpected error occurred while downloading the model: {e})')
+                    print(f'** An unexpected error occurred while downloading the model: {e})')
             if vae:
                 break
 
diff --git a/ldm/models/diffusion/cross_attention_control.py b/ldm/models/diffusion/cross_attention_control.py
index 66c5567ebdb..eab76e2cbfa 100644
--- a/ldm/models/diffusion/cross_attention_control.py
+++ b/ldm/models/diffusion/cross_attention_control.py
@@ -6,6 +6,7 @@
 import torch
 import diffusers
 from torch import nn
+from diffusers.models.unet_2d_condition import UNet2DConditionModel
 
 # adapted from bloc97's CrossAttentionControl colab
 # https://github.com/bloc97/CrossAttentionControl
@@ -338,7 +339,8 @@ def setup_cross_attention_control(model, context: Context):
 
 
 def get_cross_attention_modules(model, which: CrossAttentionType) -> list[tuple[str, InvokeAICrossAttentionMixin]]:
-    cross_attention_class: type = InvokeAIDiffusersCrossAttention
+    from ldm.modules.attention import CrossAttention # avoid circular import
+    cross_attention_class: type = InvokeAIDiffusersCrossAttention if isinstance(model,UNet2DConditionModel) else CrossAttention
     which_attn = "attn1" if which is CrossAttentionType.SELF else "attn2"
     attention_module_tuples = [(name,module) for name, module in model.named_modules() if
                 isinstance(module, cross_attention_class) and which_attn in name]
diff --git a/ldm/modules/attention.py b/ldm/modules/attention.py
index 5d9c6db7e83..6737ed2060d 100644
--- a/ldm/modules/attention.py
+++ b/ldm/modules/attention.py
@@ -164,7 +164,6 @@ def get_mem_free_total(device):
 
 class CrossAttention(nn.Module, InvokeAICrossAttentionMixin):
     def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.):
-        print(f"Warning! ldm.modules.attention.CrossAttention is no longer being maintained. Please use InvokeAICrossAttention instead.")
         super().__init__()
         InvokeAICrossAttentionMixin.__init__(self)
         inner_dim = dim_head * heads

From b72c878e44e34657fba8d3ef5cbbe027b82aa3b9 Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Fri, 23 Dec 2022 18:46:41 +0000
Subject: [PATCH 089/199] enable autoconvert

New --autoconvert CLI option will scan a designated directory for
new .ckpt files, convert them into diffuser models, and import
them into models.yaml.

Works like this:

   invoke.py --autoconvert /path/to/weights/directory

In ModelCache added two new methods:

  autoconvert_weights(config_path, weights_directory_path, models_directory_path)
  convert_and_import(ckpt_path, diffuser_path)
---
 ldm/invoke/CLI.py         | 46 ++++++++------------
 ldm/invoke/args.py        |  6 +++
 ldm/invoke/globals.py     | 14 ++++++
 ldm/invoke/model_cache.py | 91 +++++++++++++++++++++++++++++++++++++--
 4 files changed, 125 insertions(+), 32 deletions(-)

diff --git a/ldm/invoke/CLI.py b/ldm/invoke/CLI.py
index c49349de322..b412d8b8a84 100644
--- a/ldm/invoke/CLI.py
+++ b/ldm/invoke/CLI.py
@@ -17,6 +17,7 @@
 from ldm.invoke.image_util import make_grid
 from ldm.invoke.log import write_log
 from ldm.invoke.concepts_lib import HuggingFaceConceptsLibrary
+from ldm.invoke.model_cache import ModelCache
 from omegaconf import OmegaConf
 from pathlib import Path
 import pyparsing
@@ -126,6 +127,14 @@ def main():
         emergency_model_reconfigure(opt)
         sys.exit(-1)
 
+    # try to autoconvert new models
+    # autoimport new .ckpt files
+    if path := opt.autoconvert:
+        gen.model_cache.autoconvert_weights(
+            conf_path=opt.conf,
+            weights_directory=path,
+        )
+
     # web server loops forever
     if opt.web or opt.gui:
         invoke_ai_web_server_loop(gen, gfpgan, codeformer, esrgan)
@@ -591,35 +600,16 @@ def add_weights_to_config(model_path:str, gen, opt, completer):
     if write_config_file(opt.conf, gen, model_name, new_config, make_default=make_default):
         completer.add_model(model_name)
 
-def optimize_model(model_path:str, gen, opt, completer):
-    from ldm.invoke.ckpt_to_diffuser import convert_ckpt_to_diffuser
-    import transformers
-    basename = os.path.basename(os.path.splitext(model_path)[0])
-    dump_path = os.path.join(Globals.root, 'models','optimized-ckpts',basename)
-    if os.path.exists(dump_path):
-        print(f'ERROR: The path {dump_path} already exists. Please move or remove it and try again.')
+def optimize_model(ckpt_path:str, gen, opt, completer):
+    ckpt_path = Path(ckpt_path)
+    basename = ckpt_path.stem
+    diffuser_path = Path(Globals.root, 'models','optimized-ckpts',basename)
+    if diffuser_path.exists():
+        print(f'** {basename} is already optimized. Will not overwrite.')
         return
-    
-    print(f'INFO: Converting legacy weights file {model_path} to optimized diffuser model.')
-    print(f'      This operation will take 30-60s to complete.')
-    try:
-        verbosity =transformers.logging.get_verbosity()
-        transformers.logging.set_verbosity_error()
-        convert_ckpt_to_diffuser(model_path, dump_path)
-        transformers.logging.set_verbosity(verbosity)
-        print(f'Success. Optimized model is now located at {dump_path}')
-        print(f'Writing new config file entry for {basename}...')
-        model_name = basename
-        new_config = dict(
-            path=dump_path,
-            description=f'Optimized version of {basename}',
-            format='diffusers',
-        )
-        if write_config_file(opt.conf, gen, model_name, new_config):
-            completer.add_model(model_name)
-    except Exception as e:
-        print(f'** Conversion failed: {str(e)}')
-        traceback.print_exc()
+    new_config = gen.model_cache.convert_and_import(ckpt_path, diffuser_path)
+    if write_config_file(opt.conf, gen, basename, new_config, clobber=False):
+        completer.add_model(basename)
 
 def del_config(model_name:str, gen, opt, completer):
     current_model = gen.model_name
diff --git a/ldm/invoke/args.py b/ldm/invoke/args.py
index c7417900b69..3d5e28bab61 100644
--- a/ldm/invoke/args.py
+++ b/ldm/invoke/args.py
@@ -508,6 +508,12 @@ def _create_arg_parser(self):
             default=False,
             help='Check for and blur potentially NSFW images. Use --no-nsfw_checker to disable.',
         )
+        model_group.add_argument(
+            '--autoconvert',
+            default=None,
+            type=str,
+            help='Check the indicated directory for .ckpt weights files at startup and import as optimized diffuser models',
+        )
         model_group.add_argument(
             '--patchmatch',
             action=argparse.BooleanOptionalAction,
diff --git a/ldm/invoke/globals.py b/ldm/invoke/globals.py
index 8dd64b9ea85..10a673a08a8 100644
--- a/ldm/invoke/globals.py
+++ b/ldm/invoke/globals.py
@@ -13,6 +13,7 @@
 
 import os
 import os.path as osp
+from pathlib import Path
 from argparse import Namespace
 
 Globals = Namespace()
@@ -27,9 +28,22 @@
 
 # Where to look for the initialization file
 Globals.initfile = 'invokeai.init'
+Globals.models_dir = 'models'
+Globals.config_dir = 'configs'
+Globals.autoscan_dir = 'weights'
 
 # Try loading patchmatch
 Globals.try_patchmatch = True
 
 # Use CPU even if GPU is available (main use case is for debugging MPS issues)
 Globals.always_use_cpu = False
+
+def global_config_dir()->str:
+    return Path(Globals.root, Globals.config_dir)
+
+def global_models_dir()->str:
+    return Path(Globals.root, Globals.models_dir)
+
+def global_autoscan_dir()->str:
+    return Path(Globals.root, Globals.autoscan_dir)
+
diff --git a/ldm/invoke/model_cache.py b/ldm/invoke/model_cache.py
index 9f01ab9baff..c874da81173 100644
--- a/ldm/invoke/model_cache.py
+++ b/ldm/invoke/model_cache.py
@@ -21,7 +21,7 @@
 
 import torch
 import transformers
-from diffusers import AutoencoderKL
+from diffusers import AutoencoderKL, logging as dlogging
 from huggingface_hub import hf_hub_download
 from huggingface_hub.utils import RevisionNotFoundError
 from omegaconf import OmegaConf
@@ -30,7 +30,7 @@
 from picklescan.scanner import scan_file_path
 
 from ldm.invoke.generator.diffusers_pipeline import StableDiffusionGeneratorPipeline
-from ldm.invoke.globals import Globals
+from ldm.invoke.globals import Globals, global_config_dir, global_models_dir, global_autoscan_dir
 from ldm.util import instantiate_from_config, ask_user
 
 DEFAULT_MAX_MODELS=2
@@ -96,7 +96,7 @@ def get_model(self, model_name:str):
 
             except Exception as e:
                 print(f'** model {model_name} could not be loaded: {str(e)}')
-                print(traceback.format_exc())
+                traceback.print_exc()
                 assert self.current_model,'** FATAL: no current model to restore to'
                 print(f'** restoring {self.current_model}')
                 self.get_model(self.current_model)
@@ -366,6 +366,9 @@ def _load_diffusers_model(self, mconfig):
         else:
             fp_args_list = [{}]            
 
+        verbosity = dlogging.get_verbosity()
+        dlogging.set_verbosity_error()
+
         pipeline = None
         for fp_args in fp_args_list:
             try:
@@ -374,6 +377,7 @@ def _load_diffusers_model(self, mconfig):
                     **pipeline_args,
                     **fp_args,
                 )
+                
             except OSError as e:
                 if str(e).startswith('fp16 is not a valid'):
                     print(f'Could not fetch half-precision version of model {repo_id}; fetching full-precision instead')
@@ -382,6 +386,7 @@ def _load_diffusers_model(self, mconfig):
             if pipeline:
                 break
 
+            dlogging.set_verbosity_error()
             assert pipeline is not None, OSError(f'"{model_name}" could not be loaded')        
 
         pipeline.to(self.device)
@@ -430,6 +435,10 @@ def offload_model(self, model_name:str) -> None:
             torch.cuda.empty_cache()
 
     def scan_model(self, model_name, checkpoint):
+        '''
+        Apply picklescanner to the indicated checkpoint and issue a warning
+        and option to exit if an infected file is identified.
+        '''
         # scan model
         print(f'>> Scanning Model: {model_name}')
         scan_result = scan_file_path(checkpoint)
@@ -448,7 +457,81 @@ def scan_model(self, model_name, checkpoint):
                     print("### Exiting InvokeAI")
                     sys.exit()
         else:
-            print('>> Model Scanned. OK!!')
+            print('>> Model scanned ok!')
+
+    def autoconvert_weights(
+            self,
+            conf_path:Path,
+            weights_directory:Path=None,
+            dest_directory:Path=None,
+    ):
+        '''
+        Scan the indicated directory for .ckpt files, convert into diffuser models,
+        and import.
+        '''
+        weights_directory = weights_directory or global_autoscan_dir()
+        dest_directory = dest_directory or Path(global_models_dir(), 'optimized-ckpts')
+        
+        print('>> Checking for unconverted .ckpt files in {weights_directory}')
+        ckpt_files = dict()
+        for root, dirs, files in os.walk(weights_directory):
+            for f in files:
+                if not f.endswith('.ckpt'):
+                    continue
+                basename = Path(f).stem
+                dest = Path(dest_directory,basename)
+                if not dest.exists():
+                    ckpt_files[Path(root,f)]=dest
+                
+        if len(ckpt_files)==0:
+            return
+
+        print(f'>> New .ckpt file(s) found in {weights_directory}. Optimizing and importing...')
+        for ckpt in ckpt_files:
+            self.convert_and_import(ckpt, ckpt_files[ckpt])
+        self.commit(conf_path)
+
+    def convert_and_import(self, ckpt_path:Path, diffuser_path:Path)->dict:
+        '''
+        Convert a legacy ckpt weights file to diffuser model and import
+        into models.yaml.
+        '''
+        from ldm.invoke.ckpt_to_diffuser import convert_ckpt_to_diffuser
+        import transformers
+        if diffuser_path.exists():
+            print(f'ERROR: The path {str(diffuser_path)} already exists. Please move or remove it and try again.')
+            return
+
+        print(f'>> {ckpt_path.name}: optimizing (30-60s).')
+        try:
+            model_name = diffuser_path.name
+            verbosity =transformers.logging.get_verbosity()
+            transformers.logging.set_verbosity_error()
+            convert_ckpt_to_diffuser(ckpt_path, diffuser_path)
+            transformers.logging.set_verbosity(verbosity)
+            print(f'>> Success. Optimized model is now located at {str(diffuser_path)}')
+            print(f'>> Writing new config file entry for {model_name}...',end='')
+            new_config = dict(
+                path=str(diffuser_path),
+                description=f'Optimized version of {model_name}',
+                format='diffusers',
+            )
+            self.add_model(model_name, new_config, True)
+            print('done.')
+        except Exception as e:
+            print(f'** Conversion failed: {str(e)}')
+            traceback.print_exc()
+        return new_config
+
+    def del_config(model_name:str, gen, opt, completer):
+        current_model = gen.model_name
+        if model_name == current_model:
+            print("** Can't delete active model. !switch to another model first. **")
+            return
+        gen.model_cache.del_model(model_name)
+        gen.model_cache.commit(opt.conf)
+        print(f'** {model_name} deleted')
+        completer.del_model(model_name)
 
     def _make_cache_room(self) -> None:
         num_loaded_models = len(self.models)

From 2e3d42d010b6eacb17acdafe7f9ab8739781bfef Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Fri, 23 Dec 2022 18:59:29 -0800
Subject: [PATCH 090/199] diffusers: update to diffusers 0.11 (from 0.10.2)

---
 binary_installer/requirements.in                       | 2 +-
 environments-and-requirements/environment-lin-amd.yml  | 2 +-
 environments-and-requirements/environment-lin-cuda.yml | 2 +-
 environments-and-requirements/environment-mac.yml      | 2 +-
 environments-and-requirements/environment-win-cuda.yml | 2 +-
 environments-and-requirements/requirements-base.txt    | 2 +-
 6 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/binary_installer/requirements.in b/binary_installer/requirements.in
index c90da4e390b..66e0618f789 100644
--- a/binary_installer/requirements.in
+++ b/binary_installer/requirements.in
@@ -4,7 +4,7 @@
 --trusted-host https://download.pytorch.org
 accelerate~=0.15
 albumentations
-diffusers[torch]~=0.10
+diffusers[torch]~=0.11
 einops
 eventlet
 flask_cors
diff --git a/environments-and-requirements/environment-lin-amd.yml b/environments-and-requirements/environment-lin-amd.yml
index 1ad046ea848..c100b70b872 100644
--- a/environments-and-requirements/environment-lin-amd.yml
+++ b/environments-and-requirements/environment-lin-amd.yml
@@ -10,7 +10,7 @@ dependencies:
   - pip:
     - --extra-index-url  https://download.pytorch.org/whl/rocm5.2/
     - albumentations==0.4.3
-    - diffusers~=0.10
+    - diffusers~=0.11
     - einops==0.3.0
     - eventlet
     - flask==2.1.3
diff --git a/environments-and-requirements/environment-lin-cuda.yml b/environments-and-requirements/environment-lin-cuda.yml
index 8bb073d832b..fa22d6c843b 100644
--- a/environments-and-requirements/environment-lin-cuda.yml
+++ b/environments-and-requirements/environment-lin-cuda.yml
@@ -14,7 +14,7 @@ dependencies:
   - pip:
     - accelerate~=0.13
     - albumentations==0.4.3
-    - diffusers~=0.10
+    - diffusers~=0.11
     - einops==0.3.0
     - eventlet
     - flask==2.1.3
diff --git a/environments-and-requirements/environment-mac.yml b/environments-and-requirements/environment-mac.yml
index 18de1ce6c4d..de5330f40c4 100644
--- a/environments-and-requirements/environment-mac.yml
+++ b/environments-and-requirements/environment-mac.yml
@@ -22,7 +22,7 @@ dependencies:
 
   - albumentations=1.2
   - coloredlogs=15.0
-  - diffusers~=0.10
+  - diffusers~=0.11
   - einops=0.3
   - eventlet
   - grpcio=1.46
diff --git a/environments-and-requirements/environment-win-cuda.yml b/environments-and-requirements/environment-win-cuda.yml
index dd1404e1d02..306ffd67246 100644
--- a/environments-and-requirements/environment-win-cuda.yml
+++ b/environments-and-requirements/environment-win-cuda.yml
@@ -13,7 +13,7 @@ dependencies:
   - cudatoolkit=11.6
   - pip:
     - albumentations==0.4.3
-    - diffusers~=0.10
+    - diffusers~=0.11
     - einops==0.3.0
     - eventlet
     - flask==2.1.3
diff --git a/environments-and-requirements/requirements-base.txt b/environments-and-requirements/requirements-base.txt
index a7b9d48c13b..f84ebbaf6f9 100644
--- a/environments-and-requirements/requirements-base.txt
+++ b/environments-and-requirements/requirements-base.txt
@@ -1,6 +1,6 @@
 # pip will resolve the version which matches torch
 albumentations
-diffusers[torch]~=0.10
+diffusers[torch]~=0.11
 einops
 eventlet
 facexlib

From 3309baf168284711aca10c05c246af1841c619c8 Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Sat, 24 Dec 2022 03:01:28 +0000
Subject: [PATCH 091/199] fix vae loading & width/height calculation

---
 ldm/invoke/ckpt_to_diffuser.py |  5 ++++-
 ldm/invoke/model_cache.py      | 12 +++++++-----
 2 files changed, 11 insertions(+), 6 deletions(-)

diff --git a/ldm/invoke/ckpt_to_diffuser.py b/ldm/invoke/ckpt_to_diffuser.py
index 15702ccb35b..fcbe0b7ec5d 100644
--- a/ldm/invoke/ckpt_to_diffuser.py
+++ b/ldm/invoke/ckpt_to_diffuser.py
@@ -940,4 +940,7 @@ def convert_ckpt_to_diffuser(checkpoint_path:str,
         tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased")
         pipe = LDMTextToImagePipeline(vqvae=vae, bert=text_model, tokenizer=tokenizer, unet=unet, scheduler=scheduler)
 
-    pipe.save_pretrained(dump_path)
+    pipe.save_pretrained(
+        dump_path,
+#        safe_serialization=1,                 
+    )
diff --git a/ldm/invoke/model_cache.py b/ldm/invoke/model_cache.py
index c874da81173..b947504412d 100644
--- a/ldm/invoke/model_cache.py
+++ b/ldm/invoke/model_cache.py
@@ -356,8 +356,8 @@ def _load_diffusers_model(self, mconfig):
             #local_files_only=True
         )
         if 'vae' in mconfig:
-            vae = self._load_vae(mconfig['vae'])
-            pipeline_args.update(vae=vae)
+             vae = self._load_vae(mconfig['vae'])
+             pipeline_args.update(vae=vae)
         if not isinstance(name_or_path,Path):
             pipeline_args.update(cache_dir=os.path.join(Globals.root,'models',name_or_path))
         if using_fp16:
@@ -391,8 +391,9 @@ def _load_diffusers_model(self, mconfig):
 
         pipeline.to(self.device)
 
-        width = pipeline.vae.block_out_channels[-2]
-        height = pipeline.vae.block_out_channels[-1]
+        # square images???
+        width = pipeline.unet.config.sample_size * pipeline.vae_scale_factor
+        height = width
 
         print(f'  | default image dimensions = {width} x {height}')
 
@@ -657,7 +658,8 @@ def _load_vae(self, vae_config):
         vae_args = {}
         name_or_path = self.model_name_or_path(vae_config)
         using_fp16 = self.precision == 'float16'
-
+        vae_args.update(cache_dir=os.path.join(Globals.root,'models',name_or_path))
+        
         print(f'  | Loading diffusers VAE from {name_or_path}')
         if using_fp16:
             print(f'  | Using faster float16 precision')

From aaff507bdd9307c820c1ee42557823723dee6000 Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Fri, 23 Dec 2022 19:58:53 -0800
Subject: [PATCH 092/199] refactor: encapsulate these conditioning data into
 one container

---
 ldm/invoke/generator/diffusers_pipeline.py | 103 +++++++++++----------
 ldm/invoke/generator/img2img.py            |   6 +-
 ldm/invoke/generator/inpaint.py            |   8 +-
 ldm/invoke/generator/txt2img.py            |  10 +-
 ldm/invoke/generator/txt2img2img.py        |  15 ++-
 5 files changed, 70 insertions(+), 72 deletions(-)

diff --git a/ldm/invoke/generator/diffusers_pipeline.py b/ldm/invoke/generator/diffusers_pipeline.py
index 771184c4824..cbe38ee5ebe 100644
--- a/ldm/invoke/generator/diffusers_pipeline.py
+++ b/ldm/invoke/generator/diffusers_pipeline.py
@@ -1,10 +1,11 @@
 from __future__ import annotations
 
 import secrets
+import sys
 import warnings
 from dataclasses import dataclass
-import sys
 from typing import List, Optional, Union, Callable, Type, TypeVar, Generic, Any
+
 if sys.version_info < (3, 10):
     from typing_extensions import ParamSpec
 else:
@@ -12,7 +13,6 @@
 
 import PIL.Image
 import einops
-import numpy as np
 import torch
 import torchvision.transforms as T
 from diffusers.models import attention
@@ -39,7 +39,6 @@
 
 from ldm.models.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent
 from ldm.modules.textual_inversion_manager import TextualInversionManager
-from ldm.modules.encoders.modules import WeightedFrozenCLIPEmbedder
 
 
 @dataclass
@@ -189,6 +188,25 @@ def __call__(self, *args: ParamType.args,
             raise AssertionError("why was that an empty generator?")
         return result
 
+
+@dataclass(frozen=True)
+class ConditioningData:
+    unconditioned_embeddings: torch.Tensor
+    text_embeddings: torch.Tensor
+    guidance_scale: float
+    """
+    Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
+    `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf).
+    Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate
+    images that are closely linked to the text `prompt`, usually at the expense of lower image quality.
+    """
+    extra: Optional[InvokeAIDiffuserComponent.ExtraConditioningInfo] = None
+
+    @property
+    def dtype(self):
+        return self.text_embeddings.dtype
+
+
 @dataclass
 class InvokeAIStableDiffusionPipelineOutput(StableDiffusionPipelineOutput):
     r"""
@@ -275,32 +293,24 @@ def __init__(
             self.enable_xformers_memory_efficient_attention()
 
     def image_from_embeddings(self, latents: torch.Tensor, num_inference_steps: int,
-                              text_embeddings: torch.Tensor, unconditioned_embeddings: torch.Tensor,
-                              guidance_scale: float,
+                              conditioning_data: ConditioningData,
                               *, callback: Callable[[PipelineIntermediateState], None]=None,
-                              extra_conditioning_info: InvokeAIDiffuserComponent.ExtraConditioningInfo=None,
                               run_id=None,
                               **extra_step_kwargs) -> InvokeAIStableDiffusionPipelineOutput:
         r"""
         Function invoked when calling the pipeline for generation.
 
+        :param conditioning_data:
         :param latents: Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for
             image generation. Can be used to tweak the same generation with different prompts.
         :param num_inference_steps: The number of denoising steps. More denoising steps usually lead to a higher quality
             image at the expense of slower inference.
-        :param text_embeddings:
-        :param guidance_scale: Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-            `guidance_scale` is defined as `w` of equation 2. of [Imagen Paper](https://arxiv.org/pdf/2205.11487.pdf).
-             Guidance scale is enabled by setting `guidance_scale > 1`. Higher guidance scale encourages to generate
-             images that are closely linked to the text `prompt`, usually at the expense of lower image quality.
         :param callback:
-        :param extra_conditioning_info:
         :param run_id:
         :param extra_step_kwargs:
         """
         result_latents, result_attention_map_saver = self.latents_from_embeddings(
-            latents, num_inference_steps, text_embeddings, unconditioned_embeddings, guidance_scale,
-            extra_conditioning_info=extra_conditioning_info,
+            latents, num_inference_steps, conditioning_data,
             run_id=run_id, callback=callback, **extra_step_kwargs
         )
         # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
@@ -309,42 +319,40 @@ def image_from_embeddings(self, latents: torch.Tensor, num_inference_steps: int,
         with torch.inference_mode():
             image = self.decode_latents(result_latents)
             output = InvokeAIStableDiffusionPipelineOutput(images=image, nsfw_content_detected=[], attention_map_saver=result_attention_map_saver)
-            return self.check_for_safety(output, dtype=text_embeddings.dtype)
+            return self.check_for_safety(output, dtype=conditioning_data.dtype)
 
     def latents_from_embeddings(
         self, latents: torch.Tensor, num_inference_steps: int,
-        text_embeddings: torch.Tensor, unconditioned_embeddings: torch.Tensor,
-        guidance_scale: float,
+        conditioning_data: ConditioningData,
         *,
         timesteps = None,
-        extra_conditioning_info: InvokeAIDiffuserComponent.ExtraConditioningInfo = None,
         additional_guidance: List[Callable] = None,
         run_id=None,
         callback: Callable[[PipelineIntermediateState], None]=None,
         **extra_step_kwargs
-    ) -> torch.Tensor:
+    ) -> tuple[torch.Tensor, Optional[AttentionMapSaver]]:
         if timesteps is None:
             self.scheduler.set_timesteps(num_inference_steps, device=self.unet.device)
             timesteps = self.scheduler.timesteps
         infer_latents_from_embeddings = GeneratorToCallbackinator(self.generate_latents_from_embeddings, PipelineIntermediateState)
         result: PipelineIntermediateState = infer_latents_from_embeddings(
-            latents, timesteps, text_embeddings, unconditioned_embeddings, guidance_scale,
-            extra_conditioning_info=extra_conditioning_info,
+            latents, timesteps, conditioning_data,
             additional_guidance=additional_guidance,
             run_id=run_id,
             callback=callback,
             **extra_step_kwargs)
         return result.latents, result.attention_map_saver
 
-    def generate_latents_from_embeddings(self, latents: torch.Tensor, timesteps, text_embeddings: torch.Tensor,
-                                         unconditioned_embeddings: torch.Tensor, guidance_scale: float, *,
+    def generate_latents_from_embeddings(self, latents: torch.Tensor, timesteps,
+                                         conditioning_data: ConditioningData,
+                                         *,
                                          run_id: str = None,
-                                         extra_conditioning_info: InvokeAIDiffuserComponent.ExtraConditioningInfo = None,
                                          additional_guidance: List[Callable] = None, **extra_step_kwargs):
         if run_id is None:
             run_id = secrets.token_urlsafe(self.ID_LENGTH)
         if additional_guidance is None:
             additional_guidance = []
+        extra_conditioning_info = conditioning_data.extra
         if extra_conditioning_info is not None and extra_conditioning_info.wants_cross_attention_control:
             self.invokeai_diffuser.setup_cross_attention_control(extra_conditioning_info,
                                                                  step_count=len(self.scheduler.timesteps))
@@ -360,12 +368,11 @@ def generate_latents_from_embeddings(self, latents: torch.Tensor, timesteps, tex
         batched_t = torch.full((batch_size,), timesteps[0],
                                dtype=timesteps.dtype, device=self.unet.device)
 
-        attention_map_saver: AttentionMapSaver = None
+        attention_map_saver: Optional[AttentionMapSaver] = None
         self.invokeai_diffuser.remove_attention_map_saving()
         for i, t in enumerate(self.progress_bar(timesteps)):
             batched_t.fill_(t)
-            step_output = self.step(batched_t, latents, guidance_scale,
-                                    text_embeddings, unconditioned_embeddings,
+            step_output = self.step(batched_t, latents, conditioning_data,
                                     i, additional_guidance=additional_guidance,
                                     **extra_step_kwargs)
             latents = step_output.prev_sample
@@ -384,8 +391,8 @@ def generate_latents_from_embeddings(self, latents: torch.Tensor, timesteps, tex
         return latents, attention_map_saver
 
     @torch.inference_mode()
-    def step(self, t: torch.Tensor, latents: torch.Tensor, guidance_scale: float,
-             text_embeddings: torch.Tensor, unconditioned_embeddings: torch.Tensor,
+    def step(self, t: torch.Tensor, latents: torch.Tensor,
+             conditioning_data: ConditioningData,
              step_index:int | None = None, additional_guidance: List[Callable] = None,
              **extra_step_kwargs):
         # invokeai_diffuser has batched timesteps, but diffusers schedulers expect a single value
@@ -401,8 +408,8 @@ def step(self, t: torch.Tensor, latents: torch.Tensor, guidance_scale: float,
         # predict the noise residual
         noise_pred = self.invokeai_diffuser.do_diffusion_step(
             latent_model_input, t,
-            unconditioned_embeddings, text_embeddings,
-            guidance_scale,
+            conditioning_data.unconditioned_embeddings, conditioning_data.text_embeddings,
+            conditioning_data.guidance_scale,
             step_index=step_index)
 
         # compute the previous noisy sample x_t -> x_t-1
@@ -412,7 +419,7 @@ def step(self, t: torch.Tensor, latents: torch.Tensor, guidance_scale: float,
         #    But the way things are now, scheduler runs _after_ that, so there was
         #    no way to use it to apply an operation that happens after the last scheduler.step.
         for guidance in additional_guidance:
-            step_output = guidance(step_output, timestep, (unconditioned_embeddings, text_embeddings))
+            step_output = guidance(step_output, timestep, conditioning_data)
 
         return step_output
 
@@ -424,10 +431,8 @@ def img2img_from_embeddings(self,
                                 init_image: Union[torch.FloatTensor, PIL.Image.Image],
                                 strength: float,
                                 num_inference_steps: int,
-                                text_embeddings: torch.Tensor, unconditioned_embeddings: torch.Tensor,
-                                guidance_scale: float,
+                                conditioning_data: ConditioningData,
                                 *, callback: Callable[[PipelineIntermediateState], None] = None,
-                                extra_conditioning_info: InvokeAIDiffuserComponent.ExtraConditioningInfo = None,
                                 run_id=None,
                                 noise_func=None,
                                 **extra_step_kwargs) -> InvokeAIStableDiffusionPipelineOutput:
@@ -442,13 +447,15 @@ def img2img_from_embeddings(self,
         latents_dtype = self.unet.dtype
         initial_latents = self.non_noised_latents_from_image(init_image, device=device, dtype=latents_dtype)
 
-        return self.img2img_from_latents_and_embeddings(initial_latents, num_inference_steps, text_embeddings,
-                                                          unconditioned_embeddings, guidance_scale, strength,
-                                                          extra_conditioning_info, noise_func, run_id, callback,
-                                                          **extra_step_kwargs)
+        return self.img2img_from_latents_and_embeddings(initial_latents, num_inference_steps,
+                                                        conditioning_data,
+                                                        strength,
+                                                        noise_func, run_id, callback,
+                                                        **extra_step_kwargs)
 
-    def img2img_from_latents_and_embeddings(self, initial_latents, num_inference_steps, text_embeddings,
-                                            unconditioned_embeddings, guidance_scale, strength, extra_conditioning_info,
+    def img2img_from_latents_and_embeddings(self, initial_latents, num_inference_steps,
+                                            conditioning_data: ConditioningData,
+                                            strength,
                                             noise_func, run_id=None, callback=None, **extra_step_kwargs) -> InvokeAIStableDiffusionPipelineOutput:
         device = self.unet.device
         batch_size = initial_latents.size(0)
@@ -461,8 +468,7 @@ def img2img_from_latents_and_embeddings(self, initial_latents, num_inference_ste
         latents = noised_latents
 
         result_latents, result_attention_maps = self.latents_from_embeddings(
-            latents, num_inference_steps, text_embeddings, unconditioned_embeddings, guidance_scale,
-            extra_conditioning_info=extra_conditioning_info,
+            latents, num_inference_steps, conditioning_data,
             timesteps=timesteps,
             callback=callback,
             run_id=run_id, **extra_step_kwargs)
@@ -473,7 +479,7 @@ def img2img_from_latents_and_embeddings(self, initial_latents, num_inference_ste
         with torch.inference_mode():
             image = self.decode_latents(result_latents)
             output = InvokeAIStableDiffusionPipelineOutput(images=image, nsfw_content_detected=[], attention_map_saver=result_attention_maps)
-            return self.check_for_safety(output, dtype=text_embeddings.dtype)
+            return self.check_for_safety(output, dtype=conditioning_data.dtype)
 
     def inpaint_from_embeddings(
             self,
@@ -481,10 +487,8 @@ def inpaint_from_embeddings(
             mask: torch.FloatTensor,
             strength: float,
             num_inference_steps: int,
-            text_embeddings: torch.Tensor, unconditioned_embeddings: torch.Tensor,
-            guidance_scale: float,
+            conditioning_data: ConditioningData,
             *, callback: Callable[[PipelineIntermediateState], None] = None,
-            extra_conditioning_info: InvokeAIDiffuserComponent.ExtraConditioningInfo = None,
             run_id=None,
             noise_func=None,
             **extra_step_kwargs) -> InvokeAIStableDiffusionPipelineOutput:
@@ -531,8 +535,7 @@ def inpaint_from_embeddings(
 
         try:
             result_latents, result_attention_maps = self.latents_from_embeddings(
-                latents, num_inference_steps, text_embeddings, unconditioned_embeddings, guidance_scale,
-                extra_conditioning_info=extra_conditioning_info,
+                latents, num_inference_steps, conditioning_data,
                 timesteps=timesteps,
                 run_id=run_id, additional_guidance=guidance,
                 callback=callback,
@@ -546,7 +549,7 @@ def inpaint_from_embeddings(
         with torch.inference_mode():
             image = self.decode_latents(result_latents)
             output = InvokeAIStableDiffusionPipelineOutput(images=image, nsfw_content_detected=[], attention_map_saver=result_attention_maps)
-            return self.check_for_safety(output, dtype=text_embeddings.dtype)
+            return self.check_for_safety(output, dtype=conditioning_data.dtype)
 
     def non_noised_latents_from_image(self, init_image, *, device, dtype):
         init_image = init_image.to(device=device, dtype=dtype)
diff --git a/ldm/invoke/generator/img2img.py b/ldm/invoke/generator/img2img.py
index 1a470d1ebf1..551fa64ab50 100644
--- a/ldm/invoke/generator/img2img.py
+++ b/ldm/invoke/generator/img2img.py
@@ -5,7 +5,7 @@
 import torch
 
 from ldm.invoke.generator.base import Generator
-from ldm.invoke.generator.diffusers_pipeline import StableDiffusionGeneratorPipeline
+from ldm.invoke.generator.diffusers_pipeline import StableDiffusionGeneratorPipeline, ConditioningData
 
 
 class Img2Img(Generator):
@@ -24,6 +24,7 @@ def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
         self.perlin = perlin
 
         uc, c, extra_conditioning_info   = conditioning
+        conditioning_data = ConditioningData(uc, c, cfg_scale, extra_conditioning_info)
 
         # noinspection PyTypeChecker
         pipeline: StableDiffusionGeneratorPipeline = self.model
@@ -32,8 +33,7 @@ def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
         def make_image(x_T):
             # FIXME: use x_T for initial seeded noise
             pipeline_output = pipeline.img2img_from_embeddings(
-                init_image, strength, steps, c, uc, cfg_scale,
-                extra_conditioning_info=extra_conditioning_info,
+                init_image, strength, steps, conditioning_data,
                 noise_func=self.get_noise_like,
                 callback=step_callback
             )
diff --git a/ldm/invoke/generator/inpaint.py b/ldm/invoke/generator/inpaint.py
index bbee1e4b0ae..f29eaa77bb0 100644
--- a/ldm/invoke/generator/inpaint.py
+++ b/ldm/invoke/generator/inpaint.py
@@ -11,7 +11,8 @@
 import torch
 from PIL import Image, ImageFilter, ImageOps, ImageChops
 
-from ldm.invoke.generator.diffusers_pipeline import image_resized_to_grid_as_tensor, StableDiffusionGeneratorPipeline
+from ldm.invoke.generator.diffusers_pipeline import image_resized_to_grid_as_tensor, StableDiffusionGeneratorPipeline, \
+    ConditioningData
 from ldm.invoke.generator.img2img import Img2Img
 from ldm.invoke.patchmatch import PatchMatch
 from ldm.util import debug_image
@@ -242,6 +243,7 @@ def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
 
         # todo: support cross-attention control
         uc, c, _ = conditioning
+        conditioning_data = ConditioningData(uc, c, cfg_scale)
 
         # noinspection PyTypeChecker
         pipeline: StableDiffusionGeneratorPipeline = self.model
@@ -262,9 +264,7 @@ def make_image(x_T):
                 mask=1 - mask,  # expects white means "paint here."
                 strength=strength,
                 num_inference_steps=steps,
-                text_embeddings=c,
-                unconditioned_embeddings=uc,
-                guidance_scale=cfg_scale,
+                conditioning_data=conditioning_data,
                 noise_func=self.get_noise_like,
                 callback=step_callback,
             )
diff --git a/ldm/invoke/generator/txt2img.py b/ldm/invoke/generator/txt2img.py
index 6c4c7a3f13a..fc003c4af86 100644
--- a/ldm/invoke/generator/txt2img.py
+++ b/ldm/invoke/generator/txt2img.py
@@ -5,7 +5,7 @@
 import torch
 
 from .base import Generator
-from .diffusers_pipeline import StableDiffusionGeneratorPipeline
+from .diffusers_pipeline import StableDiffusionGeneratorPipeline, ConditioningData
 
 
 class Txt2Img(Generator):
@@ -24,6 +24,7 @@ def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
         """
         self.perlin = perlin
         uc, c, extra_conditioning_info   = conditioning
+        conditioning_data = ConditioningData(uc, c, cfg_scale, extra_conditioning_info)
 
         # noinspection PyTypeChecker
         pipeline: StableDiffusionGeneratorPipeline = self.model
@@ -33,11 +34,8 @@ def make_image(x_T) -> PIL.Image.Image:
             pipeline_output = pipeline.image_from_embeddings(
                 latents=x_T,
                 num_inference_steps=steps,
-                text_embeddings=c,
-                unconditioned_embeddings=uc,
-                guidance_scale=cfg_scale,
-                callback=step_callback,
-                extra_conditioning_info=extra_conditioning_info
+                conditioning_data=conditioning_data,
+                callback=step_callback
                 # TODO: eta = ddim_eta,
                 # TODO: threshold = threshold,
             )
diff --git a/ldm/invoke/generator/txt2img2img.py b/ldm/invoke/generator/txt2img2img.py
index 56ebcc5bf43..b4aa6b57749 100644
--- a/ldm/invoke/generator/txt2img2img.py
+++ b/ldm/invoke/generator/txt2img2img.py
@@ -8,7 +8,8 @@
 import torch
 
 from ldm.invoke.generator.base import Generator
-from ldm.invoke.generator.diffusers_pipeline import trim_to_multiple_of, StableDiffusionGeneratorPipeline
+from ldm.invoke.generator.diffusers_pipeline import trim_to_multiple_of, StableDiffusionGeneratorPipeline, \
+    ConditioningData
 
 
 class Txt2Img2Img(Generator):
@@ -25,6 +26,7 @@ def get_make_image(self, prompt:str, sampler, steps:int, cfg_scale:float, ddim_e
         kwargs are 'width' and 'height'
         """
         uc, c, extra_conditioning_info = conditioning
+        conditioning_data = ConditioningData(uc, c, cfg_scale, extra_conditioning_info)
         scale_dim = min(width, height)
         scale = 512 / scale_dim
 
@@ -39,11 +41,8 @@ def make_image(x_T):
             first_pass_latent_output, _ = pipeline.latents_from_embeddings(
                 latents=x_T,
                 num_inference_steps=steps,
-                text_embeddings=c,
-                unconditioned_embeddings=uc,
-                guidance_scale=cfg_scale,
+                conditioning_data=conditioning_data,
                 callback=step_callback,
-                extra_conditioning_info=extra_conditioning_info,
                 # TODO: eta = ddim_eta,
                 # TODO: threshold = threshold,
             )
@@ -62,10 +61,8 @@ def make_image(x_T):
             pipeline_output = pipeline.img2img_from_latents_and_embeddings(
                 resized_latents,
                 num_inference_steps=steps,
-                text_embeddings=c,
-                unconditioned_embeddings=uc,
-                guidance_scale=cfg_scale, strength=strength,
-                extra_conditioning_info=extra_conditioning_info,
+                conditioning_data=conditioning_data,
+                strength=strength,
                 noise_func=self.get_noise_like,
                 callback=step_callback)
 

From 644ba5edab4b8df6a3a61e8a937faa50c6faeffb Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Fri, 23 Dec 2022 20:35:34 -0800
Subject: [PATCH 093/199] diffusers: fix some noise-scaling issues by pushing
 the noise-mixing down to the common function

---
 ldm/invoke/generator/diffusers_pipeline.py | 68 +++++++++++-----------
 ldm/invoke/generator/img2img.py            |  2 +
 ldm/invoke/generator/inpaint.py            |  9 ---
 ldm/invoke/generator/txt2img.py            |  3 +-
 ldm/invoke/generator/txt2img2img.py        |  7 ++-
 5 files changed, 42 insertions(+), 47 deletions(-)

diff --git a/ldm/invoke/generator/diffusers_pipeline.py b/ldm/invoke/generator/diffusers_pipeline.py
index cbe38ee5ebe..22bada77eb1 100644
--- a/ldm/invoke/generator/diffusers_pipeline.py
+++ b/ldm/invoke/generator/diffusers_pipeline.py
@@ -294,25 +294,31 @@ def __init__(
 
     def image_from_embeddings(self, latents: torch.Tensor, num_inference_steps: int,
                               conditioning_data: ConditioningData,
-                              *, callback: Callable[[PipelineIntermediateState], None]=None,
+                              *,
+                              noise: torch.Tensor,
+                              callback: Callable[[PipelineIntermediateState], None]=None,
                               run_id=None,
                               **extra_step_kwargs) -> InvokeAIStableDiffusionPipelineOutput:
         r"""
         Function invoked when calling the pipeline for generation.
 
         :param conditioning_data:
-        :param latents: Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for
+        :param latents: Pre-generated un-noised latents, to be used as inputs for
             image generation. Can be used to tweak the same generation with different prompts.
         :param num_inference_steps: The number of denoising steps. More denoising steps usually lead to a higher quality
             image at the expense of slower inference.
+        :param noise: Noise to add to the latents, sampled from a Gaussian distribution.
         :param callback:
         :param run_id:
         :param extra_step_kwargs:
         """
         result_latents, result_attention_map_saver = self.latents_from_embeddings(
-            latents, num_inference_steps, conditioning_data,
-            run_id=run_id, callback=callback, **extra_step_kwargs
-        )
+            latents, num_inference_steps,
+            conditioning_data,
+            noise=noise,
+            run_id=run_id,
+            callback=callback,
+            **extra_step_kwargs)
         # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
         torch.cuda.empty_cache()
 
@@ -321,22 +327,20 @@ def image_from_embeddings(self, latents: torch.Tensor, num_inference_steps: int,
             output = InvokeAIStableDiffusionPipelineOutput(images=image, nsfw_content_detected=[], attention_map_saver=result_attention_map_saver)
             return self.check_for_safety(output, dtype=conditioning_data.dtype)
 
-    def latents_from_embeddings(
-        self, latents: torch.Tensor, num_inference_steps: int,
-        conditioning_data: ConditioningData,
-        *,
-        timesteps = None,
-        additional_guidance: List[Callable] = None,
-        run_id=None,
-        callback: Callable[[PipelineIntermediateState], None]=None,
-        **extra_step_kwargs
-    ) -> tuple[torch.Tensor, Optional[AttentionMapSaver]]:
+    def latents_from_embeddings(self, latents: torch.Tensor, num_inference_steps: int,
+                                conditioning_data: ConditioningData,
+                                *,
+                                noise: torch.Tensor,
+                                timesteps=None,
+                                additional_guidance: List[Callable] = None, run_id=None,
+                                callback: Callable[[PipelineIntermediateState], None] = None, **extra_step_kwargs) -> tuple[torch.Tensor, Optional[AttentionMapSaver]]:
         if timesteps is None:
             self.scheduler.set_timesteps(num_inference_steps, device=self.unet.device)
             timesteps = self.scheduler.timesteps
         infer_latents_from_embeddings = GeneratorToCallbackinator(self.generate_latents_from_embeddings, PipelineIntermediateState)
         result: PipelineIntermediateState = infer_latents_from_embeddings(
             latents, timesteps, conditioning_data,
+            noise=noise,
             additional_guidance=additional_guidance,
             run_id=run_id,
             callback=callback,
@@ -346,6 +350,7 @@ def latents_from_embeddings(
     def generate_latents_from_embeddings(self, latents: torch.Tensor, timesteps,
                                          conditioning_data: ConditioningData,
                                          *,
+                                         noise: torch.Tensor,
                                          run_id: str = None,
                                          additional_guidance: List[Callable] = None, **extra_step_kwargs):
         if run_id is None:
@@ -359,14 +364,13 @@ def generate_latents_from_embeddings(self, latents: torch.Tensor, timesteps,
         else:
             self.invokeai_diffuser.remove_cross_attention_control()
 
-        # scale the initial noise by the standard deviation required by the scheduler
-        latents *= self.scheduler.init_noise_sigma
         yield PipelineIntermediateState(run_id=run_id, step=-1, timestep=self.scheduler.num_train_timesteps,
                                         latents=latents)
 
         batch_size = latents.shape[0]
         batched_t = torch.full((batch_size,), timesteps[0],
                                dtype=timesteps.dtype, device=self.unet.device)
+        latents = self.scheduler.add_noise(latents, noise, batched_t)
 
         attention_map_saver: Optional[AttentionMapSaver] = None
         self.invokeai_diffuser.remove_attention_map_saving()
@@ -446,32 +450,30 @@ def img2img_from_embeddings(self,
         device = self.unet.device
         latents_dtype = self.unet.dtype
         initial_latents = self.non_noised_latents_from_image(init_image, device=device, dtype=latents_dtype)
+        noise = noise_func(initial_latents)
 
         return self.img2img_from_latents_and_embeddings(initial_latents, num_inference_steps,
                                                         conditioning_data,
                                                         strength,
-                                                        noise_func, run_id, callback,
+                                                        noise, run_id, callback,
                                                         **extra_step_kwargs)
 
     def img2img_from_latents_and_embeddings(self, initial_latents, num_inference_steps,
                                             conditioning_data: ConditioningData,
                                             strength,
-                                            noise_func, run_id=None, callback=None, **extra_step_kwargs) -> InvokeAIStableDiffusionPipelineOutput:
+                                            noise: torch.Tensor, run_id=None, callback=None, **extra_step_kwargs) -> InvokeAIStableDiffusionPipelineOutput:
         device = self.unet.device
-        batch_size = initial_latents.size(0)
         img2img_pipeline = StableDiffusionImg2ImgPipeline(**self.components)
         img2img_pipeline.scheduler.set_timesteps(num_inference_steps, device=device)
         timesteps, _ = img2img_pipeline.get_timesteps(num_inference_steps, strength, device=device)
-        latent_timestep = timesteps[:1].repeat(batch_size)
-        noise = noise_func(initial_latents)
-        noised_latents = self.scheduler.add_noise(initial_latents, noise, latent_timestep)
-        latents = noised_latents
 
         result_latents, result_attention_maps = self.latents_from_embeddings(
-            latents, num_inference_steps, conditioning_data,
+            initial_latents, num_inference_steps, conditioning_data,
             timesteps=timesteps,
+            noise=noise,
+            run_id=run_id,
             callback=callback,
-            run_id=run_id, **extra_step_kwargs)
+            **extra_step_kwargs)
 
         # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
         torch.cuda.empty_cache()
@@ -494,8 +496,6 @@ def inpaint_from_embeddings(
             **extra_step_kwargs) -> InvokeAIStableDiffusionPipelineOutput:
         device = self.unet.device
         latents_dtype = self.unet.dtype
-        batch_size = 1
-        num_images_per_prompt = 1
 
         if isinstance(init_image, PIL.Image.Image):
             init_image = image_resized_to_grid_as_tensor(init_image.convert('RGB'))
@@ -512,16 +512,14 @@ def inpaint_from_embeddings(
         assert img2img_pipeline.scheduler is self.scheduler
 
         # 6. Prepare latent variables
-        latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
         # can't quite use upstream StableDiffusionImg2ImgPipeline.prepare_latents
         # because we have our own noise function
         init_image_latents = self.non_noised_latents_from_image(init_image, device=device, dtype=latents_dtype)
         noise = noise_func(init_image_latents)
-        latents = self.scheduler.add_noise(init_image_latents, noise, latent_timestep)
 
         if mask.dim() == 3:
             mask = mask.unsqueeze(0)
-        mask = tv_resize(mask, latents.shape[-2:], T.InterpolationMode.BILINEAR) \
+        mask = tv_resize(mask, init_image_latents.shape[-2:], T.InterpolationMode.BILINEAR) \
             .to(device=device, dtype=latents_dtype)
 
         guidance: List[Callable] = []
@@ -535,10 +533,10 @@ def inpaint_from_embeddings(
 
         try:
             result_latents, result_attention_maps = self.latents_from_embeddings(
-                latents, num_inference_steps, conditioning_data,
-                timesteps=timesteps,
-                run_id=run_id, additional_guidance=guidance,
-                callback=callback,
+                init_image_latents, num_inference_steps,
+                conditioning_data, noise=noise, timesteps=timesteps,
+                additional_guidance=guidance,
+                run_id=run_id, callback=callback,
                 **extra_step_kwargs)
         finally:
             self.invokeai_diffuser.model_forward_callback = self._unet_forward
diff --git a/ldm/invoke/generator/img2img.py b/ldm/invoke/generator/img2img.py
index 551fa64ab50..52dec6f198d 100644
--- a/ldm/invoke/generator/img2img.py
+++ b/ldm/invoke/generator/img2img.py
@@ -32,6 +32,8 @@ def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
 
         def make_image(x_T):
             # FIXME: use x_T for initial seeded noise
+            # We're not at the moment because the pipeline automatically resizes init_image if
+            # necessary, which the x_T input might not match.
             pipeline_output = pipeline.img2img_from_embeddings(
                 init_image, strength, steps, conditioning_data,
                 noise_func=self.get_noise_like,
diff --git a/ldm/invoke/generator/inpaint.py b/ldm/invoke/generator/inpaint.py
index f29eaa77bb0..263644176c6 100644
--- a/ldm/invoke/generator/inpaint.py
+++ b/ldm/invoke/generator/inpaint.py
@@ -250,15 +250,6 @@ def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
         pipeline.scheduler = sampler
 
         def make_image(x_T):
-            # FIXME: some of this z_enc and inpaint_replace stuff was probably important
-            # # to replace masked area with latent noise, weighted by inpaint_replace strength
-            # if inpaint_replace > 0.0:
-            #     print(f'>> inpaint will replace what was under the mask with a strength of {inpaint_replace}')
-            #     l_noise = self.get_noise(kwargs['width'],kwargs['height'])
-            #     inverted_mask = 1.0-mask  # there will be 1s where the mask is
-            #     masked_region = (1.0-inpaint_replace) * inverted_mask * z_enc + inpaint_replace * inverted_mask * l_noise
-            #     z_enc   = z_enc * mask + masked_region
-
             pipeline_output = pipeline.inpaint_from_embeddings(
                 init_image=init_image,
                 mask=1 - mask,  # expects white means "paint here."
diff --git a/ldm/invoke/generator/txt2img.py b/ldm/invoke/generator/txt2img.py
index fc003c4af86..768f20abd14 100644
--- a/ldm/invoke/generator/txt2img.py
+++ b/ldm/invoke/generator/txt2img.py
@@ -32,7 +32,8 @@ def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
 
         def make_image(x_T) -> PIL.Image.Image:
             pipeline_output = pipeline.image_from_embeddings(
-                latents=x_T,
+                latents=torch.zeros_like(x_T),
+                noise=x_T,
                 num_inference_steps=steps,
                 conditioning_data=conditioning_data,
                 callback=step_callback
diff --git a/ldm/invoke/generator/txt2img2img.py b/ldm/invoke/generator/txt2img2img.py
index b4aa6b57749..a1743988f8f 100644
--- a/ldm/invoke/generator/txt2img2img.py
+++ b/ldm/invoke/generator/txt2img2img.py
@@ -39,9 +39,10 @@ def get_make_image(self, prompt:str, sampler, steps:int, cfg_scale:float, ddim_e
         def make_image(x_T):
 
             first_pass_latent_output, _ = pipeline.latents_from_embeddings(
-                latents=x_T,
+                latents=torch.zeros_like(x_T),
                 num_inference_steps=steps,
                 conditioning_data=conditioning_data,
+                noise=x_T,
                 callback=step_callback,
                 # TODO: eta = ddim_eta,
                 # TODO: threshold = threshold,
@@ -58,12 +59,14 @@ def make_image(x_T):
                 mode="bilinear"
             )
 
+            second_pass_noise = self.get_noise_like(resized_latents)
+
             pipeline_output = pipeline.img2img_from_latents_and_embeddings(
                 resized_latents,
                 num_inference_steps=steps,
                 conditioning_data=conditioning_data,
                 strength=strength,
-                noise_func=self.get_noise_like,
+                noise=second_pass_noise,
                 callback=step_callback)
 
             return pipeline.numpy_to_pil(pipeline_output.images)[0]

From 003a7f87e1d67866b21db48e1a4ef063832a77a6 Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Sat, 24 Dec 2022 12:54:31 +0000
Subject: [PATCH 094/199] add support for safetensors and accelerate

---
 configs/INITIAL_MODELS.yaml                   | 20 ++++++++++++++++++-
 .../environment-lin-aarch64.yml               |  2 ++
 .../environment-lin-amd.yml                   |  2 ++
 .../environment-lin-cuda.yml                  |  1 +
 .../environment-mac.yml                       |  3 ++-
 .../environment-win-cuda.yml                  |  2 ++
 .../requirements-base.txt                     |  2 ++
 ldm/invoke/ckpt_to_diffuser.py                |  2 +-
 ldm/invoke/model_cache.py                     |  4 +++-
 9 files changed, 34 insertions(+), 4 deletions(-)

diff --git a/configs/INITIAL_MODELS.yaml b/configs/INITIAL_MODELS.yaml
index feea63065ed..64158186faf 100644
--- a/configs/INITIAL_MODELS.yaml
+++ b/configs/INITIAL_MODELS.yaml
@@ -21,7 +21,7 @@ inpainting-1.5:
    height: 512
 stable-diffusion-1.4:
    description: The original Stable Diffusion version 1.4 weight file (4.27 GB)
-   repo_id: CompVis/stable-diffusion-v-1-4-original
+   repo_id: CompVis/stable-diffusion-v1-4
    recommended: False
    format: diffusers
    vae:
@@ -80,3 +80,21 @@ voxel_art-1.0:
    recommended: False
    width: 512
    height: 512
+ft-mse-improved-autoencoder-840000:
+   description: StabilityAI improved autoencoder fine-tuned for human faces for legacy .ckpt models only (335 MB)
+   repo_id: stabilityai/sd-vae-ft-mse-original
+   format: ckpt
+   config: VAE/default
+   file: vae-ft-mse-840000-ema-pruned.ckpt
+   recommended: True
+   width: 512
+   height: 512
+trinart_vae:
+   description: Custom autoencoder for trinart_characters for legacy .ckpt models only (335 MB)
+   repo_id: naclbit/trinart_characters_19.2m_stable_diffusion_v1
+   config: VAE/trinart
+   format: ckpt
+   file: autoencoder_fix_kl-f8-trinart_characters.ckpt
+   recommended: False
+   width: 512
+   height: 512
diff --git a/environments-and-requirements/environment-lin-aarch64.yml b/environments-and-requirements/environment-lin-aarch64.yml
index 01fc4c30c28..b2944066576 100644
--- a/environments-and-requirements/environment-lin-aarch64.yml
+++ b/environments-and-requirements/environment-lin-aarch64.yml
@@ -30,11 +30,13 @@ dependencies:
   - torchvision
   - transformers~=4.25
   - pip:
+      - accelerate
       - getpass_asterisk
       - omegaconf==2.1.1
       - picklescan
       - pyreadline3
       - realesrgan
+      - safetensors
       - taming-transformers-rom1504
       - test-tube>=0.7.5
       - git+https://github.com/openai/CLIP.git@main#egg=clip
diff --git a/environments-and-requirements/environment-lin-amd.yml b/environments-and-requirements/environment-lin-amd.yml
index c100b70b872..3c061a9a507 100644
--- a/environments-and-requirements/environment-lin-amd.yml
+++ b/environments-and-requirements/environment-lin-amd.yml
@@ -9,6 +9,7 @@ dependencies:
   - numpy=1.23.3
   - pip:
     - --extra-index-url  https://download.pytorch.org/whl/rocm5.2/
+    - accelerate
     - albumentations==0.4.3
     - diffusers~=0.11
     - einops==0.3.0
@@ -28,6 +29,7 @@ dependencies:
     - pyreadline3
     - pytorch-lightning==1.7.7
     - realesrgan
+    - safetensors
     - send2trash==1.8.0
     - streamlit==1.12.0
     - taming-transformers-rom1504
diff --git a/environments-and-requirements/environment-lin-cuda.yml b/environments-and-requirements/environment-lin-cuda.yml
index fa22d6c843b..f48f2f3110b 100644
--- a/environments-and-requirements/environment-lin-cuda.yml
+++ b/environments-and-requirements/environment-lin-cuda.yml
@@ -32,6 +32,7 @@ dependencies:
     - pyreadline3
     - pytorch-lightning==1.7.7
     - realesrgan
+    - safetensors
     - send2trash==1.8.0
     - streamlit==1.12.0
     - taming-transformers-rom1504
diff --git a/environments-and-requirements/environment-mac.yml b/environments-and-requirements/environment-mac.yml
index de5330f40c4..99b4093a0c5 100644
--- a/environments-and-requirements/environment-mac.yml
+++ b/environments-and-requirements/environment-mac.yml
@@ -19,7 +19,7 @@ dependencies:
   # sed -E 's/invokeai/invokeai-updated/;20,99s/- ([^=]+)==.+/- \1/' environment-mac.yml > environment-mac-updated.yml
   # CONDA_SUBDIR=osx-arm64 conda env create -f environment-mac-updated.yml && conda list -n invokeai-updated | awk ' {print "  - " $1 "==" $2;} '
   # ```
-
+  - accelerate
   - albumentations=1.2
   - coloredlogs=15.0
   - diffusers~=0.11
@@ -44,6 +44,7 @@ dependencies:
   - pudb=2019.2
   - protobuf=3.20
   - py-opencv=4.6
+  - safetensors
   - scipy=1.9
   - streamlit=1.12
   - sympy=1.10
diff --git a/environments-and-requirements/environment-win-cuda.yml b/environments-and-requirements/environment-win-cuda.yml
index 306ffd67246..9f91d66cb9b 100644
--- a/environments-and-requirements/environment-win-cuda.yml
+++ b/environments-and-requirements/environment-win-cuda.yml
@@ -12,6 +12,7 @@ dependencies:
   - pytorch=1.12.1
   - cudatoolkit=11.6
   - pip:
+    - accelerate
     - albumentations==0.4.3
     - diffusers~=0.11
     - einops==0.3.0
@@ -31,6 +32,7 @@ dependencies:
     - pyreadline3
     - pytorch-lightning==1.7.7
     - realesrgan
+    - safetensors
     - send2trash==1.8.0
     - streamlit==1.12.0
     - taming-transformers-rom1504
diff --git a/environments-and-requirements/requirements-base.txt b/environments-and-requirements/requirements-base.txt
index f84ebbaf6f9..ba95913651e 100644
--- a/environments-and-requirements/requirements-base.txt
+++ b/environments-and-requirements/requirements-base.txt
@@ -1,4 +1,5 @@
 # pip will resolve the version which matches torch
+accelerate
 albumentations
 diffusers[torch]~=0.11
 einops
@@ -25,6 +26,7 @@ pyreadline3
 pytorch-lightning==1.7.7
 realesrgan
 requests==2.25.1
+safetensors
 scikit-image>=0.19
 send2trash
 streamlit
diff --git a/ldm/invoke/ckpt_to_diffuser.py b/ldm/invoke/ckpt_to_diffuser.py
index fcbe0b7ec5d..7d8510f3166 100644
--- a/ldm/invoke/ckpt_to_diffuser.py
+++ b/ldm/invoke/ckpt_to_diffuser.py
@@ -942,5 +942,5 @@ def convert_ckpt_to_diffuser(checkpoint_path:str,
 
     pipe.save_pretrained(
         dump_path,
-#        safe_serialization=1,                 
+        safe_serialization=1,                 
     )
diff --git a/ldm/invoke/model_cache.py b/ldm/invoke/model_cache.py
index b947504412d..4324e8d303c 100644
--- a/ldm/invoke/model_cache.py
+++ b/ldm/invoke/model_cache.py
@@ -243,7 +243,9 @@ def _load_model(self, model_name:str):
             print(f'>> Loading {model_name} from {weights}')
             model, width, height, model_hash = self._load_ckpt_model(model_name, mconfig)
         elif model_format == 'diffusers':
-            model, width, height, model_hash = self._load_diffusers_model(mconfig)
+            with warnings.catch_warnings():
+                warnings.simplefilter('ignore')
+                model, width, height, model_hash = self._load_diffusers_model(mconfig)
         else:
             raise NotImplementedError(f"Unknown model format {model_name}: {model_format}")
 

From 8ce7c45fef4703b8bad2b446f3be14419a36177b Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Sat, 24 Dec 2022 13:52:56 +0000
Subject: [PATCH 095/199] set local_files_only when internet unreachable

---
 ldm/invoke/CLI.py         | 15 +++++++++++++++
 ldm/invoke/args.py        |  7 +++++++
 ldm/invoke/globals.py     |  4 ++++
 ldm/invoke/model_cache.py | 20 ++++++++++++++------
 4 files changed, 40 insertions(+), 6 deletions(-)

diff --git a/ldm/invoke/CLI.py b/ldm/invoke/CLI.py
index b412d8b8a84..5bbd4e1b1fe 100644
--- a/ldm/invoke/CLI.py
+++ b/ldm/invoke/CLI.py
@@ -48,6 +48,8 @@ def main():
     # alert - setting a global here
     Globals.try_patchmatch = args.patchmatch
     Globals.always_use_cpu = args.always_use_cpu
+    Globals.internet_available = args.internet_available and check_internet()
+    print(f'>> Internet connectivity is {Globals.internet_available}')
 
     if not args.conf:
         if not os.path.exists(os.path.join(Globals.root,'configs','models.yaml')):
@@ -1003,3 +1005,16 @@ def emergency_model_reconfigure(opt):
 
     import configure_invokeai
     configure_invokeai.main()
+
+def check_internet()->bool:
+    '''
+    Return true if the internet is reachable.
+    It does this by pinging huggingface.co.
+    '''
+    import urllib.request
+    host = 'http://huggingface.co'
+    try:
+        urllib.request.urlopen(host,timeout=1)
+        return True
+    except:
+        return False
diff --git a/ldm/invoke/args.py b/ldm/invoke/args.py
index 905230f4ade..6b21df47a34 100644
--- a/ldm/invoke/args.py
+++ b/ldm/invoke/args.py
@@ -500,6 +500,13 @@ def _create_arg_parser(self):
             help=f'Set model precision. Defaults to auto selected based on device. Options: {", ".join(PRECISION_CHOICES)}',
             default='auto',
         )
+        model_group.add_argument(
+            '--internet',
+            action=argparse.BooleanOptionalAction,
+            dest='internet_available',
+            default=True,
+            help='Indicate whether internet is available for just-in-time model downloading (default: probe automatically).',
+        )
         model_group.add_argument(
             '--nsfw_checker',
             '--safety_checker',
diff --git a/ldm/invoke/globals.py b/ldm/invoke/globals.py
index 10a673a08a8..7c5b765cbc3 100644
--- a/ldm/invoke/globals.py
+++ b/ldm/invoke/globals.py
@@ -38,6 +38,10 @@
 # Use CPU even if GPU is available (main use case is for debugging MPS issues)
 Globals.always_use_cpu = False
 
+# Whether the internet is reachable for dynamic downloads
+# The CLI will test connectivity at startup time.
+Globals.internet_available = True
+
 def global_config_dir()->str:
     return Path(Globals.root, Globals.config_dir)
 
diff --git a/ldm/invoke/model_cache.py b/ldm/invoke/model_cache.py
index 4324e8d303c..cb40007ddf5 100644
--- a/ldm/invoke/model_cache.py
+++ b/ldm/invoke/model_cache.py
@@ -22,7 +22,7 @@
 import torch
 import transformers
 from diffusers import AutoencoderKL, logging as dlogging
-from huggingface_hub import hf_hub_download
+from huggingface_hub import hf_hub_download, logging as hlogging
 from huggingface_hub.utils import RevisionNotFoundError
 from omegaconf import OmegaConf
 from omegaconf.dictconfig import DictConfig
@@ -355,7 +355,7 @@ def _load_diffusers_model(self, mconfig):
         # TODO: scan weights maybe?
         pipeline_args = dict(
             safety_checker=None,
-            #local_files_only=True
+            local_files_only=not Globals.internet_available
         )
         if 'vae' in mconfig:
              vae = self._load_vae(mconfig['vae'])
@@ -397,7 +397,7 @@ def _load_diffusers_model(self, mconfig):
         width = pipeline.unet.config.sample_size * pipeline.vae_scale_factor
         height = width
 
-        print(f'  | default image dimensions = {width} x {height}')
+        print(f'  | Default image dimensions = {width} x {height}')
 
         return pipeline, width, height, model_hash
 
@@ -660,11 +660,14 @@ def _load_vae(self, vae_config):
         vae_args = {}
         name_or_path = self.model_name_or_path(vae_config)
         using_fp16 = self.precision == 'float16'
-        vae_args.update(cache_dir=os.path.join(Globals.root,'models',name_or_path))
+                
+        vae_args.update(
+            cache_dir=os.path.join(Globals.root,'models',name_or_path),
+            local_files_only=not Globals.internet_available,
+        )
         
         print(f'  | Loading diffusers VAE from {name_or_path}')
         if using_fp16:
-            print(f'  | Using faster float16 precision')
             vae_args.update(torch_dtype=torch.float16)
             fp_args_list = [{'revision':'fp16'},{}]
         else:
@@ -672,6 +675,8 @@ def _load_vae(self, vae_config):
             fp_args_list = [{}]
 
         vae = None
+        deferred_error = None
+
         for fp_args in fp_args_list:
             # At some point we might need to be able to use different classes here? But for now I think
             # all Stable Diffusion VAE are AutoencoderKL.
@@ -681,10 +686,13 @@ def _load_vae(self, vae_config):
                 if str(e).startswith('fp16 is not a valid'):
                     print(f'  | Half-precision version of model not available; fetching full-precision instead')
                 else:
-                    print(f'** An unexpected error occurred while downloading the model: {e})')
+                    deferred_error = e
             if vae:
                 break
 
+        if not vae and deferred_error:
+            print(f'** Could not load VAE {name_or_path}: {str(deferred_error)}')
+
         # comment by lstein: I don't know what this does
         if 'subfolder' in vae_config:
             vae_args['subfolder'] = vae_config['subfolder']

From f0fb555c4fce0a678e3788636869a4aff0cabdcf Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Sat, 24 Dec 2022 10:24:51 -0800
Subject: [PATCH 096/199] diffusers: fix error-handling path when model repo
 has no fp16 branch

---
 ldm/invoke/model_cache.py | 34 ++++++++++++++++------------------
 1 file changed, 16 insertions(+), 18 deletions(-)

diff --git a/ldm/invoke/model_cache.py b/ldm/invoke/model_cache.py
index cb40007ddf5..83d5ca12184 100644
--- a/ldm/invoke/model_cache.py
+++ b/ldm/invoke/model_cache.py
@@ -17,20 +17,18 @@
 import traceback
 import warnings
 from pathlib import Path
-from typing import Union
+from typing import Union, Any
 
 import torch
 import transformers
 from diffusers import AutoencoderKL, logging as dlogging
-from huggingface_hub import hf_hub_download, logging as hlogging
-from huggingface_hub.utils import RevisionNotFoundError
 from omegaconf import OmegaConf
 from omegaconf.dictconfig import DictConfig
 from omegaconf.errors import ConfigAttributeError
 from picklescan.scanner import scan_file_path
 
 from ldm.invoke.generator.diffusers_pipeline import StableDiffusionGeneratorPipeline
-from ldm.invoke.globals import Globals, global_config_dir, global_models_dir, global_autoscan_dir
+from ldm.invoke.globals import Globals, global_models_dir, global_autoscan_dir
 from ldm.util import instantiate_from_config, ask_user
 
 DEFAULT_MAX_MODELS=2
@@ -146,7 +144,7 @@ def is_legacy(self,model_name:str)->bool:
         '''
         info = self.model_info(model_name)
         return info['format']=='ckpt' if info else False
-        
+
     def list_models(self) -> dict:
         '''
         Return a dict of models in the format:
@@ -353,7 +351,7 @@ def _load_diffusers_model(self, mconfig):
             print(f'  | Using more accurate float32 precision')
 
         # TODO: scan weights maybe?
-        pipeline_args = dict(
+        pipeline_args: dict[str, Any] = dict(
             safety_checker=None,
             local_files_only=not Globals.internet_available
         )
@@ -366,7 +364,7 @@ def _load_diffusers_model(self, mconfig):
             pipeline_args.update(torch_dtype=torch.float16)
             fp_args_list = [{'revision':'fp16'},{}]
         else:
-            fp_args_list = [{}]            
+            fp_args_list = [{}]
 
         verbosity = dlogging.get_verbosity()
         dlogging.set_verbosity_error()
@@ -379,17 +377,17 @@ def _load_diffusers_model(self, mconfig):
                     **pipeline_args,
                     **fp_args,
                 )
-                
+
             except OSError as e:
                 if str(e).startswith('fp16 is not a valid'):
-                    print(f'Could not fetch half-precision version of model {repo_id}; fetching full-precision instead')
+                    print(f'Could not fetch half-precision version of model {name_or_path}; fetching full-precision instead')
                 else:
                     print(f'An unexpected error occurred while downloading the model: {e})')
             if pipeline:
                 break
 
-            dlogging.set_verbosity_error()
-            assert pipeline is not None, OSError(f'"{model_name}" could not be loaded')        
+        dlogging.set_verbosity(verbosity)
+        assert pipeline is not None, OSError(f'"{name_or_path}" could not be loaded')
 
         pipeline.to(self.device)
 
@@ -405,7 +403,7 @@ def model_name_or_path(self, model_name:Union[str,DictConfig]) -> str | Path:
         if isinstance(model_name,DictConfig):
             mconfig = model_name
         elif model_name in self.config:
-            mconfig = self.config[model_name]            
+            mconfig = self.config[model_name]
         else:
             raise ValueError(f'"{model_name}" is not a known model name. Please check your models.yaml file')
 
@@ -474,7 +472,7 @@ def autoconvert_weights(
         '''
         weights_directory = weights_directory or global_autoscan_dir()
         dest_directory = dest_directory or Path(global_models_dir(), 'optimized-ckpts')
-        
+
         print('>> Checking for unconverted .ckpt files in {weights_directory}')
         ckpt_files = dict()
         for root, dirs, files in os.walk(weights_directory):
@@ -485,7 +483,7 @@ def autoconvert_weights(
                 dest = Path(dest_directory,basename)
                 if not dest.exists():
                     ckpt_files[Path(root,f)]=dest
-                
+
         if len(ckpt_files)==0:
             return
 
@@ -526,7 +524,7 @@ def convert_and_import(self, ckpt_path:Path, diffuser_path:Path)->dict:
             traceback.print_exc()
         return new_config
 
-    def del_config(model_name:str, gen, opt, completer):
+    def del_config(self, model_name:str, gen, opt, completer):
         current_model = gen.model_name
         if model_name == current_model:
             print("** Can't delete active model. !switch to another model first. **")
@@ -601,7 +599,7 @@ def _model_to_cpu(self,model):
     def _model_from_cpu(self,model):
         if self.device == 'cpu':
             return model
-        
+
         model.to(self.device)
         model.cond_stage_model.device = self.device
 
@@ -660,12 +658,12 @@ def _load_vae(self, vae_config):
         vae_args = {}
         name_or_path = self.model_name_or_path(vae_config)
         using_fp16 = self.precision == 'float16'
-                
+
         vae_args.update(
             cache_dir=os.path.join(Globals.root,'models',name_or_path),
             local_files_only=not Globals.internet_available,
         )
-        
+
         print(f'  | Loading diffusers VAE from {name_or_path}')
         if using_fp16:
             vae_args.update(torch_dtype=torch.float16)

From 76075dad06a72bcf14c35f143649a2904a222687 Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Sat, 24 Dec 2022 18:33:44 +0000
Subject: [PATCH 097/199] fix generatorinpaint error

Fixes :
  "ModuleNotFoundError: No module named 'ldm.invoke.generatorinpaint'
   https://github.com/invoke-ai/InvokeAI/pull/1583#issuecomment-1363634318
---
 ldm/generate.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/ldm/generate.py b/ldm/generate.py
index c36f6b90298..73bd4829bdf 100644
--- a/ldm/generate.py
+++ b/ldm/generate.py
@@ -790,10 +790,10 @@ def _make_txt2img2img(self):
         return self._load_generator('.txt2img2img','Txt2Img2Img')
 
     def _make_inpaint(self):
-        return self._load_generator('inpaint','Inpaint')
+        return self._load_generator('.inpaint','Inpaint')
 
     def _make_omnibus(self):
-        return self._load_generator('omnibus','Omnibus')
+        return self._load_generator('.omnibus','Omnibus')
 
     def _load_generator(self, module, class_name):
         if self.is_legacy_model(self.model_name):

From 3e8346be9e723f9340846311569cf4faf687abe0 Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Sat, 24 Dec 2022 18:52:44 +0000
Subject: [PATCH 098/199] quench diffuser safety-checker warning

---
 ldm/invoke/generator/img2img.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/ldm/invoke/generator/img2img.py b/ldm/invoke/generator/img2img.py
index 52dec6f198d..15e79b918ae 100644
--- a/ldm/invoke/generator/img2img.py
+++ b/ldm/invoke/generator/img2img.py
@@ -6,7 +6,7 @@
 
 from ldm.invoke.generator.base import Generator
 from ldm.invoke.generator.diffusers_pipeline import StableDiffusionGeneratorPipeline, ConditioningData
-
+from diffusers import logging
 
 class Img2Img(Generator):
     def __init__(self, model, precision):
@@ -34,6 +34,7 @@ def make_image(x_T):
             # FIXME: use x_T for initial seeded noise
             # We're not at the moment because the pipeline automatically resizes init_image if
             # necessary, which the x_T input might not match.
+            logging.set_verbosity_error()   # quench safety check warnings
             pipeline_output = pipeline.img2img_from_embeddings(
                 init_image, strength, steps, conditioning_data,
                 noise_func=self.get_noise_like,

From 50f131c4b4481a86fcb47c2f20079aa35e29cfad Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Tue, 27 Dec 2022 23:58:41 -0800
Subject: [PATCH 099/199] diffusers: support stochastic DDIM eta parameter

---
 ldm/generate.py                            |  4 +-
 ldm/invoke/generator/diffusers_pipeline.py | 60 +++++++++++++---------
 ldm/invoke/generator/img2img.py            | 11 ++--
 ldm/invoke/generator/inpaint.py            | 10 ++--
 ldm/invoke/generator/txt2img.py            |  8 +--
 ldm/invoke/generator/txt2img2img.py        | 13 ++---
 6 files changed, 62 insertions(+), 44 deletions(-)

diff --git a/ldm/generate.py b/ldm/generate.py
index 73bd4829bdf..542b3f4d615 100644
--- a/ldm/generate.py
+++ b/ldm/generate.py
@@ -168,7 +168,7 @@ def __init__(
         self.steps          = 50
         self.cfg_scale      = 7.5
         self.sampler_name   = sampler_name
-        self.ddim_eta       = 0.0    # same seed always produces same image
+        self.ddim_eta       = ddim_eta    # same seed always produces same image
         self.precision      = precision
         self.strength       = 0.75
         self.seamless       = False
@@ -785,7 +785,7 @@ def _make_img2img(self):
 
     def _make_embiggen(self):
         return self._load_generator('.embiggen','Embiggen')
-   
+
     def _make_txt2img2img(self):
         return self._load_generator('.txt2img2img','Txt2Img2Img')
 
diff --git a/ldm/invoke/generator/diffusers_pipeline.py b/ldm/invoke/generator/diffusers_pipeline.py
index 22bada77eb1..7174ffac0f1 100644
--- a/ldm/invoke/generator/diffusers_pipeline.py
+++ b/ldm/invoke/generator/diffusers_pipeline.py
@@ -1,9 +1,11 @@
 from __future__ import annotations
 
+import dataclasses
+import inspect
 import secrets
 import sys
 import warnings
-from dataclasses import dataclass
+from dataclasses import dataclass, field
 from typing import List, Optional, Union, Callable, Type, TypeVar, Generic, Any
 
 if sys.version_info < (3, 10):
@@ -201,11 +203,25 @@ class ConditioningData:
     images that are closely linked to the text `prompt`, usually at the expense of lower image quality.
     """
     extra: Optional[InvokeAIDiffuserComponent.ExtraConditioningInfo] = None
+    scheduler_args: dict[str, Any] = field(default_factory=dict)
+    """Additional arguments to pass to scheduler.step."""
 
     @property
     def dtype(self):
         return self.text_embeddings.dtype
 
+    def add_scheduler_args_if_applicable(self, scheduler, **kwargs):
+        scheduler_args = dict(self.scheduler_args)
+        step_method = inspect.signature(scheduler.step)
+        for name, value in kwargs.items():
+            try:
+                step_method.bind_partial(**{name: value})
+            except TypeError:
+                # FIXME: don't silently discard arguments
+                pass  # debug("%s does not accept argument named %r", scheduler, name)
+            else:
+                scheduler_args[name] = value
+        return dataclasses.replace(self, scheduler_args=scheduler_args)
 
 @dataclass
 class InvokeAIStableDiffusionPipelineOutput(StableDiffusionPipelineOutput):
@@ -297,8 +313,7 @@ def image_from_embeddings(self, latents: torch.Tensor, num_inference_steps: int,
                               *,
                               noise: torch.Tensor,
                               callback: Callable[[PipelineIntermediateState], None]=None,
-                              run_id=None,
-                              **extra_step_kwargs) -> InvokeAIStableDiffusionPipelineOutput:
+                              run_id=None) -> InvokeAIStableDiffusionPipelineOutput:
         r"""
         Function invoked when calling the pipeline for generation.
 
@@ -310,15 +325,13 @@ def image_from_embeddings(self, latents: torch.Tensor, num_inference_steps: int,
         :param noise: Noise to add to the latents, sampled from a Gaussian distribution.
         :param callback:
         :param run_id:
-        :param extra_step_kwargs:
         """
         result_latents, result_attention_map_saver = self.latents_from_embeddings(
             latents, num_inference_steps,
             conditioning_data,
             noise=noise,
             run_id=run_id,
-            callback=callback,
-            **extra_step_kwargs)
+            callback=callback)
         # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
         torch.cuda.empty_cache()
 
@@ -333,7 +346,8 @@ def latents_from_embeddings(self, latents: torch.Tensor, num_inference_steps: in
                                 noise: torch.Tensor,
                                 timesteps=None,
                                 additional_guidance: List[Callable] = None, run_id=None,
-                                callback: Callable[[PipelineIntermediateState], None] = None, **extra_step_kwargs) -> tuple[torch.Tensor, Optional[AttentionMapSaver]]:
+                                callback: Callable[[PipelineIntermediateState], None] = None
+                                ) -> tuple[torch.Tensor, Optional[AttentionMapSaver]]:
         if timesteps is None:
             self.scheduler.set_timesteps(num_inference_steps, device=self.unet.device)
             timesteps = self.scheduler.timesteps
@@ -343,8 +357,7 @@ def latents_from_embeddings(self, latents: torch.Tensor, num_inference_steps: in
             noise=noise,
             additional_guidance=additional_guidance,
             run_id=run_id,
-            callback=callback,
-            **extra_step_kwargs)
+            callback=callback)
         return result.latents, result.attention_map_saver
 
     def generate_latents_from_embeddings(self, latents: torch.Tensor, timesteps,
@@ -352,7 +365,7 @@ def generate_latents_from_embeddings(self, latents: torch.Tensor, timesteps,
                                          *,
                                          noise: torch.Tensor,
                                          run_id: str = None,
-                                         additional_guidance: List[Callable] = None, **extra_step_kwargs):
+                                         additional_guidance: List[Callable] = None):
         if run_id is None:
             run_id = secrets.token_urlsafe(self.ID_LENGTH)
         if additional_guidance is None:
@@ -377,8 +390,7 @@ def generate_latents_from_embeddings(self, latents: torch.Tensor, timesteps,
         for i, t in enumerate(self.progress_bar(timesteps)):
             batched_t.fill_(t)
             step_output = self.step(batched_t, latents, conditioning_data,
-                                    i, additional_guidance=additional_guidance,
-                                    **extra_step_kwargs)
+                                    i, additional_guidance=additional_guidance)
             latents = step_output.prev_sample
             predicted_original = getattr(step_output, 'pred_original_sample', None)
 
@@ -397,8 +409,7 @@ def generate_latents_from_embeddings(self, latents: torch.Tensor, timesteps,
     @torch.inference_mode()
     def step(self, t: torch.Tensor, latents: torch.Tensor,
              conditioning_data: ConditioningData,
-             step_index:int | None = None, additional_guidance: List[Callable] = None,
-             **extra_step_kwargs):
+             step_index:int | None = None, additional_guidance: List[Callable] = None):
         # invokeai_diffuser has batched timesteps, but diffusers schedulers expect a single value
         timestep = t[0]
 
@@ -417,7 +428,8 @@ def step(self, t: torch.Tensor, latents: torch.Tensor,
             step_index=step_index)
 
         # compute the previous noisy sample x_t -> x_t-1
-        step_output = self.scheduler.step(noise_pred, timestep, latents, **extra_step_kwargs)
+        step_output = self.scheduler.step(noise_pred, timestep, latents,
+                                          **conditioning_data.scheduler_args)
 
         # TODO: this additional_guidance extension point feels redundant with InvokeAIDiffusionComponent.
         #    But the way things are now, scheduler runs _after_ that, so there was
@@ -438,8 +450,8 @@ def img2img_from_embeddings(self,
                                 conditioning_data: ConditioningData,
                                 *, callback: Callable[[PipelineIntermediateState], None] = None,
                                 run_id=None,
-                                noise_func=None,
-                                **extra_step_kwargs) -> InvokeAIStableDiffusionPipelineOutput:
+                                noise_func=None
+                                ) -> InvokeAIStableDiffusionPipelineOutput:
         if isinstance(init_image, PIL.Image.Image):
             init_image = image_resized_to_grid_as_tensor(init_image.convert('RGB'))
 
@@ -455,13 +467,13 @@ def img2img_from_embeddings(self,
         return self.img2img_from_latents_and_embeddings(initial_latents, num_inference_steps,
                                                         conditioning_data,
                                                         strength,
-                                                        noise, run_id, callback,
-                                                        **extra_step_kwargs)
+                                                        noise, run_id, callback)
 
     def img2img_from_latents_and_embeddings(self, initial_latents, num_inference_steps,
                                             conditioning_data: ConditioningData,
                                             strength,
-                                            noise: torch.Tensor, run_id=None, callback=None, **extra_step_kwargs) -> InvokeAIStableDiffusionPipelineOutput:
+                                            noise: torch.Tensor, run_id=None, callback=None
+                                            ) -> InvokeAIStableDiffusionPipelineOutput:
         device = self.unet.device
         img2img_pipeline = StableDiffusionImg2ImgPipeline(**self.components)
         img2img_pipeline.scheduler.set_timesteps(num_inference_steps, device=device)
@@ -472,8 +484,7 @@ def img2img_from_latents_and_embeddings(self, initial_latents, num_inference_ste
             timesteps=timesteps,
             noise=noise,
             run_id=run_id,
-            callback=callback,
-            **extra_step_kwargs)
+            callback=callback)
 
         # https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
         torch.cuda.empty_cache()
@@ -493,7 +504,7 @@ def inpaint_from_embeddings(
             *, callback: Callable[[PipelineIntermediateState], None] = None,
             run_id=None,
             noise_func=None,
-            **extra_step_kwargs) -> InvokeAIStableDiffusionPipelineOutput:
+            ) -> InvokeAIStableDiffusionPipelineOutput:
         device = self.unet.device
         latents_dtype = self.unet.dtype
 
@@ -536,8 +547,7 @@ def inpaint_from_embeddings(
                 init_image_latents, num_inference_steps,
                 conditioning_data, noise=noise, timesteps=timesteps,
                 additional_guidance=guidance,
-                run_id=run_id, callback=callback,
-                **extra_step_kwargs)
+                run_id=run_id, callback=callback)
         finally:
             self.invokeai_diffuser.model_forward_callback = self._unet_forward
 
diff --git a/ldm/invoke/generator/img2img.py b/ldm/invoke/generator/img2img.py
index 15e79b918ae..0c51648bb55 100644
--- a/ldm/invoke/generator/img2img.py
+++ b/ldm/invoke/generator/img2img.py
@@ -3,10 +3,11 @@
 '''
 
 import torch
+from diffusers import logging
 
 from ldm.invoke.generator.base import Generator
 from ldm.invoke.generator.diffusers_pipeline import StableDiffusionGeneratorPipeline, ConditioningData
-from diffusers import logging
+
 
 class Img2Img(Generator):
     def __init__(self, model, precision):
@@ -23,13 +24,15 @@ def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
         """
         self.perlin = perlin
 
-        uc, c, extra_conditioning_info   = conditioning
-        conditioning_data = ConditioningData(uc, c, cfg_scale, extra_conditioning_info)
-
         # noinspection PyTypeChecker
         pipeline: StableDiffusionGeneratorPipeline = self.model
         pipeline.scheduler = sampler
 
+        uc, c, extra_conditioning_info   = conditioning
+        conditioning_data = (ConditioningData(uc, c, cfg_scale, extra_conditioning_info)
+                             .add_scheduler_args_if_applicable(pipeline.scheduler, eta=ddim_eta))
+
+
         def make_image(x_T):
             # FIXME: use x_T for initial seeded noise
             # We're not at the moment because the pipeline automatically resizes init_image if
diff --git a/ldm/invoke/generator/inpaint.py b/ldm/invoke/generator/inpaint.py
index 263644176c6..62078311ae3 100644
--- a/ldm/invoke/generator/inpaint.py
+++ b/ldm/invoke/generator/inpaint.py
@@ -241,14 +241,16 @@ def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
 
         self.mask_blur_radius = mask_blur_radius
 
-        # todo: support cross-attention control
-        uc, c, _ = conditioning
-        conditioning_data = ConditioningData(uc, c, cfg_scale)
-
         # noinspection PyTypeChecker
         pipeline: StableDiffusionGeneratorPipeline = self.model
         pipeline.scheduler = sampler
 
+        # todo: support cross-attention control
+        uc, c, _ = conditioning
+        conditioning_data = (ConditioningData(uc, c, cfg_scale)
+                             .add_scheduler_args_if_applicable(pipeline.scheduler, eta=ddim_eta))
+
+
         def make_image(x_T):
             pipeline_output = pipeline.inpaint_from_embeddings(
                 init_image=init_image,
diff --git a/ldm/invoke/generator/txt2img.py b/ldm/invoke/generator/txt2img.py
index 768f20abd14..71749432625 100644
--- a/ldm/invoke/generator/txt2img.py
+++ b/ldm/invoke/generator/txt2img.py
@@ -23,13 +23,16 @@ def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
         kwargs are 'width' and 'height'
         """
         self.perlin = perlin
-        uc, c, extra_conditioning_info   = conditioning
-        conditioning_data = ConditioningData(uc, c, cfg_scale, extra_conditioning_info)
 
         # noinspection PyTypeChecker
         pipeline: StableDiffusionGeneratorPipeline = self.model
         pipeline.scheduler = sampler
 
+        uc, c, extra_conditioning_info   = conditioning
+        conditioning_data = (ConditioningData(uc, c, cfg_scale, extra_conditioning_info)
+                             .add_scheduler_args_if_applicable(pipeline.scheduler, eta=ddim_eta))
+
+
         def make_image(x_T) -> PIL.Image.Image:
             pipeline_output = pipeline.image_from_embeddings(
                 latents=torch.zeros_like(x_T),
@@ -37,7 +40,6 @@ def make_image(x_T) -> PIL.Image.Image:
                 num_inference_steps=steps,
                 conditioning_data=conditioning_data,
                 callback=step_callback
-                # TODO: eta = ddim_eta,
                 # TODO: threshold = threshold,
             )
             if pipeline_output.attention_map_saver is not None and attention_maps_callback is not None:
diff --git a/ldm/invoke/generator/txt2img2img.py b/ldm/invoke/generator/txt2img2img.py
index a1743988f8f..2c71194532d 100644
--- a/ldm/invoke/generator/txt2img2img.py
+++ b/ldm/invoke/generator/txt2img2img.py
@@ -26,16 +26,18 @@ def get_make_image(self, prompt:str, sampler, steps:int, cfg_scale:float, ddim_e
         kwargs are 'width' and 'height'
         """
         uc, c, extra_conditioning_info = conditioning
-        conditioning_data = ConditioningData(uc, c, cfg_scale, extra_conditioning_info)
-        scale_dim = min(width, height)
-        scale = 512 / scale_dim
-
-        init_width, init_height = trim_to_multiple_of(scale * width, scale * height)
 
         # noinspection PyTypeChecker
         pipeline: StableDiffusionGeneratorPipeline = self.model
         pipeline.scheduler = sampler
 
+        conditioning_data = (ConditioningData(uc, c, cfg_scale, extra_conditioning_info)
+                             .add_scheduler_args_if_applicable(pipeline.scheduler, eta=ddim_eta))
+        scale_dim = min(width, height)
+        scale = 512 / scale_dim
+
+        init_width, init_height = trim_to_multiple_of(scale * width, scale * height)
+
         def make_image(x_T):
 
             first_pass_latent_output, _ = pipeline.latents_from_embeddings(
@@ -44,7 +46,6 @@ def make_image(x_T):
                 conditioning_data=conditioning_data,
                 noise=x_T,
                 callback=step_callback,
-                # TODO: eta = ddim_eta,
                 # TODO: threshold = threshold,
             )
 

From 72f5cbb2123932423d20f96032cd55dbe7986e5b Mon Sep 17 00:00:00 2001
From: Damian Stewart <d@damianstewart.com>
Date: Wed, 28 Dec 2022 15:07:42 +0100
Subject: [PATCH 100/199] fix conda env creation on macos

---
 environments-and-requirements/environment-mac.yml | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/environments-and-requirements/environment-mac.yml b/environments-and-requirements/environment-mac.yml
index 99b4093a0c5..e46d214db5f 100644
--- a/environments-and-requirements/environment-mac.yml
+++ b/environments-and-requirements/environment-mac.yml
@@ -1,6 +1,7 @@
 name: invokeai
 channels:
   - pytorch
+  - huggingface
   - conda-forge
   - defaults
 dependencies:
@@ -44,7 +45,6 @@ dependencies:
   - pudb=2019.2
   - protobuf=3.20
   - py-opencv=4.6
-  - safetensors
   - scipy=1.9
   - streamlit=1.12
   - sympy=1.10
@@ -52,6 +52,7 @@ dependencies:
   - tensorboard=2.10
   - transformers~=4.25
   - pip:
+      - safetensors~=0.2
       - getpass_asterisk
       - picklescan
       - taming-transformers-rom1504

From bac090158c108dafea32bfb1ba7e20e8be4627cc Mon Sep 17 00:00:00 2001
From: Damian Stewart <d@damianstewart.com>
Date: Wed, 28 Dec 2022 15:19:31 +0100
Subject: [PATCH 101/199] fix cross-attention with diffusers 0.11

---
 ldm/models/diffusion/cross_attention_control.py | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/ldm/models/diffusion/cross_attention_control.py b/ldm/models/diffusion/cross_attention_control.py
index eab76e2cbfa..7415f1435be 100644
--- a/ldm/models/diffusion/cross_attention_control.py
+++ b/ldm/models/diffusion/cross_attention_control.py
@@ -450,10 +450,12 @@ def __init__(self, **kwargs):
         super().__init__(**kwargs)
         InvokeAICrossAttentionMixin.__init__(self)
 
-    def _attention(self, query, key, value):
+    def _attention(self, query, key, value, attention_mask=None):
         #default_result = super()._attention(query,  key, value)
-        damian_result = self.get_invokeai_attention_mem_efficient(query, key, value)
+        if attention_mask is not None:
+            print(f"{type(self).__name__} ignoring passed-in attention_mask")
+        attention_result = self.get_invokeai_attention_mem_efficient(query, key, value)
 
-        hidden_states = self.reshape_batch_dim_to_heads(damian_result)
+        hidden_states = self.reshape_batch_dim_to_heads(attention_result)
         return hidden_states
 

From e294fcabeb88f8b56b7b9d9fc57a1e6ac5b72de0 Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Wed, 28 Dec 2022 11:38:59 -0800
Subject: [PATCH 102/199] diffusers: the VAE needs to be tiling as well as the
 U-Net

---
 ldm/generate.py        | 9 +++++----
 ldm/invoke/seamless.py | 1 +
 2 files changed, 6 insertions(+), 4 deletions(-)

diff --git a/ldm/generate.py b/ldm/generate.py
index 542b3f4d615..9c9cc9bff53 100644
--- a/ldm/generate.py
+++ b/ldm/generate.py
@@ -4,13 +4,13 @@
 # Copyright (c) 2022 Robin Rombach and Patrick Esser and contributors
 
 import gc
+import importlib
 import os
 import random
 import re
 import sys
 import time
 import traceback
-import importlib
 
 import cv2
 import numpy as np
@@ -35,18 +35,18 @@
 from ldm.invoke.concepts_lib import HuggingFaceConceptsLibrary
 from ldm.invoke.conditioning import get_uc_and_c_and_ec
 from ldm.invoke.devices import choose_torch_device, choose_precision
+from ldm.invoke.generator.inpaint import infill_methods
 from ldm.invoke.globals import Globals
 from ldm.invoke.image_util import InitImageResizer
 from ldm.invoke.model_cache import ModelCache
 from ldm.invoke.pngwriter import PngWriter
 from ldm.invoke.seamless import configure_model_padding
-from ldm.invoke.txt2mask import Txt2Mask, SegmentedGrayscale
-from ldm.invoke.generator.inpaint import infill_methods
-
+from ldm.invoke.txt2mask import Txt2Mask
 from ldm.models.diffusion.ddim import DDIMSampler
 from ldm.models.diffusion.ksampler import KSampler
 from ldm.models.diffusion.plms import PLMSSampler
 
+
 def fix_func(orig):
     if hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
         def new_func(*args, **kw):
@@ -408,6 +408,7 @@ def process_image(image,seed):
 
         if isinstance(model, DiffusionPipeline):
             configure_model_padding(model.unet, seamless, seamless_axes)
+            configure_model_padding(model.vae, seamless, seamless_axes)
         else:
             configure_model_padding(model, seamless, seamless_axes)
 
diff --git a/ldm/invoke/seamless.py b/ldm/invoke/seamless.py
index 3a010ef118e..fda363eb7be 100644
--- a/ldm/invoke/seamless.py
+++ b/ldm/invoke/seamless.py
@@ -12,6 +12,7 @@ def configure_model_padding(model, seamless, seamless_axes):
     """
     Modifies the 2D convolution layers to use a circular padding mode based on the `seamless` and `seamless_axes` options.
     """
+    # TODO: get an explicit interface for this in diffusers: https://github.com/huggingface/diffusers/issues/556
     for m in model.modules():
         if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
             if seamless:

From 795a5c75099b7674e95277fe2032a6ba6fde11ad Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Wed, 28 Dec 2022 12:45:30 -0800
Subject: [PATCH 103/199] diffusers: comment on subfolders

---
 ldm/invoke/model_cache.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/ldm/invoke/model_cache.py b/ldm/invoke/model_cache.py
index f66bac737d6..d60cd3f334f 100644
--- a/ldm/invoke/model_cache.py
+++ b/ldm/invoke/model_cache.py
@@ -701,6 +701,10 @@ def _load_vae(self, vae_config):
         vae = None
         deferred_error = None
 
+        # A VAE may be in a subfolder of a model's repository.
+        if 'subfolder' in vae_config:
+            vae_args['subfolder'] = vae_config['subfolder']
+
         for fp_args in fp_args_list:
             # At some point we might need to be able to use different classes here? But for now I think
             # all Stable Diffusion VAE are AutoencoderKL.
@@ -717,8 +721,4 @@ def _load_vae(self, vae_config):
         if not vae and deferred_error:
             print(f'** Could not load VAE {name_or_path}: {str(deferred_error)}')
 
-        # comment by lstein: I don't know what this does
-        if 'subfolder' in vae_config:
-            vae_args['subfolder'] = vae_config['subfolder']
-
         return vae

From 716616597277467ab99a1433fbc218e7f9a23fa8 Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Wed, 28 Dec 2022 13:35:40 -0800
Subject: [PATCH 104/199] diffusers: embiggen!

---
 ldm/invoke/generator/embiggen.py | 20 ++++++++------------
 1 file changed, 8 insertions(+), 12 deletions(-)

diff --git a/ldm/invoke/generator/embiggen.py b/ldm/invoke/generator/embiggen.py
index 2fec0606f77..e7505aab2fe 100644
--- a/ldm/invoke/generator/embiggen.py
+++ b/ldm/invoke/generator/embiggen.py
@@ -8,10 +8,8 @@
 from PIL import Image
 from tqdm import trange
 
-from ldm.invoke.devices import choose_autocast
 from ldm.invoke.generator.base import Generator
 from ldm.invoke.generator.img2img import Img2Img
-from ldm.models.diffusion.ddim import DDIMSampler
 
 
 class Embiggen(Generator):
@@ -24,7 +22,6 @@ def generate(self,prompt,iterations=1,seed=None,
                  image_callback=None, step_callback=None,
                  **kwargs):
 
-        scope      = choose_autocast(self.precision)
         make_image = self.get_make_image(
             prompt,
             step_callback = step_callback,
@@ -34,14 +31,13 @@ def generate(self,prompt,iterations=1,seed=None,
         seed                = seed if seed else self.new_seed()
 
         # Noise will be generated by the Img2Img generator when called
-        with scope(self.model.device.type), self.model.ema_scope():
-            for n in trange(iterations, desc='Generating'):
-                # make_image will call Img2Img which will do the equivalent of get_noise itself
-                image = make_image()
-                results.append([image, seed])
-                if image_callback is not None:
-                    image_callback(image, seed, prompt_in=prompt)
-                seed = self.new_seed()
+        for _ in trange(iterations, desc='Generating'):
+            # make_image will call Img2Img which will do the equivalent of get_noise itself
+            image = make_image()
+            results.append([image, seed])
+            if image_callback is not None:
+                image_callback(image, seed, prompt_in=prompt)
+            seed = self.new_seed()
         return results
 
     @torch.no_grad()
@@ -355,7 +351,7 @@ def make_image():
                     prompt,
                     iterations     = 1,
                     seed           = seed,
-                    sampler        = DDIMSampler(self.model, device=self.model.device),
+                    sampler        = sampler,
                     steps          = steps,
                     cfg_scale      = cfg_scale,
                     conditioning   = conditioning,

From 3811d07e75996f669d9c81f36919c4113062aa67 Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Thu, 29 Dec 2022 09:32:13 -0800
Subject: [PATCH 105/199] diffusers: make model_cache.list_models serializable

---
 ldm/invoke/model_cache.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/ldm/invoke/model_cache.py b/ldm/invoke/model_cache.py
index 4551ddc7f49..896185de68a 100644
--- a/ldm/invoke/model_cache.py
+++ b/ldm/invoke/model_cache.py
@@ -162,6 +162,9 @@ def list_models(self) -> dict:
             default = self.config[name].default if 'default' in self.config[name] else False
             vae = self.config[name].vae if 'vae' in self.config[name] else '<no vae>'
 
+            if isinstance(vae, DictConfig):
+                vae = OmegaConf.to_object(vae)  # so it can be JSON-serialized
+
             if self.current_model == name:
                 status = 'active'
             elif name in self.models:

From aa174a9cb25597d2b0e5faa1f4d932469060060e Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Sat, 31 Dec 2022 08:10:20 -0800
Subject: [PATCH 106/199] diffusers(inpaint): restore scaling functionality

---
 ldm/invoke/generator/inpaint.py | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/ldm/invoke/generator/inpaint.py b/ldm/invoke/generator/inpaint.py
index 62078311ae3..dd43d3cde71 100644
--- a/ldm/invoke/generator/inpaint.py
+++ b/ldm/invoke/generator/inpaint.py
@@ -264,7 +264,8 @@ def make_image(x_T):
 
             if pipeline_output.attention_map_saver is not None and attention_maps_callback is not None:
                 attention_maps_callback(pipeline_output.attention_map_saver)
-            result = pipeline.numpy_to_pil(pipeline_output.images)[0]
+
+            result = self.postprocess_size_and_mask(pipeline.numpy_to_pil(pipeline_output.images)[0])
 
             # Seam paint if this is our first pass (seam_size set to 0 during seam painting)
             if seam_size > 0:
@@ -295,6 +296,10 @@ def make_image(x_T):
 
     def sample_to_image(self, samples)->Image.Image:
         gen_result = super().sample_to_image(samples).convert('RGB')
+        return self.postprocess_size_and_mask(gen_result)
+
+
+    def postprocess_size_and_mask(self, gen_result: Image.Image) -> Image.Image:
         debug_image(gen_result, "gen_result", debug_status=self.enable_image_debugging)
 
         # Resize if necessary
@@ -304,7 +309,7 @@ def sample_to_image(self, samples)->Image.Image:
         if self.pil_image is None or self.pil_mask is None:
             return gen_result
 
-        corrected_result = super().repaste_and_color_correct(gen_result, self.pil_image, self.pil_mask, self.mask_blur_radius)
+        corrected_result = self.repaste_and_color_correct(gen_result, self.pil_image, self.pil_mask, self.mask_blur_radius)
         debug_image(corrected_result, "corrected_result", debug_status=self.enable_image_debugging)
 
         return corrected_result

From 45e34d52b95027b8edf840292154c07fc57b57e1 Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Sat, 31 Dec 2022 14:20:24 -0500
Subject: [PATCH 107/199] fix requirements clash between numba and numpy 1.24

---
 environments-and-requirements/requirements-base.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/environments-and-requirements/requirements-base.txt b/environments-and-requirements/requirements-base.txt
index ba95913651e..16a2c7ca002 100644
--- a/environments-and-requirements/requirements-base.txt
+++ b/environments-and-requirements/requirements-base.txt
@@ -15,7 +15,7 @@ huggingface-hub
 imageio
 imageio-ffmpeg
 kornia
-numpy
+numpy==1.23.*
 omegaconf
 opencv-python
 picklescan

From 4e3f5cc1ecc5202d2f9a499b9bb4548c2e51f88c Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Sat, 31 Dec 2022 19:09:13 -0800
Subject: [PATCH 108/199] diffusers: allow inpainting model to do
 non-inpainting tasks

---
 ldm/invoke/generator/diffusers_pipeline.py | 19 +++++++++++++++----
 ldm/invoke/generator/txt2img.py            |  6 ++++--
 2 files changed, 19 insertions(+), 6 deletions(-)

diff --git a/ldm/invoke/generator/diffusers_pipeline.py b/ldm/invoke/generator/diffusers_pipeline.py
index 7174ffac0f1..0e8d82f935c 100644
--- a/ldm/invoke/generator/diffusers_pipeline.py
+++ b/ldm/invoke/generator/diffusers_pipeline.py
@@ -73,10 +73,10 @@ class AddsMaskLatents:
     This class assumes the same mask and base image should apply to all items in the batch.
     """
     forward: Callable[[torch.Tensor, torch.Tensor, torch.Tensor], torch.Tensor]
-    mask: torch.FloatTensor
-    initial_image_latents: torch.FloatTensor
+    mask: torch.Tensor
+    initial_image_latents: torch.Tensor
 
-    def __call__(self, latents: torch.FloatTensor, t: torch.Tensor, text_embeddings: torch.FloatTensor) -> torch.Tensor:
+    def __call__(self, latents: torch.Tensor, t: torch.Tensor, text_embeddings: torch.Tensor) -> torch.Tensor:
         model_input = self.add_mask_channels(latents)
         return self.forward(model_input, t, text_embeddings)
 
@@ -440,7 +440,18 @@ def step(self, t: torch.Tensor, latents: torch.Tensor,
         return step_output
 
     def _unet_forward(self, latents, t, text_embeddings):
-        # predict the noise residual
+        """predict the noise residual"""
+        if is_inpainting_model(self.unet) and latents.size(1) == 4:
+            # Pad out normal non-inpainting inputs for an inpainting model.
+            # FIXME: There are too many layers of functions and we have too many different ways of
+            #     overriding things! This should get handled in a way more consistent with the other
+            #     use of AddsMaskLatents.
+            latents = AddsMaskLatents(
+                self._unet_forward,
+                mask=torch.ones_like(latents[:1, :1], device=latents.device, dtype=latents.dtype),
+                initial_image_latents=torch.zeros_like(latents[:1], device=latents.device, dtype=latents.dtype)
+            ).add_mask_channels(latents)
+
         return self.unet(latents, t, encoder_hidden_states=text_embeddings).sample
 
     def img2img_from_embeddings(self,
diff --git a/ldm/invoke/generator/txt2img.py b/ldm/invoke/generator/txt2img.py
index 71749432625..e8e3a90d195 100644
--- a/ldm/invoke/generator/txt2img.py
+++ b/ldm/invoke/generator/txt2img.py
@@ -52,15 +52,17 @@ def make_image(x_T) -> PIL.Image.Image:
     # returns a tensor filled with random numbers from a normal distribution
     def get_noise(self,width,height):
         device         = self.model.device
+        # limit noise to only the diffusion image channels, not the mask channels
+        input_channels = min(self.latent_channels, 4)
         if self.use_mps_noise or device.type == 'mps':
             x = torch.randn([1,
-                                self.latent_channels,
+                                input_channels,
                                 height // self.downsampling_factor,
                                 width  // self.downsampling_factor],
                                device='cpu').to(device)
         else:
             x = torch.randn([1,
-                                self.latent_channels,
+                                input_channels,
                                 height // self.downsampling_factor,
                                 width  // self.downsampling_factor],
                                device=device)

From 9a9068823cb2df99ea00edfa1c9f0d754a7a5efe Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Sun, 1 Jan 2023 15:10:10 -0500
Subject: [PATCH 109/199] start expanding model_cache functionality

---
 configs/INITIAL_MODELS.yaml |  1 +
 ldm/invoke/model_cache.py   | 49 ++++++++++++++++---------------------
 2 files changed, 22 insertions(+), 28 deletions(-)

diff --git a/configs/INITIAL_MODELS.yaml b/configs/INITIAL_MODELS.yaml
index 64158186faf..2432e1f453c 100644
--- a/configs/INITIAL_MODELS.yaml
+++ b/configs/INITIAL_MODELS.yaml
@@ -37,6 +37,7 @@ waifu-diffusion-1.3:
    vae:
       repo_id: stabilityai/sd-vae-ft-mse-original
       file: vae-ft-mse-840000-ema-pruned.ckpt
+   recommended: False
    width: 512
    height: 512
 trinart-2.0:
diff --git a/ldm/invoke/model_cache.py b/ldm/invoke/model_cache.py
index 896185de68a..c15e3ffadf6 100644
--- a/ldm/invoke/model_cache.py
+++ b/ldm/invoke/model_cache.py
@@ -137,6 +137,12 @@ def model_info(self, model_name:str)->dict:
             return None
         return self.config[model_name]
 
+    def models(self)->list[str]:
+        '''
+        Return a list consisting of all the names of models defined in models.yaml
+        '''
+        return self.config.keys()
+
     def is_legacy(self,model_name:str)->bool:
         '''
         Return true if this is a legacy (.ckpt) model
@@ -151,37 +157,22 @@ def list_models(self) -> dict:
                         'description': description,
                        },
           model_name2: { etc }
+        Please use model_cache.models() to get all the model names,
+        model_cache.model_info('model-name') to get the stanza for the model
+        named 'model-name', and model_cache.config to get the full OmegaConf
+        object derived from models.yaml
         '''
         models = {}
         for name in self.config:
-            description = self.config[name].description if 'description' in self.config[name] else '<no description>'
-            weights = self.config[name].weights if 'weights' in self.config[name] else '<no weights>'
-            config = self.config[name].config if 'config' in self.config[name] else '<no config>'
-            width = self.config[name].width if 'width' in self.config[name] else 512
-            height = self.config[name].height if 'height' in self.config[name] else 512
-            default = self.config[name].default if 'default' in self.config[name] else False
-            vae = self.config[name].vae if 'vae' in self.config[name] else '<no vae>'
-
-            if isinstance(vae, DictConfig):
-                vae = OmegaConf.to_object(vae)  # so it can be JSON-serialized
-
-            if self.current_model == name:
-                status = 'active'
-            elif name in self.models:
-                status = 'cached'
-            else:
-                status = 'not loaded'
-
-            models[name]={
-                'status' : status,
-                'description' : description,
-                'weights': weights,
-                'config': config,
-                'width': width,
-                'height': height,
-                'vae': vae,
-                'default': default
-            }
+            stanza = self.config[name]
+            format = stanza.get('format','diffusers')
+            config = stanza.get('config','no config')
+            models[name] = dict(
+                description = stanza.get('description',None),
+                format = 'vae' if 'VAE/default' in config else format,
+                status = 'active' if self.current_model == name else 'cached' if name is self.models else 'not loaded',
+            )
+
         return models
 
     def print_models(self) -> None:
@@ -190,6 +181,8 @@ def print_models(self) -> None:
         '''
         models = self.list_models()
         for name in models:
+            if models[name]['format'] == 'vae':
+                continue
             line = f'{name:25s} {models[name]["status"]:>10s}  {models[name]["description"]}'
             if models[name]['status'] == 'active':
                 line = f'\033[1m{line}\033[0m'

From 76fbadeaa62ee391bc430a86d49a2452662f1f51 Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Mon, 2 Jan 2023 16:35:43 -0500
Subject: [PATCH 110/199] add import_ckpt_model() and import_diffuser_model()
 methods to model_manager

- in addition, model_cache.py is now renamed to model_manager.py
---
 backend/invoke_ai_web_server.py               |  22 ++--
 ldm/generate.py                               |  14 +--
 ldm/invoke/CLI.py                             |  20 ++--
 .../{model_cache.py => model_manager.py}      | 111 ++++++++++++++++--
 ldm/util.py                                   |  33 ++++++
 scripts/configure_invokeai.py                 |   4 +-
 6 files changed, 165 insertions(+), 39 deletions(-)
 rename ldm/invoke/{model_cache.py => model_manager.py} (85%)

diff --git a/backend/invoke_ai_web_server.py b/backend/invoke_ai_web_server.py
index e89eaa66e8f..5b64e8f7aef 100644
--- a/backend/invoke_ai_web_server.py
+++ b/backend/invoke_ai_web_server.py
@@ -296,7 +296,7 @@ def load_socketio_listeners(self, socketio):
         def handle_request_capabilities():
             print(f">> System config requested")
             config = self.get_system_config()
-            config["model_list"] = self.generate.model_cache.list_models()
+            config["model_list"] = self.generate.model_manager.list_models()
             config["infill_methods"] = infill_methods()
             socketio.emit("systemConfig", config)
 
@@ -309,7 +309,7 @@ def handle_search_models(search_folder: str):
                     {'search_folder': None, 'found_models': None},
                 )
                 else:
-                    search_folder, found_models = self.generate.model_cache.search_models(search_folder)
+                    search_folder, found_models = self.generate.model_manager.search_models(search_folder)
                     socketio.emit(
                         "foundModels",
                         {'search_folder': search_folder, 'found_models': found_models},
@@ -328,17 +328,17 @@ def handle_add_model(new_model_config: dict):
                 del new_model_config['name']
                 model_attributes = new_model_config
                 update = False
-                current_model_list = self.generate.model_cache.list_models()
+                current_model_list = self.generate.model_manager.list_models()
                 if model_name in current_model_list:
                     update = True
 
                 print(f">> Adding New Model: {model_name}")
 
-                self.generate.model_cache.add_model(
+                self.generate.model_manager.add_model(
                     model_name=model_name, model_attributes=model_attributes, clobber=True)
-                self.generate.model_cache.commit(opt.conf)
+                self.generate.model_manager.commit(opt.conf)
 
-                new_model_list = self.generate.model_cache.list_models()
+                new_model_list = self.generate.model_manager.list_models()
                 socketio.emit(
                     "newModelAdded",
                     {"new_model_name": model_name,
@@ -356,9 +356,9 @@ def handle_add_model(new_model_config: dict):
         def handle_delete_model(model_name: str):
             try:
                 print(f">> Deleting Model: {model_name}")
-                self.generate.model_cache.del_model(model_name)
-                self.generate.model_cache.commit(opt.conf)
-                updated_model_list = self.generate.model_cache.list_models()
+                self.generate.model_manager.del_model(model_name)
+                self.generate.model_manager.commit(opt.conf)
+                updated_model_list = self.generate.model_manager.list_models()
                 socketio.emit(
                     "modelDeleted",
                     {"deleted_model_name": model_name,
@@ -377,7 +377,7 @@ def handle_set_model(model_name: str):
             try:
                 print(f">> Model change requested: {model_name}")
                 model = self.generate.set_model(model_name)
-                model_list = self.generate.model_cache.list_models()
+                model_list = self.generate.model_manager.list_models()
                 if model is None:
                     socketio.emit(
                         "modelChangeFailed",
@@ -789,7 +789,7 @@ def handle_delete_image(url, thumbnail, uuid, category):
 
     # App Functions
     def get_system_config(self):
-        model_list: dict = self.generate.model_cache.list_models()
+        model_list: dict = self.generate.model_manager.list_models()
         active_model_name = None
 
         for model_name, model_dict in model_list.items():
diff --git a/ldm/generate.py b/ldm/generate.py
index 9c9cc9bff53..faa3689940c 100644
--- a/ldm/generate.py
+++ b/ldm/generate.py
@@ -38,7 +38,7 @@
 from ldm.invoke.generator.inpaint import infill_methods
 from ldm.invoke.globals import Globals
 from ldm.invoke.image_util import InitImageResizer
-from ldm.invoke.model_cache import ModelCache
+from ldm.invoke.model_manager import ModelCache
 from ldm.invoke.pngwriter import PngWriter
 from ldm.invoke.seamless import configure_model_padding
 from ldm.invoke.txt2mask import Txt2Mask
@@ -163,7 +163,7 @@ def __init__(
         mconfig             = OmegaConf.load(conf)
         self.height         = None
         self.width          = None
-        self.model_cache    = None
+        self.model_manager    = None
         self.iterations     = 1
         self.steps          = 50
         self.cfg_scale      = 7.5
@@ -210,8 +210,8 @@ def __init__(
             self.precision = choose_precision(self.device)
 
         # model caching system for fast switching
-        self.model_cache = ModelCache(mconfig,self.device,self.precision,max_loaded_models=max_loaded_models)
-        self.model_name  = model or self.model_cache.default_model() or FALLBACK_MODEL_NAME
+        self.model_manager = ModelCache(mconfig,self.device,self.precision,max_loaded_models=max_loaded_models)
+        self.model_name  = model or self.model_manager.default_model() or FALLBACK_MODEL_NAME
 
         # for VRAM usage statistics
         self.session_peakmem = torch.cuda.max_memory_allocated() if self._has_cuda else None
@@ -822,7 +822,7 @@ def set_model(self,model_name):
             return self.model
 
         # the model cache does the loading and offloading
-        cache = self.model_cache
+        cache = self.model_manager
         if not cache.valid_model(model_name):
             print(f'** "{model_name}" is not a known model name. Please check your models.yaml file')
             return self.model
@@ -967,7 +967,7 @@ def sample_to_lowres_estimated_image(self, samples):
         return self._make_base().sample_to_lowres_estimated_image(samples)
 
     def is_legacy_model(self,model_name)->bool:
-        return self.model_cache.is_legacy(model_name)
+        return self.model_manager.is_legacy(model_name)
 
     def _set_sampler(self):
         if isinstance(self.model, DiffusionPipeline):
@@ -1027,7 +1027,7 @@ def _set_scheduler(self):
             sampler_class = scheduler_map[self.sampler_name]
             msg = f'>> Setting Sampler to {self.sampler_name} ({sampler_class.__name__})'
             self.sampler = sampler_class.from_pretrained(
-                self.model_cache.model_name_or_path(self.model_name),
+                self.model_manager.model_name_or_path(self.model_name),
                 subfolder="scheduler"
             )
         else:
diff --git a/ldm/invoke/CLI.py b/ldm/invoke/CLI.py
index b9203aa09d6..995364e709c 100644
--- a/ldm/invoke/CLI.py
+++ b/ldm/invoke/CLI.py
@@ -130,7 +130,7 @@ def main():
     # try to autoconvert new models
     # autoimport new .ckpt files
     if path := opt.autoconvert:
-        gen.model_cache.autoconvert_weights(
+        gen.model_manager.autoconvert_weights(
             conf_path=opt.conf,
             weights_directory=path,
         )
@@ -454,7 +454,7 @@ def do_command(command:str, gen, opt:Args, completer) -> tuple:
         operation = None
 
     elif command.startswith('!models'):
-        gen.model_cache.print_models()
+        gen.model_manager.print_models()
         completer.add_history(command)
         operation = None
 
@@ -607,7 +607,7 @@ def optimize_model(ckpt_path:str, gen, opt, completer):
     if diffuser_path.exists():
         print(f'** {basename} is already optimized. Will not overwrite.')
         return
-    new_config = gen.model_cache.convert_and_import(ckpt_path, diffuser_path)
+    new_config = gen.model_manager.convert_and_import(ckpt_path, diffuser_path)
     if write_config_file(opt.conf, gen, basename, new_config, clobber=False):
         completer.add_model(basename)
 
@@ -616,13 +616,13 @@ def del_config(model_name:str, gen, opt, completer):
     if model_name == current_model:
         print("** Can't delete active model. !switch to another model first. **")
         return
-    gen.model_cache.del_model(model_name)
-    gen.model_cache.commit(opt.conf)
+    gen.model_manager.del_model(model_name)
+    gen.model_manager.commit(opt.conf)
     print(f'** {model_name} deleted')
     completer.del_model(model_name)
 
 def edit_config(model_name:str, gen, opt, completer):
-    config = gen.model_cache.config
+    config = gen.model_manager.config
 
     if model_name not in config:
         print(f'** Unknown model {model_name}')
@@ -654,22 +654,22 @@ def write_config_file(conf_path, gen, model_name, new_config, clobber=False, mak
 
     try:
         print('>> Verifying that new model loads...')
-        gen.model_cache.add_model(model_name, new_config, clobber)
+        gen.model_manager.add_model(model_name, new_config, clobber)
         assert gen.set_model(model_name) is not None, 'model failed to load'
     except AssertionError as e:
         traceback.print_exc()
         print(f'** aborting **')
         try:
-            gen.model_cache.del_model(model_name)
+            gen.model_manager.del_model(model_name)
         except Exception:
             pass
         return False
 
     if make_default:
         print('making this default')
-        gen.model_cache.set_default_model(model_name)
+        gen.model_manager.set_default_model(model_name)
 
-    gen.model_cache.commit(conf_path)
+    gen.model_manager.commit(conf_path)
 
     do_switch = input(f'Keep model loaded? [y]')
     if len(do_switch)==0 or do_switch[0] in ('y','Y'):
diff --git a/ldm/invoke/model_cache.py b/ldm/invoke/model_manager.py
similarity index 85%
rename from ldm/invoke/model_cache.py
rename to ldm/invoke/model_manager.py
index 977c53955e6..33cdca5e07e 100644
--- a/ldm/invoke/model_cache.py
+++ b/ldm/invoke/model_manager.py
@@ -18,6 +18,7 @@
 import warnings
 from pathlib import Path
 from typing import Union, Any
+from ldm.util import download_with_progress_bar
 
 import torch
 import transformers
@@ -120,7 +121,7 @@ def default_model(self) -> str | None:
     def set_default_model(self,model_name:str) -> None:
         '''
         Set the default model. The change will not take
-        effect until you call model_cache.commit()
+        effect until you call model_manager.commit()
         '''
         assert model_name in self.models,f"unknown model '{model_name}'"
 
@@ -131,17 +132,17 @@ def set_default_model(self,model_name:str) -> None:
 
     def model_info(self, model_name:str)->dict:
         '''
-        Given a model name returns the config object describing it.
+        Given a model name returns the OmegaConf (dict-like) object describing it.
         '''
         if model_name not in self.config:
             return None
         return self.config[model_name]
 
-    def models(self)->list[str]:
+    def model_names(self)->list[str]:
         '''
         Return a list consisting of all the names of models defined in models.yaml
         '''
-        return self.config.keys()
+        return list(self.config.keys())
 
     def is_legacy(self,model_name:str)->bool:
         '''
@@ -157,9 +158,9 @@ def list_models(self) -> dict:
                         'description': description,
                        },
           model_name2: { etc }
-        Please use model_cache.models() to get all the model names,
-        model_cache.model_info('model-name') to get the stanza for the model
-        named 'model-name', and model_cache.config to get the full OmegaConf
+        Please use model_manager.models() to get all the model names,
+        model_manager.model_info('model-name') to get the stanza for the model
+        named 'model-name', and model_manager.config to get the full OmegaConf
         object derived from models.yaml
         '''
         models = {}
@@ -469,6 +470,83 @@ def scan_model(self, model_name, checkpoint):
         else:
             print('>> Model scanned ok!')
 
+    def import_diffuser_model(self,
+                        repo_or_path:Union[str,Path],
+                        model_name:str=None,
+                        description:str=None,
+                        commit_to_conf:Path=None,
+                        )->bool:
+        '''
+        Attempts to install the indicated diffuser model and returns True if successful.
+
+        "repo_or_path" can be either a repo-id or a path-like object corresponding to the
+        top of a downloaded diffusers directory.
+
+        You can optionally provide a model name and/or description. If not provided,
+        then these will be derived from the repo name. If you provide a commit_to_conf
+        path to the configuration file, then the new entry will be committed to the
+        models.yaml file.
+        '''
+        model_name = model_name or Path(repo_or_path).stem
+        description = description or f'imported diffusers model {model_name}'
+        new_config = dict(
+            description=description,
+            format='diffusers',
+        )
+        if isinstance(repo_or_path,Path) and repo_or_path.exists():
+            new_config.update(path=repo_or_path)
+        else:
+            new_config.update(repo_id=repo_or_path)
+
+        self.add_model(model_name, new_config, True)
+        if commit_to_conf:
+            self.commit(commit_to_conf)
+        return True
+
+    def import_ckpt_model(self,
+                    weights:Union[str,Path],
+                    config:Union[str,Path]='configs/stable-diffusion/v1-inference.yaml',
+                    model_name:str=None,
+                    description:str=None,
+                    commit_to_conf:Path=None,
+    )->bool:
+        '''
+        Attempts to install the indicated ckpt file and returns True if successful.
+
+        "weights" can be either a path-like object corresponding to a local .ckpt file 
+        or a http/https URL pointing to a remote model.
+
+        "config" is the model config file to use with this ckpt file. It defaults to
+        v1-inference.yaml. If a URL is provided, the config will be downloaded.
+        
+        You can optionally provide a model name and/or description. If not provided,
+        then these will be derived from the weight file name. If you provide a commit_to_conf
+        path to the configuration file, then the new entry will be committed to the
+        models.yaml file.
+        '''
+        weights_path = self._resolve_path(weights,'models/ldm/stable-diffusion-v1')
+        config_path  = self._resolve_path(config,'configs/stable-diffusion')
+
+        if weights_path is None or not weights_path.exists():
+            return False
+        if config_path is None or not config_path.exists():
+            return False
+            
+        model_name = model_name or Path(basename).stem
+        description = description or f'imported stable diffusion weights file {model_name}'
+        new_config = dict(
+            weights=str(weights_path),
+            config=str(config_path),
+            description=description,
+            format='ckpt',
+            width=512,
+            height=512
+        )
+        self.add_model(model_name, new_config, True)
+        if commit_to_conf:
+            self.commit(commit_to_conf)
+        return True
+                       
     def autoconvert_weights(
             self,
             conf_path:Path,
@@ -538,8 +616,8 @@ def del_config(self, model_name:str, gen, opt, completer):
         if model_name == current_model:
             print("** Can't delete active model. !switch to another model first. **")
             return
-        gen.model_cache.del_model(model_name)
-        gen.model_cache.commit(opt.conf)
+        gen.model_manager.del_model(model_name)
+        gen.model_manager.commit(opt.conf)
         print(f'** {model_name} deleted')
         completer.del_model(model_name)
 
@@ -599,6 +677,21 @@ def preamble(self) -> str:
             # was trained on.
         ''')
 
+    def _resolve_path(self, source:Union[str,Path], dest_directory:str)->Path:
+        resolved_path = None
+        if source.startswith('http'):
+            basename = os.path.basename(source)
+            if not os.path.isabs(dest_directory):
+                dest_directory = os.path.join(Globals.root,dest_directory)
+            dest = os.path.join(dest_directory,basename)
+            if download_with_progress_bar(source,dest):
+                resolved_path = Path(dest)
+        else:
+            if not os.path.isabs(source):
+                source = os.path.join(Globals.root,source)
+            resolved_path = Path(source)
+        return resolved_path
+
     def _invalidate_cached_model(self,model_name:str) -> None:
         self.offload_model(model_name)
         if model_name in self.stack:
diff --git a/ldm/util.py b/ldm/util.py
index 2039faf0f78..282a56c3e58 100644
--- a/ldm/util.py
+++ b/ldm/util.py
@@ -5,9 +5,14 @@
 from inspect import isfunction
 from queue import Queue
 from threading import Thread
+from urllib import request
+from tqdm import tqdm
+from pathlib import Path
 
 import numpy as np
 import torch
+import os
+import traceback
 from PIL import Image, ImageDraw, ImageFont
 
 
@@ -259,3 +264,31 @@ def debug_image(debug_image, debug_text, debug_show=True, debug_result=False, de
     if debug_result:
         return image_copy
 
+#-------------------------------------
+class ProgressBar():
+    def __init__(self,model_name='file'):
+        self.pbar = None
+        self.name = model_name
+
+    def __call__(self, block_num, block_size, total_size):
+        if not self.pbar:
+            self.pbar=tqdm(desc=self.name,
+                           initial=0,
+                           unit='iB',
+                           unit_scale=True,
+                           unit_divisor=1000,
+                           total=total_size)
+        self.pbar.update(block_size)
+
+def download_with_progress_bar(url:str, dest:Path)->bool:
+    try:
+        if not os.path.exists(dest):
+            os.makedirs((os.path.dirname(dest) or '.'), exist_ok=True)
+            request.urlretrieve(url,dest,ProgressBar(os.path.basename(dest)))
+            return True
+        else:
+            return True
+    except OSError:
+        print(traceback.format_exc())
+        return False
+
diff --git a/scripts/configure_invokeai.py b/scripts/configure_invokeai.py
index c26359cc357..202c53e7926 100755
--- a/scripts/configure_invokeai.py
+++ b/scripts/configure_invokeai.py
@@ -40,10 +40,10 @@
 transformers.logging.set_verbosity_error()
 
 try:
-    from ldm.invoke.model_cache import ModelCache
+    from ldm.invoke.model_manager import ModelCache
 except ImportError:
     sys.path.append('.')
-    from ldm.invoke.model_cache import ModelCache
+    from ldm.invoke.model_manager import ModelCache
 
 #--------------------------globals-----------------------
 Model_dir = 'models'

From 8ef3c6824a2806891def7d219fa9bccafe2aa4dd Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Mon, 2 Jan 2023 17:13:10 -0500
Subject: [PATCH 111/199] allow "recommended" flag to be optional in
 INITIAL_MODELS.yaml

---
 configs/INITIAL_MODELS.yaml   | 4 ++--
 scripts/configure_invokeai.py | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/configs/INITIAL_MODELS.yaml b/configs/INITIAL_MODELS.yaml
index 2432e1f453c..f994db5c7b4 100644
--- a/configs/INITIAL_MODELS.yaml
+++ b/configs/INITIAL_MODELS.yaml
@@ -82,12 +82,12 @@ voxel_art-1.0:
    width: 512
    height: 512
 ft-mse-improved-autoencoder-840000:
-   description: StabilityAI improved autoencoder fine-tuned for human faces for legacy .ckpt models only (335 MB)
+   description: StabilityAI improved autoencoder fine-tuned for human faces. Use with legacy .ckpt models ONLY (335 MB)
    repo_id: stabilityai/sd-vae-ft-mse-original
    format: ckpt
    config: VAE/default
    file: vae-ft-mse-840000-ema-pruned.ckpt
-   recommended: True
+   recommended: False
    width: 512
    height: 512
 trinart_vae:
diff --git a/scripts/configure_invokeai.py b/scripts/configure_invokeai.py
index 202c53e7926..4a91c13d286 100755
--- a/scripts/configure_invokeai.py
+++ b/scripts/configure_invokeai.py
@@ -169,7 +169,7 @@ def select_datasets(action:str):
                     counter += 1
         else:
             for ds in Datasets.keys():
-                if Datasets[ds]['recommended']:
+                if Datasets[ds].get('recommended',False):
                     datasets[ds]=counter
                     counter += 1
 
@@ -193,7 +193,7 @@ def select_datasets(action:str):
 def recommended_datasets()->dict:
     datasets = dict()
     for ds in Datasets.keys():
-        if Datasets[ds]['recommended']:
+        if Datasets[ds].get('recommended',False):
             datasets[ds]=True
     return datasets
 

From d8897b7a65ddc15115aa7edb568c15acc2d517cb Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Mon, 2 Jan 2023 17:24:43 -0500
Subject: [PATCH 112/199] configure_invokeai now downloads VAE diffusers in
 advance

---
 scripts/configure_invokeai.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/scripts/configure_invokeai.py b/scripts/configure_invokeai.py
index 4a91c13d286..d547a02aabc 100755
--- a/scripts/configure_invokeai.py
+++ b/scripts/configure_invokeai.py
@@ -343,6 +343,8 @@ def _download_repo_or_file(mconfig:DictConfig, access_token:str, precision:str='
         path = _download_ckpt_weights(mconfig, access_token)
     else:
         path = _download_diffusion_weights(mconfig, access_token, precision=precision)
+        if 'vae' in mconfig and 'repo_id' in mconfig['vae']:
+            _download_diffusion_weights(mconfig['vae'], access_token, precision=precision)
     return path
 
 def _download_ckpt_weights(mconfig:DictConfig, access_token:str)->Path:
@@ -358,7 +360,7 @@ def _download_ckpt_weights(mconfig:DictConfig, access_token:str)->Path:
 
 def _download_diffusion_weights(mconfig:DictConfig, access_token:str, precision:str='float32'):
     repo_id = mconfig['repo_id']
-    model_class = StableDiffusionGeneratorPipeline if mconfig['format']=='diffusers' else AutoencoderKL
+    model_class = StableDiffusionGeneratorPipeline if mconfig.get('format',None)=='diffusers' else AutoencoderKL
     extra_arg_list = [{'revision':'fp16'},{}] if precision=='float16' else [{}]
     path = None
     for extra_args in extra_arg_list:

From dfa5f56004518c3c661d6e89374474f3b787c2bf Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Mon, 2 Jan 2023 17:32:23 -0500
Subject: [PATCH 113/199] rename ModelCache to ModelManager

---
 ldm/generate.py               | 4 ++--
 ldm/invoke/model_manager.py   | 2 +-
 scripts/configure_invokeai.py | 4 ++--
 3 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/ldm/generate.py b/ldm/generate.py
index faa3689940c..70a887a693a 100644
--- a/ldm/generate.py
+++ b/ldm/generate.py
@@ -38,7 +38,7 @@
 from ldm.invoke.generator.inpaint import infill_methods
 from ldm.invoke.globals import Globals
 from ldm.invoke.image_util import InitImageResizer
-from ldm.invoke.model_manager import ModelCache
+from ldm.invoke.model_manager import ModelManager
 from ldm.invoke.pngwriter import PngWriter
 from ldm.invoke.seamless import configure_model_padding
 from ldm.invoke.txt2mask import Txt2Mask
@@ -210,7 +210,7 @@ def __init__(
             self.precision = choose_precision(self.device)
 
         # model caching system for fast switching
-        self.model_manager = ModelCache(mconfig,self.device,self.precision,max_loaded_models=max_loaded_models)
+        self.model_manager = ModelManager(mconfig,self.device,self.precision,max_loaded_models=max_loaded_models)
         self.model_name  = model or self.model_manager.default_model() or FALLBACK_MODEL_NAME
 
         # for VRAM usage statistics
diff --git a/ldm/invoke/model_manager.py b/ldm/invoke/model_manager.py
index 33cdca5e07e..d699468e55c 100644
--- a/ldm/invoke/model_manager.py
+++ b/ldm/invoke/model_manager.py
@@ -33,7 +33,7 @@
 
 DEFAULT_MAX_MODELS=2
 
-class ModelCache(object):
+class ModelManager(object):
     def __init__(self, config:OmegaConf, device_type:str, precision:str, max_loaded_models=DEFAULT_MAX_MODELS):
         '''
         Initialize with the path to the models.yaml config file,
diff --git a/scripts/configure_invokeai.py b/scripts/configure_invokeai.py
index d547a02aabc..c3a583aff2c 100755
--- a/scripts/configure_invokeai.py
+++ b/scripts/configure_invokeai.py
@@ -40,10 +40,10 @@
 transformers.logging.set_verbosity_error()
 
 try:
-    from ldm.invoke.model_manager import ModelCache
+    from ldm.invoke.model_manager import ModelManager
 except ImportError:
     sys.path.append('.')
-    from ldm.invoke.model_manager import ModelCache
+    from ldm.invoke.model_manager import ModelManager
 
 #--------------------------globals-----------------------
 Model_dir = 'models'

From faa30e4a26eed4af1ac4703577d6bd0129481538 Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Mon, 2 Jan 2023 17:33:08 -0500
Subject: [PATCH 114/199] remove support for `repo_name` in models.yaml

---
 ldm/invoke/model_manager.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/ldm/invoke/model_manager.py b/ldm/invoke/model_manager.py
index d699468e55c..a217f567f0e 100644
--- a/ldm/invoke/model_manager.py
+++ b/ldm/invoke/model_manager.py
@@ -424,8 +424,6 @@ def model_name_or_path(self, model_name:Union[str,DictConfig]) -> str | Path:
             return path
         elif 'repo_id' in mconfig:
             return mconfig['repo_id']
-        elif 'repo_name' in mconfig:
-            return mconfig['repo_name']
         else:
             raise ValueError("Model config must specify either repo_id or path.")
 

From c32a7d2bee4a35856506c580fda579a137d1d0dd Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Mon, 2 Jan 2023 18:46:25 -0500
Subject: [PATCH 115/199] check for and refuse to load embeddings trained on
 incompatible models

---
 ldm/modules/textual_inversion_manager.py | 13 +++++++++----
 scripts/invoke.py                        |  2 +-
 2 files changed, 10 insertions(+), 5 deletions(-)

diff --git a/ldm/modules/textual_inversion_manager.py b/ldm/modules/textual_inversion_manager.py
index 543fa52111e..f5eecaa949e 100644
--- a/ldm/modules/textual_inversion_manager.py
+++ b/ldm/modules/textual_inversion_manager.py
@@ -96,10 +96,13 @@ def _add_textual_inversion(self, trigger_str, embedding) -> int:
             ))
             return trigger_token_id
 
-        except ValueError:
-            traceback.print_exc()
-            print(f">> TextualInversionManager was unable to add a textual inversion with trigger string {trigger_str}.")
-            raise
+        except ValueError as e:
+            if str(e).startswith('Warning'):
+                print(f">> {str(e)}")
+            else:
+                traceback.print_exc()
+                print(f">> TextualInversionManager was unable to add a textual inversion with trigger string {trigger_str}.")
+                raise
 
 
     def has_textual_inversion_for_trigger_string(self, trigger_string: str) -> bool:
@@ -156,6 +159,8 @@ def _get_or_create_token_id_and_assign_embedding(self, token_str: str, embedding
         token_id = self.tokenizer.convert_tokens_to_ids(token_str)
         if token_id == self.tokenizer.unk_token_id:
             raise RuntimeError(f"Unable to find token id for token '{token_str}'")
+        if self.text_encoder.get_input_embeddings().weight.data[token_id].shape != embedding.shape:
+            raise ValueError(f"Warning. Cannot load embedding for {token_str}. It was trained on a model with token dimension {embedding.shape[0]}, but the current model has token dimension {self.text_encoder.get_input_embeddings().weight.data[token_id].shape[0]}.")
         self.text_encoder.get_input_embeddings().weight.data[token_id] = embedding
 
         return token_id
diff --git a/scripts/invoke.py b/scripts/invoke.py
index d38bda4cb5b..cfe6b061144 100755
--- a/scripts/invoke.py
+++ b/scripts/invoke.py
@@ -2,7 +2,7 @@
 
 import sys
 import os
-if sys.platform == 'Darsin':
+if sys.platform == 'darwin':
     os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
 
 import ldm.invoke.CLI

From 0ee5fc3222a4281accdce24551a9ae0134456f96 Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Mon, 2 Jan 2023 15:46:43 -0800
Subject: [PATCH 116/199] models.yaml.example: s/repo_name/repo_id

and remove extra INITIAL_MODELS now that the main one has diffusers models in it.
---
 configs/INITIAL_MODELS.diffusers.yaml | 56 ---------------------------
 configs/models.yaml.example           | 10 ++---
 2 files changed, 5 insertions(+), 61 deletions(-)
 delete mode 100644 configs/INITIAL_MODELS.diffusers.yaml

diff --git a/configs/INITIAL_MODELS.diffusers.yaml b/configs/INITIAL_MODELS.diffusers.yaml
deleted file mode 100644
index 73a8d77c578..00000000000
--- a/configs/INITIAL_MODELS.diffusers.yaml
+++ /dev/null
@@ -1,56 +0,0 @@
-stable-diffusion-1.5:
-   description: The newest Stable Diffusion version 1.5 weight file (4.27 GB)
-   repo_id: runwayml/stable-diffusion-v1-5
-   format: diffusers
-   recommended: true
-   width: 512
-   height: 512
-inpainting-1.5:
-   description: RunwayML SD 1.5 model optimized for inpainting (4.27 GB)
-   repo_id: runwayml/stable-diffusion-inpainting
-   format: diffusers
-   recommended: True
-   width: 512
-   height: 512
-ft-mse-improved-autoencoder-840000:
-   description: StabilityAI improved autoencoder fine-tuned for human faces (recommended; 335 MB)
-   repo_id: stabilityai/sd-vae-ft-mse
-   format: diffusers
-   recommended: True
-   width: 512
-   height: 512
-stable-diffusion-1.4:
-   description: The original Stable Diffusion version 1.4 weight file (4.27 GB)
-   repo_id: CompVis/stable-diffusion-v1-4
-   format: diffusers
-   recommended: False
-   width: 512
-   height: 512
-waifu-diffusion-1.3:
-   description: Stable Diffusion 1.4 fine tuned on anime-styled images (4.27)
-   repo_id: hakurei/waifu-diffusion
-   format: diffusers
-   recommended: False
-   width: 512
-   height: 512
-trinart-2.0:
-   description: An SD model finetuned with ~40,000 assorted high resolution manga/anime-style pictures (2.13 GB)
-   repo_id: naclbit/trinart_stable_diffusion_v2
-   format: diffusers
-   recommended: False
-   width: 512
-   height: 512
-papercut-1.0:
-   description: SD 1.5 fine-tuned for papercut art (use "PaperCut" in your prompts) (2.13 GB)
-   repo_id: Fictiverse/Stable_Diffusion_PaperCut_Model
-   format: diffusers
-   recommended: False
-   width: 512
-   height: 512
-voxel_art-1.0:
-   description: Stable Diffusion trained on voxel art (use "VoxelArt" in your prompts) (4.27 GB)
-   repo_id: Fictiverse/Stable_Diffusion_VoxelArt_Model
-   format: diffusers
-   recommended: False
-   width: 512
-   height: 512
diff --git a/configs/models.yaml.example b/configs/models.yaml.example
index 5187d29b3ea..98f8f77e62c 100644
--- a/configs/models.yaml.example
+++ b/configs/models.yaml.example
@@ -8,22 +8,22 @@
 diffusers-1.4:
   description: 🤗🧨 Stable Diffusion v1.4
   format: diffusers
-  repo_name: CompVis/stable-diffusion-v1-4
+  repo_id: CompVis/stable-diffusion-v1-4
 diffusers-1.5:
   description: 🤗🧨 Stable Diffusion v1.5
   format: diffusers
-  repo_name: runwayml/stable-diffusion-v1-5
+  repo_id: runwayml/stable-diffusion-v1-5
   default: true
 diffusers-1.5+mse:
   description: 🤗🧨 Stable Diffusion v1.5 + MSE-finetuned VAE
   format: diffusers
-  repo_name: runwayml/stable-diffusion-v1-5
+  repo_id: runwayml/stable-diffusion-v1-5
   vae:
-    repo_name: stabilityai/sd-vae-ft-mse
+    repo_id: stabilityai/sd-vae-ft-mse
 diffusers-inpainting-1.5:
   description: 🤗🧨 inpainting for Stable Diffusion v1.5
   format: diffusers
-  repo_name: runwayml/stable-diffusion-inpainting
+  repo_id: runwayml/stable-diffusion-inpainting
 stable-diffusion-1.5:
   description: The newest Stable Diffusion version 1.5 weight file (4.27 GB)
   weights: models/ldm/stable-diffusion-v1/v1-5-pruned-emaonly.ckpt

From 33d4ee6279107d26b428ec344f86d235a7b65e09 Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Mon, 2 Jan 2023 19:39:05 -0500
Subject: [PATCH 117/199] add MVP textual inversion script

---
 .../requirements-base.txt                     |   1 +
 scripts/textual_inversion.py                  | 764 ++++++++++++++++++
 2 files changed, 765 insertions(+)
 create mode 100755 scripts/textual_inversion.py

diff --git a/environments-and-requirements/requirements-base.txt b/environments-and-requirements/requirements-base.txt
index 16a2c7ca002..35072d309e4 100644
--- a/environments-and-requirements/requirements-base.txt
+++ b/environments-and-requirements/requirements-base.txt
@@ -1,6 +1,7 @@
 # pip will resolve the version which matches torch
 accelerate
 albumentations
+datasets
 diffusers[torch]~=0.11
 einops
 eventlet
diff --git a/scripts/textual_inversion.py b/scripts/textual_inversion.py
new file mode 100755
index 00000000000..04e22aaf017
--- /dev/null
+++ b/scripts/textual_inversion.py
@@ -0,0 +1,764 @@
+#!/usr/bin/env python
+
+# This script was copied from
+# https://github.com/huggingface/diffusers/blob/main/examples/textual_inversion/textual_inversion.py
+# on January 2, 2023
+# and modified slightly by Lincoln Stein (@lstein) to work with InvokeAI
+
+import argparse
+import logging
+import math
+import os
+import random
+from pathlib import Path
+from typing import Optional
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch.utils.data import Dataset
+
+import datasets
+import diffusers
+import PIL
+import transformers
+from accelerate import Accelerator
+from accelerate.logging import get_logger
+from accelerate.utils import set_seed
+from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionPipeline, UNet2DConditionModel
+from diffusers.optimization import get_scheduler
+from diffusers.utils import check_min_version
+from diffusers.utils.import_utils import is_xformers_available
+from huggingface_hub import HfFolder, Repository, whoami
+
+# invokeai stuff
+from ldm.invoke.globals import Globals
+from omegaconf import OmegaConf
+
+# TODO: remove and import from diffusers.utils when the new version of diffusers is released
+from packaging import version
+from PIL import Image
+from torchvision import transforms
+from tqdm.auto import tqdm
+from transformers import CLIPTextModel, CLIPTokenizer
+
+if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
+    PIL_INTERPOLATION = {
+        "linear": PIL.Image.Resampling.BILINEAR,
+        "bilinear": PIL.Image.Resampling.BILINEAR,
+        "bicubic": PIL.Image.Resampling.BICUBIC,
+        "lanczos": PIL.Image.Resampling.LANCZOS,
+        "nearest": PIL.Image.Resampling.NEAREST,
+    }
+else:
+    PIL_INTERPOLATION = {
+        "linear": PIL.Image.LINEAR,
+        "bilinear": PIL.Image.BILINEAR,
+        "bicubic": PIL.Image.BICUBIC,
+        "lanczos": PIL.Image.LANCZOS,
+        "nearest": PIL.Image.NEAREST,
+    }
+# ------------------------------------------------------------------------------
+
+
+# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
+check_min_version("0.10.0.dev0")
+
+
+logger = get_logger(__name__)
+
+
+def save_progress(text_encoder, placeholder_token_id, accelerator, args, save_path):
+    logger.info("Saving embeddings")
+    learned_embeds = accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[placeholder_token_id]
+    learned_embeds_dict = {args.placeholder_token: learned_embeds.detach().cpu()}
+    torch.save(learned_embeds_dict, save_path)
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Simple example of a training script.")
+    parser.add_argument(
+        "--save_steps",
+        type=int,
+        default=500,
+        help="Save learned_embeds.bin every X updates steps.",
+    )
+    parser.add_argument(
+        '--root_dir','--root',
+        type=str,
+        default=Globals.root,
+        help="Path to the invokeai runtime directory",
+    )
+    parser.add_argument(
+        "--only_save_embeds",
+        action="store_true",
+        default=False,
+        help="Save only the embeddings for the new concept.",
+    )
+    parser.add_argument(
+        "--model",
+        type=str,
+        default=None,
+        required=True,
+        help="Name of the diffusers model to train against, as defined in configs/models.yaml.",
+    )
+    parser.add_argument(
+        "--revision",
+        type=str,
+        default=None,
+        required=False,
+        help="Revision of pretrained model identifier from huggingface.co/models.",
+    )
+    parser.add_argument(
+        "--tokenizer_name",
+        type=str,
+        default=None,
+        help="Pretrained tokenizer name or path if not the same as model_name",
+    )
+    parser.add_argument(
+        "--train_data_dir", type=str, default=None, required=True, help="A folder containing the training data."
+    )
+    parser.add_argument(
+        "--placeholder_token",
+        type=str,
+        default=None,
+        required=True,
+        help="A token to use as a placeholder for the concept.",
+    )
+    parser.add_argument(
+        "--initializer_token", type=str, default=None, required=True, help="A token to use as initializer word."
+    )
+    parser.add_argument("--learnable_property", type=str, default="object", help="Choose between 'object' and 'style'")
+    parser.add_argument("--repeats", type=int, default=100, help="How many times to repeat the training data.")
+    parser.add_argument(
+        "--output_dir",
+         type=str,
+        default=f'{Globals.root}/text-inversion-model',
+        help="The output directory where the model predictions and checkpoints will be written.",
+    )
+    parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
+    parser.add_argument(
+        "--resolution",
+        type=int,
+        default=512,
+        help=(
+            "The resolution for input images, all the images in the train/validation dataset will be resized to this"
+            " resolution"
+        ),
+    )
+    parser.add_argument(
+        "--center_crop", action="store_true", help="Whether to center crop images before resizing to resolution"
+    )
+    parser.add_argument(
+        "--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader."
+    )
+    parser.add_argument("--num_train_epochs", type=int, default=100)
+    parser.add_argument(
+        "--max_train_steps",
+        type=int,
+        default=5000,
+        help="Total number of training steps to perform.  If provided, overrides num_train_epochs.",
+    )
+    parser.add_argument(
+        "--gradient_accumulation_steps",
+        type=int,
+        default=1,
+        help="Number of updates steps to accumulate before performing a backward/update pass.",
+    )
+    parser.add_argument(
+        "--gradient_checkpointing",
+        action="store_true",
+        help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
+    )
+    parser.add_argument(
+        "--learning_rate",
+        type=float,
+        default=1e-4,
+        help="Initial learning rate (after the potential warmup period) to use.",
+    )
+    parser.add_argument(
+        "--scale_lr",
+        action="store_true",
+        default=True,
+        help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
+    )
+    parser.add_argument(
+        "--lr_scheduler",
+        type=str,
+        default="constant",
+        help=(
+            'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
+            ' "constant", "constant_with_warmup"]'
+        ),
+    )
+    parser.add_argument(
+        "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
+    )
+    parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
+    parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
+    parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
+    parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
+    parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
+    parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
+    parser.add_argument(
+        "--logging_dir",
+        type=str,
+        default="logs",
+        help=(
+            "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
+            " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
+        ),
+    )
+    parser.add_argument(
+        "--mixed_precision",
+        type=str,
+        default="no",
+        choices=["no", "fp16", "bf16"],
+        help=(
+            "Whether to use mixed precision. Choose"
+            "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
+            "and an Nvidia Ampere GPU."
+        ),
+    )
+    parser.add_argument(
+        "--allow_tf32",
+        action="store_true",
+        help=(
+            "Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
+            " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
+        ),
+    )
+    parser.add_argument(
+        "--report_to",
+        type=str,
+        default="tensorboard",
+        help=(
+            'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
+            ' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
+        ),
+    )
+    parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
+    parser.add_argument(
+        "--checkpointing_steps",
+        type=int,
+        default=500,
+        help=(
+            "Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming"
+            " training using `--resume_from_checkpoint`."
+        ),
+    )
+    parser.add_argument(
+        "--resume_from_checkpoint",
+        type=str,
+        default=None,
+        help=(
+            "Whether training should be resumed from a previous checkpoint. Use a path saved by"
+            ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
+        ),
+    )
+    parser.add_argument(
+        "--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
+    )
+
+    args = parser.parse_args()
+    env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
+    if env_local_rank != -1 and env_local_rank != args.local_rank:
+        args.local_rank = env_local_rank
+
+    if args.train_data_dir is None:
+        raise ValueError("You must specify a train data directory.")
+
+    return args
+
+
+imagenet_templates_small = [
+    "a photo of a {}",
+    "a rendering of a {}",
+    "a cropped photo of the {}",
+    "the photo of a {}",
+    "a photo of a clean {}",
+    "a photo of a dirty {}",
+    "a dark photo of the {}",
+    "a photo of my {}",
+    "a photo of the cool {}",
+    "a close-up photo of a {}",
+    "a bright photo of the {}",
+    "a cropped photo of a {}",
+    "a photo of the {}",
+    "a good photo of the {}",
+    "a photo of one {}",
+    "a close-up photo of the {}",
+    "a rendition of the {}",
+    "a photo of the clean {}",
+    "a rendition of a {}",
+    "a photo of a nice {}",
+    "a good photo of a {}",
+    "a photo of the nice {}",
+    "a photo of the small {}",
+    "a photo of the weird {}",
+    "a photo of the large {}",
+    "a photo of a cool {}",
+    "a photo of a small {}",
+]
+
+imagenet_style_templates_small = [
+    "a painting in the style of {}",
+    "a rendering in the style of {}",
+    "a cropped painting in the style of {}",
+    "the painting in the style of {}",
+    "a clean painting in the style of {}",
+    "a dirty painting in the style of {}",
+    "a dark painting in the style of {}",
+    "a picture in the style of {}",
+    "a cool painting in the style of {}",
+    "a close-up painting in the style of {}",
+    "a bright painting in the style of {}",
+    "a cropped painting in the style of {}",
+    "a good painting in the style of {}",
+    "a close-up painting in the style of {}",
+    "a rendition in the style of {}",
+    "a nice painting in the style of {}",
+    "a small painting in the style of {}",
+    "a weird painting in the style of {}",
+    "a large painting in the style of {}",
+]
+
+
+class TextualInversionDataset(Dataset):
+    def __init__(
+        self,
+        data_root,
+        tokenizer,
+        learnable_property="object",  # [object, style]
+        size=512,
+        repeats=100,
+        interpolation="bicubic",
+        flip_p=0.5,
+        set="train",
+        placeholder_token="*",
+        center_crop=False,
+    ):
+        self.data_root = data_root
+        self.tokenizer = tokenizer
+        self.learnable_property = learnable_property
+        self.size = size
+        self.placeholder_token = placeholder_token
+        self.center_crop = center_crop
+        self.flip_p = flip_p
+
+        self.image_paths = [os.path.join(self.data_root, file_path) for file_path in os.listdir(self.data_root)]
+
+        self.num_images = len(self.image_paths)
+        self._length = self.num_images
+
+        if set == "train":
+            self._length = self.num_images * repeats
+
+        self.interpolation = {
+            "linear": PIL_INTERPOLATION["linear"],
+            "bilinear": PIL_INTERPOLATION["bilinear"],
+            "bicubic": PIL_INTERPOLATION["bicubic"],
+            "lanczos": PIL_INTERPOLATION["lanczos"],
+        }[interpolation]
+
+        self.templates = imagenet_style_templates_small if learnable_property == "style" else imagenet_templates_small
+        self.flip_transform = transforms.RandomHorizontalFlip(p=self.flip_p)
+
+    def __len__(self):
+        return self._length
+
+    def __getitem__(self, i):
+        example = {}
+        image = Image.open(self.image_paths[i % self.num_images])
+
+        if not image.mode == "RGB":
+            image = image.convert("RGB")
+
+        placeholder_string = self.placeholder_token
+        text = random.choice(self.templates).format(placeholder_string)
+
+        example["input_ids"] = self.tokenizer(
+            text,
+            padding="max_length",
+            truncation=True,
+            max_length=self.tokenizer.model_max_length,
+            return_tensors="pt",
+        ).input_ids[0]
+
+        # default to score-sde preprocessing
+        img = np.array(image).astype(np.uint8)
+
+        if self.center_crop:
+            crop = min(img.shape[0], img.shape[1])
+            h, w, = (
+                img.shape[0],
+                img.shape[1],
+            )
+            img = img[(h - crop) // 2 : (h + crop) // 2, (w - crop) // 2 : (w + crop) // 2]
+
+        image = Image.fromarray(img)
+        image = image.resize((self.size, self.size), resample=self.interpolation)
+
+        image = self.flip_transform(image)
+        image = np.array(image).astype(np.uint8)
+        image = (image / 127.5 - 1.0).astype(np.float32)
+
+        example["pixel_values"] = torch.from_numpy(image).permute(2, 0, 1)
+        return example
+
+
+def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None):
+    if token is None:
+        token = HfFolder.get_token()
+    if organization is None:
+        username = whoami(token)["name"]
+        return f"{username}/{model_id}"
+    else:
+        return f"{organization}/{model_id}"
+
+
+def main():
+    args = parse_args()
+
+    # setting up things the way invokeai expects them
+    if os.path.exists(args.root_dir):
+        Globals.root = args.root_dir
+    if not os.path.isabs(args.output_dir):
+        args.output_dir = os.path.join(Globals.root,args.output_dir)
+        
+    logging_dir = os.path.join(args.output_dir, args.logging_dir)
+
+    accelerator = Accelerator(
+        gradient_accumulation_steps=args.gradient_accumulation_steps,
+        mixed_precision=args.mixed_precision,
+        log_with=args.report_to,
+        logging_dir=logging_dir,
+    )
+
+    # Make one log on every process with the configuration for debugging.
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        level=logging.INFO,
+    )
+    logger.info(accelerator.state, main_process_only=False)
+    if accelerator.is_local_main_process:
+        datasets.utils.logging.set_verbosity_warning()
+        transformers.utils.logging.set_verbosity_warning()
+        diffusers.utils.logging.set_verbosity_info()
+    else:
+        datasets.utils.logging.set_verbosity_error()
+        transformers.utils.logging.set_verbosity_error()
+        diffusers.utils.logging.set_verbosity_error()
+
+    # If passed along, set the training seed now.
+    if args.seed is not None:
+        set_seed(args.seed)
+
+    # Handle the repository creation
+    if accelerator.is_main_process:
+        if args.push_to_hub:
+            if args.hub_model_id is None:
+                repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
+            else:
+                repo_name = args.hub_model_id
+            repo = Repository(args.output_dir, clone_from=repo_name)
+
+            with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
+                if "step_*" not in gitignore:
+                    gitignore.write("step_*\n")
+                if "epoch_*" not in gitignore:
+                    gitignore.write("epoch_*\n")
+        elif args.output_dir is not None:
+            os.makedirs(args.output_dir, exist_ok=True)
+
+    models_conf = OmegaConf.load(os.path.join(Globals.root,'configs/models.yaml'))
+    model_conf = models_conf.get(args.model,None)
+    assert model_conf is not None,f'Unknown model: {args.model}'
+    assert model_conf.get('format','diffusers')=='diffusers', "This script only works with models of type 'diffusers'"
+    pretrained_model_name_or_path = model_conf.get('repo_id',None) or Path(model_conf.get('path'))
+    assert pretrained_model_name_or_path, f"models.yaml error: neither 'repo_id' nor 'path' is defined for {args.model}"
+    pipeline_args = dict()
+    if not isinstance(pretrained_model_name_or_path,Path):
+        pipeline_args.update(cache_dir=os.path.join(Globals.root,'models',pretrained_model_name_or_path))
+    
+    # Load tokenizer
+    if args.tokenizer_name:
+        tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name)
+    else:
+        tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_name_or_path, subfolder="tokenizer", **pipeline_args)
+
+    # Load scheduler and models
+    noise_scheduler = DDPMScheduler.from_pretrained(pretrained_model_name_or_path, subfolder="scheduler", **pipeline_args)
+    text_encoder = CLIPTextModel.from_pretrained(
+        pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision,  **pipeline_args
+    )
+    vae = AutoencoderKL.from_pretrained(pretrained_model_name_or_path, subfolder="vae", revision=args.revision,  **pipeline_args)
+    unet = UNet2DConditionModel.from_pretrained(
+        pretrained_model_name_or_path, subfolder="unet", revision=args.revision,  **pipeline_args
+    )
+
+    # Add the placeholder token in tokenizer
+    num_added_tokens = tokenizer.add_tokens(args.placeholder_token)
+    if num_added_tokens == 0:
+        raise ValueError(
+            f"The tokenizer already contains the token {args.placeholder_token}. Please pass a different"
+            " `placeholder_token` that is not already in the tokenizer."
+        )
+
+    # Convert the initializer_token, placeholder_token to ids
+    token_ids = tokenizer.encode(args.initializer_token, add_special_tokens=False)
+    # Check if initializer_token is a single token or a sequence of tokens
+    if len(token_ids) > 1:
+        raise ValueError("The initializer token must be a single token.")
+
+    initializer_token_id = token_ids[0]
+    placeholder_token_id = tokenizer.convert_tokens_to_ids(args.placeholder_token)
+
+    # Resize the token embeddings as we are adding new special tokens to the tokenizer
+    text_encoder.resize_token_embeddings(len(tokenizer))
+
+    # Initialise the newly added placeholder token with the embeddings of the initializer token
+    token_embeds = text_encoder.get_input_embeddings().weight.data
+    token_embeds[placeholder_token_id] = token_embeds[initializer_token_id]
+
+    # Freeze vae and unet
+    vae.requires_grad_(False)
+    unet.requires_grad_(False)
+    # Freeze all parameters except for the token embeddings in text encoder
+    text_encoder.text_model.encoder.requires_grad_(False)
+    text_encoder.text_model.final_layer_norm.requires_grad_(False)
+    text_encoder.text_model.embeddings.position_embedding.requires_grad_(False)
+
+    if args.gradient_checkpointing:
+        # Keep unet in train mode if we are using gradient checkpointing to save memory.
+        # The dropout cannot be != 0 so it doesn't matter if we are in eval or train mode.
+        unet.train()
+        text_encoder.gradient_checkpointing_enable()
+        unet.enable_gradient_checkpointing()
+
+    if args.enable_xformers_memory_efficient_attention:
+        if is_xformers_available():
+            unet.enable_xformers_memory_efficient_attention()
+        else:
+            raise ValueError("xformers is not available. Make sure it is installed correctly")
+
+    # Enable TF32 for faster training on Ampere GPUs,
+    # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
+    if args.allow_tf32:
+        torch.backends.cuda.matmul.allow_tf32 = True
+
+    if args.scale_lr:
+        args.learning_rate = (
+            args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
+        )
+
+    # Initialize the optimizer
+    optimizer = torch.optim.AdamW(
+        text_encoder.get_input_embeddings().parameters(),  # only optimize the embeddings
+        lr=args.learning_rate,
+        betas=(args.adam_beta1, args.adam_beta2),
+        weight_decay=args.adam_weight_decay,
+        eps=args.adam_epsilon,
+    )
+
+    # Dataset and DataLoaders creation:
+    train_dataset = TextualInversionDataset(
+        data_root=args.train_data_dir,
+        tokenizer=tokenizer,
+        size=args.resolution,
+        placeholder_token=args.placeholder_token,
+        repeats=args.repeats,
+        learnable_property=args.learnable_property,
+        center_crop=args.center_crop,
+        set="train",
+    )
+    train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=args.train_batch_size, shuffle=True)
+
+    # Scheduler and math around the number of training steps.
+    overrode_max_train_steps = False
+    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
+    if args.max_train_steps is None:
+        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
+        overrode_max_train_steps = True
+
+    lr_scheduler = get_scheduler(
+        args.lr_scheduler,
+        optimizer=optimizer,
+        num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,
+        num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
+    )
+
+    # Prepare everything with our `accelerator`.
+    text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
+        text_encoder, optimizer, train_dataloader, lr_scheduler
+    )
+
+    # For mixed precision training we cast the text_encoder and vae weights to half-precision
+    # as these models are only used for inference, keeping weights in full precision is not required.
+    weight_dtype = torch.float32
+    if accelerator.mixed_precision == "fp16":
+        weight_dtype = torch.float16
+    elif accelerator.mixed_precision == "bf16":
+        weight_dtype = torch.bfloat16
+
+    # Move vae and unet to device and cast to weight_dtype
+    unet.to(accelerator.device, dtype=weight_dtype)
+    vae.to(accelerator.device, dtype=weight_dtype)
+
+    # We need to recalculate our total training steps as the size of the training dataloader may have changed.
+    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
+    if overrode_max_train_steps:
+        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
+    # Afterwards we recalculate our number of training epochs
+    args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
+
+    # We need to initialize the trackers we use, and also store our configuration.
+    # The trackers initializes automatically on the main process.
+    if accelerator.is_main_process:
+        accelerator.init_trackers("textual_inversion", config=vars(args))
+
+    # Train!
+    total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
+
+    logger.info("***** Running training *****")
+    logger.info(f"  Num examples = {len(train_dataset)}")
+    logger.info(f"  Num Epochs = {args.num_train_epochs}")
+    logger.info(f"  Instantaneous batch size per device = {args.train_batch_size}")
+    logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
+    logger.info(f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
+    logger.info(f"  Total optimization steps = {args.max_train_steps}")
+    global_step = 0
+    first_epoch = 0
+
+    # Potentially load in the weights and states from a previous save
+    if args.resume_from_checkpoint:
+        if args.resume_from_checkpoint != "latest":
+            path = os.path.basename(args.resume_from_checkpoint)
+        else:
+            # Get the most recent checkpoint
+            dirs = os.listdir(args.output_dir)
+            dirs = [d for d in dirs if d.startswith("checkpoint")]
+            dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
+            path = dirs[-1]
+        accelerator.print(f"Resuming from checkpoint {path}")
+        accelerator.load_state(os.path.join(args.output_dir, path))
+        global_step = int(path.split("-")[1])
+
+        resume_global_step = global_step * args.gradient_accumulation_steps
+        first_epoch = resume_global_step // num_update_steps_per_epoch
+        resume_step = resume_global_step % num_update_steps_per_epoch
+
+    # Only show the progress bar once on each machine.
+    progress_bar = tqdm(range(global_step, args.max_train_steps), disable=not accelerator.is_local_main_process)
+    progress_bar.set_description("Steps")
+
+    # keep original embeddings as reference
+    orig_embeds_params = accelerator.unwrap_model(text_encoder).get_input_embeddings().weight.data.clone()
+
+    for epoch in range(first_epoch, args.num_train_epochs):
+        text_encoder.train()
+        for step, batch in enumerate(train_dataloader):
+            # Skip steps until we reach the resumed step
+            if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step:
+                if step % args.gradient_accumulation_steps == 0:
+                    progress_bar.update(1)
+                continue
+
+            with accelerator.accumulate(text_encoder):
+                # Convert images to latent space
+                latents = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist.sample().detach()
+                latents = latents * 0.18215
+
+                # Sample noise that we'll add to the latents
+                noise = torch.randn_like(latents)
+                bsz = latents.shape[0]
+                # Sample a random timestep for each image
+                timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
+                timesteps = timesteps.long()
+
+                # Add noise to the latents according to the noise magnitude at each timestep
+                # (this is the forward diffusion process)
+                noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
+
+                # Get the text embedding for conditioning
+                encoder_hidden_states = text_encoder(batch["input_ids"])[0].to(dtype=weight_dtype)
+
+                # Predict the noise residual
+                model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
+
+                # Get the target for loss depending on the prediction type
+                if noise_scheduler.config.prediction_type == "epsilon":
+                    target = noise
+                elif noise_scheduler.config.prediction_type == "v_prediction":
+                    target = noise_scheduler.get_velocity(latents, noise, timesteps)
+                else:
+                    raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
+
+                loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
+
+                accelerator.backward(loss)
+
+                optimizer.step()
+                lr_scheduler.step()
+                optimizer.zero_grad()
+
+                # Let's make sure we don't update any embedding weights besides the newly added token
+                index_no_updates = torch.arange(len(tokenizer)) != placeholder_token_id
+                with torch.no_grad():
+                    accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[
+                        index_no_updates
+                    ] = orig_embeds_params[index_no_updates]
+
+            # Checks if the accelerator has performed an optimization step behind the scenes
+            if accelerator.sync_gradients:
+                progress_bar.update(1)
+                global_step += 1
+                if global_step % args.save_steps == 0:
+                    save_path = os.path.join(args.output_dir, f"learned_embeds-steps-{global_step}.bin")
+                    save_progress(text_encoder, placeholder_token_id, accelerator, args, save_path)
+
+                if global_step % args.checkpointing_steps == 0:
+                    if accelerator.is_main_process:
+                        save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
+                        accelerator.save_state(save_path)
+                        logger.info(f"Saved state to {save_path}")
+
+            logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
+            progress_bar.set_postfix(**logs)
+            accelerator.log(logs, step=global_step)
+
+            if global_step >= args.max_train_steps:
+                break
+
+    # Create the pipeline using using the trained modules and save it.
+    accelerator.wait_for_everyone()
+    if accelerator.is_main_process:
+        if args.push_to_hub and args.only_save_embeds:
+            logger.warn("Enabling full model saving because --push_to_hub=True was specified.")
+            save_full_model = True
+        else:
+            save_full_model = not args.only_save_embeds
+        if save_full_model:
+            pipeline = StableDiffusionPipeline.from_pretrained(
+                pretrained_model_name_or_path,
+                text_encoder=accelerator.unwrap_model(text_encoder),
+                vae=vae,
+                unet=unet,
+                tokenizer=tokenizer,
+                **pipeline_args,
+            )
+            pipeline.save_pretrained(args.output_dir)
+        # Save the newly trained embeddings
+        save_path = os.path.join(args.output_dir, "learned_embeds.bin")
+        save_progress(text_encoder, placeholder_token_id, accelerator, args, save_path)
+
+        if args.push_to_hub:
+            repo.push_to_hub(commit_message="End of training", blocking=False, auto_lfs_prune=True)
+
+    accelerator.end_training()
+
+
+if __name__ == "__main__":
+    main()

From 088e5462db3372e8b38554b1ad02f4fb4ca23eee Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Mon, 2 Jan 2023 16:14:42 -0800
Subject: [PATCH 118/199] refactor(InvokeAIDiffuserComponent): factor out
 _combine()

---
 .../diffusion/shared_invokeai_diffusion.py      | 17 +++++++++--------
 1 file changed, 9 insertions(+), 8 deletions(-)

diff --git a/ldm/models/diffusion/shared_invokeai_diffusion.py b/ldm/models/diffusion/shared_invokeai_diffusion.py
index bf1ace72e18..282dedefc04 100644
--- a/ldm/models/diffusion/shared_invokeai_diffusion.py
+++ b/ldm/models/diffusion/shared_invokeai_diffusion.py
@@ -1,11 +1,11 @@
-import traceback
 from math import ceil
 from typing import Callable, Optional, Union
 
 import torch
 
 from ldm.models.diffusion.cross_attention_control import Arguments, \
-    remove_cross_attention_control, setup_cross_attention_control, Context, get_cross_attention_modules, CrossAttentionType
+    remove_cross_attention_control, setup_cross_attention_control, Context, get_cross_attention_modules, \
+    CrossAttentionType
 from ldm.models.diffusion.cross_attention_map_saving import AttentionMapSaver
 
 
@@ -107,12 +107,7 @@ def do_diffusion_step(self, x: torch.Tensor, sigma: torch.Tensor,
         else:
             unconditioned_next_x, conditioned_next_x = self.apply_standard_conditioning(x, sigma, unconditioning, conditioning)
 
-        # to scale how much effect conditioning has, calculate the changes it does and then scale that
-        scaled_delta = (conditioned_next_x - unconditioned_next_x) * unconditional_guidance_scale
-        combined_next_x = unconditioned_next_x + scaled_delta
-
-        return combined_next_x
-
+        return self._combine(unconditioned_next_x, conditioned_next_x, unconditional_guidance_scale)
 
     # methods below are called from do_diffusion_step and should be considered private to this class.
 
@@ -183,6 +178,12 @@ def apply_cross_attention_controlled_conditioning(self, x:torch.Tensor, sigma, u
 
         return unconditioned_next_x, conditioned_next_x
 
+    def _combine(self, unconditioned_next_x, conditioned_next_x, guidance_scale):
+        # to scale how much effect conditioning has, calculate the changes it does and then scale that
+        scaled_delta = (conditioned_next_x - unconditioned_next_x) * guidance_scale
+        combined_next_x = unconditioned_next_x + scaled_delta
+        return combined_next_x
+
     def estimate_percent_through(self, step_index, sigma):
         if step_index is not None and self.cross_attention_control_context is not None:
             # percent_through will never reach 1.0 (but this is intended)

From 430f044f6f6050bf4fbfe2581f218bfba4282f61 Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Mon, 2 Jan 2023 18:38:21 -0800
Subject: [PATCH 119/199] InvokeAIDiffuserComponent: implement threshold

---
 ldm/invoke/generator/diffusers_pipeline.py    |  7 ++-
 ldm/invoke/generator/img2img.py               |  8 +++-
 ldm/invoke/generator/txt2img.py               | 11 +++--
 ldm/invoke/generator/txt2img2img.py           | 12 +++--
 .../diffusion/shared_invokeai_diffusion.py    | 48 ++++++++++++++++++-
 5 files changed, 72 insertions(+), 14 deletions(-)

diff --git a/ldm/invoke/generator/diffusers_pipeline.py b/ldm/invoke/generator/diffusers_pipeline.py
index 0e8d82f935c..b4a0703f18e 100644
--- a/ldm/invoke/generator/diffusers_pipeline.py
+++ b/ldm/invoke/generator/diffusers_pipeline.py
@@ -39,7 +39,7 @@
 from torchvision.transforms.functional import resize as tv_resize
 from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
 
-from ldm.models.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent
+from ldm.models.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent, ThresholdSettings
 from ldm.modules.textual_inversion_manager import TextualInversionManager
 
 
@@ -205,6 +205,7 @@ class ConditioningData:
     extra: Optional[InvokeAIDiffuserComponent.ExtraConditioningInfo] = None
     scheduler_args: dict[str, Any] = field(default_factory=dict)
     """Additional arguments to pass to scheduler.step."""
+    threshold: Optional[ThresholdSettings] = None
 
     @property
     def dtype(self):
@@ -425,7 +426,9 @@ def step(self, t: torch.Tensor, latents: torch.Tensor,
             latent_model_input, t,
             conditioning_data.unconditioned_embeddings, conditioning_data.text_embeddings,
             conditioning_data.guidance_scale,
-            step_index=step_index)
+            step_index=step_index,
+            threshold=conditioning_data.threshold
+        )
 
         # compute the previous noisy sample x_t -> x_t-1
         step_output = self.scheduler.step(noise_pred, timestep, latents,
diff --git a/ldm/invoke/generator/img2img.py b/ldm/invoke/generator/img2img.py
index 0c51648bb55..fedf6d3abc9 100644
--- a/ldm/invoke/generator/img2img.py
+++ b/ldm/invoke/generator/img2img.py
@@ -7,6 +7,7 @@
 
 from ldm.invoke.generator.base import Generator
 from ldm.invoke.generator.diffusers_pipeline import StableDiffusionGeneratorPipeline, ConditioningData
+from ldm.models.diffusion.shared_invokeai_diffusion import ThresholdSettings
 
 
 class Img2Img(Generator):
@@ -29,8 +30,11 @@ def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
         pipeline.scheduler = sampler
 
         uc, c, extra_conditioning_info   = conditioning
-        conditioning_data = (ConditioningData(uc, c, cfg_scale, extra_conditioning_info)
-                             .add_scheduler_args_if_applicable(pipeline.scheduler, eta=ddim_eta))
+        conditioning_data = (
+            ConditioningData(
+                uc, c, cfg_scale, extra_conditioning_info,
+                threshold = ThresholdSettings(threshold, warmup=0.2) if threshold else None)
+            .add_scheduler_args_if_applicable(pipeline.scheduler, eta=ddim_eta))
 
 
         def make_image(x_T):
diff --git a/ldm/invoke/generator/txt2img.py b/ldm/invoke/generator/txt2img.py
index e8e3a90d195..174c1e469dc 100644
--- a/ldm/invoke/generator/txt2img.py
+++ b/ldm/invoke/generator/txt2img.py
@@ -6,6 +6,7 @@
 
 from .base import Generator
 from .diffusers_pipeline import StableDiffusionGeneratorPipeline, ConditioningData
+from ...models.diffusion.shared_invokeai_diffusion import ThresholdSettings
 
 
 class Txt2Img(Generator):
@@ -29,8 +30,11 @@ def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
         pipeline.scheduler = sampler
 
         uc, c, extra_conditioning_info   = conditioning
-        conditioning_data = (ConditioningData(uc, c, cfg_scale, extra_conditioning_info)
-                             .add_scheduler_args_if_applicable(pipeline.scheduler, eta=ddim_eta))
+        conditioning_data = (
+            ConditioningData(
+                uc, c, cfg_scale, extra_conditioning_info,
+                threshold = ThresholdSettings(threshold, warmup=0.2) if threshold else None)
+            .add_scheduler_args_if_applicable(pipeline.scheduler, eta=ddim_eta))
 
 
         def make_image(x_T) -> PIL.Image.Image:
@@ -39,8 +43,7 @@ def make_image(x_T) -> PIL.Image.Image:
                 noise=x_T,
                 num_inference_steps=steps,
                 conditioning_data=conditioning_data,
-                callback=step_callback
-                # TODO: threshold = threshold,
+                callback=step_callback,
             )
             if pipeline_output.attention_map_saver is not None and attention_maps_callback is not None:
                 attention_maps_callback(pipeline_output.attention_map_saver)
diff --git a/ldm/invoke/generator/txt2img2img.py b/ldm/invoke/generator/txt2img2img.py
index 2c71194532d..e356f719c48 100644
--- a/ldm/invoke/generator/txt2img2img.py
+++ b/ldm/invoke/generator/txt2img2img.py
@@ -10,6 +10,7 @@
 from ldm.invoke.generator.base import Generator
 from ldm.invoke.generator.diffusers_pipeline import trim_to_multiple_of, StableDiffusionGeneratorPipeline, \
     ConditioningData
+from ldm.models.diffusion.shared_invokeai_diffusion import ThresholdSettings
 
 
 class Txt2Img2Img(Generator):
@@ -19,20 +20,23 @@ def __init__(self, model, precision):
 
     def get_make_image(self, prompt:str, sampler, steps:int, cfg_scale:float, ddim_eta,
                        conditioning, width:int, height:int, strength:float,
-                       step_callback:Optional[Callable]=None, **kwargs):
+                       step_callback:Optional[Callable]=None, threshold=0.0, **kwargs):
         """
         Returns a function returning an image derived from the prompt and the initial image
         Return value depends on the seed at the time you call it
         kwargs are 'width' and 'height'
         """
-        uc, c, extra_conditioning_info = conditioning
 
         # noinspection PyTypeChecker
         pipeline: StableDiffusionGeneratorPipeline = self.model
         pipeline.scheduler = sampler
 
-        conditioning_data = (ConditioningData(uc, c, cfg_scale, extra_conditioning_info)
-                             .add_scheduler_args_if_applicable(pipeline.scheduler, eta=ddim_eta))
+        uc, c, extra_conditioning_info = conditioning
+        conditioning_data = (
+            ConditioningData(
+                uc, c, cfg_scale, extra_conditioning_info,
+                threshold = ThresholdSettings(threshold, warmup=0.2) if threshold else None)
+            .add_scheduler_args_if_applicable(pipeline.scheduler, eta=ddim_eta))
         scale_dim = min(width, height)
         scale = 512 / scale_dim
 
diff --git a/ldm/models/diffusion/shared_invokeai_diffusion.py b/ldm/models/diffusion/shared_invokeai_diffusion.py
index 282dedefc04..52fe9e8ce61 100644
--- a/ldm/models/diffusion/shared_invokeai_diffusion.py
+++ b/ldm/models/diffusion/shared_invokeai_diffusion.py
@@ -1,6 +1,9 @@
+import math
+from dataclasses import dataclass
 from math import ceil
 from typing import Callable, Optional, Union
 
+import numpy as np
 import torch
 
 from ldm.models.diffusion.cross_attention_control import Arguments, \
@@ -9,6 +12,12 @@
 from ldm.models.diffusion.cross_attention_map_saving import AttentionMapSaver
 
 
+@dataclass(frozen=True)
+class ThresholdSettings:
+    threshold: float
+    warmup: float
+
+
 class InvokeAIDiffuserComponent:
     '''
     The aim of this component is to provide a single place for code that can be applied identically to
@@ -18,6 +27,7 @@ class InvokeAIDiffuserComponent:
     * Cross attention control ("prompt2prompt")
     * Hybrid conditioning (used for inpainting)
     '''
+    debug_thresholding = True
 
 
     class ExtraConditioningInfo:
@@ -78,7 +88,8 @@ def do_diffusion_step(self, x: torch.Tensor, sigma: torch.Tensor,
                                 unconditioning: Union[torch.Tensor,dict],
                                 conditioning: Union[torch.Tensor,dict],
                                 unconditional_guidance_scale: float,
-                                step_index: Optional[int]=None
+                                step_index: Optional[int]=None,
+                                threshold: Optional[ThresholdSettings]=None,
                           ):
         """
         :param x: current latents
@@ -87,6 +98,7 @@ def do_diffusion_step(self, x: torch.Tensor, sigma: torch.Tensor,
         :param conditioning: embeddings for conditioned output. for hybrid conditioning this is a dict of tensors [B x 77 x 768], otherwise a single tensor [B x 77 x 768]
         :param unconditional_guidance_scale: aka CFG scale, controls how much effect the conditioning tensor has
         :param step_index: counts upwards from 0 to (step_count-1) (as passed to setup_cross_attention_control, if using). May be called multiple times for a single step, therefore do not assume that its value will monotically increase. If None, will be estimated by comparing sigma against self.model.sigmas .
+        :param threshold: threshold to apply after each step
         :return: the new latents after applying the model to x using unscaled unconditioning and CFG-scaled conditioning.
         """
 
@@ -107,7 +119,12 @@ def do_diffusion_step(self, x: torch.Tensor, sigma: torch.Tensor,
         else:
             unconditioned_next_x, conditioned_next_x = self.apply_standard_conditioning(x, sigma, unconditioning, conditioning)
 
-        return self._combine(unconditioned_next_x, conditioned_next_x, unconditional_guidance_scale)
+        combined_next_x = self._combine(unconditioned_next_x, conditioned_next_x, unconditional_guidance_scale)
+
+        if threshold:
+            combined_next_x = self._threshold(threshold.threshold, threshold.warmup, combined_next_x, sigma)
+
+        return combined_next_x
 
     # methods below are called from do_diffusion_step and should be considered private to this class.
 
@@ -184,6 +201,33 @@ def _combine(self, unconditioned_next_x, conditioned_next_x, guidance_scale):
         combined_next_x = unconditioned_next_x + scaled_delta
         return combined_next_x
 
+    def _threshold(self, threshold, warmup, latents: torch.Tensor, sigma) -> torch.Tensor:
+        warmup_scale = (1 - sigma.item() / 1000) / warmup if warmup else math.inf
+        if warmup_scale < 1:
+            # This arithmetic based on https://github.com/invoke-ai/InvokeAI/pull/395
+            warming_threshold = 1 + (threshold - 1) * warmup_scale
+            current_threshold = np.clip(warming_threshold, 1, threshold)
+        else:
+            current_threshold = threshold
+
+        if current_threshold <= 0:
+            return latents
+        maxval = latents.max().item()
+        minval = latents.min().item()
+
+        scale = 0.7  # default value from #395
+
+        if maxval < current_threshold and minval > -current_threshold:
+            return latents
+
+        if maxval > current_threshold:
+            maxval = np.clip(maxval * scale, 1, current_threshold)
+
+        if minval < -current_threshold:
+            minval = np.clip(minval * scale, -current_threshold, -1)
+
+        return latents.clamp(minval, maxval)
+
     def estimate_percent_through(self, step_index, sigma):
         if step_index is not None and self.cross_attention_control_context is not None:
             # percent_through will never reach 1.0 (but this is intended)

From 9737077681f13ecb7c37165ee7c34118c16a022a Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Mon, 2 Jan 2023 23:52:15 -0800
Subject: [PATCH 120/199] InvokeAIDiffuserComponent: diagnostic logs for
 threshold

...this does not look right
---
 ldm/models/diffusion/shared_invokeai_diffusion.py | 14 +++++++++++++-
 1 file changed, 13 insertions(+), 1 deletion(-)

diff --git a/ldm/models/diffusion/shared_invokeai_diffusion.py b/ldm/models/diffusion/shared_invokeai_diffusion.py
index 52fe9e8ce61..06305463941 100644
--- a/ldm/models/diffusion/shared_invokeai_diffusion.py
+++ b/ldm/models/diffusion/shared_invokeai_diffusion.py
@@ -27,7 +27,7 @@ class InvokeAIDiffuserComponent:
     * Cross attention control ("prompt2prompt")
     * Hybrid conditioning (used for inpainting)
     '''
-    debug_thresholding = True
+    debug_thresholding = False
 
 
     class ExtraConditioningInfo:
@@ -217,6 +217,13 @@ def _threshold(self, threshold, warmup, latents: torch.Tensor, sigma) -> torch.T
 
         scale = 0.7  # default value from #395
 
+        if self.debug_thresholding:
+            std, mean = [i.item() for i in torch.std_mean(latents)]
+            outside = torch.count_nonzero((latents < -current_threshold) | (latents > current_threshold))
+            print(f"\nThreshold: 𝜎={sigma.item()} threshold={current_threshold:.3f} (of {threshold:.3f})\n"
+                  f"  | min, mean, max = {minval:.3f}, {mean:.3f}, {maxval:.3f}\tstd={std}\n"
+                  f"  | {outside / latents.numel() * 100:.2f}% values outside threshold")
+
         if maxval < current_threshold and minval > -current_threshold:
             return latents
 
@@ -226,6 +233,11 @@ def _threshold(self, threshold, warmup, latents: torch.Tensor, sigma) -> torch.T
         if minval < -current_threshold:
             minval = np.clip(minval * scale, -current_threshold, -1)
 
+        if self.debug_thresholding:
+            outside = torch.count_nonzero((latents < minval) | (latents > maxval))
+            print(f"  | min,     , max = {minval:.3f},        , {maxval:.3f}\t(scaled by {scale})\n"
+                  f"  | {outside / latents.numel() * 100:.2f}% values will be clamped")
+
         return latents.clamp(minval, maxval)
 
     def estimate_percent_through(self, step_index, sigma):

From 0f7d8fd434e5517f4c4f60a94b3bcddc7ba16ddd Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Tue, 3 Jan 2023 18:52:57 -0500
Subject: [PATCH 121/199] add a curses-based frontend to textual inversion

- not quite working yet
- requires npyscreen installed
- on windows will also have the windows-curses requirement, but not added
  to requirements yet
---
 .../requirements-base.txt                     |   1 +
 ldm/invoke/textual_inversion_training.py      | 768 ++++++++++++++++++
 scripts/textual_inversion.py                  | 763 +----------------
 scripts/textual_inversion_fe.py               | 186 +++++
 4 files changed, 959 insertions(+), 759 deletions(-)
 create mode 100644 ldm/invoke/textual_inversion_training.py
 create mode 100755 scripts/textual_inversion_fe.py

diff --git a/environments-and-requirements/requirements-base.txt b/environments-and-requirements/requirements-base.txt
index 35072d309e4..c0d89767ab1 100644
--- a/environments-and-requirements/requirements-base.txt
+++ b/environments-and-requirements/requirements-base.txt
@@ -16,6 +16,7 @@ huggingface-hub
 imageio
 imageio-ffmpeg
 kornia
+npyscreen
 numpy==1.23.*
 omegaconf
 opencv-python
diff --git a/ldm/invoke/textual_inversion_training.py b/ldm/invoke/textual_inversion_training.py
new file mode 100644
index 00000000000..75369d96133
--- /dev/null
+++ b/ldm/invoke/textual_inversion_training.py
@@ -0,0 +1,768 @@
+# This code was copied from
+# https://github.com/huggingface/diffusers/blob/main/examples/textual_inversion/textual_inversion.py
+# on January 2, 2023
+# and modified slightly by Lincoln Stein (@lstein) to work with InvokeAI
+
+import argparse
+from argparse import Namespace
+import logging
+import math
+import os
+import random
+from pathlib import Path
+from typing import Optional
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch.utils.data import Dataset
+
+import datasets
+import diffusers
+import PIL
+import transformers
+from accelerate import Accelerator
+from accelerate.logging import get_logger
+from accelerate.utils import set_seed
+from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionPipeline, UNet2DConditionModel
+from diffusers.optimization import get_scheduler
+from diffusers.utils import check_min_version
+from diffusers.utils.import_utils import is_xformers_available
+from huggingface_hub import HfFolder, Repository, whoami
+
+# invokeai stuff
+from ldm.invoke.globals import Globals
+from omegaconf import OmegaConf
+
+# TODO: remove and import from diffusers.utils when the new version of diffusers is released
+from packaging import version
+from PIL import Image
+from torchvision import transforms
+from tqdm.auto import tqdm
+from transformers import CLIPTextModel, CLIPTokenizer
+
+if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
+    PIL_INTERPOLATION = {
+        "linear": PIL.Image.Resampling.BILINEAR,
+        "bilinear": PIL.Image.Resampling.BILINEAR,
+        "bicubic": PIL.Image.Resampling.BICUBIC,
+        "lanczos": PIL.Image.Resampling.LANCZOS,
+        "nearest": PIL.Image.Resampling.NEAREST,
+    }
+else:
+    PIL_INTERPOLATION = {
+        "linear": PIL.Image.LINEAR,
+        "bilinear": PIL.Image.BILINEAR,
+        "bicubic": PIL.Image.BICUBIC,
+        "lanczos": PIL.Image.LANCZOS,
+        "nearest": PIL.Image.NEAREST,
+    }
+# ------------------------------------------------------------------------------
+
+
+# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
+check_min_version("0.10.0.dev0")
+
+
+logger = get_logger(__name__)
+
+
+def save_progress(text_encoder, placeholder_token_id, accelerator, args, save_path):
+    logger.info("Saving embeddings")
+    learned_embeds = accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[placeholder_token_id]
+    learned_embeds_dict = {args.placeholder_token: learned_embeds.detach().cpu()}
+    torch.save(learned_embeds_dict, save_path)
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Simple example of a training script.")
+    parser.add_argument(
+        "--save_steps",
+        type=int,
+        default=500,
+        help="Save learned_embeds.bin every X updates steps.",
+    )
+    parser.add_argument(
+        '--root_dir','--root',
+        type=str,
+        default=Globals.root,
+        help="Path to the invokeai runtime directory",
+    )
+    parser.add_argument(
+        "--only_save_embeds",
+        action="store_true",
+        default=False,
+        help="Save only the embeddings for the new concept.",
+    )
+    parser.add_argument(
+        "--model",
+        type=str,
+        default=None,
+        required=False,
+        help="Name of the diffusers model to train against, as defined in configs/models.yaml.",
+    )
+    parser.add_argument(
+        "--revision",
+        type=str,
+        default=None,
+        required=False,
+        help="Revision of pretrained model identifier from huggingface.co/models.",
+    )
+    parser.add_argument(
+        "--tokenizer_name",
+        type=str,
+        default=None,
+        help="Pretrained tokenizer name or path if not the same as model_name",
+    )
+    parser.add_argument(
+        "--train_data_dir", type=str, default=None, required=False, help="A folder containing the training data."
+    )
+    parser.add_argument(
+        "--placeholder_token",
+        type=str,
+        default=None,
+        required=False,
+        help="A token to use as a placeholder for the concept.",
+    )
+    parser.add_argument(
+        "--initializer_token", type=str, default=None, required=False, help="A token to use as initializer word."
+    )
+    parser.add_argument("--learnable_property", type=str, default="object", help="Choose between 'object' and 'style'")
+    parser.add_argument("--repeats", type=int, default=100, help="How many times to repeat the training data.")
+    parser.add_argument(
+        "--output_dir",
+         type=str,
+        default=f'{Globals.root}/text-inversion-model',
+        help="The output directory where the model predictions and checkpoints will be written.",
+    )
+    parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
+    parser.add_argument(
+        "--resolution",
+        type=int,
+        default=512,
+        help=(
+            "The resolution for input images, all the images in the train/validation dataset will be resized to this"
+            " resolution"
+        ),
+    )
+    parser.add_argument(
+        "--center_crop", action="store_true", help="Whether to center crop images before resizing to resolution"
+    )
+    parser.add_argument(
+        "--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader."
+    )
+    parser.add_argument("--num_train_epochs", type=int, default=100)
+    parser.add_argument(
+        "--max_train_steps",
+        type=int,
+        default=5000,
+        help="Total number of training steps to perform.  If provided, overrides num_train_epochs.",
+    )
+    parser.add_argument(
+        "--gradient_accumulation_steps",
+        type=int,
+        default=1,
+        help="Number of updates steps to accumulate before performing a backward/update pass.",
+    )
+    parser.add_argument(
+        "--gradient_checkpointing",
+        action="store_true",
+        help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
+    )
+    parser.add_argument(
+        "--learning_rate",
+        type=float,
+        default=1e-4,
+        help="Initial learning rate (after the potential warmup period) to use.",
+    )
+    parser.add_argument(
+        "--scale_lr",
+        action="store_true",
+        default=True,
+        help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
+    )
+    parser.add_argument(
+        "--lr_scheduler",
+        type=str,
+        default="constant",
+        help=(
+            'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
+            ' "constant", "constant_with_warmup"]'
+        ),
+    )
+    parser.add_argument(
+        "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
+    )
+    parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
+    parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
+    parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
+    parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
+    parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
+    parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
+    parser.add_argument(
+        "--logging_dir",
+        type=str,
+        default="logs",
+        help=(
+            "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
+            " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
+        ),
+    )
+    parser.add_argument(
+        "--mixed_precision",
+        type=str,
+        default="no",
+        choices=["no", "fp16", "bf16"],
+        help=(
+            "Whether to use mixed precision. Choose"
+            "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
+            "and an Nvidia Ampere GPU."
+        ),
+    )
+    parser.add_argument(
+        "--allow_tf32",
+        action="store_true",
+        help=(
+            "Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
+            " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
+        ),
+    )
+    parser.add_argument(
+        "--report_to",
+        type=str,
+        default="tensorboard",
+        help=(
+            'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
+            ' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
+        ),
+    )
+    parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
+    parser.add_argument(
+        "--checkpointing_steps",
+        type=int,
+        default=500,
+        help=(
+            "Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming"
+            " training using `--resume_from_checkpoint`."
+        ),
+    )
+    parser.add_argument(
+        "--resume_from_checkpoint",
+        type=str,
+        default=None,
+        help=(
+            "Whether training should be resumed from a previous checkpoint. Use a path saved by"
+            ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
+        ),
+    )
+    parser.add_argument(
+        "--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
+    )
+
+    args = parser.parse_args()
+    env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
+    if env_local_rank != -1 and env_local_rank != args.local_rank:
+        args.local_rank = env_local_rank
+    return args
+
+
+imagenet_templates_small = [
+    "a photo of a {}",
+    "a rendering of a {}",
+    "a cropped photo of the {}",
+    "the photo of a {}",
+    "a photo of a clean {}",
+    "a photo of a dirty {}",
+    "a dark photo of the {}",
+    "a photo of my {}",
+    "a photo of the cool {}",
+    "a close-up photo of a {}",
+    "a bright photo of the {}",
+    "a cropped photo of a {}",
+    "a photo of the {}",
+    "a good photo of the {}",
+    "a photo of one {}",
+    "a close-up photo of the {}",
+    "a rendition of the {}",
+    "a photo of the clean {}",
+    "a rendition of a {}",
+    "a photo of a nice {}",
+    "a good photo of a {}",
+    "a photo of the nice {}",
+    "a photo of the small {}",
+    "a photo of the weird {}",
+    "a photo of the large {}",
+    "a photo of a cool {}",
+    "a photo of a small {}",
+]
+
+imagenet_style_templates_small = [
+    "a painting in the style of {}",
+    "a rendering in the style of {}",
+    "a cropped painting in the style of {}",
+    "the painting in the style of {}",
+    "a clean painting in the style of {}",
+    "a dirty painting in the style of {}",
+    "a dark painting in the style of {}",
+    "a picture in the style of {}",
+    "a cool painting in the style of {}",
+    "a close-up painting in the style of {}",
+    "a bright painting in the style of {}",
+    "a cropped painting in the style of {}",
+    "a good painting in the style of {}",
+    "a close-up painting in the style of {}",
+    "a rendition in the style of {}",
+    "a nice painting in the style of {}",
+    "a small painting in the style of {}",
+    "a weird painting in the style of {}",
+    "a large painting in the style of {}",
+]
+
+
+class TextualInversionDataset(Dataset):
+    def __init__(
+        self,
+        data_root,
+        tokenizer,
+        learnable_property="object",  # [object, style]
+        size=512,
+        repeats=100,
+        interpolation="bicubic",
+        flip_p=0.5,
+        set="train",
+        placeholder_token="*",
+        center_crop=False,
+    ):
+        self.data_root = data_root
+        self.tokenizer = tokenizer
+        self.learnable_property = learnable_property
+        self.size = size
+        self.placeholder_token = placeholder_token
+        self.center_crop = center_crop
+        self.flip_p = flip_p
+
+        self.image_paths = [os.path.join(self.data_root, file_path) for file_path in os.listdir(self.data_root)]
+
+        self.num_images = len(self.image_paths)
+        self._length = self.num_images
+
+        if set == "train":
+            self._length = self.num_images * repeats
+
+        self.interpolation = {
+            "linear": PIL_INTERPOLATION["linear"],
+            "bilinear": PIL_INTERPOLATION["bilinear"],
+            "bicubic": PIL_INTERPOLATION["bicubic"],
+            "lanczos": PIL_INTERPOLATION["lanczos"],
+        }[interpolation]
+
+        self.templates = imagenet_style_templates_small if learnable_property == "style" else imagenet_templates_small
+        self.flip_transform = transforms.RandomHorizontalFlip(p=self.flip_p)
+
+    def __len__(self):
+        return self._length
+
+    def __getitem__(self, i):
+        example = {}
+        image = Image.open(self.image_paths[i % self.num_images])
+
+        if not image.mode == "RGB":
+            image = image.convert("RGB")
+
+        placeholder_string = self.placeholder_token
+        text = random.choice(self.templates).format(placeholder_string)
+
+        example["input_ids"] = self.tokenizer(
+            text,
+            padding="max_length",
+            truncation=True,
+            max_length=self.tokenizer.model_max_length,
+            return_tensors="pt",
+        ).input_ids[0]
+
+        # default to score-sde preprocessing
+        img = np.array(image).astype(np.uint8)
+
+        if self.center_crop:
+            crop = min(img.shape[0], img.shape[1])
+            h, w, = (
+                img.shape[0],
+                img.shape[1],
+            )
+            img = img[(h - crop) // 2 : (h + crop) // 2, (w - crop) // 2 : (w + crop) // 2]
+
+        image = Image.fromarray(img)
+        image = image.resize((self.size, self.size), resample=self.interpolation)
+
+        image = self.flip_transform(image)
+        image = np.array(image).astype(np.uint8)
+        image = (image / 127.5 - 1.0).astype(np.float32)
+
+        example["pixel_values"] = torch.from_numpy(image).permute(2, 0, 1)
+        return example
+
+
+def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None):
+    if token is None:
+        token = HfFolder.get_token()
+    if organization is None:
+        username = whoami(token)["name"]
+        return f"{username}/{model_id}"
+    else:
+        return f"{organization}/{model_id}"
+
+
+def do_textual_inversion_training(args:Namespace):
+
+    # Out of laziness, we are getting our parameterse from an argparse Namespace
+    # which is a legacy of the original script. This needs to be changed.
+    
+    if args.train_data_dir is None:
+        raise ValueError("You must specify a training data directory with --train_data_dir.")
+
+    if args.placeholder_token is None:
+        raise ValueError("You must specify a placeholder token with --placeholder_token.")
+
+    if args.initializer__token is None:
+        raise ValueError("You must specify an initializer token with --initializer_token.")
+
+    if args.model is None:
+        raise ValueError("You must specify a model name with --model.")
+
+    # setting up things the way invokeai expects them
+    if os.path.exists(args.root_dir):
+        Globals.root = args.root_dir
+    if not os.path.isabs(args.output_dir):
+        args.output_dir = os.path.join(Globals.root,args.output_dir)
+        
+    logging_dir = os.path.join(args.output_dir, args.logging_dir)
+
+    accelerator = Accelerator(
+        gradient_accumulation_steps=args.gradient_accumulation_steps,
+        mixed_precision=args.mixed_precision,
+        log_with=args.report_to,
+        logging_dir=logging_dir,
+    )
+
+    # Make one log on every process with the configuration for debugging.
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        level=logging.INFO,
+    )
+    logger.info(accelerator.state, main_process_only=False)
+    if accelerator.is_local_main_process:
+        datasets.utils.logging.set_verbosity_warning()
+        transformers.utils.logging.set_verbosity_warning()
+        diffusers.utils.logging.set_verbosity_info()
+    else:
+        datasets.utils.logging.set_verbosity_error()
+        transformers.utils.logging.set_verbosity_error()
+        diffusers.utils.logging.set_verbosity_error()
+
+    # If passed along, set the training seed now.
+    if args.seed is not None:
+        set_seed(args.seed)
+
+    # Handle the repository creation
+    if accelerator.is_main_process:
+        if args.push_to_hub:
+            if args.hub_model_id is None:
+                repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
+            else:
+                repo_name = args.hub_model_id
+            repo = Repository(args.output_dir, clone_from=repo_name)
+
+            with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
+                if "step_*" not in gitignore:
+                    gitignore.write("step_*\n")
+                if "epoch_*" not in gitignore:
+                    gitignore.write("epoch_*\n")
+        elif args.output_dir is not None:
+            os.makedirs(args.output_dir, exist_ok=True)
+
+    models_conf = OmegaConf.load(os.path.join(Globals.root,'configs/models.yaml'))
+    model_conf = models_conf.get(args.model,None)
+    assert model_conf is not None,f'Unknown model: {args.model}'
+    assert model_conf.get('format','diffusers')=='diffusers', "This script only works with models of type 'diffusers'"
+    pretrained_model_name_or_path = model_conf.get('repo_id',None) or Path(model_conf.get('path'))
+    assert pretrained_model_name_or_path, f"models.yaml error: neither 'repo_id' nor 'path' is defined for {args.model}"
+    pipeline_args = dict()
+    if not isinstance(pretrained_model_name_or_path,Path):
+        pipeline_args.update(cache_dir=os.path.join(Globals.root,'models',pretrained_model_name_or_path))
+    
+    # Load tokenizer
+    if args.tokenizer_name:
+        tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name)
+    else:
+        tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_name_or_path, subfolder="tokenizer", **pipeline_args)
+
+    # Load scheduler and models
+    noise_scheduler = DDPMScheduler.from_pretrained(pretrained_model_name_or_path, subfolder="scheduler", **pipeline_args)
+    text_encoder = CLIPTextModel.from_pretrained(
+        pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision,  **pipeline_args
+    )
+    vae = AutoencoderKL.from_pretrained(pretrained_model_name_or_path, subfolder="vae", revision=args.revision,  **pipeline_args)
+    unet = UNet2DConditionModel.from_pretrained(
+        pretrained_model_name_or_path, subfolder="unet", revision=args.revision,  **pipeline_args
+    )
+
+    # Add the placeholder token in tokenizer
+    num_added_tokens = tokenizer.add_tokens(args.placeholder_token)
+    if num_added_tokens == 0:
+        raise ValueError(
+            f"The tokenizer already contains the token {args.placeholder_token}. Please pass a different"
+            " `placeholder_token` that is not already in the tokenizer."
+        )
+
+    # Convert the initializer_token, placeholder_token to ids
+    token_ids = tokenizer.encode(args.initializer_token, add_special_tokens=False)
+    # Check if initializer_token is a single token or a sequence of tokens
+    if len(token_ids) > 1:
+        raise ValueError("The initializer token must be a single token.")
+
+    initializer_token_id = token_ids[0]
+    placeholder_token_id = tokenizer.convert_tokens_to_ids(args.placeholder_token)
+
+    # Resize the token embeddings as we are adding new special tokens to the tokenizer
+    text_encoder.resize_token_embeddings(len(tokenizer))
+
+    # Initialise the newly added placeholder token with the embeddings of the initializer token
+    token_embeds = text_encoder.get_input_embeddings().weight.data
+    token_embeds[placeholder_token_id] = token_embeds[initializer_token_id]
+
+    # Freeze vae and unet
+    vae.requires_grad_(False)
+    unet.requires_grad_(False)
+    # Freeze all parameters except for the token embeddings in text encoder
+    text_encoder.text_model.encoder.requires_grad_(False)
+    text_encoder.text_model.final_layer_norm.requires_grad_(False)
+    text_encoder.text_model.embeddings.position_embedding.requires_grad_(False)
+
+    if args.gradient_checkpointing:
+        # Keep unet in train mode if we are using gradient checkpointing to save memory.
+        # The dropout cannot be != 0 so it doesn't matter if we are in eval or train mode.
+        unet.train()
+        text_encoder.gradient_checkpointing_enable()
+        unet.enable_gradient_checkpointing()
+
+    if args.enable_xformers_memory_efficient_attention:
+        if is_xformers_available():
+            unet.enable_xformers_memory_efficient_attention()
+        else:
+            raise ValueError("xformers is not available. Make sure it is installed correctly")
+
+    # Enable TF32 for faster training on Ampere GPUs,
+    # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
+    if args.allow_tf32:
+        torch.backends.cuda.matmul.allow_tf32 = True
+
+    if args.scale_lr:
+        args.learning_rate = (
+            args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
+        )
+
+    # Initialize the optimizer
+    optimizer = torch.optim.AdamW(
+        text_encoder.get_input_embeddings().parameters(),  # only optimize the embeddings
+        lr=args.learning_rate,
+        betas=(args.adam_beta1, args.adam_beta2),
+        weight_decay=args.adam_weight_decay,
+        eps=args.adam_epsilon,
+    )
+
+    # Dataset and DataLoaders creation:
+    train_dataset = TextualInversionDataset(
+        data_root=args.train_data_dir,
+        tokenizer=tokenizer,
+        size=args.resolution,
+        placeholder_token=args.placeholder_token,
+        repeats=args.repeats,
+        learnable_property=args.learnable_property,
+        center_crop=args.center_crop,
+        set="train",
+    )
+    train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=args.train_batch_size, shuffle=True)
+
+    # Scheduler and math around the number of training steps.
+    overrode_max_train_steps = False
+    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
+    if args.max_train_steps is None:
+        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
+        overrode_max_train_steps = True
+
+    lr_scheduler = get_scheduler(
+        args.lr_scheduler,
+        optimizer=optimizer,
+        num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,
+        num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
+    )
+
+    # Prepare everything with our `accelerator`.
+    text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
+        text_encoder, optimizer, train_dataloader, lr_scheduler
+    )
+
+    # For mixed precision training we cast the text_encoder and vae weights to half-precision
+    # as these models are only used for inference, keeping weights in full precision is not required.
+    weight_dtype = torch.float32
+    if accelerator.mixed_precision == "fp16":
+        weight_dtype = torch.float16
+    elif accelerator.mixed_precision == "bf16":
+        weight_dtype = torch.bfloat16
+
+    # Move vae and unet to device and cast to weight_dtype
+    unet.to(accelerator.device, dtype=weight_dtype)
+    vae.to(accelerator.device, dtype=weight_dtype)
+
+    # We need to recalculate our total training steps as the size of the training dataloader may have changed.
+    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
+    if overrode_max_train_steps:
+        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
+    # Afterwards we recalculate our number of training epochs
+    args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
+
+    # We need to initialize the trackers we use, and also store our configuration.
+    # The trackers initializes automatically on the main process.
+    if accelerator.is_main_process:
+        accelerator.init_trackers("textual_inversion", config=vars(args))
+
+    # Train!
+    total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
+
+    logger.info("***** Running training *****")
+    logger.info(f"  Num examples = {len(train_dataset)}")
+    logger.info(f"  Num Epochs = {args.num_train_epochs}")
+    logger.info(f"  Instantaneous batch size per device = {args.train_batch_size}")
+    logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
+    logger.info(f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
+    logger.info(f"  Total optimization steps = {args.max_train_steps}")
+    global_step = 0
+    first_epoch = 0
+
+    # Potentially load in the weights and states from a previous save
+    if args.resume_from_checkpoint:
+        if args.resume_from_checkpoint != "latest":
+            path = os.path.basename(args.resume_from_checkpoint)
+        else:
+            # Get the most recent checkpoint
+            dirs = os.listdir(args.output_dir)
+            dirs = [d for d in dirs if d.startswith("checkpoint")]
+            dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
+            path = dirs[-1]
+        accelerator.print(f"Resuming from checkpoint {path}")
+        accelerator.load_state(os.path.join(args.output_dir, path))
+        global_step = int(path.split("-")[1])
+
+        resume_global_step = global_step * args.gradient_accumulation_steps
+        first_epoch = resume_global_step // num_update_steps_per_epoch
+        resume_step = resume_global_step % num_update_steps_per_epoch
+
+    # Only show the progress bar once on each machine.
+    progress_bar = tqdm(range(global_step, args.max_train_steps), disable=not accelerator.is_local_main_process)
+    progress_bar.set_description("Steps")
+
+    # keep original embeddings as reference
+    orig_embeds_params = accelerator.unwrap_model(text_encoder).get_input_embeddings().weight.data.clone()
+
+    for epoch in range(first_epoch, args.num_train_epochs):
+        text_encoder.train()
+        for step, batch in enumerate(train_dataloader):
+            # Skip steps until we reach the resumed step
+            if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step:
+                if step % args.gradient_accumulation_steps == 0:
+                    progress_bar.update(1)
+                continue
+
+            with accelerator.accumulate(text_encoder):
+                # Convert images to latent space
+                latents = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist.sample().detach()
+                latents = latents * 0.18215
+
+                # Sample noise that we'll add to the latents
+                noise = torch.randn_like(latents)
+                bsz = latents.shape[0]
+                # Sample a random timestep for each image
+                timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
+                timesteps = timesteps.long()
+
+                # Add noise to the latents according to the noise magnitude at each timestep
+                # (this is the forward diffusion process)
+                noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
+
+                # Get the text embedding for conditioning
+                encoder_hidden_states = text_encoder(batch["input_ids"])[0].to(dtype=weight_dtype)
+
+                # Predict the noise residual
+                model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
+
+                # Get the target for loss depending on the prediction type
+                if noise_scheduler.config.prediction_type == "epsilon":
+                    target = noise
+                elif noise_scheduler.config.prediction_type == "v_prediction":
+                    target = noise_scheduler.get_velocity(latents, noise, timesteps)
+                else:
+                    raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
+
+                loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
+
+                accelerator.backward(loss)
+
+                optimizer.step()
+                lr_scheduler.step()
+                optimizer.zero_grad()
+
+                # Let's make sure we don't update any embedding weights besides the newly added token
+                index_no_updates = torch.arange(len(tokenizer)) != placeholder_token_id
+                with torch.no_grad():
+                    accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[
+                        index_no_updates
+                    ] = orig_embeds_params[index_no_updates]
+
+            # Checks if the accelerator has performed an optimization step behind the scenes
+            if accelerator.sync_gradients:
+                progress_bar.update(1)
+                global_step += 1
+                if global_step % args.save_steps == 0:
+                    save_path = os.path.join(args.output_dir, f"learned_embeds-steps-{global_step}.bin")
+                    save_progress(text_encoder, placeholder_token_id, accelerator, args, save_path)
+
+                if global_step % args.checkpointing_steps == 0:
+                    if accelerator.is_main_process:
+                        save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
+                        accelerator.save_state(save_path)
+                        logger.info(f"Saved state to {save_path}")
+
+            logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
+            progress_bar.set_postfix(**logs)
+            accelerator.log(logs, step=global_step)
+
+            if global_step >= args.max_train_steps:
+                break
+
+    # Create the pipeline using using the trained modules and save it.
+    accelerator.wait_for_everyone()
+    if accelerator.is_main_process:
+        if args.push_to_hub and args.only_save_embeds:
+            logger.warn("Enabling full model saving because --push_to_hub=True was specified.")
+            save_full_model = True
+        else:
+            save_full_model = not args.only_save_embeds
+        if save_full_model:
+            pipeline = StableDiffusionPipeline.from_pretrained(
+                pretrained_model_name_or_path,
+                text_encoder=accelerator.unwrap_model(text_encoder),
+                vae=vae,
+                unet=unet,
+                tokenizer=tokenizer,
+                **pipeline_args,
+            )
+            pipeline.save_pretrained(args.output_dir)
+        # Save the newly trained embeddings
+        save_path = os.path.join(args.output_dir, "learned_embeds.bin")
+        save_progress(text_encoder, placeholder_token_id, accelerator, args, save_path)
+
+        if args.push_to_hub:
+            repo.push_to_hub(commit_message="End of training", blocking=False, auto_lfs_prune=True)
+
+    accelerator.end_training()
diff --git a/scripts/textual_inversion.py b/scripts/textual_inversion.py
index 04e22aaf017..81784f25349 100755
--- a/scripts/textual_inversion.py
+++ b/scripts/textual_inversion.py
@@ -1,764 +1,9 @@
 #!/usr/bin/env python
 
-# This script was copied from
-# https://github.com/huggingface/diffusers/blob/main/examples/textual_inversion/textual_inversion.py
-# on January 2, 2023
-# and modified slightly by Lincoln Stein (@lstein) to work with InvokeAI
-
-import argparse
-import logging
-import math
-import os
-import random
-from pathlib import Path
-from typing import Optional
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-import torch.utils.checkpoint
-from torch.utils.data import Dataset
-
-import datasets
-import diffusers
-import PIL
-import transformers
-from accelerate import Accelerator
-from accelerate.logging import get_logger
-from accelerate.utils import set_seed
-from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionPipeline, UNet2DConditionModel
-from diffusers.optimization import get_scheduler
-from diffusers.utils import check_min_version
-from diffusers.utils.import_utils import is_xformers_available
-from huggingface_hub import HfFolder, Repository, whoami
-
-# invokeai stuff
-from ldm.invoke.globals import Globals
-from omegaconf import OmegaConf
-
-# TODO: remove and import from diffusers.utils when the new version of diffusers is released
-from packaging import version
-from PIL import Image
-from torchvision import transforms
-from tqdm.auto import tqdm
-from transformers import CLIPTextModel, CLIPTokenizer
-
-if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
-    PIL_INTERPOLATION = {
-        "linear": PIL.Image.Resampling.BILINEAR,
-        "bilinear": PIL.Image.Resampling.BILINEAR,
-        "bicubic": PIL.Image.Resampling.BICUBIC,
-        "lanczos": PIL.Image.Resampling.LANCZOS,
-        "nearest": PIL.Image.Resampling.NEAREST,
-    }
-else:
-    PIL_INTERPOLATION = {
-        "linear": PIL.Image.LINEAR,
-        "bilinear": PIL.Image.BILINEAR,
-        "bicubic": PIL.Image.BICUBIC,
-        "lanczos": PIL.Image.LANCZOS,
-        "nearest": PIL.Image.NEAREST,
-    }
-# ------------------------------------------------------------------------------
-
-
-# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
-check_min_version("0.10.0.dev0")
-
-
-logger = get_logger(__name__)
-
-
-def save_progress(text_encoder, placeholder_token_id, accelerator, args, save_path):
-    logger.info("Saving embeddings")
-    learned_embeds = accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[placeholder_token_id]
-    learned_embeds_dict = {args.placeholder_token: learned_embeds.detach().cpu()}
-    torch.save(learned_embeds_dict, save_path)
-
-
-def parse_args():
-    parser = argparse.ArgumentParser(description="Simple example of a training script.")
-    parser.add_argument(
-        "--save_steps",
-        type=int,
-        default=500,
-        help="Save learned_embeds.bin every X updates steps.",
-    )
-    parser.add_argument(
-        '--root_dir','--root',
-        type=str,
-        default=Globals.root,
-        help="Path to the invokeai runtime directory",
-    )
-    parser.add_argument(
-        "--only_save_embeds",
-        action="store_true",
-        default=False,
-        help="Save only the embeddings for the new concept.",
-    )
-    parser.add_argument(
-        "--model",
-        type=str,
-        default=None,
-        required=True,
-        help="Name of the diffusers model to train against, as defined in configs/models.yaml.",
-    )
-    parser.add_argument(
-        "--revision",
-        type=str,
-        default=None,
-        required=False,
-        help="Revision of pretrained model identifier from huggingface.co/models.",
-    )
-    parser.add_argument(
-        "--tokenizer_name",
-        type=str,
-        default=None,
-        help="Pretrained tokenizer name or path if not the same as model_name",
-    )
-    parser.add_argument(
-        "--train_data_dir", type=str, default=None, required=True, help="A folder containing the training data."
-    )
-    parser.add_argument(
-        "--placeholder_token",
-        type=str,
-        default=None,
-        required=True,
-        help="A token to use as a placeholder for the concept.",
-    )
-    parser.add_argument(
-        "--initializer_token", type=str, default=None, required=True, help="A token to use as initializer word."
-    )
-    parser.add_argument("--learnable_property", type=str, default="object", help="Choose between 'object' and 'style'")
-    parser.add_argument("--repeats", type=int, default=100, help="How many times to repeat the training data.")
-    parser.add_argument(
-        "--output_dir",
-         type=str,
-        default=f'{Globals.root}/text-inversion-model',
-        help="The output directory where the model predictions and checkpoints will be written.",
-    )
-    parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
-    parser.add_argument(
-        "--resolution",
-        type=int,
-        default=512,
-        help=(
-            "The resolution for input images, all the images in the train/validation dataset will be resized to this"
-            " resolution"
-        ),
-    )
-    parser.add_argument(
-        "--center_crop", action="store_true", help="Whether to center crop images before resizing to resolution"
-    )
-    parser.add_argument(
-        "--train_batch_size", type=int, default=16, help="Batch size (per device) for the training dataloader."
-    )
-    parser.add_argument("--num_train_epochs", type=int, default=100)
-    parser.add_argument(
-        "--max_train_steps",
-        type=int,
-        default=5000,
-        help="Total number of training steps to perform.  If provided, overrides num_train_epochs.",
-    )
-    parser.add_argument(
-        "--gradient_accumulation_steps",
-        type=int,
-        default=1,
-        help="Number of updates steps to accumulate before performing a backward/update pass.",
-    )
-    parser.add_argument(
-        "--gradient_checkpointing",
-        action="store_true",
-        help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
-    )
-    parser.add_argument(
-        "--learning_rate",
-        type=float,
-        default=1e-4,
-        help="Initial learning rate (after the potential warmup period) to use.",
-    )
-    parser.add_argument(
-        "--scale_lr",
-        action="store_true",
-        default=True,
-        help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
-    )
-    parser.add_argument(
-        "--lr_scheduler",
-        type=str,
-        default="constant",
-        help=(
-            'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
-            ' "constant", "constant_with_warmup"]'
-        ),
-    )
-    parser.add_argument(
-        "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
-    )
-    parser.add_argument("--adam_beta1", type=float, default=0.9, help="The beta1 parameter for the Adam optimizer.")
-    parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
-    parser.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
-    parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer")
-    parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
-    parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
-    parser.add_argument(
-        "--logging_dir",
-        type=str,
-        default="logs",
-        help=(
-            "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
-            " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
-        ),
-    )
-    parser.add_argument(
-        "--mixed_precision",
-        type=str,
-        default="no",
-        choices=["no", "fp16", "bf16"],
-        help=(
-            "Whether to use mixed precision. Choose"
-            "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
-            "and an Nvidia Ampere GPU."
-        ),
-    )
-    parser.add_argument(
-        "--allow_tf32",
-        action="store_true",
-        help=(
-            "Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
-            " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
-        ),
-    )
-    parser.add_argument(
-        "--report_to",
-        type=str,
-        default="tensorboard",
-        help=(
-            'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
-            ' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
-        ),
-    )
-    parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
-    parser.add_argument(
-        "--checkpointing_steps",
-        type=int,
-        default=500,
-        help=(
-            "Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming"
-            " training using `--resume_from_checkpoint`."
-        ),
-    )
-    parser.add_argument(
-        "--resume_from_checkpoint",
-        type=str,
-        default=None,
-        help=(
-            "Whether training should be resumed from a previous checkpoint. Use a path saved by"
-            ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
-        ),
-    )
-    parser.add_argument(
-        "--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
-    )
-
-    args = parser.parse_args()
-    env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
-    if env_local_rank != -1 and env_local_rank != args.local_rank:
-        args.local_rank = env_local_rank
-
-    if args.train_data_dir is None:
-        raise ValueError("You must specify a train data directory.")
-
-    return args
-
-
-imagenet_templates_small = [
-    "a photo of a {}",
-    "a rendering of a {}",
-    "a cropped photo of the {}",
-    "the photo of a {}",
-    "a photo of a clean {}",
-    "a photo of a dirty {}",
-    "a dark photo of the {}",
-    "a photo of my {}",
-    "a photo of the cool {}",
-    "a close-up photo of a {}",
-    "a bright photo of the {}",
-    "a cropped photo of a {}",
-    "a photo of the {}",
-    "a good photo of the {}",
-    "a photo of one {}",
-    "a close-up photo of the {}",
-    "a rendition of the {}",
-    "a photo of the clean {}",
-    "a rendition of a {}",
-    "a photo of a nice {}",
-    "a good photo of a {}",
-    "a photo of the nice {}",
-    "a photo of the small {}",
-    "a photo of the weird {}",
-    "a photo of the large {}",
-    "a photo of a cool {}",
-    "a photo of a small {}",
-]
-
-imagenet_style_templates_small = [
-    "a painting in the style of {}",
-    "a rendering in the style of {}",
-    "a cropped painting in the style of {}",
-    "the painting in the style of {}",
-    "a clean painting in the style of {}",
-    "a dirty painting in the style of {}",
-    "a dark painting in the style of {}",
-    "a picture in the style of {}",
-    "a cool painting in the style of {}",
-    "a close-up painting in the style of {}",
-    "a bright painting in the style of {}",
-    "a cropped painting in the style of {}",
-    "a good painting in the style of {}",
-    "a close-up painting in the style of {}",
-    "a rendition in the style of {}",
-    "a nice painting in the style of {}",
-    "a small painting in the style of {}",
-    "a weird painting in the style of {}",
-    "a large painting in the style of {}",
-]
-
-
-class TextualInversionDataset(Dataset):
-    def __init__(
-        self,
-        data_root,
-        tokenizer,
-        learnable_property="object",  # [object, style]
-        size=512,
-        repeats=100,
-        interpolation="bicubic",
-        flip_p=0.5,
-        set="train",
-        placeholder_token="*",
-        center_crop=False,
-    ):
-        self.data_root = data_root
-        self.tokenizer = tokenizer
-        self.learnable_property = learnable_property
-        self.size = size
-        self.placeholder_token = placeholder_token
-        self.center_crop = center_crop
-        self.flip_p = flip_p
-
-        self.image_paths = [os.path.join(self.data_root, file_path) for file_path in os.listdir(self.data_root)]
-
-        self.num_images = len(self.image_paths)
-        self._length = self.num_images
-
-        if set == "train":
-            self._length = self.num_images * repeats
-
-        self.interpolation = {
-            "linear": PIL_INTERPOLATION["linear"],
-            "bilinear": PIL_INTERPOLATION["bilinear"],
-            "bicubic": PIL_INTERPOLATION["bicubic"],
-            "lanczos": PIL_INTERPOLATION["lanczos"],
-        }[interpolation]
-
-        self.templates = imagenet_style_templates_small if learnable_property == "style" else imagenet_templates_small
-        self.flip_transform = transforms.RandomHorizontalFlip(p=self.flip_p)
-
-    def __len__(self):
-        return self._length
-
-    def __getitem__(self, i):
-        example = {}
-        image = Image.open(self.image_paths[i % self.num_images])
-
-        if not image.mode == "RGB":
-            image = image.convert("RGB")
-
-        placeholder_string = self.placeholder_token
-        text = random.choice(self.templates).format(placeholder_string)
-
-        example["input_ids"] = self.tokenizer(
-            text,
-            padding="max_length",
-            truncation=True,
-            max_length=self.tokenizer.model_max_length,
-            return_tensors="pt",
-        ).input_ids[0]
-
-        # default to score-sde preprocessing
-        img = np.array(image).astype(np.uint8)
-
-        if self.center_crop:
-            crop = min(img.shape[0], img.shape[1])
-            h, w, = (
-                img.shape[0],
-                img.shape[1],
-            )
-            img = img[(h - crop) // 2 : (h + crop) // 2, (w - crop) // 2 : (w + crop) // 2]
-
-        image = Image.fromarray(img)
-        image = image.resize((self.size, self.size), resample=self.interpolation)
-
-        image = self.flip_transform(image)
-        image = np.array(image).astype(np.uint8)
-        image = (image / 127.5 - 1.0).astype(np.float32)
-
-        example["pixel_values"] = torch.from_numpy(image).permute(2, 0, 1)
-        return example
-
-
-def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None):
-    if token is None:
-        token = HfFolder.get_token()
-    if organization is None:
-        username = whoami(token)["name"]
-        return f"{username}/{model_id}"
-    else:
-        return f"{organization}/{model_id}"
-
-
-def main():
-    args = parse_args()
-
-    # setting up things the way invokeai expects them
-    if os.path.exists(args.root_dir):
-        Globals.root = args.root_dir
-    if not os.path.isabs(args.output_dir):
-        args.output_dir = os.path.join(Globals.root,args.output_dir)
-        
-    logging_dir = os.path.join(args.output_dir, args.logging_dir)
-
-    accelerator = Accelerator(
-        gradient_accumulation_steps=args.gradient_accumulation_steps,
-        mixed_precision=args.mixed_precision,
-        log_with=args.report_to,
-        logging_dir=logging_dir,
-    )
-
-    # Make one log on every process with the configuration for debugging.
-    logging.basicConfig(
-        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
-        datefmt="%m/%d/%Y %H:%M:%S",
-        level=logging.INFO,
-    )
-    logger.info(accelerator.state, main_process_only=False)
-    if accelerator.is_local_main_process:
-        datasets.utils.logging.set_verbosity_warning()
-        transformers.utils.logging.set_verbosity_warning()
-        diffusers.utils.logging.set_verbosity_info()
-    else:
-        datasets.utils.logging.set_verbosity_error()
-        transformers.utils.logging.set_verbosity_error()
-        diffusers.utils.logging.set_verbosity_error()
-
-    # If passed along, set the training seed now.
-    if args.seed is not None:
-        set_seed(args.seed)
-
-    # Handle the repository creation
-    if accelerator.is_main_process:
-        if args.push_to_hub:
-            if args.hub_model_id is None:
-                repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
-            else:
-                repo_name = args.hub_model_id
-            repo = Repository(args.output_dir, clone_from=repo_name)
-
-            with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
-                if "step_*" not in gitignore:
-                    gitignore.write("step_*\n")
-                if "epoch_*" not in gitignore:
-                    gitignore.write("epoch_*\n")
-        elif args.output_dir is not None:
-            os.makedirs(args.output_dir, exist_ok=True)
-
-    models_conf = OmegaConf.load(os.path.join(Globals.root,'configs/models.yaml'))
-    model_conf = models_conf.get(args.model,None)
-    assert model_conf is not None,f'Unknown model: {args.model}'
-    assert model_conf.get('format','diffusers')=='diffusers', "This script only works with models of type 'diffusers'"
-    pretrained_model_name_or_path = model_conf.get('repo_id',None) or Path(model_conf.get('path'))
-    assert pretrained_model_name_or_path, f"models.yaml error: neither 'repo_id' nor 'path' is defined for {args.model}"
-    pipeline_args = dict()
-    if not isinstance(pretrained_model_name_or_path,Path):
-        pipeline_args.update(cache_dir=os.path.join(Globals.root,'models',pretrained_model_name_or_path))
-    
-    # Load tokenizer
-    if args.tokenizer_name:
-        tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name)
-    else:
-        tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_name_or_path, subfolder="tokenizer", **pipeline_args)
-
-    # Load scheduler and models
-    noise_scheduler = DDPMScheduler.from_pretrained(pretrained_model_name_or_path, subfolder="scheduler", **pipeline_args)
-    text_encoder = CLIPTextModel.from_pretrained(
-        pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision,  **pipeline_args
-    )
-    vae = AutoencoderKL.from_pretrained(pretrained_model_name_or_path, subfolder="vae", revision=args.revision,  **pipeline_args)
-    unet = UNet2DConditionModel.from_pretrained(
-        pretrained_model_name_or_path, subfolder="unet", revision=args.revision,  **pipeline_args
-    )
-
-    # Add the placeholder token in tokenizer
-    num_added_tokens = tokenizer.add_tokens(args.placeholder_token)
-    if num_added_tokens == 0:
-        raise ValueError(
-            f"The tokenizer already contains the token {args.placeholder_token}. Please pass a different"
-            " `placeholder_token` that is not already in the tokenizer."
-        )
-
-    # Convert the initializer_token, placeholder_token to ids
-    token_ids = tokenizer.encode(args.initializer_token, add_special_tokens=False)
-    # Check if initializer_token is a single token or a sequence of tokens
-    if len(token_ids) > 1:
-        raise ValueError("The initializer token must be a single token.")
-
-    initializer_token_id = token_ids[0]
-    placeholder_token_id = tokenizer.convert_tokens_to_ids(args.placeholder_token)
-
-    # Resize the token embeddings as we are adding new special tokens to the tokenizer
-    text_encoder.resize_token_embeddings(len(tokenizer))
-
-    # Initialise the newly added placeholder token with the embeddings of the initializer token
-    token_embeds = text_encoder.get_input_embeddings().weight.data
-    token_embeds[placeholder_token_id] = token_embeds[initializer_token_id]
-
-    # Freeze vae and unet
-    vae.requires_grad_(False)
-    unet.requires_grad_(False)
-    # Freeze all parameters except for the token embeddings in text encoder
-    text_encoder.text_model.encoder.requires_grad_(False)
-    text_encoder.text_model.final_layer_norm.requires_grad_(False)
-    text_encoder.text_model.embeddings.position_embedding.requires_grad_(False)
-
-    if args.gradient_checkpointing:
-        # Keep unet in train mode if we are using gradient checkpointing to save memory.
-        # The dropout cannot be != 0 so it doesn't matter if we are in eval or train mode.
-        unet.train()
-        text_encoder.gradient_checkpointing_enable()
-        unet.enable_gradient_checkpointing()
-
-    if args.enable_xformers_memory_efficient_attention:
-        if is_xformers_available():
-            unet.enable_xformers_memory_efficient_attention()
-        else:
-            raise ValueError("xformers is not available. Make sure it is installed correctly")
-
-    # Enable TF32 for faster training on Ampere GPUs,
-    # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
-    if args.allow_tf32:
-        torch.backends.cuda.matmul.allow_tf32 = True
-
-    if args.scale_lr:
-        args.learning_rate = (
-            args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
-        )
-
-    # Initialize the optimizer
-    optimizer = torch.optim.AdamW(
-        text_encoder.get_input_embeddings().parameters(),  # only optimize the embeddings
-        lr=args.learning_rate,
-        betas=(args.adam_beta1, args.adam_beta2),
-        weight_decay=args.adam_weight_decay,
-        eps=args.adam_epsilon,
-    )
-
-    # Dataset and DataLoaders creation:
-    train_dataset = TextualInversionDataset(
-        data_root=args.train_data_dir,
-        tokenizer=tokenizer,
-        size=args.resolution,
-        placeholder_token=args.placeholder_token,
-        repeats=args.repeats,
-        learnable_property=args.learnable_property,
-        center_crop=args.center_crop,
-        set="train",
-    )
-    train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=args.train_batch_size, shuffle=True)
-
-    # Scheduler and math around the number of training steps.
-    overrode_max_train_steps = False
-    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
-    if args.max_train_steps is None:
-        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
-        overrode_max_train_steps = True
-
-    lr_scheduler = get_scheduler(
-        args.lr_scheduler,
-        optimizer=optimizer,
-        num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,
-        num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
-    )
-
-    # Prepare everything with our `accelerator`.
-    text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
-        text_encoder, optimizer, train_dataloader, lr_scheduler
-    )
-
-    # For mixed precision training we cast the text_encoder and vae weights to half-precision
-    # as these models are only used for inference, keeping weights in full precision is not required.
-    weight_dtype = torch.float32
-    if accelerator.mixed_precision == "fp16":
-        weight_dtype = torch.float16
-    elif accelerator.mixed_precision == "bf16":
-        weight_dtype = torch.bfloat16
-
-    # Move vae and unet to device and cast to weight_dtype
-    unet.to(accelerator.device, dtype=weight_dtype)
-    vae.to(accelerator.device, dtype=weight_dtype)
-
-    # We need to recalculate our total training steps as the size of the training dataloader may have changed.
-    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
-    if overrode_max_train_steps:
-        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
-    # Afterwards we recalculate our number of training epochs
-    args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
-
-    # We need to initialize the trackers we use, and also store our configuration.
-    # The trackers initializes automatically on the main process.
-    if accelerator.is_main_process:
-        accelerator.init_trackers("textual_inversion", config=vars(args))
-
-    # Train!
-    total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
-
-    logger.info("***** Running training *****")
-    logger.info(f"  Num examples = {len(train_dataset)}")
-    logger.info(f"  Num Epochs = {args.num_train_epochs}")
-    logger.info(f"  Instantaneous batch size per device = {args.train_batch_size}")
-    logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
-    logger.info(f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
-    logger.info(f"  Total optimization steps = {args.max_train_steps}")
-    global_step = 0
-    first_epoch = 0
-
-    # Potentially load in the weights and states from a previous save
-    if args.resume_from_checkpoint:
-        if args.resume_from_checkpoint != "latest":
-            path = os.path.basename(args.resume_from_checkpoint)
-        else:
-            # Get the most recent checkpoint
-            dirs = os.listdir(args.output_dir)
-            dirs = [d for d in dirs if d.startswith("checkpoint")]
-            dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
-            path = dirs[-1]
-        accelerator.print(f"Resuming from checkpoint {path}")
-        accelerator.load_state(os.path.join(args.output_dir, path))
-        global_step = int(path.split("-")[1])
-
-        resume_global_step = global_step * args.gradient_accumulation_steps
-        first_epoch = resume_global_step // num_update_steps_per_epoch
-        resume_step = resume_global_step % num_update_steps_per_epoch
-
-    # Only show the progress bar once on each machine.
-    progress_bar = tqdm(range(global_step, args.max_train_steps), disable=not accelerator.is_local_main_process)
-    progress_bar.set_description("Steps")
-
-    # keep original embeddings as reference
-    orig_embeds_params = accelerator.unwrap_model(text_encoder).get_input_embeddings().weight.data.clone()
-
-    for epoch in range(first_epoch, args.num_train_epochs):
-        text_encoder.train()
-        for step, batch in enumerate(train_dataloader):
-            # Skip steps until we reach the resumed step
-            if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step:
-                if step % args.gradient_accumulation_steps == 0:
-                    progress_bar.update(1)
-                continue
-
-            with accelerator.accumulate(text_encoder):
-                # Convert images to latent space
-                latents = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist.sample().detach()
-                latents = latents * 0.18215
-
-                # Sample noise that we'll add to the latents
-                noise = torch.randn_like(latents)
-                bsz = latents.shape[0]
-                # Sample a random timestep for each image
-                timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=latents.device)
-                timesteps = timesteps.long()
-
-                # Add noise to the latents according to the noise magnitude at each timestep
-                # (this is the forward diffusion process)
-                noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
-
-                # Get the text embedding for conditioning
-                encoder_hidden_states = text_encoder(batch["input_ids"])[0].to(dtype=weight_dtype)
-
-                # Predict the noise residual
-                model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
-
-                # Get the target for loss depending on the prediction type
-                if noise_scheduler.config.prediction_type == "epsilon":
-                    target = noise
-                elif noise_scheduler.config.prediction_type == "v_prediction":
-                    target = noise_scheduler.get_velocity(latents, noise, timesteps)
-                else:
-                    raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
-
-                loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
-
-                accelerator.backward(loss)
-
-                optimizer.step()
-                lr_scheduler.step()
-                optimizer.zero_grad()
-
-                # Let's make sure we don't update any embedding weights besides the newly added token
-                index_no_updates = torch.arange(len(tokenizer)) != placeholder_token_id
-                with torch.no_grad():
-                    accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[
-                        index_no_updates
-                    ] = orig_embeds_params[index_no_updates]
-
-            # Checks if the accelerator has performed an optimization step behind the scenes
-            if accelerator.sync_gradients:
-                progress_bar.update(1)
-                global_step += 1
-                if global_step % args.save_steps == 0:
-                    save_path = os.path.join(args.output_dir, f"learned_embeds-steps-{global_step}.bin")
-                    save_progress(text_encoder, placeholder_token_id, accelerator, args, save_path)
-
-                if global_step % args.checkpointing_steps == 0:
-                    if accelerator.is_main_process:
-                        save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
-                        accelerator.save_state(save_path)
-                        logger.info(f"Saved state to {save_path}")
-
-            logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]}
-            progress_bar.set_postfix(**logs)
-            accelerator.log(logs, step=global_step)
-
-            if global_step >= args.max_train_steps:
-                break
-
-    # Create the pipeline using using the trained modules and save it.
-    accelerator.wait_for_everyone()
-    if accelerator.is_main_process:
-        if args.push_to_hub and args.only_save_embeds:
-            logger.warn("Enabling full model saving because --push_to_hub=True was specified.")
-            save_full_model = True
-        else:
-            save_full_model = not args.only_save_embeds
-        if save_full_model:
-            pipeline = StableDiffusionPipeline.from_pretrained(
-                pretrained_model_name_or_path,
-                text_encoder=accelerator.unwrap_model(text_encoder),
-                vae=vae,
-                unet=unet,
-                tokenizer=tokenizer,
-                **pipeline_args,
-            )
-            pipeline.save_pretrained(args.output_dir)
-        # Save the newly trained embeddings
-        save_path = os.path.join(args.output_dir, "learned_embeds.bin")
-        save_progress(text_encoder, placeholder_token_id, accelerator, args, save_path)
-
-        if args.push_to_hub:
-            repo.push_to_hub(commit_message="End of training", blocking=False, auto_lfs_prune=True)
-
-    accelerator.end_training()
+# Copyright 2023, Lincoln Stein @lstein
 
+from ldm.invoke.textual_inversion_training import parse_args, do_textual_inversion_training
 
 if __name__ == "__main__":
-    main()
+    args = parse_args()
+    do_textual_inversion_training(args)
diff --git a/scripts/textual_inversion_fe.py b/scripts/textual_inversion_fe.py
new file mode 100755
index 00000000000..b07b4ede682
--- /dev/null
+++ b/scripts/textual_inversion_fe.py
@@ -0,0 +1,186 @@
+#!/usr/bin/env python
+
+import npyscreen
+import os
+import sys
+import curses
+from ldm.invoke.globals import Globals
+from omegaconf import OmegaConf
+from pathlib import Path
+from typing import List
+from argparse import Namespace
+
+class textualInversionForm(npyscreen.FormMultiPageAction):
+    resolutions = [512, 768, 1024]
+    lr_schedulers = [
+                "linear", "cosine", "cosine_with_restarts",
+                "polynomial","constant", "constant_with_warmup"
+    ]
+
+    def afterEditing(self):
+        self.parentApp.setNextForm(None)
+
+    def create(self):
+        self.model_names, default = self.get_model_names()
+        default_token = 'cat-toy'
+
+        self.model = self.add_widget_intelligent(
+            npyscreen.TitleSelectOne,
+            name='Model Name',
+            values=self.model_names,
+            value=default,
+            max_height=len(self.model_names)+1
+        )
+        self.initializer_token = self.add_widget_intelligent(
+            npyscreen.TitleText,
+            name="Initializer Token",
+            value=default_token,
+        )
+        self.placeholder_token = self.add_widget_intelligent(
+            npyscreen.TitleText,
+            name="Placeholder Token",
+            value=f'<{default_token}>'
+        )
+        self.train_data_dir = self.add_widget_intelligent(
+            npyscreen.TitleFilenameCombo,
+            name='Data Training Directory',
+            select_dir=True,
+            must_exist=True,
+            value=Path(Globals.root) / 'training-data' / default_token
+        )
+        self.output_dir = self.add_widget_intelligent(
+            npyscreen.TitleFilenameCombo,
+            name='Output Destination Directory',
+            select_dir=True,
+            must_exist=False,
+            value=Path(Globals.root) / 'embeddings' / default_token
+        )
+        self.resolution = self.add_widget_intelligent(
+            npyscreen.TitleSelectOne,
+            name='Resolution',
+            values = self.resolutions,
+            value=0,
+            scroll_exit = True,
+            max_height=3
+        )
+        self.train_batch_size = self.add_widget_intelligent(
+            npyscreen.TitleSlider,
+            name='Batch Size',
+            out_of=10,
+            step=1,
+            lowest=1,
+            value=1
+        )
+        self.gradient_accumulation_steps = self.add_widget_intelligent(
+            npyscreen.TitleSlider,
+            name='Gradient Accumulation Steps',
+            out_of=10,
+            step=1,
+            lowest=1,
+            value=4
+        )
+        self.max_train_steps = self.add_widget_intelligent(
+            npyscreen.TitleSlider,
+            name='Max Training Steps',
+            out_of=10000,
+            step=500,
+            lowest=1,
+            value=3000
+        )
+        self.learning_rate = self.add_widget_intelligent(
+            npyscreen.TitleText,
+            name="Learning Rate",
+            value='5.0e-04',
+        )
+        self.scale_lr = self.add_widget_intelligent(
+            npyscreen.Checkbox,
+            name="Scale learning rate by number GPUs, steps and batch size",
+            value=True
+        )
+        self.lr_scheduler = self.add_widget_intelligent(
+            npyscreen.TitleSelectOne,
+            name='Learning rate scheduler',
+            values = self.lr_schedulers,
+            max_height=7,
+            scroll_exit = True,
+            value=4)
+        self.lr_warmup_steps = self.add_widget_intelligent(
+            npyscreen.TitleSlider,
+            name='Warmup Steps',
+            out_of=100,
+            step=1,
+            lowest=0,
+            value=0
+        )
+        self.initializer_token.when_value_edited = self.initializer_changed
+
+    def initializer_changed(self):
+        new_value = self.initializer_token.value
+        self.placeholder_token.value = f'<{new_value}>'
+        self.train_data_dir.value = Path(Globals.root) / 'training-data' / new_value
+        self.output_dir.value = Path(Globals.root) / 'embeddings' / new_value
+        
+    def on_ok(self):
+        if self.validate_field_values():
+            self.parentApp.setNextForm(None)
+            self.editing = False
+            self.run_textual_inversion()
+        else:
+            self.editing = True
+
+    def validate_field_values(self)->bool:
+        bad_fields = []
+        if self.model.value is None:
+            bad_fields.append('Model Name must correspond to a known model in models.yaml')
+        if self.train_data_dir.value is None:
+            bad_fields.append('Data Training Directory cannot be empty')
+        if self.output_dir.value is None:
+            bad_fields.append('The Output Destination Directory cannot be empty')
+        if len(bad_fields) > 0:
+            message = 'The following problems were detected and must be corrected:'
+            for problem in bad_fields:
+                message += f'\n* {problem}'
+            npyscreen.notify_confirm(message)
+            return False
+        else:
+            return True
+
+    def get_model_names(self)->(List[str],int):
+        conf = OmegaConf.load(os.path.join(Globals.root,'configs/models.yaml'))
+        model_names = list(conf.keys())
+        defaults = [idx for idx in range(len(model_names)) if 'default' in conf[model_names[idx]]]
+        return (model_names,defaults[0])
+
+    def create_namespace(self):
+        args = Namespace()
+
+        # the choices
+        args.model = self.model_names[self.model.value[0]]
+        args.resolution = self.resolutions[self.resolution.value[0]]
+        args.lr_scheduler = self.lr_schedulers[self.lr_scheduler.value[0]]
+
+        # all the strings
+        for attr in ('initializer_token','placeholder_token','train_data_dir','output_dir','scale_lr'):
+            setattr(args,attr,getattr(self,attr).value)
+        # all the integers
+        for attr in ('train_batch_size','gradient_accumulation_steps',
+                     'max_train_steps','lr_warmup_steps'):
+            setattr(args,attr,int(getattr(self,attr).value))
+        # the floats (just one)
+        args.learning_rate = float(self.learning_rate.value)
+        return args
+
+    def run_textual_inversion(self):
+        npyscreen.notify('Launching textual inversion training. This will take a while...')
+        from ldm.invoke.textual_inversion_training import do_textual_inversion_training, parse_args
+        args = parse_args()
+        args.root_dir = Globals.root
+        do_textual_inversion_training(args)
+
+class MyApplication(npyscreen.NPSAppManaged):
+    def onStart(self):
+        npyscreen.setTheme(npyscreen.Themes.DefaultTheme)
+        self.main = self.addForm('MAIN', textualInversionForm, name='Textual Inversion Settings')
+
+if __name__ == '__main__':
+   TestApp = MyApplication().run()

From 1ff402f8f56fb7e8f57e9896e61ac96c4b036e2d Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Tue, 3 Jan 2023 23:42:14 -0500
Subject: [PATCH 122/199] add curses-based interface for textual inversion

---
 .../requirements-base.txt                     |   1 +
 installer/templates/invoke.bat.in             |   9 +-
 installer/templates/invoke.sh.in              |  11 +-
 ldm/invoke/textual_inversion_training.py      | 257 ++++++++++--------
 scripts/textual_inversion.py                  |   6 +-
 scripts/textual_inversion_fe.py               | 120 +++++---
 setup.py                                      |   3 +-
 7 files changed, 251 insertions(+), 156 deletions(-)

diff --git a/environments-and-requirements/requirements-base.txt b/environments-and-requirements/requirements-base.txt
index c0d89767ab1..fda97aaa2d8 100644
--- a/environments-and-requirements/requirements-base.txt
+++ b/environments-and-requirements/requirements-base.txt
@@ -37,6 +37,7 @@ test-tube>=0.7.5
 torch-fidelity
 torchmetrics
 transformers~=4.25
+windows-curses; sys_platform == 'win32'
 https://github.com/Birch-san/k-diffusion/archive/refs/heads/mps.zip#egg=k-diffusion
 https://github.com/invoke-ai/PyPatchMatch/archive/refs/tags/0.1.5.zip#egg=pypatchmatch
 https://github.com/openai/CLIP/archive/eaa22acb90a5876642d0507623e859909230a52d.zip#egg=clip
diff --git a/installer/templates/invoke.bat.in b/installer/templates/invoke.bat.in
index 19885d84678..3a67cb2f577 100644
--- a/installer/templates/invoke.bat.in
+++ b/installer/templates/invoke.bat.in
@@ -9,8 +9,10 @@ set INVOKEAI_ROOT=.
 echo Do you want to generate images using the
 echo 1. command-line
 echo 2. browser-based UI
-echo 3. open the developer console
-set /P restore="Please enter 1, 2 or 3: "
+echo 3. textual inversion training
+echo 4. open the developer console
+set /P restore="Please enter 1, 2, 3 or 4: [1] "
+if not defined restore set restore=1
 IF /I "%restore%" == "1" (
     echo Starting the InvokeAI command-line..
     python .venv\Scripts\invoke.py %*
@@ -18,6 +20,9 @@ IF /I "%restore%" == "1" (
     echo Starting the InvokeAI browser-based UI..
     python .venv\Scripts\invoke.py --web %*
 ) ELSE IF /I "%restore%" == "3" (
+    echo Starting textual inversion training..
+    python .venv\Scripts\textual_inversion_fe.py --web %*
+) ELSE IF /I "%restore%" == "4" (
     echo Developer Console
     echo Python command is:
     where python
diff --git a/installer/templates/invoke.sh.in b/installer/templates/invoke.sh.in
index 7b67c95a60c..143ce49beaf 100644
--- a/installer/templates/invoke.sh.in
+++ b/installer/templates/invoke.sh.in
@@ -19,12 +19,15 @@ if [ "$0" != "bash" ]; then
     echo "Do you want to generate images using the"
     echo "1. command-line"
     echo "2. browser-based UI"
-    echo "3. open the developer console"
-    read -p "Please enter 1, 2, or 3: " yn
-    case $yn in
+    echo "3. textual inversion training"
+    echo "4. open the developer console"
+    read -p "Please enter 1, 2, 3 or 4: [1] " yn
+    choice=${yn:='1'}
+    case $choice in
         1 ) printf "\nStarting the InvokeAI command-line..\n"; .venv/bin/python .venv/bin/invoke.py  $*;;
         2 ) printf "\nStarting the InvokeAI browser-based UI..\n"; .venv/bin/python .venv/bin/invoke.py --web $*;;
-        3 ) printf "\nDeveloper Console:\n"; file_name=$(basename "${BASH_SOURCE[0]}"); bash --init-file "$file_name";;
+        3 ) printf "\nStarting Textual Inversion:\n"; .venv/bin/python .venv/bin/textual_inversion_fe.py $*;;
+	4 ) printf "\nDeveloper Console:\n"; file_name=$(basename "${BASH_SOURCE[0]}"); bash --init-file "$file_name";;
         * ) echo "Invalid selection"; exit;;
     esac
 else # in developer console
diff --git a/ldm/invoke/textual_inversion_training.py b/ldm/invoke/textual_inversion_training.py
index 75369d96133..34e83718d6c 100644
--- a/ldm/invoke/textual_inversion_training.py
+++ b/ldm/invoke/textual_inversion_training.py
@@ -84,7 +84,7 @@ def parse_args():
     )
     parser.add_argument(
         '--root_dir','--root',
-        type=str,
+        type=Path,
         default=Globals.root,
         help="Path to the invokeai runtime directory",
     )
@@ -98,7 +98,7 @@ def parse_args():
         "--model",
         type=str,
         default=None,
-        required=False,
+        required=True,
         help="Name of the diffusers model to train against, as defined in configs/models.yaml.",
     )
     parser.add_argument(
@@ -115,23 +115,31 @@ def parse_args():
         help="Pretrained tokenizer name or path if not the same as model_name",
     )
     parser.add_argument(
-        "--train_data_dir", type=str, default=None, required=False, help="A folder containing the training data."
+        "--train_data_dir",
+        type=Path,
+        default=None,
+        required=True,
+        help="A folder containing the training data."
     )
     parser.add_argument(
         "--placeholder_token",
         type=str,
         default=None,
-        required=False,
+        required=True,
         help="A token to use as a placeholder for the concept.",
     )
     parser.add_argument(
-        "--initializer_token", type=str, default=None, required=False, help="A token to use as initializer word."
+        "--initializer_token",
+        type=str,
+        default=None,
+        required=False,
+        help="A token to use as initializer word."
     )
     parser.add_argument("--learnable_property", type=str, default="object", help="Choose between 'object' and 'style'")
     parser.add_argument("--repeats", type=int, default=100, help="How many times to repeat the training data.")
     parser.add_argument(
         "--output_dir",
-         type=str,
+        type=Path,
         default=f'{Globals.root}/text-inversion-model',
         help="The output directory where the model predictions and checkpoints will be written.",
     )
@@ -201,7 +209,7 @@ def parse_args():
     parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
     parser.add_argument(
         "--logging_dir",
-        type=str,
+        type=Path,
         default="logs",
         help=(
             "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
@@ -248,7 +256,7 @@ def parse_args():
     )
     parser.add_argument(
         "--resume_from_checkpoint",
-        type=str,
+        type=Path,
         default=None,
         help=(
             "Whether training should be resumed from a previous checkpoint. Use a path saved by"
@@ -260,9 +268,6 @@ def parse_args():
     )
 
     args = parser.parse_args()
-    env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
-    if env_local_rank != -1 and env_local_rank != args.local_rank:
-        args.local_rank = env_local_rank
     return args
 
 
@@ -412,35 +417,60 @@ def get_full_repo_name(model_id: str, organization: Optional[str] = None, token:
         return f"{organization}/{model_id}"
 
 
-def do_textual_inversion_training(args:Namespace):
-
-    # Out of laziness, we are getting our parameterse from an argparse Namespace
-    # which is a legacy of the original script. This needs to be changed.
-    
-    if args.train_data_dir is None:
-        raise ValueError("You must specify a training data directory with --train_data_dir.")
-
-    if args.placeholder_token is None:
-        raise ValueError("You must specify a placeholder token with --placeholder_token.")
-
-    if args.initializer__token is None:
-        raise ValueError("You must specify an initializer token with --initializer_token.")
-
-    if args.model is None:
-        raise ValueError("You must specify a model name with --model.")
+def do_textual_inversion_training(
+        model:str,
+        train_data_dir:Path,
+        output_dir:Path,
+        placeholder_token:str,
+        initializer_token:str,
+        save_steps:int=500,
+        only_save_embeds:bool=False,
+        revision:str=None,
+        tokenizer_name:str=None,
+        learnable_property:str='object',
+        repeats:int=100,
+        seed:int=None,
+        resolution:int=512,
+        center_crop:bool=False,
+        train_batch_size:int=16,
+        num_train_epochs:int=100,
+        max_train_steps:int=5000,
+        gradient_accumulation_steps:int=1,
+        gradient_checkpointing:bool=False,
+        learning_rate:float=1e-4,
+        scale_lr:bool=True,
+        lr_scheduler:str='constant',
+        lr_warmup_steps:int=500,
+        adam_beta1:float=0.9,
+        adam_beta2:float=0.999,
+        adam_weight_decay:float=1e-02,
+        adam_epsilon:float=1e-08,
+        push_to_hub:bool=False,
+        hub_token:str=None,
+        logging_dir:Path=Path('logs'),
+        mixed_precision:str='no',
+        allow_tf32:bool=False,
+        report_to:str='tensorboard',
+        local_rank:int=-1,
+        checkpointing_steps:int=500,
+        resume_from_checkpoint:Path=None,
+        enable_xformers_memory_efficient_attention:bool=False,
+        root_dir:Path=None
+):
+    env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
+    if env_local_rank != -1 and env_local_rank != local_rank:
+        local_rank = env_local_rank
 
     # setting up things the way invokeai expects them
-    if os.path.exists(args.root_dir):
-        Globals.root = args.root_dir
-    if not os.path.isabs(args.output_dir):
-        args.output_dir = os.path.join(Globals.root,args.output_dir)
+    if not os.path.isabs(output_dir):
+        output_dir = os.path.join(Globals.root,output_dir)
         
-    logging_dir = os.path.join(args.output_dir, args.logging_dir)
+    logging_dir = output_dir / logging_dir
 
     accelerator = Accelerator(
-        gradient_accumulation_steps=args.gradient_accumulation_steps,
-        mixed_precision=args.mixed_precision,
-        log_with=args.report_to,
+        gradient_accumulation_steps=gradient_accumulation_steps,
+        mixed_precision=mixed_precision,
+        log_with=report_to,
         logging_dir=logging_dir,
     )
 
@@ -461,68 +491,70 @@ def do_textual_inversion_training(args:Namespace):
         diffusers.utils.logging.set_verbosity_error()
 
     # If passed along, set the training seed now.
-    if args.seed is not None:
-        set_seed(args.seed)
+    if seed is not None:
+        set_seed(seed)
 
     # Handle the repository creation
     if accelerator.is_main_process:
-        if args.push_to_hub:
-            if args.hub_model_id is None:
-                repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
+        if push_to_hub:
+            if hub_model_id is None:
+                repo_name = get_full_repo_name(Path(output_dir).name, token=hub_token)
             else:
-                repo_name = args.hub_model_id
-            repo = Repository(args.output_dir, clone_from=repo_name)
+                repo_name = hub_model_id
+            repo = Repository(output_dir, clone_from=repo_name)
 
-            with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
+            with open(os.path.join(output_dir, ".gitignore"), "w+") as gitignore:
                 if "step_*" not in gitignore:
                     gitignore.write("step_*\n")
                 if "epoch_*" not in gitignore:
                     gitignore.write("epoch_*\n")
-        elif args.output_dir is not None:
-            os.makedirs(args.output_dir, exist_ok=True)
+        elif output_dir is not None:
+            os.makedirs(output_dir, exist_ok=True)
 
     models_conf = OmegaConf.load(os.path.join(Globals.root,'configs/models.yaml'))
-    model_conf = models_conf.get(args.model,None)
-    assert model_conf is not None,f'Unknown model: {args.model}'
+    model_conf = models_conf.get(model,None)
+    assert model_conf is not None,f'Unknown model: {model}'
     assert model_conf.get('format','diffusers')=='diffusers', "This script only works with models of type 'diffusers'"
     pretrained_model_name_or_path = model_conf.get('repo_id',None) or Path(model_conf.get('path'))
-    assert pretrained_model_name_or_path, f"models.yaml error: neither 'repo_id' nor 'path' is defined for {args.model}"
+    assert pretrained_model_name_or_path, f"models.yaml error: neither 'repo_id' nor 'path' is defined for {model}"
     pipeline_args = dict()
     if not isinstance(pretrained_model_name_or_path,Path):
-        pipeline_args.update(cache_dir=os.path.join(Globals.root,'models',pretrained_model_name_or_path))
+        pipeline_args.update(
+            cache_dir=os.path.join(Globals.root,'models',pretrained_model_name_or_path)
+        )
     
     # Load tokenizer
-    if args.tokenizer_name:
-        tokenizer = CLIPTokenizer.from_pretrained(args.tokenizer_name)
+    if tokenizer_name:
+        tokenizer = CLIPTokenizer.from_pretrained(tokenizer_name)
     else:
         tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_name_or_path, subfolder="tokenizer", **pipeline_args)
 
     # Load scheduler and models
     noise_scheduler = DDPMScheduler.from_pretrained(pretrained_model_name_or_path, subfolder="scheduler", **pipeline_args)
     text_encoder = CLIPTextModel.from_pretrained(
-        pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision,  **pipeline_args
+        pretrained_model_name_or_path, subfolder="text_encoder", revision=revision,  **pipeline_args
     )
-    vae = AutoencoderKL.from_pretrained(pretrained_model_name_or_path, subfolder="vae", revision=args.revision,  **pipeline_args)
+    vae = AutoencoderKL.from_pretrained(pretrained_model_name_or_path, subfolder="vae", revision=revision,  **pipeline_args)
     unet = UNet2DConditionModel.from_pretrained(
-        pretrained_model_name_or_path, subfolder="unet", revision=args.revision,  **pipeline_args
+        pretrained_model_name_or_path, subfolder="unet", revision=revision,  **pipeline_args
     )
 
     # Add the placeholder token in tokenizer
-    num_added_tokens = tokenizer.add_tokens(args.placeholder_token)
+    num_added_tokens = tokenizer.add_tokens(placeholder_token)
     if num_added_tokens == 0:
         raise ValueError(
-            f"The tokenizer already contains the token {args.placeholder_token}. Please pass a different"
+            f"The tokenizer already contains the token {placeholder_token}. Please pass a different"
             " `placeholder_token` that is not already in the tokenizer."
         )
 
     # Convert the initializer_token, placeholder_token to ids
-    token_ids = tokenizer.encode(args.initializer_token, add_special_tokens=False)
+    token_ids = tokenizer.encode(initializer_token, add_special_tokens=False)
     # Check if initializer_token is a single token or a sequence of tokens
     if len(token_ids) > 1:
-        raise ValueError("The initializer token must be a single token.")
+        raise ValueError(f"The initializer token must be a single token. Provided initializer={initializer_token}. Token ids={token_ids}")
 
     initializer_token_id = token_ids[0]
-    placeholder_token_id = tokenizer.convert_tokens_to_ids(args.placeholder_token)
+    placeholder_token_id = tokenizer.convert_tokens_to_ids(placeholder_token)
 
     # Resize the token embeddings as we are adding new special tokens to the tokenizer
     text_encoder.resize_token_embeddings(len(tokenizer))
@@ -539,14 +571,14 @@ def do_textual_inversion_training(args:Namespace):
     text_encoder.text_model.final_layer_norm.requires_grad_(False)
     text_encoder.text_model.embeddings.position_embedding.requires_grad_(False)
 
-    if args.gradient_checkpointing:
+    if gradient_checkpointing:
         # Keep unet in train mode if we are using gradient checkpointing to save memory.
         # The dropout cannot be != 0 so it doesn't matter if we are in eval or train mode.
         unet.train()
         text_encoder.gradient_checkpointing_enable()
         unet.enable_gradient_checkpointing()
 
-    if args.enable_xformers_memory_efficient_attention:
+    if enable_xformers_memory_efficient_attention:
         if is_xformers_available():
             unet.enable_xformers_memory_efficient_attention()
         else:
@@ -554,48 +586,48 @@ def do_textual_inversion_training(args:Namespace):
 
     # Enable TF32 for faster training on Ampere GPUs,
     # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
-    if args.allow_tf32:
+    if allow_tf32:
         torch.backends.cuda.matmul.allow_tf32 = True
 
-    if args.scale_lr:
-        args.learning_rate = (
-            args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes
+    if scale_lr:
+        learning_rate = (
+            learning_rate * gradient_accumulation_steps * train_batch_size * accelerator.num_processes
         )
 
     # Initialize the optimizer
     optimizer = torch.optim.AdamW(
         text_encoder.get_input_embeddings().parameters(),  # only optimize the embeddings
-        lr=args.learning_rate,
-        betas=(args.adam_beta1, args.adam_beta2),
-        weight_decay=args.adam_weight_decay,
-        eps=args.adam_epsilon,
+        lr=learning_rate,
+        betas=(adam_beta1, adam_beta2),
+        weight_decay=adam_weight_decay,
+        eps=adam_epsilon,
     )
 
     # Dataset and DataLoaders creation:
     train_dataset = TextualInversionDataset(
-        data_root=args.train_data_dir,
+        data_root=train_data_dir,
         tokenizer=tokenizer,
-        size=args.resolution,
-        placeholder_token=args.placeholder_token,
-        repeats=args.repeats,
-        learnable_property=args.learnable_property,
-        center_crop=args.center_crop,
+        size=resolution,
+        placeholder_token=placeholder_token,
+        repeats=repeats,
+        learnable_property=learnable_property,
+        center_crop=center_crop,
         set="train",
     )
-    train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=args.train_batch_size, shuffle=True)
+    train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=train_batch_size, shuffle=True)
 
     # Scheduler and math around the number of training steps.
     overrode_max_train_steps = False
-    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
-    if args.max_train_steps is None:
-        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
+    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / gradient_accumulation_steps)
+    if max_train_steps is None:
+        max_train_steps = num_train_epochs * num_update_steps_per_epoch
         overrode_max_train_steps = True
 
     lr_scheduler = get_scheduler(
-        args.lr_scheduler,
+        lr_scheduler,
         optimizer=optimizer,
-        num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps,
-        num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
+        num_warmup_steps=lr_warmup_steps * gradient_accumulation_steps,
+        num_training_steps=max_train_steps * gradient_accumulation_steps,
     )
 
     # Prepare everything with our `accelerator`.
@@ -616,61 +648,64 @@ def do_textual_inversion_training(args:Namespace):
     vae.to(accelerator.device, dtype=weight_dtype)
 
     # We need to recalculate our total training steps as the size of the training dataloader may have changed.
-    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
+    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / gradient_accumulation_steps)
     if overrode_max_train_steps:
-        args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
+        max_train_steps = num_train_epochs * num_update_steps_per_epoch
     # Afterwards we recalculate our number of training epochs
-    args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
+    num_train_epochs = math.ceil(max_train_steps / num_update_steps_per_epoch)
 
     # We need to initialize the trackers we use, and also store our configuration.
     # The trackers initializes automatically on the main process.
     if accelerator.is_main_process:
-        accelerator.init_trackers("textual_inversion", config=vars(args))
+        params = locals()
+        for k in params: # init_trackers() doesn't like objects
+            params[k] = str(params[k]) if isinstance(params[k],object) else params[k]
+        accelerator.init_trackers("textual_inversion", config=params)
 
     # Train!
-    total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
+    total_batch_size = train_batch_size * accelerator.num_processes * gradient_accumulation_steps
 
     logger.info("***** Running training *****")
     logger.info(f"  Num examples = {len(train_dataset)}")
-    logger.info(f"  Num Epochs = {args.num_train_epochs}")
-    logger.info(f"  Instantaneous batch size per device = {args.train_batch_size}")
+    logger.info(f"  Num Epochs = {num_train_epochs}")
+    logger.info(f"  Instantaneous batch size per device = {train_batch_size}")
     logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
-    logger.info(f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
-    logger.info(f"  Total optimization steps = {args.max_train_steps}")
+    logger.info(f"  Gradient Accumulation steps = {gradient_accumulation_steps}")
+    logger.info(f"  Total optimization steps = {max_train_steps}")
     global_step = 0
     first_epoch = 0
 
     # Potentially load in the weights and states from a previous save
-    if args.resume_from_checkpoint:
-        if args.resume_from_checkpoint != "latest":
-            path = os.path.basename(args.resume_from_checkpoint)
+    if resume_from_checkpoint:
+        if resume_from_checkpoint != "latest":
+            path = os.path.basename(resume_from_checkpoint)
         else:
             # Get the most recent checkpoint
-            dirs = os.listdir(args.output_dir)
+            dirs = os.listdir(output_dir)
             dirs = [d for d in dirs if d.startswith("checkpoint")]
             dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
             path = dirs[-1]
         accelerator.print(f"Resuming from checkpoint {path}")
-        accelerator.load_state(os.path.join(args.output_dir, path))
+        accelerator.load_state(os.path.join(output_dir, path))
         global_step = int(path.split("-")[1])
 
-        resume_global_step = global_step * args.gradient_accumulation_steps
+        resume_global_step = global_step * gradient_accumulation_steps
         first_epoch = resume_global_step // num_update_steps_per_epoch
         resume_step = resume_global_step % num_update_steps_per_epoch
 
     # Only show the progress bar once on each machine.
-    progress_bar = tqdm(range(global_step, args.max_train_steps), disable=not accelerator.is_local_main_process)
+    progress_bar = tqdm(range(global_step, max_train_steps), disable=not accelerator.is_local_main_process)
     progress_bar.set_description("Steps")
 
     # keep original embeddings as reference
     orig_embeds_params = accelerator.unwrap_model(text_encoder).get_input_embeddings().weight.data.clone()
 
-    for epoch in range(first_epoch, args.num_train_epochs):
+    for epoch in range(first_epoch, num_train_epochs):
         text_encoder.train()
         for step, batch in enumerate(train_dataloader):
             # Skip steps until we reach the resumed step
-            if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step:
-                if step % args.gradient_accumulation_steps == 0:
+            if resume_from_checkpoint and epoch == first_epoch and step < resume_step:
+                if step % gradient_accumulation_steps == 0:
                     progress_bar.update(1)
                 continue
 
@@ -723,13 +758,13 @@ def do_textual_inversion_training(args:Namespace):
             if accelerator.sync_gradients:
                 progress_bar.update(1)
                 global_step += 1
-                if global_step % args.save_steps == 0:
-                    save_path = os.path.join(args.output_dir, f"learned_embeds-steps-{global_step}.bin")
+                if global_step % save_steps == 0:
+                    save_path = os.path.join(output_dir, f"learned_embeds-steps-{global_step}.bin")
                     save_progress(text_encoder, placeholder_token_id, accelerator, args, save_path)
 
-                if global_step % args.checkpointing_steps == 0:
+                if global_step % checkpointing_steps == 0:
                     if accelerator.is_main_process:
-                        save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}")
+                        save_path = os.path.join(output_dir, f"checkpoint-{global_step}")
                         accelerator.save_state(save_path)
                         logger.info(f"Saved state to {save_path}")
 
@@ -737,17 +772,17 @@ def do_textual_inversion_training(args:Namespace):
             progress_bar.set_postfix(**logs)
             accelerator.log(logs, step=global_step)
 
-            if global_step >= args.max_train_steps:
+            if global_step >= max_train_steps:
                 break
 
     # Create the pipeline using using the trained modules and save it.
     accelerator.wait_for_everyone()
     if accelerator.is_main_process:
-        if args.push_to_hub and args.only_save_embeds:
+        if push_to_hub and only_save_embeds:
             logger.warn("Enabling full model saving because --push_to_hub=True was specified.")
             save_full_model = True
         else:
-            save_full_model = not args.only_save_embeds
+            save_full_model = not only_save_embeds
         if save_full_model:
             pipeline = StableDiffusionPipeline.from_pretrained(
                 pretrained_model_name_or_path,
@@ -757,12 +792,12 @@ def do_textual_inversion_training(args:Namespace):
                 tokenizer=tokenizer,
                 **pipeline_args,
             )
-            pipeline.save_pretrained(args.output_dir)
+            pipeline.save_pretrained(output_dir)
         # Save the newly trained embeddings
-        save_path = os.path.join(args.output_dir, "learned_embeds.bin")
+        save_path = os.path.join(output_dir, "learned_embeds.bin")
         save_progress(text_encoder, placeholder_token_id, accelerator, args, save_path)
 
-        if args.push_to_hub:
+        if push_to_hub:
             repo.push_to_hub(commit_message="End of training", blocking=False, auto_lfs_prune=True)
 
     accelerator.end_training()
diff --git a/scripts/textual_inversion.py b/scripts/textual_inversion.py
index 81784f25349..24ce18a0077 100755
--- a/scripts/textual_inversion.py
+++ b/scripts/textual_inversion.py
@@ -1,9 +1,11 @@
 #!/usr/bin/env python
 
 # Copyright 2023, Lincoln Stein @lstein
-
+from ldm.invoke.globals import Globals
 from ldm.invoke.textual_inversion_training import parse_args, do_textual_inversion_training
 
 if __name__ == "__main__":
     args = parse_args()
-    do_textual_inversion_training(args)
+    Globals.root = args.root_dir or Globals.root
+    kwargs = vars(args)
+    do_textual_inversion_training(**kwargs)
diff --git a/scripts/textual_inversion_fe.py b/scripts/textual_inversion_fe.py
index b07b4ede682..79d96a5a437 100755
--- a/scripts/textual_inversion_fe.py
+++ b/scripts/textual_inversion_fe.py
@@ -4,11 +4,12 @@
 import os
 import sys
 import curses
+import re
 from ldm.invoke.globals import Globals
 from omegaconf import OmegaConf
 from pathlib import Path
 from typing import List
-from argparse import Namespace
+import argparse
 
 class textualInversionForm(npyscreen.FormMultiPageAction):
     resolutions = [512, 768, 1024]
@@ -22,7 +23,8 @@ def afterEditing(self):
 
     def create(self):
         self.model_names, default = self.get_model_names()
-        default_token = 'cat-toy'
+        default_initializer_token = '★'
+        default_placeholder_token = ''
 
         self.model = self.add_widget_intelligent(
             npyscreen.TitleSelectOne,
@@ -31,45 +33,65 @@ def create(self):
             value=default,
             max_height=len(self.model_names)+1
         )
-        self.initializer_token = self.add_widget_intelligent(
+        self.placeholder_token = self.add_widget_intelligent(
             npyscreen.TitleText,
-            name="Initializer Token",
-            value=default_token,
+            name="Placeholder token",
+            value='',
         )
-        self.placeholder_token = self.add_widget_intelligent(
+        self.nextrely -= 1
+        self.nextrelx += 30
+        self.prompt_token = self.add_widget_intelligent(
+            npyscreen.FixedText,
+            name="Trigger term for use in prompt",
+            value='',
+        )
+        self.nextrelx -= 30
+        self.initializer_token = self.add_widget_intelligent(
             npyscreen.TitleText,
-            name="Placeholder Token",
-            value=f'<{default_token}>'
+            name="Initializer token",
+            value=default_initializer_token,
+        )
+        self.learnable_property = self.add_widget_intelligent(
+            npyscreen.TitleSelectOne,
+            name="Learnable property",
+            values=['object','style'],
+            value=0,
+            max_height=3,
         )
         self.train_data_dir = self.add_widget_intelligent(
             npyscreen.TitleFilenameCombo,
             name='Data Training Directory',
             select_dir=True,
             must_exist=True,
-            value=Path(Globals.root) / 'training-data' / default_token
+            value=Path(Globals.root) / 'training-data' / default_placeholder_token
         )
         self.output_dir = self.add_widget_intelligent(
             npyscreen.TitleFilenameCombo,
             name='Output Destination Directory',
             select_dir=True,
             must_exist=False,
-            value=Path(Globals.root) / 'embeddings' / default_token
+            value=Path(Globals.root) / 'embeddings' / default_placeholder_token
         )
         self.resolution = self.add_widget_intelligent(
             npyscreen.TitleSelectOne,
-            name='Resolution',
+            name='Image resolution (pixels)',
             values = self.resolutions,
             value=0,
             scroll_exit = True,
-            max_height=3
+            max_height=4,
+        )
+        self.center_crop = self.add_widget_intelligent(
+            npyscreen.Checkbox,
+            name="Center crop images before resizing to resolution",
+            value=False,
         )
         self.train_batch_size = self.add_widget_intelligent(
             npyscreen.TitleSlider,
             name='Batch Size',
-            out_of=10,
+            out_of=50,
             step=1,
             lowest=1,
-            value=1
+            value=16
         )
         self.gradient_accumulation_steps = self.add_widget_intelligent(
             npyscreen.TitleSlider,
@@ -97,6 +119,11 @@ def create(self):
             name="Scale learning rate by number GPUs, steps and batch size",
             value=True
         )
+        self.enable_xformers_memory_efficient_attention = self.add_widget_intelligent(
+            npyscreen.Checkbox,
+            name="Use xformers acceleration",
+            value=False,
+        )
         self.lr_scheduler = self.add_widget_intelligent(
             npyscreen.TitleSelectOne,
             name='Learning rate scheduler',
@@ -112,19 +139,22 @@ def create(self):
             lowest=0,
             value=0
         )
-        self.initializer_token.when_value_edited = self.initializer_changed
+        self.placeholder_token.when_value_edited = self.initializer_changed
 
     def initializer_changed(self):
-        new_value = self.initializer_token.value
-        self.placeholder_token.value = f'<{new_value}>'
-        self.train_data_dir.value = Path(Globals.root) / 'training-data' / new_value
-        self.output_dir.value = Path(Globals.root) / 'embeddings' / new_value
+        placeholder = self.placeholder_token.value
+        self.prompt_token.value = f'(Trigger by using <{placeholder}> in your prompts)'
+        self.train_data_dir.value = Path(Globals.root) / 'training-data' / placeholder
+        self.output_dir.value = Path(Globals.root) / 'embeddings' / placeholder
         
     def on_ok(self):
         if self.validate_field_values():
             self.parentApp.setNextForm(None)
             self.editing = False
-            self.run_textual_inversion()
+            self.parentApp.ti_arguments = self.marshall_arguments()
+            npyscreen.notify('Launching textual inversion training. This will take a while...')
+            # The module load takes a while, so we do it while the form and message are still up
+            import ldm.invoke.textual_inversion_training
         else:
             self.editing = True
 
@@ -151,36 +181,54 @@ def get_model_names(self)->(List[str],int):
         defaults = [idx for idx in range(len(model_names)) if 'default' in conf[model_names[idx]]]
         return (model_names,defaults[0])
 
-    def create_namespace(self):
-        args = Namespace()
+    def marshall_arguments(self):
+        args = dict()
 
         # the choices
-        args.model = self.model_names[self.model.value[0]]
-        args.resolution = self.resolutions[self.resolution.value[0]]
-        args.lr_scheduler = self.lr_schedulers[self.lr_scheduler.value[0]]
+        args.update(
+            model = self.model_names[self.model.value[0]],
+            resolution = self.resolutions[self.resolution.value[0]],
+            lr_scheduler = self.lr_schedulers[self.lr_scheduler.value[0]]
+        )
 
         # all the strings
         for attr in ('initializer_token','placeholder_token','train_data_dir','output_dir','scale_lr'):
-            setattr(args,attr,getattr(self,attr).value)
+            args[attr] = getattr(self,attr).value
         # all the integers
         for attr in ('train_batch_size','gradient_accumulation_steps',
                      'max_train_steps','lr_warmup_steps'):
-            setattr(args,attr,int(getattr(self,attr).value))
+            args[attr] = int(getattr(self,attr).value)
+
         # the floats (just one)
-        args.learning_rate = float(self.learning_rate.value)
+        args.update(
+            learning_rate = float(self.learning_rate.value)
+        )
         return args
 
-    def run_textual_inversion(self):
-        npyscreen.notify('Launching textual inversion training. This will take a while...')
-        from ldm.invoke.textual_inversion_training import do_textual_inversion_training, parse_args
-        args = parse_args()
-        args.root_dir = Globals.root
-        do_textual_inversion_training(args)
-
 class MyApplication(npyscreen.NPSAppManaged):
+    def __init__(self):
+        super().__init__()
+        self.ti_arguments=None
+
     def onStart(self):
         npyscreen.setTheme(npyscreen.Themes.DefaultTheme)
         self.main = self.addForm('MAIN', textualInversionForm, name='Textual Inversion Settings')
 
 if __name__ == '__main__':
-   TestApp = MyApplication().run()
+    parser = argparse.ArgumentParser(description='InvokeAI textual inversion training')
+    parser.add_argument(
+        '--root_dir','--root-dir',
+        type=Path,
+        default=Globals.root,
+        help='Path to the invokeai runtime directory',
+    )
+    args = parser.parse_args()
+    Globals.root = args.root_dir
+    
+    myapplication = MyApplication()
+    myapplication.run()
+    
+    from ldm.invoke.textual_inversion_training import do_textual_inversion_training
+    if args := myapplication.ti_arguments:
+        do_textual_inversion_training(**args)
+    
diff --git a/setup.py b/setup.py
index 3324a0184f4..06e53057e3b 100644
--- a/setup.py
+++ b/setup.py
@@ -85,7 +85,8 @@ def list_files(directory):
         'Topic :: Scientific/Engineering :: Image Processing',
     ],
     scripts = ['scripts/invoke.py','scripts/configure_invokeai.py', 'scripts/sd-metadata.py',
-               'scripts/preload_models.py', 'scripts/images2prompt.py','scripts/merge_embeddings.py'
+               'scripts/preload_models.py', 'scripts/images2prompt.py','scripts/merge_embeddings.py',
+               'scripts/textual_inversion_fe.py','scripts/textual_inversion.py'
     ],
     data_files=FRONTEND_FILES,
 )

From 690365a82d647a8ea32930cff8c12bf66d17d4b9 Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Tue, 3 Jan 2023 23:45:20 -0500
Subject: [PATCH 123/199] fix crash in convert_and_import()

- This corrects a "local variable referenced before assignment" error
  in model_manager.convert_and_import()
---
 ldm/invoke/CLI.py           | 2 +-
 ldm/invoke/model_manager.py | 1 +
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/ldm/invoke/CLI.py b/ldm/invoke/CLI.py
index 27986609a83..d347ff93399 100644
--- a/ldm/invoke/CLI.py
+++ b/ldm/invoke/CLI.py
@@ -610,7 +610,7 @@ def optimize_model(ckpt_path:str, gen, opt, completer):
         print(f'** {basename} is already optimized. Will not overwrite.')
         return
     new_config = gen.model_manager.convert_and_import(ckpt_path, diffuser_path)
-    if write_config_file(opt.conf, gen, basename, new_config, clobber=False):
+    if new_config and write_config_file(opt.conf, gen, basename, new_config, clobber=False):
         completer.add_model(basename)
 
 def del_config(model_name:str, gen, opt, completer):
diff --git a/ldm/invoke/model_manager.py b/ldm/invoke/model_manager.py
index a217f567f0e..ac39591d276 100644
--- a/ldm/invoke/model_manager.py
+++ b/ldm/invoke/model_manager.py
@@ -582,6 +582,7 @@ def convert_and_import(self, ckpt_path:Path, diffuser_path:Path)->dict:
         Convert a legacy ckpt weights file to diffuser model and import
         into models.yaml.
         '''
+        new_config = None
         from ldm.invoke.ckpt_to_diffuser import convert_ckpt_to_diffuser
         import transformers
         if diffuser_path.exists():

From 13129fd56b129fa8748de7513ddc40e8352526eb Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Tue, 3 Jan 2023 23:54:50 -0500
Subject: [PATCH 124/199] potential workaround for no 'state_dict' key error

- As reported in https://github.com/huggingface/diffusers/issues/1876
---
 ldm/invoke/ckpt_to_diffuser.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/ldm/invoke/ckpt_to_diffuser.py b/ldm/invoke/ckpt_to_diffuser.py
index 7d8510f3166..f04c68a814e 100644
--- a/ldm/invoke/ckpt_to_diffuser.py
+++ b/ldm/invoke/ckpt_to_diffuser.py
@@ -798,7 +798,10 @@ def convert_ckpt_to_diffuser(checkpoint_path:str,
     else:
         print("global_step key not found in model")
         global_step = None
-    checkpoint = checkpoint["state_dict"]
+
+    # sometimes there is a state_dict key and sometimes not
+    if 'state_dict' in checkpoint:
+        checkpoint = checkpoint["state_dict"]
 
     upcast_attention = False
     if original_config_file is None:

From 8bcf4b7d5f21e77bc1daf58ed03aec8f4e9cd6aa Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Wed, 4 Jan 2023 00:02:45 -0500
Subject: [PATCH 125/199] create TI output dir if needed

---
 scripts/textual_inversion_fe.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/scripts/textual_inversion_fe.py b/scripts/textual_inversion_fe.py
index 79d96a5a437..9a8363a500b 100755
--- a/scripts/textual_inversion_fe.py
+++ b/scripts/textual_inversion_fe.py
@@ -70,7 +70,7 @@ def create(self):
             name='Output Destination Directory',
             select_dir=True,
             must_exist=False,
-            value=Path(Globals.root) / 'embeddings' / default_placeholder_token
+            value=Path(Globals.root) / 'trained-checkpoints' / default_placeholder_token
         )
         self.resolution = self.add_widget_intelligent(
             npyscreen.TitleSelectOne,
@@ -230,5 +230,6 @@ def onStart(self):
     
     from ldm.invoke.textual_inversion_training import do_textual_inversion_training
     if args := myapplication.ti_arguments:
+        os.makedirs(args['output_dir'],exist_ok=True)
         do_textual_inversion_training(**args)
     

From 2ac2f8120ccec323a97fb08d6dd850eaf2d02d66 Mon Sep 17 00:00:00 2001
From: Wybartel-luxmc <37852506+Wybartel-luxmc@users.noreply.github.com>
Date: Wed, 4 Jan 2023 11:25:59 -0800
Subject: [PATCH 126/199] Update environment-lin-cuda.yml (#2159)

Fixing line 42 to be the proper order to define the transformers requirement: ~= instead of =~
---
 environments-and-requirements/environment-lin-cuda.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/environments-and-requirements/environment-lin-cuda.yml b/environments-and-requirements/environment-lin-cuda.yml
index f48f2f3110b..82b3a8a3703 100644
--- a/environments-and-requirements/environment-lin-cuda.yml
+++ b/environments-and-requirements/environment-lin-cuda.yml
@@ -39,7 +39,7 @@ dependencies:
     - test-tube>=0.7.5
     - torch-fidelity==0.3.0
     - torchmetrics==0.7.0
-    - transformers=~4.25
+    - transformers~=4.25
     - git+https://github.com/openai/CLIP.git@main#egg=clip
     - git+https://github.com/Birch-san/k-diffusion.git@mps#egg=k-diffusion
     - git+https://github.com/invoke-ai/clipseg.git@relaxed-python-requirement#egg=clipseg

From 34626f35f2ec57f3f1fd902adb2ebdc5eced0e48 Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Wed, 4 Jan 2023 14:07:39 -0800
Subject: [PATCH 127/199] diffusers: update sampler-to-scheduler mapping

based on https://github.com/huggingface/diffusers/issues/277#issuecomment-1371428672
---
 ldm/generate.py | 29 ++++++++++++-----------------
 1 file changed, 12 insertions(+), 17 deletions(-)

diff --git a/ldm/generate.py b/ldm/generate.py
index 70a887a693a..4267eb1b4b2 100644
--- a/ldm/generate.py
+++ b/ldm/generate.py
@@ -13,20 +13,13 @@
 import traceback
 
 import cv2
+import diffusers
 import numpy as np
 import skimage
 import torch
 import transformers
 from PIL import Image, ImageOps
-from diffusers import HeunDiscreteScheduler
 from diffusers.pipeline_utils import DiffusionPipeline
-from diffusers.schedulers.scheduling_ddim import DDIMScheduler
-from diffusers.schedulers.scheduling_dpmsolver_multistep import DPMSolverMultistepScheduler
-from diffusers.schedulers.scheduling_euler_ancestral_discrete import EulerAncestralDiscreteScheduler
-from diffusers.schedulers.scheduling_euler_discrete import EulerDiscreteScheduler
-from diffusers.schedulers.scheduling_ipndm import IPNDMScheduler
-from diffusers.schedulers.scheduling_lms_discrete import LMSDiscreteScheduler
-from diffusers.schedulers.scheduling_pndm import PNDMScheduler
 from omegaconf import OmegaConf
 from pytorch_lightning import seed_everything, logging
 
@@ -1008,19 +1001,21 @@ def _set_sampler_legacy(self):
     def _set_scheduler(self):
         default = self.model.scheduler
 
+        # See https://github.com/huggingface/diffusers/issues/277#issuecomment-1371428672
         scheduler_map = dict(
-            ddim=DDIMScheduler,
-            dpmpp_2=DPMSolverMultistepScheduler,
-            ipndm=IPNDMScheduler,
+            ddim=diffusers.DDIMScheduler,
+            dpmpp_2=diffusers.DPMSolverMultistepScheduler,
+            k_dpm_2=diffusers.KDPM2DiscreteScheduler,
+            k_dpm_2_a=diffusers.KDPM2AncestralDiscreteScheduler,
             # DPMSolverMultistepScheduler is technically not `k_` anything, as it is neither
             # the k-diffusers implementation nor included in EDM (Karras 2022), but we can
             # provide an alias for compatibility.
-            k_dpmpp_2=DPMSolverMultistepScheduler,
-            k_euler=EulerDiscreteScheduler,
-            k_euler_a=EulerAncestralDiscreteScheduler,
-            k_heun=HeunDiscreteScheduler,
-            k_lms=LMSDiscreteScheduler,
-            plms=PNDMScheduler,
+            k_dpmpp_2=diffusers.DPMSolverMultistepScheduler,
+            k_euler=diffusers.EulerDiscreteScheduler,
+            k_euler_a=diffusers.EulerAncestralDiscreteScheduler,
+            k_heun=diffusers.HeunDiscreteScheduler,
+            k_lms=diffusers.LMSDiscreteScheduler,
+            plms=diffusers.PNDMScheduler,
         )
 
         if self.sampler_name in scheduler_map:

From 7d65152a73f8244a6fa3c8df9c3e3c5666506fab Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Wed, 4 Jan 2023 19:00:37 -0500
Subject: [PATCH 128/199] improve user exp for ckt to diffusers conversion

- !optimize_models command now operates on an existing ckpt file entry in models.yaml
- replaces existing entry, rather than adding a new one
- offers to delete the ckpt file after conversion
---
 ldm/invoke/CLI.py           | 54 ++++++++++++++++++++++++++-----------
 ldm/invoke/model_manager.py | 27 ++++++++++++++-----
 ldm/invoke/readline.py      | 34 +++++++++++++----------
 3 files changed, 78 insertions(+), 37 deletions(-)

diff --git a/ldm/invoke/CLI.py b/ldm/invoke/CLI.py
index d347ff93399..ec51b47637d 100644
--- a/ldm/invoke/CLI.py
+++ b/ldm/invoke/CLI.py
@@ -168,7 +168,7 @@ def main_loop(gen, opt):
     # The readline completer reads history from the .dream_history file located in the
     # output directory specified at the time of script launch. We do not currently support
     # changing the history file midstream when the output directory is changed.
-    completer   = get_completer(opt, models=list(model_config.keys()))
+    completer   = get_completer(opt, models=gen.model_manager.list_models())
     set_default_output_dir(opt, completer)
     add_embedding_terms(gen, completer)
     output_cntr = completer.get_current_history_length()+1
@@ -474,9 +474,9 @@ def do_command(command:str, gen, opt:Args, completer) -> tuple:
     elif command.startswith('!optimize'):
         path = shlex.split(command)
         if len(path) < 2:
-            print('** please provide a path to a .ckpt file')
-        elif not os.path.exists(path[1]):
-            print(f'** {path[1]}: file not found')
+            print('** please provide an installed model name')
+        elif not path[1] in gen.model_manager.list_models():
+            print(f'** {path[1]}: model not found')
         else:
             optimize_model(path[1], gen, opt, completer)
         completer.add_history(command)
@@ -600,18 +600,41 @@ def add_weights_to_config(model_path:str, gen, opt, completer):
     make_default = input('Make this the default model? [n] ') in ('y','Y')
 
     if write_config_file(opt.conf, gen, model_name, new_config, make_default=make_default):
-        completer.add_model(model_name)
+        completer.models(gen.model_manager.list_models())
+
+def optimize_model(model_name:str, gen, opt, completer):
+    manager = gen.model_manager
+    if not (model_info := manager.model_info(model_name)):
+        print(f'** unknown model: {model_name}')
+        return
+    ckpt_path = Path(model_info['weights'])
+    if not ckpt_path.is_absolute():
+        ckpt_path = Path(Globals.root,ckpt_path)
 
-def optimize_model(ckpt_path:str, gen, opt, completer):
-    ckpt_path = Path(ckpt_path)
     basename = ckpt_path.stem
     diffuser_path = Path(Globals.root, 'models','optimized-ckpts',basename)
     if diffuser_path.exists():
-        print(f'** {basename} is already optimized. Will not overwrite.')
+        print(f'** {model_name} is already optimized. Will not overwrite. If this is an error, please remove the directory {diffuser_path} and try again.')
+        return
+     
+    new_config = gen.model_manager.convert_and_import(
+        ckpt_path,
+        diffuser_path,
+        model_name=model_name,
+        model_description=model_info['description'],
+        commit_to_conf=opt.conf,
+    )
+    if not new_config:
         return
-    new_config = gen.model_manager.convert_and_import(ckpt_path, diffuser_path)
-    if new_config and write_config_file(opt.conf, gen, basename, new_config, clobber=False):
-        completer.add_model(basename)
+
+    response = input(f'OK to delete original .ckpt file at ({ckpt_path} ? [n] ')
+    if response.startswith(('y','Y')):
+        ckpt_path.unlink(missing_ok=True)
+        print(f'{ckpt_path} deleted')
+
+    completer.update_models(gen.model_manager.list_models())
+    if input(f'Load optimized model {model_name}? [y] ') not in ('n','N'):
+        gen.set_model(model_name)
 
 def del_config(model_name:str, gen, opt, completer):
     current_model = gen.model_name
@@ -621,7 +644,7 @@ def del_config(model_name:str, gen, opt, completer):
     gen.model_manager.del_model(model_name)
     gen.model_manager.commit(opt.conf)
     print(f'** {model_name} deleted')
-    completer.del_model(model_name)
+    completer.update_models(gen.model_manager.list_models())
 
 def edit_config(model_name:str, gen, opt, completer):
     config = gen.model_manager.config
@@ -643,15 +666,15 @@ def edit_config(model_name:str, gen, opt, completer):
     completer.complete_extensions(None)
     write_config_file(opt.conf, gen, model_name, new_config, clobber=True, make_default=make_default)
 
-def write_config_file(conf_path, gen, model_name, new_config, clobber=False, make_default=False):
+def write_config_file(conf_path, gen, model_name, new_config, clobber=False, make_default=False)->bool:
     current_model = gen.model_name
 
-    op = 'modify' if clobber else 'import'
+    op = 'write' if clobber else 'import'
     print('\n>> New configuration:')
     if make_default:
         new_config['default'] = True
     print(yaml.dump({model_name:new_config}))
-    if input(f'OK to {op} [n]? ') not in ('y','Y'):
+    if input(f'OK to {op} [y]? ').startswith(('n','N')):
         return False
 
     try:
@@ -672,7 +695,6 @@ def write_config_file(conf_path, gen, model_name, new_config, clobber=False, mak
         gen.model_manager.set_default_model(model_name)
 
     gen.model_manager.commit(conf_path)
-
     do_switch = input(f'Keep model loaded? [y]')
     if len(do_switch)==0 or do_switch[0] in ('y','Y'):
         pass
diff --git a/ldm/invoke/model_manager.py b/ldm/invoke/model_manager.py
index ac39591d276..1b8c3e81170 100644
--- a/ldm/invoke/model_manager.py
+++ b/ldm/invoke/model_manager.py
@@ -16,6 +16,7 @@
 import time
 import traceback
 import warnings
+import shutil
 from pathlib import Path
 from typing import Union, Any
 from ldm.util import download_with_progress_bar
@@ -156,6 +157,7 @@ def list_models(self) -> dict:
         Return a dict of models in the format:
         { model_name1: {'status': ('active'|'cached'|'not loaded'),
                         'description': description,
+                        'format': ('ckpt'|'diffusers'|'vae'),
                        },
           model_name2: { etc }
         Please use model_manager.models() to get all the model names,
@@ -184,7 +186,7 @@ def print_models(self) -> None:
         for name in models:
             if models[name]['format'] == 'vae':
                 continue
-            line = f'{name:25s} {models[name]["status"]:>10s}  {models[name]["description"]}'
+            line = f'{name:25s} {models[name]["status"]:>10s}  {models[name]["format"]:10s} {models[name]["description"]}'
             if models[name]['status'] == 'active':
                 line = f'\033[1m{line}\033[0m'
             print(line)
@@ -577,7 +579,13 @@ def autoconvert_weights(
             self.convert_and_import(ckpt, ckpt_files[ckpt])
         self.commit(conf_path)
 
-    def convert_and_import(self, ckpt_path:Path, diffuser_path:Path)->dict:
+    def convert_and_import(self,
+                           ckpt_path:Path,
+                           diffuser_path:Path,
+                           model_name=None,
+                           model_description=None,
+                           commit_to_conf:Path=None,
+    )->dict:
         '''
         Convert a legacy ckpt weights file to diffuser model and import
         into models.yaml.
@@ -589,25 +597,30 @@ def convert_and_import(self, ckpt_path:Path, diffuser_path:Path)->dict:
             print(f'ERROR: The path {str(diffuser_path)} already exists. Please move or remove it and try again.')
             return
 
-        print(f'>> {ckpt_path.name}: optimizing (30-60s).')
+        model_name = model_name or diffuser_path.name
+        model_description = model_description or 'Optimized version of {model_name}'
+        print(f'>> {model_name}: optimizing (30-60s).')
         try:
-            model_name = diffuser_path.name
             verbosity =transformers.logging.get_verbosity()
             transformers.logging.set_verbosity_error()
-            convert_ckpt_to_diffuser(ckpt_path, diffuser_path)
+            convert_ckpt_to_diffuser(ckpt_path, diffuser_path,extract_ema=True)
             transformers.logging.set_verbosity(verbosity)
             print(f'>> Success. Optimized model is now located at {str(diffuser_path)}')
             print(f'>> Writing new config file entry for {model_name}...',end='')
             new_config = dict(
                 path=str(diffuser_path),
-                description=f'Optimized version of {model_name}',
+                description=model_description,
                 format='diffusers',
             )
+            self.del_model(model_name)
             self.add_model(model_name, new_config, True)
-            print('done.')
+            if commit_to_conf:
+                self.commit(commit_to_conf)
         except Exception as e:
             print(f'** Conversion failed: {str(e)}')
             traceback.print_exc()
+            
+        print('done.')
         return new_config
 
     def del_config(self, model_name:str, gen, opt, completer):
diff --git a/ldm/invoke/readline.py b/ldm/invoke/readline.py
index 3023d6db971..02fc46cec6d 100644
--- a/ldm/invoke/readline.py
+++ b/ldm/invoke/readline.py
@@ -67,8 +67,11 @@
     '!edit_model',
     '!del_model',
     )
+CKPT_MODEL_COMMANDS = (
+    '!optimize_model',
+)
 WEIGHT_COMMANDS = (
-    '!import_model','!optimize_model',
+    '!import_model',
     )
 IMG_PATH_COMMANDS = (
     '--outdir[=\s]',
@@ -91,9 +94,9 @@
 text_regexp = '(' + '|'.join(TEXT_PATH_COMMANDS) + ')\s*\S*$'
 
 class Completer(object):
-    def __init__(self, options, models=[]):
+    def __init__(self, options, models={}):
         self.options     = sorted(options)
-        self.models      = sorted(models)
+        self.models      = models
         self.seeds       = set()
         self.matches     = list()
         self.default_dir = None
@@ -134,6 +137,10 @@ def complete(self, text, state):
             elif re.match('^'+'|'.join(MODEL_COMMANDS),buffer):
                 self.matches= self._model_completions(text, state)
 
+            # looking for a ckpt model 
+            elif re.match('^'+'|'.join(CKPT_MODEL_COMMANDS),buffer):
+                self.matches= self._model_completions(text, state, ckpt_only=True)
+
             elif re.search(weight_regexp,buffer):
                 self.matches = self._path_completions(
                     text,
@@ -242,18 +249,12 @@ def set_line(self,line)->None:
         self.linebuffer = line
         readline.redisplay()
 
-    def add_model(self,model_name:str)->None:
-        '''
-        add a model name to the completion list
-        '''
-        self.models.append(model_name)
-
-    def del_model(self,model_name:str)->None:
+    def update_models(self,models:dict)->None:
         '''
-        removes a model name from the completion list
+        update our list of models
         '''
-        self.models.remove(model_name)
-
+        self.models = models
+        
     def _seed_completions(self, text, state):
         m = re.search('(-S\s?|--seed[=\s]?)(\d*)',text)
         if m:
@@ -294,7 +295,7 @@ def _concept_completions(self, text, state):
         matches.sort()
         return matches
 
-    def _model_completions(self, text, state):
+    def _model_completions(self, text, state, ckpt_only=False):
         m = re.search('(!switch\s+)(\w*)',text)
         if m:
             switch  = m.groups()[0]
@@ -304,6 +305,11 @@ def _model_completions(self, text, state):
             partial = text
         matches = list()
         for s in self.models:
+            format = self.models[s]['format']
+            if format == 'vae':
+                continue
+            if ckpt_only and format != 'ckpt':
+                continue
             if s.startswith(partial):
                 matches.append(switch+s)
         matches.sort()

From ec981745e7301ad5a007a62c8d600bb5c7cd6e92 Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Wed, 4 Jan 2023 19:42:12 -0800
Subject: [PATCH 129/199] web: adapt progress callback to deal with old
 generator or new diffusers pipeline

---
 backend/invoke_ai_web_server.py | 13 +++++++++----
 1 file changed, 9 insertions(+), 4 deletions(-)

diff --git a/backend/invoke_ai_web_server.py b/backend/invoke_ai_web_server.py
index 3e0301346e0..f41385bad64 100644
--- a/backend/invoke_ai_web_server.py
+++ b/backend/invoke_ai_web_server.py
@@ -933,9 +933,7 @@ def generate_images(
                 init_img_path = self.get_image_path_from_url(init_img_url)
                 generation_parameters["init_img"] = Image.open(init_img_path).convert('RGB')
 
-            def image_progress(progress_state: PipelineIntermediateState):
-                step = progress_state.step
-                sample = progress_state.latents
+            def image_progress(sample, step):
                 if self.canceled.is_set():
                     raise CanceledException
 
@@ -1206,9 +1204,16 @@ def image_done(image, seed, first_seed, attention_maps_image=None):
 
             print(generation_parameters)
 
+            def diffusers_step_callback_adapter(*cb_args, **kwargs):
+                if isinstance(cb_args[0], PipelineIntermediateState):
+                    progress_state: PipelineIntermediateState = cb_args[0]
+                    return image_progress(progress_state.latents, progress_state.step)
+                else:
+                    return image_progress(*cb_args, **kwargs)
+
             self.generate.prompt2image(
                 **generation_parameters,
-                step_callback=image_progress,
+                step_callback=diffusers_step_callback_adapter,
                 image_callback=image_done
             )
 

From fb178d52c94a5b25e93ed6a5acaf19297885b50c Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Thu, 5 Jan 2023 15:10:09 -0500
Subject: [PATCH 130/199] clean-up model_manager code

- add_model() verified to work for .ckpt local paths,
  .ckpt remote URLs, diffusers local paths, and
  diffusers repo_ids

- convert_and_import() verified to work for local and
  remove .ckpt files
---
 ldm/invoke/CLI.py           | 146 +++++++++++++++++++++++-------------
 ldm/invoke/model_manager.py |  16 ++--
 2 files changed, 104 insertions(+), 58 deletions(-)

diff --git a/ldm/invoke/CLI.py b/ldm/invoke/CLI.py
index ec51b47637d..be1a00311fb 100644
--- a/ldm/invoke/CLI.py
+++ b/ldm/invoke/CLI.py
@@ -463,11 +463,9 @@ def do_command(command:str, gen, opt:Args, completer) -> tuple:
     elif command.startswith('!import'):
         path = shlex.split(command)
         if len(path) < 2:
-            print('** please provide a path to a .ckpt or .vae model file')
-        elif not os.path.exists(path[1]):
-            print(f'** {path[1]}: file not found')
+            print('** please provide (1) a URL to a .ckpt file to import; (2) a local path to a .ckpt file; or (3) a diffusers repository id in the form stabilityai/stable-diffusion-2-1')
         else:
-            add_weights_to_config(path[1], gen, opt, completer)
+            import_model(path[1], gen, opt, completer)
         completer.add_history(command)
         operation = None
 
@@ -548,59 +546,101 @@ def set_default_output_dir(opt:Args, completer:Completer):
     completer.set_default_dir(opt.outdir)
 
 
-def add_weights_to_config(model_path:str, gen, opt, completer):
-    print(f'>> Model import in process. Please enter the values needed to configure this model:')
-    print()
+def import_model(model_path:str, gen, opt, completer):
+    '''
+    model_path can be (1) a URL to a .ckpt file; (2) a local .ckpt file path; or
+    (3) a huggingface repository id
+    '''
+    model_name = None
+    
+    if model_path.startswith('http') or os.path.exists(model_path):
+        model_name = import_ckpt_model(model_path, gen, opt, completer)
+    elif re.match('^[\w-]+/[\w-]+$',model_path):
+        model_name = import_diffuser_model(model_path, gen, opt, completer)
+    else:
+        print(f'** {model_path} is neither the path to a .ckpt file nor a diffusers repository id. Can\'t import.')
 
-    new_config = {}
-    new_config['weights'] = model_path
-    new_config['format'] = 'ckpt'
+    if not model_name:
+        return
+        
+    if not _verify_load(model_name, gen):
+        print(f'** model failed to load. Discarding configuration entry')
+        gen.model_manager.del_model(model_name)
+        return
+    
+    if input('Make this the default model? [n] ') in ('y','Y'):
+        gen.model_manager.set_default_model(model_name)
 
-    done = False
-    while not done:
-        model_name = input('Short name for this model: ')
-        if not re.match('^[\w._-]+$',model_name):
-            print('** model name must contain only words, digits and the characters [._-] **')
-        else:
-            done = True
-    new_config['description'] = input('Description of this model: ')
+    gen.model_manager.commit(opt.conf)
+    completer.update_models(gen.model_manager.list_models())
+    print(f'>> {model_name} successfully installed')
+
+def import_diffuser_model(path_or_repo:str, gen, opt, completer)->str:
+    manager = gen.model_manager
+    model_name, model_description = _get_model_name_and_desc(manager)
+
+    if not manager.import_diffuser_model(
+            path_or_repo,
+            model_name = model_name,
+            description = model_description):
+        print(f'** model failed to import')
+        return None
+    return model_name
+
+def import_ckpt_model(path_or_url:str, gen, opt, completer)->str:
+    manager = gen.model_manager
+    model_name, model_description = _get_model_name_and_desc(manager)
+    config_file = None
 
     completer.complete_extensions(('.yaml','.yml'))
     completer.linebuffer = 'configs/stable-diffusion/v1-inference.yaml'
-
     done = False
     while not done:
-        new_config['config'] = input('Configuration file for this model: ')
-        done = os.path.exists(new_config['config'])
+        config_file = input('Configuration file for this model: ')
+        done = os.path.exists(config_file)
+    completer.complete_extensions(None)
 
-    done = False
-    completer.complete_extensions(('.vae.pt','.vae','.ckpt'))
-    while not done:
-        vae = input('VAE autoencoder file for this model [None]: ')
-        if os.path.exists(vae):
-            new_config['vae'] = vae
-            done = True
-        else:
-            done = len(vae)==0
+    if not manager.import_ckpt_model(
+            path_or_url,
+            config = config_file,
+            model_name = model_name,
+            model_description = model_description
+    ):
+        print(f'** model failed to import')
+        return None
 
-    completer.complete_extensions(None)
+    return model_name
 
-    for field in ('width','height'):
-        done = False
-        while not done:
-            try:
-                completer.linebuffer = '512'
-                value = int(input(f'Default image {field}: '))
-                assert value >= 64 and value <= 2048
-                new_config[field] = value
-                done = True
-            except:
-                print('** Please enter a valid integer between 64 and 2048')
+def _verify_load(model_name:str, gen)->bool:
+    print('>> Verifying that new model loads...')
+    current_model = gen.model_name
+    if not gen.model_manager.get_model(model_name):
+        return False
+    do_switch = input(f'Keep model loaded? [y] ')
+    if len(do_switch)>0 and do_switch[0] not in ('y','Y'):
+        gen.set_model(model_name)
+    else:
+        print('>> Restoring previous model')
+        gen.set_model(current_model)
+    return True
 
-    make_default = input('Make this the default model? [n] ') in ('y','Y')
+def _get_model_name_and_desc(model_manager):
+    model_name = None
+    model_description = None
+    existing_models = model_manager.list_models()
 
-    if write_config_file(opt.conf, gen, model_name, new_config, make_default=make_default):
-        completer.models(gen.model_manager.list_models())
+    done = False
+    while not done:
+        model_name = input('Short name for this model: ')
+        if not re.match('^[\w._-]+$',model_name):
+            print('** model name must contain only words, digits and the characters [._-] **')
+        elif model_name in existing_models:
+            print(f'** the name {model_name} is already in use. Pick another.')
+        else:
+            done = True
+    model_description = input('Description for this model: ')
+   
+    return model_name, model_description
 
 def optimize_model(model_name:str, gen, opt, completer):
     manager = gen.model_manager
@@ -627,15 +667,15 @@ def optimize_model(model_name:str, gen, opt, completer):
     if not new_config:
         return
 
-    response = input(f'OK to delete original .ckpt file at ({ckpt_path} ? [n] ')
-    if response.startswith(('y','Y')):
-        ckpt_path.unlink(missing_ok=True)
-        print(f'{ckpt_path} deleted')
-
     completer.update_models(gen.model_manager.list_models())
     if input(f'Load optimized model {model_name}? [y] ') not in ('n','N'):
         gen.set_model(model_name)
 
+    response = input(f'Delete the original .ckpt file at ({ckpt_path} ? [n] ')
+    if response.startswith(('y','Y')):
+        ckpt_path.unlink(missing_ok=True)
+        print(f'{ckpt_path} deleted')
+
 def del_config(model_name:str, gen, opt, completer):
     current_model = gen.model_name
     if model_name == current_model:
@@ -646,6 +686,9 @@ def del_config(model_name:str, gen, opt, completer):
     print(f'** {model_name} deleted')
     completer.update_models(gen.model_manager.list_models())
 
+
+# NOTE: edit_config() must be rewritten for diffusers
+# We should only allow user to change: name, description, default
 def edit_config(model_name:str, gen, opt, completer):
     config = gen.model_manager.config
 
@@ -666,6 +709,7 @@ def edit_config(model_name:str, gen, opt, completer):
     completer.complete_extensions(None)
     write_config_file(opt.conf, gen, model_name, new_config, clobber=True, make_default=make_default)
 
+# NOTE: delete this whole function
 def write_config_file(conf_path, gen, model_name, new_config, clobber=False, make_default=False)->bool:
     current_model = gen.model_name
 
@@ -695,7 +739,7 @@ def write_config_file(conf_path, gen, model_name, new_config, clobber=False, mak
         gen.model_manager.set_default_model(model_name)
 
     gen.model_manager.commit(conf_path)
-    do_switch = input(f'Keep model loaded? [y]')
+    do_switch = input(f'Keep model loaded? [y] ')
     if len(do_switch)==0 or do_switch[0] in ('y','Y'):
         pass
     else:
diff --git a/ldm/invoke/model_manager.py b/ldm/invoke/model_manager.py
index 1b8c3e81170..467aaac2f60 100644
--- a/ldm/invoke/model_manager.py
+++ b/ldm/invoke/model_manager.py
@@ -99,7 +99,7 @@ def get_model(self, model_name:str):
                 assert self.current_model,'** FATAL: no current model to restore to'
                 print(f'** restoring {self.current_model}')
                 self.get_model(self.current_model)
-                return
+                return None
 
         self.current_model = model_name
         self._push_newest_model(model_name)
@@ -150,7 +150,9 @@ def is_legacy(self,model_name:str)->bool:
         Return true if this is a legacy (.ckpt) model
         '''
         info = self.model_info(model_name)
-        return info['format']=='ckpt' if info else False
+        if 'weights' in info and info['weights'].endswith('.ckpt'):
+            return True
+        return False
 
     def list_models(self) -> dict:
         '''
@@ -507,7 +509,7 @@ def import_ckpt_model(self,
                     weights:Union[str,Path],
                     config:Union[str,Path]='configs/stable-diffusion/v1-inference.yaml',
                     model_name:str=None,
-                    description:str=None,
+                    model_description:str=None,
                     commit_to_conf:Path=None,
     )->bool:
         '''
@@ -532,12 +534,12 @@ def import_ckpt_model(self,
         if config_path is None or not config_path.exists():
             return False
             
-        model_name = model_name or Path(basename).stem
-        description = description or f'imported stable diffusion weights file {model_name}'
+        model_name = model_name or Path(weights).stem
+        model_description = model_description or f'imported stable diffusion weights file {model_name}'
         new_config = dict(
             weights=str(weights_path),
             config=str(config_path),
-            description=description,
+            description=model_description,
             format='ckpt',
             width=512,
             height=512
@@ -668,7 +670,7 @@ def commit(self,config_file_path:str) -> None:
         '''
         yaml_str = OmegaConf.to_yaml(self.config)
         if not os.path.isabs(config_file_path):
-            config_file_path = os.path.normpath(os.path.join(Globals.root,opt.conf))
+            config_file_path = os.path.normpath(os.path.join(Globals.root,config_file_path))
         tmpfile = os.path.join(os.path.dirname(config_file_path),'new_config.tmp')
         with open(tmpfile, 'w') as outfile:
             outfile.write(self.preamble())

From 61d8be1b5597e48394c279b666aa68a9fd41875d Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Thu, 5 Jan 2023 20:54:10 -0500
Subject: [PATCH 131/199] handle edge cases for import_model() and
 convert_model()

---
 ldm/invoke/CLI.py           | 172 +++++++++++++++++++-----------------
 ldm/invoke/args.py          |   7 +-
 ldm/invoke/model_manager.py |   2 +-
 ldm/invoke/readline.py      |   5 +-
 4 files changed, 101 insertions(+), 85 deletions(-)

diff --git a/ldm/invoke/CLI.py b/ldm/invoke/CLI.py
index be1a00311fb..eb01b2af78e 100644
--- a/ldm/invoke/CLI.py
+++ b/ldm/invoke/CLI.py
@@ -469,6 +469,18 @@ def do_command(command:str, gen, opt:Args, completer) -> tuple:
         completer.add_history(command)
         operation = None
 
+    elif command.startswith('!convert'):
+        path = shlex.split(command)
+        if len(path) < 2:
+            print('** please provide the path to a .ckpt or .safetensors model')
+        elif not os.path.exists(path[1]):
+            print(f'** {path[1]}: model not found')
+        else:
+            optimize_model(path[1], gen, opt, completer)
+        completer.add_history(command)
+        operation = None
+        
+
     elif command.startswith('!optimize'):
         path = shlex.split(command)
         if len(path) < 2:
@@ -553,7 +565,7 @@ def import_model(model_path:str, gen, opt, completer):
     '''
     model_name = None
     
-    if model_path.startswith('http') or os.path.exists(model_path):
+    if model_path.startswith(('http:','https:','ftp:')) or os.path.exists(model_path):
         model_name = import_ckpt_model(model_path, gen, opt, completer)
     elif re.match('^[\w-]+/[\w-]+$',model_path):
         model_name = import_diffuser_model(model_path, gen, opt, completer)
@@ -577,7 +589,14 @@ def import_model(model_path:str, gen, opt, completer):
 
 def import_diffuser_model(path_or_repo:str, gen, opt, completer)->str:
     manager = gen.model_manager
-    model_name, model_description = _get_model_name_and_desc(manager)
+    default_name = Path(path_or_url).stem
+    default_description = f'Imported model {default_name}'
+    model_name, model_description = _get_model_name_and_desc(
+        manager,
+        completer,
+        model_name=default_name,
+        model_description=default_description
+    )
 
     if not manager.import_diffuser_model(
             path_or_repo,
@@ -589,7 +608,14 @@ def import_diffuser_model(path_or_repo:str, gen, opt, completer)->str:
 
 def import_ckpt_model(path_or_url:str, gen, opt, completer)->str:
     manager = gen.model_manager
-    model_name, model_description = _get_model_name_and_desc(manager)
+    default_name = Path(path_or_url).stem
+    default_description = f'Imported model {default_name}'
+    model_name, model_description = _get_model_name_and_desc(
+        manager,
+        completer,
+        model_name=default_name,
+        model_description=default_description
+    )
     config_file = None
 
     completer.complete_extensions(('.yaml','.yml'))
@@ -617,51 +643,56 @@ def _verify_load(model_name:str, gen)->bool:
     if not gen.model_manager.get_model(model_name):
         return False
     do_switch = input(f'Keep model loaded? [y] ')
-    if len(do_switch)>0 and do_switch[0] not in ('y','Y'):
+    if len(do_switch)==0 or do_switch[0] in ('y','Y'):
         gen.set_model(model_name)
     else:
         print('>> Restoring previous model')
         gen.set_model(current_model)
     return True
 
-def _get_model_name_and_desc(model_manager):
-    model_name = None
-    model_description = None
-    existing_models = model_manager.list_models()
-
-    done = False
-    while not done:
-        model_name = input('Short name for this model: ')
-        if not re.match('^[\w._-]+$',model_name):
-            print('** model name must contain only words, digits and the characters [._-] **')
-        elif model_name in existing_models:
-            print(f'** the name {model_name} is already in use. Pick another.')
-        else:
-            done = True
-    model_description = input('Description for this model: ')
-   
+def _get_model_name_and_desc(model_manager,completer,model_name:str='',model_description:str=''):
+    model_name = _get_model_name(model_manager.list_models(),completer,model_name)
+    completer.linebuffer = model_description
+    model_description = input(f'Description for this model [{model_description}]: ') or model_description
     return model_name, model_description
 
-def optimize_model(model_name:str, gen, opt, completer):
+def optimize_model(model_name_or_path:str, gen, opt, completer):
     manager = gen.model_manager
-    if not (model_info := manager.model_info(model_name)):
-        print(f'** unknown model: {model_name}')
+    ckpt_path = None
+
+    if (model_info := manager.model_info(model_name_or_path)):
+        if 'weights' in model_info:
+            ckpt_path = Path(model_info['weights'])
+            model_name = model_name_or_path
+            model_description = model_info['description']
+        else:
+            printf('** {model_name_or_path} is not a legacy .ckpt weights file')
+            return
+    elif os.path.exists(model_name_or_path):
+        ckpt_path = Path(model_name_or_path)
+        model_name,model_description = _get_model_name_and_desc(
+            manager,
+            completer,
+            ckpt_path.stem,
+            f'Converted model {ckpt_path.stem}'
+        )
+    else:
+        print(f'** {model_name_or_path} is neither an existing model nor the path to a .ckpt file')
         return
-    ckpt_path = Path(model_info['weights'])
+    
     if not ckpt_path.is_absolute():
         ckpt_path = Path(Globals.root,ckpt_path)
 
-    basename = ckpt_path.stem
-    diffuser_path = Path(Globals.root, 'models','optimized-ckpts',basename)
+    diffuser_path = Path(Globals.root, 'models','optimized-ckpts',model_name)
     if diffuser_path.exists():
-        print(f'** {model_name} is already optimized. Will not overwrite. If this is an error, please remove the directory {diffuser_path} and try again.')
+        print(f'** {model_name_or_path} is already optimized. Will not overwrite. If this is an error, please remove the directory {diffuser_path} and try again.')
         return
      
     new_config = gen.model_manager.convert_and_import(
         ckpt_path,
         diffuser_path,
         model_name=model_name,
-        model_description=model_info['description'],
+        model_description=model_description,
         commit_to_conf=opt.conf,
     )
     if not new_config:
@@ -686,65 +717,46 @@ def del_config(model_name:str, gen, opt, completer):
     print(f'** {model_name} deleted')
     completer.update_models(gen.model_manager.list_models())
 
-
-# NOTE: edit_config() must be rewritten for diffusers
-# We should only allow user to change: name, description, default
 def edit_config(model_name:str, gen, opt, completer):
-    config = gen.model_manager.config
+    current_model = gen.model_name
+    if model_name == current_model:
+        print("** Can't edit the active model. !switch to another model first. **")
+        return
 
-    if model_name not in config:
+    manager = gen.model_manager
+    if not (info := manager.model_info(model_name)):
         print(f'** Unknown model {model_name}')
         return
 
     print(f'\n>> Editing model {model_name} from configuration file {opt.conf}')
+    new_name,new_description = _get_model_name_and_desc(gen.model_manager,
+                                                        completer,
+                                                        model_name=model_name,
+                                                        model_description=info['description']
+    )
+    info['description'] = new_description
+    if new_name != model_name:
+        manager.add_model(new_name,info)
+        manager.del_model(model_name)
+    manager.commit(opt.conf)
+    completer.update_models(manager.list_models())
+    print('>> Model successfully updated')
+
+def _get_model_name(existing_names,completer,default_name:str='')->str:
+    done = False
+    completer.linebuffer = default_name
+    while not done:
+        model_name = input(f'Short name for this model [{default_name}]: ')
+        if len(model_name)==0:
+            model_name = default_name
+        if not re.match('^[\w._-]+$',model_name):
+            print('** model name must contain only words, digits and the characters [._-] **')
+        elif model_name in existing_names:
+            print(f'** the name {model_name} is already in use. Pick another.')
+        else:
+            done = True
+    return model_name
 
-    conf = config[model_name]
-    new_config = {}
-    completer.complete_extensions(('.yaml','.yml','.ckpt','.vae.pt'))
-    for field in ('description', 'weights', 'vae', 'config', 'width', 'height', 'format'):
-        completer.linebuffer = str(conf[field]) if field in conf else ''
-        new_value = input(f'{field}: ')
-        new_config[field] = int(new_value) if field in ('width','height') else new_value
-    make_default = input('Make this the default model? [n] ') in ('y','Y')
-    completer.complete_extensions(None)
-    write_config_file(opt.conf, gen, model_name, new_config, clobber=True, make_default=make_default)
-
-# NOTE: delete this whole function
-def write_config_file(conf_path, gen, model_name, new_config, clobber=False, make_default=False)->bool:
-    current_model = gen.model_name
-
-    op = 'write' if clobber else 'import'
-    print('\n>> New configuration:')
-    if make_default:
-        new_config['default'] = True
-    print(yaml.dump({model_name:new_config}))
-    if input(f'OK to {op} [y]? ').startswith(('n','N')):
-        return False
-
-    try:
-        print('>> Verifying that new model loads...')
-        gen.model_manager.add_model(model_name, new_config, clobber)
-        assert gen.set_model(model_name) is not None, 'model failed to load'
-    except AssertionError as e:
-        traceback.print_exc()
-        print(f'** aborting **')
-        try:
-            gen.model_manager.del_model(model_name)
-        except Exception:
-            pass
-        return False
-
-    if make_default:
-        print('making this default')
-        gen.model_manager.set_default_model(model_name)
-
-    gen.model_manager.commit(conf_path)
-    do_switch = input(f'Keep model loaded? [y] ')
-    if len(do_switch)==0 or do_switch[0] in ('y','Y'):
-        pass
-    else:
-        gen.set_model(current_model)
-    return True
 
 def do_textmask(gen, opt, callback):
     image_path = opt.prompt
diff --git a/ldm/invoke/args.py b/ldm/invoke/args.py
index 6b21df47a34..7d160e825ec 100644
--- a/ldm/invoke/args.py
+++ b/ldm/invoke/args.py
@@ -745,8 +745,11 @@ def _create_dream_cmd_parser(self):
             *Model manipulation*
             !models                                   -- list models in configs/models.yaml
             !switch <model_name>                      -- switch to model named <model_name>
-            !import_model path/to/weights/file.ckpt   -- adds a .ckpt model to your config
-            !optimize_model path/to/weights/file.ckpt -- converts a .ckpt file model a diffusers model
+            !import_model /path/to/weights/file.ckpt  -- adds a .ckpt model to your config
+            !import_model http://path_to_model.ckpt   -- downloads and adds a .ckpt model to your config
+            !import_model hakurei/waifu-diffusion     -- downloads and adds a diffusers model to your config
+            !optimize_model <model_name>              -- converts a .ckpt model to a diffusers model
+            !convert_model /path/to/weights/file.ckpt -- converts a .ckpt file path to a diffusers model
             !edit_model <model_name>                  -- edit a model's description
             !del_model <model_name>                   -- delete a model
             """
diff --git a/ldm/invoke/model_manager.py b/ldm/invoke/model_manager.py
index 467aaac2f60..e7887ffc2a4 100644
--- a/ldm/invoke/model_manager.py
+++ b/ldm/invoke/model_manager.py
@@ -693,7 +693,7 @@ def preamble(self) -> str:
 
     def _resolve_path(self, source:Union[str,Path], dest_directory:str)->Path:
         resolved_path = None
-        if source.startswith('http'):
+        if source.startswith(('http:','https:','ftp:')):
             basename = os.path.basename(source)
             if not os.path.isabs(dest_directory):
                 dest_directory = os.path.join(Globals.root,dest_directory)
diff --git a/ldm/invoke/readline.py b/ldm/invoke/readline.py
index 02fc46cec6d..f14af0714f5 100644
--- a/ldm/invoke/readline.py
+++ b/ldm/invoke/readline.py
@@ -24,7 +24,7 @@
     readline_available = False
 
 IMG_EXTENSIONS     = ('.png','.jpg','.jpeg','.PNG','.JPG','.JPEG','.gif','.GIF')
-WEIGHT_EXTENSIONS  = ('.ckpt','.bae')
+WEIGHT_EXTENSIONS  = ('.ckpt','.vae','.safetensors')
 TEXT_EXTENSIONS  = ('.txt','.TXT')
 CONFIG_EXTENSIONS  = ('.yaml','.yml')
 COMMANDS = (
@@ -59,7 +59,7 @@
     '--png_compression','-z',
     '--text_mask','-tm',
     '!fix','!fetch','!replay','!history','!search','!clear',
-    '!models','!switch','!import_model','!optimize_model','!edit_model','!del_model',
+    '!models','!switch','!import_model','!optimize_model','!convert_model','!edit_model','!del_model',
     '!mask',
     )
 MODEL_COMMANDS = (
@@ -72,6 +72,7 @@
 )
 WEIGHT_COMMANDS = (
     '!import_model',
+    '!convert_model',
     )
 IMG_PATH_COMMANDS = (
     '--outdir[=\s]',

From f14dac4ad692927cc023aaf228f3051d027c3e9c Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Thu, 5 Jan 2023 21:28:23 -0500
Subject: [PATCH 132/199] add support for safetensor .ckpt files

---
 ldm/invoke/ckpt_to_diffuser.py | 4 +++-
 ldm/invoke/model_manager.py    | 8 +++++++-
 2 files changed, 10 insertions(+), 2 deletions(-)

diff --git a/ldm/invoke/ckpt_to_diffuser.py b/ldm/invoke/ckpt_to_diffuser.py
index f04c68a814e..64fbb0925c7 100644
--- a/ldm/invoke/ckpt_to_diffuser.py
+++ b/ldm/invoke/ckpt_to_diffuser.py
@@ -19,7 +19,9 @@
 import os
 import re
 import torch
+from pathlib import Path
 from ldm.invoke.globals import Globals
+from safetensors.torch import load_file
 
 try:
     from omegaconf import OmegaConf
@@ -790,7 +792,7 @@ def convert_ckpt_to_diffuser(checkpoint_path:str,
                              upcast_attn:bool=False,
                              ):
 
-    checkpoint = torch.load(checkpoint_path)
+    checkpoint = load_file(checkpoint_path) if Path(checkpoint_path).suffix == '.safetensors' else torch.load(checkpoint_path)
 
     # Sometimes models don't have the global_step item
     if "global_step" in checkpoint:
diff --git a/ldm/invoke/model_manager.py b/ldm/invoke/model_manager.py
index e7887ffc2a4..6c6659e5c79 100644
--- a/ldm/invoke/model_manager.py
+++ b/ldm/invoke/model_manager.py
@@ -17,11 +17,13 @@
 import traceback
 import warnings
 import shutil
+from safetensors.torch import load_file
 from pathlib import Path
 from typing import Union, Any
 from ldm.util import download_with_progress_bar
 
 import torch
+import safetensors
 import transformers
 from diffusers import AutoencoderKL, logging as dlogging
 from omegaconf import OmegaConf
@@ -300,7 +302,11 @@ def _load_ckpt_model(self, model_name, mconfig):
         with open(weights,'rb') as f:
             weight_bytes = f.read()
         model_hash = self._cached_sha256(weights, weight_bytes)
-        sd = torch.load(io.BytesIO(weight_bytes), map_location='cpu')
+        sd = None
+        if weights.endswith('.safetensors'):
+            sd = safetensors.torch.load(weight_bytes)
+        else:
+            sd = torch.load(io.BytesIO(weight_bytes), map_location='cpu')
         del weight_bytes
         # merged models from auto11 merge board are flat for some reason
         if 'state_dict' in sd:

From b1e98199fc73ac42c4f300b6d4a9c6ec6d9842e3 Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Thu, 5 Jan 2023 22:11:41 -0500
Subject: [PATCH 133/199] fix name error

---
 ldm/invoke/CLI.py              | 2 +-
 ldm/invoke/ckpt_to_diffuser.py | 1 +
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/ldm/invoke/CLI.py b/ldm/invoke/CLI.py
index eb01b2af78e..c87fd4560c8 100644
--- a/ldm/invoke/CLI.py
+++ b/ldm/invoke/CLI.py
@@ -589,7 +589,7 @@ def import_model(model_path:str, gen, opt, completer):
 
 def import_diffuser_model(path_or_repo:str, gen, opt, completer)->str:
     manager = gen.model_manager
-    default_name = Path(path_or_url).stem
+    default_name = Path(path_or_repo).stem
     default_description = f'Imported model {default_name}'
     model_name, model_description = _get_model_name_and_desc(
         manager,
diff --git a/ldm/invoke/ckpt_to_diffuser.py b/ldm/invoke/ckpt_to_diffuser.py
index 64fbb0925c7..6e8e2194787 100644
--- a/ldm/invoke/ckpt_to_diffuser.py
+++ b/ldm/invoke/ckpt_to_diffuser.py
@@ -14,6 +14,7 @@
 # limitations under the License.
 #
 # Adapted for use as a module by Lincoln Stein <lstein@gmail.com>
+# Original file at: https://github.com/huggingface/diffusers/blob/main/scripts/convert_ldm_original_checkpoint_to_diffusers.py
 """ Conversion script for the LDM checkpoints. """
 
 import os

From 282c0ef98a31e86dcc7ac77debd8f8f74b07c16c Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Thu, 5 Jan 2023 22:53:25 -0500
Subject: [PATCH 134/199] code cleanup with pyflake

---
 ldm/generate.py             | 10 +++++-----
 ldm/invoke/CLI.py           | 22 ++++++++--------------
 ldm/invoke/args.py          | 14 +++++---------
 ldm/invoke/model_manager.py | 21 ++++++++++-----------
 4 files changed, 28 insertions(+), 39 deletions(-)

diff --git a/ldm/generate.py b/ldm/generate.py
index 4267eb1b4b2..314fae9775e 100644
--- a/ldm/generate.py
+++ b/ldm/generate.py
@@ -545,7 +545,7 @@ def process_image(image,seed):
                 print('**Interrupted** Partial results will be returned.')
             else:
                 raise KeyboardInterrupt
-        except RuntimeError as e:
+        except RuntimeError:
             print(traceback.format_exc(), file=sys.stderr)
             print('>> Could not generate image.')
 
@@ -557,7 +557,7 @@ def process_image(image,seed):
         )
         if self._has_cuda():
             print(
-                f'>>   Max VRAM used for this generation:',
+                '>>   Max VRAM used for this generation:',
                 '%4.2fG.' % (torch.cuda.max_memory_allocated() / 1e9),
                 'Current VRAM utilization:',
                 '%4.2fG' % (torch.cuda.memory_allocated() / 1e9),
@@ -567,7 +567,7 @@ def process_image(image,seed):
                 self.session_peakmem, torch.cuda.max_memory_allocated()
             )
             print(
-                f'>>   Max VRAM used since script start: ',
+                '>>   Max VRAM used since script start: ',
                 '%4.2fG' % (self.session_peakmem / 1e9),
             )
         return results
@@ -643,7 +643,7 @@ def apply_postprocessor(
                 try:
                     extend_instructions[direction]=int(pixels)
                 except ValueError:
-                    print(f'** invalid extension instruction. Use <directions> <pixels>..., as in "top 64 left 128 right 64 bottom 64"')
+                    print('** invalid extension instruction. Use <directions> <pixels>..., as in "top 64 left 128 right 64 bottom 64"')
 
             opt.seed = seed
             opt.prompt = prompt
@@ -691,7 +691,7 @@ def apply_postprocessor(
             )
 
         elif tool is None:
-            print(f'* please provide at least one postprocessing option, such as -G or -U')
+            print('* please provide at least one postprocessing option, such as -G or -U')
             return None
         else:
             print(f'* postprocessing tool {tool} is not yet supported')
diff --git a/ldm/invoke/CLI.py b/ldm/invoke/CLI.py
index c87fd4560c8..c88b0927d42 100644
--- a/ldm/invoke/CLI.py
+++ b/ldm/invoke/CLI.py
@@ -2,11 +2,7 @@
 import re
 import sys
 import shlex
-import copy
-import warnings
-import time
 import traceback
-import yaml
 
 from ldm.invoke.globals import Globals
 from ldm.generate import Generate
@@ -16,7 +12,6 @@
 from ldm.invoke.pngwriter import PngWriter, retrieve_metadata, write_metadata
 from ldm.invoke.image_util import make_grid
 from ldm.invoke.log import write_log
-from omegaconf import OmegaConf
 from pathlib import Path
 import pyparsing
 import ldm.invoke
@@ -53,8 +48,8 @@ def main():
     if not args.conf:
         if not os.path.exists(os.path.join(Globals.root,'configs','models.yaml')):
             print(f"\n** Error. The file {os.path.join(Globals.root,'configs','models.yaml')} could not be found.")
-            print(f'** Please check the location of your invokeai directory and use the --root_dir option to point to the correct path.')
-            print(f'** This script will now exit.')
+            print('** Please check the location of your invokeai directory and use the --root_dir option to point to the correct path.')
+            print('** This script will now exit.')
             sys.exit(-1)
 
     print(f'>> {ldm.invoke.__app_name__} {ldm.invoke.__version__}')
@@ -163,7 +158,6 @@ def main_loop(gen, opt):
     doneAfterInFile = infile is not None
     path_filter = re.compile(r'[<>:"/\\|?*]')
     last_results = list()
-    model_config = OmegaConf.load(opt.conf)
 
     # The readline completer reads history from the .dream_history file located in the
     # output directory specified at the time of script launch. We do not currently support
@@ -576,7 +570,7 @@ def import_model(model_path:str, gen, opt, completer):
         return
         
     if not _verify_load(model_name, gen):
-        print(f'** model failed to load. Discarding configuration entry')
+        print('** model failed to load. Discarding configuration entry')
         gen.model_manager.del_model(model_name)
         return
     
@@ -602,7 +596,7 @@ def import_diffuser_model(path_or_repo:str, gen, opt, completer)->str:
             path_or_repo,
             model_name = model_name,
             description = model_description):
-        print(f'** model failed to import')
+        print('** model failed to import')
         return None
     return model_name
 
@@ -632,7 +626,7 @@ def import_ckpt_model(path_or_url:str, gen, opt, completer)->str:
             model_name = model_name,
             model_description = model_description
     ):
-        print(f'** model failed to import')
+        print('** model failed to import')
         return None
 
     return model_name
@@ -642,7 +636,7 @@ def _verify_load(model_name:str, gen)->bool:
     current_model = gen.model_name
     if not gen.model_manager.get_model(model_name):
         return False
-    do_switch = input(f'Keep model loaded? [y] ')
+    do_switch = input('Keep model loaded? [y] ')
     if len(do_switch)==0 or do_switch[0] in ('y','Y'):
         gen.set_model(model_name)
     else:
@@ -666,7 +660,7 @@ def optimize_model(model_name_or_path:str, gen, opt, completer):
             model_name = model_name_or_path
             model_description = model_info['description']
         else:
-            printf('** {model_name_or_path} is not a legacy .ckpt weights file')
+            print(f'** {model_name_or_path} is not a legacy .ckpt weights file')
             return
     elif os.path.exists(model_name_or_path):
         ckpt_path = Path(model_name_or_path)
@@ -867,7 +861,7 @@ def prepare_image_metadata(
         except KeyError as e:
             print(f'** The filename format contains an unknown key \'{e.args[0]}\'. Will use \'{{prefix}}.{{seed}}.png\' instead')
             filename = f'{prefix}.{seed}.png'
-        except IndexError as e:
+        except IndexError:
             print(f'** The filename format is broken or complete. Will use \'{{prefix}}.{{seed}}.png\' instead')
             filename = f'{prefix}.{seed}.png'
 
diff --git a/ldm/invoke/args.py b/ldm/invoke/args.py
index 7d160e825ec..9ad12c53dbe 100644
--- a/ldm/invoke/args.py
+++ b/ldm/invoke/args.py
@@ -91,10 +91,6 @@
 import re
 import shlex
 import sys
-import copy
-import base64
-import functools
-import warnings
 import ldm.invoke
 import ldm.invoke.pngwriter
 
@@ -279,7 +275,7 @@ def dream_prompt_str(self,**kwargs):
             switches.append(f'-I {a["init_img"]}')
             switches.append(f'-A {a["sampler_name"]}')
             if a['fit']:
-                switches.append(f'--fit')
+                switches.append('--fit')
             if a['init_mask'] and len(a['init_mask'])>0:
                 switches.append(f'-M {a["init_mask"]}')
             if a['init_color'] and len(a['init_color'])>0:
@@ -287,7 +283,7 @@ def dream_prompt_str(self,**kwargs):
             if a['strength'] and a['strength']>0:
                 switches.append(f'-f {a["strength"]}')
             if a['inpaint_replace']:
-                switches.append(f'--inpaint_replace')
+                switches.append('--inpaint_replace')
             if a['text_mask']:
                 switches.append(f'-tm {" ".join([str(u) for u in a["text_mask"]])}')
         else:
@@ -1090,7 +1086,7 @@ def _create_dream_cmd_parser(self):
         return parser
 
 def format_metadata(**kwargs):
-    print(f'format_metadata() is deprecated. Please use metadata_dumps()')
+    print('format_metadata() is deprecated. Please use metadata_dumps()')
     return metadata_dumps(kwargs)
 
 def metadata_dumps(opt,
@@ -1157,7 +1153,7 @@ def metadata_dumps(opt,
         rfc_dict.pop('strength')
 
     if len(seeds)==0 and opt.seed:
-        seeds=[seed]
+        seeds=[opt.seed]
 
     if opt.grid:
         images = []
@@ -1228,7 +1224,7 @@ def metadata_loads(metadata) -> list:
             opt = Args()
             opt._cmd_switches = Namespace(**image)
             results.append(opt)
-    except Exception as e:
+    except Exception:
         import sys, traceback
         print('>> could not read metadata',file=sys.stderr)
         print(traceback.format_exc(), file=sys.stderr)
diff --git a/ldm/invoke/model_manager.py b/ldm/invoke/model_manager.py
index 6c6659e5c79..73fdc0d74c9 100644
--- a/ldm/invoke/model_manager.py
+++ b/ldm/invoke/model_manager.py
@@ -16,8 +16,7 @@
 import time
 import traceback
 import warnings
-import shutil
-from safetensors.torch import load_file
+import safetensors.torch
 from pathlib import Path
 from typing import Union, Any
 from ldm.util import download_with_progress_bar
@@ -213,7 +212,7 @@ def add_model(self, model_name:str, model_attributes:dict, clobber:bool=False) -
         attributes are incorrect or the model name is missing.
         '''
         omega = self.config
-        assert 'format' in model_attributes, f'missing required field "format"'
+        assert 'format' in model_attributes, 'missing required field "format"'
         if model_attributes['format']=='diffusers':
             assert 'description' in model_attributes, 'required field "description" is missing'
             assert 'path' in model_attributes or 'repo_id' in model_attributes,'model must have either the "path" or "repo_id" fields defined'
@@ -262,7 +261,7 @@ def _load_model(self, model_name:str):
 
         # usage statistics
         toc = time.time()
-        print(f'>> Model loaded in', '%4.2fs' % (toc - tic))
+        print('>> Model loaded in', '%4.2fs' % (toc - tic))
         if self._has_cuda():
             print(
                 '>> Max VRAM used to load the model:',
@@ -312,7 +311,7 @@ def _load_ckpt_model(self, model_name, mconfig):
         if 'state_dict' in sd:
             sd = sd['state_dict']
 
-        print(f'   | Forcing garbage collection prior to loading new model')
+        print('   | Forcing garbage collection prior to loading new model')
         gc.collect()
         model = instantiate_from_config(omega_config.model)
         model.load_state_dict(sd, strict=False)
@@ -347,7 +346,7 @@ def _load_ckpt_model(self, model_name, mconfig):
 
         # usage statistics
         toc = time.time()
-        print(f'>> Model loaded in', '%4.2fs' % (toc - tic))
+        print('>> Model loaded in', '%4.2fs' % (toc - tic))
 
         if self._has_cuda():
             print(
@@ -366,9 +365,9 @@ def _load_diffusers_model(self, mconfig):
 
         print(f'>> Loading diffusers model from {name_or_path}')
         if using_fp16:
-            print(f'  | Using faster float16 precision')
+            print('  | Using faster float16 precision')
         else:
-            print(f'  | Using more accurate float32 precision')
+            print('  | Using more accurate float32 precision')
 
         # TODO: scan weights maybe?
         pipeline_args: dict[str, Any] = dict(
@@ -782,7 +781,7 @@ def _cached_sha256(self,path,data) -> Union[str, bytes]:
                 hash = f.read()
             return hash
 
-        print(f'>> Calculating sha256 hash of weights file')
+        print('>> Calculating sha256 hash of weights file')
         tic = time.time()
         sha = hashlib.sha256()
         sha.update(data)
@@ -809,7 +808,7 @@ def _load_vae(self, vae_config):
             vae_args.update(torch_dtype=torch.float16)
             fp_args_list = [{'revision':'fp16'},{}]
         else:
-            print(f'  | Using more accurate float32 precision')
+            print('  | Using more accurate float32 precision')
             fp_args_list = [{}]
 
         vae = None
@@ -826,7 +825,7 @@ def _load_vae(self, vae_config):
                 vae = AutoencoderKL.from_pretrained(name_or_path, **vae_args, **fp_args)
             except OSError as e:
                 if str(e).startswith('fp16 is not a valid'):
-                    print(f'  | Half-precision version of model not available; fetching full-precision instead')
+                    print('  | Half-precision version of model not available; fetching full-precision instead')
                 else:
                     deferred_error = e
             if vae:

From a8591cd2ad9064920072aea4e098e8862f11968a Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Fri, 6 Jan 2023 14:06:29 -0500
Subject: [PATCH 135/199] improve model setting behavior

- If the user enters an invalid model name at startup time, will not
  try to load it, warn, and use default model
- CLI UI enhancement: include currently active model in the command
  line prompt.
---
 ldm/generate.py   | 11 ++++++++---
 ldm/invoke/CLI.py | 36 +++++++++++++++++++-----------------
 2 files changed, 27 insertions(+), 20 deletions(-)

diff --git a/ldm/generate.py b/ldm/generate.py
index 314fae9775e..d899268ec95 100644
--- a/ldm/generate.py
+++ b/ldm/generate.py
@@ -204,7 +204,12 @@ def __init__(
 
         # model caching system for fast switching
         self.model_manager = ModelManager(mconfig,self.device,self.precision,max_loaded_models=max_loaded_models)
-        self.model_name  = model or self.model_manager.default_model() or FALLBACK_MODEL_NAME
+        # don't accept invalid models
+        fallback = self.model_manager.default_model() or FALLBACK_MODEL_NAME        
+        if not self.model_manager.valid_model(model):
+            print(f'** "{model}" is not a known model name; falling back to {fallback}.')
+            model = None
+        self.model_name  = model or fallback
 
         # for VRAM usage statistics
         self.session_peakmem = torch.cuda.max_memory_allocated() if self._has_cuda else None
@@ -804,7 +809,7 @@ def load_model(self):
         '''
         preload model identified in self.model_name
         '''
-        self.set_model(self.model_name)
+        return self.set_model(self.model_name)
 
     def set_model(self,model_name):
         """
@@ -817,7 +822,7 @@ def set_model(self,model_name):
         # the model cache does the loading and offloading
         cache = self.model_manager
         if not cache.valid_model(model_name):
-            print(f'** "{model_name}" is not a known model name. Please check your models.yaml file')
+            print(f'** "{model_name}" is not a known model name. Cannot change.')
             return self.model
 
         cache.print_vram_usage()
diff --git a/ldm/invoke/CLI.py b/ldm/invoke/CLI.py
index c88b0927d42..b5872884c9e 100644
--- a/ldm/invoke/CLI.py
+++ b/ldm/invoke/CLI.py
@@ -13,6 +13,7 @@
 from ldm.invoke.image_util import make_grid
 from ldm.invoke.log import write_log
 from pathlib import Path
+from argparse import Namespace
 import pyparsing
 import ldm.invoke
 
@@ -107,9 +108,8 @@ def main():
             safety_checker=opt.safety_checker,
             max_loaded_models=opt.max_loaded_models,
             )
-    except (FileNotFoundError, TypeError, AssertionError):
-        emergency_model_reconfigure(opt)
-        sys.exit(-1)
+    except (FileNotFoundError, TypeError, AssertionError) as e:
+        report_model_error(opt,e)
     except (IOError, KeyError) as e:
         print(f'{e}. Aborting.')
         sys.exit(-1)
@@ -120,9 +120,8 @@ def main():
     # preload the model
     try:
         gen.load_model()
-    except AssertionError:
-        emergency_model_reconfigure(opt)
-        sys.exit(-1)
+    except AssertionError as e:
+        report_model_error(opt, e)
 
     # try to autoconvert new models
     # autoimport new .ckpt files
@@ -164,7 +163,8 @@ def main_loop(gen, opt):
     # changing the history file midstream when the output directory is changed.
     completer   = get_completer(opt, models=gen.model_manager.list_models())
     set_default_output_dir(opt, completer)
-    add_embedding_terms(gen, completer)
+    if gen.model:
+        add_embedding_terms(gen, completer)
     output_cntr = completer.get_current_history_length()+1
 
     # os.pathconf is not available on Windows
@@ -180,7 +180,7 @@ def main_loop(gen, opt):
         operation = 'generate'
 
         try:
-            command = get_next_command(infile)
+            command = get_next_command(infile, gen.model_name)
         except EOFError:
             done = infile is None or doneAfterInFile
             infile = None
@@ -897,9 +897,9 @@ def choose_postprocess_name(opt,prefix,seed) -> str:
         counter += 1
     return filename
 
-def get_next_command(infile=None) -> str:  # command string
+def get_next_command(infile=None, model_name='no model') -> str:  # command string
     if infile is None:
-        command = input('invoke> ')
+        command = input(f'({model_name}) invoke> ')
     else:
         command = infile.readline()
         if not command:
@@ -1054,13 +1054,15 @@ def write_commands(opt, file_path:str, outfilepath:str):
             f.write('\n'.join(commands))
         print(f'>> File {outfilepath} with commands created')
 
-def emergency_model_reconfigure(opt):
-    print()
-    print('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!')
-    print('   You appear to have a missing or misconfigured model file(s).                   ')
-    print('   The script will now exit and run configure_invokeai.py to help fix the problem.')
-    print('   After reconfiguration is done, please relaunch invoke.py.                      ')
-    print('!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!')
+def report_model_error(opt:Namespace, e:Exception):
+    print('**   An error occurred while attempting to initialize the model.  **')
+    print(f'**   The error was: {str(e)}                                      **')
+    print('**   This can be caused by a missing or corrupted models file,    **')
+    print('**   and can sometimes be fixed by (re)installing the models.     **')
+    response = input('Do you want to run configure_invokeai.py to select and/or reinstall models? [Yn] ')
+    if response.startswith(('n','N')):
+        return
+
     print('configure_invokeai is launching....\n')
 
     # Match arguments that were set on the CLI

From 54179e613b321c13be8c36a28af981821ed57870 Mon Sep 17 00:00:00 2001
From: mauwii <Mauwii@outlook.de>
Date: Fri, 6 Jan 2023 20:29:19 +0100
Subject: [PATCH 136/199] update test-invoke-pip.yml - fix model cache path to
 point to runwayml/stable-diffusion-v1-5 - remove `skip-sd-weights` from
 configure_invokeai.py args

---
 .github/workflows/test-invoke-pip.yml | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/test-invoke-pip.yml b/.github/workflows/test-invoke-pip.yml
index dacee4685c9..a03bf87d0cd 100644
--- a/.github/workflows/test-invoke-pip.yml
+++ b/.github/workflows/test-invoke-pip.yml
@@ -76,13 +76,13 @@ jobs:
           mkdir -p ${{ env.INVOKEAI_ROOT }}/configs
           cp configs/models.yaml.example ${{ env.INVOKEAI_ROOT }}/configs/models.yaml
 
-      - name: Use Cached Stable Diffusion Model
+      - name: Use Cached diffusers-1.5
         id: cache-sd-model
         uses: actions/cache@v3
         env:
           cache-name: huggingface-${{ matrix.stable-diffusion-model }}
         with:
-          path: ~/.cache/huggingface
+          path: ${{ env.INVOKEAI_ROOT }}/models/runwayml/stable-diffusion-v1-5
           key: ${{ env.cache-name }}
 
       - name: set test prompt to main branch validation
@@ -119,7 +119,7 @@ jobs:
           HUGGING_FACE_HUB_TOKEN: ${{ secrets.HUGGINGFACE_TOKEN }}
         run: >
           python3 scripts/configure_invokeai.py
-          --skip-sd-weights --yes
+          --yes
           --full-precision  # can't use fp16 weights without a GPU
 
       - name: Run the tests

From 0c03f6c43cd787e4c87f018d0f7dcb074e16378a Mon Sep 17 00:00:00 2001
From: mauwii <Mauwii@outlook.de>
Date: Fri, 6 Jan 2023 20:50:09 +0100
Subject: [PATCH 137/199] exclude dev/diffusers from "fail for draft PRs"

---
 .github/workflows/test-invoke-pip.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/test-invoke-pip.yml b/.github/workflows/test-invoke-pip.yml
index a03bf87d0cd..b4dd3bff8df 100644
--- a/.github/workflows/test-invoke-pip.yml
+++ b/.github/workflows/test-invoke-pip.yml
@@ -16,7 +16,7 @@ concurrency:
 
 jobs:
   fail_if_pull_request_is_draft:
-    if: github.event.pull_request.draft == true
+    if: github.event.pull_request.draft == true && github.head_ref != 'dev/diffusers'
     runs-on: ubuntu-18.04
     steps:
     - name: Fails in order to indicate that pull request needs to be marked as ready to review and unit tests workflow needs to pass.

From 12b454b604bd37c1cee7549a11db2b635e88aaec Mon Sep 17 00:00:00 2001
From: mauwii <Mauwii@outlook.de>
Date: Fri, 6 Jan 2023 20:59:18 +0100
Subject: [PATCH 138/199] disable "fail on PR jobs"

---
 .github/workflows/test-invoke-conda.yml | 14 +++++++-------
 .github/workflows/test-invoke-pip.yml   | 14 +++++++-------
 2 files changed, 14 insertions(+), 14 deletions(-)

diff --git a/.github/workflows/test-invoke-conda.yml b/.github/workflows/test-invoke-conda.yml
index 87974f358fd..7e9630689d6 100644
--- a/.github/workflows/test-invoke-conda.yml
+++ b/.github/workflows/test-invoke-conda.yml
@@ -15,15 +15,15 @@ concurrency:
    cancel-in-progress: true
 
 jobs:
-  fail_if_pull_request_is_draft:
-    if: github.event.pull_request.draft == true
-    runs-on: ubuntu-22.04
-    steps:
-    - name: Fails in order to indicate that pull request needs to be marked as ready to review and unit tests workflow needs to pass.
-      run: exit 1
+  # fail_if_pull_request_is_draft:
+  #   if: github.event.pull_request.draft == true
+  #   runs-on: ubuntu-22.04
+  #   steps:
+  #   - name: Fails in order to indicate that pull request needs to be marked as ready to review and unit tests workflow needs to pass.
+  #     run: exit 1
 
   matrix:
-    if: github.event.pull_request.draft == false
+    if: github.event.pull_request.draft == false ||  github.head_ref == 'dev/diffusers'
     strategy:
       matrix:
         stable-diffusion-model:
diff --git a/.github/workflows/test-invoke-pip.yml b/.github/workflows/test-invoke-pip.yml
index b4dd3bff8df..8dec7ad53e6 100644
--- a/.github/workflows/test-invoke-pip.yml
+++ b/.github/workflows/test-invoke-pip.yml
@@ -15,14 +15,14 @@ concurrency:
    cancel-in-progress: true
 
 jobs:
-  fail_if_pull_request_is_draft:
-    if: github.event.pull_request.draft == true && github.head_ref != 'dev/diffusers'
-    runs-on: ubuntu-18.04
-    steps:
-    - name: Fails in order to indicate that pull request needs to be marked as ready to review and unit tests workflow needs to pass.
-      run: exit 1
+  # fail_if_pull_request_is_draft:
+  #   if: github.event.pull_request.draft == true && github.head_ref != 'dev/diffusers'
+  #   runs-on: ubuntu-18.04
+  #   steps:
+  #   - name: Fails in order to indicate that pull request needs to be marked as ready to review and unit tests workflow needs to pass.
+  #     run: exit 1
   matrix:
-    if: github.event.pull_request.draft == false
+    if: github.event.pull_request.draft == false ||  github.head_ref == 'dev/diffusers'
     strategy:
       matrix:
         stable-diffusion-model:

From 18b1b8cf414605393779ecabfb29b431dcb054d5 Mon Sep 17 00:00:00 2001
From: mauwii <Mauwii@outlook.de>
Date: Fri, 6 Jan 2023 21:14:52 +0100
Subject: [PATCH 139/199] re-add `--skip-sd-weights` since no space

---
 .github/workflows/test-invoke-pip.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.github/workflows/test-invoke-pip.yml b/.github/workflows/test-invoke-pip.yml
index 8dec7ad53e6..412453e43cb 100644
--- a/.github/workflows/test-invoke-pip.yml
+++ b/.github/workflows/test-invoke-pip.yml
@@ -119,6 +119,7 @@ jobs:
           HUGGING_FACE_HUB_TOKEN: ${{ secrets.HUGGINGFACE_TOKEN }}
         run: >
           python3 scripts/configure_invokeai.py
+          --skip-sd-weights
           --yes
           --full-precision  # can't use fp16 weights without a GPU
 

From b5c9d2fa545cb3413597921d83d1de4ec8b439ea Mon Sep 17 00:00:00 2001
From: mauwii <Mauwii@outlook.de>
Date: Fri, 6 Jan 2023 21:20:07 +0100
Subject: [PATCH 140/199] update workflow environments - include
 `INVOKE_MODEL_RECONFIGURE: '--yes'`

---
 .github/workflows/test-invoke-conda.yml | 1 +
 .github/workflows/test-invoke-pip.yml   | 1 +
 2 files changed, 2 insertions(+)

diff --git a/.github/workflows/test-invoke-conda.yml b/.github/workflows/test-invoke-conda.yml
index 7e9630689d6..6372efd6f80 100644
--- a/.github/workflows/test-invoke-conda.yml
+++ b/.github/workflows/test-invoke-conda.yml
@@ -53,6 +53,7 @@ jobs:
     name: ${{ matrix.environment-yaml }} on ${{ matrix.os }}
     runs-on: ${{ matrix.os }}
     env:
+      INVOKE_MODEL_RECONFIGURE: '--yes'
       CONDA_ENV_NAME: invokeai
       INVOKEAI_ROOT: '${{ github.workspace }}/invokeai'
       PYTHONUNBUFFERED: 1
diff --git a/.github/workflows/test-invoke-pip.yml b/.github/workflows/test-invoke-pip.yml
index 412453e43cb..1d01b7a7cb1 100644
--- a/.github/workflows/test-invoke-pip.yml
+++ b/.github/workflows/test-invoke-pip.yml
@@ -51,6 +51,7 @@ jobs:
     name: ${{ matrix.requirements-file }} on ${{ matrix.python-version }}
     runs-on: ${{ matrix.os }}
     env:
+      INVOKE_MODEL_RECONFIGURE: '--yes'
       INVOKEAI_ROOT: '${{ github.workspace }}/invokeai'
       PYTHONUNBUFFERED: 1
       HAVE_SECRETS: ${{ secrets.HUGGINGFACE_TOKEN != '' }}

From 52147cc7acecc474acc29d78dded3337d70a8444 Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Sat, 7 Jan 2023 10:10:21 -0500
Subject: [PATCH 141/199] clean up model load failure handling

- Allow CLI to run even when no model is defined or loadable.
- Inhibit stack trace when model load fails - only show last error
- Give user *option* to run configure_invokeai.py when no models
  successfully load.
- Restart invokeai after reconfiguration.
---
 ldm/generate.py             |  8 +++++---
 ldm/invoke/CLI.py           | 30 ++++++++++++++++++------------
 ldm/invoke/model_manager.py |  7 +++----
 3 files changed, 26 insertions(+), 19 deletions(-)

diff --git a/ldm/generate.py b/ldm/generate.py
index d899268ec95..3cb1f78cb95 100644
--- a/ldm/generate.py
+++ b/ldm/generate.py
@@ -835,9 +835,11 @@ def set_model(self,model_name):
         gc.collect()
 
         model_data = cache.get_model(model_name)
-        if model_data is None:  # restore previous
-            model_data = cache.get_model(self.model_name)
-            model_name = self.model_name # addresses Issue #1547
+        assert model_data,'an error occurred while loading the model'
+#        if model_data is None:
+#            return
+#            model_data = cache.get_model(self.model_name)
+#            model_name = self.model_name # addresses Issue #1547
 
         self.model = model_data['model']
         self.width = model_data['width']
diff --git a/ldm/invoke/CLI.py b/ldm/invoke/CLI.py
index b5872884c9e..092ee1f8432 100644
--- a/ldm/invoke/CLI.py
+++ b/ldm/invoke/CLI.py
@@ -559,9 +559,13 @@ def import_model(model_path:str, gen, opt, completer):
     '''
     model_name = None
     
-    if model_path.startswith(('http:','https:','ftp:')) or os.path.exists(model_path):
+    if model_path.startswith(('http:','https:','ftp:')):
         model_name = import_ckpt_model(model_path, gen, opt, completer)
-    elif re.match('^[\w-]+/[\w-]+$',model_path):
+    elif os.path.exists(model_path) and model_path.endswith('.ckpt') and os.path.isfile(model_path):
+        model_name = import_ckpt_model(model_path, gen, opt, completer)        
+    elif re.match('^[\w.-]+/[\w.-]+$',model_path):
+        model_name = import_diffuser_model(model_path, gen, opt, completer)
+    elif os.path.isdir(model_path):
         model_name = import_diffuser_model(model_path, gen, opt, completer)
     else:
         print(f'** {model_path} is neither the path to a .ckpt file nor a diffusers repository id. Can\'t import.')
@@ -616,7 +620,7 @@ def import_ckpt_model(path_or_url:str, gen, opt, completer)->str:
     completer.linebuffer = 'configs/stable-diffusion/v1-inference.yaml'
     done = False
     while not done:
-        config_file = input('Configuration file for this model: ')
+        config_file = input('Configuration file for this model: ').strip()
         done = os.path.exists(config_file)
     completer.complete_extensions(None)
 
@@ -647,7 +651,7 @@ def _verify_load(model_name:str, gen)->bool:
 def _get_model_name_and_desc(model_manager,completer,model_name:str='',model_description:str=''):
     model_name = _get_model_name(model_manager.list_models(),completer,model_name)
     completer.linebuffer = model_description
-    model_description = input(f'Description for this model [{model_description}]: ') or model_description
+    model_description = input(f'Description for this model [{model_description}]: ').strip() or model_description
     return model_name, model_description
 
 def optimize_model(model_name_or_path:str, gen, opt, completer):
@@ -740,7 +744,7 @@ def _get_model_name(existing_names,completer,default_name:str='')->str:
     done = False
     completer.linebuffer = default_name
     while not done:
-        model_name = input(f'Short name for this model [{default_name}]: ')
+        model_name = input(f'Short name for this model [{default_name}]: ').strip()
         if len(model_name)==0:
             model_name = default_name
         if not re.match('^[\w._-]+$',model_name):
@@ -899,7 +903,7 @@ def choose_postprocess_name(opt,prefix,seed) -> str:
 
 def get_next_command(infile=None, model_name='no model') -> str:  # command string
     if infile is None:
-        command = input(f'({model_name}) invoke> ')
+        command = input(f'({model_name}) invoke> ').strip()
     else:
         command = infile.readline()
         if not command:
@@ -1055,11 +1059,9 @@ def write_commands(opt, file_path:str, outfilepath:str):
         print(f'>> File {outfilepath} with commands created')
 
 def report_model_error(opt:Namespace, e:Exception):
-    print('**   An error occurred while attempting to initialize the model.  **')
-    print(f'**   The error was: {str(e)}                                      **')
-    print('**   This can be caused by a missing or corrupted models file,    **')
-    print('**   and can sometimes be fixed by (re)installing the models.     **')
-    response = input('Do you want to run configure_invokeai.py to select and/or reinstall models? [Yn] ')
+    print(f'** An error occurred while attempting to initialize the model: "{str(e)}"')
+    print('** This can be caused by a missing or corrupted models file, and can sometimes be fixed by (re)installing the models.')
+    response = input('Do you want to run configure_invokeai.py to select and/or reinstall models? [y] ')
     if response.startswith(('n','N')):
         return
 
@@ -1070,7 +1072,7 @@ def report_model_error(opt:Namespace, e:Exception):
     root_dir = ["--root", opt.root_dir] if opt.root_dir is not None else []
     config = ["--config", opt.conf] if opt.conf is not None else []
     yes_to_all = os.environ.get('INVOKE_MODEL_RECONFIGURE')
-
+    previous_args = sys.argv
     sys.argv = [ 'configure_invokeai' ]
     sys.argv.extend(root_dir)
     sys.argv.extend(config)
@@ -1079,6 +1081,10 @@ def report_model_error(opt:Namespace, e:Exception):
 
     import configure_invokeai
     configure_invokeai.main()
+    print('** InvokeAI will now restart')
+    sys.argv = previous_args
+    main() # would rather do a os.exec(), but doesn't exist?
+    sys.exit(0)
 
 def check_internet()->bool:
     '''
diff --git a/ldm/invoke/model_manager.py b/ldm/invoke/model_manager.py
index 73fdc0d74c9..24c99832480 100644
--- a/ldm/invoke/model_manager.py
+++ b/ldm/invoke/model_manager.py
@@ -96,11 +96,9 @@ def get_model(self, model_name:str):
 
             except Exception as e:
                 print(f'** model {model_name} could not be loaded: {str(e)}')
-                traceback.print_exc()
-                assert self.current_model,'** FATAL: no current model to restore to'
+                assert self.current_model,f'no model loaded and no previous model to fall back to'
                 print(f'** restoring {self.current_model}')
-                self.get_model(self.current_model)
-                return None
+                return self.get_model(self.current_model)
 
         self.current_model = model_name
         self._push_newest_model(model_name)
@@ -236,6 +234,7 @@ def _load_model(self, model_name:str):
         """Load and initialize the model from configuration variables passed at object creation time"""
         if model_name not in self.config:
             print(f'"{model_name}" is not a known model name. Please check your models.yaml file')
+            return
 
         mconfig = self.config[model_name]
 

From 6be47b559df5392cf4995ff52de719560ed934b5 Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Sat, 7 Jan 2023 10:52:52 -0500
Subject: [PATCH 142/199] further edge-case handling

1) only one model in models.yaml file, and that model is broken
2) no models in models.yaml
3) models.yaml doesn't exist at all
---
 ldm/generate.py             | 24 +++++++++++++++---------
 ldm/invoke/CLI.py           | 12 +++++++++---
 ldm/invoke/model_manager.py | 21 +++++++--------------
 3 files changed, 31 insertions(+), 26 deletions(-)

diff --git a/ldm/generate.py b/ldm/generate.py
index 3cb1f78cb95..6e0a49380f7 100644
--- a/ldm/generate.py
+++ b/ldm/generate.py
@@ -819,11 +819,15 @@ def set_model(self,model_name):
         if self.model_name == model_name and self.model is not None:
             return self.model
 
+        previous_model_name = self.model_name
+
         # the model cache does the loading and offloading
         cache = self.model_manager
         if not cache.valid_model(model_name):
-            print(f'** "{model_name}" is not a known model name. Cannot change.')
-            return self.model
+            raise KeyError(f'** "{model_name}" is not a known model name. Cannot change.')
+        #if not cache.valid_model(model_name):
+        #print(f'** "{model_name}" is not a known model name. Cannot change.')
+        #return self.model
 
         cache.print_vram_usage()
 
@@ -833,13 +837,15 @@ def set_model(self,model_name):
         self.sampler = None
         self.generators = {}
         gc.collect()
-
-        model_data = cache.get_model(model_name)
-        assert model_data,'an error occurred while loading the model'
-#        if model_data is None:
-#            return
-#            model_data = cache.get_model(self.model_name)
-#            model_name = self.model_name # addresses Issue #1547
+        try:
+            model_data = cache.get_model(model_name)
+        except Exception as e:
+            print(f'** model {model_name} could not be loaded: {str(e)}')
+            assert previous_model_name,'no previous model to restore'
+            print(f'** trying to reload previous model')
+            model_data = cache.get_model(previous_model_name) # load previous
+            assert model_data,e
+            model_name = previous_model_name
 
         self.model = model_data['model']
         self.width = model_data['width']
diff --git a/ldm/invoke/CLI.py b/ldm/invoke/CLI.py
index 092ee1f8432..2f1a824ed81 100644
--- a/ldm/invoke/CLI.py
+++ b/ldm/invoke/CLI.py
@@ -120,7 +120,7 @@ def main():
     # preload the model
     try:
         gen.load_model()
-    except AssertionError as e:
+    except Exception as e:
         report_model_error(opt, e)
 
     # try to autoconvert new models
@@ -444,8 +444,14 @@ def do_command(command:str, gen, opt:Args, completer) -> tuple:
 
     elif command.startswith('!switch'):
         model_name = command.replace('!switch ','',1)
-        gen.set_model(model_name)
-        add_embedding_terms(gen, completer)
+        try:
+            gen.set_model(model_name)
+            add_embedding_terms(gen, completer)
+        except AssertionError as e:
+            report_model_error(opt,e)
+        except KeyError as e:
+            print(str(e))
+            pass
         completer.add_history(command)
         operation = None
 
diff --git a/ldm/invoke/model_manager.py b/ldm/invoke/model_manager.py
index 24c99832480..7d0ea7a5f2d 100644
--- a/ldm/invoke/model_manager.py
+++ b/ldm/invoke/model_manager.py
@@ -85,20 +85,13 @@ def get_model(self, model_name:str):
             hash = self.models[model_name]['hash']
 
         else: # we're about to load a new model, so potentially offload the least recently used one
-            try:
-                requested_model, width, height, hash = self._load_model(model_name)
-                self.models[model_name] = {
-                    'model': requested_model,
-                    'width': width,
-                    'height': height,
-                    'hash': hash,
-                }
-
-            except Exception as e:
-                print(f'** model {model_name} could not be loaded: {str(e)}')
-                assert self.current_model,f'no model loaded and no previous model to fall back to'
-                print(f'** restoring {self.current_model}')
-                return self.get_model(self.current_model)
+            requested_model, width, height, hash = self._load_model(model_name)
+            self.models[model_name] = {
+                'model': requested_model,
+                'width': width,
+                'height': height,
+                'hash': hash,
+            }
 
         self.current_model = model_name
         self._push_newest_model(model_name)

From b0935981093615dc35fa7c82e593f03af25c2743 Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Sat, 7 Jan 2023 11:08:08 -0500
Subject: [PATCH 143/199] fix incorrect model status listing

- "cached" was not being returned from list_models()
- normalize handling of exceptions during model loading:
   - Passing an invalid model name to generate.set_model() will return
     a KeyError
   - All other exceptions are returned as the appropriate Exception
---
 ldm/generate.py             | 15 +++++++++------
 ldm/invoke/CLI.py           |  7 ++++---
 ldm/invoke/model_manager.py |  2 +-
 3 files changed, 14 insertions(+), 10 deletions(-)

diff --git a/ldm/generate.py b/ldm/generate.py
index 6e0a49380f7..06608eafb0d 100644
--- a/ldm/generate.py
+++ b/ldm/generate.py
@@ -815,6 +815,10 @@ def set_model(self,model_name):
         """
         Given the name of a model defined in models.yaml, will load and initialize it
         and return the model object. Previously-used models will be cached.
+
+        If the passed model_name is invalid, raises a KeyError.
+        If the model fails to load for some reason, will attempt to load the previously-
+        loaded model (if any). If that fallback fails, will raise an AssertionError
         """
         if self.model_name == model_name and self.model is not None:
             return self.model
@@ -824,10 +828,7 @@ def set_model(self,model_name):
         # the model cache does the loading and offloading
         cache = self.model_manager
         if not cache.valid_model(model_name):
-            raise KeyError(f'** "{model_name}" is not a known model name. Cannot change.')
-        #if not cache.valid_model(model_name):
-        #print(f'** "{model_name}" is not a known model name. Cannot change.')
-        #return self.model
+            raise KeyError('** "{model_name}" is not a known model name. Cannot change.')
 
         cache.print_vram_usage()
 
@@ -841,10 +842,12 @@ def set_model(self,model_name):
             model_data = cache.get_model(model_name)
         except Exception as e:
             print(f'** model {model_name} could not be loaded: {str(e)}')
-            assert previous_model_name,'no previous model to restore'
+            if previous_model_name is None:
+                raise e
             print(f'** trying to reload previous model')
             model_data = cache.get_model(previous_model_name) # load previous
-            assert model_data,e
+            if model_data is None:
+                raise e
             model_name = previous_model_name
 
         self.model = model_data['model']
diff --git a/ldm/invoke/CLI.py b/ldm/invoke/CLI.py
index 2f1a824ed81..1cb59e51e3c 100644
--- a/ldm/invoke/CLI.py
+++ b/ldm/invoke/CLI.py
@@ -120,6 +120,8 @@ def main():
     # preload the model
     try:
         gen.load_model()
+    except KeyError as e:
+        pass
     except Exception as e:
         report_model_error(opt, e)
 
@@ -447,11 +449,10 @@ def do_command(command:str, gen, opt:Args, completer) -> tuple:
         try:
             gen.set_model(model_name)
             add_embedding_terms(gen, completer)
-        except AssertionError as e:
-            report_model_error(opt,e)
         except KeyError as e:
             print(str(e))
-            pass
+        except Exception as e:
+            report_model_error(opt,e)
         completer.add_history(command)
         operation = None
 
diff --git a/ldm/invoke/model_manager.py b/ldm/invoke/model_manager.py
index 7d0ea7a5f2d..9152bd29b13 100644
--- a/ldm/invoke/model_manager.py
+++ b/ldm/invoke/model_manager.py
@@ -167,7 +167,7 @@ def list_models(self) -> dict:
             models[name] = dict(
                 description = stanza.get('description',None),
                 format = 'vae' if 'VAE/default' in config else format,
-                status = 'active' if self.current_model == name else 'cached' if name is self.models else 'not loaded',
+                status = 'active' if self.current_model == name else 'cached' if name in self.models else 'not loaded',
             )
 
         return models

From 11425b14b5590f7cba6bae0bd2062eaeab140677 Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Sat, 7 Jan 2023 15:22:15 -0800
Subject: [PATCH 144/199] CI: do download weights (if not already cached)

---
 .github/workflows/test-invoke-pip.yml | 8 +-------
 1 file changed, 1 insertion(+), 7 deletions(-)

diff --git a/.github/workflows/test-invoke-pip.yml b/.github/workflows/test-invoke-pip.yml
index 1d01b7a7cb1..c3fcd99241d 100644
--- a/.github/workflows/test-invoke-pip.yml
+++ b/.github/workflows/test-invoke-pip.yml
@@ -72,11 +72,6 @@ jobs:
           echo "INVOKEAI_ROOT=${{ github.workspace }}/invokeai" >> ${{ matrix.github-env }}
           echo "INVOKEAI_OUTDIR=${{ github.workspace }}/invokeai/outputs" >> ${{ matrix.github-env }}
 
-      - name: create models.yaml from example
-        run: |
-          mkdir -p ${{ env.INVOKEAI_ROOT }}/configs
-          cp configs/models.yaml.example ${{ env.INVOKEAI_ROOT }}/configs/models.yaml
-
       - name: Use Cached diffusers-1.5
         id: cache-sd-model
         uses: actions/cache@v3
@@ -119,8 +114,7 @@ jobs:
         env:
           HUGGING_FACE_HUB_TOKEN: ${{ secrets.HUGGINGFACE_TOKEN }}
         run: >
-          python3 scripts/configure_invokeai.py
-          --skip-sd-weights
+          configure_invokeai.py
           --yes
           --full-precision  # can't use fp16 weights without a GPU
 

From 785c36603a2373ce7bf677919292ea491e5ea644 Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Sat, 7 Jan 2023 15:49:35 -0800
Subject: [PATCH 145/199] diffusers: fix scheduler loading in offline mode

---
 ldm/generate.py | 7 ++-----
 1 file changed, 2 insertions(+), 5 deletions(-)

diff --git a/ldm/generate.py b/ldm/generate.py
index 06608eafb0d..a5511fcc206 100644
--- a/ldm/generate.py
+++ b/ldm/generate.py
@@ -205,7 +205,7 @@ def __init__(
         # model caching system for fast switching
         self.model_manager = ModelManager(mconfig,self.device,self.precision,max_loaded_models=max_loaded_models)
         # don't accept invalid models
-        fallback = self.model_manager.default_model() or FALLBACK_MODEL_NAME        
+        fallback = self.model_manager.default_model() or FALLBACK_MODEL_NAME
         if not self.model_manager.valid_model(model):
             print(f'** "{model}" is not a known model name; falling back to {fallback}.')
             model = None
@@ -1037,10 +1037,7 @@ def _set_scheduler(self):
         if self.sampler_name in scheduler_map:
             sampler_class = scheduler_map[self.sampler_name]
             msg = f'>> Setting Sampler to {self.sampler_name} ({sampler_class.__name__})'
-            self.sampler = sampler_class.from_pretrained(
-                self.model_manager.model_name_or_path(self.model_name),
-                subfolder="scheduler"
-            )
+            self.sampler = sampler_class.from_config(self.model.scheduler.config)
         else:
             msg = (f'>> Unsupported Sampler: {self.sampler_name} '
                   f'Defaulting to {default}')

From 697279afa45a1754975011860711692d4934b0e7 Mon Sep 17 00:00:00 2001
From: Kevin Turner <83819+keturn@users.noreply.github.com>
Date: Sat, 7 Jan 2023 16:42:50 -0800
Subject: [PATCH 146/199] CI: fix model name (no longer has `diffusers-`
 prefix)

---
 .github/workflows/test-invoke-pip.yml | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/test-invoke-pip.yml b/.github/workflows/test-invoke-pip.yml
index c3fcd99241d..34c90f10c54 100644
--- a/.github/workflows/test-invoke-pip.yml
+++ b/.github/workflows/test-invoke-pip.yml
@@ -26,7 +26,7 @@ jobs:
     strategy:
       matrix:
         stable-diffusion-model:
-          - diffusers-1.5
+          - stable-diffusion-1.5
         requirements-file:
           - requirements-lin-cuda.txt
           - requirements-lin-amd.txt
@@ -78,7 +78,10 @@ jobs:
         env:
           cache-name: huggingface-${{ matrix.stable-diffusion-model }}
         with:
-          path: ${{ env.INVOKEAI_ROOT }}/models/runwayml/stable-diffusion-v1-5
+          path: |
+            ${{ env.INVOKEAI_ROOT }}/models/runwayml
+            ${{ env.INVOKEAI_ROOT }}/models/stabilityai
+            ${{ env.INVOKEAI_ROOT }}/models/CompVis
           key: ${{ env.cache-name }}
 
       - name: set test prompt to main branch validation

From d9e41f17f54370dbf20f95a5f29fd463e548b546 Mon Sep 17 00:00:00 2001
From: mickr777 <115216705+mickr777@users.noreply.github.com>
Date: Mon, 9 Jan 2023 13:07:58 +1100
Subject: [PATCH 147/199] Update txt2img2img.py (#2256)

---
 ldm/invoke/ckpt_generator/txt2img2img.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ldm/invoke/ckpt_generator/txt2img2img.py b/ldm/invoke/ckpt_generator/txt2img2img.py
index 8c650f9ebda..167debb98ea 100644
--- a/ldm/invoke/ckpt_generator/txt2img2img.py
+++ b/ldm/invoke/ckpt_generator/txt2img2img.py
@@ -12,7 +12,7 @@
 from ldm.models.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent
 from PIL import Image
 
-class CkptTxt2Img2Img(Generator):
+class CkptTxt2Img2Img(CkptGenerator):
     def __init__(self, model, precision):
         super().__init__(model, precision)
         self.init_latent = None    # for get_noise()

From da12663509a64a15f0f4918cc1a274429594612a Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Mon, 9 Jan 2023 11:56:19 -0500
Subject: [PATCH 148/199] fixes to share models with HuggingFace cache system

- If HF_HOME environment variable is defined, then all huggingface models
  are stored in that directory following the standard conventions.
- For seamless interoperability, set HF_HOME to ~/.cache/huggingface
- If HF_HOME not defined, then models are stored in ~/invokeai/models.
  This is equivalent to setting HF_HOME to ~/invokeai/models

A future commit will add a migration mechanism so that this change doesn't
break previous installs.
---
 ldm/generate.py                          |  1 +
 ldm/invoke/CLI.py                        | 21 ++++++++++++---------
 ldm/invoke/args.py                       |  3 ++-
 ldm/invoke/globals.py                    | 22 +++++++++++++++++++---
 ldm/invoke/model_manager.py              |  6 +++---
 ldm/invoke/textual_inversion_training.py |  4 ----
 ldm/modules/encoders/modules.py          |  6 +++---
 scripts/configure_invokeai.py            |  7 ++++---
 scripts/textual_inversion.py             |  4 ++--
 scripts/textual_inversion_fe.py          |  4 ++--
 10 files changed, 48 insertions(+), 30 deletions(-)

diff --git a/ldm/generate.py b/ldm/generate.py
index a5511fcc206..8081ffc89dd 100644
--- a/ldm/generate.py
+++ b/ldm/generate.py
@@ -206,6 +206,7 @@ def __init__(
         self.model_manager = ModelManager(mconfig,self.device,self.precision,max_loaded_models=max_loaded_models)
         # don't accept invalid models
         fallback = self.model_manager.default_model() or FALLBACK_MODEL_NAME
+        model = model or fallback
         if not self.model_manager.valid_model(model):
             print(f'** "{model}" is not a known model name; falling back to {fallback}.')
             model = None
diff --git a/ldm/invoke/CLI.py b/ldm/invoke/CLI.py
index 1cb59e51e3c..1db1b4f6927 100644
--- a/ldm/invoke/CLI.py
+++ b/ldm/invoke/CLI.py
@@ -659,7 +659,8 @@ def _get_model_name_and_desc(model_manager,completer,model_name:str='',model_des
     model_name = _get_model_name(model_manager.list_models(),completer,model_name)
     completer.linebuffer = model_description
     model_description = input(f'Description for this model [{model_description}]: ').strip() or model_description
-    return model_name, model_description
+    default = input(f'Make this model the default? [n] ').startswith(('y','Y'))
+    return model_name, model_description, default
 
 def optimize_model(model_name_or_path:str, gen, opt, completer):
     manager = gen.model_manager
@@ -724,9 +725,9 @@ def del_config(model_name:str, gen, opt, completer):
 
 def edit_config(model_name:str, gen, opt, completer):
     current_model = gen.model_name
-    if model_name == current_model:
-        print("** Can't edit the active model. !switch to another model first. **")
-        return
+#    if model_name == current_model:
+#        print("** Can't edit the active model. !switch to another model first. **")
+#        return
 
     manager = gen.model_manager
     if not (info := manager.model_info(model_name)):
@@ -734,15 +735,17 @@ def edit_config(model_name:str, gen, opt, completer):
         return
 
     print(f'\n>> Editing model {model_name} from configuration file {opt.conf}')
-    new_name,new_description = _get_model_name_and_desc(gen.model_manager,
-                                                        completer,
-                                                        model_name=model_name,
-                                                        model_description=info['description']
+    new_name,new_description,default = _get_model_name_and_desc(gen.model_manager,
+                                                                completer,
+                                                                model_name=model_name,
+                                                                model_description=info['description']
     )
     info['description'] = new_description
     if new_name != model_name:
         manager.add_model(new_name,info)
         manager.del_model(model_name)
+    if default:
+        manager.set_default_model(new_name)
     manager.commit(opt.conf)
     completer.update_models(manager.list_models())
     print('>> Model successfully updated')
@@ -756,7 +759,7 @@ def _get_model_name(existing_names,completer,default_name:str='')->str:
             model_name = default_name
         if not re.match('^[\w._-]+$',model_name):
             print('** model name must contain only words, digits and the characters [._-] **')
-        elif model_name in existing_names:
+        elif model_name != default_name and model_name in existing_names:
             print(f'** the name {model_name} is already in use. Pick another.')
         else:
             done = True
diff --git a/ldm/invoke/args.py b/ldm/invoke/args.py
index 9ad12c53dbe..400d1f720d0 100644
--- a/ldm/invoke/args.py
+++ b/ldm/invoke/args.py
@@ -97,6 +97,7 @@
 from ldm.invoke.globals import Globals
 from ldm.invoke.prompt_parser import split_weighted_subprompts
 from argparse import Namespace
+from pathlib import Path
 
 APP_ID = ldm.invoke.__app_id__
 APP_NAME = ldm.invoke.__app_name__
@@ -183,7 +184,7 @@ def parse_args(self):
                 sys.exit(0)
 
             print('* Initializing, be patient...')
-            Globals.root = os.path.abspath(switches.root_dir or Globals.root)
+            Globals.root = Path(os.path.abspath(switches.root_dir or Globals.root))
             Globals.try_patchmatch = switches.patchmatch
 
             # now use root directory to find the init file
diff --git a/ldm/invoke/globals.py b/ldm/invoke/globals.py
index 7c5b765cbc3..c67dbac145c 100644
--- a/ldm/invoke/globals.py
+++ b/ldm/invoke/globals.py
@@ -15,6 +15,7 @@
 import os.path as osp
 from pathlib import Path
 from argparse import Namespace
+from typing import Union
 
 Globals = Namespace()
 
@@ -42,12 +43,27 @@
 # The CLI will test connectivity at startup time.
 Globals.internet_available = True
 
-def global_config_dir()->str:
+def global_config_dir()->Path:
     return Path(Globals.root, Globals.config_dir)
 
-def global_models_dir()->str:
+def global_models_dir()->Path:
     return Path(Globals.root, Globals.models_dir)
 
-def global_autoscan_dir()->str:
+def global_autoscan_dir()->Path:
     return Path(Globals.root, Globals.autoscan_dir)
 
+def global_set_root(root_dir:Union[str,Path]):
+    Globals.root = root_dir
+
+def global_cache_dir(subdir:Union[str,Path]='')->Path:
+    '''
+    Returns Path to the model cache directory. If a subdirectory
+    is provided, it will be appended to the end of the path, allowing
+    for huggingface-style conventions: 
+         global_cache_dir('diffusers')
+         global_cache_dir('transformers')
+    '''
+    if (home := os.environ.get('HF_HOME')):
+        return Path(home,subdir)
+    else:
+        return Path(Globals.root,'models',subdir)
diff --git a/ldm/invoke/model_manager.py b/ldm/invoke/model_manager.py
index 9152bd29b13..a9db776b03c 100644
--- a/ldm/invoke/model_manager.py
+++ b/ldm/invoke/model_manager.py
@@ -30,7 +30,7 @@
 from picklescan.scanner import scan_file_path
 
 from ldm.invoke.generator.diffusers_pipeline import StableDiffusionGeneratorPipeline
-from ldm.invoke.globals import Globals, global_models_dir, global_autoscan_dir
+from ldm.invoke.globals import Globals, global_models_dir, global_autoscan_dir, global_cache_dir
 from ldm.util import instantiate_from_config, ask_user
 
 DEFAULT_MAX_MODELS=2
@@ -370,7 +370,7 @@ def _load_diffusers_model(self, mconfig):
              vae = self._load_vae(mconfig['vae'])
              pipeline_args.update(vae=vae)
         if not isinstance(name_or_path,Path):
-            pipeline_args.update(cache_dir=os.path.join(Globals.root,'models',name_or_path))
+            pipeline_args.update(cache_dir=global_cache_dir('diffusers'))
         if using_fp16:
             pipeline_args.update(torch_dtype=torch.float16)
             fp_args_list = [{'revision':'fp16'},{}]
@@ -791,7 +791,7 @@ def _load_vae(self, vae_config):
         using_fp16 = self.precision == 'float16'
 
         vae_args.update(
-            cache_dir=os.path.join(Globals.root,'models',name_or_path),
+            cache_dir=global_cache_dir('diffusers'),
             local_files_only=not Globals.internet_available,
         )
 
diff --git a/ldm/invoke/textual_inversion_training.py b/ldm/invoke/textual_inversion_training.py
index 34e83718d6c..7615353ecb6 100644
--- a/ldm/invoke/textual_inversion_training.py
+++ b/ldm/invoke/textual_inversion_training.py
@@ -518,10 +518,6 @@ def do_textual_inversion_training(
     pretrained_model_name_or_path = model_conf.get('repo_id',None) or Path(model_conf.get('path'))
     assert pretrained_model_name_or_path, f"models.yaml error: neither 'repo_id' nor 'path' is defined for {model}"
     pipeline_args = dict()
-    if not isinstance(pretrained_model_name_or_path,Path):
-        pipeline_args.update(
-            cache_dir=os.path.join(Globals.root,'models',pretrained_model_name_or_path)
-        )
     
     # Load tokenizer
     if tokenizer_name:
diff --git a/ldm/modules/encoders/modules.py b/ldm/modules/encoders/modules.py
index e119bdef5ad..f4706bcb3f4 100644
--- a/ldm/modules/encoders/modules.py
+++ b/ldm/modules/encoders/modules.py
@@ -10,7 +10,7 @@
 from transformers import CLIPTokenizer, CLIPTextModel
 import kornia
 from ldm.invoke.devices import choose_torch_device
-from ldm.invoke.globals import Globals
+from ldm.invoke.globals import Globals, global_cache_dir
 #from ldm.modules.textual_inversion_manager import TextualInversionManager
 
 from ldm.modules.x_transformer import (
@@ -109,7 +109,7 @@ def __init__(
             BertTokenizerFast,
         )
 
-        cache = os.path.join(Globals.root,'models/bert-base-uncased')
+        cache = global_cache_dir('hub')
         try:
             self.tokenizer = BertTokenizerFast.from_pretrained(
                 'bert-base-uncased',
@@ -249,7 +249,7 @@ def __init__(
         transformer:Optional[CLIPTextModel]=None,
     ):
         super().__init__()
-        cache = os.path.join(Globals.root,'models',version)
+        cache = global_cache_dir('hub')
         self.tokenizer = tokenizer or CLIPTokenizer.from_pretrained(
             version,
             cache_dir=cache,
diff --git a/scripts/configure_invokeai.py b/scripts/configure_invokeai.py
index c3a583aff2c..64ae1e356df 100755
--- a/scripts/configure_invokeai.py
+++ b/scripts/configure_invokeai.py
@@ -32,7 +32,7 @@
 from tqdm import tqdm
 from transformers import CLIPTokenizer, CLIPTextModel
 
-from ldm.invoke.globals import Globals
+from ldm.invoke.globals import Globals, global_cache_dir
 from ldm.invoke.readline import generic_completer
 
 warnings.filterwarnings('ignore')
@@ -368,6 +368,7 @@ def _download_diffusion_weights(mconfig:DictConfig, access_token:str, precision:
             path = download_from_hf(
                 model_class,
                 repo_id,
+                cache_subdir='diffusers',
                 safety_checker=None,
                 **extra_args,
             )
@@ -535,9 +536,9 @@ def download_bert():
         print('...success',file=sys.stderr)
 
 #---------------------------------------------
-def download_from_hf(model_class:object, model_name:str, **kwargs):
+def download_from_hf(model_class:object, model_name:str, cache_subdir:Path=Path('hub'), **kwargs):
     print('',file=sys.stderr)  # to prevent tqdm from overwriting
-    path = os.path.join(Globals.root,Model_dir,model_name)
+    path = global_cache_dir(cache_subdir)
     model = model_class.from_pretrained(model_name,
                                         cache_dir=path,
                                         resume_download=True,
diff --git a/scripts/textual_inversion.py b/scripts/textual_inversion.py
index 24ce18a0077..fb176a5eecc 100755
--- a/scripts/textual_inversion.py
+++ b/scripts/textual_inversion.py
@@ -1,11 +1,11 @@
 #!/usr/bin/env python
 
 # Copyright 2023, Lincoln Stein @lstein
-from ldm.invoke.globals import Globals
+from ldm.invoke.globals import Globals, set_root
 from ldm.invoke.textual_inversion_training import parse_args, do_textual_inversion_training
 
 if __name__ == "__main__":
     args = parse_args()
-    Globals.root = args.root_dir or Globals.root
+    set_root(args.root_dir or Globals.root)
     kwargs = vars(args)
     do_textual_inversion_training(**kwargs)
diff --git a/scripts/textual_inversion_fe.py b/scripts/textual_inversion_fe.py
index 9a8363a500b..187ac089ba6 100755
--- a/scripts/textual_inversion_fe.py
+++ b/scripts/textual_inversion_fe.py
@@ -5,7 +5,7 @@
 import sys
 import curses
 import re
-from ldm.invoke.globals import Globals
+from ldm.invoke.globals import Globals, set_root
 from omegaconf import OmegaConf
 from pathlib import Path
 from typing import List
@@ -223,7 +223,7 @@ def onStart(self):
         help='Path to the invokeai runtime directory',
     )
     args = parser.parse_args()
-    Globals.root = args.root_dir
+    set_root(args.root_dir)
     
     myapplication = MyApplication()
     myapplication.run()

From 5aeba074317fe3b3ee8fe991d24717fb62db52d8 Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Mon, 9 Jan 2023 12:04:34 -0500
Subject: [PATCH 149/199] feat - make model storage compatible with hugging
 face caching system

This commit alters the InvokeAI model directory to be compatible with
hugging face, making it easier to share diffusers (and other models)
across different programs.

- If the HF_HOME environment variable is not set, then models are
  cached in ~/invokeai/models in a format that is identical to the
  HuggingFace cache.

- If HF_HOME is set, then models are cached wherever HF_HOME points.

- To enable sharing with other HuggingFace library clients, set
  HF_HOME to ~/.cache/huggingface to set the default cache location
  or to ~/invokeai/models to have huggingface cache inside InvokeAI.
---
 ldm/generate.py                          |  1 -
 ldm/invoke/CLI.py                        | 21 +++++++++------------
 ldm/invoke/args.py                       |  3 +--
 ldm/invoke/globals.py                    | 22 +++-------------------
 ldm/invoke/model_manager.py              |  6 +++---
 ldm/invoke/textual_inversion_training.py |  4 ++++
 ldm/modules/encoders/modules.py          |  6 +++---
 scripts/configure_invokeai.py            |  7 +++----
 scripts/textual_inversion.py             |  4 ++--
 scripts/textual_inversion_fe.py          |  4 ++--
 10 files changed, 30 insertions(+), 48 deletions(-)

diff --git a/ldm/generate.py b/ldm/generate.py
index 8081ffc89dd..a5511fcc206 100644
--- a/ldm/generate.py
+++ b/ldm/generate.py
@@ -206,7 +206,6 @@ def __init__(
         self.model_manager = ModelManager(mconfig,self.device,self.precision,max_loaded_models=max_loaded_models)
         # don't accept invalid models
         fallback = self.model_manager.default_model() or FALLBACK_MODEL_NAME
-        model = model or fallback
         if not self.model_manager.valid_model(model):
             print(f'** "{model}" is not a known model name; falling back to {fallback}.')
             model = None
diff --git a/ldm/invoke/CLI.py b/ldm/invoke/CLI.py
index 1db1b4f6927..1cb59e51e3c 100644
--- a/ldm/invoke/CLI.py
+++ b/ldm/invoke/CLI.py
@@ -659,8 +659,7 @@ def _get_model_name_and_desc(model_manager,completer,model_name:str='',model_des
     model_name = _get_model_name(model_manager.list_models(),completer,model_name)
     completer.linebuffer = model_description
     model_description = input(f'Description for this model [{model_description}]: ').strip() or model_description
-    default = input(f'Make this model the default? [n] ').startswith(('y','Y'))
-    return model_name, model_description, default
+    return model_name, model_description
 
 def optimize_model(model_name_or_path:str, gen, opt, completer):
     manager = gen.model_manager
@@ -725,9 +724,9 @@ def del_config(model_name:str, gen, opt, completer):
 
 def edit_config(model_name:str, gen, opt, completer):
     current_model = gen.model_name
-#    if model_name == current_model:
-#        print("** Can't edit the active model. !switch to another model first. **")
-#        return
+    if model_name == current_model:
+        print("** Can't edit the active model. !switch to another model first. **")
+        return
 
     manager = gen.model_manager
     if not (info := manager.model_info(model_name)):
@@ -735,17 +734,15 @@ def edit_config(model_name:str, gen, opt, completer):
         return
 
     print(f'\n>> Editing model {model_name} from configuration file {opt.conf}')
-    new_name,new_description,default = _get_model_name_and_desc(gen.model_manager,
-                                                                completer,
-                                                                model_name=model_name,
-                                                                model_description=info['description']
+    new_name,new_description = _get_model_name_and_desc(gen.model_manager,
+                                                        completer,
+                                                        model_name=model_name,
+                                                        model_description=info['description']
     )
     info['description'] = new_description
     if new_name != model_name:
         manager.add_model(new_name,info)
         manager.del_model(model_name)
-    if default:
-        manager.set_default_model(new_name)
     manager.commit(opt.conf)
     completer.update_models(manager.list_models())
     print('>> Model successfully updated')
@@ -759,7 +756,7 @@ def _get_model_name(existing_names,completer,default_name:str='')->str:
             model_name = default_name
         if not re.match('^[\w._-]+$',model_name):
             print('** model name must contain only words, digits and the characters [._-] **')
-        elif model_name != default_name and model_name in existing_names:
+        elif model_name in existing_names:
             print(f'** the name {model_name} is already in use. Pick another.')
         else:
             done = True
diff --git a/ldm/invoke/args.py b/ldm/invoke/args.py
index 400d1f720d0..9ad12c53dbe 100644
--- a/ldm/invoke/args.py
+++ b/ldm/invoke/args.py
@@ -97,7 +97,6 @@
 from ldm.invoke.globals import Globals
 from ldm.invoke.prompt_parser import split_weighted_subprompts
 from argparse import Namespace
-from pathlib import Path
 
 APP_ID = ldm.invoke.__app_id__
 APP_NAME = ldm.invoke.__app_name__
@@ -184,7 +183,7 @@ def parse_args(self):
                 sys.exit(0)
 
             print('* Initializing, be patient...')
-            Globals.root = Path(os.path.abspath(switches.root_dir or Globals.root))
+            Globals.root = os.path.abspath(switches.root_dir or Globals.root)
             Globals.try_patchmatch = switches.patchmatch
 
             # now use root directory to find the init file
diff --git a/ldm/invoke/globals.py b/ldm/invoke/globals.py
index c67dbac145c..7c5b765cbc3 100644
--- a/ldm/invoke/globals.py
+++ b/ldm/invoke/globals.py
@@ -15,7 +15,6 @@
 import os.path as osp
 from pathlib import Path
 from argparse import Namespace
-from typing import Union
 
 Globals = Namespace()
 
@@ -43,27 +42,12 @@
 # The CLI will test connectivity at startup time.
 Globals.internet_available = True
 
-def global_config_dir()->Path:
+def global_config_dir()->str:
     return Path(Globals.root, Globals.config_dir)
 
-def global_models_dir()->Path:
+def global_models_dir()->str:
     return Path(Globals.root, Globals.models_dir)
 
-def global_autoscan_dir()->Path:
+def global_autoscan_dir()->str:
     return Path(Globals.root, Globals.autoscan_dir)
 
-def global_set_root(root_dir:Union[str,Path]):
-    Globals.root = root_dir
-
-def global_cache_dir(subdir:Union[str,Path]='')->Path:
-    '''
-    Returns Path to the model cache directory. If a subdirectory
-    is provided, it will be appended to the end of the path, allowing
-    for huggingface-style conventions: 
-         global_cache_dir('diffusers')
-         global_cache_dir('transformers')
-    '''
-    if (home := os.environ.get('HF_HOME')):
-        return Path(home,subdir)
-    else:
-        return Path(Globals.root,'models',subdir)
diff --git a/ldm/invoke/model_manager.py b/ldm/invoke/model_manager.py
index a9db776b03c..9152bd29b13 100644
--- a/ldm/invoke/model_manager.py
+++ b/ldm/invoke/model_manager.py
@@ -30,7 +30,7 @@
 from picklescan.scanner import scan_file_path
 
 from ldm.invoke.generator.diffusers_pipeline import StableDiffusionGeneratorPipeline
-from ldm.invoke.globals import Globals, global_models_dir, global_autoscan_dir, global_cache_dir
+from ldm.invoke.globals import Globals, global_models_dir, global_autoscan_dir
 from ldm.util import instantiate_from_config, ask_user
 
 DEFAULT_MAX_MODELS=2
@@ -370,7 +370,7 @@ def _load_diffusers_model(self, mconfig):
              vae = self._load_vae(mconfig['vae'])
              pipeline_args.update(vae=vae)
         if not isinstance(name_or_path,Path):
-            pipeline_args.update(cache_dir=global_cache_dir('diffusers'))
+            pipeline_args.update(cache_dir=os.path.join(Globals.root,'models',name_or_path))
         if using_fp16:
             pipeline_args.update(torch_dtype=torch.float16)
             fp_args_list = [{'revision':'fp16'},{}]
@@ -791,7 +791,7 @@ def _load_vae(self, vae_config):
         using_fp16 = self.precision == 'float16'
 
         vae_args.update(
-            cache_dir=global_cache_dir('diffusers'),
+            cache_dir=os.path.join(Globals.root,'models',name_or_path),
             local_files_only=not Globals.internet_available,
         )
 
diff --git a/ldm/invoke/textual_inversion_training.py b/ldm/invoke/textual_inversion_training.py
index 7615353ecb6..34e83718d6c 100644
--- a/ldm/invoke/textual_inversion_training.py
+++ b/ldm/invoke/textual_inversion_training.py
@@ -518,6 +518,10 @@ def do_textual_inversion_training(
     pretrained_model_name_or_path = model_conf.get('repo_id',None) or Path(model_conf.get('path'))
     assert pretrained_model_name_or_path, f"models.yaml error: neither 'repo_id' nor 'path' is defined for {model}"
     pipeline_args = dict()
+    if not isinstance(pretrained_model_name_or_path,Path):
+        pipeline_args.update(
+            cache_dir=os.path.join(Globals.root,'models',pretrained_model_name_or_path)
+        )
     
     # Load tokenizer
     if tokenizer_name:
diff --git a/ldm/modules/encoders/modules.py b/ldm/modules/encoders/modules.py
index f4706bcb3f4..e119bdef5ad 100644
--- a/ldm/modules/encoders/modules.py
+++ b/ldm/modules/encoders/modules.py
@@ -10,7 +10,7 @@
 from transformers import CLIPTokenizer, CLIPTextModel
 import kornia
 from ldm.invoke.devices import choose_torch_device
-from ldm.invoke.globals import Globals, global_cache_dir
+from ldm.invoke.globals import Globals
 #from ldm.modules.textual_inversion_manager import TextualInversionManager
 
 from ldm.modules.x_transformer import (
@@ -109,7 +109,7 @@ def __init__(
             BertTokenizerFast,
         )
 
-        cache = global_cache_dir('hub')
+        cache = os.path.join(Globals.root,'models/bert-base-uncased')
         try:
             self.tokenizer = BertTokenizerFast.from_pretrained(
                 'bert-base-uncased',
@@ -249,7 +249,7 @@ def __init__(
         transformer:Optional[CLIPTextModel]=None,
     ):
         super().__init__()
-        cache = global_cache_dir('hub')
+        cache = os.path.join(Globals.root,'models',version)
         self.tokenizer = tokenizer or CLIPTokenizer.from_pretrained(
             version,
             cache_dir=cache,
diff --git a/scripts/configure_invokeai.py b/scripts/configure_invokeai.py
index 64ae1e356df..c3a583aff2c 100755
--- a/scripts/configure_invokeai.py
+++ b/scripts/configure_invokeai.py
@@ -32,7 +32,7 @@
 from tqdm import tqdm
 from transformers import CLIPTokenizer, CLIPTextModel
 
-from ldm.invoke.globals import Globals, global_cache_dir
+from ldm.invoke.globals import Globals
 from ldm.invoke.readline import generic_completer
 
 warnings.filterwarnings('ignore')
@@ -368,7 +368,6 @@ def _download_diffusion_weights(mconfig:DictConfig, access_token:str, precision:
             path = download_from_hf(
                 model_class,
                 repo_id,
-                cache_subdir='diffusers',
                 safety_checker=None,
                 **extra_args,
             )
@@ -536,9 +535,9 @@ def download_bert():
         print('...success',file=sys.stderr)
 
 #---------------------------------------------
-def download_from_hf(model_class:object, model_name:str, cache_subdir:Path=Path('hub'), **kwargs):
+def download_from_hf(model_class:object, model_name:str, **kwargs):
     print('',file=sys.stderr)  # to prevent tqdm from overwriting
-    path = global_cache_dir(cache_subdir)
+    path = os.path.join(Globals.root,Model_dir,model_name)
     model = model_class.from_pretrained(model_name,
                                         cache_dir=path,
                                         resume_download=True,
diff --git a/scripts/textual_inversion.py b/scripts/textual_inversion.py
index fb176a5eecc..24ce18a0077 100755
--- a/scripts/textual_inversion.py
+++ b/scripts/textual_inversion.py
@@ -1,11 +1,11 @@
 #!/usr/bin/env python
 
 # Copyright 2023, Lincoln Stein @lstein
-from ldm.invoke.globals import Globals, set_root
+from ldm.invoke.globals import Globals
 from ldm.invoke.textual_inversion_training import parse_args, do_textual_inversion_training
 
 if __name__ == "__main__":
     args = parse_args()
-    set_root(args.root_dir or Globals.root)
+    Globals.root = args.root_dir or Globals.root
     kwargs = vars(args)
     do_textual_inversion_training(**kwargs)
diff --git a/scripts/textual_inversion_fe.py b/scripts/textual_inversion_fe.py
index 187ac089ba6..9a8363a500b 100755
--- a/scripts/textual_inversion_fe.py
+++ b/scripts/textual_inversion_fe.py
@@ -5,7 +5,7 @@
 import sys
 import curses
 import re
-from ldm.invoke.globals import Globals, set_root
+from ldm.invoke.globals import Globals
 from omegaconf import OmegaConf
 from pathlib import Path
 from typing import List
@@ -223,7 +223,7 @@ def onStart(self):
         help='Path to the invokeai runtime directory',
     )
     args = parser.parse_args()
-    set_root(args.root_dir)
+    Globals.root = args.root_dir
     
     myapplication = MyApplication()
     myapplication.run()

From a46d1750b51a4d2669d72c32901e2f16f8966855 Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Mon, 9 Jan 2023 12:17:31 -0500
Subject: [PATCH 150/199]     fixes to share models with HuggingFace cache
 system

    - If HF_HOME environment variable is defined, then all huggingface models
      are stored in that directory following the standard conventions.
    - For seamless interoperability, set HF_HOME to ~/.cache/huggingface
    - If HF_HOME not defined, then models are stored in ~/invokeai/models.
      This is equivalent to setting HF_HOME to ~/invokeai/models

    A future commit will add a migration mechanism so that this change doesn't
    break previous installs.
---
 ldm/generate.py                          |  1 +
 ldm/invoke/CLI.py                        | 21 ++++++++++++---------
 ldm/invoke/args.py                       |  3 ++-
 ldm/invoke/ckpt_generator/inpaint.py     |  2 +-
 ldm/invoke/globals.py                    | 22 +++++++++++++++++++---
 ldm/invoke/model_manager.py              |  6 +++---
 ldm/invoke/textual_inversion_training.py |  4 ----
 ldm/modules/encoders/modules.py          |  6 +++---
 scripts/configure_invokeai.py            |  7 ++++---
 scripts/textual_inversion.py             |  4 ++--
 scripts/textual_inversion_fe.py          |  4 ++--
 11 files changed, 49 insertions(+), 31 deletions(-)

diff --git a/ldm/generate.py b/ldm/generate.py
index a5511fcc206..8081ffc89dd 100644
--- a/ldm/generate.py
+++ b/ldm/generate.py
@@ -206,6 +206,7 @@ def __init__(
         self.model_manager = ModelManager(mconfig,self.device,self.precision,max_loaded_models=max_loaded_models)
         # don't accept invalid models
         fallback = self.model_manager.default_model() or FALLBACK_MODEL_NAME
+        model = model or fallback
         if not self.model_manager.valid_model(model):
             print(f'** "{model}" is not a known model name; falling back to {fallback}.')
             model = None
diff --git a/ldm/invoke/CLI.py b/ldm/invoke/CLI.py
index 1cb59e51e3c..1db1b4f6927 100644
--- a/ldm/invoke/CLI.py
+++ b/ldm/invoke/CLI.py
@@ -659,7 +659,8 @@ def _get_model_name_and_desc(model_manager,completer,model_name:str='',model_des
     model_name = _get_model_name(model_manager.list_models(),completer,model_name)
     completer.linebuffer = model_description
     model_description = input(f'Description for this model [{model_description}]: ').strip() or model_description
-    return model_name, model_description
+    default = input(f'Make this model the default? [n] ').startswith(('y','Y'))
+    return model_name, model_description, default
 
 def optimize_model(model_name_or_path:str, gen, opt, completer):
     manager = gen.model_manager
@@ -724,9 +725,9 @@ def del_config(model_name:str, gen, opt, completer):
 
 def edit_config(model_name:str, gen, opt, completer):
     current_model = gen.model_name
-    if model_name == current_model:
-        print("** Can't edit the active model. !switch to another model first. **")
-        return
+#    if model_name == current_model:
+#        print("** Can't edit the active model. !switch to another model first. **")
+#        return
 
     manager = gen.model_manager
     if not (info := manager.model_info(model_name)):
@@ -734,15 +735,17 @@ def edit_config(model_name:str, gen, opt, completer):
         return
 
     print(f'\n>> Editing model {model_name} from configuration file {opt.conf}')
-    new_name,new_description = _get_model_name_and_desc(gen.model_manager,
-                                                        completer,
-                                                        model_name=model_name,
-                                                        model_description=info['description']
+    new_name,new_description,default = _get_model_name_and_desc(gen.model_manager,
+                                                                completer,
+                                                                model_name=model_name,
+                                                                model_description=info['description']
     )
     info['description'] = new_description
     if new_name != model_name:
         manager.add_model(new_name,info)
         manager.del_model(model_name)
+    if default:
+        manager.set_default_model(new_name)
     manager.commit(opt.conf)
     completer.update_models(manager.list_models())
     print('>> Model successfully updated')
@@ -756,7 +759,7 @@ def _get_model_name(existing_names,completer,default_name:str='')->str:
             model_name = default_name
         if not re.match('^[\w._-]+$',model_name):
             print('** model name must contain only words, digits and the characters [._-] **')
-        elif model_name in existing_names:
+        elif model_name != default_name and model_name in existing_names:
             print(f'** the name {model_name} is already in use. Pick another.')
         else:
             done = True
diff --git a/ldm/invoke/args.py b/ldm/invoke/args.py
index 9ad12c53dbe..400d1f720d0 100644
--- a/ldm/invoke/args.py
+++ b/ldm/invoke/args.py
@@ -97,6 +97,7 @@
 from ldm.invoke.globals import Globals
 from ldm.invoke.prompt_parser import split_weighted_subprompts
 from argparse import Namespace
+from pathlib import Path
 
 APP_ID = ldm.invoke.__app_id__
 APP_NAME = ldm.invoke.__app_name__
@@ -183,7 +184,7 @@ def parse_args(self):
                 sys.exit(0)
 
             print('* Initializing, be patient...')
-            Globals.root = os.path.abspath(switches.root_dir or Globals.root)
+            Globals.root = Path(os.path.abspath(switches.root_dir or Globals.root))
             Globals.try_patchmatch = switches.patchmatch
 
             # now use root directory to find the init file
diff --git a/ldm/invoke/ckpt_generator/inpaint.py b/ldm/invoke/ckpt_generator/inpaint.py
index ac99430a12d..3b965b0ee36 100644
--- a/ldm/invoke/ckpt_generator/inpaint.py
+++ b/ldm/invoke/ckpt_generator/inpaint.py
@@ -27,7 +27,7 @@ def infill_methods()->list[str]:
     methods.append('tile')
     return methods
 
-class Inpaint(CkptImg2Img):
+class CkptInpaint(CkptImg2Img):
     def __init__(self, model, precision):
         self.init_latent = None
         self.pil_image = None
diff --git a/ldm/invoke/globals.py b/ldm/invoke/globals.py
index 7c5b765cbc3..c67dbac145c 100644
--- a/ldm/invoke/globals.py
+++ b/ldm/invoke/globals.py
@@ -15,6 +15,7 @@
 import os.path as osp
 from pathlib import Path
 from argparse import Namespace
+from typing import Union
 
 Globals = Namespace()
 
@@ -42,12 +43,27 @@
 # The CLI will test connectivity at startup time.
 Globals.internet_available = True
 
-def global_config_dir()->str:
+def global_config_dir()->Path:
     return Path(Globals.root, Globals.config_dir)
 
-def global_models_dir()->str:
+def global_models_dir()->Path:
     return Path(Globals.root, Globals.models_dir)
 
-def global_autoscan_dir()->str:
+def global_autoscan_dir()->Path:
     return Path(Globals.root, Globals.autoscan_dir)
 
+def global_set_root(root_dir:Union[str,Path]):
+    Globals.root = root_dir
+
+def global_cache_dir(subdir:Union[str,Path]='')->Path:
+    '''
+    Returns Path to the model cache directory. If a subdirectory
+    is provided, it will be appended to the end of the path, allowing
+    for huggingface-style conventions: 
+         global_cache_dir('diffusers')
+         global_cache_dir('transformers')
+    '''
+    if (home := os.environ.get('HF_HOME')):
+        return Path(home,subdir)
+    else:
+        return Path(Globals.root,'models',subdir)
diff --git a/ldm/invoke/model_manager.py b/ldm/invoke/model_manager.py
index 9152bd29b13..a9db776b03c 100644
--- a/ldm/invoke/model_manager.py
+++ b/ldm/invoke/model_manager.py
@@ -30,7 +30,7 @@
 from picklescan.scanner import scan_file_path
 
 from ldm.invoke.generator.diffusers_pipeline import StableDiffusionGeneratorPipeline
-from ldm.invoke.globals import Globals, global_models_dir, global_autoscan_dir
+from ldm.invoke.globals import Globals, global_models_dir, global_autoscan_dir, global_cache_dir
 from ldm.util import instantiate_from_config, ask_user
 
 DEFAULT_MAX_MODELS=2
@@ -370,7 +370,7 @@ def _load_diffusers_model(self, mconfig):
              vae = self._load_vae(mconfig['vae'])
              pipeline_args.update(vae=vae)
         if not isinstance(name_or_path,Path):
-            pipeline_args.update(cache_dir=os.path.join(Globals.root,'models',name_or_path))
+            pipeline_args.update(cache_dir=global_cache_dir('diffusers'))
         if using_fp16:
             pipeline_args.update(torch_dtype=torch.float16)
             fp_args_list = [{'revision':'fp16'},{}]
@@ -791,7 +791,7 @@ def _load_vae(self, vae_config):
         using_fp16 = self.precision == 'float16'
 
         vae_args.update(
-            cache_dir=os.path.join(Globals.root,'models',name_or_path),
+            cache_dir=global_cache_dir('diffusers'),
             local_files_only=not Globals.internet_available,
         )
 
diff --git a/ldm/invoke/textual_inversion_training.py b/ldm/invoke/textual_inversion_training.py
index 34e83718d6c..7615353ecb6 100644
--- a/ldm/invoke/textual_inversion_training.py
+++ b/ldm/invoke/textual_inversion_training.py
@@ -518,10 +518,6 @@ def do_textual_inversion_training(
     pretrained_model_name_or_path = model_conf.get('repo_id',None) or Path(model_conf.get('path'))
     assert pretrained_model_name_or_path, f"models.yaml error: neither 'repo_id' nor 'path' is defined for {model}"
     pipeline_args = dict()
-    if not isinstance(pretrained_model_name_or_path,Path):
-        pipeline_args.update(
-            cache_dir=os.path.join(Globals.root,'models',pretrained_model_name_or_path)
-        )
     
     # Load tokenizer
     if tokenizer_name:
diff --git a/ldm/modules/encoders/modules.py b/ldm/modules/encoders/modules.py
index e119bdef5ad..f4706bcb3f4 100644
--- a/ldm/modules/encoders/modules.py
+++ b/ldm/modules/encoders/modules.py
@@ -10,7 +10,7 @@
 from transformers import CLIPTokenizer, CLIPTextModel
 import kornia
 from ldm.invoke.devices import choose_torch_device
-from ldm.invoke.globals import Globals
+from ldm.invoke.globals import Globals, global_cache_dir
 #from ldm.modules.textual_inversion_manager import TextualInversionManager
 
 from ldm.modules.x_transformer import (
@@ -109,7 +109,7 @@ def __init__(
             BertTokenizerFast,
         )
 
-        cache = os.path.join(Globals.root,'models/bert-base-uncased')
+        cache = global_cache_dir('hub')
         try:
             self.tokenizer = BertTokenizerFast.from_pretrained(
                 'bert-base-uncased',
@@ -249,7 +249,7 @@ def __init__(
         transformer:Optional[CLIPTextModel]=None,
     ):
         super().__init__()
-        cache = os.path.join(Globals.root,'models',version)
+        cache = global_cache_dir('hub')
         self.tokenizer = tokenizer or CLIPTokenizer.from_pretrained(
             version,
             cache_dir=cache,
diff --git a/scripts/configure_invokeai.py b/scripts/configure_invokeai.py
index c3a583aff2c..64ae1e356df 100755
--- a/scripts/configure_invokeai.py
+++ b/scripts/configure_invokeai.py
@@ -32,7 +32,7 @@
 from tqdm import tqdm
 from transformers import CLIPTokenizer, CLIPTextModel
 
-from ldm.invoke.globals import Globals
+from ldm.invoke.globals import Globals, global_cache_dir
 from ldm.invoke.readline import generic_completer
 
 warnings.filterwarnings('ignore')
@@ -368,6 +368,7 @@ def _download_diffusion_weights(mconfig:DictConfig, access_token:str, precision:
             path = download_from_hf(
                 model_class,
                 repo_id,
+                cache_subdir='diffusers',
                 safety_checker=None,
                 **extra_args,
             )
@@ -535,9 +536,9 @@ def download_bert():
         print('...success',file=sys.stderr)
 
 #---------------------------------------------
-def download_from_hf(model_class:object, model_name:str, **kwargs):
+def download_from_hf(model_class:object, model_name:str, cache_subdir:Path=Path('hub'), **kwargs):
     print('',file=sys.stderr)  # to prevent tqdm from overwriting
-    path = os.path.join(Globals.root,Model_dir,model_name)
+    path = global_cache_dir(cache_subdir)
     model = model_class.from_pretrained(model_name,
                                         cache_dir=path,
                                         resume_download=True,
diff --git a/scripts/textual_inversion.py b/scripts/textual_inversion.py
index 24ce18a0077..fb176a5eecc 100755
--- a/scripts/textual_inversion.py
+++ b/scripts/textual_inversion.py
@@ -1,11 +1,11 @@
 #!/usr/bin/env python
 
 # Copyright 2023, Lincoln Stein @lstein
-from ldm.invoke.globals import Globals
+from ldm.invoke.globals import Globals, set_root
 from ldm.invoke.textual_inversion_training import parse_args, do_textual_inversion_training
 
 if __name__ == "__main__":
     args = parse_args()
-    Globals.root = args.root_dir or Globals.root
+    set_root(args.root_dir or Globals.root)
     kwargs = vars(args)
     do_textual_inversion_training(**kwargs)
diff --git a/scripts/textual_inversion_fe.py b/scripts/textual_inversion_fe.py
index 9a8363a500b..187ac089ba6 100755
--- a/scripts/textual_inversion_fe.py
+++ b/scripts/textual_inversion_fe.py
@@ -5,7 +5,7 @@
 import sys
 import curses
 import re
-from ldm.invoke.globals import Globals
+from ldm.invoke.globals import Globals, set_root
 from omegaconf import OmegaConf
 from pathlib import Path
 from typing import List
@@ -223,7 +223,7 @@ def onStart(self):
         help='Path to the invokeai runtime directory',
     )
     args = parser.parse_args()
-    Globals.root = args.root_dir
+    set_root(args.root_dir)
     
     myapplication = MyApplication()
     myapplication.run()

From 30b74c4c6ba98a2614c9bffe6b8d06eb2ca54934 Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Mon, 9 Jan 2023 12:19:02 -0500
Subject: [PATCH 151/199] fix error "no attribute CkptInpaint"

---
 ldm/invoke/ckpt_generator/inpaint.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ldm/invoke/ckpt_generator/inpaint.py b/ldm/invoke/ckpt_generator/inpaint.py
index ac99430a12d..3b965b0ee36 100644
--- a/ldm/invoke/ckpt_generator/inpaint.py
+++ b/ldm/invoke/ckpt_generator/inpaint.py
@@ -27,7 +27,7 @@ def infill_methods()->list[str]:
     methods.append('tile')
     return methods
 
-class Inpaint(CkptImg2Img):
+class CkptInpaint(CkptImg2Img):
     def __init__(self, model, precision):
         self.init_latent = None
         self.pil_image = None

From 81ded862cab10854583f3ca95ce0aaf0885c9696 Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Mon, 9 Jan 2023 14:11:35 -0500
Subject: [PATCH 152/199] model_manager.list_models() returns entire model
 config stanza+status

---
 ldm/invoke/model_manager.py | 12 +++++-------
 1 file changed, 5 insertions(+), 7 deletions(-)

diff --git a/ldm/invoke/model_manager.py b/ldm/invoke/model_manager.py
index 9152bd29b13..d9cc9c6f744 100644
--- a/ldm/invoke/model_manager.py
+++ b/ldm/invoke/model_manager.py
@@ -162,14 +162,12 @@ def list_models(self) -> dict:
         models = {}
         for name in self.config:
             stanza = self.config[name]
-            format = stanza.get('format','diffusers')
+            format = stanza.get('format','ckpt')
             config = stanza.get('config','no config')
-            models[name] = dict(
-                description = stanza.get('description',None),
-                format = 'vae' if 'VAE/default' in config else format,
-                status = 'active' if self.current_model == name else 'cached' if name in self.models else 'not loaded',
-            )
-
+            models[name]=dict()
+            models[name].update(stanza)
+            models[name]['format'] = 'vae' if 'VAE/default' in config else format
+            models[name]['status'] = 'active' if self.current_model == name else 'cached' if name in self.models else 'not loaded'
         return models
 
     def print_models(self) -> None:

From e728b4625c88c0ced5268827eb9013f9000b46cd Mon Sep 17 00:00:00 2001
From: blessedcoolant <54517381+blessedcoolant@users.noreply.github.com>
Date: Tue, 10 Jan 2023 09:49:21 +1300
Subject: [PATCH 153/199] Initial Draft - Model Manager Diffusers

---
 frontend/src/app/invokeai.d.ts                |  11 +
 frontend/src/app/socketio/actions.ts          |   6 +-
 .../components/ModelManager/AddModel.tsx      |   1 +
 .../ModelManager/DiffusersModelEdit.tsx       | 210 ++++++++++++++++++
 .../components/ModelManager/ModelEdit.tsx     |   2 +
 .../components/ModelManager/ModelList.tsx     |  76 +++++--
 .../ModelManager/ModelManagerModal.tsx        |  17 +-
 .../components/ModelManager/SearchModels.tsx  |   1 +
 ldm/invoke/model_manager.py                   |  51 ++++-
 9 files changed, 344 insertions(+), 31 deletions(-)
 create mode 100644 frontend/src/features/system/components/ModelManager/DiffusersModelEdit.tsx

diff --git a/frontend/src/app/invokeai.d.ts b/frontend/src/app/invokeai.d.ts
index d5ee6adbbc8..8938e7265cb 100644
--- a/frontend/src/app/invokeai.d.ts
+++ b/frontend/src/app/invokeai.d.ts
@@ -170,6 +170,8 @@ export declare type Model = {
   width?: number;
   height?: number;
   default?: boolean;
+  format?: string;
+  repo_id?: string;
 };
 
 export declare type ModelList = Record<string, Model>;
@@ -188,6 +190,15 @@ export declare type InvokeModelConfigProps = {
   width: number | undefined;
   height: number | undefined;
   default: boolean | undefined;
+  format: string | undefined;
+};
+
+export declare type InvokeDiffusersModelConfigProps = {
+  name: string | undefined;
+  description: string | undefined;
+  repo_id: string | undefined;
+  vae: string | undefined;
+  default: boolean | undefined;
 };
 
 /**
diff --git a/frontend/src/app/socketio/actions.ts b/frontend/src/app/socketio/actions.ts
index e07b12ebf4f..60ea433ac33 100644
--- a/frontend/src/app/socketio/actions.ts
+++ b/frontend/src/app/socketio/actions.ts
@@ -32,9 +32,9 @@ export const requestSystemConfig = createAction<undefined>(
 
 export const searchForModels = createAction<string>('socketio/searchForModels');
 
-export const addNewModel = createAction<InvokeAI.InvokeModelConfigProps>(
-  'socketio/addNewModel'
-);
+export const addNewModel = createAction<
+  InvokeAI.InvokeModelConfigProps | InvokeAI.InvokeDiffusersModelConfigProps
+>('socketio/addNewModel');
 
 export const deleteModel = createAction<string>('socketio/deleteModel');
 
diff --git a/frontend/src/features/system/components/ModelManager/AddModel.tsx b/frontend/src/features/system/components/ModelManager/AddModel.tsx
index bc08fa08b6f..dac2e2cc34d 100644
--- a/frontend/src/features/system/components/ModelManager/AddModel.tsx
+++ b/frontend/src/features/system/components/ModelManager/AddModel.tsx
@@ -65,6 +65,7 @@ export default function AddModel() {
     vae: '',
     width: 512,
     height: 512,
+    format: 'ckpt',
     default: false,
   };
 
diff --git a/frontend/src/features/system/components/ModelManager/DiffusersModelEdit.tsx b/frontend/src/features/system/components/ModelManager/DiffusersModelEdit.tsx
new file mode 100644
index 00000000000..f9de0faa691
--- /dev/null
+++ b/frontend/src/features/system/components/ModelManager/DiffusersModelEdit.tsx
@@ -0,0 +1,210 @@
+import { createSelector } from '@reduxjs/toolkit';
+
+import React, { useEffect, useState } from 'react';
+import IAIInput from 'common/components/IAIInput';
+import IAIButton from 'common/components/IAIButton';
+
+import { useAppDispatch, useAppSelector } from 'app/storeHooks';
+import { systemSelector } from 'features/system/store/systemSelectors';
+
+import {
+  Flex,
+  FormControl,
+  FormErrorMessage,
+  FormHelperText,
+  FormLabel,
+  Text,
+  VStack,
+} from '@chakra-ui/react';
+
+import { Field, Formik } from 'formik';
+import { useTranslation } from 'react-i18next';
+import { addNewModel } from 'app/socketio/actions';
+
+import _ from 'lodash';
+
+import type { RootState } from 'app/store';
+import type { InvokeDiffusersModelConfigProps } from 'app/invokeai';
+
+const selector = createSelector(
+  [systemSelector],
+  (system) => {
+    const { openModel, model_list } = system;
+    return {
+      model_list,
+      openModel,
+    };
+  },
+  {
+    memoizeOptions: {
+      resultEqualityCheck: _.isEqual,
+    },
+  }
+);
+
+export default function DiffusersModelEdit() {
+  const { openModel, model_list } = useAppSelector(selector);
+  const isProcessing = useAppSelector(
+    (state: RootState) => state.system.isProcessing
+  );
+
+  const dispatch = useAppDispatch();
+
+  const { t } = useTranslation();
+
+  const [editModelFormValues, setEditModelFormValues] =
+    useState<InvokeDiffusersModelConfigProps>({
+      name: '',
+      description: '',
+      repo_id: '',
+      vae: '',
+      default: false,
+    });
+
+  useEffect(() => {
+    if (openModel) {
+      const retrievedModel = _.pickBy(model_list, (val, key) => {
+        return _.isEqual(key, openModel);
+      });
+      setEditModelFormValues({
+        name: openModel,
+        description: retrievedModel[openModel]?.description,
+        repo_id: retrievedModel[openModel]?.repo_id,
+        vae: retrievedModel[openModel]?.vae
+          ? retrievedModel[openModel]?.vae
+          : '',
+        default: retrievedModel[openModel]?.default,
+      });
+    }
+  }, [model_list, openModel]);
+
+  const editModelFormSubmitHandler = (
+    values: InvokeDiffusersModelConfigProps
+  ) => {
+    dispatch(addNewModel(values));
+  };
+
+  return openModel ? (
+    <Flex flexDirection="column" rowGap="1rem" width="100%">
+      <Flex alignItems="center">
+        <Text fontSize="lg" fontWeight="bold">
+          {openModel}
+        </Text>
+      </Flex>
+      <Flex
+        flexDirection="column"
+        maxHeight={window.innerHeight - 270}
+        overflowY="scroll"
+        paddingRight="2rem"
+      >
+        <Formik
+          enableReinitialize={true}
+          initialValues={editModelFormValues}
+          onSubmit={editModelFormSubmitHandler}
+        >
+          {({ handleSubmit, errors, touched }) => (
+            <form onSubmit={handleSubmit}>
+              <VStack rowGap={'0.5rem'} alignItems="start">
+                {/* Description */}
+                <FormControl
+                  isInvalid={!!errors.description && touched.description}
+                  isRequired
+                >
+                  <FormLabel htmlFor="description" fontSize="sm">
+                    {t('modelmanager:description')}
+                  </FormLabel>
+                  <VStack alignItems={'start'}>
+                    <Field
+                      as={IAIInput}
+                      id="description"
+                      name="description"
+                      type="text"
+                      width="lg"
+                    />
+                    {!!errors.description && touched.description ? (
+                      <FormErrorMessage>{errors.description}</FormErrorMessage>
+                    ) : (
+                      <FormHelperText margin={0}>
+                        {t('modelmanager:descriptionValidationMsg')}
+                      </FormHelperText>
+                    )}
+                  </VStack>
+                </FormControl>
+
+                {/* Repo ID */}
+                <FormControl
+                  isInvalid={!!errors.repo_id && touched.repo_id}
+                  isRequired
+                >
+                  <FormLabel htmlFor="config" fontSize="sm">
+                    {t('modelmanager:modelLocation')}
+                  </FormLabel>
+                  <VStack alignItems={'start'}>
+                    <Field
+                      as={IAIInput}
+                      id="repo_id"
+                      name="repo_id"
+                      type="text"
+                      width="lg"
+                    />
+                    {!!errors.repo_id && touched.repo_id ? (
+                      <FormErrorMessage>{errors.repo_id}</FormErrorMessage>
+                    ) : (
+                      <FormHelperText margin={0}>
+                        {t('modelmanager:modelLocationValidationMsg')}
+                      </FormHelperText>
+                    )}
+                  </VStack>
+                </FormControl>
+
+                {/* VAE */}
+                <FormControl isInvalid={!!errors.vae && touched.vae}>
+                  <FormLabel htmlFor="vae" fontSize="sm">
+                    {t('modelmanager:vaeLocation')}
+                  </FormLabel>
+                  <VStack alignItems={'start'}>
+                    <Field
+                      as={IAIInput}
+                      id="vae"
+                      name="vae"
+                      type="text"
+                      width="lg"
+                    />
+                    {!!errors.vae && touched.vae ? (
+                      <FormErrorMessage>{errors.vae}</FormErrorMessage>
+                    ) : (
+                      <FormHelperText margin={0}>
+                        {t('modelmanager:vaeLocationValidationMsg')}
+                      </FormHelperText>
+                    )}
+                  </VStack>
+                </FormControl>
+
+                <IAIButton
+                  type="submit"
+                  className="modal-close-btn"
+                  isLoading={isProcessing}
+                >
+                  {t('modelmanager:updateModel')}
+                </IAIButton>
+              </VStack>
+            </form>
+          )}
+        </Formik>
+      </Flex>
+    </Flex>
+  ) : (
+    <Flex
+      width="100%"
+      height="250px"
+      justifyContent="center"
+      alignItems="center"
+      backgroundColor="var(--background-color)"
+      borderRadius="0.5rem"
+    >
+      <Text fontWeight="bold" color="var(--subtext-color-bright)">
+        Pick A Model To Edit
+      </Text>
+    </Flex>
+  );
+}
diff --git a/frontend/src/features/system/components/ModelManager/ModelEdit.tsx b/frontend/src/features/system/components/ModelManager/ModelEdit.tsx
index fcb8d701ba6..5d816e170e6 100644
--- a/frontend/src/features/system/components/ModelManager/ModelEdit.tsx
+++ b/frontend/src/features/system/components/ModelManager/ModelEdit.tsx
@@ -68,6 +68,7 @@ export default function ModelEdit() {
       width: 512,
       height: 512,
       default: false,
+      format: 'ckpt',
     });
 
   useEffect(() => {
@@ -84,6 +85,7 @@ export default function ModelEdit() {
         width: retrievedModel[openModel]?.width,
         height: retrievedModel[openModel]?.height,
         default: retrievedModel[openModel]?.default,
+        format: 'ckpt',
       });
     }
   }, [model_list, openModel]);
diff --git a/frontend/src/features/system/components/ModelManager/ModelList.tsx b/frontend/src/features/system/components/ModelManager/ModelList.tsx
index f5b2419100c..fa607bb503f 100644
--- a/frontend/src/features/system/components/ModelManager/ModelList.tsx
+++ b/frontend/src/features/system/components/ModelManager/ModelList.tsx
@@ -1,5 +1,5 @@
 import { useState } from 'react';
-import { Flex, Text } from '@chakra-ui/react';
+import { Box, Flex, Text } from '@chakra-ui/react';
 import { createSelector } from '@reduxjs/toolkit';
 import IAIInput from 'common/components/IAIInput';
 
@@ -22,17 +22,12 @@ const modelListSelector = createSelector(
       return { name: key, ...model };
     });
 
-    const activeModel = models.find((model) => model.status === 'active');
-
-    return {
-      models,
-      activeModel: activeModel,
-    };
+    return models;
   }
 );
 
 const ModelList = () => {
-  const { models } = useAppSelector(modelListSelector);
+  const models = useAppSelector(modelListSelector);
 
   const [searchText, setSearchText] = useState<string>('');
 
@@ -43,7 +38,8 @@ const ModelList = () => {
   }, 400);
 
   const renderModelListItems = () => {
-    const modelListItemsToRender: ReactNode[] = [];
+    const ckptModelListItemsToRender: ReactNode[] = [];
+    const diffusersModelListItemsToRender: ReactNode[] = [];
     const filteredModelListItemsToRender: ReactNode[] = [];
 
     models.forEach((model, i) => {
@@ -57,19 +53,59 @@ const ModelList = () => {
           />
         );
       }
-      modelListItemsToRender.push(
-        <ModelListItem
-          key={i}
-          name={model.name}
-          status={model.status}
-          description={model.description}
-        />
-      );
+      if (model.format !== 'diffusers') {
+        ckptModelListItemsToRender.push(
+          <ModelListItem
+            key={i}
+            name={model.name}
+            status={model.status}
+            description={model.description}
+          />
+        );
+      } else {
+        diffusersModelListItemsToRender.push(
+          <ModelListItem
+            key={i}
+            name={model.name}
+            status={model.status}
+            description={model.description}
+          />
+        );
+      }
     });
 
-    return searchText !== ''
-      ? filteredModelListItemsToRender
-      : modelListItemsToRender;
+    return searchText !== '' ? (
+      filteredModelListItemsToRender
+    ) : (
+      <Flex flexDirection="column" rowGap="1.5rem">
+        <Box>
+          <Text
+            fontWeight="bold"
+            backgroundColor="var(--background-color)"
+            padding="0.5rem 1rem"
+            borderRadius="0.5rem"
+            marginBottom="0.5rem"
+            width="max-content"
+          >
+            Checkpoint Models
+          </Text>
+          {ckptModelListItemsToRender}
+        </Box>
+        <Box>
+          <Text
+            fontWeight="bold"
+            backgroundColor="var(--background-color)"
+            padding="0.5rem 1rem"
+            borderRadius="0.5rem"
+            marginBottom="0.5rem"
+            width="max-content"
+          >
+            Diffusers Models
+          </Text>
+          {diffusersModelListItemsToRender}
+        </Box>
+      </Flex>
+    );
   };
 
   return (
diff --git a/frontend/src/features/system/components/ModelManager/ModelManagerModal.tsx b/frontend/src/features/system/components/ModelManager/ModelManagerModal.tsx
index 8fecf121982..25d3305863a 100644
--- a/frontend/src/features/system/components/ModelManager/ModelManagerModal.tsx
+++ b/frontend/src/features/system/components/ModelManager/ModelManagerModal.tsx
@@ -14,6 +14,9 @@ import ModelEdit from './ModelEdit';
 import ModelList from './ModelList';
 
 import type { ReactElement } from 'react';
+import { useAppSelector } from 'app/storeHooks';
+import { RootState } from 'app/store';
+import DiffusersModelEdit from './DiffusersModelEdit';
 
 type ModelManagerModalProps = {
   children: ReactElement;
@@ -28,6 +31,14 @@ export default function ModelManagerModal({
     onClose: onModelManagerModalClose,
   } = useDisclosure();
 
+  const model_list = useAppSelector(
+    (state: RootState) => state.system.model_list
+  );
+
+  const openModel = useAppSelector(
+    (state: RootState) => state.system.openModel
+  );
+
   const { t } = useTranslation();
 
   return (
@@ -50,7 +61,11 @@ export default function ModelManagerModal({
             columnGap={'2rem'}
           >
             <ModelList />
-            <ModelEdit />
+            {openModel && model_list[openModel]['format'] === 'diffusers' ? (
+              <DiffusersModelEdit />
+            ) : (
+              <ModelEdit />
+            )}
           </Flex>
         </ModalContent>
       </Modal>
diff --git a/frontend/src/features/system/components/ModelManager/SearchModels.tsx b/frontend/src/features/system/components/ModelManager/SearchModels.tsx
index f5a67220be0..ab129bf19f4 100644
--- a/frontend/src/features/system/components/ModelManager/SearchModels.tsx
+++ b/frontend/src/features/system/components/ModelManager/SearchModels.tsx
@@ -178,6 +178,7 @@ export default function SearchModels() {
         width: 512,
         height: 512,
         default: false,
+        format: 'ckpt',
       };
       dispatch(addNewModel(modelFormat));
     });
diff --git a/ldm/invoke/model_manager.py b/ldm/invoke/model_manager.py
index d9cc9c6f744..34854099532 100644
--- a/ldm/invoke/model_manager.py
+++ b/ldm/invoke/model_manager.py
@@ -162,12 +162,49 @@ def list_models(self) -> dict:
         models = {}
         for name in self.config:
             stanza = self.config[name]
-            format = stanza.get('format','ckpt')
-            config = stanza.get('config','no config')
-            models[name]=dict()
-            models[name].update(stanza)
-            models[name]['format'] = 'vae' if 'VAE/default' in config else format
-            models[name]['status'] = 'active' if self.current_model == name else 'cached' if name in self.models else 'not loaded'
+            models[name] = dict()
+            
+            format = stanza.get('format','ckpt') # Determine Format
+
+            # Common Attribs
+            description = stanza.get('description', None)
+            if self.current_model == name:
+                status = 'active'
+            elif name in self.models:
+                status = 'cached'
+            else:
+                status = 'not loaded'
+            
+            # Checkpoint Config Parse
+            if format == 'ckpt':
+                config = stanza.get('config', None)
+                weights = stanza.get('weights', None)
+                vae = stanza.get('vae', None)
+                width = stanza.get('width', 512)
+                height = stanza.get('height', 512)
+                models[name].update(
+                    description = description,
+                    format = format,
+                    config = config,
+                    weights = weights,
+                    vae = vae,
+                    width = width,
+                    height = height,
+                    status = status
+                )
+                
+            # Diffusers Config Parse
+            if format == 'diffusers':
+                repo_id = stanza.get('repo_id', None)
+                vae = stanza.get('vae', None)['repo_id']
+                models[name].update(
+                    description = description,
+                    format = format,
+                    repo_id = repo_id,
+                    vae = vae,
+                    status = status
+                )
+
         return models
 
     def print_models(self) -> None:
@@ -667,7 +704,7 @@ def commit(self,config_file_path:str) -> None:
         if not os.path.isabs(config_file_path):
             config_file_path = os.path.normpath(os.path.join(Globals.root,config_file_path))
         tmpfile = os.path.join(os.path.dirname(config_file_path),'new_config.tmp')
-        with open(tmpfile, 'w') as outfile:
+        with open(tmpfile, 'w', encoding="utf-8") as outfile:
             outfile.write(self.preamble())
             outfile.write(yaml_str)
         os.replace(tmpfile,config_file_path)

From a17f9014abb9dc3d90626785853278a20ed17fd3 Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Mon, 9 Jan 2023 20:56:15 +0000
Subject: [PATCH 154/199] added hash function to diffusers

---
 ldm/invoke/model_manager.py | 33 ++++++++++++++++++++++++++++++++-
 1 file changed, 32 insertions(+), 1 deletion(-)

diff --git a/ldm/invoke/model_manager.py b/ldm/invoke/model_manager.py
index a9db776b03c..998603e592d 100644
--- a/ldm/invoke/model_manager.py
+++ b/ldm/invoke/model_manager.py
@@ -352,7 +352,6 @@ def _load_ckpt_model(self, model_name, mconfig):
 
     def _load_diffusers_model(self, mconfig):
         name_or_path = self.model_name_or_path(mconfig)
-        model_hash = 'FIXME'
         using_fp16 = self.precision == 'float16'
 
         print(f'>> Loading diffusers model from {name_or_path}')
@@ -402,6 +401,8 @@ def _load_diffusers_model(self, mconfig):
 
         pipeline.to(self.device)
 
+        model_hash = self._hash_diffuser(name_or_path)
+
         # square images???
         width = pipeline.unet.config.sample_size * pipeline.vae_scale_factor
         height = width
@@ -762,6 +763,36 @@ def _push_newest_model(self,model_name:str) -> None:
     def _has_cuda(self) -> bool:
         return self.device.type == 'cuda'
 
+    def _diffuser_sha256(self,name_or_path:Union[str, Path])->Union[str,bytes]:
+        path = None
+        if isinstance(name_or_path,Path):
+           path = name_or_path
+        else:
+            owner,repo = name_or_path.split('/')
+            path = Path(global_cache_dir('diffusers') / f'models--{owner}--{repo}')
+        if not path.exists():
+            return None
+        hashpath = path / 'checksum.sha256'
+        if hashpath.exists() and path.stat().st_mtime <= hashpath.stat().st_mtime:
+            with open(hashpath) as f:
+                hash = f.read()
+            return hash
+        print('>> Calculating sha256 hash of models files')
+        tic = time.time()
+        sha = hashlib.sha256()
+        count = 0
+        for root, dirs, files in os.walk(path):
+            for name in files:
+                count += 1
+                with open(os.path.join(root,name),'rb') as f:
+                    sha.update(f.read())
+        hash = sha.hexdigest()
+        toc = time.time()
+        print(f'>> sha256 = {hash} ({count} files hashed in','%4.2fs)' % (toc - tic))
+        with open(hashpath,'w') as f:
+            f.write(hash)
+        return hash
+
     def _cached_sha256(self,path,data) -> Union[str, bytes]:
         dirname    = os.path.dirname(path)
         basename   = os.path.basename(path)

From 1637cbdea678f7c02d388d118ee5dc8bf7e4b62b Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Mon, 9 Jan 2023 16:21:12 -0500
Subject: [PATCH 155/199] implement sha256 hashes on diffusers models

---
 ldm/invoke/model_manager.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/ldm/invoke/model_manager.py b/ldm/invoke/model_manager.py
index 998603e592d..74863ba0cd3 100644
--- a/ldm/invoke/model_manager.py
+++ b/ldm/invoke/model_manager.py
@@ -401,7 +401,7 @@ def _load_diffusers_model(self, mconfig):
 
         pipeline.to(self.device)
 
-        model_hash = self._hash_diffuser(name_or_path)
+        model_hash = self._diffuser_sha256(name_or_path)
 
         # square images???
         width = pipeline.unet.config.sample_size * pipeline.vae_scale_factor
@@ -777,18 +777,18 @@ def _diffuser_sha256(self,name_or_path:Union[str, Path])->Union[str,bytes]:
             with open(hashpath) as f:
                 hash = f.read()
             return hash
-        print('>> Calculating sha256 hash of models files')
+        print('  | Calculating sha256 hash of model files')
         tic = time.time()
         sha = hashlib.sha256()
         count = 0
-        for root, dirs, files in os.walk(path):
+        for root, dirs, files in os.walk(path, followlinks=False):
             for name in files:
                 count += 1
                 with open(os.path.join(root,name),'rb') as f:
                     sha.update(f.read())
         hash = sha.hexdigest()
         toc = time.time()
-        print(f'>> sha256 = {hash} ({count} files hashed in','%4.2fs)' % (toc - tic))
+        print(f'  | sha256 = {hash} ({count} files hashed in','%4.2fs)' % (toc - tic))
         with open(hashpath,'w') as f:
             f.write(hash)
         return hash
@@ -804,7 +804,7 @@ def _cached_sha256(self,path,data) -> Union[str, bytes]:
                 hash = f.read()
             return hash
 
-        print('>> Calculating sha256 hash of weights file')
+        print('   | Calculating sha256 hash of weights file')
         tic = time.time()
         sha = hashlib.sha256()
         sha.update(data)

From 1cd93a714e62f8dd178d911bc99f1af2d35d0999 Mon Sep 17 00:00:00 2001
From: blessedcoolant <54517381+blessedcoolant@users.noreply.github.com>
Date: Tue, 10 Jan 2023 12:50:46 +1300
Subject: [PATCH 156/199] Add Model Manager Support for Diffusers

---
 frontend/public/locales/common/en.json        |   1 +
 .../public/locales/modelmanager/en-US.json    |   1 +
 frontend/public/locales/modelmanager/en.json  |   1 +
 frontend/src/app/invokeai.d.ts                |   5 +-
 .../features/options/store/optionsSlice.ts    |   9 +
 .../ModelManager/AddCheckpointModel.tsx       | 328 ++++++++++++++++
 .../ModelManager/AddDiffusersModel.tsx        | 205 ++++++++++
 .../components/ModelManager/AddModel.tsx      | 359 +++---------------
 ...{ModelEdit.tsx => CheckpointModelEdit.tsx} |   2 +-
 .../ModelManager/DiffusersModelEdit.tsx       |  25 +-
 .../components/ModelManager/ModelList.tsx     |   2 +-
 .../ModelManager/ModelManagerModal.tsx        |  15 +-
 12 files changed, 626 insertions(+), 327 deletions(-)
 create mode 100644 frontend/src/features/system/components/ModelManager/AddCheckpointModel.tsx
 create mode 100644 frontend/src/features/system/components/ModelManager/AddDiffusersModel.tsx
 rename frontend/src/features/system/components/ModelManager/{ModelEdit.tsx => CheckpointModelEdit.tsx} (99%)

diff --git a/frontend/public/locales/common/en.json b/frontend/public/locales/common/en.json
index aed5882710d..b67799e7895 100644
--- a/frontend/public/locales/common/en.json
+++ b/frontend/public/locales/common/en.json
@@ -34,6 +34,7 @@
   "upload": "Upload",
   "close": "Close",
   "load": "Load",
+  "back": "Back",
   "statusConnected": "Connected",
   "statusDisconnected": "Disconnected",
   "statusError": "Error",
diff --git a/frontend/public/locales/modelmanager/en-US.json b/frontend/public/locales/modelmanager/en-US.json
index aa824590afa..30bb6004490 100644
--- a/frontend/public/locales/modelmanager/en-US.json
+++ b/frontend/public/locales/modelmanager/en-US.json
@@ -16,6 +16,7 @@
   "config": "Config",
   "configValidationMsg": "Path to the config file of your model.",
   "modelLocation": "Model Location",
+  "repo_id": "Repo ID",
   "modelLocationValidationMsg": "Path to where your model is located.",
   "vaeLocation": "VAE Location",
   "vaeLocationValidationMsg": "Path to where your VAE is located.",
diff --git a/frontend/public/locales/modelmanager/en.json b/frontend/public/locales/modelmanager/en.json
index 820a87f8498..f57d237d11a 100644
--- a/frontend/public/locales/modelmanager/en.json
+++ b/frontend/public/locales/modelmanager/en.json
@@ -16,6 +16,7 @@
   "config": "Config",
   "configValidationMsg": "Path to the config file of your model.",
   "modelLocation": "Model Location",
+  "repo_id": "Repo ID",
   "modelLocationValidationMsg": "Path to where your model is located.",
   "vaeLocation": "VAE Location",
   "vaeLocationValidationMsg": "Path to where your VAE is located.",
diff --git a/frontend/src/app/invokeai.d.ts b/frontend/src/app/invokeai.d.ts
index 8938e7265cb..d3b027b58c4 100644
--- a/frontend/src/app/invokeai.d.ts
+++ b/frontend/src/app/invokeai.d.ts
@@ -197,8 +197,11 @@ export declare type InvokeDiffusersModelConfigProps = {
   name: string | undefined;
   description: string | undefined;
   repo_id: string | undefined;
-  vae: string | undefined;
   default: boolean | undefined;
+  format: string | undefined;
+  vae: {
+    repo_id: string | undefined;
+  };
 };
 
 /**
diff --git a/frontend/src/features/options/store/optionsSlice.ts b/frontend/src/features/options/store/optionsSlice.ts
index 6ef16050128..9e38e78f579 100644
--- a/frontend/src/features/options/store/optionsSlice.ts
+++ b/frontend/src/features/options/store/optionsSlice.ts
@@ -57,6 +57,7 @@ export interface OptionsState {
   width: number;
   shouldUseCanvasBetaLayout: boolean;
   shouldShowExistingModelsInSearch: boolean;
+  addNewModelUIOption: 'ckpt' | 'diffusers' | null;
 }
 
 const initialOptionsState: OptionsState = {
@@ -105,6 +106,7 @@ const initialOptionsState: OptionsState = {
   width: 512,
   shouldUseCanvasBetaLayout: false,
   shouldShowExistingModelsInSearch: false,
+  addNewModelUIOption: null,
 };
 
 const initialState: OptionsState = initialOptionsState;
@@ -412,6 +414,12 @@ export const optionsSlice = createSlice({
     ) => {
       state.shouldShowExistingModelsInSearch = action.payload;
     },
+    setAddNewModelUIOption: (
+      state,
+      action: PayloadAction<'ckpt' | 'diffusers' | null>
+    ) => {
+      state.addNewModelUIOption = action.payload;
+    },
   },
 });
 
@@ -469,6 +477,7 @@ export const {
   setWidth,
   setShouldUseCanvasBetaLayout,
   setShouldShowExistingModelsInSearch,
+  setAddNewModelUIOption,
 } = optionsSlice.actions;
 
 export default optionsSlice.reducer;
diff --git a/frontend/src/features/system/components/ModelManager/AddCheckpointModel.tsx b/frontend/src/features/system/components/ModelManager/AddCheckpointModel.tsx
new file mode 100644
index 00000000000..7b996900690
--- /dev/null
+++ b/frontend/src/features/system/components/ModelManager/AddCheckpointModel.tsx
@@ -0,0 +1,328 @@
+import {
+  FormControl,
+  FormErrorMessage,
+  FormHelperText,
+  FormLabel,
+  HStack,
+  Text,
+  VStack,
+} from '@chakra-ui/react';
+
+import React from 'react';
+import IAIInput from 'common/components/IAIInput';
+import IAINumberInput from 'common/components/IAINumberInput';
+import IAICheckbox from 'common/components/IAICheckbox';
+import IAIButton from 'common/components/IAIButton';
+
+import SearchModels from './SearchModels';
+
+import { addNewModel } from 'app/socketio/actions';
+
+import { useAppDispatch, useAppSelector } from 'app/storeHooks';
+
+import { Field, Formik } from 'formik';
+import { useTranslation } from 'react-i18next';
+
+import type { FieldInputProps, FormikProps } from 'formik';
+import type { RootState } from 'app/store';
+import type { InvokeModelConfigProps } from 'app/invokeai';
+import { setAddNewModelUIOption } from 'features/options/store/optionsSlice';
+import IAIIconButton from 'common/components/IAIIconButton';
+import { BiArrowBack } from 'react-icons/bi';
+
+const MIN_MODEL_SIZE = 64;
+const MAX_MODEL_SIZE = 2048;
+
+export default function AddCheckpointModel() {
+  const dispatch = useAppDispatch();
+  const { t } = useTranslation();
+
+  const isProcessing = useAppSelector(
+    (state: RootState) => state.system.isProcessing
+  );
+
+  function hasWhiteSpace(s: string) {
+    return /\\s/g.test(s);
+  }
+
+  function baseValidation(value: string) {
+    let error;
+    if (hasWhiteSpace(value)) error = t('modelmanager:cannotUseSpaces');
+    return error;
+  }
+
+  const addModelFormValues: InvokeModelConfigProps = {
+    name: '',
+    description: '',
+    config: 'configs/stable-diffusion/v1-inference.yaml',
+    weights: '',
+    vae: '',
+    width: 512,
+    height: 512,
+    format: 'ckpt',
+    default: false,
+  };
+
+  const addModelFormSubmitHandler = (values: InvokeModelConfigProps) => {
+    dispatch(addNewModel(values));
+    dispatch(setAddNewModelUIOption(null));
+  };
+
+  const [addManually, setAddmanually] = React.useState<boolean>(false);
+
+  return (
+    <>
+      <IAIIconButton
+        aria-label={t('common:back')}
+        tooltip={t('common:back')}
+        onClick={() => dispatch(setAddNewModelUIOption(null))}
+        width="max-content"
+        position="absolute"
+        zIndex={1}
+        size="sm"
+        right={12}
+        top={3}
+        icon={<BiArrowBack />}
+      />
+
+      <SearchModels />
+      <IAICheckbox
+        label={t('modelmanager:addManually')}
+        isChecked={addManually}
+        onChange={() => setAddmanually(!addManually)}
+      />
+
+      {addManually && (
+        <Formik
+          initialValues={addModelFormValues}
+          onSubmit={addModelFormSubmitHandler}
+        >
+          {({ handleSubmit, errors, touched }) => (
+            <form onSubmit={handleSubmit}>
+              <VStack rowGap={'0.5rem'}>
+                <Text fontSize={20} fontWeight="bold" alignSelf={'start'}>
+                  {t('modelmanager:manual')}
+                </Text>
+                {/* Name */}
+                <FormControl
+                  isInvalid={!!errors.name && touched.name}
+                  isRequired
+                >
+                  <FormLabel htmlFor="name" fontSize="sm">
+                    {t('modelmanager:name')}
+                  </FormLabel>
+                  <VStack alignItems={'start'}>
+                    <Field
+                      as={IAIInput}
+                      id="name"
+                      name="name"
+                      type="text"
+                      validate={baseValidation}
+                      width="2xl"
+                    />
+                    {!!errors.name && touched.name ? (
+                      <FormErrorMessage>{errors.name}</FormErrorMessage>
+                    ) : (
+                      <FormHelperText margin={0}>
+                        {t('modelmanager:nameValidationMsg')}
+                      </FormHelperText>
+                    )}
+                  </VStack>
+                </FormControl>
+
+                {/* Description */}
+                <FormControl
+                  isInvalid={!!errors.description && touched.description}
+                  isRequired
+                >
+                  <FormLabel htmlFor="description" fontSize="sm">
+                    {t('modelmanager:description')}
+                  </FormLabel>
+                  <VStack alignItems={'start'}>
+                    <Field
+                      as={IAIInput}
+                      id="description"
+                      name="description"
+                      type="text"
+                      width="2xl"
+                    />
+                    {!!errors.description && touched.description ? (
+                      <FormErrorMessage>{errors.description}</FormErrorMessage>
+                    ) : (
+                      <FormHelperText margin={0}>
+                        {t('modelmanager:descriptionValidationMsg')}
+                      </FormHelperText>
+                    )}
+                  </VStack>
+                </FormControl>
+
+                {/* Config */}
+                <FormControl
+                  isInvalid={!!errors.config && touched.config}
+                  isRequired
+                >
+                  <FormLabel htmlFor="config" fontSize="sm">
+                    {t('modelmanager:config')}
+                  </FormLabel>
+                  <VStack alignItems={'start'}>
+                    <Field
+                      as={IAIInput}
+                      id="config"
+                      name="config"
+                      type="text"
+                      width="2xl"
+                    />
+                    {!!errors.config && touched.config ? (
+                      <FormErrorMessage>{errors.config}</FormErrorMessage>
+                    ) : (
+                      <FormHelperText margin={0}>
+                        {t('modelmanager:configValidationMsg')}
+                      </FormHelperText>
+                    )}
+                  </VStack>
+                </FormControl>
+
+                {/* Weights */}
+                <FormControl
+                  isInvalid={!!errors.weights && touched.weights}
+                  isRequired
+                >
+                  <FormLabel htmlFor="config" fontSize="sm">
+                    {t('modelmanager:modelLocation')}
+                  </FormLabel>
+                  <VStack alignItems={'start'}>
+                    <Field
+                      as={IAIInput}
+                      id="weights"
+                      name="weights"
+                      type="text"
+                      width="2xl"
+                    />
+                    {!!errors.weights && touched.weights ? (
+                      <FormErrorMessage>{errors.weights}</FormErrorMessage>
+                    ) : (
+                      <FormHelperText margin={0}>
+                        {t('modelmanager:modelLocationValidationMsg')}
+                      </FormHelperText>
+                    )}
+                  </VStack>
+                </FormControl>
+
+                {/* VAE */}
+                <FormControl isInvalid={!!errors.vae && touched.vae}>
+                  <FormLabel htmlFor="vae" fontSize="sm">
+                    {t('modelmanager:vaeLocation')}
+                  </FormLabel>
+                  <VStack alignItems={'start'}>
+                    <Field
+                      as={IAIInput}
+                      id="vae"
+                      name="vae"
+                      type="text"
+                      width="2xl"
+                    />
+                    {!!errors.vae && touched.vae ? (
+                      <FormErrorMessage>{errors.vae}</FormErrorMessage>
+                    ) : (
+                      <FormHelperText margin={0}>
+                        {t('modelmanager:vaeLocationValidationMsg')}
+                      </FormHelperText>
+                    )}
+                  </VStack>
+                </FormControl>
+
+                <HStack width={'100%'}>
+                  {/* Width */}
+                  <FormControl isInvalid={!!errors.width && touched.width}>
+                    <FormLabel htmlFor="width" fontSize="sm">
+                      {t('modelmanager:width')}
+                    </FormLabel>
+                    <VStack alignItems={'start'}>
+                      <Field id="width" name="width">
+                        {({
+                          field,
+                          form,
+                        }: {
+                          field: FieldInputProps<number>;
+                          form: FormikProps<InvokeModelConfigProps>;
+                        }) => (
+                          <IAINumberInput
+                            id="width"
+                            name="width"
+                            min={MIN_MODEL_SIZE}
+                            max={MAX_MODEL_SIZE}
+                            step={64}
+                            width="90%"
+                            value={form.values.width}
+                            onChange={(value) =>
+                              form.setFieldValue(field.name, Number(value))
+                            }
+                          />
+                        )}
+                      </Field>
+
+                      {!!errors.width && touched.width ? (
+                        <FormErrorMessage>{errors.width}</FormErrorMessage>
+                      ) : (
+                        <FormHelperText margin={0}>
+                          {t('modelmanager:widthValidationMsg')}
+                        </FormHelperText>
+                      )}
+                    </VStack>
+                  </FormControl>
+
+                  {/* Height */}
+                  <FormControl isInvalid={!!errors.height && touched.height}>
+                    <FormLabel htmlFor="height" fontSize="sm">
+                      {t('modelmanager:height')}
+                    </FormLabel>
+                    <VStack alignItems={'start'}>
+                      <Field id="height" name="height">
+                        {({
+                          field,
+                          form,
+                        }: {
+                          field: FieldInputProps<number>;
+                          form: FormikProps<InvokeModelConfigProps>;
+                        }) => (
+                          <IAINumberInput
+                            id="height"
+                            name="height"
+                            min={MIN_MODEL_SIZE}
+                            max={MAX_MODEL_SIZE}
+                            width="90%"
+                            step={64}
+                            value={form.values.height}
+                            onChange={(value) =>
+                              form.setFieldValue(field.name, Number(value))
+                            }
+                          />
+                        )}
+                      </Field>
+
+                      {!!errors.height && touched.height ? (
+                        <FormErrorMessage>{errors.height}</FormErrorMessage>
+                      ) : (
+                        <FormHelperText margin={0}>
+                          {t('modelmanager:heightValidationMsg')}
+                        </FormHelperText>
+                      )}
+                    </VStack>
+                  </FormControl>
+                </HStack>
+
+                <IAIButton
+                  type="submit"
+                  className="modal-close-btn"
+                  isLoading={isProcessing}
+                >
+                  {t('modelmanager:addModel')}
+                </IAIButton>
+              </VStack>
+            </form>
+          )}
+        </Formik>
+      )}
+    </>
+  );
+}
diff --git a/frontend/src/features/system/components/ModelManager/AddDiffusersModel.tsx b/frontend/src/features/system/components/ModelManager/AddDiffusersModel.tsx
new file mode 100644
index 00000000000..ee7940e5df3
--- /dev/null
+++ b/frontend/src/features/system/components/ModelManager/AddDiffusersModel.tsx
@@ -0,0 +1,205 @@
+import {
+  Flex,
+  FormControl,
+  FormErrorMessage,
+  FormHelperText,
+  FormLabel,
+  VStack,
+} from '@chakra-ui/react';
+import { useAppDispatch, useAppSelector } from 'app/storeHooks';
+import IAIButton from 'common/components/IAIButton';
+import IAIIconButton from 'common/components/IAIIconButton';
+import IAIInput from 'common/components/IAIInput';
+import { setAddNewModelUIOption } from 'features/options/store/optionsSlice';
+import { Field, Formik } from 'formik';
+import React from 'react';
+import { useTranslation } from 'react-i18next';
+import { BiArrowBack } from 'react-icons/bi';
+
+import type { RootState } from 'app/store';
+import { InvokeDiffusersModelConfigProps } from 'app/invokeai';
+import { addNewModel } from 'app/socketio/actions';
+
+export default function AddDiffusersModel() {
+  const dispatch = useAppDispatch();
+  const { t } = useTranslation();
+
+  const isProcessing = useAppSelector(
+    (state: RootState) => state.system.isProcessing
+  );
+
+  function hasWhiteSpace(s: string) {
+    return /\\s/g.test(s);
+  }
+
+  function baseValidation(value: string) {
+    let error;
+    if (hasWhiteSpace(value)) error = t('modelmanager:cannotUseSpaces');
+    return error;
+  }
+
+  const addModelFormValues: InvokeDiffusersModelConfigProps = {
+    name: '',
+    description: '',
+    repo_id: '',
+    format: 'diffusers',
+    default: false,
+    vae: {
+      repo_id: '',
+    },
+  };
+
+  const addModelFormSubmitHandler = (
+    values: InvokeDiffusersModelConfigProps
+  ) => {
+    let diffusersModelToAdd = values;
+
+    if (values.vae.repo_id == '') {
+      diffusersModelToAdd = {
+        ...diffusersModelToAdd,
+        vae: { repo_id: values.repo_id + '/vae' },
+      };
+    }
+
+    dispatch(addNewModel(diffusersModelToAdd));
+    dispatch(setAddNewModelUIOption(null));
+  };
+
+  return (
+    <Flex>
+      <IAIIconButton
+        aria-label={t('common:back')}
+        tooltip={t('common:back')}
+        onClick={() => dispatch(setAddNewModelUIOption(null))}
+        width="max-content"
+        position="absolute"
+        zIndex={1}
+        size="sm"
+        right={12}
+        top={3}
+        icon={<BiArrowBack />}
+      />
+      <Formik
+        initialValues={addModelFormValues}
+        onSubmit={addModelFormSubmitHandler}
+      >
+        {({ handleSubmit, errors, touched }) => (
+          <form onSubmit={handleSubmit}>
+            <VStack rowGap={'0.5rem'}>
+              {/* Name */}
+              <FormControl isInvalid={!!errors.name && touched.name} isRequired>
+                <FormLabel htmlFor="name" fontSize="sm">
+                  {t('modelmanager:name')}
+                </FormLabel>
+                <VStack alignItems={'start'}>
+                  <Field
+                    as={IAIInput}
+                    id="name"
+                    name="name"
+                    type="text"
+                    validate={baseValidation}
+                    width="2xl"
+                  />
+                  {!!errors.name && touched.name ? (
+                    <FormErrorMessage>{errors.name}</FormErrorMessage>
+                  ) : (
+                    <FormHelperText margin={0}>
+                      {t('modelmanager:nameValidationMsg')}
+                    </FormHelperText>
+                  )}
+                </VStack>
+              </FormControl>
+
+              {/* Description */}
+              <FormControl
+                isInvalid={!!errors.description && touched.description}
+                isRequired
+              >
+                <FormLabel htmlFor="description" fontSize="sm">
+                  {t('modelmanager:description')}
+                </FormLabel>
+                <VStack alignItems={'start'}>
+                  <Field
+                    as={IAIInput}
+                    id="description"
+                    name="description"
+                    type="text"
+                    width="2xl"
+                  />
+                  {!!errors.description && touched.description ? (
+                    <FormErrorMessage>{errors.description}</FormErrorMessage>
+                  ) : (
+                    <FormHelperText margin={0}>
+                      {t('modelmanager:descriptionValidationMsg')}
+                    </FormHelperText>
+                  )}
+                </VStack>
+              </FormControl>
+
+              {/* Repo ID */}
+              <FormControl
+                isInvalid={!!errors.repo_id && touched.repo_id}
+                isRequired
+              >
+                <FormLabel htmlFor="repo_id" fontSize="sm">
+                  {t('modelmanager:modelLocation')} /{' '}
+                  {t('modelmanager:repo_id')}
+                </FormLabel>
+                <VStack alignItems={'start'}>
+                  <Field
+                    as={IAIInput}
+                    id="repo_id"
+                    name="repo_id"
+                    type="text"
+                    width="2xl"
+                  />
+                  {!!errors.repo_id && touched.repo_id ? (
+                    <FormErrorMessage>{errors.repo_id}</FormErrorMessage>
+                  ) : (
+                    <FormHelperText margin={0}>
+                      {t('modelmanager:modelLocationValidationMsg')}
+                    </FormHelperText>
+                  )}
+                </VStack>
+              </FormControl>
+
+              {/* VAE */}
+              <FormControl
+                isInvalid={!!errors.vae?.repo_id && touched.vae?.repo_id}
+                isRequired
+              >
+                <FormLabel htmlFor="vae.repo_id" fontSize="sm">
+                  {t('modelmanager:vaeLocation')}
+                </FormLabel>
+                <VStack alignItems={'start'}>
+                  <Field
+                    as={IAIInput}
+                    id="vae.repo_id"
+                    name="vae.repo_id"
+                    type="text"
+                    width="2xl"
+                  />
+                  {!!errors.vae?.repo_id && touched.vae?.repo_id ? (
+                    <FormErrorMessage>{errors.vae?.repo_id}</FormErrorMessage>
+                  ) : (
+                    <FormHelperText margin={0}>
+                      {t('modelmanager:vaeLocationValidationMsg')}
+                    </FormHelperText>
+                  )}
+                </VStack>
+              </FormControl>
+
+              <IAIButton
+                type="submit"
+                className="modal-close-btn"
+                isLoading={isProcessing}
+              >
+                {t('modelmanager:addModel')}
+              </IAIButton>
+            </VStack>
+          </form>
+        )}
+      </Formik>
+    </Flex>
+  );
+}
diff --git a/frontend/src/features/system/components/ModelManager/AddModel.tsx b/frontend/src/features/system/components/ModelManager/AddModel.tsx
index dac2e2cc34d..3447f3a1a0e 100644
--- a/frontend/src/features/system/components/ModelManager/AddModel.tsx
+++ b/frontend/src/features/system/components/ModelManager/AddModel.tsx
@@ -1,10 +1,5 @@
 import {
   Flex,
-  FormControl,
-  FormErrorMessage,
-  FormHelperText,
-  FormLabel,
-  HStack,
   Modal,
   ModalBody,
   ModalCloseButton,
@@ -13,73 +8,63 @@ import {
   ModalOverlay,
   Text,
   useDisclosure,
-  VStack,
 } from '@chakra-ui/react';
 
 import React from 'react';
-import IAIInput from 'common/components/IAIInput';
-import IAINumberInput from 'common/components/IAINumberInput';
-import IAICheckbox from 'common/components/IAICheckbox';
 import IAIButton from 'common/components/IAIButton';
 
-import SearchModels from './SearchModels';
-
-import { addNewModel } from 'app/socketio/actions';
-
-import { useAppDispatch, useAppSelector } from 'app/storeHooks';
 import { FaPlus } from 'react-icons/fa';
-import { Field, Formik } from 'formik';
+
 import { useTranslation } from 'react-i18next';
+import { useAppDispatch, useAppSelector } from 'app/storeHooks';
 
-import type { FieldInputProps, FormikProps } from 'formik';
 import type { RootState } from 'app/store';
-import type { InvokeModelConfigProps } from 'app/invokeai';
-
-const MIN_MODEL_SIZE = 64;
-const MAX_MODEL_SIZE = 2048;
+import { setAddNewModelUIOption } from 'features/options/store/optionsSlice';
+import AddCheckpointModel from './AddCheckpointModel';
+import AddDiffusersModel from './AddDiffusersModel';
+
+function AddModelBox({
+  text,
+  onClick,
+}: {
+  text: string;
+  onClick?: () => void;
+}) {
+  return (
+    <Flex
+      position="relative"
+      width="50%"
+      height="200px"
+      backgroundColor="var(--background-color)"
+      borderRadius="0.5rem"
+      justifyContent="center"
+      alignItems="center"
+      _hover={{
+        cursor: 'pointer',
+        backgroundColor: 'var(--accent-color)',
+      }}
+      onClick={onClick}
+    >
+      <Text fontWeight="bold">{text}</Text>
+    </Flex>
+  );
+}
 
 export default function AddModel() {
   const { isOpen, onOpen, onClose } = useDisclosure();
-  const dispatch = useAppDispatch();
-  const { t } = useTranslation();
 
-  const isProcessing = useAppSelector(
-    (state: RootState) => state.system.isProcessing
+  const addNewModelUIOption = useAppSelector(
+    (state: RootState) => state.options.addNewModelUIOption
   );
 
-  function hasWhiteSpace(s: string) {
-    return /\\s/g.test(s);
-  }
-
-  function baseValidation(value: string) {
-    let error;
-    if (hasWhiteSpace(value)) error = t('modelmanager:cannotUseSpaces');
-    return error;
-  }
-
-  const addModelFormValues: InvokeModelConfigProps = {
-    name: '',
-    description: '',
-    config: 'configs/stable-diffusion/v1-inference.yaml',
-    weights: '',
-    vae: '',
-    width: 512,
-    height: 512,
-    format: 'ckpt',
-    default: false,
-  };
+  const dispatch = useAppDispatch();
 
-  const addModelFormSubmitHandler = (values: InvokeModelConfigProps) => {
-    dispatch(addNewModel(values));
-    onClose();
-  };
+  const { t } = useTranslation();
 
   const addModelModalClose = () => {
     onClose();
   };
 
-  const [addManually, setAddmanually] = React.useState<boolean>(false);
-
   return (
     <>
       <IAIButton
@@ -102,266 +87,24 @@ export default function AddModel() {
         closeOnOverlayClick={false}
       >
         <ModalOverlay />
-        <ModalContent className="modal add-model-modal">
+        <ModalContent className="modal add-model-modal" fontFamily="Inter">
           <ModalHeader>{t('modelmanager:addNewModel')}</ModalHeader>
-          <ModalCloseButton />
+          <ModalCloseButton marginTop="0.3rem" />
           <ModalBody className="add-model-modal-body">
-            <SearchModels />
-            <IAICheckbox
-              label={t('modelmanager:addManually')}
-              isChecked={addManually}
-              onChange={() => setAddmanually(!addManually)}
-            />
-
-            {addManually && (
-              <Formik
-                initialValues={addModelFormValues}
-                onSubmit={addModelFormSubmitHandler}
-              >
-                {({ handleSubmit, errors, touched }) => (
-                  <form onSubmit={handleSubmit}>
-                    <VStack rowGap={'0.5rem'}>
-                      <Text fontSize={20} fontWeight="bold" alignSelf={'start'}>
-                        {t('modelmanager:manual')}
-                      </Text>
-                      {/* Name */}
-                      <FormControl
-                        isInvalid={!!errors.name && touched.name}
-                        isRequired
-                      >
-                        <FormLabel htmlFor="name" fontSize="sm">
-                          {t('modelmanager:name')}
-                        </FormLabel>
-                        <VStack alignItems={'start'}>
-                          <Field
-                            as={IAIInput}
-                            id="name"
-                            name="name"
-                            type="text"
-                            validate={baseValidation}
-                            width="2xl"
-                          />
-                          {!!errors.name && touched.name ? (
-                            <FormErrorMessage>{errors.name}</FormErrorMessage>
-                          ) : (
-                            <FormHelperText margin={0}>
-                              {t('modelmanager:nameValidationMsg')}
-                            </FormHelperText>
-                          )}
-                        </VStack>
-                      </FormControl>
-
-                      {/* Description */}
-                      <FormControl
-                        isInvalid={!!errors.description && touched.description}
-                        isRequired
-                      >
-                        <FormLabel htmlFor="description" fontSize="sm">
-                          {t('modelmanager:description')}
-                        </FormLabel>
-                        <VStack alignItems={'start'}>
-                          <Field
-                            as={IAIInput}
-                            id="description"
-                            name="description"
-                            type="text"
-                            width="2xl"
-                          />
-                          {!!errors.description && touched.description ? (
-                            <FormErrorMessage>
-                              {errors.description}
-                            </FormErrorMessage>
-                          ) : (
-                            <FormHelperText margin={0}>
-                              {t('modelmanager:descriptionValidationMsg')}
-                            </FormHelperText>
-                          )}
-                        </VStack>
-                      </FormControl>
-
-                      {/* Config */}
-                      <FormControl
-                        isInvalid={!!errors.config && touched.config}
-                        isRequired
-                      >
-                        <FormLabel htmlFor="config" fontSize="sm">
-                          {t('modelmanager:config')}
-                        </FormLabel>
-                        <VStack alignItems={'start'}>
-                          <Field
-                            as={IAIInput}
-                            id="config"
-                            name="config"
-                            type="text"
-                            width="2xl"
-                          />
-                          {!!errors.config && touched.config ? (
-                            <FormErrorMessage>{errors.config}</FormErrorMessage>
-                          ) : (
-                            <FormHelperText margin={0}>
-                              {t('modelmanager:configValidationMsg')}
-                            </FormHelperText>
-                          )}
-                        </VStack>
-                      </FormControl>
-
-                      {/* Weights */}
-                      <FormControl
-                        isInvalid={!!errors.weights && touched.weights}
-                        isRequired
-                      >
-                        <FormLabel htmlFor="config" fontSize="sm">
-                          {t('modelmanager:modelLocation')}
-                        </FormLabel>
-                        <VStack alignItems={'start'}>
-                          <Field
-                            as={IAIInput}
-                            id="weights"
-                            name="weights"
-                            type="text"
-                            width="2xl"
-                          />
-                          {!!errors.weights && touched.weights ? (
-                            <FormErrorMessage>
-                              {errors.weights}
-                            </FormErrorMessage>
-                          ) : (
-                            <FormHelperText margin={0}>
-                              {t('modelmanager:modelLocationValidationMsg')}
-                            </FormHelperText>
-                          )}
-                        </VStack>
-                      </FormControl>
-
-                      {/* VAE */}
-                      <FormControl isInvalid={!!errors.vae && touched.vae}>
-                        <FormLabel htmlFor="vae" fontSize="sm">
-                          {t('modelmanager:vaeLocation')}
-                        </FormLabel>
-                        <VStack alignItems={'start'}>
-                          <Field
-                            as={IAIInput}
-                            id="vae"
-                            name="vae"
-                            type="text"
-                            width="2xl"
-                          />
-                          {!!errors.vae && touched.vae ? (
-                            <FormErrorMessage>{errors.vae}</FormErrorMessage>
-                          ) : (
-                            <FormHelperText margin={0}>
-                              {t('modelmanager:vaeLocationValidationMsg')}
-                            </FormHelperText>
-                          )}
-                        </VStack>
-                      </FormControl>
-
-                      <HStack width={'100%'}>
-                        {/* Width */}
-                        <FormControl
-                          isInvalid={!!errors.width && touched.width}
-                        >
-                          <FormLabel htmlFor="width" fontSize="sm">
-                            {t('modelmanager:width')}
-                          </FormLabel>
-                          <VStack alignItems={'start'}>
-                            <Field id="width" name="width">
-                              {({
-                                field,
-                                form,
-                              }: {
-                                field: FieldInputProps<number>;
-                                form: FormikProps<InvokeModelConfigProps>;
-                              }) => (
-                                <IAINumberInput
-                                  id="width"
-                                  name="width"
-                                  min={MIN_MODEL_SIZE}
-                                  max={MAX_MODEL_SIZE}
-                                  step={64}
-                                  width="90%"
-                                  value={form.values.width}
-                                  onChange={(value) =>
-                                    form.setFieldValue(
-                                      field.name,
-                                      Number(value)
-                                    )
-                                  }
-                                />
-                              )}
-                            </Field>
-
-                            {!!errors.width && touched.width ? (
-                              <FormErrorMessage>
-                                {errors.width}
-                              </FormErrorMessage>
-                            ) : (
-                              <FormHelperText margin={0}>
-                                {t('modelmanager:widthValidationMsg')}
-                              </FormHelperText>
-                            )}
-                          </VStack>
-                        </FormControl>
-
-                        {/* Height */}
-                        <FormControl
-                          isInvalid={!!errors.height && touched.height}
-                        >
-                          <FormLabel htmlFor="height" fontSize="sm">
-                            {t('modelmanager:height')}
-                          </FormLabel>
-                          <VStack alignItems={'start'}>
-                            <Field id="height" name="height">
-                              {({
-                                field,
-                                form,
-                              }: {
-                                field: FieldInputProps<number>;
-                                form: FormikProps<InvokeModelConfigProps>;
-                              }) => (
-                                <IAINumberInput
-                                  id="height"
-                                  name="height"
-                                  min={MIN_MODEL_SIZE}
-                                  max={MAX_MODEL_SIZE}
-                                  width="90%"
-                                  step={64}
-                                  value={form.values.height}
-                                  onChange={(value) =>
-                                    form.setFieldValue(
-                                      field.name,
-                                      Number(value)
-                                    )
-                                  }
-                                />
-                              )}
-                            </Field>
-
-                            {!!errors.height && touched.height ? (
-                              <FormErrorMessage>
-                                {errors.height}
-                              </FormErrorMessage>
-                            ) : (
-                              <FormHelperText margin={0}>
-                                {t('modelmanager:heightValidationMsg')}
-                              </FormHelperText>
-                            )}
-                          </VStack>
-                        </FormControl>
-                      </HStack>
-
-                      <IAIButton
-                        type="submit"
-                        className="modal-close-btn"
-                        isLoading={isProcessing}
-                      >
-                        {t('modelmanager:addModel')}
-                      </IAIButton>
-                    </VStack>
-                  </form>
-                )}
-              </Formik>
+            {addNewModelUIOption == null && (
+              <Flex columnGap="1rem">
+                <AddModelBox
+                  text="Add Checkpoint Model"
+                  onClick={() => dispatch(setAddNewModelUIOption('ckpt'))}
+                />
+                <AddModelBox
+                  text="Add Diffusers Model"
+                  onClick={() => dispatch(setAddNewModelUIOption('diffusers'))}
+                />
+              </Flex>
             )}
+            {addNewModelUIOption == 'ckpt' && <AddCheckpointModel />}
+            {addNewModelUIOption == 'diffusers' && <AddDiffusersModel />}
           </ModalBody>
         </ModalContent>
       </Modal>
diff --git a/frontend/src/features/system/components/ModelManager/ModelEdit.tsx b/frontend/src/features/system/components/ModelManager/CheckpointModelEdit.tsx
similarity index 99%
rename from frontend/src/features/system/components/ModelManager/ModelEdit.tsx
rename to frontend/src/features/system/components/ModelManager/CheckpointModelEdit.tsx
index 5d816e170e6..8999f71281b 100644
--- a/frontend/src/features/system/components/ModelManager/ModelEdit.tsx
+++ b/frontend/src/features/system/components/ModelManager/CheckpointModelEdit.tsx
@@ -48,7 +48,7 @@ const selector = createSelector(
 const MIN_MODEL_SIZE = 64;
 const MAX_MODEL_SIZE = 2048;
 
-export default function ModelEdit() {
+export default function CheckpointModelEdit() {
   const { openModel, model_list } = useAppSelector(selector);
   const isProcessing = useAppSelector(
     (state: RootState) => state.system.isProcessing
diff --git a/frontend/src/features/system/components/ModelManager/DiffusersModelEdit.tsx b/frontend/src/features/system/components/ModelManager/DiffusersModelEdit.tsx
index f9de0faa691..6dc0d7fbbcb 100644
--- a/frontend/src/features/system/components/ModelManager/DiffusersModelEdit.tsx
+++ b/frontend/src/features/system/components/ModelManager/DiffusersModelEdit.tsx
@@ -57,8 +57,9 @@ export default function DiffusersModelEdit() {
       name: '',
       description: '',
       repo_id: '',
-      vae: '',
+      vae: { repo_id: '' },
       default: false,
+      format: 'diffusers',
     });
 
   useEffect(() => {
@@ -66,14 +67,18 @@ export default function DiffusersModelEdit() {
       const retrievedModel = _.pickBy(model_list, (val, key) => {
         return _.isEqual(key, openModel);
       });
+
       setEditModelFormValues({
         name: openModel,
         description: retrievedModel[openModel]?.description,
         repo_id: retrievedModel[openModel]?.repo_id,
-        vae: retrievedModel[openModel]?.vae
-          ? retrievedModel[openModel]?.vae
-          : '',
+        vae: {
+          repo_id: retrievedModel[openModel]?.vae
+            ? retrievedModel[openModel]?.vae
+            : '',
+        },
         default: retrievedModel[openModel]?.default,
+        format: 'diffusers',
       });
     }
   }, [model_list, openModel]);
@@ -158,20 +163,22 @@ export default function DiffusersModelEdit() {
                 </FormControl>
 
                 {/* VAE */}
-                <FormControl isInvalid={!!errors.vae && touched.vae}>
+                <FormControl
+                  isInvalid={!!errors.vae?.repo_id && touched.vae?.repo_id}
+                >
                   <FormLabel htmlFor="vae" fontSize="sm">
                     {t('modelmanager:vaeLocation')}
                   </FormLabel>
                   <VStack alignItems={'start'}>
                     <Field
                       as={IAIInput}
-                      id="vae"
-                      name="vae"
+                      id="vae.repo_id"
+                      name="vae.repo_id"
                       type="text"
                       width="lg"
                     />
-                    {!!errors.vae && touched.vae ? (
-                      <FormErrorMessage>{errors.vae}</FormErrorMessage>
+                    {!!errors.vae?.repo_id && touched.vae?.repo_id ? (
+                      <FormErrorMessage>{errors.vae?.repo_id}</FormErrorMessage>
                     ) : (
                       <FormHelperText margin={0}>
                         {t('modelmanager:vaeLocationValidationMsg')}
diff --git a/frontend/src/features/system/components/ModelManager/ModelList.tsx b/frontend/src/features/system/components/ModelManager/ModelList.tsx
index fa607bb503f..f335e4b23ce 100644
--- a/frontend/src/features/system/components/ModelManager/ModelList.tsx
+++ b/frontend/src/features/system/components/ModelManager/ModelList.tsx
@@ -43,7 +43,7 @@ const ModelList = () => {
     const filteredModelListItemsToRender: ReactNode[] = [];
 
     models.forEach((model, i) => {
-      if (model.name.startsWith(searchText)) {
+      if (model.name.toLowerCase().startsWith(searchText.toLowerCase())) {
         filteredModelListItemsToRender.push(
           <ModelListItem
             key={i}
diff --git a/frontend/src/features/system/components/ModelManager/ModelManagerModal.tsx b/frontend/src/features/system/components/ModelManager/ModelManagerModal.tsx
index 25d3305863a..7e8c9724f38 100644
--- a/frontend/src/features/system/components/ModelManager/ModelManagerModal.tsx
+++ b/frontend/src/features/system/components/ModelManager/ModelManagerModal.tsx
@@ -8,15 +8,16 @@ import {
   useDisclosure,
 } from '@chakra-ui/react';
 import React, { cloneElement } from 'react';
-import { useTranslation } from 'react-i18next';
-
-import ModelEdit from './ModelEdit';
-import ModelList from './ModelList';
 
-import type { ReactElement } from 'react';
+import { useTranslation } from 'react-i18next';
 import { useAppSelector } from 'app/storeHooks';
 import { RootState } from 'app/store';
+
+import type { ReactElement } from 'react';
+
+import ModelList from './ModelList';
 import DiffusersModelEdit from './DiffusersModelEdit';
+import CheckpointModelEdit from './CheckpointModelEdit';
 
 type ModelManagerModalProps = {
   children: ReactElement;
@@ -52,7 +53,7 @@ export default function ModelManagerModal({
         size="6xl"
       >
         <ModalOverlay />
-        <ModalContent className=" modal">
+        <ModalContent className="modal" fontFamily="Inter">
           <ModalCloseButton className="modal-close-btn" />
           <ModalHeader>{t('modelmanager:modelManager')}</ModalHeader>
           <Flex
@@ -64,7 +65,7 @@ export default function ModelManagerModal({
             {openModel && model_list[openModel]['format'] === 'diffusers' ? (
               <DiffusersModelEdit />
             ) : (
-              <ModelEdit />
+              <CheckpointModelEdit />
             )}
           </Flex>
         </ModalContent>

From ee03e984f62d89a29dc2d551be7a3caeed9ace16 Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Tue, 10 Jan 2023 03:02:27 +0000
Subject: [PATCH 157/199] fix various problems with model manager

- in cli import functions, fix not enough values to unpack from
  _get_name_and_desc()
- fix crash when using old-style vae: value with new-style diffuser
---
 ldm/invoke/CLI.py           | 10 +++++++---
 ldm/invoke/model_manager.py |  4 +++-
 2 files changed, 10 insertions(+), 4 deletions(-)

diff --git a/ldm/invoke/CLI.py b/ldm/invoke/CLI.py
index 1db1b4f6927..bddc691ee00 100644
--- a/ldm/invoke/CLI.py
+++ b/ldm/invoke/CLI.py
@@ -596,7 +596,7 @@ def import_diffuser_model(path_or_repo:str, gen, opt, completer)->str:
     manager = gen.model_manager
     default_name = Path(path_or_repo).stem
     default_description = f'Imported model {default_name}'
-    model_name, model_description = _get_model_name_and_desc(
+    model_name, model_description, default = _get_model_name_and_desc(
         manager,
         completer,
         model_name=default_name,
@@ -609,13 +609,15 @@ def import_diffuser_model(path_or_repo:str, gen, opt, completer)->str:
             description = model_description):
         print('** model failed to import')
         return None
+    if default:
+        manager.set_default_model(model_name)
     return model_name
 
 def import_ckpt_model(path_or_url:str, gen, opt, completer)->str:
     manager = gen.model_manager
     default_name = Path(path_or_url).stem
     default_description = f'Imported model {default_name}'
-    model_name, model_description = _get_model_name_and_desc(
+    model_name, model_description, default = _get_model_name_and_desc(
         manager,
         completer,
         model_name=default_name,
@@ -640,6 +642,8 @@ def import_ckpt_model(path_or_url:str, gen, opt, completer)->str:
         print('** model failed to import')
         return None
 
+    if default:
+        manager.set_model_default(model_name)
     return model_name
 
 def _verify_load(model_name:str, gen)->bool:
@@ -676,7 +680,7 @@ def optimize_model(model_name_or_path:str, gen, opt, completer):
             return
     elif os.path.exists(model_name_or_path):
         ckpt_path = Path(model_name_or_path)
-        model_name,model_description = _get_model_name_and_desc(
+        model_name,model_description, default = _get_model_name_and_desc(
             manager,
             completer,
             ckpt_path.stem,
diff --git a/ldm/invoke/model_manager.py b/ldm/invoke/model_manager.py
index 442bc848f86..212d71df39d 100644
--- a/ldm/invoke/model_manager.py
+++ b/ldm/invoke/model_manager.py
@@ -196,7 +196,9 @@ def list_models(self) -> dict:
             # Diffusers Config Parse
             if format == 'diffusers':
                 repo_id = stanza.get('repo_id', None)
-                vae = stanza.get('vae', None)['repo_id']
+                vae = None
+                if 'vae' in stanza:
+                    vae = stanza['vae'].get('repo_id',None)
                 models[name].update(
                     description = description,
                     format = format,

From 7f867ab2d7315e12fabe5aa6b74c9e1ffa4462eb Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Tue, 10 Jan 2023 03:09:23 +0000
Subject: [PATCH 158/199] rebuild frontend

---
 ...y-5c5a479d.js => index-legacy-474a75fe.js} |  8 +-
 .../{index.ec2d89c6.js => index.1b59e83a.js}  | 98 +++++++++----------
 frontend/dist/index.html                      |  4 +-
 3 files changed, 55 insertions(+), 55 deletions(-)
 rename frontend/dist/assets/{index-legacy-5c5a479d.js => index-legacy-474a75fe.js} (78%)
 rename frontend/dist/assets/{index.ec2d89c6.js => index.1b59e83a.js} (65%)

diff --git a/frontend/dist/assets/index-legacy-5c5a479d.js b/frontend/dist/assets/index-legacy-474a75fe.js
similarity index 78%
rename from frontend/dist/assets/index-legacy-5c5a479d.js
rename to frontend/dist/assets/index-legacy-474a75fe.js
index 3babd3134aa..b48c03f8f8b 100644
--- a/frontend/dist/assets/index-legacy-5c5a479d.js
+++ b/frontend/dist/assets/index-legacy-474a75fe.js
@@ -18,7 +18,7 @@
              * This source code is licensed under the MIT license found in the
              * LICENSE file in the root directory of this source tree.
              */
-var Y=a.exports,Z=G.exports;function X(e){for(var t="https://reactjs.org/docs/error-decoder.html?invariant="+e,n=1;n<arguments.length;n++)t+="&args[]="+encodeURIComponent(arguments[n]);return"Minified React error #"+e+"; visit "+t+" for the full message or use the non-minified dev environment for full errors and additional helpful warnings."}var K=new Set,Q={};function J(e,t){ee(e,t),ee(e+"Capture",t)}function ee(e,t){for(Q[e]=t,e=0;e<t.length;e++)K.add(t[e])}var te=!("undefined"==typeof window||void 0===window.document||void 0===window.document.createElement),ne=Object.prototype.hasOwnProperty,re=/^[:A-Z_a-z\u00C0-\u00D6\u00D8-\u00F6\u00F8-\u02FF\u0370-\u037D\u037F-\u1FFF\u200C-\u200D\u2070-\u218F\u2C00-\u2FEF\u3001-\uD7FF\uF900-\uFDCF\uFDF0-\uFFFD][:A-Z_a-z\u00C0-\u00D6\u00D8-\u00F6\u00F8-\u02FF\u0370-\u037D\u037F-\u1FFF\u200C-\u200D\u2070-\u218F\u2C00-\u2FEF\u3001-\uD7FF\uF900-\uFDCF\uFDF0-\uFFFD\-.0-9\u00B7\u0300-\u036F\u203F-\u2040]*$/,oe={},ie={};function 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t=e.stateNode;t.pendingContext?_i(0,t.pendingContext,t.pendingContext!==t.context):t.context&&_i(0,t.context,!1),Ya(e,t.containerInfo)}function wl(e,t,n,r,o){return oa(),ia(o),t.flags|=256,dl(e,t,n,r),t.child}var kl,Sl,Cl,_l={dehydrated:null,treeContext:null,retryLane:0};function El(e){return{baseLanes:e,cachePool:null,transitions:null}}function Pl(e,t,n){var r,o=t.pendingProps,i=Qa.current,a=!1,s=0!=(128&t.flags);if((r=s)||(r=(null===e||null!==e.memoizedState)&&0!=(2&i)),r?(a=!0,t.flags&=-129):null!==e&&null===e.memoizedState||(i|=1),vi(Qa,1&i),null===e)return 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Xy(e){const{focusBorderColor:t,errorBorderColor:n}=e;return{focusBorderColor:t||Gm("blue.500","blue.300")(e),errorBorderColor:n||Gm("red.500","red.300")(e)}}var Ky=Uy((e=>{const{theme:t}=e,{focusBorderColor:n,errorBorderColor:r}=Xy(e);return{field:{border:"1px solid",borderColor:"inherit",bg:"inherit",_hover:{borderColor:Gm("gray.300","whiteAlpha.400")(e)},_readOnly:{boxShadow:"none !important",userSelect:"all"},_invalid:{borderColor:Hm(t,r),boxShadow:`0 0 0 1px ${Hm(t,r)}`},_focusVisible:{zIndex:1,borderColor:Hm(t,n),boxShadow:`0 0 0 1px ${Hm(t,n)}`}},addon:{border:"1px solid",borderColor:Gm("inherit","whiteAlpha.50")(e),bg:Gm("gray.100","whiteAlpha.300")(e)}}})),Qy=Uy((e=>{const{theme:t}=e,{focusBorderColor:n,errorBorderColor:r}=Xy(e);return{field:{border:"2px solid",borderColor:"transparent",bg:Gm("gray.100","whiteAlpha.50")(e),_hover:{bg:Gm("gray.200","whiteAlpha.100")(e)},_readOnly:{boxShadow:"none 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$P={animation:OE((({visualElement:e,animate:t})=>{e.animationState||(e.animationState=HP(e)),Dk(t)&&a.exports.useEffect((()=>t.subscribe(e)),[t])})),exit:OE((e=>{const{custom:t,visualElement:n}=e,[r,o]=TE(),i=a.exports.useContext(Lk);a.exports.useEffect((()=>{n.isPresent=r;const e=n.animationState&&n.animationState.setActive(CC.Exit,!r,{custom:i&&i.custom||t});e&&!r&&e.then(o)}),[r])}))};class UP{constructor(e,t,{transformPagePoint:n}={}){if(this.startEvent=null,this.lastMoveEvent=null,this.lastMoveEventInfo=null,this.handlers={},this.updatePoint=()=>{if(!this.lastMoveEvent||!this.lastMoveEventInfo)return;const e=YP(this.lastMoveEventInfo,this.history),t=null!==this.startEvent,n=uE(e.offset,{x:0,y:0})>=3;if(!t&&!n)return;const{point:r}=e,{timestamp:o}=cP();this.history.push({...r,timestamp:o});const{onStart:i,onMove:a}=this.handlers;t||(i&&i(this.lastMoveEvent,e),this.startEvent=this.lastMoveEvent),a&&a(this.lastMoveEvent,e)},this.handlePointerMove=(e,t)=>{this.lastMoveEvent=e,this.lastMoveEventInfo=GP(t,this.transformPagePoint),PC(e)&&0===e.buttons?this.handlePointerUp(e,t):rP.update(this.updatePoint,!0)},this.handlePointerUp=(e,t)=>{this.end();const{onEnd:n,onSessionEnd:r}=this.handlers,o=YP(GP(t,this.transformPagePoint),this.history);this.startEvent&&n&&n(e,o),r&&r(e,o)},LC(e)&&e.touches.length>1)return;this.handlers=t,this.transformPagePoint=n;const r=GP(AC(e),this.transformPagePoint),{point:o}=r,{timestamp:i}=cP();this.history=[{...o,timestamp:i}];const{onSessionStart:a}=t;a&&a(e,YP(r,this.history)),this.removeListeners=f_(zC(window,"pointermove",this.handlePointerMove),zC(window,"pointerup",this.handlePointerUp),zC(window,"pointercancel",this.handlePointerUp))}updateHandlers(e){this.handlers=e}end(){this.removeListeners&&this.removeListeners(),oP.update(this.updatePoint)}}function GP(e,t){return t?{point:t(e.point)}:e}function qP(e,t){return{x:e.x-t.x,y:e.y-t.y}}function YP({point:e},t){return{point:e,delta:qP(e,XP(t)),offset:qP(e,ZP(t)),velocity:KP(t,.1)}}function ZP(e){return e[0]}function XP(e){return e[e.length-1]}function KP(e,t){if(e.length<2)return{x:0,y:0};let n=e.length-1,r=null;const o=XP(e);for(;n>=0&&(r=e[n],!(o.timestamp-r.timestamp>RE(t)));)n--;if(!r)return{x:0,y:0};const i=(o.timestamp-r.timestamp)/1e3;if(0===i)return{x:0,y:0};const a={x:(o.x-r.x)/i,y:(o.y-r.y)/i};return 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r=this.getAxisMotionValue(e).get()||0;if(kS.test(r)){const o=null===(n=null===(t=this.visualElement.projection)||void 0===t?void 0:t.layout)||void 0===n?void 0:n.layoutBox[e];if(o){r=QP(o)*(parseFloat(r)/100)}}this.originPoint[e]=r})),null==i||i(e,t),null===(n=this.visualElement.animationState)||void 0===n||n.setActive(CC.Drag,!0))},onMove:(e,t)=>{const{dragPropagation:n,dragDirectionLock:r,onDirectionLock:o,onDrag:i}=this.getProps();if(!n&&!this.openGlobalLock)return;const{offset:a}=t;if(r&&null===this.currentDirection)return this.currentDirection=function(e,t=10){let n=null;Math.abs(e.y)>t?n="y":Math.abs(e.x)>t&&(n="x");return n}(a),void(null!==this.currentDirection&&(null==o||o(this.currentDirection)));this.updateAxis("x",t.point,a),this.updateAxis("y",t.point,a),this.visualElement.render(),null==i||i(e,t)},onSessionEnd:(e,t)=>this.stop(e,t)},{transformPagePoint:this.visualElement.getTransformPagePoint()})}stop(e,t){const n=this.isDragging;if(this.cancel(),!n)return;const{velocity:r}=t;this.startAnimation(r);const{onDragEnd:o}=this.getProps();null==o||o(e,t)}cancel(){var e,t;this.isDragging=!1,this.visualElement.projection&&(this.visualElement.projection.isAnimationBlocked=!1),null===(e=this.panSession)||void 0===e||e.end(),this.panSession=void 0;const{dragPropagation:n}=this.getProps();!n&&this.openGlobalLock&&(this.openGlobalLock(),this.openGlobalLock=null),null===(t=this.visualElement.animationState)||void 0===t||t.setActive(CC.Drag,!1)}updateAxis(e,t,n){const{drag:r}=this.getProps();if(!n||!OL(e,r,this.currentDirection))return;const o=this.getAxisMotionValue(e);let i=this.originPoint[e]+n[e];this.constraints&&this.constraints[e]&&(i=function(e,{min:t,max:n},r){return void 0!==t&&e<t?e=r?o_(t,e,r.min):Math.max(e,t):void 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n=e.current,{projection:r}=this.visualElement;if(!r||!r.layout)return!1;const o=function(e,t,n){const r=EL(e,n),{scroll:o}=t;return o&&(wL(r.x,o.offset.x),wL(r.y,o.offset.y)),r}(n,r.root,this.visualElement.getTransformPagePoint());let i=function(e,t){return{x:aL(e.x,t.x),y:aL(e.y,t.y)}}(r.layout.layoutBox,o);if(t){const e=t(function({x:e,y:t}){return{top:t.min,right:e.max,bottom:t.max,left:e.min}}(i));this.hasMutatedConstraints=!!e,e&&(i=dL(e))}return i}startAnimation(e){const{drag:t,dragMomentum:n,dragElastic:r,dragTransition:o,dragSnapToOrigin:i,onDragTransitionEnd:a}=this.getProps(),s=this.constraints||{},l=uL((a=>{var l;if(!OL(a,t,this.currentDirection))return;let c=null!==(l=null==s?void 0:s[a])&&void 0!==l?l:{};i&&(c={min:0,max:0});const u=r?200:1e6,d=r?40:1e7,h={type:"inertia",velocity:n?e[a]:0,bounceStiffness:u,bounceDamping:d,timeConstant:750,restDelta:1,restSpeed:10,...o,...c};return this.startAxisValueAnimation(a,h)}));return Promise.all(l).then(a)}startAxisValueAnimation(e,t){return mP(e,this.getAxisMotionValue(e),0,t)}stopAnimation(){uL((e=>this.getAxisMotionValue(e).stop()))}getAxisMotionValue(e){var t,n;const r="_drag"+e.toUpperCase(),o=this.visualElement.getProps()[r];return o||this.visualElement.getValue(e,null!==(n=null===(t=this.visualElement.getProps().initial)||void 0===t?void 0:t[e])&&void 0!==n?n:0)}snapToCursor(e){uL((t=>{const{drag:n}=this.getProps();if(!OL(t,n,this.currentDirection))return;const{projection:r}=this.visualElement,o=this.getAxisMotionValue(t);if(r&&r.layout){const{min:n,max:i}=r.layout.layoutBox[t];o.set(e[t]-o_(n,i,.5))}}))}scalePositionWithinConstraints(){var e;if(!this.visualElement.current)return;const{drag:t,dragConstraints:n}=this.getProps(),{projection:r}=this.visualElement;if(!Ik(n)||!r||!this.constraints)return;this.stopAnimation();const o={x:0,y:0};uL((e=>{const t=this.getAxisMotionValue(e);if(t){const n=t.get();o[e]=function(e,t){let n=.5;const r=QP(e),o=QP(t);return 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i=_C(window,"resize",(()=>this.scalePositionWithinConstraints())),a=r.addEventListener("didUpdate",(({delta:e,hasLayoutChanged:t})=>{this.isDragging&&t&&(uL((t=>{const n=this.getAxisMotionValue(t);n&&(this.originPoint[t]+=e[t].translate,n.set(n.get()+e[t].translate))})),this.visualElement.render())}));return()=>{i(),t(),o(),null==a||a()}}getProps(){const e=this.visualElement.getProps(),{drag:t=!1,dragDirectionLock:n=!1,dragPropagation:r=!1,dragConstraints:o=!1,dragElastic:i=sL,dragMomentum:a=!0}=e;return{...e,drag:t,dragDirectionLock:n,dragPropagation:r,dragConstraints:o,dragElastic:i,dragMomentum:a}}}function OL(e,t,n){return!(!0!==t&&t!==e||null!==n&&n!==e)}const ML={pan:OE((function({onPan:e,onPanStart:t,onPanEnd:n,onPanSessionStart:r,visualElement:o}){const 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o=Object.keys(t).filter(NL);let i=[],a=!1;const s=[];if(o.forEach((o=>{const l=e.getValue(o);if(!e.hasValue(o))return;let c=n[o],u=EP(c);const d=t[o];let h;if(vC(d)){const e=d.length,t=null===d[0]?1:0;c=d[t],u=EP(c);for(let n=t;n<e;n++)h?EP(d[n]):h=EP(d[n])}else h=EP(d);if(u!==h)if(zL(u)&&zL(h)){const e=l.get();"string"==typeof e&&l.set(parseFloat(e)),"string"==typeof d?t[o]=parseFloat(d):Array.isArray(d)&&h===SS&&(t[o]=d.map(parseFloat))}else(null==u?void 0:u.transform)&&(null==h?void 0:h.transform)&&(0===c||0===d)?0===c?l.set(h.transform(c)):t[o]=u.transform(d):(a||(i=function(e){const t=[];return WL.forEach((n=>{const r=e.getValue(n);void 0!==r&&(t.push([n,r.get()]),r.set(n.startsWith("scale")?1:0))})),t.length&&e.render(),t}(e),a=!0),s.push(o),r[o]=void 0!==r[o]?r[o]:t[o],DL(l,d))})),s.length){const n=s.indexOf("height")>=0?window.pageYOffset:null,o=((e,t,n)=>{const r=t.measureViewportBox(),o=t.current,i=getComputedStyle(o),{display:a}=i,s={};"none"===a&&t.setStaticValue("display",e.display||"block"),n.forEach((e=>{s[e]=VL[e](r,i)})),t.render();const l=t.measureViewportBox();return n.forEach((n=>{const r=t.getValue(n);DL(r,s[n]),e[n]=VL[n](l,i)})),e})(t,e,s);return i.length&&i.forEach((([t,n])=>{e.getValue(t).set(n)})),e.render(),Ok&&null!==n&&window.scrollTo({top:n}),{target:o,transitionEnd:r}}return{target:t,transitionEnd:r}};function UL(e,t,n,r){return(e=>Object.keys(e).some(NL))(t)?$L(e,t,n,r):{target:t,transitionEnd:r}}const GL=(e,t,n,r)=>{const o=function(e,{...t},n){const r=e.current;if(!(r instanceof Element))return{target:t,transitionEnd:n};n&&(n={...n}),e.values.forEach((e=>{const t=e.get();if(!TL(t))return;const n=IL(t,r);n&&e.set(n)}));for(const o in t){const e=t[o];if(!TL(e))continue;const i=IL(e,r);i&&(t[o]=i,n&&void 0===n[o]&&(n[o]=e))}return{target:t,transitionEnd:n}}(e,t,r);return UL(e,t=o.target,n,r=o.transitionEnd)},qL={current:null},YL={current:!1};const ZL=Object.keys(Vk),XL=ZL.length,KL=["AnimationStart","AnimationComplete","Update","Unmount","BeforeLayoutMeasure","LayoutMeasure","LayoutAnimationStart","LayoutAnimationComplete"];class QL{constructor({parent:e,props:t,reducedMotionConfig:n,visualState:r},o={}){this.current=null,this.children=new Set,this.isVariantNode=!1,this.isControllingVariants=!1,this.shouldReduceMotion=null,this.values=new Map,this.isPresent=!0,this.valueSubscriptions=new Map,this.prevMotionValues={},this.events={},this.propEventSubscriptions={},this.notifyUpdate=()=>this.notify("Update",this.latestValues),this.render=()=>{this.current&&(this.triggerBuild(),this.renderInstance(this.current,this.renderState,this.props.style,this.projection))},this.scheduleRender=()=>rP.render(this.render,!1,!0);const{latestValues:i,renderState:a}=r;this.latestValues=i,this.baseTarget={...i},this.initialValues=t.initial?{...i}:{},this.renderState=a,this.parent=e,this.props=t,this.depth=e?e.depth+1:0,this.reducedMotionConfig=n,this.options=o,this.isControllingVariants=Bk(t),this.isVariantNode=jk(t),this.isVariantNode&&(this.variantChildren=new Set),this.manuallyAnimateOnMount=Boolean(e&&e.current);const{willChange:s,...l}=this.scrapeMotionValuesFromProps(t);for(const c in l){const e=l[c];void 0!==i[c]&&aS(e)&&(e.set(i[c],!1),AP(s)&&s.add(c))}}scrapeMotionValuesFromProps(e){return{}}mount(e){var t;this.current=e,this.projection&&this.projection.mount(e),this.parent&&this.isVariantNode&&!this.isControllingVariants&&(this.removeFromVariantTree=null===(t=this.parent)||void 0===t?void 0:t.addVariantChild(this)),this.values.forEach(((e,t)=>this.bindToMotionValue(t,e))),YL.current||function(){if(YL.current=!0,Ok)if(window.matchMedia){const e=window.matchMedia("(prefers-reduced-motion)"),t=()=>qL.current=e.matches;e.addListener(t),t()}else qL.current=!1}(),this.shouldReduceMotion="never"!==this.reducedMotionConfig&&("always"===this.reducedMotionConfig||qL.current),this.parent&&this.parent.children.add(this),this.setProps(this.props)}unmount(){var e,t,n;null===(e=this.projection)||void 0===e||e.unmount(),oP.update(this.notifyUpdate),oP.render(this.render),this.valueSubscriptions.forEach((e=>e())),null===(t=this.removeFromVariantTree)||void 0===t||t.call(this),null===(n=this.parent)||void 0===n||n.children.delete(this);for(const r in this.events)this.events[r].clear();this.current=null}bindToMotionValue(e,t){const n=oS.has(e),r=t.onChange((t=>{this.latestValues[e]=t,this.props.onUpdate&&rP.update(this.notifyUpdate,!1,!0),n&&this.projection&&(this.projection.isProjectionDirty=!0)})),o=t.onRenderRequest(this.scheduleRender);this.valueSubscriptions.set(e,(()=>{r(),o()}))}sortNodePosition(e){return this.current&&this.sortInstanceNodePosition&&this.type===e.type?this.sortInstanceNodePosition(this.current,e.current):0}loadFeatures(e,t,n,r,o,i){const s=[];for(let l=0;l<XL;l++){const t=ZL[l],{isEnabled:n,Component:r}=Vk[t];n(e)&&r&&s.push(a.exports.createElement(r,{key:t,...e,visualElement:this}))}if(!this.projection&&o){this.projection=new o(r,this.latestValues,this.parent&&this.parent.projection);const{layoutId:t,layout:n,drag:a,dragConstraints:s,layoutScroll:l}=e;this.projection.setOptions({layoutId:t,layout:n,alwaysMeasureLayout:Boolean(a)||s&&Ik(s),visualElement:this,scheduleRender:()=>this.scheduleRender(),animationType:"string"==typeof n?n:"both",initialPromotionConfig:i,layoutScroll:l})}return s}triggerBuild(){this.build(this.renderState,this.latestValues,this.options,this.props)}measureViewportBox(){return this.current?this.measureInstanceViewportBox(this.current,this.props):{x:{min:0,max:0},y:{min:0,max:0}}}getStaticValue(e){return this.latestValues[e]}setStaticValue(e,t){this.latestValues[e]=t}makeTargetAnimatable(e,t=!0){return this.makeTargetAnimatableFromInstance(e,this.props,t)}setProps(e){(e.transformTemplate||this.props.transformTemplate)&&this.scheduleRender(),this.props=e;for(let t=0;t<KL.length;t++){const n=KL[t];this.propEventSubscriptions[n]&&(this.propEventSubscriptions[n](),delete this.propEventSubscriptions[n]);const r=e["on"+n];r&&(this.propEventSubscriptions[n]=this.on(n,r))}this.prevMotionValues=function(e,t,n){const{willChange:r}=t;for(const o in t){const i=t[o],a=n[o];if(aS(i))e.addValue(o,i),AP(r)&&r.add(o);else if(aS(a))e.addValue(o,SP(i)),AP(r)&&r.remove(o);else if(a!==i)if(e.hasValue(o)){const t=e.getValue(o);!t.hasAnimated&&t.set(i)}else{const t=e.getStaticValue(o);e.addValue(o,SP(void 0!==t?t:i))}}for(const o in n)void 0===t[o]&&e.removeValue(o);return t}(this,this.scrapeMotionValuesFromProps(e),this.prevMotionValues)}getProps(){return this.props}getVariant(e){var t;return null===(t=this.props.variants)||void 0===t?void 0:t[e]}getDefaultTransition(){return this.props.transition}getTransformPagePoint(){return this.props.transformPagePoint}getClosestVariantNode(){var e;return this.isVariantNode?this:null===(e=this.parent)||void 0===e?void 0:e.getClosestVariantNode()}getVariantContext(e=!1){var t,n;if(e)return null===(t=this.parent)||void 0===t?void 0:t.getVariantContext();if(!this.isControllingVariants){const e=(null===(n=this.parent)||void 0===n?void 0:n.getVariantContext())||{};return void 0!==this.props.initial&&(e.initial=this.props.initial),e}const r={};for(let o=0;o<eO;o++){const e=JL[o],t=this.props[e];(Nk(t)||!1===t)&&(r[e]=t)}return r}addVariantChild(e){var t;const n=this.getClosestVariantNode();if(n)return null===(t=n.variantChildren)||void 0===t||t.add(e),()=>n.variantChildren.delete(e)}addValue(e,t){this.hasValue(e)&&this.removeValue(e),this.values.set(e,t),this.latestValues[e]=t.get(),this.bindToMotionValue(e,t)}removeValue(e){var t;this.values.delete(e),null===(t=this.valueSubscriptions.get(e))||void 0===t||t(),this.valueSubscriptions.delete(e),delete this.latestValues[e],this.removeValueFromRenderState(e,this.renderState)}hasValue(e){return this.values.has(e)}getValue(e,t){if(this.props.values&&this.props.values[e])return this.props.values[e];let n=this.values.get(e);return void 0===n&&void 0!==t&&(n=SP(t),this.addValue(e,n)),n}readValue(e){return void 0===this.latestValues[e]&&this.current?this.readValueFromInstance(this.current,e,this.options):this.latestValues[e]}setBaseTarget(e,t){this.baseTarget[e]=t}getBaseTarget(e){var t;const{initial:n}=this.props,r="string"==typeof n||"object"==typeof n?null===(t=mC(this.props,n))||void 0===t?void 0:t[e]:void 0;if(n&&void 0!==r)return r;const o=this.getBaseTargetFromProps(this.props,e);return void 0===o||aS(o)?void 0!==this.initialValues[e]&&void 0===r?void 0:this.baseTarget[e]:o}on(e,t){return this.events[e]||(this.events[e]=new wP),this.events[e].add(t)}notify(e,...t){var n;null===(n=this.events[e])||void 0===n||n.notify(...t)}}const JL=["initial",...zP],eO=JL.length;class tO extends QL{sortInstanceNodePosition(e,t){return 2&e.compareDocumentPosition(t)?1:-1}getBaseTargetFromProps(e,t){var n;return null===(n=e.style)||void 0===n?void 0:n[t]}removeValueFromRenderState(e,{vars:t,style:n}){delete t[e],delete n[e]}makeTargetAnimatableFromInstance({transition:e,transitionEnd:t,...n},{transformValues:r},o){let i=function(e,t,n){var r;const o={};for(const i in e){const e=TP(i,t);o[i]=void 0!==e?e:null===(r=n.getValue(i))||void 0===r?void 0:r.get()}return o}(n,e||{},this);if(r&&(t&&(t=r(t)),n&&(n=r(n)),i&&(i=r(i))),o){!function(e,t,n){var r,o;const i=Object.keys(t).filter((t=>!e.hasValue(t))),a=i.length;if(a)for(let s=0;s<a;s++){const a=i[s],l=t[a];let c=null;Array.isArray(l)&&(c=l[0]),null===c&&(c=null!==(o=null!==(r=n[a])&&void 0!==r?r:e.readValue(a))&&void 0!==o?o:t[a]),null!=c&&("string"==typeof c&&(vP(c)||yP(c))?c=parseFloat(c):!LP(c)&&HS.test(l)&&(c=GE(a,l)),e.addValue(a,SP(c)),void 0===n[a]&&(n[a]=c),null!==c&&e.setBaseTarget(a,c))}}(this,n,i);const e=GL(this,n,i,t);t=e.transitionEnd,n=e.target}return{transition:e,transitionEnd:t,...n}}}class nO extends tO{readValueFromInstance(e,t){if(oS.has(t)){const e=UE(t);return e&&e.default||0}{const r=(n=e,window.getComputedStyle(n)),o=(cS(t)?r.getPropertyValue(t):r[t])||0;return"string"==typeof o?o.trim():o}var n}measureInstanceViewportBox(e,{transformPagePoint:t}){return EL(e,t)}build(e,t,n,r){YS(e,t,n,r.transformTemplate)}scrapeMotionValuesFromProps(e){return pC(e)}renderInstance(e,t,n,r){dC(e,t,n,r)}}class rO extends tO{getBaseTargetFromProps(e,t){return e[t]}readValueFromInstance(e,t){var n;return oS.has(t)?(null===(n=UE(t))||void 0===n?void 0:n.default)||0:(t=hC.has(t)?t:uC(t),e.getAttribute(t))}measureInstanceViewportBox(){return{x:{min:0,max:0},y:{min:0,max:0}}}scrapeMotionValuesFromProps(e){return gC(e)}build(e,t,n,r){aC(e,t,n,r.transformTemplate)}renderInstance(e,t,n,r){fC(e,t,0,r)}}const oO=(e,t)=>tS(e)?new rO(t,{enableHardwareAcceleration:!1}):new nO(t,{enableHardwareAcceleration:!0});function iO(e,t){return t.max===t.min?0:e/(t.max-t.min)*100}const aO={correct:(e,t)=>{if(!t.target)return e;if("string"==typeof e){if(!SS.test(e))return e;e=parseFloat(e)}return`${iO(e,t.target.x)}% ${iO(e,t.target.y)}%`}},sO="_$css",lO={correct:(e,{treeScale:t,projectionDelta:n})=>{const r=e,o=e.includes("var("),i=[];o&&(e=e.replace(AL,(e=>(i.push(e),sO))));const a=HS.parse(e);if(a.length>5)return r;const s=HS.createTransformer(e),l="number"!=typeof a[0]?1:0,c=n.x.scale*t.x,u=n.y.scale*t.y;a[0+l]/=c,a[1+l]/=u;const d=o_(c,u,.5);"number"==typeof a[2+l]&&(a[2+l]/=d),"number"==typeof a[3+l]&&(a[3+l]/=d);let h=s(a);if(o){let e=0;h=h.replace(sO,(()=>{const t=i[e];return e++,t}))}return h}};class cO extends H.Component{componentDidMount(){const{visualElement:e,layoutGroup:t,switchLayoutGroup:n,layoutId:r}=this.props,{projection:o}=e;var i;i=uO,Object.assign(nS,i),o&&(t.group&&t.group.add(o),n&&n.register&&r&&n.register(o),o.root.didUpdate(),o.addEventListener("animationComplete",(()=>{this.safeToRemove()})),o.setOptions({...o.options,onExitComplete:()=>this.safeToRemove()})),Uk.hasEverUpdated=!0}getSnapshotBeforeUpdate(e){const{layoutDependency:t,visualElement:n,drag:r,isPresent:o}=this.props,i=n.projection;return i?(i.isPresent=o,r||e.layoutDependency!==t||void 0===t?i.willUpdate():this.safeToRemove(),e.isPresent!==o&&(o?i.promote():i.relegate()||rP.postRender((()=>{var e;(null===(e=i.getStack())||void 0===e?void 0:e.members.length)||this.safeToRemove()}))),null):null}componentDidUpdate(){const{projection:e}=this.props.visualElement;e&&(e.root.didUpdate(),!e.currentAnimation&&e.isLead()&&this.safeToRemove())}componentWillUnmount(){const{visualElement:e,layoutGroup:t,switchLayoutGroup:n}=this.props,{projection:r}=e;r&&(r.scheduleCheckAfterUnmount(),(null==t?void 0:t.group)&&t.group.remove(r),(null==n?void 0:n.deregister)&&n.deregister(r))}safeToRemove(){const{safeToRemove:e}=this.props;null==e||e()}render(){return null}}const uO={borderRadius:{...aO,applyTo:["borderTopLeftRadius","borderTopRightRadius","borderBottomLeftRadius","borderBottomRightRadius"]},borderTopLeftRadius:aO,borderTopRightRadius:aO,borderBottomLeftRadius:aO,borderBottomRightRadius:aO,boxShadow:lO},dO={measureLayout:function(e){const[t,n]=TE(),r=a.exports.useContext(qk);return ld(cO,{...e,layoutGroup:r,switchLayoutGroup:a.exports.useContext(Zk),isPresent:t,safeToRemove:n})}};const hO=["TopLeft","TopRight","BottomLeft","BottomRight"],fO=hO.length,pO=e=>"string"==typeof e?parseFloat(e):e,gO=e=>"number"==typeof e||SS.test(e);function mO(e,t){var n;return null!==(n=e[t])&&void 0!==n?n:e.borderRadius}const vO=bO(0,.5,T_),yO=bO(.5,.95,__);function bO(e,t,n){return r=>r<e?0:r>t?1:n(r_(e,t,r))}function xO(e,t){e.min=t.min,e.max=t.max}function wO(e,t){xO(e.x,t.x),xO(e.y,t.y)}function kO(e,t,n,r,o){return e=vL(e-=t,1/n,r),void 0!==o&&(e=vL(e,1/o,r)),e}function SO(e,t,[n,r,o],i,a){!function(e,t=0,n=1,r=.5,o,i=e,a=e){kS.test(t)&&(t=parseFloat(t),t=o_(a.min,a.max,t/100)-a.min);if("number"!=typeof t)return;let s=o_(i.min,i.max,r);e===i&&(s-=t),e.min=kO(e.min,t,n,s,o),e.max=kO(e.max,t,n,s,o)}(e,t[n],t[r],t[o],t.scale,i,a)}const CO=["x","scaleX","originX"],_O=["y","scaleY","originY"];function EO(e,t,n,r){SO(e.x,t,CO,null==n?void 0:n.x,null==r?void 0:r.x),SO(e.y,t,_O,null==n?void 0:n.y,null==r?void 0:r.y)}function PO(e){return 0===e.translate&&1===e.scale}function LO(e){return PO(e.x)&&PO(e.y)}function OO(e,t){return e.x.min===t.x.min&&e.x.max===t.x.max&&e.y.min===t.y.min&&e.y.max===t.y.max}function MO(e){return QP(e.x)/QP(e.y)}class TO{constructor(){this.members=[]}add(e){bP(this.members,e),e.scheduleRender()}remove(e){if(xP(this.members,e),e===this.prevLead&&(this.prevLead=void 0),e===this.lead){const e=this.members[this.members.length-1];e&&this.promote(e)}}relegate(e){const t=this.members.findIndex((t=>e===t));if(0===t)return!1;let n;for(let r=t;r>=0;r--){const e=this.members[r];if(!1!==e.isPresent){n=e;break}}return!!n&&(this.promote(n),!0)}promote(e,t){var n;const r=this.lead;if(e!==r&&(this.prevLead=r,this.lead=e,e.show(),r)){r.instance&&r.scheduleRender(),e.scheduleRender(),e.resumeFrom=r,t&&(e.resumeFrom.preserveOpacity=!0),r.snapshot&&(e.snapshot=r.snapshot,e.snapshot.latestValues=r.animationValues||r.latestValues),(null===(n=e.root)||void 0===n?void 0:n.isUpdating)&&(e.isLayoutDirty=!0);const{crossfade:o}=e.options;!1===o&&r.hide()}}exitAnimationComplete(){this.members.forEach((e=>{var t,n,r,o,i;null===(n=(t=e.options).onExitComplete)||void 0===n||n.call(t),null===(i=null===(r=e.resumingFrom)||void 0===r?void 0:(o=r.options).onExitComplete)||void 0===i||i.call(o)}))}scheduleRender(){this.members.forEach((e=>{e.instance&&e.scheduleRender(!1)}))}removeLeadSnapshot(){this.lead&&this.lead.snapshot&&(this.lead.snapshot=void 0)}}function AO(e,t,n){let r="";const o=e.x.translate/t.x,i=e.y.translate/t.y;if((o||i)&&(r=`translate3d(${o}px, ${i}px, 0) `),1===t.x&&1===t.y||(r+=`scale(${1/t.x}, ${1/t.y}) `),n){const{rotate:e,rotateX:t,rotateY:o}=n;e&&(r+=`rotate(${e}deg) `),t&&(r+=`rotateX(${t}deg) `),o&&(r+=`rotateY(${o}deg) `)}const a=e.x.scale*t.x,s=e.y.scale*t.y;return 1===a&&1===s||(r+=`scale(${a}, ${s})`),r||"none"}const IO=(e,t)=>e.depth-t.depth;class RO{constructor(){this.children=[],this.isDirty=!1}add(e){bP(this.children,e),this.isDirty=!0}remove(e){xP(this.children,e),this.isDirty=!0}forEach(e){this.isDirty&&this.children.sort(IO),this.isDirty=!1,this.children.forEach(e)}}const NO=["","X","Y","Z"];let DO=0;function zO({attachResizeListener:e,defaultParent:t,measureScroll:n,checkIsScrollRoot:r,resetTransform:o}){return class{constructor(e,n={},r=(null==t?void 0:t())){this.id=DO++,this.animationId=0,this.children=new Set,this.options={},this.isTreeAnimating=!1,this.isAnimationBlocked=!1,this.isLayoutDirty=!1,this.isProjectionDirty=!1,this.updateManuallyBlocked=!1,this.updateBlockedByResize=!1,this.isUpdating=!1,this.isSVG=!1,this.needsReset=!1,this.shouldResetTransform=!1,this.treeScale={x:1,y:1},this.eventHandlers=new Map,this.potentialNodes=new Map,this.checkUpdateFailed=()=>{this.isUpdating&&(this.isUpdating=!1,this.clearAllSnapshots())},this.updateProjection=()=>{this.nodes.forEach($O),this.nodes.forEach(UO)},this.hasProjected=!1,this.isVisible=!0,this.animationProgress=0,this.sharedNodes=new Map,this.elementId=e,this.latestValues=n,this.root=r?r.root||r:this,this.path=r?[...r.path,r]:[],this.parent=r,this.depth=r?r.depth+1:0,e&&this.root.registerPotentialNode(e,this);for(let t=0;t<this.path.length;t++)this.path[t].shouldResetTransform=!0;this.root===this&&(this.nodes=new RO)}addEventListener(e,t){return this.eventHandlers.has(e)||this.eventHandlers.set(e,new wP),this.eventHandlers.get(e).add(t)}notifyListeners(e,...t){const n=this.eventHandlers.get(e);null==n||n.notify(...t)}hasListeners(e){return this.eventHandlers.has(e)}registerPotentialNode(e,t){this.potentialNodes.set(e,t)}mount(t,n=!1){var r;if(this.instance)return;this.isSVG=t instanceof SVGElement&&"svg"!==t.tagName,this.instance=t;const{layoutId:o,layout:i,visualElement:a}=this.options;if(a&&!a.current&&a.mount(t),this.root.nodes.add(this),null===(r=this.parent)||void 0===r||r.children.add(this),this.elementId&&this.root.potentialNodes.delete(this.elementId),n&&(i||o)&&(this.isLayoutDirty=!0),e){let n;const r=()=>this.root.updateBlockedByResize=!1;e(t,(()=>{this.root.updateBlockedByResize=!0,n&&n(),n=uP(r,250),Uk.hasAnimatedSinceResize&&(Uk.hasAnimatedSinceResize=!1,this.nodes.forEach(VO))}))}o&&this.root.registerSharedNode(o,this),!1!==this.options.animate&&a&&(o||i)&&this.addEventListener("didUpdate",(({delta:e,hasLayoutChanged:t,hasRelativeTargetChanged:n,layout:r})=>{var o,i,s,l,c;if(this.isTreeAnimationBlocked())return this.target=void 0,void(this.relativeTarget=void 0);const u=null!==(i=null!==(o=this.options.transition)&&void 0!==o?o:a.getDefaultTransition())&&void 0!==i?i:KO,{onLayoutAnimationStart:d,onLayoutAnimationComplete:h}=a.getProps(),f=!this.targetLayout||!OO(this.targetLayout,r)||n,p=!t&&n;if((null===(s=this.resumeFrom)||void 0===s?void 0:s.instance)||p||t&&(f||!this.currentAnimation)){this.resumeFrom&&(this.resumingFrom=this.resumeFrom,this.resumingFrom.resumingFrom=void 0),this.setAnimationOrigin(e,p);const t={...gP(u,"layout"),onPlay:d,onComplete:h};a.shouldReduceMotion&&(t.delay=0,t.type=!1),this.startAnimation(t)}else t||0!==this.animationProgress||VO(this),this.isLead()&&(null===(c=(l=this.options).onExitComplete)||void 0===c||c.call(l));this.targetLayout=r}))}unmount(){var e,t;this.options.layoutId&&this.willUpdate(),this.root.nodes.remove(this),null===(e=this.getStack())||void 0===e||e.remove(this),null===(t=this.parent)||void 0===t||t.children.delete(this),this.instance=void 0,oP.preRender(this.updateProjection)}blockUpdate(){this.updateManuallyBlocked=!0}unblockUpdate(){this.updateManuallyBlocked=!1}isUpdateBlocked(){return this.updateManuallyBlocked||this.updateBlockedByResize}isTreeAnimationBlocked(){var e;return this.isAnimationBlocked||(null===(e=this.parent)||void 0===e?void 0:e.isTreeAnimationBlocked())||!1}startUpdate(){var e;this.isUpdateBlocked()||(this.isUpdating=!0,null===(e=this.nodes)||void 0===e||e.forEach(GO),this.animationId++)}willUpdate(e=!0){var t,n,r;if(this.root.isUpdateBlocked())return void(null===(n=(t=this.options).onExitComplete)||void 0===n||n.call(t));if(!this.root.isUpdating&&this.root.startUpdate(),this.isLayoutDirty)return;this.isLayoutDirty=!0;for(let s=0;s<this.path.length;s++){const e=this.path[s];e.shouldResetTransform=!0,e.updateScroll("snapshot")}const{layoutId:o,layout:i}=this.options;if(void 0===o&&!i)return;const a=null===(r=this.options.visualElement)||void 0===r?void 0:r.getProps().transformTemplate;this.prevTransformTemplateValue=null==a?void 0:a(this.latestValues,""),this.updateSnapshot(),e&&this.notifyListeners("willUpdate")}didUpdate(){if(this.isUpdateBlocked())return this.unblockUpdate(),this.clearAllSnapshots(),void this.nodes.forEach(HO);this.isUpdating&&(this.isUpdating=!1,this.potentialNodes.size&&(this.potentialNodes.forEach(QO),this.potentialNodes.clear()),this.nodes.forEach(WO),this.nodes.forEach(BO),this.nodes.forEach(jO),this.clearAllSnapshots(),iP.update(),iP.preRender(),iP.render())}clearAllSnapshots(){this.nodes.forEach(FO),this.sharedNodes.forEach(qO)}scheduleUpdateProjection(){rP.preRender(this.updateProjection,!1,!0)}scheduleCheckAfterUnmount(){rP.postRender((()=>{this.isLayoutDirty?this.root.didUpdate():this.root.checkUpdateFailed()}))}updateSnapshot(){!this.snapshot&&this.instance&&(this.snapshot=this.measure())}updateLayout(){var e;if(!this.instance)return;if(this.updateScroll(),!(this.options.alwaysMeasureLayout&&this.isLead()||this.isLayoutDirty))return;if(this.resumeFrom&&!this.resumeFrom.instance)for(let n=0;n<this.path.length;n++){this.path[n].updateScroll()}const t=this.layout;this.layout=this.measure(!1),this.layoutCorrected={x:{min:0,max:0},y:{min:0,max:0}},this.isLayoutDirty=!1,this.projectionDelta=void 0,this.notifyListeners("measure",this.layout.layoutBox),null===(e=this.options.visualElement)||void 0===e||e.notify("LayoutMeasure",this.layout.layoutBox,null==t?void 0:t.layoutBox)}updateScroll(e="measure"){let t=Boolean(this.options.layoutScroll&&this.instance);this.scroll&&this.scroll.animationId===this.root.animationId&&this.scroll.phase===e&&(t=!1),t&&(this.scroll={animationId:this.root.animationId,phase:e,isRoot:r(this.instance),offset:n(this.instance)})}resetTransform(){var e;if(!o)return;const t=this.isLayoutDirty||this.shouldResetTransform,n=this.projectionDelta&&!LO(this.projectionDelta),r=null===(e=this.options.visualElement)||void 0===e?void 0:e.getProps().transformTemplate,i=null==r?void 0:r(this.latestValues,""),a=i!==this.prevTransformTemplateValue;t&&(n||pL(this.latestValues)||a)&&(o(this.instance,i),this.shouldResetTransform=!1,this.scheduleRender())}measure(e=!0){var t;const n=this.measurePageBox();let r=this.removeElementScroll(n);var o;e&&(r=this.removeTransform(r)),JO((o=r).x),JO(o.y);const i=null===(t=this.options.visualElement)||void 0===t?void 0:t.readValue("position"),a="fixed"===i||"sticky"===i?i:"static";return{animationId:this.root.animationId,measuredBox:n,layoutBox:r,latestValues:{},source:this.id,position:a}}measurePageBox(){const{visualElement:e}=this.options;if(!e)return{x:{min:0,max:0},y:{min:0,max:0}};const t=e.measureViewportBox(),{scroll:n}=this.root;return n&&(wL(t.x,n.offset.x),wL(t.y,n.offset.y)),t}removeElementScroll(e){const t={x:{min:0,max:0},y:{min:0,max:0}};wO(t,e);for(let n=0;n<this.path.length;n++){const r=this.path[n],{scroll:o,options:i}=r;if(r!==this.root&&o&&i.layoutScroll){if(o.isRoot){wO(t,e);const{scroll:n}=this.root;n&&(wL(t.x,-n.offset.x),wL(t.y,-n.offset.y))}wL(t.x,o.offset.x),wL(t.y,o.offset.y)}}return t}applyTransform(e,t=!1){const n={x:{min:0,max:0},y:{min:0,max:0}};wO(n,e);for(let r=0;r<this.path.length;r++){const e=this.path[r];!t&&e.options.layoutScroll&&e.scroll&&e!==e.root&&_L(n,{x:-e.scroll.offset.x,y:-e.scroll.offset.y}),pL(e.latestValues)&&_L(n,e.latestValues)}return pL(this.latestValues)&&_L(n,this.latestValues),n}removeTransform(e){var t;const n={x:{min:0,max:0},y:{min:0,max:0}};wO(n,e);for(let r=0;r<this.path.length;r++){const e=this.path[r];if(!e.instance)continue;if(!pL(e.latestValues))continue;fL(e.latestValues)&&e.updateSnapshot();const o={x:{min:0,max:0},y:{min:0,max:0}};wO(o,e.measurePageBox()),EO(n,e.latestValues,null===(t=e.snapshot)||void 0===t?void 0:t.layoutBox,o)}return pL(this.latestValues)&&EO(n,this.latestValues),n}setTargetDelta(e){this.targetDelta=e,this.isProjectionDirty=!0,this.root.scheduleUpdateProjection()}setOptions(e){this.options={...this.options,...e,crossfade:void 0===e.crossfade||e.crossfade}}clearMeasurements(){this.scroll=void 0,this.layout=void 0,this.snapshot=void 0,this.prevTransformTemplateValue=void 0,this.targetDelta=void 0,this.target=void 0,this.isLayoutDirty=!1}resolveTargetDelta(){var e;if(this.isProjectionDirty||(this.isProjectionDirty=this.getLead().isProjectionDirty||Boolean(this.parent&&this.parent.isProjectionDirty)),!this.isProjectionDirty)return;const{layout:t,layoutId:n}=this.options;if(this.layout&&(t||n)){if(!this.targetDelta&&!this.relativeTarget){const e=this.getClosestProjectingParent();e&&e.layout?(this.relativeParent=e,this.relativeTarget={x:{min:0,max:0},y:{min:0,max:0}},this.relativeTargetOrigin={x:{min:0,max:0},y:{min:0,max:0}},oL(this.relativeTargetOrigin,this.layout.layoutBox,e.layout.layoutBox),wO(this.relativeTarget,this.relativeTargetOrigin)):this.relativeParent=this.relativeTarget=void 0}var r,o,i;if(this.relativeTarget||this.targetDelta)if(this.target||(this.target={x:{min:0,max:0},y:{min:0,max:0}},this.targetWithTransforms={x:{min:0,max:0},y:{min:0,max:0}}),this.relativeTarget&&this.relativeTargetOrigin&&(null===(e=this.relativeParent)||void 0===e?void 0:e.target)?(r=this.target,o=this.relativeTarget,i=this.relativeParent.target,nL(r.x,o.x,i.x),nL(r.y,o.y,i.y)):this.targetDelta?(Boolean(this.resumingFrom)?this.target=this.applyTransform(this.layout.layoutBox):wO(this.target,this.layout.layoutBox),xL(this.target,this.targetDelta)):wO(this.target,this.layout.layoutBox),this.attemptToResolveRelativeTarget){this.attemptToResolveRelativeTarget=!1;const e=this.getClosestProjectingParent();e&&Boolean(e.resumingFrom)===Boolean(this.resumingFrom)&&!e.options.layoutScroll&&e.target?(this.relativeParent=e,this.relativeTarget={x:{min:0,max:0},y:{min:0,max:0}},this.relativeTargetOrigin={x:{min:0,max:0},y:{min:0,max:0}},oL(this.relativeTargetOrigin,this.target,e.target),wO(this.relativeTarget,this.relativeTargetOrigin)):this.relativeParent=this.relativeTarget=void 0}}}getClosestProjectingParent(){if(this.parent&&!fL(this.parent.latestValues)&&!gL(this.parent.latestValues))return(this.parent.relativeTarget||this.parent.targetDelta)&&this.parent.layout?this.parent:this.parent.getClosestProjectingParent()}calcProjection(){var e;if(!this.isProjectionDirty)return;this.isProjectionDirty=!1;const{layout:t,layoutId:n}=this.options;if(this.isTreeAnimating=Boolean((null===(e=this.parent)||void 0===e?void 0:e.isTreeAnimating)||this.currentAnimation||this.pendingAnimation),this.isTreeAnimating||(this.targetDelta=this.relativeTarget=void 0),!this.layout||!t&&!n)return;const r=this.getLead();wO(this.layoutCorrected,this.layout.layoutBox),function(e,t,n,r=!1){var o,i;const a=n.length;if(!a)return;let s,l;t.x=t.y=1;for(let c=0;c<a;c++)s=n[c],l=s.projectionDelta,"contents"!==(null===(i=null===(o=s.instance)||void 0===o?void 0:o.style)||void 0===i?void 0:i.display)&&(r&&s.options.layoutScroll&&s.scroll&&s!==s.root&&_L(e,{x:-s.scroll.offset.x,y:-s.scroll.offset.y}),l&&(t.x*=l.x.scale,t.y*=l.y.scale,xL(e,l)),r&&pL(s.latestValues)&&_L(e,s.latestValues))}(this.layoutCorrected,this.treeScale,this.path,Boolean(this.resumingFrom)||this!==r);const{target:o}=r;if(!o)return;this.projectionDelta||(this.projectionDelta={x:{translate:0,scale:1,origin:0,originPoint:0},y:{translate:0,scale:1,origin:0,originPoint:0}},this.projectionDeltaWithTransform={x:{translate:0,scale:1,origin:0,originPoint:0},y:{translate:0,scale:1,origin:0,originPoint:0}});const i=this.treeScale.x,a=this.treeScale.y,s=this.projectionTransform;tL(this.projectionDelta,this.layoutCorrected,o,this.latestValues),this.projectionTransform=AO(this.projectionDelta,this.treeScale),this.projectionTransform===s&&this.treeScale.x===i&&this.treeScale.y===a||(this.hasProjected=!0,this.scheduleRender(),this.notifyListeners("projectionUpdate",o))}hide(){this.isVisible=!1}show(){this.isVisible=!0}scheduleRender(e=!0){var t,n,r;null===(n=(t=this.options).scheduleRender)||void 0===n||n.call(t),e&&(null===(r=this.getStack())||void 0===r||r.scheduleRender()),this.resumingFrom&&!this.resumingFrom.instance&&(this.resumingFrom=void 0)}setAnimationOrigin(e,t=!1){var n,r;const o=this.snapshot,i=(null==o?void 0:o.latestValues)||{},a={...this.latestValues},s={x:{translate:0,scale:1,origin:0,originPoint:0},y:{translate:0,scale:1,origin:0,originPoint:0}};this.relativeTarget=this.relativeTargetOrigin=void 0,this.attemptToResolveRelativeTarget=!t;const l={x:{min:0,max:0},y:{min:0,max:0}},c=(null==o?void 0:o.source)!==(null===(n=this.layout)||void 0===n?void 0:n.source),u=((null===(r=this.getStack())||void 0===r?void 0:r.members.length)||0)<=1,d=Boolean(c&&!u&&!0===this.options.crossfade&&!this.path.some(XO));this.animationProgress=0,this.mixTargetDelta=t=>{var n;const r=t/1e3;YO(s.x,e.x,r),YO(s.y,e.y,r),this.setTargetDelta(s),this.relativeTarget&&this.relativeTargetOrigin&&this.layout&&(null===(n=this.relativeParent)||void 0===n?void 0:n.layout)&&(oL(l,this.layout.layoutBox,this.relativeParent.layout.layoutBox),function(e,t,n,r){ZO(e.x,t.x,n.x,r),ZO(e.y,t.y,n.y,r)}(this.relativeTarget,this.relativeTargetOrigin,l,r)),c&&(this.animationValues=a,function(e,t,n,r,o,i){var a,s,l,c;o?(e.opacity=o_(0,null!==(a=n.opacity)&&void 0!==a?a:1,vO(r)),e.opacityExit=o_(null!==(s=t.opacity)&&void 0!==s?s:1,0,yO(r))):i&&(e.opacity=o_(null!==(l=t.opacity)&&void 0!==l?l:1,null!==(c=n.opacity)&&void 0!==c?c:1,r));for(let u=0;u<fO;u++){const o=`border${hO[u]}Radius`;let i=mO(t,o),a=mO(n,o);void 0===i&&void 0===a||(i||(i=0),a||(a=0),0===i||0===a||gO(i)===gO(a)?(e[o]=Math.max(o_(pO(i),pO(a),r),0),(kS.test(a)||kS.test(i))&&(e[o]+="%")):e[o]=a)}(t.rotate||n.rotate)&&(e.rotate=o_(t.rotate||0,n.rotate||0,r))}(a,i,this.latestValues,r,d,u)),this.root.scheduleUpdateProjection(),this.scheduleRender(),this.animationProgress=r},this.mixTargetDelta(0)}startAnimation(e){var t,n;this.notifyListeners("animationStart"),null===(t=this.currentAnimation)||void 0===t||t.stop(),this.resumingFrom&&(null===(n=this.resumingFrom.currentAnimation)||void 0===n||n.stop()),this.pendingAnimation&&(oP.update(this.pendingAnimation),this.pendingAnimation=void 0),this.pendingAnimation=rP.update((()=>{Uk.hasAnimatedSinceResize=!0,this.currentAnimation=function(e,t,n={}){const r=aS(e)?e:SP(e);return mP("",r,t,n),{stop:()=>r.stop(),isAnimating:()=>r.isAnimating()}}(0,1e3,{...e,onUpdate:t=>{var n;this.mixTargetDelta(t),null===(n=e.onUpdate)||void 0===n||n.call(e,t)},onComplete:()=>{var t;null===(t=e.onComplete)||void 0===t||t.call(e),this.completeAnimation()}}),this.resumingFrom&&(this.resumingFrom.currentAnimation=this.currentAnimation),this.pendingAnimation=void 0}))}completeAnimation(){var e;this.resumingFrom&&(this.resumingFrom.currentAnimation=void 0,this.resumingFrom.preserveOpacity=void 0),null===(e=this.getStack())||void 0===e||e.exitAnimationComplete(),this.resumingFrom=this.currentAnimation=this.animationValues=void 0,this.notifyListeners("animationComplete")}finishAnimation(){var e;this.currentAnimation&&(null===(e=this.mixTargetDelta)||void 0===e||e.call(this,1e3),this.currentAnimation.stop()),this.completeAnimation()}applyTransformsToTarget(){const e=this.getLead();let{targetWithTransforms:t,target:n,layout:r,latestValues:o}=e;if(t&&n&&r){if(this!==e&&this.layout&&r&&eM(this.options.animationType,this.layout.layoutBox,r.layoutBox)){n=this.target||{x:{min:0,max:0},y:{min:0,max:0}};const t=QP(this.layout.layoutBox.x);n.x.min=e.target.x.min,n.x.max=n.x.min+t;const r=QP(this.layout.layoutBox.y);n.y.min=e.target.y.min,n.y.max=n.y.min+r}wO(t,n),_L(t,o),tL(this.projectionDeltaWithTransform,this.layoutCorrected,t,o)}}registerSharedNode(e,t){var n,r,o;this.sharedNodes.has(e)||this.sharedNodes.set(e,new TO);this.sharedNodes.get(e).add(t),t.promote({transition:null===(n=t.options.initialPromotionConfig)||void 0===n?void 0:n.transition,preserveFollowOpacity:null===(o=null===(r=t.options.initialPromotionConfig)||void 0===r?void 0:r.shouldPreserveFollowOpacity)||void 0===o?void 0:o.call(r,t)})}isLead(){const e=this.getStack();return!e||e.lead===this}getLead(){var e;const{layoutId:t}=this.options;return t&&(null===(e=this.getStack())||void 0===e?void 0:e.lead)||this}getPrevLead(){var e;const{layoutId:t}=this.options;return t?null===(e=this.getStack())||void 0===e?void 0:e.prevLead:void 0}getStack(){const{layoutId:e}=this.options;if(e)return this.root.sharedNodes.get(e)}promote({needsReset:e,transition:t,preserveFollowOpacity:n}={}){const r=this.getStack();r&&r.promote(this,n),e&&(this.projectionDelta=void 0,this.needsReset=!0),t&&this.setOptions({transition:t})}relegate(){const e=this.getStack();return!!e&&e.relegate(this)}resetRotation(){const{visualElement:e}=this.options;if(!e)return;let t=!1;const{latestValues:n}=e;if((n.rotate||n.rotateX||n.rotateY||n.rotateZ)&&(t=!0),!t)return;const r={};for(let o=0;o<NO.length;o++){const t="rotate"+NO[o];n[t]&&(r[t]=n[t],e.setStaticValue(t,0))}null==e||e.render();for(const o in r)e.setStaticValue(o,r[o]);e.scheduleRender()}getProjectionStyles(e={}){var t,n,r;const o={};if(!this.instance||this.isSVG)return o;if(!this.isVisible)return{visibility:"hidden"};o.visibility="";const i=null===(t=this.options.visualElement)||void 0===t?void 0:t.getProps().transformTemplate;if(this.needsReset)return this.needsReset=!1,o.opacity="",o.pointerEvents=bC(e.pointerEvents)||"",o.transform=i?i(this.latestValues,""):"none",o;const a=this.getLead();if(!this.projectionDelta||!this.layout||!a.target){const t={};return this.options.layoutId&&(t.opacity=void 0!==this.latestValues.opacity?this.latestValues.opacity:1,t.pointerEvents=bC(e.pointerEvents)||""),this.hasProjected&&!pL(this.latestValues)&&(t.transform=i?i({},""):"none",this.hasProjected=!1),t}const s=a.animationValues||a.latestValues;this.applyTransformsToTarget(),o.transform=AO(this.projectionDeltaWithTransform,this.treeScale,s),i&&(o.transform=i(s,o.transform));const{x:l,y:c}=this.projectionDelta;o.transformOrigin=`${100*l.origin}% ${100*c.origin}% 0`,a.animationValues?o.opacity=a===this?null!==(r=null!==(n=s.opacity)&&void 0!==n?n:this.latestValues.opacity)&&void 0!==r?r:1:this.preserveOpacity?this.latestValues.opacity:s.opacityExit:o.opacity=a===this?void 0!==s.opacity?s.opacity:"":void 0!==s.opacityExit?s.opacityExit:0;for(const u 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Xy(e){const{focusBorderColor:t,errorBorderColor:n}=e;return{focusBorderColor:t||Gm("blue.500","blue.300")(e),errorBorderColor:n||Gm("red.500","red.300")(e)}}var Ky=Uy((e=>{const{theme:t}=e,{focusBorderColor:n,errorBorderColor:r}=Xy(e);return{field:{border:"1px solid",borderColor:"inherit",bg:"inherit",_hover:{borderColor:Gm("gray.300","whiteAlpha.400")(e)},_readOnly:{boxShadow:"none !important",userSelect:"all"},_invalid:{borderColor:Hm(t,r),boxShadow:`0 0 0 1px ${Hm(t,r)}`},_focusVisible:{zIndex:1,borderColor:Hm(t,n),boxShadow:`0 0 0 1px ${Hm(t,n)}`}},addon:{border:"1px solid",borderColor:Gm("inherit","whiteAlpha.50")(e),bg:Gm("gray.100","whiteAlpha.300")(e)}}})),Qy=Uy((e=>{const{theme:t}=e,{focusBorderColor:n,errorBorderColor:r}=Xy(e);return{field:{border:"2px solid",borderColor:"transparent",bg:Gm("gray.100","whiteAlpha.50")(e),_hover:{bg:Gm("gray.200","whiteAlpha.100")(e)},_readOnly:{boxShadow:"none 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$P={animation:OE((({visualElement:e,animate:t})=>{e.animationState||(e.animationState=HP(e)),Dk(t)&&a.exports.useEffect((()=>t.subscribe(e)),[t])})),exit:OE((e=>{const{custom:t,visualElement:n}=e,[r,o]=TE(),i=a.exports.useContext(Lk);a.exports.useEffect((()=>{n.isPresent=r;const e=n.animationState&&n.animationState.setActive(CC.Exit,!r,{custom:i&&i.custom||t});e&&!r&&e.then(o)}),[r])}))};class UP{constructor(e,t,{transformPagePoint:n}={}){if(this.startEvent=null,this.lastMoveEvent=null,this.lastMoveEventInfo=null,this.handlers={},this.updatePoint=()=>{if(!this.lastMoveEvent||!this.lastMoveEventInfo)return;const e=YP(this.lastMoveEventInfo,this.history),t=null!==this.startEvent,n=uE(e.offset,{x:0,y:0})>=3;if(!t&&!n)return;const{point:r}=e,{timestamp:o}=cP();this.history.push({...r,timestamp:o});const{onStart:i,onMove:a}=this.handlers;t||(i&&i(this.lastMoveEvent,e),this.startEvent=this.lastMoveEvent),a&&a(this.lastMoveEvent,e)},this.handlePointerMove=(e,t)=>{this.lastMoveEvent=e,this.lastMoveEventInfo=GP(t,this.transformPagePoint),PC(e)&&0===e.buttons?this.handlePointerUp(e,t):rP.update(this.updatePoint,!0)},this.handlePointerUp=(e,t)=>{this.end();const{onEnd:n,onSessionEnd:r}=this.handlers,o=YP(GP(t,this.transformPagePoint),this.history);this.startEvent&&n&&n(e,o),r&&r(e,o)},LC(e)&&e.touches.length>1)return;this.handlers=t,this.transformPagePoint=n;const r=GP(AC(e),this.transformPagePoint),{point:o}=r,{timestamp:i}=cP();this.history=[{...o,timestamp:i}];const{onSessionStart:a}=t;a&&a(e,YP(r,this.history)),this.removeListeners=f_(zC(window,"pointermove",this.handlePointerMove),zC(window,"pointerup",this.handlePointerUp),zC(window,"pointercancel",this.handlePointerUp))}updateHandlers(e){this.handlers=e}end(){this.removeListeners&&this.removeListeners(),oP.update(this.updatePoint)}}function GP(e,t){return t?{point:t(e.point)}:e}function qP(e,t){return{x:e.x-t.x,y:e.y-t.y}}function YP({point:e},t){return{point:e,delta:qP(e,XP(t)),offset:qP(e,ZP(t)),velocity:KP(t,.1)}}function ZP(e){return e[0]}function XP(e){return e[e.length-1]}function KP(e,t){if(e.length<2)return{x:0,y:0};let n=e.length-1,r=null;const o=XP(e);for(;n>=0&&(r=e[n],!(o.timestamp-r.timestamp>RE(t)));)n--;if(!r)return{x:0,y:0};const i=(o.timestamp-r.timestamp)/1e3;if(0===i)return{x:0,y:0};const a={x:(o.x-r.x)/i,y:(o.y-r.y)/i};return 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r=this.getAxisMotionValue(e).get()||0;if(kS.test(r)){const o=null===(n=null===(t=this.visualElement.projection)||void 0===t?void 0:t.layout)||void 0===n?void 0:n.layoutBox[e];if(o){r=QP(o)*(parseFloat(r)/100)}}this.originPoint[e]=r})),null==i||i(e,t),null===(n=this.visualElement.animationState)||void 0===n||n.setActive(CC.Drag,!0))},onMove:(e,t)=>{const{dragPropagation:n,dragDirectionLock:r,onDirectionLock:o,onDrag:i}=this.getProps();if(!n&&!this.openGlobalLock)return;const{offset:a}=t;if(r&&null===this.currentDirection)return this.currentDirection=function(e,t=10){let n=null;Math.abs(e.y)>t?n="y":Math.abs(e.x)>t&&(n="x");return n}(a),void(null!==this.currentDirection&&(null==o||o(this.currentDirection)));this.updateAxis("x",t.point,a),this.updateAxis("y",t.point,a),this.visualElement.render(),null==i||i(e,t)},onSessionEnd:(e,t)=>this.stop(e,t)},{transformPagePoint:this.visualElement.getTransformPagePoint()})}stop(e,t){const n=this.isDragging;if(this.cancel(),!n)return;const{velocity:r}=t;this.startAnimation(r);const{onDragEnd:o}=this.getProps();null==o||o(e,t)}cancel(){var e,t;this.isDragging=!1,this.visualElement.projection&&(this.visualElement.projection.isAnimationBlocked=!1),null===(e=this.panSession)||void 0===e||e.end(),this.panSession=void 0;const{dragPropagation:n}=this.getProps();!n&&this.openGlobalLock&&(this.openGlobalLock(),this.openGlobalLock=null),null===(t=this.visualElement.animationState)||void 0===t||t.setActive(CC.Drag,!1)}updateAxis(e,t,n){const{drag:r}=this.getProps();if(!n||!OL(e,r,this.currentDirection))return;const o=this.getAxisMotionValue(e);let i=this.originPoint[e]+n[e];this.constraints&&this.constraints[e]&&(i=function(e,{min:t,max:n},r){return void 0!==t&&e<t?e=r?o_(t,e,r.min):Math.max(e,t):void 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n=e.current,{projection:r}=this.visualElement;if(!r||!r.layout)return!1;const o=function(e,t,n){const r=EL(e,n),{scroll:o}=t;return o&&(wL(r.x,o.offset.x),wL(r.y,o.offset.y)),r}(n,r.root,this.visualElement.getTransformPagePoint());let i=function(e,t){return{x:aL(e.x,t.x),y:aL(e.y,t.y)}}(r.layout.layoutBox,o);if(t){const e=t(function({x:e,y:t}){return{top:t.min,right:e.max,bottom:t.max,left:e.min}}(i));this.hasMutatedConstraints=!!e,e&&(i=dL(e))}return i}startAnimation(e){const{drag:t,dragMomentum:n,dragElastic:r,dragTransition:o,dragSnapToOrigin:i,onDragTransitionEnd:a}=this.getProps(),s=this.constraints||{},l=uL((a=>{var l;if(!OL(a,t,this.currentDirection))return;let c=null!==(l=null==s?void 0:s[a])&&void 0!==l?l:{};i&&(c={min:0,max:0});const u=r?200:1e6,d=r?40:1e7,h={type:"inertia",velocity:n?e[a]:0,bounceStiffness:u,bounceDamping:d,timeConstant:750,restDelta:1,restSpeed:10,...o,...c};return this.startAxisValueAnimation(a,h)}));return Promise.all(l).then(a)}startAxisValueAnimation(e,t){return mP(e,this.getAxisMotionValue(e),0,t)}stopAnimation(){uL((e=>this.getAxisMotionValue(e).stop()))}getAxisMotionValue(e){var t,n;const r="_drag"+e.toUpperCase(),o=this.visualElement.getProps()[r];return o||this.visualElement.getValue(e,null!==(n=null===(t=this.visualElement.getProps().initial)||void 0===t?void 0:t[e])&&void 0!==n?n:0)}snapToCursor(e){uL((t=>{const{drag:n}=this.getProps();if(!OL(t,n,this.currentDirection))return;const{projection:r}=this.visualElement,o=this.getAxisMotionValue(t);if(r&&r.layout){const{min:n,max:i}=r.layout.layoutBox[t];o.set(e[t]-o_(n,i,.5))}}))}scalePositionWithinConstraints(){var e;if(!this.visualElement.current)return;const{drag:t,dragConstraints:n}=this.getProps(),{projection:r}=this.visualElement;if(!Ik(n)||!r||!this.constraints)return;this.stopAnimation();const o={x:0,y:0};uL((e=>{const t=this.getAxisMotionValue(e);if(t){const n=t.get();o[e]=function(e,t){let n=.5;const r=QP(e),o=QP(t);return 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i=_C(window,"resize",(()=>this.scalePositionWithinConstraints())),a=r.addEventListener("didUpdate",(({delta:e,hasLayoutChanged:t})=>{this.isDragging&&t&&(uL((t=>{const n=this.getAxisMotionValue(t);n&&(this.originPoint[t]+=e[t].translate,n.set(n.get()+e[t].translate))})),this.visualElement.render())}));return()=>{i(),t(),o(),null==a||a()}}getProps(){const e=this.visualElement.getProps(),{drag:t=!1,dragDirectionLock:n=!1,dragPropagation:r=!1,dragConstraints:o=!1,dragElastic:i=sL,dragMomentum:a=!0}=e;return{...e,drag:t,dragDirectionLock:n,dragPropagation:r,dragConstraints:o,dragElastic:i,dragMomentum:a}}}function OL(e,t,n){return!(!0!==t&&t!==e||null!==n&&n!==e)}const ML={pan:OE((function({onPan:e,onPanStart:t,onPanEnd:n,onPanSessionStart:r,visualElement:o}){const 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o=Object.keys(t).filter(NL);let i=[],a=!1;const s=[];if(o.forEach((o=>{const l=e.getValue(o);if(!e.hasValue(o))return;let c=n[o],u=EP(c);const d=t[o];let h;if(vC(d)){const e=d.length,t=null===d[0]?1:0;c=d[t],u=EP(c);for(let n=t;n<e;n++)h?EP(d[n]):h=EP(d[n])}else h=EP(d);if(u!==h)if(zL(u)&&zL(h)){const e=l.get();"string"==typeof e&&l.set(parseFloat(e)),"string"==typeof d?t[o]=parseFloat(d):Array.isArray(d)&&h===SS&&(t[o]=d.map(parseFloat))}else(null==u?void 0:u.transform)&&(null==h?void 0:h.transform)&&(0===c||0===d)?0===c?l.set(h.transform(c)):t[o]=u.transform(d):(a||(i=function(e){const t=[];return WL.forEach((n=>{const r=e.getValue(n);void 0!==r&&(t.push([n,r.get()]),r.set(n.startsWith("scale")?1:0))})),t.length&&e.render(),t}(e),a=!0),s.push(o),r[o]=void 0!==r[o]?r[o]:t[o],DL(l,d))})),s.length){const n=s.indexOf("height")>=0?window.pageYOffset:null,o=((e,t,n)=>{const r=t.measureViewportBox(),o=t.current,i=getComputedStyle(o),{display:a}=i,s={};"none"===a&&t.setStaticValue("display",e.display||"block"),n.forEach((e=>{s[e]=VL[e](r,i)})),t.render();const l=t.measureViewportBox();return n.forEach((n=>{const r=t.getValue(n);DL(r,s[n]),e[n]=VL[n](l,i)})),e})(t,e,s);return i.length&&i.forEach((([t,n])=>{e.getValue(t).set(n)})),e.render(),Ok&&null!==n&&window.scrollTo({top:n}),{target:o,transitionEnd:r}}return{target:t,transitionEnd:r}};function UL(e,t,n,r){return(e=>Object.keys(e).some(NL))(t)?$L(e,t,n,r):{target:t,transitionEnd:r}}const GL=(e,t,n,r)=>{const o=function(e,{...t},n){const r=e.current;if(!(r instanceof Element))return{target:t,transitionEnd:n};n&&(n={...n}),e.values.forEach((e=>{const t=e.get();if(!TL(t))return;const n=IL(t,r);n&&e.set(n)}));for(const o in t){const e=t[o];if(!TL(e))continue;const i=IL(e,r);i&&(t[o]=i,n&&void 0===n[o]&&(n[o]=e))}return{target:t,transitionEnd:n}}(e,t,r);return UL(e,t=o.target,n,r=o.transitionEnd)},qL={current:null},YL={current:!1};const ZL=Object.keys(Vk),XL=ZL.length,KL=["AnimationStart","AnimationComplete","Update","Unmount","BeforeLayoutMeasure","LayoutMeasure","LayoutAnimationStart","LayoutAnimationComplete"];class QL{constructor({parent:e,props:t,reducedMotionConfig:n,visualState:r},o={}){this.current=null,this.children=new Set,this.isVariantNode=!1,this.isControllingVariants=!1,this.shouldReduceMotion=null,this.values=new Map,this.isPresent=!0,this.valueSubscriptions=new Map,this.prevMotionValues={},this.events={},this.propEventSubscriptions={},this.notifyUpdate=()=>this.notify("Update",this.latestValues),this.render=()=>{this.current&&(this.triggerBuild(),this.renderInstance(this.current,this.renderState,this.props.style,this.projection))},this.scheduleRender=()=>rP.render(this.render,!1,!0);const{latestValues:i,renderState:a}=r;this.latestValues=i,this.baseTarget={...i},this.initialValues=t.initial?{...i}:{},this.renderState=a,this.parent=e,this.props=t,this.depth=e?e.depth+1:0,this.reducedMotionConfig=n,this.options=o,this.isControllingVariants=Bk(t),this.isVariantNode=jk(t),this.isVariantNode&&(this.variantChildren=new Set),this.manuallyAnimateOnMount=Boolean(e&&e.current);const{willChange:s,...l}=this.scrapeMotionValuesFromProps(t);for(const c in l){const e=l[c];void 0!==i[c]&&aS(e)&&(e.set(i[c],!1),AP(s)&&s.add(c))}}scrapeMotionValuesFromProps(e){return{}}mount(e){var t;this.current=e,this.projection&&this.projection.mount(e),this.parent&&this.isVariantNode&&!this.isControllingVariants&&(this.removeFromVariantTree=null===(t=this.parent)||void 0===t?void 0:t.addVariantChild(this)),this.values.forEach(((e,t)=>this.bindToMotionValue(t,e))),YL.current||function(){if(YL.current=!0,Ok)if(window.matchMedia){const e=window.matchMedia("(prefers-reduced-motion)"),t=()=>qL.current=e.matches;e.addListener(t),t()}else qL.current=!1}(),this.shouldReduceMotion="never"!==this.reducedMotionConfig&&("always"===this.reducedMotionConfig||qL.current),this.parent&&this.parent.children.add(this),this.setProps(this.props)}unmount(){var e,t,n;null===(e=this.projection)||void 0===e||e.unmount(),oP.update(this.notifyUpdate),oP.render(this.render),this.valueSubscriptions.forEach((e=>e())),null===(t=this.removeFromVariantTree)||void 0===t||t.call(this),null===(n=this.parent)||void 0===n||n.children.delete(this);for(const r in this.events)this.events[r].clear();this.current=null}bindToMotionValue(e,t){const n=oS.has(e),r=t.onChange((t=>{this.latestValues[e]=t,this.props.onUpdate&&rP.update(this.notifyUpdate,!1,!0),n&&this.projection&&(this.projection.isProjectionDirty=!0)})),o=t.onRenderRequest(this.scheduleRender);this.valueSubscriptions.set(e,(()=>{r(),o()}))}sortNodePosition(e){return this.current&&this.sortInstanceNodePosition&&this.type===e.type?this.sortInstanceNodePosition(this.current,e.current):0}loadFeatures(e,t,n,r,o,i){const s=[];for(let l=0;l<XL;l++){const t=ZL[l],{isEnabled:n,Component:r}=Vk[t];n(e)&&r&&s.push(a.exports.createElement(r,{key:t,...e,visualElement:this}))}if(!this.projection&&o){this.projection=new o(r,this.latestValues,this.parent&&this.parent.projection);const{layoutId:t,layout:n,drag:a,dragConstraints:s,layoutScroll:l}=e;this.projection.setOptions({layoutId:t,layout:n,alwaysMeasureLayout:Boolean(a)||s&&Ik(s),visualElement:this,scheduleRender:()=>this.scheduleRender(),animationType:"string"==typeof n?n:"both",initialPromotionConfig:i,layoutScroll:l})}return s}triggerBuild(){this.build(this.renderState,this.latestValues,this.options,this.props)}measureViewportBox(){return this.current?this.measureInstanceViewportBox(this.current,this.props):{x:{min:0,max:0},y:{min:0,max:0}}}getStaticValue(e){return this.latestValues[e]}setStaticValue(e,t){this.latestValues[e]=t}makeTargetAnimatable(e,t=!0){return this.makeTargetAnimatableFromInstance(e,this.props,t)}setProps(e){(e.transformTemplate||this.props.transformTemplate)&&this.scheduleRender(),this.props=e;for(let t=0;t<KL.length;t++){const n=KL[t];this.propEventSubscriptions[n]&&(this.propEventSubscriptions[n](),delete this.propEventSubscriptions[n]);const r=e["on"+n];r&&(this.propEventSubscriptions[n]=this.on(n,r))}this.prevMotionValues=function(e,t,n){const{willChange:r}=t;for(const o in t){const i=t[o],a=n[o];if(aS(i))e.addValue(o,i),AP(r)&&r.add(o);else if(aS(a))e.addValue(o,SP(i)),AP(r)&&r.remove(o);else if(a!==i)if(e.hasValue(o)){const t=e.getValue(o);!t.hasAnimated&&t.set(i)}else{const t=e.getStaticValue(o);e.addValue(o,SP(void 0!==t?t:i))}}for(const o in n)void 0===t[o]&&e.removeValue(o);return t}(this,this.scrapeMotionValuesFromProps(e),this.prevMotionValues)}getProps(){return this.props}getVariant(e){var t;return null===(t=this.props.variants)||void 0===t?void 0:t[e]}getDefaultTransition(){return this.props.transition}getTransformPagePoint(){return this.props.transformPagePoint}getClosestVariantNode(){var e;return this.isVariantNode?this:null===(e=this.parent)||void 0===e?void 0:e.getClosestVariantNode()}getVariantContext(e=!1){var t,n;if(e)return null===(t=this.parent)||void 0===t?void 0:t.getVariantContext();if(!this.isControllingVariants){const e=(null===(n=this.parent)||void 0===n?void 0:n.getVariantContext())||{};return void 0!==this.props.initial&&(e.initial=this.props.initial),e}const r={};for(let o=0;o<eO;o++){const e=JL[o],t=this.props[e];(Nk(t)||!1===t)&&(r[e]=t)}return r}addVariantChild(e){var t;const n=this.getClosestVariantNode();if(n)return null===(t=n.variantChildren)||void 0===t||t.add(e),()=>n.variantChildren.delete(e)}addValue(e,t){this.hasValue(e)&&this.removeValue(e),this.values.set(e,t),this.latestValues[e]=t.get(),this.bindToMotionValue(e,t)}removeValue(e){var t;this.values.delete(e),null===(t=this.valueSubscriptions.get(e))||void 0===t||t(),this.valueSubscriptions.delete(e),delete this.latestValues[e],this.removeValueFromRenderState(e,this.renderState)}hasValue(e){return this.values.has(e)}getValue(e,t){if(this.props.values&&this.props.values[e])return this.props.values[e];let n=this.values.get(e);return void 0===n&&void 0!==t&&(n=SP(t),this.addValue(e,n)),n}readValue(e){return void 0===this.latestValues[e]&&this.current?this.readValueFromInstance(this.current,e,this.options):this.latestValues[e]}setBaseTarget(e,t){this.baseTarget[e]=t}getBaseTarget(e){var t;const{initial:n}=this.props,r="string"==typeof n||"object"==typeof n?null===(t=mC(this.props,n))||void 0===t?void 0:t[e]:void 0;if(n&&void 0!==r)return r;const o=this.getBaseTargetFromProps(this.props,e);return void 0===o||aS(o)?void 0!==this.initialValues[e]&&void 0===r?void 0:this.baseTarget[e]:o}on(e,t){return this.events[e]||(this.events[e]=new wP),this.events[e].add(t)}notify(e,...t){var n;null===(n=this.events[e])||void 0===n||n.notify(...t)}}const JL=["initial",...zP],eO=JL.length;class tO extends QL{sortInstanceNodePosition(e,t){return 2&e.compareDocumentPosition(t)?1:-1}getBaseTargetFromProps(e,t){var n;return null===(n=e.style)||void 0===n?void 0:n[t]}removeValueFromRenderState(e,{vars:t,style:n}){delete t[e],delete n[e]}makeTargetAnimatableFromInstance({transition:e,transitionEnd:t,...n},{transformValues:r},o){let i=function(e,t,n){var r;const o={};for(const i in e){const e=TP(i,t);o[i]=void 0!==e?e:null===(r=n.getValue(i))||void 0===r?void 0:r.get()}return o}(n,e||{},this);if(r&&(t&&(t=r(t)),n&&(n=r(n)),i&&(i=r(i))),o){!function(e,t,n){var r,o;const i=Object.keys(t).filter((t=>!e.hasValue(t))),a=i.length;if(a)for(let s=0;s<a;s++){const a=i[s],l=t[a];let c=null;Array.isArray(l)&&(c=l[0]),null===c&&(c=null!==(o=null!==(r=n[a])&&void 0!==r?r:e.readValue(a))&&void 0!==o?o:t[a]),null!=c&&("string"==typeof c&&(vP(c)||yP(c))?c=parseFloat(c):!LP(c)&&HS.test(l)&&(c=GE(a,l)),e.addValue(a,SP(c)),void 0===n[a]&&(n[a]=c),null!==c&&e.setBaseTarget(a,c))}}(this,n,i);const e=GL(this,n,i,t);t=e.transitionEnd,n=e.target}return{transition:e,transitionEnd:t,...n}}}class nO extends tO{readValueFromInstance(e,t){if(oS.has(t)){const e=UE(t);return e&&e.default||0}{const r=(n=e,window.getComputedStyle(n)),o=(cS(t)?r.getPropertyValue(t):r[t])||0;return"string"==typeof o?o.trim():o}var n}measureInstanceViewportBox(e,{transformPagePoint:t}){return EL(e,t)}build(e,t,n,r){YS(e,t,n,r.transformTemplate)}scrapeMotionValuesFromProps(e){return pC(e)}renderInstance(e,t,n,r){dC(e,t,n,r)}}class rO extends tO{getBaseTargetFromProps(e,t){return e[t]}readValueFromInstance(e,t){var n;return oS.has(t)?(null===(n=UE(t))||void 0===n?void 0:n.default)||0:(t=hC.has(t)?t:uC(t),e.getAttribute(t))}measureInstanceViewportBox(){return{x:{min:0,max:0},y:{min:0,max:0}}}scrapeMotionValuesFromProps(e){return gC(e)}build(e,t,n,r){aC(e,t,n,r.transformTemplate)}renderInstance(e,t,n,r){fC(e,t,0,r)}}const oO=(e,t)=>tS(e)?new rO(t,{enableHardwareAcceleration:!1}):new nO(t,{enableHardwareAcceleration:!0});function iO(e,t){return t.max===t.min?0:e/(t.max-t.min)*100}const aO={correct:(e,t)=>{if(!t.target)return e;if("string"==typeof e){if(!SS.test(e))return e;e=parseFloat(e)}return`${iO(e,t.target.x)}% ${iO(e,t.target.y)}%`}},sO="_$css",lO={correct:(e,{treeScale:t,projectionDelta:n})=>{const r=e,o=e.includes("var("),i=[];o&&(e=e.replace(AL,(e=>(i.push(e),sO))));const a=HS.parse(e);if(a.length>5)return r;const s=HS.createTransformer(e),l="number"!=typeof a[0]?1:0,c=n.x.scale*t.x,u=n.y.scale*t.y;a[0+l]/=c,a[1+l]/=u;const d=o_(c,u,.5);"number"==typeof a[2+l]&&(a[2+l]/=d),"number"==typeof a[3+l]&&(a[3+l]/=d);let h=s(a);if(o){let e=0;h=h.replace(sO,(()=>{const t=i[e];return e++,t}))}return h}};class cO extends H.Component{componentDidMount(){const{visualElement:e,layoutGroup:t,switchLayoutGroup:n,layoutId:r}=this.props,{projection:o}=e;var i;i=uO,Object.assign(nS,i),o&&(t.group&&t.group.add(o),n&&n.register&&r&&n.register(o),o.root.didUpdate(),o.addEventListener("animationComplete",(()=>{this.safeToRemove()})),o.setOptions({...o.options,onExitComplete:()=>this.safeToRemove()})),Uk.hasEverUpdated=!0}getSnapshotBeforeUpdate(e){const{layoutDependency:t,visualElement:n,drag:r,isPresent:o}=this.props,i=n.projection;return i?(i.isPresent=o,r||e.layoutDependency!==t||void 0===t?i.willUpdate():this.safeToRemove(),e.isPresent!==o&&(o?i.promote():i.relegate()||rP.postRender((()=>{var e;(null===(e=i.getStack())||void 0===e?void 0:e.members.length)||this.safeToRemove()}))),null):null}componentDidUpdate(){const{projection:e}=this.props.visualElement;e&&(e.root.didUpdate(),!e.currentAnimation&&e.isLead()&&this.safeToRemove())}componentWillUnmount(){const{visualElement:e,layoutGroup:t,switchLayoutGroup:n}=this.props,{projection:r}=e;r&&(r.scheduleCheckAfterUnmount(),(null==t?void 0:t.group)&&t.group.remove(r),(null==n?void 0:n.deregister)&&n.deregister(r))}safeToRemove(){const{safeToRemove:e}=this.props;null==e||e()}render(){return null}}const uO={borderRadius:{...aO,applyTo:["borderTopLeftRadius","borderTopRightRadius","borderBottomLeftRadius","borderBottomRightRadius"]},borderTopLeftRadius:aO,borderTopRightRadius:aO,borderBottomLeftRadius:aO,borderBottomRightRadius:aO,boxShadow:lO},dO={measureLayout:function(e){const[t,n]=TE(),r=a.exports.useContext(qk);return ld(cO,{...e,layoutGroup:r,switchLayoutGroup:a.exports.useContext(Zk),isPresent:t,safeToRemove:n})}};const hO=["TopLeft","TopRight","BottomLeft","BottomRight"],fO=hO.length,pO=e=>"string"==typeof e?parseFloat(e):e,gO=e=>"number"==typeof e||SS.test(e);function mO(e,t){var n;return null!==(n=e[t])&&void 0!==n?n:e.borderRadius}const vO=bO(0,.5,T_),yO=bO(.5,.95,__);function bO(e,t,n){return r=>r<e?0:r>t?1:n(r_(e,t,r))}function xO(e,t){e.min=t.min,e.max=t.max}function wO(e,t){xO(e.x,t.x),xO(e.y,t.y)}function kO(e,t,n,r,o){return e=vL(e-=t,1/n,r),void 0!==o&&(e=vL(e,1/o,r)),e}function SO(e,t,[n,r,o],i,a){!function(e,t=0,n=1,r=.5,o,i=e,a=e){kS.test(t)&&(t=parseFloat(t),t=o_(a.min,a.max,t/100)-a.min);if("number"!=typeof t)return;let s=o_(i.min,i.max,r);e===i&&(s-=t),e.min=kO(e.min,t,n,s,o),e.max=kO(e.max,t,n,s,o)}(e,t[n],t[r],t[o],t.scale,i,a)}const CO=["x","scaleX","originX"],_O=["y","scaleY","originY"];function EO(e,t,n,r){SO(e.x,t,CO,null==n?void 0:n.x,null==r?void 0:r.x),SO(e.y,t,_O,null==n?void 0:n.y,null==r?void 0:r.y)}function PO(e){return 0===e.translate&&1===e.scale}function LO(e){return PO(e.x)&&PO(e.y)}function OO(e,t){return e.x.min===t.x.min&&e.x.max===t.x.max&&e.y.min===t.y.min&&e.y.max===t.y.max}function MO(e){return QP(e.x)/QP(e.y)}class TO{constructor(){this.members=[]}add(e){bP(this.members,e),e.scheduleRender()}remove(e){if(xP(this.members,e),e===this.prevLead&&(this.prevLead=void 0),e===this.lead){const e=this.members[this.members.length-1];e&&this.promote(e)}}relegate(e){const t=this.members.findIndex((t=>e===t));if(0===t)return!1;let n;for(let r=t;r>=0;r--){const e=this.members[r];if(!1!==e.isPresent){n=e;break}}return!!n&&(this.promote(n),!0)}promote(e,t){var n;const r=this.lead;if(e!==r&&(this.prevLead=r,this.lead=e,e.show(),r)){r.instance&&r.scheduleRender(),e.scheduleRender(),e.resumeFrom=r,t&&(e.resumeFrom.preserveOpacity=!0),r.snapshot&&(e.snapshot=r.snapshot,e.snapshot.latestValues=r.animationValues||r.latestValues),(null===(n=e.root)||void 0===n?void 0:n.isUpdating)&&(e.isLayoutDirty=!0);const{crossfade:o}=e.options;!1===o&&r.hide()}}exitAnimationComplete(){this.members.forEach((e=>{var t,n,r,o,i;null===(n=(t=e.options).onExitComplete)||void 0===n||n.call(t),null===(i=null===(r=e.resumingFrom)||void 0===r?void 0:(o=r.options).onExitComplete)||void 0===i||i.call(o)}))}scheduleRender(){this.members.forEach((e=>{e.instance&&e.scheduleRender(!1)}))}removeLeadSnapshot(){this.lead&&this.lead.snapshot&&(this.lead.snapshot=void 0)}}function AO(e,t,n){let r="";const o=e.x.translate/t.x,i=e.y.translate/t.y;if((o||i)&&(r=`translate3d(${o}px, ${i}px, 0) `),1===t.x&&1===t.y||(r+=`scale(${1/t.x}, ${1/t.y}) `),n){const{rotate:e,rotateX:t,rotateY:o}=n;e&&(r+=`rotate(${e}deg) `),t&&(r+=`rotateX(${t}deg) `),o&&(r+=`rotateY(${o}deg) `)}const a=e.x.scale*t.x,s=e.y.scale*t.y;return 1===a&&1===s||(r+=`scale(${a}, ${s})`),r||"none"}const IO=(e,t)=>e.depth-t.depth;class RO{constructor(){this.children=[],this.isDirty=!1}add(e){bP(this.children,e),this.isDirty=!0}remove(e){xP(this.children,e),this.isDirty=!0}forEach(e){this.isDirty&&this.children.sort(IO),this.isDirty=!1,this.children.forEach(e)}}const NO=["","X","Y","Z"];let DO=0;function zO({attachResizeListener:e,defaultParent:t,measureScroll:n,checkIsScrollRoot:r,resetTransform:o}){return class{constructor(e,n={},r=(null==t?void 0:t())){this.id=DO++,this.animationId=0,this.children=new Set,this.options={},this.isTreeAnimating=!1,this.isAnimationBlocked=!1,this.isLayoutDirty=!1,this.isProjectionDirty=!1,this.updateManuallyBlocked=!1,this.updateBlockedByResize=!1,this.isUpdating=!1,this.isSVG=!1,this.needsReset=!1,this.shouldResetTransform=!1,this.treeScale={x:1,y:1},this.eventHandlers=new Map,this.potentialNodes=new Map,this.checkUpdateFailed=()=>{this.isUpdating&&(this.isUpdating=!1,this.clearAllSnapshots())},this.updateProjection=()=>{this.nodes.forEach($O),this.nodes.forEach(UO)},this.hasProjected=!1,this.isVisible=!0,this.animationProgress=0,this.sharedNodes=new Map,this.elementId=e,this.latestValues=n,this.root=r?r.root||r:this,this.path=r?[...r.path,r]:[],this.parent=r,this.depth=r?r.depth+1:0,e&&this.root.registerPotentialNode(e,this);for(let t=0;t<this.path.length;t++)this.path[t].shouldResetTransform=!0;this.root===this&&(this.nodes=new RO)}addEventListener(e,t){return this.eventHandlers.has(e)||this.eventHandlers.set(e,new wP),this.eventHandlers.get(e).add(t)}notifyListeners(e,...t){const n=this.eventHandlers.get(e);null==n||n.notify(...t)}hasListeners(e){return this.eventHandlers.has(e)}registerPotentialNode(e,t){this.potentialNodes.set(e,t)}mount(t,n=!1){var r;if(this.instance)return;this.isSVG=t instanceof SVGElement&&"svg"!==t.tagName,this.instance=t;const{layoutId:o,layout:i,visualElement:a}=this.options;if(a&&!a.current&&a.mount(t),this.root.nodes.add(this),null===(r=this.parent)||void 0===r||r.children.add(this),this.elementId&&this.root.potentialNodes.delete(this.elementId),n&&(i||o)&&(this.isLayoutDirty=!0),e){let n;const r=()=>this.root.updateBlockedByResize=!1;e(t,(()=>{this.root.updateBlockedByResize=!0,n&&n(),n=uP(r,250),Uk.hasAnimatedSinceResize&&(Uk.hasAnimatedSinceResize=!1,this.nodes.forEach(VO))}))}o&&this.root.registerSharedNode(o,this),!1!==this.options.animate&&a&&(o||i)&&this.addEventListener("didUpdate",(({delta:e,hasLayoutChanged:t,hasRelativeTargetChanged:n,layout:r})=>{var o,i,s,l,c;if(this.isTreeAnimationBlocked())return this.target=void 0,void(this.relativeTarget=void 0);const u=null!==(i=null!==(o=this.options.transition)&&void 0!==o?o:a.getDefaultTransition())&&void 0!==i?i:KO,{onLayoutAnimationStart:d,onLayoutAnimationComplete:h}=a.getProps(),f=!this.targetLayout||!OO(this.targetLayout,r)||n,p=!t&&n;if((null===(s=this.resumeFrom)||void 0===s?void 0:s.instance)||p||t&&(f||!this.currentAnimation)){this.resumeFrom&&(this.resumingFrom=this.resumeFrom,this.resumingFrom.resumingFrom=void 0),this.setAnimationOrigin(e,p);const t={...gP(u,"layout"),onPlay:d,onComplete:h};a.shouldReduceMotion&&(t.delay=0,t.type=!1),this.startAnimation(t)}else t||0!==this.animationProgress||VO(this),this.isLead()&&(null===(c=(l=this.options).onExitComplete)||void 0===c||c.call(l));this.targetLayout=r}))}unmount(){var e,t;this.options.layoutId&&this.willUpdate(),this.root.nodes.remove(this),null===(e=this.getStack())||void 0===e||e.remove(this),null===(t=this.parent)||void 0===t||t.children.delete(this),this.instance=void 0,oP.preRender(this.updateProjection)}blockUpdate(){this.updateManuallyBlocked=!0}unblockUpdate(){this.updateManuallyBlocked=!1}isUpdateBlocked(){return this.updateManuallyBlocked||this.updateBlockedByResize}isTreeAnimationBlocked(){var e;return this.isAnimationBlocked||(null===(e=this.parent)||void 0===e?void 0:e.isTreeAnimationBlocked())||!1}startUpdate(){var e;this.isUpdateBlocked()||(this.isUpdating=!0,null===(e=this.nodes)||void 0===e||e.forEach(GO),this.animationId++)}willUpdate(e=!0){var t,n,r;if(this.root.isUpdateBlocked())return void(null===(n=(t=this.options).onExitComplete)||void 0===n||n.call(t));if(!this.root.isUpdating&&this.root.startUpdate(),this.isLayoutDirty)return;this.isLayoutDirty=!0;for(let s=0;s<this.path.length;s++){const e=this.path[s];e.shouldResetTransform=!0,e.updateScroll("snapshot")}const{layoutId:o,layout:i}=this.options;if(void 0===o&&!i)return;const a=null===(r=this.options.visualElement)||void 0===r?void 0:r.getProps().transformTemplate;this.prevTransformTemplateValue=null==a?void 0:a(this.latestValues,""),this.updateSnapshot(),e&&this.notifyListeners("willUpdate")}didUpdate(){if(this.isUpdateBlocked())return this.unblockUpdate(),this.clearAllSnapshots(),void this.nodes.forEach(HO);this.isUpdating&&(this.isUpdating=!1,this.potentialNodes.size&&(this.potentialNodes.forEach(QO),this.potentialNodes.clear()),this.nodes.forEach(WO),this.nodes.forEach(BO),this.nodes.forEach(jO),this.clearAllSnapshots(),iP.update(),iP.preRender(),iP.render())}clearAllSnapshots(){this.nodes.forEach(FO),this.sharedNodes.forEach(qO)}scheduleUpdateProjection(){rP.preRender(this.updateProjection,!1,!0)}scheduleCheckAfterUnmount(){rP.postRender((()=>{this.isLayoutDirty?this.root.didUpdate():this.root.checkUpdateFailed()}))}updateSnapshot(){!this.snapshot&&this.instance&&(this.snapshot=this.measure())}updateLayout(){var e;if(!this.instance)return;if(this.updateScroll(),!(this.options.alwaysMeasureLayout&&this.isLead()||this.isLayoutDirty))return;if(this.resumeFrom&&!this.resumeFrom.instance)for(let n=0;n<this.path.length;n++){this.path[n].updateScroll()}const t=this.layout;this.layout=this.measure(!1),this.layoutCorrected={x:{min:0,max:0},y:{min:0,max:0}},this.isLayoutDirty=!1,this.projectionDelta=void 0,this.notifyListeners("measure",this.layout.layoutBox),null===(e=this.options.visualElement)||void 0===e||e.notify("LayoutMeasure",this.layout.layoutBox,null==t?void 0:t.layoutBox)}updateScroll(e="measure"){let t=Boolean(this.options.layoutScroll&&this.instance);this.scroll&&this.scroll.animationId===this.root.animationId&&this.scroll.phase===e&&(t=!1),t&&(this.scroll={animationId:this.root.animationId,phase:e,isRoot:r(this.instance),offset:n(this.instance)})}resetTransform(){var e;if(!o)return;const t=this.isLayoutDirty||this.shouldResetTransform,n=this.projectionDelta&&!LO(this.projectionDelta),r=null===(e=this.options.visualElement)||void 0===e?void 0:e.getProps().transformTemplate,i=null==r?void 0:r(this.latestValues,""),a=i!==this.prevTransformTemplateValue;t&&(n||pL(this.latestValues)||a)&&(o(this.instance,i),this.shouldResetTransform=!1,this.scheduleRender())}measure(e=!0){var t;const n=this.measurePageBox();let r=this.removeElementScroll(n);var o;e&&(r=this.removeTransform(r)),JO((o=r).x),JO(o.y);const i=null===(t=this.options.visualElement)||void 0===t?void 0:t.readValue("position"),a="fixed"===i||"sticky"===i?i:"static";return{animationId:this.root.animationId,measuredBox:n,layoutBox:r,latestValues:{},source:this.id,position:a}}measurePageBox(){const{visualElement:e}=this.options;if(!e)return{x:{min:0,max:0},y:{min:0,max:0}};const t=e.measureViewportBox(),{scroll:n}=this.root;return n&&(wL(t.x,n.offset.x),wL(t.y,n.offset.y)),t}removeElementScroll(e){const t={x:{min:0,max:0},y:{min:0,max:0}};wO(t,e);for(let n=0;n<this.path.length;n++){const r=this.path[n],{scroll:o,options:i}=r;if(r!==this.root&&o&&i.layoutScroll){if(o.isRoot){wO(t,e);const{scroll:n}=this.root;n&&(wL(t.x,-n.offset.x),wL(t.y,-n.offset.y))}wL(t.x,o.offset.x),wL(t.y,o.offset.y)}}return t}applyTransform(e,t=!1){const n={x:{min:0,max:0},y:{min:0,max:0}};wO(n,e);for(let r=0;r<this.path.length;r++){const e=this.path[r];!t&&e.options.layoutScroll&&e.scroll&&e!==e.root&&_L(n,{x:-e.scroll.offset.x,y:-e.scroll.offset.y}),pL(e.latestValues)&&_L(n,e.latestValues)}return pL(this.latestValues)&&_L(n,this.latestValues),n}removeTransform(e){var t;const n={x:{min:0,max:0},y:{min:0,max:0}};wO(n,e);for(let r=0;r<this.path.length;r++){const e=this.path[r];if(!e.instance)continue;if(!pL(e.latestValues))continue;fL(e.latestValues)&&e.updateSnapshot();const o={x:{min:0,max:0},y:{min:0,max:0}};wO(o,e.measurePageBox()),EO(n,e.latestValues,null===(t=e.snapshot)||void 0===t?void 0:t.layoutBox,o)}return pL(this.latestValues)&&EO(n,this.latestValues),n}setTargetDelta(e){this.targetDelta=e,this.isProjectionDirty=!0,this.root.scheduleUpdateProjection()}setOptions(e){this.options={...this.options,...e,crossfade:void 0===e.crossfade||e.crossfade}}clearMeasurements(){this.scroll=void 0,this.layout=void 0,this.snapshot=void 0,this.prevTransformTemplateValue=void 0,this.targetDelta=void 0,this.target=void 0,this.isLayoutDirty=!1}resolveTargetDelta(){var e;if(this.isProjectionDirty||(this.isProjectionDirty=this.getLead().isProjectionDirty||Boolean(this.parent&&this.parent.isProjectionDirty)),!this.isProjectionDirty)return;const{layout:t,layoutId:n}=this.options;if(this.layout&&(t||n)){if(!this.targetDelta&&!this.relativeTarget){const e=this.getClosestProjectingParent();e&&e.layout?(this.relativeParent=e,this.relativeTarget={x:{min:0,max:0},y:{min:0,max:0}},this.relativeTargetOrigin={x:{min:0,max:0},y:{min:0,max:0}},oL(this.relativeTargetOrigin,this.layout.layoutBox,e.layout.layoutBox),wO(this.relativeTarget,this.relativeTargetOrigin)):this.relativeParent=this.relativeTarget=void 0}var r,o,i;if(this.relativeTarget||this.targetDelta)if(this.target||(this.target={x:{min:0,max:0},y:{min:0,max:0}},this.targetWithTransforms={x:{min:0,max:0},y:{min:0,max:0}}),this.relativeTarget&&this.relativeTargetOrigin&&(null===(e=this.relativeParent)||void 0===e?void 0:e.target)?(r=this.target,o=this.relativeTarget,i=this.relativeParent.target,nL(r.x,o.x,i.x),nL(r.y,o.y,i.y)):this.targetDelta?(Boolean(this.resumingFrom)?this.target=this.applyTransform(this.layout.layoutBox):wO(this.target,this.layout.layoutBox),xL(this.target,this.targetDelta)):wO(this.target,this.layout.layoutBox),this.attemptToResolveRelativeTarget){this.attemptToResolveRelativeTarget=!1;const e=this.getClosestProjectingParent();e&&Boolean(e.resumingFrom)===Boolean(this.resumingFrom)&&!e.options.layoutScroll&&e.target?(this.relativeParent=e,this.relativeTarget={x:{min:0,max:0},y:{min:0,max:0}},this.relativeTargetOrigin={x:{min:0,max:0},y:{min:0,max:0}},oL(this.relativeTargetOrigin,this.target,e.target),wO(this.relativeTarget,this.relativeTargetOrigin)):this.relativeParent=this.relativeTarget=void 0}}}getClosestProjectingParent(){if(this.parent&&!fL(this.parent.latestValues)&&!gL(this.parent.latestValues))return(this.parent.relativeTarget||this.parent.targetDelta)&&this.parent.layout?this.parent:this.parent.getClosestProjectingParent()}calcProjection(){var e;if(!this.isProjectionDirty)return;this.isProjectionDirty=!1;const{layout:t,layoutId:n}=this.options;if(this.isTreeAnimating=Boolean((null===(e=this.parent)||void 0===e?void 0:e.isTreeAnimating)||this.currentAnimation||this.pendingAnimation),this.isTreeAnimating||(this.targetDelta=this.relativeTarget=void 0),!this.layout||!t&&!n)return;const r=this.getLead();wO(this.layoutCorrected,this.layout.layoutBox),function(e,t,n,r=!1){var o,i;const a=n.length;if(!a)return;let s,l;t.x=t.y=1;for(let c=0;c<a;c++)s=n[c],l=s.projectionDelta,"contents"!==(null===(i=null===(o=s.instance)||void 0===o?void 0:o.style)||void 0===i?void 0:i.display)&&(r&&s.options.layoutScroll&&s.scroll&&s!==s.root&&_L(e,{x:-s.scroll.offset.x,y:-s.scroll.offset.y}),l&&(t.x*=l.x.scale,t.y*=l.y.scale,xL(e,l)),r&&pL(s.latestValues)&&_L(e,s.latestValues))}(this.layoutCorrected,this.treeScale,this.path,Boolean(this.resumingFrom)||this!==r);const{target:o}=r;if(!o)return;this.projectionDelta||(this.projectionDelta={x:{translate:0,scale:1,origin:0,originPoint:0},y:{translate:0,scale:1,origin:0,originPoint:0}},this.projectionDeltaWithTransform={x:{translate:0,scale:1,origin:0,originPoint:0},y:{translate:0,scale:1,origin:0,originPoint:0}});const i=this.treeScale.x,a=this.treeScale.y,s=this.projectionTransform;tL(this.projectionDelta,this.layoutCorrected,o,this.latestValues),this.projectionTransform=AO(this.projectionDelta,this.treeScale),this.projectionTransform===s&&this.treeScale.x===i&&this.treeScale.y===a||(this.hasProjected=!0,this.scheduleRender(),this.notifyListeners("projectionUpdate",o))}hide(){this.isVisible=!1}show(){this.isVisible=!0}scheduleRender(e=!0){var t,n,r;null===(n=(t=this.options).scheduleRender)||void 0===n||n.call(t),e&&(null===(r=this.getStack())||void 0===r||r.scheduleRender()),this.resumingFrom&&!this.resumingFrom.instance&&(this.resumingFrom=void 0)}setAnimationOrigin(e,t=!1){var n,r;const o=this.snapshot,i=(null==o?void 0:o.latestValues)||{},a={...this.latestValues},s={x:{translate:0,scale:1,origin:0,originPoint:0},y:{translate:0,scale:1,origin:0,originPoint:0}};this.relativeTarget=this.relativeTargetOrigin=void 0,this.attemptToResolveRelativeTarget=!t;const l={x:{min:0,max:0},y:{min:0,max:0}},c=(null==o?void 0:o.source)!==(null===(n=this.layout)||void 0===n?void 0:n.source),u=((null===(r=this.getStack())||void 0===r?void 0:r.members.length)||0)<=1,d=Boolean(c&&!u&&!0===this.options.crossfade&&!this.path.some(XO));this.animationProgress=0,this.mixTargetDelta=t=>{var n;const r=t/1e3;YO(s.x,e.x,r),YO(s.y,e.y,r),this.setTargetDelta(s),this.relativeTarget&&this.relativeTargetOrigin&&this.layout&&(null===(n=this.relativeParent)||void 0===n?void 0:n.layout)&&(oL(l,this.layout.layoutBox,this.relativeParent.layout.layoutBox),function(e,t,n,r){ZO(e.x,t.x,n.x,r),ZO(e.y,t.y,n.y,r)}(this.relativeTarget,this.relativeTargetOrigin,l,r)),c&&(this.animationValues=a,function(e,t,n,r,o,i){var a,s,l,c;o?(e.opacity=o_(0,null!==(a=n.opacity)&&void 0!==a?a:1,vO(r)),e.opacityExit=o_(null!==(s=t.opacity)&&void 0!==s?s:1,0,yO(r))):i&&(e.opacity=o_(null!==(l=t.opacity)&&void 0!==l?l:1,null!==(c=n.opacity)&&void 0!==c?c:1,r));for(let u=0;u<fO;u++){const o=`border${hO[u]}Radius`;let i=mO(t,o),a=mO(n,o);void 0===i&&void 0===a||(i||(i=0),a||(a=0),0===i||0===a||gO(i)===gO(a)?(e[o]=Math.max(o_(pO(i),pO(a),r),0),(kS.test(a)||kS.test(i))&&(e[o]+="%")):e[o]=a)}(t.rotate||n.rotate)&&(e.rotate=o_(t.rotate||0,n.rotate||0,r))}(a,i,this.latestValues,r,d,u)),this.root.scheduleUpdateProjection(),this.scheduleRender(),this.animationProgress=r},this.mixTargetDelta(0)}startAnimation(e){var t,n;this.notifyListeners("animationStart"),null===(t=this.currentAnimation)||void 0===t||t.stop(),this.resumingFrom&&(null===(n=this.resumingFrom.currentAnimation)||void 0===n||n.stop()),this.pendingAnimation&&(oP.update(this.pendingAnimation),this.pendingAnimation=void 0),this.pendingAnimation=rP.update((()=>{Uk.hasAnimatedSinceResize=!0,this.currentAnimation=function(e,t,n={}){const r=aS(e)?e:SP(e);return mP("",r,t,n),{stop:()=>r.stop(),isAnimating:()=>r.isAnimating()}}(0,1e3,{...e,onUpdate:t=>{var n;this.mixTargetDelta(t),null===(n=e.onUpdate)||void 0===n||n.call(e,t)},onComplete:()=>{var t;null===(t=e.onComplete)||void 0===t||t.call(e),this.completeAnimation()}}),this.resumingFrom&&(this.resumingFrom.currentAnimation=this.currentAnimation),this.pendingAnimation=void 0}))}completeAnimation(){var e;this.resumingFrom&&(this.resumingFrom.currentAnimation=void 0,this.resumingFrom.preserveOpacity=void 0),null===(e=this.getStack())||void 0===e||e.exitAnimationComplete(),this.resumingFrom=this.currentAnimation=this.animationValues=void 0,this.notifyListeners("animationComplete")}finishAnimation(){var e;this.currentAnimation&&(null===(e=this.mixTargetDelta)||void 0===e||e.call(this,1e3),this.currentAnimation.stop()),this.completeAnimation()}applyTransformsToTarget(){const e=this.getLead();let{targetWithTransforms:t,target:n,layout:r,latestValues:o}=e;if(t&&n&&r){if(this!==e&&this.layout&&r&&eM(this.options.animationType,this.layout.layoutBox,r.layoutBox)){n=this.target||{x:{min:0,max:0},y:{min:0,max:0}};const t=QP(this.layout.layoutBox.x);n.x.min=e.target.x.min,n.x.max=n.x.min+t;const r=QP(this.layout.layoutBox.y);n.y.min=e.target.y.min,n.y.max=n.y.min+r}wO(t,n),_L(t,o),tL(this.projectionDeltaWithTransform,this.layoutCorrected,t,o)}}registerSharedNode(e,t){var n,r,o;this.sharedNodes.has(e)||this.sharedNodes.set(e,new TO);this.sharedNodes.get(e).add(t),t.promote({transition:null===(n=t.options.initialPromotionConfig)||void 0===n?void 0:n.transition,preserveFollowOpacity:null===(o=null===(r=t.options.initialPromotionConfig)||void 0===r?void 0:r.shouldPreserveFollowOpacity)||void 0===o?void 0:o.call(r,t)})}isLead(){const e=this.getStack();return!e||e.lead===this}getLead(){var e;const{layoutId:t}=this.options;return t&&(null===(e=this.getStack())||void 0===e?void 0:e.lead)||this}getPrevLead(){var e;const{layoutId:t}=this.options;return t?null===(e=this.getStack())||void 0===e?void 0:e.prevLead:void 0}getStack(){const{layoutId:e}=this.options;if(e)return this.root.sharedNodes.get(e)}promote({needsReset:e,transition:t,preserveFollowOpacity:n}={}){const r=this.getStack();r&&r.promote(this,n),e&&(this.projectionDelta=void 0,this.needsReset=!0),t&&this.setOptions({transition:t})}relegate(){const e=this.getStack();return!!e&&e.relegate(this)}resetRotation(){const{visualElement:e}=this.options;if(!e)return;let t=!1;const{latestValues:n}=e;if((n.rotate||n.rotateX||n.rotateY||n.rotateZ)&&(t=!0),!t)return;const r={};for(let o=0;o<NO.length;o++){const t="rotate"+NO[o];n[t]&&(r[t]=n[t],e.setStaticValue(t,0))}null==e||e.render();for(const o in r)e.setStaticValue(o,r[o]);e.scheduleRender()}getProjectionStyles(e={}){var t,n,r;const o={};if(!this.instance||this.isSVG)return o;if(!this.isVisible)return{visibility:"hidden"};o.visibility="";const i=null===(t=this.options.visualElement)||void 0===t?void 0:t.getProps().transformTemplate;if(this.needsReset)return this.needsReset=!1,o.opacity="",o.pointerEvents=bC(e.pointerEvents)||"",o.transform=i?i(this.latestValues,""):"none",o;const a=this.getLead();if(!this.projectionDelta||!this.layout||!a.target){const t={};return this.options.layoutId&&(t.opacity=void 0!==this.latestValues.opacity?this.latestValues.opacity:1,t.pointerEvents=bC(e.pointerEvents)||""),this.hasProjected&&!pL(this.latestValues)&&(t.transform=i?i({},""):"none",this.hasProjected=!1),t}const s=a.animationValues||a.latestValues;this.applyTransformsToTarget(),o.transform=AO(this.projectionDeltaWithTransform,this.treeScale,s),i&&(o.transform=i(s,o.transform));const{x:l,y:c}=this.projectionDelta;o.transformOrigin=`${100*l.origin}% ${100*c.origin}% 0`,a.animationValues?o.opacity=a===this?null!==(r=null!==(n=s.opacity)&&void 0!==n?n:this.latestValues.opacity)&&void 0!==r?r:1:this.preserveOpacity?this.latestValues.opacity:s.opacityExit:o.opacity=a===this?void 0!==s.opacity?s.opacity:"":void 0!==s.opacityExit?s.opacityExit:0;for(const u 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a=[e,t];n&&(a=a.concat(i?n.split(i):n)),e.indexOf(".")>-1&&(r=t,t=(a=e.split("."))[1]),this.addNamespaces(t),wG(this.data,a,r),o.silent||this.emit("added",e,t,n,r)}},{key:"addResources",value:function(e,t,n){var r=arguments.length>3&&void 0!==arguments[3]?arguments[3]:{silent:!1};for(var o in n)"string"!=typeof n[o]&&"[object Array]"!==Object.prototype.toString.apply(n[o])||this.addResource(e,t,o,n[o],{silent:!0});r.silent||this.emit("added",e,t,n)}},{key:"addResourceBundle",value:function(e,t,n,r,o){var i=arguments.length>5&&void 0!==arguments[5]?arguments[5]:{silent:!1},a=[e,t];e.indexOf(".")>-1&&(r=n,n=t,t=(a=e.split("."))[1]),this.addNamespaces(t);var s=kG(this.data,a)||{};r?CG(s,n,o):s=TG(TG({},s),n),wG(this.data,a,s),i.silent||this.emit("added",e,t,n)}},{key:"removeResourceBundle",value:function(e,t){this.hasResourceBundle(e,t)&&delete this.data[e][t],this.removeNamespaces(t),this.emit("removed",e,t)}},{key:"hasResourceBundle",value:function(e,t){return void 0!==this.getResource(e,t)}},{key:"getResourceBundle",value:function(e,t){return t||(t=this.options.defaultNS),"v1"===this.options.compatibilityAPI?TG(TG({},{}),this.getResource(e,t)):this.getResource(e,t)}},{key:"getDataByLanguage",value:function(e){return this.data[e]}},{key:"hasLanguageSomeTranslations",value:function(e){var t=this.getDataByLanguage(e);return!!(t&&Object.keys(t)||[]).find((function(e){return t[e]&&Object.keys(t[e]).length>0}))}},{key:"toJSON",value:function(){return this.data}}]),n}(mG),NG={processors:{},addPostProcessor:function(e){this.processors[e.name]=e},handle:function(e,t,n,r,o){var i=this;return e.forEach((function(e){i.processors[e]&&(t=i.processors[e].process(t,n,r,o))})),t}};function DG(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter((function(t){return Object.getOwnPropertyDescriptor(e,t).enumerable}))),n.push.apply(n,r)}return n}function zG(e){for(var t=1;t<arguments.length;t++){var n=null!=arguments[t]?arguments[t]:{};t%2?DG(Object(n),!0).forEach((function(t){lG(e,t,n[t])})):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(n)):DG(Object(n)).forEach((function(t){Object.defineProperty(e,t,Object.getOwnPropertyDescriptor(n,t))}))}return e}function BG(e){var t=function(){if("undefined"==typeof Reflect||!Reflect.construct)return!1;if(Reflect.construct.sham)return!1;if("function"==typeof Proxy)return!0;try{return Boolean.prototype.valueOf.call(Reflect.construct(Boolean,[],(function(){}))),!0}catch(B2){return!1}}();return function(){var n,r=sG(e);if(t){var o=sG(this).constructor;n=Reflect.construct(r,arguments,o)}else n=r.apply(this,arguments);return aG(this,n)}}var jG={},FG=function(e){iG(n,e);var t=BG(n);function n(e){var r,o=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};return JU(this,n),r=t.call(this),LG&&mG.call(rG(r)),bG(["resourceStore","languageUtils","pluralResolver","interpolator","backendConnector","i18nFormat","utils"],e,rG(r)),r.options=o,void 0===r.options.keySeparator&&(r.options.keySeparator="."),r.logger=gG.create("translator"),r}return nG(n,[{key:"changeLanguage",value:function(e){e&&(this.language=e)}},{key:"exists",value:function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{interpolation:{}};if(null==e)return!1;var n=this.resolve(e,t);return n&&void 0!==n.res}},{key:"extractFromKey",value:function(e,t){var n=void 0!==t.nsSeparator?t.nsSeparator:this.options.nsSeparator;void 0===n&&(n=":");var r=void 0!==t.keySeparator?t.keySeparator:this.options.keySeparator,o=t.ns||this.options.defaultNS||[],i=n&&e.indexOf(n)>-1,a=!(this.options.userDefinedKeySeparator||t.keySeparator||this.options.userDefinedNsSeparator||t.nsSeparator||function(e,t,n){t=t||"",n=n||"";var r=OG.filter((function(e){return t.indexOf(e)<0&&n.indexOf(e)<0}));if(0===r.length)return!0;var o=new RegExp("(".concat(r.map((function(e){return"?"===e?"\\?":e})).join("|"),")")),i=!o.test(e);if(!i){var a=e.indexOf(n);a>0&&!o.test(e.substring(0,a))&&(i=!0)}return i}(e,n,r));if(i&&!a){var s=e.match(this.interpolator.nestingRegexp);if(s&&s.length>0)return{key:e,namespaces:o};var l=e.split(n);(n!==r||n===r&&this.options.ns.indexOf(l[0])>-1)&&(o=l.shift()),e=l.join(r)}return"string"==typeof o&&(o=[o]),{key:e,namespaces:o}}},{key:"translate",value:function(e,t,r){var o=this;if("object"!==QU(t)&&this.options.overloadTranslationOptionHandler&&(t=this.options.overloadTranslationOptionHandler(arguments)),t||(t={}),null==e)return"";Array.isArray(e)||(e=[String(e)]);var i=void 0!==t.returnDetails?t.returnDetails:this.options.returnDetails,a=void 0!==t.keySeparator?t.keySeparator:this.options.keySeparator,s=this.extractFromKey(e[e.length-1],t),l=s.key,c=s.namespaces,u=c[c.length-1],d=t.lng||this.language,h=t.appendNamespaceToCIMode||this.options.appendNamespaceToCIMode;if(d&&"cimode"===d.toLowerCase()){if(h){var f=t.nsSeparator||this.options.nsSeparator;return i?(p.res="".concat(u).concat(f).concat(l),p):"".concat(u).concat(f).concat(l)}return i?(p.res=l,p):l}var p=this.resolve(e,t),g=p&&p.res,m=p&&p.usedKey||l,v=p&&p.exactUsedKey||l,y=Object.prototype.toString.apply(g),b=["[object Number]","[object Function]","[object RegExp]"],x=void 0!==t.joinArrays?t.joinArrays:this.options.joinArrays,w=!this.i18nFormat||this.i18nFormat.handleAsObject,k="string"!=typeof g&&"boolean"!=typeof g&&"number"!=typeof g;if(w&&g&&k&&b.indexOf(y)<0&&("string"!=typeof x||"[object Array]"!==y)){if(!t.returnObjects&&!this.options.returnObjects){this.options.returnedObjectHandler||this.logger.warn("accessing an object - but returnObjects options is not enabled!");var S=this.options.returnedObjectHandler?this.options.returnedObjectHandler(m,g,zG(zG({},t),{},{ns:c})):"key '".concat(l," (").concat(this.language,")' returned an object instead of string.");return i?(p.res=S,p):S}if(a){var C="[object Array]"===y,_=C?[]:{},E=C?v:m;for(var P in g)if(Object.prototype.hasOwnProperty.call(g,P)){var L="".concat(E).concat(a).concat(P);_[P]=this.translate(L,zG(zG({},t),{joinArrays:!1,ns:c})),_[P]===L&&(_[P]=g[P])}g=_}}else if(w&&"string"==typeof x&&"[object Array]"===y)(g=g.join(x))&&(g=this.extendTranslation(g,e,t,r));else{var O=!1,M=!1,T=void 0!==t.count&&"string"!=typeof t.count,A=n.hasDefaultValue(t),I=T?this.pluralResolver.getSuffix(d,t.count,t):"",R=t["defaultValue".concat(I)]||t.defaultValue;!this.isValidLookup(g)&&A&&(O=!0,g=R),this.isValidLookup(g)||(M=!0,g=l);var N=t.missingKeyNoValueFallbackToKey||this.options.missingKeyNoValueFallbackToKey,D=N&&M?void 0:g,z=A&&R!==g&&this.options.updateMissing;if(M||O||z){if(this.logger.log(z?"updateKey":"missingKey",d,u,l,z?R:g),a){var B=this.resolve(l,zG(zG({},t),{},{keySeparator:!1}));B&&B.res&&this.logger.warn("Seems the loaded translations were in flat JSON format instead of nested. Either set keySeparator: false on init or make sure your translations are published in nested format.")}var j=[],F=this.languageUtils.getFallbackCodes(this.options.fallbackLng,t.lng||this.language);if("fallback"===this.options.saveMissingTo&&F&&F[0])for(var H=0;H<F.length;H++)j.push(F[H]);else"all"===this.options.saveMissingTo?j=this.languageUtils.toResolveHierarchy(t.lng||this.language):j.push(t.lng||this.language);var W=function(e,n,r){var i=A&&r!==g?r:D;o.options.missingKeyHandler?o.options.missingKeyHandler(e,u,n,i,z,t):o.backendConnector&&o.backendConnector.saveMissing&&o.backendConnector.saveMissing(e,u,n,i,z,t),o.emit("missingKey",e,u,n,g)};this.options.saveMissing&&(this.options.saveMissingPlurals&&T?j.forEach((function(e){o.pluralResolver.getSuffixes(e,t).forEach((function(n){W([e],l+n,t["defaultValue".concat(n)]||R)}))})):W(j,l,R))}g=this.extendTranslation(g,e,t,p,r),M&&g===l&&this.options.appendNamespaceToMissingKey&&(g="".concat(u,":").concat(l)),(M||O)&&this.options.parseMissingKeyHandler&&(g="v1"!==this.options.compatibilityAPI?this.options.parseMissingKeyHandler(this.options.appendNamespaceToMissingKey?"".concat(u,":").concat(l):l,O?g:void 0):this.options.parseMissingKeyHandler(g))}return i?(p.res=g,p):g}},{key:"extendTranslation",value:function(e,t,n,r,o){var i=this;if(this.i18nFormat&&this.i18nFormat.parse)e=this.i18nFormat.parse(e,zG(zG({},this.options.interpolation.defaultVariables),n),r.usedLng,r.usedNS,r.usedKey,{resolved:r});else if(!n.skipInterpolation){n.interpolation&&this.interpolator.init(zG(zG({},n),{interpolation:zG(zG({},this.options.interpolation),n.interpolation)}));var a,s="string"==typeof e&&(n&&n.interpolation&&void 0!==n.interpolation.skipOnVariables?n.interpolation.skipOnVariables:this.options.interpolation.skipOnVariables);if(s){var l=e.match(this.interpolator.nestingRegexp);a=l&&l.length}var c=n.replace&&"string"!=typeof n.replace?n.replace:n;if(this.options.interpolation.defaultVariables&&(c=zG(zG({},this.options.interpolation.defaultVariables),c)),e=this.interpolator.interpolate(e,c,n.lng||this.language,n),s){var u=e.match(this.interpolator.nestingRegexp);a<(u&&u.length)&&(n.nest=!1)}!1!==n.nest&&(e=this.interpolator.nest(e,(function(){for(var e=arguments.length,r=new Array(e),a=0;a<e;a++)r[a]=arguments[a];return o&&o[0]===r[0]&&!n.context?(i.logger.warn("It seems you are nesting recursively key: ".concat(r[0]," in key: ").concat(t[0])),null):i.translate.apply(i,r.concat([t]))}),n)),n.interpolation&&this.interpolator.reset()}var d=n.postProcess||this.options.postProcess,h="string"==typeof d?[d]:d;return null!=e&&h&&h.length&&!1!==n.applyPostProcessor&&(e=NG.handle(h,e,t,this.options&&this.options.postProcessPassResolved?zG({i18nResolved:r},n):n,this)),e}},{key:"resolve",value:function(e){var t,n,r,o,i,a=this,s=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};return"string"==typeof e&&(e=[e]),e.forEach((function(e){if(!a.isValidLookup(t)){var l=a.extractFromKey(e,s),c=l.key;n=c;var u=l.namespaces;a.options.fallbackNS&&(u=u.concat(a.options.fallbackNS));var d=void 0!==s.count&&"string"!=typeof s.count,h=d&&!s.ordinal&&0===s.count&&a.pluralResolver.shouldUseIntlApi(),f=void 0!==s.context&&("string"==typeof s.context||"number"==typeof s.context)&&""!==s.context,p=s.lngs?s.lngs:a.languageUtils.toResolveHierarchy(s.lng||a.language,s.fallbackLng);u.forEach((function(e){a.isValidLookup(t)||(i=e,!jG["".concat(p[0],"-").concat(e)]&&a.utils&&a.utils.hasLoadedNamespace&&!a.utils.hasLoadedNamespace(i)&&(jG["".concat(p[0],"-").concat(e)]=!0,a.logger.warn('key "'.concat(n,'" for languages "').concat(p.join(", "),'" won\'t get resolved as namespace "').concat(i,'" was not yet loaded'),"This means something IS WRONG in your setup. You access the t function before i18next.init / i18next.loadNamespace / i18next.changeLanguage was done. Wait for the callback or Promise to resolve before accessing it!!!")),p.forEach((function(n){if(!a.isValidLookup(t)){o=n;var i,l=[c];if(a.i18nFormat&&a.i18nFormat.addLookupKeys)a.i18nFormat.addLookupKeys(l,c,n,e,s);else{var u;d&&(u=a.pluralResolver.getSuffix(n,s.count,s));var p="".concat(a.options.pluralSeparator,"zero");if(d&&(l.push(c+u),h&&l.push(c+p)),f){var g="".concat(c).concat(a.options.contextSeparator).concat(s.context);l.push(g),d&&(l.push(g+u),h&&l.push(g+p))}}for(;i=l.pop();)a.isValidLookup(t)||(r=i,t=a.getResource(n,e,i,s))}})))}))}})),{res:t,usedKey:n,exactUsedKey:r,usedLng:o,usedNS:i}}},{key:"isValidLookup",value:function(e){return!(void 0===e||!this.options.returnNull&&null===e||!this.options.returnEmptyString&&""===e)}},{key:"getResource",value:function(e,t,n){var r=arguments.length>3&&void 0!==arguments[3]?arguments[3]:{};return this.i18nFormat&&this.i18nFormat.getResource?this.i18nFormat.getResource(e,t,n,r):this.resourceStore.getResource(e,t,n,r)}}],[{key:"hasDefaultValue",value:function(e){var t="defaultValue";for(var n in e)if(Object.prototype.hasOwnProperty.call(e,n)&&t===n.substring(0,t.length)&&void 0!==e[n])return!0;return!1}}]),n}(mG);function HG(e){return e.charAt(0).toUpperCase()+e.slice(1)}var WG=function(){function e(t){JU(this,e),this.options=t,this.supportedLngs=this.options.supportedLngs||!1,this.logger=gG.create("languageUtils")}return nG(e,[{key:"getScriptPartFromCode",value:function(e){if(!e||e.indexOf("-")<0)return null;var t=e.split("-");return 2===t.length?null:(t.pop(),"x"===t[t.length-1].toLowerCase()?null:this.formatLanguageCode(t.join("-")))}},{key:"getLanguagePartFromCode",value:function(e){if(!e||e.indexOf("-")<0)return e;var t=e.split("-");return this.formatLanguageCode(t[0])}},{key:"formatLanguageCode",value:function(e){if("string"==typeof e&&e.indexOf("-")>-1){var t=["hans","hant","latn","cyrl","cans","mong","arab"],n=e.split("-");return this.options.lowerCaseLng?n=n.map((function(e){return e.toLowerCase()})):2===n.length?(n[0]=n[0].toLowerCase(),n[1]=n[1].toUpperCase(),t.indexOf(n[1].toLowerCase())>-1&&(n[1]=HG(n[1].toLowerCase()))):3===n.length&&(n[0]=n[0].toLowerCase(),2===n[1].length&&(n[1]=n[1].toUpperCase()),"sgn"!==n[0]&&2===n[2].length&&(n[2]=n[2].toUpperCase()),t.indexOf(n[1].toLowerCase())>-1&&(n[1]=HG(n[1].toLowerCase())),t.indexOf(n[2].toLowerCase())>-1&&(n[2]=HG(n[2].toLowerCase()))),n.join("-")}return this.options.cleanCode||this.options.lowerCaseLng?e.toLowerCase():e}},{key:"isSupportedCode",value:function(e){return("languageOnly"===this.options.load||this.options.nonExplicitSupportedLngs)&&(e=this.getLanguagePartFromCode(e)),!this.supportedLngs||!this.supportedLngs.length||this.supportedLngs.indexOf(e)>-1}},{key:"getBestMatchFromCodes",value:function(e){var t,n=this;return e?(e.forEach((function(e){if(!t){var r=n.formatLanguageCode(e);n.options.supportedLngs&&!n.isSupportedCode(r)||(t=r)}})),!t&&this.options.supportedLngs&&e.forEach((function(e){if(!t){var r=n.getLanguagePartFromCode(e);if(n.isSupportedCode(r))return t=r;t=n.options.supportedLngs.find((function(e){if(0===e.indexOf(r))return e}))}})),t||(t=this.getFallbackCodes(this.options.fallbackLng)[0]),t):null}},{key:"getFallbackCodes",value:function(e,t){if(!e)return[];if("function"==typeof e&&(e=e(t)),"string"==typeof e&&(e=[e]),"[object Array]"===Object.prototype.toString.apply(e))return e;if(!t)return e.default||[];var n=e[t];return n||(n=e[this.getScriptPartFromCode(t)]),n||(n=e[this.formatLanguageCode(t)]),n||(n=e[this.getLanguagePartFromCode(t)]),n||(n=e.default),n||[]}},{key:"toResolveHierarchy",value:function(e,t){var n=this,r=this.getFallbackCodes(t||this.options.fallbackLng||[],e),o=[],i=function(e){e&&(n.isSupportedCode(e)?o.push(e):n.logger.warn("rejecting language code not found in supportedLngs: ".concat(e)))};return"string"==typeof e&&e.indexOf("-")>-1?("languageOnly"!==this.options.load&&i(this.formatLanguageCode(e)),"languageOnly"!==this.options.load&&"currentOnly"!==this.options.load&&i(this.getScriptPartFromCode(e)),"currentOnly"!==this.options.load&&i(this.getLanguagePartFromCode(e))):"string"==typeof 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aq(){return{debug:!1,initImmediate:!0,ns:["translation"],defaultNS:["translation"],fallbackLng:["dev"],fallbackNS:!1,supportedLngs:!1,nonExplicitSupportedLngs:!1,load:"all",preload:!1,simplifyPluralSuffix:!0,keySeparator:".",nsSeparator:":",pluralSeparator:"_",contextSeparator:"_",partialBundledLanguages:!1,saveMissing:!1,updateMissing:!1,saveMissingTo:"fallback",saveMissingPlurals:!0,missingKeyHandler:!1,missingInterpolationHandler:!1,postProcess:!1,postProcessPassResolved:!1,returnNull:!0,returnEmptyString:!0,returnObjects:!1,joinArrays:!1,returnedObjectHandler:!1,parseMissingKeyHandler:!1,appendNamespaceToMissingKey:!1,appendNamespaceToCIMode:!1,overloadTranslationOptionHandler:function(e){var t={};if("object"===QU(e[1])&&(t=e[1]),"string"==typeof e[1]&&(t.defaultValue=e[1]),"string"==typeof e[2]&&(t.tDescription=e[2]),"object"===QU(e[2])||"object"===QU(e[3])){var n=e[3]||e[2];Object.keys(n).forEach((function(e){t[e]=n[e]}))}return t},interpolation:{escapeValue:!0,format:function(e,t,n,r){return e},prefix:"{{",suffix:"}}",formatSeparator:",",unescapePrefix:"-",nestingPrefix:"$t(",nestingSuffix:")",nestingOptionsSeparator:",",maxReplaces:1e3,skipOnVariables:!0}}}function sq(e){return"string"==typeof e.ns&&(e.ns=[e.ns]),"string"==typeof e.fallbackLng&&(e.fallbackLng=[e.fallbackLng]),"string"==typeof e.fallbackNS&&(e.fallbackNS=[e.fallbackNS]),e.supportedLngs&&e.supportedLngs.indexOf("cimode")<0&&(e.supportedLngs=e.supportedLngs.concat(["cimode"])),e}function lq(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter((function(t){return Object.getOwnPropertyDescriptor(e,t).enumerable}))),n.push.apply(n,r)}return n}function cq(e){for(var t=1;t<arguments.length;t++){var n=null!=arguments[t]?arguments[t]:{};t%2?lq(Object(n),!0).forEach((function(t){lG(e,t,n[t])})):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(n)):lq(Object(n)).forEach((function(t){Object.defineProperty(e,t,Object.getOwnPropertyDescriptor(n,t))}))}return e}function uq(e){var t=function(){if("undefined"==typeof Reflect||!Reflect.construct)return!1;if(Reflect.construct.sham)return!1;if("function"==typeof Proxy)return!0;try{return Boolean.prototype.valueOf.call(Reflect.construct(Boolean,[],(function(){}))),!0}catch(B2){return!1}}();return function(){var n,r=sG(e);if(t){var o=sG(this).constructor;n=Reflect.construct(r,arguments,o)}else n=r.apply(this,arguments);return aG(this,n)}}function dq(){}function hq(e){Object.getOwnPropertyNames(Object.getPrototypeOf(e)).forEach((function(t){"function"==typeof e[t]&&(e[t]=e[t].bind(e))}))}var fq=function(e){iG(n,e);var t=uq(n);function n(){var e,r=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{},o=arguments.length>1?arguments[1]:void 0;if(JU(this,n),e=t.call(this),LG&&mG.call(rG(e)),e.options=sq(r),e.services={},e.logger=gG,e.modules={external:[]},hq(rG(e)),o&&!e.isInitialized&&!r.isClone){if(!e.options.initImmediate)return e.init(r,o),aG(e,rG(e));setTimeout((function(){e.init(r,o)}),0)}return e}return nG(n,[{key:"init",value:function(){var e=this,t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{},n=arguments.length>1?arguments[1]:void 0;"function"==typeof t&&(n=t,t={}),!t.defaultNS&&!1!==t.defaultNS&&t.ns&&("string"==typeof t.ns?t.defaultNS=t.ns:t.ns.indexOf("translation")<0&&(t.defaultNS=t.ns[0]));var r=aq();function o(e){return e?"function"==typeof e?new e:e:null}if(this.options=cq(cq(cq({},r),this.options),sq(t)),"v1"!==this.options.compatibilityAPI&&(this.options.interpolation=cq(cq({},r.interpolation),this.options.interpolation)),void 0!==t.keySeparator&&(this.options.userDefinedKeySeparator=t.keySeparator),void 0!==t.nsSeparator&&(this.options.userDefinedNsSeparator=t.nsSeparator),!this.options.isClone){var i;this.modules.logger?gG.init(o(this.modules.logger),this.options):gG.init(null,this.options),this.modules.formatter?i=this.modules.formatter:"undefined"!=typeof Intl&&(i=tq);var a=new WG(this.options);this.store=new RG(this.options.resources,this.options);var s=this.services;s.logger=gG,s.resourceStore=this.store,s.languageUtils=a,s.pluralResolver=new YG(a,{prepend:this.options.pluralSeparator,compatibilityJSON:this.options.compatibilityJSON,simplifyPluralSuffix:this.options.simplifyPluralSuffix}),!i||this.options.interpolation.format&&this.options.interpolation.format!==r.interpolation.format||(s.formatter=o(i),s.formatter.init(s,this.options),this.options.interpolation.format=s.formatter.format.bind(s.formatter)),s.interpolator=new KG(this.options),s.utils={hasLoadedNamespace:this.hasLoadedNamespace.bind(this)},s.backendConnector=new iq(o(this.modules.backend),s.resourceStore,s,this.options),s.backendConnector.on("*",(function(t){for(var n=arguments.length,r=new Array(n>1?n-1:0),o=1;o<n;o++)r[o-1]=arguments[o];e.emit.apply(e,[t].concat(r))})),this.modules.languageDetector&&(s.languageDetector=o(this.modules.languageDetector),s.languageDetector.init&&s.languageDetector.init(s,this.options.detection,this.options)),this.modules.i18nFormat&&(s.i18nFormat=o(this.modules.i18nFormat),s.i18nFormat.init&&s.i18nFormat.init(this)),this.translator=new FG(this.services,this.options),this.translator.on("*",(function(t){for(var n=arguments.length,r=new Array(n>1?n-1:0),o=1;o<n;o++)r[o-1]=arguments[o];e.emit.apply(e,[t].concat(r))})),this.modules.external.forEach((function(t){t.init&&t.init(e)}))}if(this.format=this.options.interpolation.format,n||(n=dq),this.options.fallbackLng&&!this.services.languageDetector&&!this.options.lng){var l=this.services.languageUtils.getFallbackCodes(this.options.fallbackLng);l.length>0&&"dev"!==l[0]&&(this.options.lng=l[0])}this.services.languageDetector||this.options.lng||this.logger.warn("init: no languageDetector is used and no lng is defined");var c=["getResource","hasResourceBundle","getResourceBundle","getDataByLanguage"];c.forEach((function(t){e[t]=function(){var n;return(n=e.store)[t].apply(n,arguments)}}));var u=["addResource","addResources","addResourceBundle","removeResourceBundle"];u.forEach((function(t){e[t]=function(){var n;return(n=e.store)[t].apply(n,arguments),e}}));var d=vG(),h=function(){var t=function(t,r){e.isInitialized&&!e.initializedStoreOnce&&e.logger.warn("init: i18next is already initialized. 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Please check the object you are passing to i18next.use()");if(!e.type)throw new Error("You are passing a wrong module! Please check the object you are passing to i18next.use()");return"backend"===e.type&&(this.modules.backend=e),("logger"===e.type||e.log&&e.warn&&e.error)&&(this.modules.logger=e),"languageDetector"===e.type&&(this.modules.languageDetector=e),"i18nFormat"===e.type&&(this.modules.i18nFormat=e),"postProcessor"===e.type&&NG.addPostProcessor(e),"formatter"===e.type&&(this.modules.formatter=e),"3rdParty"===e.type&&this.modules.external.push(e),this}},{key:"setResolvedLanguage",value:function(e){if(e&&this.languages&&!(["cimode","dev"].indexOf(e)>-1))for(var t=0;t<this.languages.length;t++){var n=this.languages[t];if(!(["cimode","dev"].indexOf(n)>-1)&&this.store.hasLanguageSomeTranslations(n)){this.resolvedLanguage=n;break}}}},{key:"changeLanguage",value:function(e,t){var n=this;this.isLanguageChangingTo=e;var r=vG();this.emit("languageChanging",e);var o=function(e){n.language=e,n.languages=n.services.languageUtils.toResolveHierarchy(e),n.resolvedLanguage=void 0,n.setResolvedLanguage(e)},i=function(i){e||i||!n.services.languageDetector||(i=[]);var a="string"==typeof i?i:n.services.languageUtils.getBestMatchFromCodes(i);a&&(n.language||o(a),n.translator.language||n.translator.changeLanguage(a),n.services.languageDetector&&n.services.languageDetector.cacheUserLanguage&&n.services.languageDetector.cacheUserLanguage(a)),n.loadResources(a,(function(e){!function(e,i){i?(o(i),n.translator.changeLanguage(i),n.isLanguageChangingTo=void 0,n.emit("languageChanged",i),n.logger.log("languageChanged",i)):n.isLanguageChangingTo=void 0,r.resolve((function(){return n.t.apply(n,arguments)})),t&&t(e,(function(){return n.t.apply(n,arguments)}))}(e,a)}))};return e||!this.services.languageDetector||this.services.languageDetector.async?!e&&this.services.languageDetector&&this.services.languageDetector.async?0===this.services.languageDetector.detect.length?this.services.languageDetector.detect().then(i):this.services.languageDetector.detect(i):i(e):i(this.services.languageDetector.detect()),r}},{key:"getFixedT",value:function(e,t,n){var r=this,o=function e(t,o){var i;if("object"!==QU(o)){for(var a=arguments.length,s=new Array(a>2?a-2:0),l=2;l<a;l++)s[l-2]=arguments[l];i=r.options.overloadTranslationOptionHandler([t,o].concat(s))}else i=cq({},o);i.lng=i.lng||e.lng,i.lngs=i.lngs||e.lngs,i.ns=i.ns||e.ns,i.keyPrefix=i.keyPrefix||n||e.keyPrefix;var c=r.options.keySeparator||".",u=i.keyPrefix?"".concat(i.keyPrefix).concat(c).concat(t):t;return r.t(u,i)};return"string"==typeof e?o.lng=e:o.lngs=e,o.ns=t,o.keyPrefix=n,o}},{key:"t",value:function(){var e;return this.translator&&(e=this.translator).translate.apply(e,arguments)}},{key:"exists",value:function(){var e;return this.translator&&(e=this.translator).exists.apply(e,arguments)}},{key:"setDefaultNamespace",value:function(e){this.options.defaultNS=e}},{key:"hasLoadedNamespace",value:function(e){var t=this,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};if(!this.isInitialized)return this.logger.warn("hasLoadedNamespace: i18next was not initialized",this.languages),!1;if(!this.languages||!this.languages.length)return this.logger.warn("hasLoadedNamespace: i18n.languages were undefined or empty",this.languages),!1;var r=this.resolvedLanguage||this.languages[0],o=!!this.options&&this.options.fallbackLng,i=this.languages[this.languages.length-1];if("cimode"===r.toLowerCase())return!0;var a=function(e,n){var r=t.services.backendConnector.state["".concat(e,"|").concat(n)];return-1===r||2===r};if(n.precheck){var s=n.precheck(this,a);if(void 0!==s)return s}return!!this.hasResourceBundle(r,e)||(!(this.services.backendConnector.backend&&(!this.options.resources||this.options.partialBundledLanguages))||!(!a(r,e)||o&&!a(i,e)))}},{key:"loadNamespaces",value:function(e,t){var n=this,r=vG();return this.options.ns?("string"==typeof e&&(e=[e]),e.forEach((function(e){n.options.ns.indexOf(e)<0&&n.options.ns.push(e)})),this.loadResources((function(e){r.resolve(),t&&t(e)})),r):(t&&t(),Promise.resolve())}},{key:"loadLanguages",value:function(e,t){var n=vG();"string"==typeof e&&(e=[e]);var r=this.options.preload||[],o=e.filter((function(e){return r.indexOf(e)<0}));return o.length?(this.options.preload=r.concat(o),this.loadResources((function(e){n.resolve(),t&&t(e)})),n):(t&&t(),Promise.resolve())}},{key:"dir",value:function(e){if(e||(e=this.resolvedLanguage||(this.languages&&this.languages.length>0?this.languages[0]:this.language)),!e)return"rtl";var t=this.services&&this.services.languageUtils||new WG(aq());return["ar","shu","sqr","ssh","xaa","yhd","yud","aao","abh","abv","acm","acq","acw","acx","acy","adf","ads","aeb","aec","afb","ajp","apc","apd","arb","arq","ars","ary","arz","auz","avl","ayh","ayl","ayn","ayp","bbz","pga","he","iw","ps","pbt","pbu","pst","prp","prd","ug","ur","ydd","yds","yih","ji","yi","hbo","men","xmn","fa","jpr","peo","pes","prs","dv","sam","ckb"].indexOf(t.getLanguagePartFromCode(e))>-1||e.toLowerCase().indexOf("-arab")>1?"rtl":"ltr"}},{key:"cloneInstance",value:function(){var e=this,t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{},r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:dq,o=cq(cq(cq({},this.options),t),{isClone:!0}),i=new n(o);void 0===t.debug&&void 0===t.prefix||(i.logger=i.logger.clone(t));var a=["store","services","language"];return a.forEach((function(t){i[t]=e[t]})),i.services=cq({},this.services),i.services.utils={hasLoadedNamespace:i.hasLoadedNamespace.bind(i)},i.translator=new FG(i.services,i.options),i.translator.on("*",(function(e){for(var t=arguments.length,n=new Array(t>1?t-1:0),r=1;r<t;r++)n[r-1]=arguments[r];i.emit.apply(i,[e].concat(n))})),i.init(o,r),i.translator.options=i.options,i.translator.backendConnector.services.utils={hasLoadedNamespace:i.hasLoadedNamespace.bind(i)},i}},{key:"toJSON",value:function(){return{options:this.options,store:this.store,language:this.language,languages:this.languages,resolvedLanguage:this.resolvedLanguage}}}]),n}(mG);lG(fq,"createInstance",(function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{},t=arguments.length>1?arguments[1]:void 0;return new fq(e,t)}));var pq=fq.createInstance();function gq(e,t){if(!(e instanceof t))throw new TypeError("Cannot call a class as a function")}function mq(e){return mq="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(e){return typeof e}:function(e){return e&&"function"==typeof Symbol&&e.constructor===Symbol&&e!==Symbol.prototype?"symbol":typeof e},mq(e)}function vq(e){var t=function(e,t){if("object"!==mq(e)||null===e)return e;var n=e[Symbol.toPrimitive];if(void 0!==n){var r=n.call(e,t||"default");if("object"!==mq(r))return r;throw new TypeError("@@toPrimitive must return a primitive value.")}return("string"===t?String:Number)(e)}(e,"string");return"symbol"===mq(t)?t:String(t)}function yq(e,t){for(var n=0;n<t.length;n++){var r=t[n];r.enumerable=r.enumerable||!1,r.configurable=!0,"value"in r&&(r.writable=!0),Object.defineProperty(e,vq(r.key),r)}}pq.createInstance=fq.createInstance,pq.createInstance,pq.dir,pq.init,pq.loadResources,pq.reloadResources,pq.use,pq.changeLanguage,pq.getFixedT,pq.t,pq.exists,pq.setDefaultNamespace,pq.hasLoadedNamespace,pq.loadNamespaces,pq.loadLanguages;var bq=[],xq=bq.forEach,wq=bq.slice;function kq(e){return xq.call(wq.call(arguments,1),(function(t){if(t)for(var n in t)void 0===e[n]&&(e[n]=t[n])})),e}var Sq=/^[\u0009\u0020-\u007e\u0080-\u00ff]+$/,Cq=function(e,t,n){var r=n||{};r.path=r.path||"/";var o=encodeURIComponent(t),i="".concat(e,"=").concat(o);if(r.maxAge>0){var a=r.maxAge-0;if(Number.isNaN(a))throw new Error("maxAge should be a Number");i+="; Max-Age=".concat(Math.floor(a))}if(r.domain){if(!Sq.test(r.domain))throw new TypeError("option domain is invalid");i+="; Domain=".concat(r.domain)}if(r.path){if(!Sq.test(r.path))throw new TypeError("option path is invalid");i+="; Path=".concat(r.path)}if(r.expires){if("function"!=typeof r.expires.toUTCString)throw new TypeError("option expires is invalid");i+="; Expires=".concat(r.expires.toUTCString())}if(r.httpOnly&&(i+="; HttpOnly"),r.secure&&(i+="; Secure"),r.sameSite)switch("string"==typeof r.sameSite?r.sameSite.toLowerCase():r.sameSite){case!0:i+="; SameSite=Strict";break;case"lax":i+="; SameSite=Lax";break;case"strict":i+="; SameSite=Strict";break;case"none":i+="; SameSite=None";break;default:throw new TypeError("option sameSite is invalid")}return i},_q=function(e,t,n,r){var o=arguments.length>4&&void 0!==arguments[4]?arguments[4]:{path:"/",sameSite:"strict"};n&&(o.expires=new Date,o.expires.setTime(o.expires.getTime()+60*n*1e3)),r&&(o.domain=r),document.cookie=Cq(e,encodeURIComponent(t),o)},Eq=function(e){for(var t="".concat(e,"="),n=document.cookie.split(";"),r=0;r<n.length;r++){for(var o=n[r];" "===o.charAt(0);)o=o.substring(1,o.length);if(0===o.indexOf(t))return o.substring(t.length,o.length)}return null},Pq={name:"cookie",lookup:function(e){var t;if(e.lookupCookie&&"undefined"!=typeof document){var n=Eq(e.lookupCookie);n&&(t=n)}return t},cacheUserLanguage:function(e,t){t.lookupCookie&&"undefined"!=typeof document&&_q(t.lookupCookie,e,t.cookieMinutes,t.cookieDomain,t.cookieOptions)}},Lq={name:"querystring",lookup:function(e){var t;if("undefined"!=typeof window){var n=window.location.search;!window.location.search&&window.location.hash&&window.location.hash.indexOf("?")>-1&&(n=window.location.hash.substring(window.location.hash.indexOf("?")));for(var r=n.substring(1).split("&"),o=0;o<r.length;o++){var i=r[o].indexOf("=");if(i>0)r[o].substring(0,i)===e.lookupQuerystring&&(t=r[o].substring(i+1))}}return t}},Oq=null,Mq=function(){if(null!==Oq)return Oq;try{Oq="undefined"!==window&&null!==window.localStorage;var e="i18next.translate.boo";window.localStorage.setItem(e,"foo"),window.localStorage.removeItem(e)}catch(B2){Oq=!1}return Oq},Tq={name:"localStorage",lookup:function(e){var t;if(e.lookupLocalStorage&&Mq()){var n=window.localStorage.getItem(e.lookupLocalStorage);n&&(t=n)}return t},cacheUserLanguage:function(e,t){t.lookupLocalStorage&&Mq()&&window.localStorage.setItem(t.lookupLocalStorage,e)}},Aq=null,Iq=function(){if(null!==Aq)return Aq;try{Aq="undefined"!==window&&null!==window.sessionStorage;var e="i18next.translate.boo";window.sessionStorage.setItem(e,"foo"),window.sessionStorage.removeItem(e)}catch(B2){Aq=!1}return Aq},Rq={name:"sessionStorage",lookup:function(e){var t;if(e.lookupSessionStorage&&Iq()){var n=window.sessionStorage.getItem(e.lookupSessionStorage);n&&(t=n)}return t},cacheUserLanguage:function(e,t){t.lookupSessionStorage&&Iq()&&window.sessionStorage.setItem(t.lookupSessionStorage,e)}},Nq={name:"navigator",lookup:function(e){var t=[];if("undefined"!=typeof navigator){if(navigator.languages)for(var n=0;n<navigator.languages.length;n++)t.push(navigator.languages[n]);navigator.userLanguage&&t.push(navigator.userLanguage),navigator.language&&t.push(navigator.language)}return t.length>0?t:void 0}},Dq={name:"htmlTag",lookup:function(e){var t,n=e.htmlTag||("undefined"!=typeof document?document.documentElement:null);return n&&"function"==typeof n.getAttribute&&(t=n.getAttribute("lang")),t}},zq={name:"path",lookup:function(e){var t;if("undefined"!=typeof window){var n=window.location.pathname.match(/\/([a-zA-Z-]*)/g);if(n instanceof Array)if("number"==typeof e.lookupFromPathIndex){if("string"!=typeof n[e.lookupFromPathIndex])return;t=n[e.lookupFromPathIndex].replace("/","")}else 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n=/\/{2,9}/g,r=t.replace(n,"/").split("/");"/"!=t.slice(0,1)&&0!==t.length||r.splice(0,1);"/"==t.slice(-1)&&r.splice(r.length-1,1);return r}(0,i.path),i.queryKey=function(e,t){const n={};return t.replace(/(?:^|&)([^&=]*)=?([^&]*)/g,(function(e,t,r){t&&(n[t]=r)})),n}(0,i.query),i}class NK extends oK{constructor(e,t={}){super(),e&&"object"==typeof e&&(t=e,e=null),e?(e=RK(e),t.hostname=e.host,t.secure="https"===e.protocol||"wss"===e.protocol,t.port=e.port,e.query&&(t.query=e.query)):t.host&&(t.hostname=RK(t.host).host),cK(this,t),this.secure=null!=t.secure?t.secure:"undefined"!=typeof location&&"https:"===location.protocol,t.hostname&&!t.port&&(t.port=this.secure?"443":"80"),this.hostname=t.hostname||("undefined"!=typeof location?location.hostname:"localhost"),this.port=t.port||("undefined"!=typeof location&&location.port?location.port:this.secure?"443":"80"),this.transports=t.transports||["polling","websocket"],this.readyState="",this.writeBuffer=[],this.prevBufferLen=0,this.opts=Object.assign({path:"/engine.io",agent:!1,withCredentials:!1,upgrade:!0,timestampParam:"t",rememberUpgrade:!1,rejectUnauthorized:!0,perMessageDeflate:{threshold:1024},transportOptions:{},closeOnBeforeunload:!0},t),this.opts.path=this.opts.path.replace(/\/$/,"")+"/","string"==typeof this.opts.query&&(this.opts.query=function(e){let t={},n=e.split("&");for(let r=0,o=n.length;r<o;r++){let e=n[r].split("=");t[decodeURIComponent(e[0])]=decodeURIComponent(e[1])}return t}(this.opts.query)),this.id=null,this.upgrades=null,this.pingInterval=null,this.pingTimeout=null,this.pingTimeoutTimer=null,"function"==typeof addEventListener&&(this.opts.closeOnBeforeunload&&(this.beforeunloadEventListener=()=>{this.transport&&(this.transport.removeAllListeners(),this.transport.close())},addEventListener("beforeunload",this.beforeunloadEventListener,!1)),"localhost"!==this.hostname&&(this.offlineEventListener=()=>{this.onClose("transport close",{description:"network connection lost"})},addEventListener("offline",this.offlineEventListener,!1))),this.open()}createTransport(e){const t=Object.assign({},this.opts.query);t.EIO=4,t.transport=e,this.id&&(t.sid=this.id);const n=Object.assign({},this.opts.transportOptions[e],this.opts,{query:t,socket:this,hostname:this.hostname,secure:this.secure,port:this.port});return new TK[e](n)}open(){let e;if(this.opts.rememberUpgrade&&NK.priorWebsocketSuccess&&-1!==this.transports.indexOf("websocket"))e="websocket";else{if(0===this.transports.length)return void this.setTimeoutFn((()=>{this.emitReserved("error","No transports available")}),0);e=this.transports[0]}this.readyState="opening";try{e=this.createTransport(e)}catch(B2){return this.transports.shift(),void this.open()}e.open(),this.setTransport(e)}setTransport(e){this.transport&&this.transport.removeAllListeners(),this.transport=e,e.on("drain",this.onDrain.bind(this)).on("packet",this.onPacket.bind(this)).on("error",this.onError.bind(this)).on("close",(e=>this.onClose("transport close",e)))}probe(e){let t=this.createTransport(e),n=!1;NK.priorWebsocketSuccess=!1;const r=()=>{n||(t.send([{type:"ping",data:"probe"}]),t.once("packet",(e=>{if(!n)if("pong"===e.type&&"probe"===e.data){if(this.upgrading=!0,this.emitReserved("upgrading",t),!t)return;NK.priorWebsocketSuccess="websocket"===t.name,this.transport.pause((()=>{n||"closed"!==this.readyState&&(c(),this.setTransport(t),t.send([{type:"upgrade"}]),this.emitReserved("upgrade",t),t=null,this.upgrading=!1,this.flush())}))}else{const e=new Error("probe error");e.transport=t.name,this.emitReserved("upgradeError",e)}})))};function o(){n||(n=!0,c(),t.close(),t=null)}const i=e=>{const n=new Error("probe error: "+e);n.transport=t.name,o(),this.emitReserved("upgradeError",n)};function a(){i("transport closed")}function s(){i("socket closed")}function l(e){t&&e.name!==t.name&&o()}const c=()=>{t.removeListener("open",r),t.removeListener("error",i),t.removeListener("close",a),this.off("close",s),this.off("upgrading",l)};t.once("open",r),t.once("error",i),t.once("close",a),this.once("close",s),this.once("upgrading",l),t.open()}onOpen(){if(this.readyState="open",NK.priorWebsocketSuccess="websocket"===this.transport.name,this.emitReserved("open"),this.flush(),"open"===this.readyState&&this.opts.upgrade&&this.transport.pause){let e=0;const t=this.upgrades.length;for(;e<t;e++)this.probe(this.upgrades[e])}}onPacket(e){if("opening"===this.readyState||"open"===this.readyState||"closing"===this.readyState)switch(this.emitReserved("packet",e),this.emitReserved("heartbeat"),e.type){case"open":this.onHandshake(JSON.parse(e.data));break;case"ping":this.resetPingTimeout(),this.sendPacket("pong"),this.emitReserved("ping"),this.emitReserved("pong");break;case"error":const t=new Error("server error");t.code=e.data,this.onError(t);break;case"message":this.emitReserved("data",e.data),this.emitReserved("message",e.data)}}onHandshake(e){this.emitReserved("handshake",e),this.id=e.sid,this.transport.query.sid=e.sid,this.upgrades=this.filterUpgrades(e.upgrades),this.pingInterval=e.pingInterval,this.pingTimeout=e.pingTimeout,this.maxPayload=e.maxPayload,this.onOpen(),"closed"!==this.readyState&&this.resetPingTimeout()}resetPingTimeout(){this.clearTimeoutFn(this.pingTimeoutTimer),this.pingTimeoutTimer=this.setTimeoutFn((()=>{this.onClose("ping timeout")}),this.pingInterval+this.pingTimeout),this.opts.autoUnref&&this.pingTimeoutTimer.unref()}onDrain(){this.writeBuffer.splice(0,this.prevBufferLen),this.prevBufferLen=0,0===this.writeBuffer.length?this.emitReserved("drain"):this.flush()}flush(){if("closed"!==this.readyState&&this.transport.writable&&!this.upgrading&&this.writeBuffer.length){const e=this.getWritablePackets();this.transport.send(e),this.prevBufferLen=e.length,this.emitReserved("flush")}}getWritablePackets(){if(!(this.maxPayload&&"polling"===this.transport.name&&this.writeBuffer.length>1))return this.writeBuffer;let e=1;for(let t=0;t<this.writeBuffer.length;t++){const n=this.writeBuffer[t].data;if(n&&(e+=uK(n)),t>0&&e>this.maxPayload)return this.writeBuffer.slice(0,t);e+=2}return this.writeBuffer}write(e,t,n){return this.sendPacket("message",e,t,n),this}send(e,t,n){return this.sendPacket("message",e,t,n),this}sendPacket(e,t,n,r){if("function"==typeof t&&(r=t,t=void 0),"function"==typeof n&&(r=n,n=null),"closing"===this.readyState||"closed"===this.readyState)return;(n=n||{}).compress=!1!==n.compress;const o={type:e,data:t,options:n};this.emitReserved("packetCreate",o),this.writeBuffer.push(o),r&&this.once("flush",r),this.flush()}close(){const e=()=>{this.onClose("forced close"),this.transport.close()},t=()=>{this.off("upgrade",t),this.off("upgradeError",t),e()},n=()=>{this.once("upgrade",t),this.once("upgradeError",t)};return"opening"!==this.readyState&&"open"!==this.readyState||(this.readyState="closing",this.writeBuffer.length?this.once("drain",(()=>{this.upgrading?n():e()})):this.upgrading?n():e()),this}onError(e){NK.priorWebsocketSuccess=!1,this.emitReserved("error",e),this.onClose("transport error",e)}onClose(e,t){"opening"!==this.readyState&&"open"!==this.readyState&&"closing"!==this.readyState||(this.clearTimeoutFn(this.pingTimeoutTimer),this.transport.removeAllListeners("close"),this.transport.close(),this.transport.removeAllListeners(),"function"==typeof removeEventListener&&(removeEventListener("beforeunload",this.beforeunloadEventListener,!1),removeEventListener("offline",this.offlineEventListener,!1)),this.readyState="closed",this.id=null,this.emitReserved("close",e,t),this.writeBuffer=[],this.prevBufferLen=0)}filterUpgrades(e){const t=[];let n=0;const r=e.length;for(;n<r;n++)~this.transports.indexOf(e[n])&&t.push(e[n]);return t}}NK.protocol=4;const DK="function"==typeof ArrayBuffer,zK=Object.prototype.toString,BK="function"==typeof Blob||"undefined"!=typeof Blob&&"[object BlobConstructor]"===zK.call(Blob),jK="function"==typeof File||"undefined"!=typeof File&&"[object FileConstructor]"===zK.call(File);function FK(e){return DK&&(e instanceof ArrayBuffer||(e=>"function"==typeof ArrayBuffer.isView?ArrayBuffer.isView(e):e.buffer instanceof ArrayBuffer)(e))||BK&&e instanceof Blob||jK&&e instanceof File}function HK(e,t){if(!e||"object"!=typeof e)return!1;if(Array.isArray(e)){for(let t=0,n=e.length;t<n;t++)if(HK(e[t]))return!0;return!1}if(FK(e))return!0;if(e.toJSON&&"function"==typeof e.toJSON&&1===arguments.length)return HK(e.toJSON(),!0);for(const n in e)if(Object.prototype.hasOwnProperty.call(e,n)&&HK(e[n]))return!0;return!1}function WK(e){const t=[],n=e.data,r=e;return r.data=VK(n,t),r.attachments=t.length,{packet:r,buffers:t}}function VK(e,t){if(!e)return e;if(FK(e)){const n={_placeholder:!0,num:t.length};return t.push(e),n}if(Array.isArray(e)){const n=new Array(e.length);for(let r=0;r<e.length;r++)n[r]=VK(e[r],t);return n}if("object"==typeof e&&!(e instanceof Date)){const n={};for(const r in e)Object.prototype.hasOwnProperty.call(e,r)&&(n[r]=VK(e[r],t));return n}return e}function $K(e,t){return e.data=UK(e.data,t),e.attachments=void 0,e}function UK(e,t){if(!e)return e;if(e&&!0===e._placeholder){if("number"==typeof e.num&&e.num>=0&&e.num<t.length)return t[e.num];throw new Error("illegal attachments")}if(Array.isArray(e))for(let n=0;n<e.length;n++)e[n]=UK(e[n],t);else if("object"==typeof e)for(const n in e)Object.prototype.hasOwnProperty.call(e,n)&&(e[n]=UK(e[n],t));return e}var GK;!function(e){e[e.CONNECT=0]="CONNECT",e[e.DISCONNECT=1]="DISCONNECT",e[e.EVENT=2]="EVENT",e[e.ACK=3]="ACK",e[e.CONNECT_ERROR=4]="CONNECT_ERROR",e[e.BINARY_EVENT=5]="BINARY_EVENT",e[e.BINARY_ACK=6]="BINARY_ACK"}(GK||(GK={}));class qK extends oK{constructor(e){super(),this.reviver=e}add(e){let t;if("string"==typeof e){if(this.reconstructor)throw new Error("got plaintext data when reconstructing a packet");t=this.decodeString(e),t.type===GK.BINARY_EVENT||t.type===GK.BINARY_ACK?(this.reconstructor=new YK(t),0===t.attachments&&super.emitReserved("decoded",t)):super.emitReserved("decoded",t)}else{if(!FK(e)&&!e.base64)throw new Error("Unknown type: "+e);if(!this.reconstructor)throw new Error("got binary data when not reconstructing a packet");t=this.reconstructor.takeBinaryData(e),t&&(this.reconstructor=null,super.emitReserved("decoded",t))}}decodeString(e){let t=0;const n={type:Number(e.charAt(0))};if(void 0===GK[n.type])throw new Error("unknown packet type "+n.type);if(n.type===GK.BINARY_EVENT||n.type===GK.BINARY_ACK){const r=t+1;for(;"-"!==e.charAt(++t)&&t!=e.length;);const o=e.substring(r,t);if(o!=Number(o)||"-"!==e.charAt(t))throw new Error("Illegal attachments");n.attachments=Number(o)}if("/"===e.charAt(t+1)){const r=t+1;for(;++t;){if(","===e.charAt(t))break;if(t===e.length)break}n.nsp=e.substring(r,t)}else n.nsp="/";const r=e.charAt(t+1);if(""!==r&&Number(r)==r){const r=t+1;for(;++t;){const n=e.charAt(t);if(null==n||Number(n)!=n){--t;break}if(t===e.length)break}n.id=Number(e.substring(r,t+1))}if(e.charAt(++t)){const r=this.tryParse(e.substr(t));if(!qK.isPayloadValid(n.type,r))throw new Error("invalid payload");n.data=r}return n}tryParse(e){try{return JSON.parse(e,this.reviver)}catch(B2){return!1}}static isPayloadValid(e,t){switch(e){case GK.CONNECT:return"object"==typeof t;case GK.DISCONNECT:return void 0===t;case GK.CONNECT_ERROR:return"string"==typeof t||"object"==typeof t;case GK.EVENT:case GK.BINARY_EVENT:return Array.isArray(t)&&t.length>0;case GK.ACK:case GK.BINARY_ACK:return Array.isArray(t)}}destroy(){this.reconstructor&&this.reconstructor.finishedReconstruction()}}class YK{constructor(e){this.packet=e,this.buffers=[],this.reconPack=e}takeBinaryData(e){if(this.buffers.push(e),this.buffers.length===this.reconPack.attachments){const e=$K(this.reconPack,this.buffers);return this.finishedReconstruction(),e}return null}finishedReconstruction(){this.reconPack=null,this.buffers=[]}}const ZK=Object.freeze(Object.defineProperty({__proto__:null,protocol:5,get PacketType(){return GK},Encoder:class{constructor(e){this.replacer=e}encode(e){return e.type!==GK.EVENT&&e.type!==GK.ACK||!HK(e)?[this.encodeAsString(e)]:(e.type=e.type===GK.EVENT?GK.BINARY_EVENT:GK.BINARY_ACK,this.encodeAsBinary(e))}encodeAsString(e){let t=""+e.type;return e.type!==GK.BINARY_EVENT&&e.type!==GK.BINARY_ACK||(t+=e.attachments+"-"),e.nsp&&"/"!==e.nsp&&(t+=e.nsp+","),null!=e.id&&(t+=e.id),null!=e.data&&(t+=JSON.stringify(e.data,this.replacer)),t}encodeAsBinary(e){const t=WK(e),n=this.encodeAsString(t.packet),r=t.buffers;return r.unshift(n),r}},Decoder:qK},Symbol.toStringTag,{value:"Module"}));function XK(e,t,n){return e.on(t,n),function(){e.off(t,n)}}const KK=Object.freeze({connect:1,connect_error:1,disconnect:1,disconnecting:1,newListener:1,removeListener:1});class QK extends oK{constructor(e,t,n){super(),this.connected=!1,this.receiveBuffer=[],this.sendBuffer=[],this.ids=0,this.acks={},this.flags={},this.io=e,this.nsp=t,n&&n.auth&&(this.auth=n.auth),this.io._autoConnect&&this.open()}get disconnected(){return!this.connected}subEvents(){if(this.subs)return;const e=this.io;this.subs=[XK(e,"open",this.onopen.bind(this)),XK(e,"packet",this.onpacket.bind(this)),XK(e,"error",this.onerror.bind(this)),XK(e,"close",this.onclose.bind(this))]}get active(){return!!this.subs}connect(){return this.connected||(this.subEvents(),this.io._reconnecting||this.io.open(),"open"===this.io._readyState&&this.onopen()),this}open(){return this.connect()}send(...e){return e.unshift("message"),this.emit.apply(this,e),this}emit(e,...t){if(KK.hasOwnProperty(e))throw new Error('"'+e.toString()+'" is a reserved event name');t.unshift(e);const n={type:GK.EVENT,data:t,options:{}};if(n.options.compress=!1!==this.flags.compress,"function"==typeof t[t.length-1]){const e=this.ids++,r=t.pop();this._registerAckCallback(e,r),n.id=e}const r=this.io.engine&&this.io.engine.transport&&this.io.engine.transport.writable;return this.flags.volatile&&(!r||!this.connected)||(this.connected?(this.notifyOutgoingListeners(n),this.packet(n)):this.sendBuffer.push(n)),this.flags={},this}_registerAckCallback(e,t){const n=this.flags.timeout;if(void 0===n)return void(this.acks[e]=t);const r=this.io.setTimeoutFn((()=>{delete this.acks[e];for(let t=0;t<this.sendBuffer.length;t++)this.sendBuffer[t].id===e&&this.sendBuffer.splice(t,1);t.call(this,new Error("operation has timed out"))}),n);this.acks[e]=(...e)=>{this.io.clearTimeoutFn(r),t.apply(this,[null,...e])}}packet(e){e.nsp=this.nsp,this.io._packet(e)}onopen(){"function"==typeof this.auth?this.auth((e=>{this.packet({type:GK.CONNECT,data:e})})):this.packet({type:GK.CONNECT,data:this.auth})}onerror(e){this.connected||this.emitReserved("connect_error",e)}onclose(e,t){this.connected=!1,delete this.id,this.emitReserved("disconnect",e,t)}onpacket(e){if(e.nsp===this.nsp)switch(e.type){case GK.CONNECT:if(e.data&&e.data.sid){const t=e.data.sid;this.onconnect(t)}else this.emitReserved("connect_error",new Error("It seems you are trying to reach a Socket.IO server in v2.x with a v3.x client, but they are not compatible (more information here: https://socket.io/docs/v3/migrating-from-2-x-to-3-0/)"));break;case GK.EVENT:case GK.BINARY_EVENT:this.onevent(e);break;case GK.ACK:case GK.BINARY_ACK:this.onack(e);break;case GK.DISCONNECT:this.ondisconnect();break;case GK.CONNECT_ERROR:this.destroy();const t=new Error(e.data.message);t.data=e.data.data,this.emitReserved("connect_error",t)}}onevent(e){const t=e.data||[];null!=e.id&&t.push(this.ack(e.id)),this.connected?this.emitEvent(t):this.receiveBuffer.push(Object.freeze(t))}emitEvent(e){if(this._anyListeners&&this._anyListeners.length){const t=this._anyListeners.slice();for(const n of t)n.apply(this,e)}super.emit.apply(this,e)}ack(e){const t=this;let n=!1;return function(...r){n||(n=!0,t.packet({type:GK.ACK,id:e,data:r}))}}onack(e){const t=this.acks[e.id];"function"==typeof t&&(t.apply(this,e.data),delete this.acks[e.id])}onconnect(e){this.id=e,this.connected=!0,this.emitBuffered(),this.emitReserved("connect")}emitBuffered(){this.receiveBuffer.forEach((e=>this.emitEvent(e))),this.receiveBuffer=[],this.sendBuffer.forEach((e=>{this.notifyOutgoingListeners(e),this.packet(e)})),this.sendBuffer=[]}ondisconnect(){this.destroy(),this.onclose("io server disconnect")}destroy(){this.subs&&(this.subs.forEach((e=>e())),this.subs=void 0),this.io._destroy(this)}disconnect(){return this.connected&&this.packet({type:GK.DISCONNECT}),this.destroy(),this.connected&&this.onclose("io client disconnect"),this}close(){return this.disconnect()}compress(e){return this.flags.compress=e,this}get volatile(){return this.flags.volatile=!0,this}timeout(e){return this.flags.timeout=e,this}onAny(e){return this._anyListeners=this._anyListeners||[],this._anyListeners.push(e),this}prependAny(e){return this._anyListeners=this._anyListeners||[],this._anyListeners.unshift(e),this}offAny(e){if(!this._anyListeners)return this;if(e){const t=this._anyListeners;for(let n=0;n<t.length;n++)if(e===t[n])return t.splice(n,1),this}else this._anyListeners=[];return this}listenersAny(){return this._anyListeners||[]}onAnyOutgoing(e){return this._anyOutgoingListeners=this._anyOutgoingListeners||[],this._anyOutgoingListeners.push(e),this}prependAnyOutgoing(e){return this._anyOutgoingListeners=this._anyOutgoingListeners||[],this._anyOutgoingListeners.unshift(e),this}offAnyOutgoing(e){if(!this._anyOutgoingListeners)return this;if(e){const t=this._anyOutgoingListeners;for(let n=0;n<t.length;n++)if(e===t[n])return t.splice(n,1),this}else this._anyOutgoingListeners=[];return this}listenersAnyOutgoing(){return this._anyOutgoingListeners||[]}notifyOutgoingListeners(e){if(this._anyOutgoingListeners&&this._anyOutgoingListeners.length){const t=this._anyOutgoingListeners.slice();for(const n of t)n.apply(this,e.data)}}}function JK(e){e=e||{},this.ms=e.min||100,this.max=e.max||1e4,this.factor=e.factor||2,this.jitter=e.jitter>0&&e.jitter<=1?e.jitter:0,this.attempts=0}JK.prototype.duration=function(){var e=this.ms*Math.pow(this.factor,this.attempts++);if(this.jitter){var t=Math.random(),n=Math.floor(t*this.jitter*e);e=0==(1&Math.floor(10*t))?e-n:e+n}return 0|Math.min(e,this.max)},JK.prototype.reset=function(){this.attempts=0},JK.prototype.setMin=function(e){this.ms=e},JK.prototype.setMax=function(e){this.max=e},JK.prototype.setJitter=function(e){this.jitter=e};class eQ extends oK{constructor(e,t){var n;super(),this.nsps={},this.subs=[],e&&"object"==typeof e&&(t=e,e=void 0),(t=t||{}).path=t.path||"/socket.io",this.opts=t,cK(this,t),this.reconnection(!1!==t.reconnection),this.reconnectionAttempts(t.reconnectionAttempts||1/0),this.reconnectionDelay(t.reconnectionDelay||1e3),this.reconnectionDelayMax(t.reconnectionDelayMax||5e3),this.randomizationFactor(null!==(n=t.randomizationFactor)&&void 0!==n?n:.5),this.backoff=new JK({min:this.reconnectionDelay(),max:this.reconnectionDelayMax(),jitter:this.randomizationFactor()}),this.timeout(null==t.timeout?2e4:t.timeout),this._readyState="closed",this.uri=e;const r=t.parser||ZK;this.encoder=new r.Encoder,this.decoder=new r.Decoder,this._autoConnect=!1!==t.autoConnect,this._autoConnect&&this.open()}reconnection(e){return arguments.length?(this._reconnection=!!e,this):this._reconnection}reconnectionAttempts(e){return void 0===e?this._reconnectionAttempts:(this._reconnectionAttempts=e,this)}reconnectionDelay(e){var t;return void 0===e?this._reconnectionDelay:(this._reconnectionDelay=e,null===(t=this.backoff)||void 0===t||t.setMin(e),this)}randomizationFactor(e){var t;return void 0===e?this._randomizationFactor:(this._randomizationFactor=e,null===(t=this.backoff)||void 0===t||t.setJitter(e),this)}reconnectionDelayMax(e){var t;return void 0===e?this._reconnectionDelayMax:(this._reconnectionDelayMax=e,null===(t=this.backoff)||void 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this._context.getImageData(e,t,n,r)}lineTo(e,t){this._context.lineTo(e,t)}moveTo(e,t){this._context.moveTo(e,t)}rect(e,t,n,r){this._context.rect(e,t,n,r)}putImageData(e,t,n){this._context.putImageData(e,t,n)}quadraticCurveTo(e,t,n,r){this._context.quadraticCurveTo(e,t,n,r)}restore(){this._context.restore()}rotate(e){this._context.rotate(e)}save(){this._context.save()}scale(e,t){this._context.scale(e,t)}setLineDash(e){this._context.setLineDash?this._context.setLineDash(e):"mozDash"in this._context?this._context.mozDash=e:"webkitLineDash"in this._context&&(this._context.webkitLineDash=e)}getLineDash(){return this._context.getLineDash()}setTransform(e,t,n,r,o,i){this._context.setTransform(e,t,n,r,o,i)}stroke(e){e?this._context.stroke(e):this._context.stroke()}strokeText(e,t,n,r){this._context.strokeText(e,t,n,r)}transform(e,t,n,r,o,i){this._context.transform(e,t,n,r,o,i)}translate(e,t){this._context.translate(e,t)}_enableTrace(){var e,t,n=this,r=fJ.length,o=this.setAttr,i=function(e){var r,o=n[e];n[e]=function(){return t=hJ(Array.prototype.slice.call(arguments,0)),r=o.apply(n,arguments),n._trace({method:e,args:t}),r}};for(e=0;e<r;e++)i(fJ[e]);n.setAttr=function(){o.apply(n,arguments);var e=arguments[0],t=arguments[1];"shadowOffsetX"!==e&&"shadowOffsetY"!==e&&"shadowBlur"!==e||(t/=this.canvas.getPixelRatio()),n._trace({property:e,val:t})}}_applyGlobalCompositeOperation(e){const t=e.attrs.globalCompositeOperation;!t||"source-over"===t||this.setAttr("globalCompositeOperation",t)}}["fillStyle","strokeStyle","shadowColor","shadowBlur","shadowOffsetX","shadowOffsetY","lineCap","lineDashOffset","lineJoin","lineWidth","miterLimit","font","textAlign","textBaseline","globalAlpha","globalCompositeOperation","imageSmoothingEnabled"].forEach((function(e){Object.defineProperty(pJ.prototype,e,{get(){return this._context[e]},set(t){this._context[e]=t}})}));class gJ extends pJ{constructor(e){super(e),this._context=e._canvas.getContext("2d")}_fillColor(e){var t=e.fill();this.setAttr("fillStyle",t),e._fillFunc(this)}_fillPattern(e){this.setAttr("fillStyle",e._getFillPattern()),e._fillFunc(this)}_fillLinearGradient(e){var t=e._getLinearGradient();t&&(this.setAttr("fillStyle",t),e._fillFunc(this))}_fillRadialGradient(e){var t=e._getRadialGradient();t&&(this.setAttr("fillStyle",t),e._fillFunc(this))}_fill(e){var t=e.fill(),n=e.getFillPriority();if(t&&"color"===n)this._fillColor(e);else{var r=e.getFillPatternImage();if(r&&"pattern"===n)this._fillPattern(e);else{var o=e.getFillLinearGradientColorStops();if(o&&"linear-gradient"===n)this._fillLinearGradient(e);else{var i=e.getFillRadialGradientColorStops();i&&"radial-gradient"===n?this._fillRadialGradient(e):t?this._fillColor(e):r?this._fillPattern(e):o?this._fillLinearGradient(e):i&&this._fillRadialGradient(e)}}}}_strokeLinearGradient(e){var t=e.getStrokeLinearGradientStartPoint(),n=e.getStrokeLinearGradientEndPoint(),r=e.getStrokeLinearGradientColorStops(),o=this.createLinearGradient(t.x,t.y,n.x,n.y);if(r){for(var i=0;i<r.length;i+=2)o.addColorStop(r[i],r[i+1]);this.setAttr("strokeStyle",o)}}_stroke(e){var t=e.dash(),n=e.getStrokeScaleEnabled();if(e.hasStroke()){if(!n){this.save();var r=this.getCanvas().getPixelRatio();this.setTransform(r,0,0,r,0,0)}this._applyLineCap(e),t&&e.dashEnabled()&&(this.setLineDash(t),this.setAttr("lineDashOffset",e.dashOffset())),this.setAttr("lineWidth",e.strokeWidth()),e.getShadowForStrokeEnabled()||this.setAttr("shadowColor","rgba(0,0,0,0)"),e.getStrokeLinearGradientColorStops()?this._strokeLinearGradient(e):this.setAttr("strokeStyle",e.stroke()),e._strokeFunc(this),n||this.restore()}}_applyShadow(e){var t,n,r,o=null!==(t=e.getShadowRGBA())&&void 0!==t?t:"black",i=null!==(n=e.getShadowBlur())&&void 0!==n?n:5,a=null!==(r=e.getShadowOffset())&&void 0!==r?r:{x:0,y:0},s=e.getAbsoluteScale(),l=this.canvas.getPixelRatio(),c=s.x*l,u=s.y*l;this.setAttr("shadowColor",o),this.setAttr("shadowBlur",i*Math.min(Math.abs(c),Math.abs(u))),this.setAttr("shadowOffsetX",a.x*c),this.setAttr("shadowOffsetY",a.y*u)}}class mJ extends pJ{constructor(e){super(e),this._context=e._canvas.getContext("2d",{willReadFrequently:!0})}_fill(e){this.save(),this.setAttr("fillStyle",e.colorKey),e._fillFuncHit(this),this.restore()}strokeShape(e){e.hasHitStroke()&&this._stroke(e)}_stroke(e){if(e.hasHitStroke()){var t=e.getStrokeScaleEnabled();if(!t){this.save();var n=this.getCanvas().getPixelRatio();this.setTransform(n,0,0,n,0,0)}this._applyLineCap(e);var r=e.hitStrokeWidth(),o="auto"===r?e.strokeWidth():r;this.setAttr("lineWidth",o),this.setAttr("strokeStyle",e.colorKey),e._strokeFuncHit(this),t||this.restore()}}}var vJ;class yJ{constructor(e){this.pixelRatio=1,this.width=0,this.height=0,this.isCache=!1;var t=(e||{}).pixelRatio||$Q.pixelRatio||function(){if(vJ)return vJ;var e=eJ.createCanvasElement(),t=e.getContext("2d");return vJ=($Q._global.devicePixelRatio||1)/(t.webkitBackingStorePixelRatio||t.mozBackingStorePixelRatio||t.msBackingStorePixelRatio||t.oBackingStorePixelRatio||t.backingStorePixelRatio||1),eJ.releaseCanvas(e),vJ}();this.pixelRatio=t,this._canvas=eJ.createCanvasElement(),this._canvas.style.padding="0",this._canvas.style.margin="0",this._canvas.style.border="0",this._canvas.style.background="transparent",this._canvas.style.position="absolute",this._canvas.style.top="0",this._canvas.style.left="0"}getContext(){return this.context}getPixelRatio(){return this.pixelRatio}setPixelRatio(e){var t=this.pixelRatio;this.pixelRatio=e,this.setSize(this.getWidth()/t,this.getHeight()/t)}setWidth(e){this.width=this._canvas.width=e*this.pixelRatio,this._canvas.style.width=e+"px";var t=this.pixelRatio;this.getContext()._context.scale(t,t)}setHeight(e){this.height=this._canvas.height=e*this.pixelRatio,this._canvas.style.height=e+"px";var t=this.pixelRatio;this.getContext()._context.scale(t,t)}getWidth(){return this.width}getHeight(){return this.height}setSize(e,t){this.setWidth(e||0),this.setHeight(t||0)}toDataURL(e,t){try{return this._canvas.toDataURL(e,t)}catch(B2){try{return this._canvas.toDataURL()}catch(Hhe){return eJ.error("Unable to get data URL. "+Hhe.message+" For more info read https://konvajs.org/docs/posts/Tainted_Canvas.html."),""}}}}dJ.addGetterSetter(yJ,"pixelRatio",void 0,rJ());class bJ extends yJ{constructor(e={width:0,height:0}){super(e),this.context=new gJ(this),this.setSize(e.width,e.height)}}class xJ extends yJ{constructor(e={width:0,height:0}){super(e),this.hitCanvas=!0,this.context=new mJ(this),this.setSize(e.width,e.height)}}const wJ={get isDragging(){var e=!1;return wJ._dragElements.forEach((t=>{"dragging"===t.dragStatus&&(e=!0)})),e},justDragged:!1,get node(){var e;return wJ._dragElements.forEach((t=>{e=t.node})),e},_dragElements:new Map,_drag(e){const t=[];wJ._dragElements.forEach(((n,r)=>{const{node:o}=n,i=o.getStage();i.setPointersPositions(e),void 0===n.pointerId&&(n.pointerId=eJ._getFirstPointerId(e));const a=i._changedPointerPositions.find((e=>e.id===n.pointerId));if(a){if("dragging"!==n.dragStatus){var s=o.dragDistance();if(Math.max(Math.abs(a.x-n.startPointerPos.x),Math.abs(a.y-n.startPointerPos.y))<s)return;if(o.startDrag({evt:e}),!o.isDragging())return}o._setDragPosition(e,n),t.push(o)}})),t.forEach((t=>{t.fire("dragmove",{type:"dragmove",target:t,evt:e},!0)}))},_endDragBefore(e){const t=[];wJ._dragElements.forEach((n=>{const{node:r}=n,o=r.getStage();e&&o.setPointersPositions(e);if(!o._changedPointerPositions.find((e=>e.id===n.pointerId)))return;"dragging"!==n.dragStatus&&"stopped"!==n.dragStatus||(wJ.justDragged=!0,$Q._mouseListenClick=!1,$Q._touchListenClick=!1,$Q._pointerListenClick=!1,n.dragStatus="stopped");const i=n.node.getLayer()||n.node instanceof $Q.Stage&&n.node;i&&-1===t.indexOf(i)&&t.push(i)})),t.forEach((e=>{e.draw()}))},_endDragAfter(e){wJ._dragElements.forEach(((t,n)=>{"stopped"===t.dragStatus&&t.node.fire("dragend",{type:"dragend",target:t.node,evt:e},!0),"dragging"!==t.dragStatus&&wJ._dragElements.delete(n)}))}};$Q.isBrowser&&(window.addEventListener("mouseup",wJ._endDragBefore,!0),window.addEventListener("touchend",wJ._endDragBefore,!0),window.addEventListener("mousemove",wJ._drag),window.addEventListener("touchmove",wJ._drag),window.addEventListener("mouseup",wJ._endDragAfter,!1),window.addEventListener("touchend",wJ._endDragAfter,!1));var kJ="absoluteOpacity",SJ="allEventListeners",CJ="absoluteTransform",_J="absoluteScale",EJ="canvas",PJ="listening",LJ="mouseenter",OJ="mouseleave",MJ="Shape",TJ=" ",AJ="stage",IJ="transform",RJ="visible",NJ=["xChange.konva","yChange.konva","scaleXChange.konva","scaleYChange.konva","skewXChange.konva","skewYChange.konva","rotationChange.konva","offsetXChange.konva","offsetYChange.konva","transformsEnabledChange.konva"].join(TJ);let DJ=1;class zJ{constructor(e){this._id=DJ++,this.eventListeners={},this.attrs={},this.index=0,this._allEventListeners=null,this.parent=null,this._cache=new Map,this._attachedDepsListeners=new Map,this._lastPos=null,this._batchingTransformChange=!1,this._needClearTransformCache=!1,this._filterUpToDate=!1,this._isUnderCache=!1,this._dragEventId=null,this._shouldFireChangeEvents=!1,this.setAttrs(e),this._shouldFireChangeEvents=!0}hasChildren(){return!1}_clearCache(e){e!==IJ&&e!==CJ||!this._cache.get(e)?e?this._cache.delete(e):this._cache.clear():this._cache.get(e).dirty=!0}_getCache(e,t){var n=this._cache.get(e);return(void 0===n||(e===IJ||e===CJ)&&!0===n.dirty)&&(n=t.call(this),this._cache.set(e,n)),n}_calculate(e,t,n){if(!this._attachedDepsListeners.get(e)){const n=t.map((e=>e+"Change.konva")).join(TJ);this.on(n,(()=>{this._clearCache(e)})),this._attachedDepsListeners.set(e,!0)}return this._getCache(e,n)}_getCanvasCache(){return this._cache.get(EJ)}_clearSelfAndDescendantCache(e){this._clearCache(e),e===CJ&&this.fire("absoluteTransformChange")}clearCache(){if(this._cache.has(EJ)){const{scene:e,filter:t,hit:n}=this._cache.get(EJ);eJ.releaseCanvas(e,t,n),this._cache.delete(EJ)}return this._clearSelfAndDescendantCache(),this._requestDraw(),this}cache(e){var t=e||{},n={};void 0!==t.x&&void 0!==t.y&&void 0!==t.width&&void 0!==t.height||(n=this.getClientRect({skipTransform:!0,relativeTo:this.getParent()}));var r=Math.ceil(t.width||n.width),o=Math.ceil(t.height||n.height),i=t.pixelRatio,a=void 0===t.x?Math.floor(n.x):t.x,s=void 0===t.y?Math.floor(n.y):t.y,l=t.offset||0,c=t.drawBorder||!1,u=t.hitCanvasPixelRatio||1;if(r&&o){a-=l,s-=l;var d=new bJ({pixelRatio:i,width:r+=2*l+1,height:o+=2*l+1}),h=new bJ({pixelRatio:i,width:0,height:0}),f=new xJ({pixelRatio:u,width:r,height:o}),p=d.getContext(),g=f.getContext();return f.isCache=!0,d.isCache=!0,this._cache.delete(EJ),this._filterUpToDate=!1,!1===t.imageSmoothingEnabled&&(d.getContext()._context.imageSmoothingEnabled=!1,h.getContext()._context.imageSmoothingEnabled=!1),p.save(),g.save(),p.translate(-a,-s),g.translate(-a,-s),this._isUnderCache=!0,this._clearSelfAndDescendantCache(kJ),this._clearSelfAndDescendantCache(_J),this.drawScene(d,this),this.drawHit(f,this),this._isUnderCache=!1,p.restore(),g.restore(),c&&(p.save(),p.beginPath(),p.rect(0,0,r,o),p.closePath(),p.setAttr("strokeStyle","red"),p.setAttr("lineWidth",5),p.stroke(),p.restore()),this._cache.set(EJ,{scene:d,filter:h,hit:f,x:a,y:s}),this._requestDraw(),this}eJ.error("Can not cache the node. Width or height of the node equals 0. Caching is skipped.")}isCached(){return this._cache.has(EJ)}getClientRect(e){throw new Error('abstract "getClientRect" method call')}_transformedRect(e,t){var n,r,o,i,a=[{x:e.x,y:e.y},{x:e.x+e.width,y:e.y},{x:e.x+e.width,y:e.y+e.height},{x:e.x,y:e.y+e.height}],s=this.getAbsoluteTransform(t);return a.forEach((function(e){var t=s.point(e);void 0===n&&(n=o=t.x,r=i=t.y),n=Math.min(n,t.x),r=Math.min(r,t.y),o=Math.max(o,t.x),i=Math.max(i,t.y)})),{x:n,y:r,width:o-n,height:i-r}}_drawCachedSceneCanvas(e){e.save(),e._applyOpacity(this),e._applyGlobalCompositeOperation(this);const t=this._getCanvasCache();e.translate(t.x,t.y);var n=this._getCachedSceneCanvas(),r=n.pixelRatio;e.drawImage(n._canvas,0,0,n.width/r,n.height/r),e.restore()}_drawCachedHitCanvas(e){var t=this._getCanvasCache(),n=t.hit;e.save(),e.translate(t.x,t.y),e.drawImage(n._canvas,0,0,n.width/n.pixelRatio,n.height/n.pixelRatio),e.restore()}_getCachedSceneCanvas(){var e,t,n,r,o=this.filters(),i=this._getCanvasCache(),a=i.scene,s=i.filter,l=s.getContext();if(o){if(!this._filterUpToDate){var c=a.pixelRatio;s.setSize(a.width/a.pixelRatio,a.height/a.pixelRatio);try{for(e=o.length,l.clear(),l.drawImage(a._canvas,0,0,a.getWidth()/c,a.getHeight()/c),t=l.getImageData(0,0,s.getWidth(),s.getHeight()),n=0;n<e;n++)"function"==typeof(r=o[n])?(r.call(this,t),l.putImageData(t,0,0)):eJ.error("Filter should be type of function, but got "+typeof r+" instead. Please check correct filters")}catch(B2){eJ.error("Unable to apply filter. "+B2.message+" This post my help you https://konvajs.org/docs/posts/Tainted_Canvas.html.")}this._filterUpToDate=!0}return s}return a}on(e,t){if(this._cache&&this._cache.delete(SJ),3===arguments.length)return this._delegate.apply(this,arguments);var n,r,o,i,a=e.split(TJ),s=a.length;for(n=0;n<s;n++)o=(r=a[n].split("."))[0],i=r[1]||"",this.eventListeners[o]||(this.eventListeners[o]=[]),this.eventListeners[o].push({name:i,handler:t});return this}off(e,t){var n,r,o,i,a,s=(e||"").split(TJ),l=s.length;if(this._cache&&this._cache.delete(SJ),!e)for(r in this.eventListeners)this._off(r);for(n=0;n<l;n++)if(i=(o=s[n].split("."))[0],a=o[1],i)this.eventListeners[i]&&this._off(i,a,t);else for(r in this.eventListeners)this._off(r,a,t);return this}dispatchEvent(e){var t={target:this,type:e.type,evt:e};return this.fire(e.type,t),this}addEventListener(e,t){return this.on(e,(function(e){t.call(this,e.evt)})),this}removeEventListener(e){return this.off(e),this}_delegate(e,t,n){var r=this;this.on(e,(function(e){for(var o=e.target.findAncestors(t,!0,r),i=0;i<o.length;i++)(e=eJ.cloneObject(e)).currentTarget=o[i],n.call(o[i],e)}))}remove(){return this.isDragging()&&this.stopDrag(),wJ._dragElements.delete(this._id),this._remove(),this}_clearCaches(){this._clearSelfAndDescendantCache(CJ),this._clearSelfAndDescendantCache(kJ),this._clearSelfAndDescendantCache(_J),this._clearSelfAndDescendantCache(AJ),this._clearSelfAndDescendantCache(RJ),this._clearSelfAndDescendantCache(PJ)}_remove(){this._clearCaches();var e=this.getParent();e&&e.children&&(e.children.splice(this.index,1),e._setChildrenIndices(),this.parent=null)}destroy(){return this.remove(),this.clearCache(),this}getAttr(e){var t="get"+eJ._capitalize(e);return eJ._isFunction(this[t])?this[t]():this.attrs[e]}getAncestors(){for(var e=this.getParent(),t=[];e;)t.push(e),e=e.getParent();return t}getAttrs(){return this.attrs||{}}setAttrs(e){return this._batchTransformChanges((()=>{var t,n;if(!e)return this;for(t in e)"children"!==t&&(n="set"+eJ._capitalize(t),eJ._isFunction(this[n])?this[n](e[t]):this._setAttr(t,e[t]))})),this}isListening(){return this._getCache(PJ,this._isListening)}_isListening(e){if(!this.listening())return!1;const t=this.getParent();return!t||t===e||this===e||t._isListening(e)}isVisible(){return this._getCache(RJ,this._isVisible)}_isVisible(e){if(!this.visible())return!1;const t=this.getParent();return!t||t===e||this===e||t._isVisible(e)}shouldDrawHit(e,t=!1){if(e)return this._isVisible(e)&&this._isListening(e);var n=this.getLayer(),r=!1;wJ._dragElements.forEach((e=>{"dragging"===e.dragStatus&&("Stage"===e.node.nodeType||e.node.getLayer()===n)&&(r=!0)}));var o=!t&&!$Q.hitOnDragEnabled&&r;return this.isListening()&&this.isVisible()&&!o}show(){return this.visible(!0),this}hide(){return this.visible(!1),this}getZIndex(){return this.index||0}getAbsoluteZIndex(){var e,t,n,r,o=this.getDepth(),i=this,a=0;return"Stage"!==i.nodeType&&function s(l){for(e=[],t=l.length,n=0;n<t;n++)r=l[n],a++,r.nodeType!==MJ&&(e=e.concat(r.getChildren().slice())),r._id===i._id&&(n=t);e.length>0&&e[0].getDepth()<=o&&s(e)}(i.getStage().getChildren()),a}getDepth(){for(var e=0,t=this.parent;t;)e++,t=t.parent;return e}_batchTransformChanges(e){this._batchingTransformChange=!0,e(),this._batchingTransformChange=!1,this._needClearTransformCache&&(this._clearCache(IJ),this._clearSelfAndDescendantCache(CJ)),this._needClearTransformCache=!1}setPosition(e){return this._batchTransformChanges((()=>{this.x(e.x),this.y(e.y)})),this}getPosition(){return{x:this.x(),y:this.y()}}getRelativePointerPosition(){if(!this.getStage())return null;var e=this.getStage().getPointerPosition();if(!e)return null;var t=this.getAbsoluteTransform().copy();return t.invert(),t.point(e)}getAbsolutePosition(e){let t=!1,n=this.parent;for(;n;){if(n.isCached()){t=!0;break}n=n.parent}t&&!e&&(e=!0);var r=this.getAbsoluteTransform(e).getMatrix(),o=new GQ,i=this.offset();return o.m=r.slice(),o.translate(i.x,i.y),o.getTranslation()}setAbsolutePosition(e){var t=this._clearTransform();this.attrs.x=t.x,this.attrs.y=t.y,delete t.x,delete t.y,this._clearCache(IJ);var n=this._getAbsoluteTransform().copy();return n.invert(),n.translate(e.x,e.y),e={x:this.attrs.x+n.getTranslation().x,y:this.attrs.y+n.getTranslation().y},this._setTransform(t),this.setPosition({x:e.x,y:e.y}),this._clearCache(IJ),this._clearSelfAndDescendantCache(CJ),this}_setTransform(e){var t;for(t in e)this.attrs[t]=e[t]}_clearTransform(){var e={x:this.x(),y:this.y(),rotation:this.rotation(),scaleX:this.scaleX(),scaleY:this.scaleY(),offsetX:this.offsetX(),offsetY:this.offsetY(),skewX:this.skewX(),skewY:this.skewY()};return this.attrs.x=0,this.attrs.y=0,this.attrs.rotation=0,this.attrs.scaleX=1,this.attrs.scaleY=1,this.attrs.offsetX=0,this.attrs.offsetY=0,this.attrs.skewX=0,this.attrs.skewY=0,e}move(e){var t=e.x,n=e.y,r=this.x(),o=this.y();return void 0!==t&&(r+=t),void 0!==n&&(o+=n),this.setPosition({x:r,y:o}),this}_eachAncestorReverse(e,t){var n,r,o=[],i=this.getParent();if(!t||t._id!==this._id){for(o.unshift(this);i&&(!t||i._id!==t._id);)o.unshift(i),i=i.parent;for(n=o.length,r=0;r<n;r++)e(o[r])}}rotate(e){return this.rotation(this.rotation()+e),this}moveToTop(){if(!this.parent)return eJ.warn("Node has no parent. moveToTop function is ignored."),!1;var e=this.index;return e<this.parent.getChildren().length-1&&(this.parent.children.splice(e,1),this.parent.children.push(this),this.parent._setChildrenIndices(),!0)}moveUp(){if(!this.parent)return eJ.warn("Node has no parent. moveUp function is ignored."),!1;var e=this.index;return e<this.parent.getChildren().length-1&&(this.parent.children.splice(e,1),this.parent.children.splice(e+1,0,this),this.parent._setChildrenIndices(),!0)}moveDown(){if(!this.parent)return eJ.warn("Node has no parent. moveDown function is ignored."),!1;var e=this.index;return e>0&&(this.parent.children.splice(e,1),this.parent.children.splice(e-1,0,this),this.parent._setChildrenIndices(),!0)}moveToBottom(){if(!this.parent)return eJ.warn("Node has no parent. moveToBottom function is ignored."),!1;var e=this.index;return e>0&&(this.parent.children.splice(e,1),this.parent.children.unshift(this),this.parent._setChildrenIndices(),!0)}setZIndex(e){if(!this.parent)return eJ.warn("Node has no parent. zIndex parameter is ignored."),this;(e<0||e>=this.parent.children.length)&&eJ.warn("Unexpected value "+e+" for zIndex property. zIndex is just index of a node in children of its parent. Expected value is from 0 to "+(this.parent.children.length-1)+".");var t=this.index;return this.parent.children.splice(t,1),this.parent.children.splice(e,0,this),this.parent._setChildrenIndices(),this}getAbsoluteOpacity(){return this._getCache(kJ,this._getAbsoluteOpacity)}_getAbsoluteOpacity(){var e=this.opacity(),t=this.getParent();return t&&!t._isUnderCache&&(e*=t.getAbsoluteOpacity()),e}moveTo(e){return this.getParent()!==e&&(this._remove(),e.add(this)),this}toObject(){var e,t,n,r,o={},i=this.getAttrs();for(e in o.attrs={},i)t=i[e],eJ.isObject(t)&&!eJ._isPlainObject(t)&&!eJ._isArray(t)||(n="function"==typeof this[e]&&this[e],delete i[e],r=n?n.call(this):null,i[e]=t,r!==t&&(o.attrs[e]=t));return o.className=this.getClassName(),eJ._prepareToStringify(o)}toJSON(){return JSON.stringify(this.toObject())}getParent(){return this.parent}findAncestors(e,t,n){var r=[];t&&this._isMatch(e)&&r.push(this);for(var o=this.parent;o;){if(o===n)return r;o._isMatch(e)&&r.push(o),o=o.parent}return r}isAncestorOf(e){return!1}findAncestor(e,t,n){return this.findAncestors(e,t,n)[0]}_isMatch(e){if(!e)return!1;if("function"==typeof e)return e(this);var t,n,r=e.replace(/ /g,"").split(","),o=r.length;for(t=0;t<o;t++)if(n=r[t],eJ.isValidSelector(n)||(eJ.warn('Selector "'+n+'" is invalid. Allowed selectors examples are "#foo", ".bar" or "Group".'),eJ.warn('If you have a custom shape with such className, please change it to start with upper letter like "Triangle".'),eJ.warn("Konva is awesome, right?")),"#"===n.charAt(0)){if(this.id()===n.slice(1))return!0}else if("."===n.charAt(0)){if(this.hasName(n.slice(1)))return!0}else if(this.className===n||this.nodeType===n)return!0;return!1}getLayer(){var e=this.getParent();return e?e.getLayer():null}getStage(){return this._getCache(AJ,this._getStage)}_getStage(){var e=this.getParent();return e?e.getStage():void 0}fire(e,t={},n){return t.target=t.target||this,n?this._fireAndBubble(e,t):this._fire(e,t),this}getAbsoluteTransform(e){return e?this._getAbsoluteTransform(e):this._getCache(CJ,this._getAbsoluteTransform)}_getAbsoluteTransform(e){var t;if(e)return t=new GQ,this._eachAncestorReverse((function(e){var n=e.transformsEnabled();"all"===n?t.multiply(e.getTransform()):"position"===n&&t.translate(e.x()-e.offsetX(),e.y()-e.offsetY())}),e),t;t=this._cache.get(CJ)||new GQ,this.parent?this.parent.getAbsoluteTransform().copyInto(t):t.reset();var n=this.transformsEnabled();if("all"===n)t.multiply(this.getTransform());else if("position"===n){const e=this.attrs.x||0,n=this.attrs.y||0,r=this.attrs.offsetX||0,o=this.attrs.offsetY||0;t.translate(e-r,n-o)}return t.dirty=!1,t}getAbsoluteScale(e){for(var t=this;t;)t._isUnderCache&&(e=t),t=t.getParent();const n=this.getAbsoluteTransform(e).decompose();return{x:n.scaleX,y:n.scaleY}}getAbsoluteRotation(){return this.getAbsoluteTransform().decompose().rotation}getTransform(){return this._getCache(IJ,this._getTransform)}_getTransform(){var e,t,n=this._cache.get(IJ)||new GQ;n.reset();var r=this.x(),o=this.y(),i=$Q.getAngle(this.rotation()),a=null!==(e=this.attrs.scaleX)&&void 0!==e?e:1,s=null!==(t=this.attrs.scaleY)&&void 0!==t?t:1,l=this.attrs.skewX||0,c=this.attrs.skewY||0,u=this.attrs.offsetX||0,d=this.attrs.offsetY||0;return 0===r&&0===o||n.translate(r,o),0!==i&&n.rotate(i),0===l&&0===c||n.skew(l,c),1===a&&1===s||n.scale(a,s),0===u&&0===d||n.translate(-1*u,-1*d),n.dirty=!1,n}clone(e){var t,n,r,o,i,a=eJ.cloneObject(this.attrs);for(t in e)a[t]=e[t];var s=new this.constructor(a);for(t in this.eventListeners)for(r=(n=this.eventListeners[t]).length,o=0;o<r;o++)(i=n[o]).name.indexOf("konva")<0&&(s.eventListeners[t]||(s.eventListeners[t]=[]),s.eventListeners[t].push(i));return s}_toKonvaCanvas(e){e=e||{};var t=this.getClientRect(),n=this.getStage(),r=void 0!==e.x?e.x:Math.floor(t.x),o=void 0!==e.y?e.y:Math.floor(t.y),i=e.pixelRatio||1,a=new bJ({width:e.width||Math.ceil(t.width)||(n?n.width():0),height:e.height||Math.ceil(t.height)||(n?n.height():0),pixelRatio:i}),s=a.getContext();return!1===e.imageSmoothingEnabled&&(s._context.imageSmoothingEnabled=!1),s.save(),(r||o)&&s.translate(-1*r,-1*o),this.drawScene(a),s.restore(),a}toCanvas(e){return this._toKonvaCanvas(e)._canvas}toDataURL(e){var t=(e=e||{}).mimeType||null,n=e.quality||null,r=this._toKonvaCanvas(e).toDataURL(t,n);return e.callback&&e.callback(r),r}toImage(e){return new Promise(((t,n)=>{try{const n=null==e?void 0:e.callback;n&&delete e.callback,eJ._urlToImage(this.toDataURL(e),(function(e){t(e),null==n||n(e)}))}catch(Hhe){n(Hhe)}}))}toBlob(e){return new Promise(((t,n)=>{try{const n=null==e?void 0:e.callback;n&&delete e.callback,this.toCanvas(e).toBlob((e=>{t(e),null==n||n(e)}))}catch(Hhe){n(Hhe)}}))}setSize(e){return this.width(e.width),this.height(e.height),this}getSize(){return{width:this.width(),height:this.height()}}getClassName(){return this.className||this.nodeType}getType(){return this.nodeType}getDragDistance(){return void 0!==this.attrs.dragDistance?this.attrs.dragDistance:this.parent?this.parent.getDragDistance():$Q.dragDistance}_off(e,t,n){var r,o,i,a=this.eventListeners[e];for(r=0;r<a.length;r++)if(o=a[r].name,i=a[r].handler,!("konva"===o&&"konva"!==t||t&&o!==t||n&&n!==i)){if(a.splice(r,1),0===a.length){delete this.eventListeners[e];break}r--}}_fireChangeEvent(e,t,n){this._fire(e+"Change",{oldVal:t,newVal:n})}addName(e){if(!this.hasName(e)){var t=this.name(),n=t?t+" "+e:e;this.name(n)}return this}hasName(e){if(!e)return!1;const t=this.name();return!!t&&-1!==(t||"").split(/\s/g).indexOf(e)}removeName(e){var t=(this.name()||"").split(/\s/g),n=t.indexOf(e);return-1!==n&&(t.splice(n,1),this.name(t.join(" "))),this}setAttr(e,t){var n=this["set"+eJ._capitalize(e)];return eJ._isFunction(n)?n.call(this,t):this._setAttr(e,t),this}_requestDraw(){if($Q.autoDrawEnabled){const e=this.getLayer()||this.getStage();null==e||e.batchDraw()}}_setAttr(e,t){var n=this.attrs[e];(n!==t||eJ.isObject(t))&&(null==t?delete this.attrs[e]:this.attrs[e]=t,this._shouldFireChangeEvents&&this._fireChangeEvent(e,n,t),this._requestDraw())}_setComponentAttr(e,t,n){var r;void 0!==n&&((r=this.attrs[e])||(this.attrs[e]=this.getAttr(e)),this.attrs[e][t]=n,this._fireChangeEvent(e,r,n))}_fireAndBubble(e,t,n){if(t&&this.nodeType===MJ&&(t.target=this),!((e===LJ||e===OJ)&&(n&&(this===n||this.isAncestorOf&&this.isAncestorOf(n))||"Stage"===this.nodeType&&!n))){this._fire(e,t);var r=(e===LJ||e===OJ)&&n&&n.isAncestorOf&&n.isAncestorOf(this)&&!n.isAncestorOf(this.parent);(t&&!t.cancelBubble||!t)&&this.parent&&this.parent.isListening()&&!r&&(n&&n.parent?this._fireAndBubble.call(this.parent,e,t,n):this._fireAndBubble.call(this.parent,e,t))}}_getProtoListeners(e){let t=this._cache.get(SJ);if(!t){t={};let e=Object.getPrototypeOf(this);for(;e;)if(e.eventListeners){for(var n in e.eventListeners){const r=e.eventListeners[n],o=t[n]||[];t[n]=r.concat(o)}e=Object.getPrototypeOf(e)}else e=Object.getPrototypeOf(e);this._cache.set(SJ,t)}return t[e]}_fire(e,t){(t=t||{}).currentTarget=this,t.type=e;const n=this._getProtoListeners(e);if(n)for(var r=0;r<n.length;r++)n[r].handler.call(this,t);const o=this.eventListeners[e];if(o)for(r=0;r<o.length;r++)o[r].handler.call(this,t)}draw(){return this.drawScene(),this.drawHit(),this}_createDragElement(e){var t=e?e.pointerId:void 0,n=this.getStage(),r=this.getAbsolutePosition(),o=n._getPointerById(t)||n._changedPointerPositions[0]||r;wJ._dragElements.set(this._id,{node:this,startPointerPos:o,offset:{x:o.x-r.x,y:o.y-r.y},dragStatus:"ready",pointerId:t})}startDrag(e,t=!0){wJ._dragElements.has(this._id)||this._createDragElement(e);wJ._dragElements.get(this._id).dragStatus="dragging",this.fire("dragstart",{type:"dragstart",target:this,evt:e&&e.evt},t)}_setDragPosition(e,t){const n=this.getStage()._getPointerById(t.pointerId);if(n){var r={x:n.x-t.offset.x,y:n.y-t.offset.y},o=this.dragBoundFunc();if(void 0!==o){const t=o.call(this,r,e);t?r=t:eJ.warn("dragBoundFunc did not return any value. That is unexpected behavior. You must return new absolute position from dragBoundFunc.")}this._lastPos&&this._lastPos.x===r.x&&this._lastPos.y===r.y||(this.setAbsolutePosition(r),this._requestDraw()),this._lastPos=r}}stopDrag(e){const t=wJ._dragElements.get(this._id);t&&(t.dragStatus="stopped"),wJ._endDragBefore(e),wJ._endDragAfter(e)}setDraggable(e){this._setAttr("draggable",e),this._dragChange()}isDragging(){const e=wJ._dragElements.get(this._id);return!!e&&"dragging"===e.dragStatus}_listenDrag(){this._dragCleanup(),this.on("mousedown.konva touchstart.konva",(function(e){if((!(void 0!==e.evt.button)||$Q.dragButtons.indexOf(e.evt.button)>=0)&&!this.isDragging()){var t=!1;wJ._dragElements.forEach((e=>{this.isAncestorOf(e.node)&&(t=!0)})),t||this._createDragElement(e)}}))}_dragChange(){if(this.attrs.draggable)this._listenDrag();else{if(this._dragCleanup(),!this.getStage())return;const e=wJ._dragElements.get(this._id),t=e&&"dragging"===e.dragStatus,n=e&&"ready"===e.dragStatus;t?this.stopDrag():n&&wJ._dragElements.delete(this._id)}}_dragCleanup(){this.off("mousedown.konva"),this.off("touchstart.konva")}isClientRectOnScreen(e={x:0,y:0}){const t=this.getStage();if(!t)return!1;const n={x:-e.x,y:-e.y,width:t.width()+2*e.x,height:t.height()+2*e.y};return eJ.haveIntersection(n,this.getClientRect())}static create(e,t){return eJ._isString(e)&&(e=JSON.parse(e)),this._createNode(e,t)}static _createNode(e,t){var n,r,o,i=zJ.prototype.getClassName.call(e),a=e.children;t&&(e.attrs.container=t),$Q[i]||(eJ.warn('Can not find a node with class name "'+i+'". Fallback to "Shape".'),i="Shape");if(n=new(0,$Q[i])(e.attrs),a)for(r=a.length,o=0;o<r;o++)n.add(zJ._createNode(a[o]));return n}}zJ.prototype.nodeType="Node",zJ.prototype._attrsAffectingSize=[],zJ.prototype.eventListeners={},zJ.prototype.on.call(zJ.prototype,NJ,(function(){this._batchingTransformChange?this._needClearTransformCache=!0:(this._clearCache(IJ),this._clearSelfAndDescendantCache(CJ))})),zJ.prototype.on.call(zJ.prototype,"visibleChange.konva",(function(){this._clearSelfAndDescendantCache(RJ)})),zJ.prototype.on.call(zJ.prototype,"listeningChange.konva",(function(){this._clearSelfAndDescendantCache(PJ)})),zJ.prototype.on.call(zJ.prototype,"opacityChange.konva",(function(){this._clearSelfAndDescendantCache(kJ)}));const BJ=dJ.addGetterSetter;BJ(zJ,"zIndex"),BJ(zJ,"absolutePosition"),BJ(zJ,"position"),BJ(zJ,"x",0,rJ()),BJ(zJ,"y",0,rJ()),BJ(zJ,"globalCompositeOperation","source-over",aJ()),BJ(zJ,"opacity",1,rJ()),BJ(zJ,"name","",aJ()),BJ(zJ,"id","",aJ()),BJ(zJ,"rotation",0,rJ()),dJ.addComponentsGetterSetter(zJ,"scale",["x","y"]),BJ(zJ,"scaleX",1,rJ()),BJ(zJ,"scaleY",1,rJ()),dJ.addComponentsGetterSetter(zJ,"skew",["x","y"]),BJ(zJ,"skewX",0,rJ()),BJ(zJ,"skewY",0,rJ()),dJ.addComponentsGetterSetter(zJ,"offset",["x","y"]),BJ(zJ,"offsetX",0,rJ()),BJ(zJ,"offsetY",0,rJ()),BJ(zJ,"dragDistance",null,rJ()),BJ(zJ,"width",0,rJ()),BJ(zJ,"height",0,rJ()),BJ(zJ,"listening",!0,lJ()),BJ(zJ,"preventDefault",!0,lJ()),BJ(zJ,"filters",null,(function(e){return this._filterUpToDate=!1,e})),BJ(zJ,"visible",!0,lJ()),BJ(zJ,"transformsEnabled","all",aJ()),BJ(zJ,"size"),BJ(zJ,"dragBoundFunc"),BJ(zJ,"draggable",!1,lJ()),dJ.backCompat(zJ,{rotateDeg:"rotate",setRotationDeg:"setRotation",getRotationDeg:"getRotation"});class jJ extends zJ{constructor(){super(...arguments),this.children=[]}getChildren(e){if(!e)return this.children||[];const t=this.children||[];var n=[];return t.forEach((function(t){e(t)&&n.push(t)})),n}hasChildren(){return this.getChildren().length>0}removeChildren(){return this.getChildren().forEach((e=>{e.parent=null,e.index=0,e.remove()})),this.children=[],this._requestDraw(),this}destroyChildren(){return this.getChildren().forEach((e=>{e.parent=null,e.index=0,e.destroy()})),this.children=[],this._requestDraw(),this}add(...e){if(arguments.length>1){for(var t=0;t<arguments.length;t++)this.add(arguments[t]);return this}var n=e[0];return n.getParent()?(n.moveTo(this),this):(this._validateAdd(n),n.index=this.getChildren().length,n.parent=this,n._clearCaches(),this.getChildren().push(n),this._fire("add",{child:n}),this._requestDraw(),this)}destroy(){return this.hasChildren()&&this.destroyChildren(),super.destroy(),this}find(e){return this._generalFind(e,!1)}findOne(e){var t=this._generalFind(e,!0);return t.length>0?t[0]:void 0}_generalFind(e,t){var n=[];return this._descendants((r=>{const o=r._isMatch(e);return o&&n.push(r),!(!o||!t)})),n}_descendants(e){let t=!1;const n=this.getChildren();for(const r of n){if(t=e(r),t)return!0;if(r.hasChildren()&&(t=r._descendants(e),t))return!0}return!1}toObject(){var e=zJ.prototype.toObject.call(this);return e.children=[],this.getChildren().forEach((t=>{e.children.push(t.toObject())})),e}isAncestorOf(e){for(var t=e.getParent();t;){if(t._id===this._id)return!0;t=t.getParent()}return!1}clone(e){var t=zJ.prototype.clone.call(this,e);return this.getChildren().forEach((function(e){t.add(e.clone())})),t}getAllIntersections(e){var t=[];return this.find("Shape").forEach((function(n){n.isVisible()&&n.intersects(e)&&t.push(n)})),t}_clearSelfAndDescendantCache(e){var t;super._clearSelfAndDescendantCache(e),this.isCached()||null===(t=this.children)||void 0===t||t.forEach((function(t){t._clearSelfAndDescendantCache(e)}))}_setChildrenIndices(){var e;null===(e=this.children)||void 0===e||e.forEach((function(e,t){e.index=t})),this._requestDraw()}drawScene(e,t){var n=this.getLayer(),r=e||n&&n.getCanvas(),o=r&&r.getContext(),i=this._getCanvasCache(),a=i&&i.scene,s=r&&r.isCache;if(!this.isVisible()&&!s)return this;if(a){o.save();var l=this.getAbsoluteTransform(t).getMatrix();o.transform(l[0],l[1],l[2],l[3],l[4],l[5]),this._drawCachedSceneCanvas(o),o.restore()}else this._drawChildren("drawScene",r,t);return this}drawHit(e,t){if(!this.shouldDrawHit(t))return this;var n=this.getLayer(),r=e||n&&n.hitCanvas,o=r&&r.getContext(),i=this._getCanvasCache();if(i&&i.hit){o.save();var a=this.getAbsoluteTransform(t).getMatrix();o.transform(a[0],a[1],a[2],a[3],a[4],a[5]),this._drawCachedHitCanvas(o),o.restore()}else this._drawChildren("drawHit",r,t);return this}_drawChildren(e,t,n){var r,o=t&&t.getContext(),i=this.clipWidth(),a=this.clipHeight(),s=this.clipFunc(),l=i&&a||s;const c=n===this;if(l){o.save();var u=this.getAbsoluteTransform(n),d=u.getMatrix();if(o.transform(d[0],d[1],d[2],d[3],d[4],d[5]),o.beginPath(),s)s.call(this,o,this);else{var h=this.clipX(),f=this.clipY();o.rect(h,f,i,a)}o.clip(),d=u.copy().invert().getMatrix(),o.transform(d[0],d[1],d[2],d[3],d[4],d[5])}var p=!c&&"source-over"!==this.globalCompositeOperation()&&"drawScene"===e;p&&(o.save(),o._applyGlobalCompositeOperation(this)),null===(r=this.children)||void 0===r||r.forEach((function(r){r[e](t,n)})),p&&o.restore(),l&&o.restore()}getClientRect(e){var t,n,r,o,i,a=(e=e||{}).skipTransform,s=e.relativeTo,l={x:1/0,y:1/0,width:0,height:0},c=this;null===(t=this.children)||void 0===t||t.forEach((function(t){if(t.visible()){var a=t.getClientRect({relativeTo:c,skipShadow:e.skipShadow,skipStroke:e.skipStroke});0===a.width&&0===a.height||(void 0===n?(n=a.x,r=a.y,o=a.x+a.width,i=a.y+a.height):(n=Math.min(n,a.x),r=Math.min(r,a.y),o=Math.max(o,a.x+a.width),i=Math.max(i,a.y+a.height)))}}));for(var u=this.find("Shape"),d=!1,h=0;h<u.length;h++){if(u[h]._isVisible(this)){d=!0;break}}return l=d&&void 0!==n?{x:n,y:r,width:o-n,height:i-r}:{x:0,y:0,width:0,height:0},a?l:this._transformedRect(l,s)}}dJ.addComponentsGetterSetter(jJ,"clip",["x","y","width","height"]),dJ.addGetterSetter(jJ,"clipX",void 0,rJ()),dJ.addGetterSetter(jJ,"clipY",void 0,rJ()),dJ.addGetterSetter(jJ,"clipWidth",void 0,rJ()),dJ.addGetterSetter(jJ,"clipHeight",void 0,rJ()),dJ.addGetterSetter(jJ,"clipFunc");const FJ=new Map,HJ=void 0!==$Q._global.PointerEvent;function WJ(e){return FJ.get(e)}function VJ(e){return{evt:e,pointerId:e.pointerId}}function $J(e,t){return FJ.get(e)===t}function UJ(e,t){GJ(e);t.getStage()&&(FJ.set(e,t),HJ&&t._fire("gotpointercapture",VJ(new PointerEvent("gotpointercapture"))))}function GJ(e,t){const n=FJ.get(e);if(!n)return;const r=n.getStage();r&&r.content,FJ.delete(e),HJ&&n._fire("lostpointercapture",VJ(new PointerEvent("lostpointercapture")))}var qJ="mouseleave",YJ="mouseover",ZJ="mouseenter",XJ="mousemove",KJ="mousedown",QJ="mouseup",JJ="pointermove",e0="pointerdown",t0="pointerup",n0="pointercancel",r0="pointerout",o0="pointerleave",i0="pointerover",a0="pointerenter",s0="contextmenu",l0="touchstart",c0="touchend",u0="touchmove",d0="touchcancel",h0="wheel",f0=[[ZJ,"_pointerenter"],[KJ,"_pointerdown"],[XJ,"_pointermove"],[QJ,"_pointerup"],[qJ,"_pointerleave"],[l0,"_pointerdown"],[u0,"_pointermove"],[c0,"_pointerup"],[d0,"_pointercancel"],[YJ,"_pointerover"],[h0,"_wheel"],[s0,"_contextmenu"],[e0,"_pointerdown"],[JJ,"_pointermove"],[t0,"_pointerup"],[n0,"_pointercancel"],["lostpointercapture","_lostpointercapture"]];const p0={mouse:{[r0]:"mouseout",[o0]:qJ,[i0]:YJ,[a0]:ZJ,[JJ]:XJ,[e0]:KJ,[t0]:QJ,[n0]:"mousecancel",pointerclick:"click",pointerdblclick:"dblclick"},touch:{[r0]:"touchout",[o0]:"touchleave",[i0]:"touchover",[a0]:"touchenter",[JJ]:u0,[e0]:l0,[t0]:c0,[n0]:d0,pointerclick:"tap",pointerdblclick:"dbltap"},pointer:{[r0]:r0,[o0]:o0,[i0]:i0,[a0]:a0,[JJ]:JJ,[e0]:e0,[t0]:t0,[n0]:n0,pointerclick:"pointerclick",pointerdblclick:"pointerdblclick"}},g0=e=>e.indexOf("pointer")>=0?"pointer":e.indexOf("touch")>=0?"touch":"mouse",m0=e=>{const t=g0(e);return"pointer"===t?$Q.pointerEventsEnabled&&p0.pointer:"touch"===t?p0.touch:"mouse"===t?p0.mouse:void 0};function v0(e={}){return(e.clipFunc||e.clipWidth||e.clipHeight)&&eJ.warn("Stage does not support clipping. Please use clip for Layers or Groups."),e}const y0=[];class b0 extends jJ{constructor(e){super(v0(e)),this._pointerPositions=[],this._changedPointerPositions=[],this._buildDOM(),this._bindContentEvents(),y0.push(this),this.on("widthChange.konva heightChange.konva",this._resizeDOM),this.on("visibleChange.konva",this._checkVisibility),this.on("clipWidthChange.konva clipHeightChange.konva clipFuncChange.konva",(()=>{v0(this.attrs)})),this._checkVisibility()}_validateAdd(e){const t="Layer"===e.getType(),n="FastLayer"===e.getType();t||n||eJ.throw("You may only add layers to the stage.")}_checkVisibility(){if(!this.content)return;const e=this.visible()?"":"none";this.content.style.display=e}setContainer(e){if("string"==typeof e){if("."===e.charAt(0)){var t=e.slice(1);e=document.getElementsByClassName(t)[0]}else{var n;n="#"!==e.charAt(0)?e:e.slice(1),e=document.getElementById(n)}if(!e)throw"Can not find container in document with id "+n}return this._setAttr("container",e),this.content&&(this.content.parentElement&&this.content.parentElement.removeChild(this.content),e.appendChild(this.content)),this}shouldDrawHit(){return!0}clear(){var e,t=this.children,n=t.length;for(e=0;e<n;e++)t[e].clear();return this}clone(e){return e||(e={}),e.container="undefined"!=typeof document&&document.createElement("div"),jJ.prototype.clone.call(this,e)}destroy(){super.destroy();var e=this.content;e&&eJ._isInDocument(e)&&this.container().removeChild(e);var t=y0.indexOf(this);return t>-1&&y0.splice(t,1),eJ.releaseCanvas(this.bufferCanvas._canvas,this.bufferHitCanvas._canvas),this}getPointerPosition(){const e=this._pointerPositions[0]||this._changedPointerPositions[0];return e?{x:e.x,y:e.y}:(eJ.warn("Pointer position is missing and not registered by the stage. Looks like it is outside of the stage container. You can set it manually from event: stage.setPointersPositions(event);"),null)}_getPointerById(e){return this._pointerPositions.find((t=>t.id===e))}getPointersPositions(){return this._pointerPositions}getStage(){return this}getContent(){return this.content}_toKonvaCanvas(e){(e=e||{}).x=e.x||0,e.y=e.y||0,e.width=e.width||this.width(),e.height=e.height||this.height();var t=new bJ({width:e.width,height:e.height,pixelRatio:e.pixelRatio||1}),n=t.getContext()._context,r=this.children;return(e.x||e.y)&&n.translate(-1*e.x,-1*e.y),r.forEach((function(t){if(t.isVisible()){var r=t._toKonvaCanvas(e);n.drawImage(r._canvas,e.x,e.y,r.getWidth()/r.getPixelRatio(),r.getHeight()/r.getPixelRatio())}})),t}getIntersection(e){if(!e)return null;var t,n=this.children;for(t=n.length-1;t>=0;t--){const r=n[t].getIntersection(e);if(r)return r}return null}_resizeDOM(){var e=this.width(),t=this.height();this.content&&(this.content.style.width=e+"px",this.content.style.height=t+"px"),this.bufferCanvas.setSize(e,t),this.bufferHitCanvas.setSize(e,t),this.children.forEach((n=>{n.setSize({width:e,height:t}),n.draw()}))}add(e,...t){if(arguments.length>1){for(var n=0;n<arguments.length;n++)this.add(arguments[n]);return this}super.add(e);var r=this.children.length;return r>5&&eJ.warn("The stage has "+r+" layers. Recommended maximum number of layers is 3-5. Adding more layers into the stage may drop the performance. Rethink your tree structure, you can use Konva.Group."),e.setSize({width:this.width(),height:this.height()}),e.draw(),$Q.isBrowser&&this.content.appendChild(e.canvas._canvas),this}getParent(){return null}getLayer(){return null}hasPointerCapture(e){return $J(e,this)}setPointerCapture(e){UJ(e,this)}releaseCapture(e){GJ(e)}getLayers(){return this.children}_bindContentEvents(){$Q.isBrowser&&f0.forEach((([e,t])=>{this.content.addEventListener(e,(e=>{this[t](e)}),{passive:!1})}))}_pointerenter(e){this.setPointersPositions(e);const t=m0(e.type);this._fire(t.pointerenter,{evt:e,target:this,currentTarget:this})}_pointerover(e){this.setPointersPositions(e);const t=m0(e.type);this._fire(t.pointerover,{evt:e,target:this,currentTarget:this})}_getTargetShape(e){let t=this[e+"targetShape"];return t&&!t.getStage()&&(t=null),t}_pointerleave(e){const t=m0(e.type),n=g0(e.type);if(t){this.setPointersPositions(e);var r=this._getTargetShape(n),o=!wJ.isDragging||$Q.hitOnDragEnabled;r&&o?(r._fireAndBubble(t.pointerout,{evt:e}),r._fireAndBubble(t.pointerleave,{evt:e}),this._fire(t.pointerleave,{evt:e,target:this,currentTarget:this}),this[n+"targetShape"]=null):o&&(this._fire(t.pointerleave,{evt:e,target:this,currentTarget:this}),this._fire(t.pointerout,{evt:e,target:this,currentTarget:this})),this.pointerPos=void 0,this._pointerPositions=[]}}_pointerdown(e){const t=m0(e.type),n=g0(e.type);if(t){this.setPointersPositions(e);var r=!1;this._changedPointerPositions.forEach((o=>{var i=this.getIntersection(o);wJ.justDragged=!1,$Q["_"+n+"ListenClick"]=!0;if(!(i&&i.isListening()))return;$Q.capturePointerEventsEnabled&&i.setPointerCapture(o.id),this[n+"ClickStartShape"]=i,i._fireAndBubble(t.pointerdown,{evt:e,pointerId:o.id}),r=!0;const a=e.type.indexOf("touch")>=0;i.preventDefault()&&e.cancelable&&a&&e.preventDefault()})),r||this._fire(t.pointerdown,{evt:e,target:this,currentTarget:this,pointerId:this._pointerPositions[0].id})}}_pointermove(e){const t=m0(e.type),n=g0(e.type);if(!t)return;if(wJ.isDragging&&wJ.node.preventDefault()&&e.cancelable&&e.preventDefault(),this.setPointersPositions(e),!(!wJ.isDragging||$Q.hitOnDragEnabled))return;var r={};let o=!1;var i=this._getTargetShape(n);this._changedPointerPositions.forEach((a=>{const s=WJ(a.id)||this.getIntersection(a),l=a.id,c={evt:e,pointerId:l};var u=i!==s;if(u&&i&&(i._fireAndBubble(t.pointerout,Object.assign({},c),s),i._fireAndBubble(t.pointerleave,Object.assign({},c),s)),s){if(r[s._id])return;r[s._id]=!0}s&&s.isListening()?(o=!0,u&&(s._fireAndBubble(t.pointerover,Object.assign({},c),i),s._fireAndBubble(t.pointerenter,Object.assign({},c),i),this[n+"targetShape"]=s),s._fireAndBubble(t.pointermove,Object.assign({},c))):i&&(this._fire(t.pointerover,{evt:e,target:this,currentTarget:this,pointerId:l}),this[n+"targetShape"]=null)})),o||this._fire(t.pointermove,{evt:e,target:this,currentTarget:this,pointerId:this._changedPointerPositions[0].id})}_pointerup(e){const t=m0(e.type),n=g0(e.type);if(!t)return;this.setPointersPositions(e);const r=this[n+"ClickStartShape"],o=this[n+"ClickEndShape"];var i={};let a=!1;this._changedPointerPositions.forEach((s=>{const l=WJ(s.id)||this.getIntersection(s);if(l){if(l.releaseCapture(s.id),i[l._id])return;i[l._id]=!0}const c=s.id,u={evt:e,pointerId:c};let d=!1;$Q["_"+n+"InDblClickWindow"]?(d=!0,clearTimeout(this[n+"DblTimeout"])):wJ.justDragged||($Q["_"+n+"InDblClickWindow"]=!0,clearTimeout(this[n+"DblTimeout"])),this[n+"DblTimeout"]=setTimeout((function(){$Q["_"+n+"InDblClickWindow"]=!1}),$Q.dblClickWindow),l&&l.isListening()?(a=!0,this[n+"ClickEndShape"]=l,l._fireAndBubble(t.pointerup,Object.assign({},u)),$Q["_"+n+"ListenClick"]&&r&&r===l&&(l._fireAndBubble(t.pointerclick,Object.assign({},u)),d&&o&&o===l&&l._fireAndBubble(t.pointerdblclick,Object.assign({},u)))):(this[n+"ClickEndShape"]=null,$Q["_"+n+"ListenClick"]&&this._fire(t.pointerclick,{evt:e,target:this,currentTarget:this,pointerId:c}),d&&this._fire(t.pointerdblclick,{evt:e,target:this,currentTarget:this,pointerId:c}))})),a||this._fire(t.pointerup,{evt:e,target:this,currentTarget:this,pointerId:this._changedPointerPositions[0].id}),$Q["_"+n+"ListenClick"]=!1,e.cancelable&&"touch"!==n&&e.preventDefault()}_contextmenu(e){this.setPointersPositions(e);var t=this.getIntersection(this.getPointerPosition());t&&t.isListening()?t._fireAndBubble(s0,{evt:e}):this._fire(s0,{evt:e,target:this,currentTarget:this})}_wheel(e){this.setPointersPositions(e);var t=this.getIntersection(this.getPointerPosition());t&&t.isListening()?t._fireAndBubble(h0,{evt:e}):this._fire(h0,{evt:e,target:this,currentTarget:this})}_pointercancel(e){this.setPointersPositions(e);const t=WJ(e.pointerId)||this.getIntersection(this.getPointerPosition());t&&t._fireAndBubble(t0,VJ(e)),GJ(e.pointerId)}_lostpointercapture(e){GJ(e.pointerId)}setPointersPositions(e){var t=this._getContentPosition(),n=null,r=null;void 0!==(e=e||window.event).touches?(this._pointerPositions=[],this._changedPointerPositions=[],Array.prototype.forEach.call(e.touches,(e=>{this._pointerPositions.push({id:e.identifier,x:(e.clientX-t.left)/t.scaleX,y:(e.clientY-t.top)/t.scaleY})})),Array.prototype.forEach.call(e.changedTouches||e.touches,(e=>{this._changedPointerPositions.push({id:e.identifier,x:(e.clientX-t.left)/t.scaleX,y:(e.clientY-t.top)/t.scaleY})}))):(n=(e.clientX-t.left)/t.scaleX,r=(e.clientY-t.top)/t.scaleY,this.pointerPos={x:n,y:r},this._pointerPositions=[{x:n,y:r,id:eJ._getFirstPointerId(e)}],this._changedPointerPositions=[{x:n,y:r,id:eJ._getFirstPointerId(e)}])}_setPointerPosition(e){eJ.warn('Method _setPointerPosition is deprecated. Use "stage.setPointersPositions(event)" instead.'),this.setPointersPositions(e)}_getContentPosition(){if(!this.content||!this.content.getBoundingClientRect)return{top:0,left:0,scaleX:1,scaleY:1};var e=this.content.getBoundingClientRect();return{top:e.top,left:e.left,scaleX:e.width/this.content.clientWidth||1,scaleY:e.height/this.content.clientHeight||1}}_buildDOM(){if(this.bufferCanvas=new bJ({width:this.width(),height:this.height()}),this.bufferHitCanvas=new xJ({pixelRatio:1,width:this.width(),height:this.height()}),$Q.isBrowser){var e=this.container();if(!e)throw"Stage has no container. A container is required.";e.innerHTML="",this.content=document.createElement("div"),this.content.style.position="relative",this.content.style.userSelect="none",this.content.className="konvajs-content",this.content.setAttribute("role","presentation"),e.appendChild(this.content),this._resizeDOM()}}cache(){return eJ.warn("Cache function is not allowed for stage. You may use cache only for layers, groups and shapes."),this}clearCache(){return this}batchDraw(){return this.getChildren().forEach((function(e){e.batchDraw()})),this}}b0.prototype.nodeType="Stage",UQ(b0),dJ.addGetterSetter(b0,"container");var x0="hasShadow",w0="shadowRGBA",k0="patternImage",S0="linearGradient",C0="radialGradient";let _0;function E0(){return _0||(_0=eJ.createCanvasElement().getContext("2d"),_0)}const P0={};class L0 extends zJ{constructor(e){let t;for(super(e);t=eJ.getRandomColor(),!t||t in P0;);this.colorKey=t,P0[t]=this}getContext(){return eJ.warn("shape.getContext() method is deprecated. Please do not use it."),this.getLayer().getContext()}getCanvas(){return eJ.warn("shape.getCanvas() method is deprecated. Please do not use it."),this.getLayer().getCanvas()}getSceneFunc(){return this.attrs.sceneFunc||this._sceneFunc}getHitFunc(){return this.attrs.hitFunc||this._hitFunc}hasShadow(){return this._getCache(x0,this._hasShadow)}_hasShadow(){return this.shadowEnabled()&&0!==this.shadowOpacity()&&!!(this.shadowColor()||this.shadowBlur()||this.shadowOffsetX()||this.shadowOffsetY())}_getFillPattern(){return this._getCache(k0,this.__getFillPattern)}__getFillPattern(){if(this.fillPatternImage()){const e=E0().createPattern(this.fillPatternImage(),this.fillPatternRepeat()||"repeat");if(e&&e.setTransform){const t=new GQ;t.translate(this.fillPatternX(),this.fillPatternY()),t.rotate($Q.getAngle(this.fillPatternRotation())),t.scale(this.fillPatternScaleX(),this.fillPatternScaleY()),t.translate(-1*this.fillPatternOffsetX(),-1*this.fillPatternOffsetY());const n=t.getMatrix(),r="undefined"==typeof DOMMatrix?{a:n[0],b:n[1],c:n[2],d:n[3],e:n[4],f:n[5]}:new DOMMatrix(n);e.setTransform(r)}return e}}_getLinearGradient(){return this._getCache(S0,this.__getLinearGradient)}__getLinearGradient(){var e=this.fillLinearGradientColorStops();if(e){for(var t=E0(),n=this.fillLinearGradientStartPoint(),r=this.fillLinearGradientEndPoint(),o=t.createLinearGradient(n.x,n.y,r.x,r.y),i=0;i<e.length;i+=2)o.addColorStop(e[i],e[i+1]);return o}}_getRadialGradient(){return this._getCache(C0,this.__getRadialGradient)}__getRadialGradient(){var e=this.fillRadialGradientColorStops();if(e){for(var t=E0(),n=this.fillRadialGradientStartPoint(),r=this.fillRadialGradientEndPoint(),o=t.createRadialGradient(n.x,n.y,this.fillRadialGradientStartRadius(),r.x,r.y,this.fillRadialGradientEndRadius()),i=0;i<e.length;i+=2)o.addColorStop(e[i],e[i+1]);return o}}getShadowRGBA(){return this._getCache(w0,this._getShadowRGBA)}_getShadowRGBA(){if(this.hasShadow()){var e=eJ.colorToRGBA(this.shadowColor());return e?"rgba("+e.r+","+e.g+","+e.b+","+e.a*(this.shadowOpacity()||1)+")":void 0}}hasFill(){return this._calculate("hasFill",["fillEnabled","fill","fillPatternImage","fillLinearGradientColorStops","fillRadialGradientColorStops"],(()=>this.fillEnabled()&&!!(this.fill()||this.fillPatternImage()||this.fillLinearGradientColorStops()||this.fillRadialGradientColorStops())))}hasStroke(){return this._calculate("hasStroke",["strokeEnabled","strokeWidth","stroke","strokeLinearGradientColorStops"],(()=>this.strokeEnabled()&&this.strokeWidth()&&!(!this.stroke()&&!this.strokeLinearGradientColorStops())))}hasHitStroke(){const e=this.hitStrokeWidth();return"auto"===e?this.hasStroke():this.strokeEnabled()&&!!e}intersects(e){var t=this.getStage().bufferHitCanvas;return t.getContext().clear(),this.drawHit(t,null,!0),t.context.getImageData(Math.round(e.x),Math.round(e.y),1,1).data[3]>0}destroy(){return zJ.prototype.destroy.call(this),delete P0[this.colorKey],delete this.colorKey,this}_useBufferCanvas(e){var t;if(!this.getStage())return!1;if(!(null===(t=this.attrs.perfectDrawEnabled)||void 0===t||t))return!1;const n=e||this.hasFill(),r=this.hasStroke(),o=1!==this.getAbsoluteOpacity();if(n&&r&&o)return!0;const i=this.hasShadow(),a=this.shadowForStrokeEnabled();return!!(n&&r&&i&&a)}setStrokeHitEnabled(e){eJ.warn("strokeHitEnabled property is deprecated. Please use hitStrokeWidth instead."),e?this.hitStrokeWidth("auto"):this.hitStrokeWidth(0)}getStrokeHitEnabled(){return 0!==this.hitStrokeWidth()}getSelfRect(){var e=this.size();return{x:this._centroid?-e.width/2:0,y:this._centroid?-e.height/2:0,width:e.width,height:e.height}}getClientRect(e={}){const t=e.skipTransform,n=e.relativeTo,r=this.getSelfRect(),o=!e.skipStroke&&this.hasStroke()&&this.strokeWidth()||0,i=r.width+o,a=r.height+o,s=!e.skipShadow&&this.hasShadow(),l=s?this.shadowOffsetX():0,c=s?this.shadowOffsetY():0,u=i+Math.abs(l),d=a+Math.abs(c),h=s&&this.shadowBlur()||0,f={width:u+2*h,height:d+2*h,x:-(o/2+h)+Math.min(l,0)+r.x,y:-(o/2+h)+Math.min(c,0)+r.y};return t?f:this._transformedRect(f,n)}drawScene(e,t){var n,r,o=this.getLayer(),i=e||o.getCanvas(),a=i.getContext(),s=this._getCanvasCache(),l=this.getSceneFunc(),c=this.hasShadow(),u=i.isCache,d=t===this;if(!this.isVisible()&&!d)return this;if(s){a.save();var h=this.getAbsoluteTransform(t).getMatrix();return a.transform(h[0],h[1],h[2],h[3],h[4],h[5]),this._drawCachedSceneCanvas(a),a.restore(),this}if(!l)return this;if(a.save(),this._useBufferCanvas()&&!u){(r=(n=this.getStage().bufferCanvas).getContext()).clear(),r.save(),r._applyLineJoin(this);var f=this.getAbsoluteTransform(t).getMatrix();r.transform(f[0],f[1],f[2],f[3],f[4],f[5]),l.call(this,r,this),r.restore();var p=n.pixelRatio;c&&a._applyShadow(this),a._applyOpacity(this),a._applyGlobalCompositeOperation(this),a.drawImage(n._canvas,0,0,n.width/p,n.height/p)}else{if(a._applyLineJoin(this),!d){f=this.getAbsoluteTransform(t).getMatrix();a.transform(f[0],f[1],f[2],f[3],f[4],f[5]),a._applyOpacity(this),a._applyGlobalCompositeOperation(this)}c&&a._applyShadow(this),l.call(this,a,this)}return a.restore(),this}drawHit(e,t,n=!1){if(!this.shouldDrawHit(t,n))return this;var r=this.getLayer(),o=e||r.hitCanvas,i=o&&o.getContext(),a=this.hitFunc()||this.sceneFunc(),s=this._getCanvasCache(),l=s&&s.hit;if(this.colorKey||eJ.warn("Looks like your canvas has a destroyed shape in it. Do not reuse shape after you destroyed it. If you want to reuse shape you should call remove() instead of destroy()"),l){i.save();var c=this.getAbsoluteTransform(t).getMatrix();return i.transform(c[0],c[1],c[2],c[3],c[4],c[5]),this._drawCachedHitCanvas(i),i.restore(),this}if(!a)return this;i.save(),i._applyLineJoin(this);if(!(this===t)){var u=this.getAbsoluteTransform(t).getMatrix();i.transform(u[0],u[1],u[2],u[3],u[4],u[5])}return a.call(this,i,this),i.restore(),this}drawHitFromCache(e=0){var t,n,r,o,i,a=this._getCanvasCache(),s=this._getCachedSceneCanvas(),l=a.hit,c=l.getContext(),u=l.getWidth(),d=l.getHeight();c.clear(),c.drawImage(s._canvas,0,0,u,d);try{for(r=(n=(t=c.getImageData(0,0,u,d)).data).length,o=eJ._hexToRgb(this.colorKey),i=0;i<r;i+=4)n[i+3]>e?(n[i]=o.r,n[i+1]=o.g,n[i+2]=o.b,n[i+3]=255):n[i+3]=0;c.putImageData(t,0,0)}catch(B2){eJ.error("Unable to draw hit graph from cached scene canvas. "+B2.message)}return this}hasPointerCapture(e){return $J(e,this)}setPointerCapture(e){UJ(e,this)}releaseCapture(e){GJ(e)}}L0.prototype._fillFunc=function(e){e.fill()},L0.prototype._strokeFunc=function(e){e.stroke()},L0.prototype._fillFuncHit=function(e){e.fill()},L0.prototype._strokeFuncHit=function(e){e.stroke()},L0.prototype._centroid=!1,L0.prototype.nodeType="Shape",UQ(L0),L0.prototype.eventListeners={},L0.prototype.on.call(L0.prototype,"shadowColorChange.konva shadowBlurChange.konva shadowOffsetChange.konva shadowOpacityChange.konva shadowEnabledChange.konva",(function(){this._clearCache(x0)})),L0.prototype.on.call(L0.prototype,"shadowColorChange.konva shadowOpacityChange.konva shadowEnabledChange.konva",(function(){this._clearCache(w0)})),L0.prototype.on.call(L0.prototype,"fillPriorityChange.konva fillPatternImageChange.konva fillPatternRepeatChange.konva fillPatternScaleXChange.konva fillPatternScaleYChange.konva fillPatternOffsetXChange.konva fillPatternOffsetYChange.konva fillPatternXChange.konva fillPatternYChange.konva fillPatternRotationChange.konva",(function(){this._clearCache(k0)})),L0.prototype.on.call(L0.prototype,"fillPriorityChange.konva fillLinearGradientColorStopsChange.konva fillLinearGradientStartPointXChange.konva fillLinearGradientStartPointYChange.konva fillLinearGradientEndPointXChange.konva fillLinearGradientEndPointYChange.konva",(function(){this._clearCache(S0)})),L0.prototype.on.call(L0.prototype,"fillPriorityChange.konva fillRadialGradientColorStopsChange.konva fillRadialGradientStartPointXChange.konva fillRadialGradientStartPointYChange.konva fillRadialGradientEndPointXChange.konva fillRadialGradientEndPointYChange.konva fillRadialGradientStartRadiusChange.konva fillRadialGradientEndRadiusChange.konva",(function(){this._clearCache(C0)})),dJ.addGetterSetter(L0,"stroke",void 0,sJ()),dJ.addGetterSetter(L0,"strokeWidth",2,rJ()),dJ.addGetterSetter(L0,"fillAfterStrokeEnabled",!1),dJ.addGetterSetter(L0,"hitStrokeWidth","auto",iJ()),dJ.addGetterSetter(L0,"strokeHitEnabled",!0,lJ()),dJ.addGetterSetter(L0,"perfectDrawEnabled",!0,lJ()),dJ.addGetterSetter(L0,"shadowForStrokeEnabled",!0,lJ()),dJ.addGetterSetter(L0,"lineJoin"),dJ.addGetterSetter(L0,"lineCap"),dJ.addGetterSetter(L0,"sceneFunc"),dJ.addGetterSetter(L0,"hitFunc"),dJ.addGetterSetter(L0,"dash"),dJ.addGetterSetter(L0,"dashOffset",0,rJ()),dJ.addGetterSetter(L0,"shadowColor",void 0,aJ()),dJ.addGetterSetter(L0,"shadowBlur",0,rJ()),dJ.addGetterSetter(L0,"shadowOpacity",1,rJ()),dJ.addComponentsGetterSetter(L0,"shadowOffset",["x","y"]),dJ.addGetterSetter(L0,"shadowOffsetX",0,rJ()),dJ.addGetterSetter(L0,"shadowOffsetY",0,rJ()),dJ.addGetterSetter(L0,"fillPatternImage"),dJ.addGetterSetter(L0,"fill",void 0,sJ()),dJ.addGetterSetter(L0,"fillPatternX",0,rJ()),dJ.addGetterSetter(L0,"fillPatternY",0,rJ()),dJ.addGetterSetter(L0,"fillLinearGradientColorStops"),dJ.addGetterSetter(L0,"strokeLinearGradientColorStops"),dJ.addGetterSetter(L0,"fillRadialGradientStartRadius",0),dJ.addGetterSetter(L0,"fillRadialGradientEndRadius",0),dJ.addGetterSetter(L0,"fillRadialGradientColorStops"),dJ.addGetterSetter(L0,"fillPatternRepeat","repeat"),dJ.addGetterSetter(L0,"fillEnabled",!0),dJ.addGetterSetter(L0,"strokeEnabled",!0),dJ.addGetterSetter(L0,"shadowEnabled",!0),dJ.addGetterSetter(L0,"dashEnabled",!0),dJ.addGetterSetter(L0,"strokeScaleEnabled",!0),dJ.addGetterSetter(L0,"fillPriority","color"),dJ.addComponentsGetterSetter(L0,"fillPatternOffset",["x","y"]),dJ.addGetterSetter(L0,"fillPatternOffsetX",0,rJ()),dJ.addGetterSetter(L0,"fillPatternOffsetY",0,rJ()),dJ.addComponentsGetterSetter(L0,"fillPatternScale",["x","y"]),dJ.addGetterSetter(L0,"fillPatternScaleX",1,rJ()),dJ.addGetterSetter(L0,"fillPatternScaleY",1,rJ()),dJ.addComponentsGetterSetter(L0,"fillLinearGradientStartPoint",["x","y"]),dJ.addComponentsGetterSetter(L0,"strokeLinearGradientStartPoint",["x","y"]),dJ.addGetterSetter(L0,"fillLinearGradientStartPointX",0),dJ.addGetterSetter(L0,"strokeLinearGradientStartPointX",0),dJ.addGetterSetter(L0,"fillLinearGradientStartPointY",0),dJ.addGetterSetter(L0,"strokeLinearGradientStartPointY",0),dJ.addComponentsGetterSetter(L0,"fillLinearGradientEndPoint",["x","y"]),dJ.addComponentsGetterSetter(L0,"strokeLinearGradientEndPoint",["x","y"]),dJ.addGetterSetter(L0,"fillLinearGradientEndPointX",0),dJ.addGetterSetter(L0,"strokeLinearGradientEndPointX",0),dJ.addGetterSetter(L0,"fillLinearGradientEndPointY",0),dJ.addGetterSetter(L0,"strokeLinearGradientEndPointY",0),dJ.addComponentsGetterSetter(L0,"fillRadialGradientStartPoint",["x","y"]),dJ.addGetterSetter(L0,"fillRadialGradientStartPointX",0),dJ.addGetterSetter(L0,"fillRadialGradientStartPointY",0),dJ.addComponentsGetterSetter(L0,"fillRadialGradientEndPoint",["x","y"]),dJ.addGetterSetter(L0,"fillRadialGradientEndPointX",0),dJ.addGetterSetter(L0,"fillRadialGradientEndPointY",0),dJ.addGetterSetter(L0,"fillPatternRotation",0),dJ.backCompat(L0,{dashArray:"dash",getDashArray:"getDash",setDashArray:"getDash",drawFunc:"sceneFunc",getDrawFunc:"getSceneFunc",setDrawFunc:"setSceneFunc",drawHitFunc:"hitFunc",getDrawHitFunc:"getHitFunc",setDrawHitFunc:"setHitFunc"});var O0=[{x:0,y:0},{x:-1,y:-1},{x:1,y:-1},{x:1,y:1},{x:-1,y:1}],M0=O0.length;class T0 extends jJ{constructor(e){super(e),this.canvas=new bJ,this.hitCanvas=new xJ({pixelRatio:1}),this._waitingForDraw=!1,this.on("visibleChange.konva",this._checkVisibility),this._checkVisibility(),this.on("imageSmoothingEnabledChange.konva",this._setSmoothEnabled),this._setSmoothEnabled()}createPNGStream(){return this.canvas._canvas.createPNGStream()}getCanvas(){return this.canvas}getNativeCanvasElement(){return this.canvas._canvas}getHitCanvas(){return this.hitCanvas}getContext(){return this.getCanvas().getContext()}clear(e){return this.getContext().clear(e),this.getHitCanvas().getContext().clear(e),this}setZIndex(e){super.setZIndex(e);var t=this.getStage();return t&&t.content&&(t.content.removeChild(this.getNativeCanvasElement()),e<t.children.length-1?t.content.insertBefore(this.getNativeCanvasElement(),t.children[e+1].getCanvas()._canvas):t.content.appendChild(this.getNativeCanvasElement())),this}moveToTop(){zJ.prototype.moveToTop.call(this);var e=this.getStage();return e&&e.content&&(e.content.removeChild(this.getNativeCanvasElement()),e.content.appendChild(this.getNativeCanvasElement())),!0}moveUp(){if(!zJ.prototype.moveUp.call(this))return!1;var e=this.getStage();return!(!e||!e.content)&&(e.content.removeChild(this.getNativeCanvasElement()),this.index<e.children.length-1?e.content.insertBefore(this.getNativeCanvasElement(),e.children[this.index+1].getCanvas()._canvas):e.content.appendChild(this.getNativeCanvasElement()),!0)}moveDown(){if(zJ.prototype.moveDown.call(this)){var e=this.getStage();if(e){var t=e.children;e.content&&(e.content.removeChild(this.getNativeCanvasElement()),e.content.insertBefore(this.getNativeCanvasElement(),t[this.index+1].getCanvas()._canvas))}return!0}return!1}moveToBottom(){if(zJ.prototype.moveToBottom.call(this)){var e=this.getStage();if(e){var t=e.children;e.content&&(e.content.removeChild(this.getNativeCanvasElement()),e.content.insertBefore(this.getNativeCanvasElement(),t[1].getCanvas()._canvas))}return!0}return!1}getLayer(){return this}remove(){var e=this.getNativeCanvasElement();return zJ.prototype.remove.call(this),e&&e.parentNode&&eJ._isInDocument(e)&&e.parentNode.removeChild(e),this}getStage(){return this.parent}setSize({width:e,height:t}){return this.canvas.setSize(e,t),this.hitCanvas.setSize(e,t),this._setSmoothEnabled(),this}_validateAdd(e){var t=e.getType();"Group"!==t&&"Shape"!==t&&eJ.throw("You may only add groups and shapes to a layer.")}_toKonvaCanvas(e){return(e=e||{}).width=e.width||this.getWidth(),e.height=e.height||this.getHeight(),e.x=void 0!==e.x?e.x:this.x(),e.y=void 0!==e.y?e.y:this.y(),zJ.prototype._toKonvaCanvas.call(this,e)}_checkVisibility(){const e=this.visible();this.canvas._canvas.style.display=e?"block":"none"}_setSmoothEnabled(){this.getContext()._context.imageSmoothingEnabled=this.imageSmoothingEnabled()}getWidth(){if(this.parent)return this.parent.width()}setWidth(){eJ.warn('Can not change width of layer. Use "stage.width(value)" function instead.')}getHeight(){if(this.parent)return this.parent.height()}setHeight(){eJ.warn('Can not change height of layer. Use "stage.height(value)" function instead.')}batchDraw(){return this._waitingForDraw||(this._waitingForDraw=!0,eJ.requestAnimFrame((()=>{this.draw(),this._waitingForDraw=!1}))),this}getIntersection(e){if(!this.isListening()||!this.isVisible())return null;for(var t=1,n=!1;;){for(let r=0;r<M0;r++){const o=O0[r],i=this._getIntersection({x:e.x+o.x*t,y:e.y+o.y*t}),a=i.shape;if(a)return a;if(n=!!i.antialiased,!i.antialiased)break}if(!n)return null;t+=1}}_getIntersection(e){const t=this.hitCanvas.pixelRatio,n=this.hitCanvas.context.getImageData(Math.round(e.x*t),Math.round(e.y*t),1,1).data,r=n[3];if(255===r){const e=eJ._rgbToHex(n[0],n[1],n[2]),t=P0["#"+e];return t?{shape:t}:{antialiased:!0}}return r>0?{antialiased:!0}:{}}drawScene(e,t){var n=this.getLayer(),r=e||n&&n.getCanvas();return this._fire("beforeDraw",{node:this}),this.clearBeforeDraw()&&r.getContext().clear(),jJ.prototype.drawScene.call(this,r,t),this._fire("draw",{node:this}),this}drawHit(e,t){var n=this.getLayer(),r=e||n&&n.hitCanvas;return n&&n.clearBeforeDraw()&&n.getHitCanvas().getContext().clear(),jJ.prototype.drawHit.call(this,r,t),this}enableHitGraph(){return this.hitGraphEnabled(!0),this}disableHitGraph(){return this.hitGraphEnabled(!1),this}setHitGraphEnabled(e){eJ.warn("hitGraphEnabled method is deprecated. Please use layer.listening() instead."),this.listening(e)}getHitGraphEnabled(e){return eJ.warn("hitGraphEnabled method is deprecated. Please use layer.listening() instead."),this.listening()}toggleHitCanvas(){if(this.parent&&this.parent.content){var e=this.parent;!!this.hitCanvas._canvas.parentNode?e.content.removeChild(this.hitCanvas._canvas):e.content.appendChild(this.hitCanvas._canvas)}}destroy(){return eJ.releaseCanvas(this.getNativeCanvasElement(),this.getHitCanvas()._canvas),super.destroy()}}T0.prototype.nodeType="Layer",UQ(T0),dJ.addGetterSetter(T0,"imageSmoothingEnabled",!0),dJ.addGetterSetter(T0,"clearBeforeDraw",!0),dJ.addGetterSetter(T0,"hitGraphEnabled",!0,lJ());class A0 extends T0{constructor(e){super(e),this.listening(!1),eJ.warn('Konva.Fast layer is deprecated. Please use "new Konva.Layer({ listening: false })" instead.')}}A0.prototype.nodeType="FastLayer",UQ(A0);class I0 extends jJ{_validateAdd(e){var t=e.getType();"Group"!==t&&"Shape"!==t&&eJ.throw("You may only add groups and shapes to groups.")}}I0.prototype.nodeType="Group",UQ(I0);var R0=VQ.performance&&VQ.performance.now?function(){return VQ.performance.now()}:function(){return(new Date).getTime()};class N0{constructor(e,t){this.id=N0.animIdCounter++,this.frame={time:0,timeDiff:0,lastTime:R0(),frameRate:0},this.func=e,this.setLayers(t)}setLayers(e){var t=[];return t=e?e.length>0?e:[e]:[],this.layers=t,this}getLayers(){return this.layers}addLayer(e){var t,n=this.layers,r=n.length;for(t=0;t<r;t++)if(n[t]._id===e._id)return!1;return this.layers.push(e),!0}isRunning(){var e,t=N0.animations,n=t.length;for(e=0;e<n;e++)if(t[e].id===this.id)return!0;return!1}start(){return this.stop(),this.frame.timeDiff=0,this.frame.lastTime=R0(),N0._addAnimation(this),this}stop(){return N0._removeAnimation(this),this}_updateFrameObject(e){this.frame.timeDiff=e-this.frame.lastTime,this.frame.lastTime=e,this.frame.time+=this.frame.timeDiff,this.frame.frameRate=1e3/this.frame.timeDiff}static _addAnimation(e){this.animations.push(e),this._handleAnimation()}static _removeAnimation(e){var t,n=e.id,r=this.animations,o=r.length;for(t=0;t<o;t++)if(r[t].id===n){this.animations.splice(t,1);break}}static _runFrames(){var e,t,n,r,o,i,a,s,l={},c=this.animations;for(r=0;r<c.length;r++)if(t=(e=c[r]).layers,n=e.func,e._updateFrameObject(R0()),i=t.length,!n||!1!==n.call(e,e.frame))for(o=0;o<i;o++)void 0!==(a=t[o])._id&&(l[a._id]=a);for(s in l)l.hasOwnProperty(s)&&l[s].batchDraw()}static _animationLoop(){var e=N0;e.animations.length?(e._runFrames(),eJ.requestAnimFrame(e._animationLoop)):e.animRunning=!1}static _handleAnimation(){this.animRunning||(this.animRunning=!0,eJ.requestAnimFrame(this._animationLoop))}}N0.animations=[],N0.animIdCounter=0,N0.animRunning=!1;var D0={node:1,duration:1,easing:1,onFinish:1,yoyo:1},z0=0,B0=["fill","stroke","shadowColor"];class j0{constructor(e,t,n,r,o,i,a){this.prop=e,this.propFunc=t,this.begin=r,this._pos=r,this.duration=i,this._change=0,this.prevPos=0,this.yoyo=a,this._time=0,this._position=0,this._startTime=0,this._finish=0,this.func=n,this._change=o-this.begin,this.pause()}fire(e){var t=this[e];t&&t()}setTime(e){e>this.duration?this.yoyo?(this._time=this.duration,this.reverse()):this.finish():e<0?this.yoyo?(this._time=0,this.play()):this.reset():(this._time=e,this.update())}getTime(){return this._time}setPosition(e){this.prevPos=this._pos,this.propFunc(e),this._pos=e}getPosition(e){return void 0===e&&(e=this._time),this.func(e,this.begin,this._change,this.duration)}play(){this.state=2,this._startTime=this.getTimer()-this._time,this.onEnterFrame(),this.fire("onPlay")}reverse(){this.state=3,this._time=this.duration-this._time,this._startTime=this.getTimer()-this._time,this.onEnterFrame(),this.fire("onReverse")}seek(e){this.pause(),this._time=e,this.update(),this.fire("onSeek")}reset(){this.pause(),this._time=0,this.update(),this.fire("onReset")}finish(){this.pause(),this._time=this.duration,this.update(),this.fire("onFinish")}update(){this.setPosition(this.getPosition(this._time)),this.fire("onUpdate")}onEnterFrame(){var e=this.getTimer()-this._startTime;2===this.state?this.setTime(e):3===this.state&&this.setTime(this.duration-e)}pause(){this.state=1,this.fire("onPause")}getTimer(){return(new Date).getTime()}}class F0{constructor(e){var t,n,r=this,o=e.node,i=o._id,a=e.easing||H0.Linear,s=!!e.yoyo;t=void 0===e.duration?.3:0===e.duration?.001:e.duration,this.node=o,this._id=z0++;var l=o.getLayer()||(o instanceof $Q.Stage?o.getLayers():null);for(n in l||eJ.error("Tween constructor have `node` that is not in a layer. Please add node into layer first."),this.anim=new N0((function(){r.tween.onEnterFrame()}),l),this.tween=new j0(n,(function(e){r._tweenFunc(e)}),a,0,1,1e3*t,s),this._addListeners(),F0.attrs[i]||(F0.attrs[i]={}),F0.attrs[i][this._id]||(F0.attrs[i][this._id]={}),F0.tweens[i]||(F0.tweens[i]={}),e)void 0===D0[n]&&this._addAttr(n,e[n]);this.reset(),this.onFinish=e.onFinish,this.onReset=e.onReset,this.onUpdate=e.onUpdate}_addAttr(e,t){var n,r,o,i,a,s,l,c,u=this.node,d=u._id;if((o=F0.tweens[d][e])&&delete F0.attrs[d][o][e],n=u.getAttr(e),eJ._isArray(t))if(r=[],a=Math.max(t.length,n.length),"points"===e&&t.length!==n.length&&(t.length>n.length?(l=n,n=eJ._prepareArrayForTween(n,t,u.closed())):(s=t,t=eJ._prepareArrayForTween(t,n,u.closed()))),0===e.indexOf("fill"))for(i=0;i<a;i++)if(i%2==0)r.push(t[i]-n[i]);else{var h=eJ.colorToRGBA(n[i]);c=eJ.colorToRGBA(t[i]),n[i]=h,r.push({r:c.r-h.r,g:c.g-h.g,b:c.b-h.b,a:c.a-h.a})}else for(i=0;i<a;i++)r.push(t[i]-n[i]);else-1!==B0.indexOf(e)?(n=eJ.colorToRGBA(n),r={r:(c=eJ.colorToRGBA(t)).r-n.r,g:c.g-n.g,b:c.b-n.b,a:c.a-n.a}):r=t-n;F0.attrs[d][this._id][e]={start:n,diff:r,end:t,trueEnd:s,trueStart:l},F0.tweens[d][e]=this._id}_tweenFunc(e){var t,n,r,o,i,a,s,l,c=this.node,u=F0.attrs[c._id][this._id];for(t in u){if(r=(n=u[t]).start,o=n.diff,l=n.end,eJ._isArray(r))if(i=[],s=Math.max(r.length,l.length),0===t.indexOf("fill"))for(a=0;a<s;a++)a%2==0?i.push((r[a]||0)+o[a]*e):i.push("rgba("+Math.round(r[a].r+o[a].r*e)+","+Math.round(r[a].g+o[a].g*e)+","+Math.round(r[a].b+o[a].b*e)+","+(r[a].a+o[a].a*e)+")");else for(a=0;a<s;a++)i.push((r[a]||0)+o[a]*e);else i=-1!==B0.indexOf(t)?"rgba("+Math.round(r.r+o.r*e)+","+Math.round(r.g+o.g*e)+","+Math.round(r.b+o.b*e)+","+(r.a+o.a*e)+")":r+o*e;c.setAttr(t,i)}}_addListeners(){this.tween.onPlay=()=>{this.anim.start()},this.tween.onReverse=()=>{this.anim.start()},this.tween.onPause=()=>{this.anim.stop()},this.tween.onFinish=()=>{var e=this.node,t=F0.attrs[e._id][this._id];t.points&&t.points.trueEnd&&e.setAttr("points",t.points.trueEnd),this.onFinish&&this.onFinish.call(this)},this.tween.onReset=()=>{var e=this.node,t=F0.attrs[e._id][this._id];t.points&&t.points.trueStart&&e.points(t.points.trueStart),this.onReset&&this.onReset()},this.tween.onUpdate=()=>{this.onUpdate&&this.onUpdate.call(this)}}play(){return this.tween.play(),this}reverse(){return this.tween.reverse(),this}reset(){return this.tween.reset(),this}seek(e){return this.tween.seek(1e3*e),this}pause(){return this.tween.pause(),this}finish(){return this.tween.finish(),this}destroy(){var e,t=this.node._id,n=this._id,r=F0.tweens[t];for(e in this.pause(),r)delete F0.tweens[t][e];delete F0.attrs[t][n]}}F0.attrs={},F0.tweens={},zJ.prototype.to=function(e){var t=e.onFinish;e.node=this,e.onFinish=function(){this.destroy(),t&&t()},new F0(e).play()};const H0={BackEaseIn(e,t,n,r){var o=1.70158;return n*(e/=r)*e*((o+1)*e-o)+t},BackEaseOut(e,t,n,r){var o=1.70158;return n*((e=e/r-1)*e*((o+1)*e+o)+1)+t},BackEaseInOut(e,t,n,r){var o=1.70158;return(e/=r/2)<1?n/2*(e*e*((1+(o*=1.525))*e-o))+t:n/2*((e-=2)*e*((1+(o*=1.525))*e+o)+2)+t},ElasticEaseIn(e,t,n,r,o,i){var a=0;return 0===e?t:1==(e/=r)?t+n:(i||(i=.3*r),!o||o<Math.abs(n)?(o=n,a=i/4):a=i/(2*Math.PI)*Math.asin(n/o),-o*Math.pow(2,10*(e-=1))*Math.sin((e*r-a)*(2*Math.PI)/i)+t)},ElasticEaseOut(e,t,n,r,o,i){var a=0;return 0===e?t:1==(e/=r)?t+n:(i||(i=.3*r),!o||o<Math.abs(n)?(o=n,a=i/4):a=i/(2*Math.PI)*Math.asin(n/o),o*Math.pow(2,-10*e)*Math.sin((e*r-a)*(2*Math.PI)/i)+n+t)},ElasticEaseInOut(e,t,n,r,o,i){var a=0;return 0===e?t:2==(e/=r/2)?t+n:(i||(i=r*(.3*1.5)),!o||o<Math.abs(n)?(o=n,a=i/4):a=i/(2*Math.PI)*Math.asin(n/o),e<1?o*Math.pow(2,10*(e-=1))*Math.sin((e*r-a)*(2*Math.PI)/i)*-.5+t:o*Math.pow(2,-10*(e-=1))*Math.sin((e*r-a)*(2*Math.PI)/i)*.5+n+t)},BounceEaseOut:(e,t,n,r)=>(e/=r)<1/2.75?n*(7.5625*e*e)+t:e<2/2.75?n*(7.5625*(e-=1.5/2.75)*e+.75)+t:e<2.5/2.75?n*(7.5625*(e-=2.25/2.75)*e+.9375)+t:n*(7.5625*(e-=2.625/2.75)*e+.984375)+t,BounceEaseIn:(e,t,n,r)=>n-H0.BounceEaseOut(r-e,0,n,r)+t,BounceEaseInOut:(e,t,n,r)=>e<r/2?.5*H0.BounceEaseIn(2*e,0,n,r)+t:.5*H0.BounceEaseOut(2*e-r,0,n,r)+.5*n+t,EaseIn:(e,t,n,r)=>n*(e/=r)*e+t,EaseOut:(e,t,n,r)=>-n*(e/=r)*(e-2)+t,EaseInOut:(e,t,n,r)=>(e/=r/2)<1?n/2*e*e+t:-n/2*(--e*(e-2)-1)+t,StrongEaseIn:(e,t,n,r)=>n*(e/=r)*e*e*e*e+t,StrongEaseOut:(e,t,n,r)=>n*((e=e/r-1)*e*e*e*e+1)+t,StrongEaseInOut:(e,t,n,r)=>(e/=r/2)<1?n/2*e*e*e*e*e+t:n/2*((e-=2)*e*e*e*e+2)+t,Linear:(e,t,n,r)=>n*e/r+t},W0=eJ._assign($Q,{Util:eJ,Transform:GQ,Node:zJ,Container:jJ,Stage:b0,stages:y0,Layer:T0,FastLayer:A0,Group:I0,DD:wJ,Shape:L0,shapes:P0,Animation:N0,Tween:F0,Easings:H0,Context:pJ,Canvas:yJ});class V0 extends L0{_sceneFunc(e){var t=$Q.getAngle(this.angle()),n=this.clockwise();e.beginPath(),e.arc(0,0,this.outerRadius(),0,t,n),e.arc(0,0,this.innerRadius(),t,0,!n),e.closePath(),e.fillStrokeShape(this)}getWidth(){return 2*this.outerRadius()}getHeight(){return 2*this.outerRadius()}setWidth(e){this.outerRadius(e/2)}setHeight(e){this.outerRadius(e/2)}getSelfRect(){const e=this.innerRadius(),t=this.outerRadius(),n=this.clockwise(),r=$Q.getAngle(n?360-this.angle():this.angle()),o=Math.cos(Math.min(r,Math.PI)),i=Math.sin(Math.min(Math.max(Math.PI,r),3*Math.PI/2)),a=Math.sin(Math.min(r,Math.PI/2)),s=o*(o>0?e:t),l=i*(i>0?e:t),c=a*(a>0?t:e);return{x:s,y:n?-1*c:l,width:1*t-s,height:c-l}}}function $0(e,t,n,r,o,i,a){var s=Math.sqrt(Math.pow(n-e,2)+Math.pow(r-t,2)),l=Math.sqrt(Math.pow(o-n,2)+Math.pow(i-r,2)),c=a*s/(s+l),u=a*l/(s+l);return[n-c*(o-e),r-c*(i-t),n+u*(o-e),r+u*(i-t)]}function U0(e,t){var n,r,o=e.length,i=[];for(n=2;n<o-2;n+=2)r=$0(e[n-2],e[n-1],e[n],e[n+1],e[n+2],e[n+3],t),isNaN(r[0])||(i.push(r[0]),i.push(r[1]),i.push(e[n]),i.push(e[n+1]),i.push(r[2]),i.push(r[3]));return i}V0.prototype._centroid=!0,V0.prototype.className="Arc",V0.prototype._attrsAffectingSize=["innerRadius","outerRadius"],UQ(V0),dJ.addGetterSetter(V0,"innerRadius",0,rJ()),dJ.addGetterSetter(V0,"outerRadius",0,rJ()),dJ.addGetterSetter(V0,"angle",0,rJ()),dJ.addGetterSetter(V0,"clockwise",!1,lJ());class G0 extends L0{constructor(e){super(e),this.on("pointsChange.konva tensionChange.konva closedChange.konva bezierChange.konva",(function(){this._clearCache("tensionPoints")}))}_sceneFunc(e){var t,n,r,o=this.points(),i=o.length,a=this.tension(),s=this.closed(),l=this.bezier();if(i){if(e.beginPath(),e.moveTo(o[0],o[1]),0!==a&&i>4){for(n=(t=this.getTensionPoints()).length,r=s?0:4,s||e.quadraticCurveTo(t[0],t[1],t[2],t[3]);r<n-2;)e.bezierCurveTo(t[r++],t[r++],t[r++],t[r++],t[r++],t[r++]);s||e.quadraticCurveTo(t[n-2],t[n-1],o[i-2],o[i-1])}else if(l)for(r=2;r<i;)e.bezierCurveTo(o[r++],o[r++],o[r++],o[r++],o[r++],o[r++]);else for(r=2;r<i;r+=2)e.lineTo(o[r],o[r+1]);s?(e.closePath(),e.fillStrokeShape(this)):e.strokeShape(this)}}getTensionPoints(){return this._getCache("tensionPoints",this._getTensionPoints)}_getTensionPoints(){return this.closed()?this._getTensionPointsClosed():U0(this.points(),this.tension())}_getTensionPointsClosed(){var e=this.points(),t=e.length,n=this.tension(),r=$0(e[t-2],e[t-1],e[0],e[1],e[2],e[3],n),o=$0(e[t-4],e[t-3],e[t-2],e[t-1],e[0],e[1],n),i=U0(e,n);return[r[2],r[3]].concat(i).concat([o[0],o[1],e[t-2],e[t-1],o[2],o[3],r[0],r[1],e[0],e[1]])}getWidth(){return this.getSelfRect().width}getHeight(){return this.getSelfRect().height}getSelfRect(){var e=this.points();if(e.length<4)return{x:e[0]||0,y:e[1]||0,width:0,height:0};for(var t,n,r=(e=0!==this.tension()?[e[0],e[1],...this._getTensionPoints(),e[e.length-2],e[e.length-1]]:this.points())[0],o=e[0],i=e[1],a=e[1],s=0;s<e.length/2;s++)t=e[2*s],n=e[2*s+1],r=Math.min(r,t),o=Math.max(o,t),i=Math.min(i,n),a=Math.max(a,n);return{x:r,y:i,width:o-r,height:a-i}}}G0.prototype.className="Line",G0.prototype._attrsAffectingSize=["points","bezier","tension"],UQ(G0),dJ.addGetterSetter(G0,"closed",!1),dJ.addGetterSetter(G0,"bezier",!1),dJ.addGetterSetter(G0,"tension",0,rJ()),dJ.addGetterSetter(G0,"points",[],function(){if($Q.isUnminified)return function(e,t){const n=Int8Array?Object.getPrototypeOf(Int8Array):null;return n&&e instanceof n||(eJ._isArray(e)?e.forEach((function(e){eJ._isNumber(e)||eJ.warn('"'+t+'" attribute has non numeric element '+e+". Make sure that all elements are numbers.")})):eJ.warn(tJ(e)+' is a not valid value for "'+t+'" attribute. The value should be a array of numbers.')),e}}());class q0 extends L0{constructor(e){super(e),this.dataArray=[],this.pathLength=0,this.dataArray=q0.parsePathData(this.data()),this.pathLength=0;for(var t=0;t<this.dataArray.length;++t)this.pathLength+=this.dataArray[t].pathLength;this.on("dataChange.konva",(function(){this.dataArray=q0.parsePathData(this.data()),this.pathLength=0;for(var e=0;e<this.dataArray.length;++e)this.pathLength+=this.dataArray[e].pathLength}))}_sceneFunc(e){var t=this.dataArray;e.beginPath();for(var n=!1,r=0;r<t.length;r++){var o=t[r].command,i=t[r].points;switch(o){case"L":e.lineTo(i[0],i[1]);break;case"M":e.moveTo(i[0],i[1]);break;case"C":e.bezierCurveTo(i[0],i[1],i[2],i[3],i[4],i[5]);break;case"Q":e.quadraticCurveTo(i[0],i[1],i[2],i[3]);break;case"A":var a=i[0],s=i[1],l=i[2],c=i[3],u=i[4],d=i[5],h=i[6],f=i[7],p=l>c?l:c,g=l>c?1:l/c,m=l>c?c/l:1;e.translate(a,s),e.rotate(h),e.scale(g,m),e.arc(0,0,p,u,u+d,1-f),e.scale(1/g,1/m),e.rotate(-h),e.translate(-a,-s);break;case"z":n=!0,e.closePath()}}n||this.hasFill()?e.fillStrokeShape(this):e.strokeShape(this)}getSelfRect(){var e=[];this.dataArray.forEach((function(t){if("A"===t.command){var n=t.points[4],r=t.points[5],o=t.points[4]+r,i=Math.PI/180;if(Math.abs(n-o)<i&&(i=Math.abs(n-o)),r<0)for(let r=n-i;r>o;r-=i){const n=q0.getPointOnEllipticalArc(t.points[0],t.points[1],t.points[2],t.points[3],r,0);e.push(n.x,n.y)}else for(let r=n+i;r<o;r+=i){const n=q0.getPointOnEllipticalArc(t.points[0],t.points[1],t.points[2],t.points[3],r,0);e.push(n.x,n.y)}}else if("C"===t.command)for(let a=0;a<=1;a+=.01){const n=q0.getPointOnCubicBezier(a,t.start.x,t.start.y,t.points[0],t.points[1],t.points[2],t.points[3],t.points[4],t.points[5]);e.push(n.x,n.y)}else e=e.concat(t.points)}));for(var t,n,r=e[0],o=e[0],i=e[1],a=e[1],s=0;s<e.length/2;s++)t=e[2*s],n=e[2*s+1],isNaN(t)||(r=Math.min(r,t),o=Math.max(o,t)),isNaN(n)||(i=Math.min(i,n),a=Math.max(a,n));return{x:r,y:i,width:o-r,height:a-i}}getLength(){return this.pathLength}getPointAtLength(e){var t,n=0,r=this.dataArray.length;if(!r)return null;for(;n<r&&e>this.dataArray[n].pathLength;)e-=this.dataArray[n].pathLength,++n;if(n===r)return{x:(t=this.dataArray[n-1].points.slice(-2))[0],y:t[1]};if(e<.01)return{x:(t=this.dataArray[n].points.slice(0,2))[0],y:t[1]};var o=this.dataArray[n],i=o.points;switch(o.command){case"L":return q0.getPointOnLine(e,o.start.x,o.start.y,i[0],i[1]);case"C":return q0.getPointOnCubicBezier(e/o.pathLength,o.start.x,o.start.y,i[0],i[1],i[2],i[3],i[4],i[5]);case"Q":return q0.getPointOnQuadraticBezier(e/o.pathLength,o.start.x,o.start.y,i[0],i[1],i[2],i[3]);case"A":var a=i[0],s=i[1],l=i[2],c=i[3],u=i[4],d=i[5],h=i[6];return u+=d*e/o.pathLength,q0.getPointOnEllipticalArc(a,s,l,c,u,h)}return null}static getLineLength(e,t,n,r){return Math.sqrt((n-e)*(n-e)+(r-t)*(r-t))}static getPointOnLine(e,t,n,r,o,i,a){void 0===i&&(i=t),void 0===a&&(a=n);var s=(o-n)/(r-t+1e-8),l=Math.sqrt(e*e/(1+s*s));r<t&&(l*=-1);var c,u=s*l;if(r===t)c={x:i,y:a+u};else if((a-n)/(i-t+1e-8)===s)c={x:i+l,y:a+u};else{var d,h,f=this.getLineLength(t,n,r,o),p=(i-t)*(r-t)+(a-n)*(o-n);d=t+(p/=f*f)*(r-t),h=n+p*(o-n);var g=this.getLineLength(i,a,d,h),m=Math.sqrt(e*e-g*g);l=Math.sqrt(m*m/(1+s*s)),r<t&&(l*=-1),c={x:d+l,y:h+(u=s*l)}}return c}static getPointOnCubicBezier(e,t,n,r,o,i,a,s,l){function c(e){return e*e*e}function u(e){return 3*e*e*(1-e)}function d(e){return 3*e*(1-e)*(1-e)}function h(e){return(1-e)*(1-e)*(1-e)}return{x:s*c(e)+i*u(e)+r*d(e)+t*h(e),y:l*c(e)+a*u(e)+o*d(e)+n*h(e)}}static getPointOnQuadraticBezier(e,t,n,r,o,i,a){function s(e){return e*e}function l(e){return 2*e*(1-e)}function c(e){return(1-e)*(1-e)}return{x:i*s(e)+r*l(e)+t*c(e),y:a*s(e)+o*l(e)+n*c(e)}}static getPointOnEllipticalArc(e,t,n,r,o,i){var a=Math.cos(i),s=Math.sin(i),l=n*Math.cos(o),c=r*Math.sin(o);return{x:e+(l*a-c*s),y:t+(l*s+c*a)}}static parsePathData(e){if(!e)return[];var t=e,n=["m","M","l","L","v","V","h","H","z","Z","c","C","q","Q","t","T","s","S","a","A"];t=t.replace(new RegExp(" ","g"),",");for(var r=0;r<n.length;r++)t=t.replace(new RegExp(n[r],"g"),"|"+n[r]);var o,i=t.split("|"),a=[],s=[],l=0,c=0,u=/([-+]?((\d+\.\d+)|((\d+)|(\.\d+)))(?:e[-+]?\d+)?)/gi;for(r=1;r<i.length;r++){var d=i[r],h=d.charAt(0);for(d=d.slice(1),s.length=0;o=u.exec(d);)s.push(o[0]);for(var f=[],p=0,g=s.length;p<g;p++)if("00"!==s[p]){var m=parseFloat(s[p]);isNaN(m)?f.push(0):f.push(m)}else f.push(0,0);for(;f.length>0&&!isNaN(f[0]);){var v,y,b,x,w,k,S,C,_,E,P=null,L=[],O=l,M=c;switch(h){case"l":l+=f.shift(),c+=f.shift(),P="L",L.push(l,c);break;case"L":l=f.shift(),c=f.shift(),L.push(l,c);break;case"m":var T=f.shift(),A=f.shift();if(l+=T,c+=A,P="M",a.length>2&&"z"===a[a.length-1].command)for(var I=a.length-2;I>=0;I--)if("M"===a[I].command){l=a[I].points[0]+T,c=a[I].points[1]+A;break}L.push(l,c),h="l";break;case"M":l=f.shift(),c=f.shift(),P="M",L.push(l,c),h="L";break;case"h":l+=f.shift(),P="L",L.push(l,c);break;case"H":l=f.shift(),P="L",L.push(l,c);break;case"v":c+=f.shift(),P="L",L.push(l,c);break;case"V":c=f.shift(),P="L",L.push(l,c);break;case"C":L.push(f.shift(),f.shift(),f.shift(),f.shift()),l=f.shift(),c=f.shift(),L.push(l,c);break;case"c":L.push(l+f.shift(),c+f.shift(),l+f.shift(),c+f.shift()),l+=f.shift(),c+=f.shift(),P="C",L.push(l,c);break;case"S":y=l,b=c,"C"===(v=a[a.length-1]).command&&(y=l+(l-v.points[2]),b=c+(c-v.points[3])),L.push(y,b,f.shift(),f.shift()),l=f.shift(),c=f.shift(),P="C",L.push(l,c);break;case"s":y=l,b=c,"C"===(v=a[a.length-1]).command&&(y=l+(l-v.points[2]),b=c+(c-v.points[3])),L.push(y,b,l+f.shift(),c+f.shift()),l+=f.shift(),c+=f.shift(),P="C",L.push(l,c);break;case"Q":L.push(f.shift(),f.shift()),l=f.shift(),c=f.shift(),L.push(l,c);break;case"q":L.push(l+f.shift(),c+f.shift()),l+=f.shift(),c+=f.shift(),P="Q",L.push(l,c);break;case"T":y=l,b=c,"Q"===(v=a[a.length-1]).command&&(y=l+(l-v.points[0]),b=c+(c-v.points[1])),l=f.shift(),c=f.shift(),P="Q",L.push(y,b,l,c);break;case"t":y=l,b=c,"Q"===(v=a[a.length-1]).command&&(y=l+(l-v.points[0]),b=c+(c-v.points[1])),l+=f.shift(),c+=f.shift(),P="Q",L.push(y,b,l,c);break;case"A":x=f.shift(),w=f.shift(),k=f.shift(),S=f.shift(),C=f.shift(),_=l,E=c,l=f.shift(),c=f.shift(),P="A",L=this.convertEndpointToCenterParameterization(_,E,l,c,S,C,x,w,k);break;case"a":x=f.shift(),w=f.shift(),k=f.shift(),S=f.shift(),C=f.shift(),_=l,E=c,l+=f.shift(),c+=f.shift(),P="A",L=this.convertEndpointToCenterParameterization(_,E,l,c,S,C,x,w,k)}a.push({command:P||h,points:L,start:{x:O,y:M},pathLength:this.calcLength(O,M,P||h,L)})}"z"!==h&&"Z"!==h||a.push({command:"z",points:[],start:void 0,pathLength:0})}return a}static calcLength(e,t,n,r){var o,i,a,s,l=q0;switch(n){case"L":return l.getLineLength(e,t,r[0],r[1]);case"C":for(o=0,i=l.getPointOnCubicBezier(0,e,t,r[0],r[1],r[2],r[3],r[4],r[5]),s=.01;s<=1;s+=.01)a=l.getPointOnCubicBezier(s,e,t,r[0],r[1],r[2],r[3],r[4],r[5]),o+=l.getLineLength(i.x,i.y,a.x,a.y),i=a;return o;case"Q":for(o=0,i=l.getPointOnQuadraticBezier(0,e,t,r[0],r[1],r[2],r[3]),s=.01;s<=1;s+=.01)a=l.getPointOnQuadraticBezier(s,e,t,r[0],r[1],r[2],r[3]),o+=l.getLineLength(i.x,i.y,a.x,a.y),i=a;return o;case"A":o=0;var c=r[4],u=r[5],d=r[4]+u,h=Math.PI/180;if(Math.abs(c-d)<h&&(h=Math.abs(c-d)),i=l.getPointOnEllipticalArc(r[0],r[1],r[2],r[3],c,0),u<0)for(s=c-h;s>d;s-=h)a=l.getPointOnEllipticalArc(r[0],r[1],r[2],r[3],s,0),o+=l.getLineLength(i.x,i.y,a.x,a.y),i=a;else for(s=c+h;s<d;s+=h)a=l.getPointOnEllipticalArc(r[0],r[1],r[2],r[3],s,0),o+=l.getLineLength(i.x,i.y,a.x,a.y),i=a;return a=l.getPointOnEllipticalArc(r[0],r[1],r[2],r[3],d,0),o+=l.getLineLength(i.x,i.y,a.x,a.y)}return 0}static convertEndpointToCenterParameterization(e,t,n,r,o,i,a,s,l){var c=l*(Math.PI/180),u=Math.cos(c)*(e-n)/2+Math.sin(c)*(t-r)/2,d=-1*Math.sin(c)*(e-n)/2+Math.cos(c)*(t-r)/2,h=u*u/(a*a)+d*d/(s*s);h>1&&(a*=Math.sqrt(h),s*=Math.sqrt(h));var f=Math.sqrt((a*a*(s*s)-a*a*(d*d)-s*s*(u*u))/(a*a*(d*d)+s*s*(u*u)));o===i&&(f*=-1),isNaN(f)&&(f=0);var p=f*a*d/s,g=f*-s*u/a,m=(e+n)/2+Math.cos(c)*p-Math.sin(c)*g,v=(t+r)/2+Math.sin(c)*p+Math.cos(c)*g,y=function(e){return Math.sqrt(e[0]*e[0]+e[1]*e[1])},b=function(e,t){return(e[0]*t[0]+e[1]*t[1])/(y(e)*y(t))},x=function(e,t){return(e[0]*t[1]<e[1]*t[0]?-1:1)*Math.acos(b(e,t))},w=x([1,0],[(u-p)/a,(d-g)/s]),k=[(u-p)/a,(d-g)/s],S=[(-1*u-p)/a,(-1*d-g)/s],C=x(k,S);return b(k,S)<=-1&&(C=Math.PI),b(k,S)>=1&&(C=0),0===i&&C>0&&(C-=2*Math.PI),1===i&&C<0&&(C+=2*Math.PI),[m,v,a,s,w,C,c,i]}}q0.prototype.className="Path",q0.prototype._attrsAffectingSize=["data"],UQ(q0),dJ.addGetterSetter(q0,"data");class Y0 extends G0{_sceneFunc(e){super._sceneFunc(e);var t=2*Math.PI,n=this.points(),r=n,o=0!==this.tension()&&n.length>4;o&&(r=this.getTensionPoints());var i,a,s=this.pointerLength(),l=n.length;if(o){const e=[r[r.length-4],r[r.length-3],r[r.length-2],r[r.length-1],n[l-2],n[l-1]],t=q0.calcLength(r[r.length-4],r[r.length-3],"C",e),o=q0.getPointOnQuadraticBezier(Math.min(1,1-s/t),e[0],e[1],e[2],e[3],e[4],e[5]);i=n[l-2]-o.x,a=n[l-1]-o.y}else i=n[l-2]-n[l-4],a=n[l-1]-n[l-3];var c=(Math.atan2(a,i)+t)%t,u=this.pointerWidth();this.pointerAtEnding()&&(e.save(),e.beginPath(),e.translate(n[l-2],n[l-1]),e.rotate(c),e.moveTo(0,0),e.lineTo(-s,u/2),e.lineTo(-s,-u/2),e.closePath(),e.restore(),this.__fillStroke(e)),this.pointerAtBeginning()&&(e.save(),e.beginPath(),e.translate(n[0],n[1]),o?(i=(r[0]+r[2])/2-n[0],a=(r[1]+r[3])/2-n[1]):(i=n[2]-n[0],a=n[3]-n[1]),e.rotate((Math.atan2(-a,-i)+t)%t),e.moveTo(0,0),e.lineTo(-s,u/2),e.lineTo(-s,-u/2),e.closePath(),e.restore(),this.__fillStroke(e))}__fillStroke(e){var t=this.dashEnabled();t&&(this.attrs.dashEnabled=!1,e.setLineDash([])),e.fillStrokeShape(this),t&&(this.attrs.dashEnabled=!0)}getSelfRect(){const e=super.getSelfRect(),t=this.pointerWidth()/2;return{x:e.x-t,y:e.y-t,width:e.width+2*t,height:e.height+2*t}}}Y0.prototype.className="Arrow",UQ(Y0),dJ.addGetterSetter(Y0,"pointerLength",10,rJ()),dJ.addGetterSetter(Y0,"pointerWidth",10,rJ()),dJ.addGetterSetter(Y0,"pointerAtBeginning",!1),dJ.addGetterSetter(Y0,"pointerAtEnding",!0);class Z0 extends L0{_sceneFunc(e){e.beginPath(),e.arc(0,0,this.attrs.radius||0,0,2*Math.PI,!1),e.closePath(),e.fillStrokeShape(this)}getWidth(){return 2*this.radius()}getHeight(){return 2*this.radius()}setWidth(e){this.radius()!==e/2&&this.radius(e/2)}setHeight(e){this.radius()!==e/2&&this.radius(e/2)}}Z0.prototype._centroid=!0,Z0.prototype.className="Circle",Z0.prototype._attrsAffectingSize=["radius"],UQ(Z0),dJ.addGetterSetter(Z0,"radius",0,rJ());class X0 extends L0{_sceneFunc(e){var t=this.radiusX(),n=this.radiusY();e.beginPath(),e.save(),t!==n&&e.scale(1,n/t),e.arc(0,0,t,0,2*Math.PI,!1),e.restore(),e.closePath(),e.fillStrokeShape(this)}getWidth(){return 2*this.radiusX()}getHeight(){return 2*this.radiusY()}setWidth(e){this.radiusX(e/2)}setHeight(e){this.radiusY(e/2)}}X0.prototype.className="Ellipse",X0.prototype._centroid=!0,X0.prototype._attrsAffectingSize=["radiusX","radiusY"],UQ(X0),dJ.addComponentsGetterSetter(X0,"radius",["x","y"]),dJ.addGetterSetter(X0,"radiusX",0,rJ()),dJ.addGetterSetter(X0,"radiusY",0,rJ());class K0 extends L0{constructor(e){super(e),this.on("imageChange.konva",(()=>{this._setImageLoad()})),this._setImageLoad()}_setImageLoad(){const e=this.image();e&&e.complete||e&&4===e.readyState||e&&e.addEventListener&&e.addEventListener("load",(()=>{this._requestDraw()}))}_useBufferCanvas(){return super._useBufferCanvas(!0)}_sceneFunc(e){const t=this.getWidth(),n=this.getHeight(),r=this.attrs.image;let o;if(r){const e=this.attrs.cropWidth,i=this.attrs.cropHeight;o=e&&i?[r,this.cropX(),this.cropY(),e,i,0,0,t,n]:[r,0,0,t,n]}(this.hasFill()||this.hasStroke())&&(e.beginPath(),e.rect(0,0,t,n),e.closePath(),e.fillStrokeShape(this)),r&&e.drawImage.apply(e,o)}_hitFunc(e){var t=this.width(),n=this.height();e.beginPath(),e.rect(0,0,t,n),e.closePath(),e.fillStrokeShape(this)}getWidth(){var e,t;return null!==(e=this.attrs.width)&&void 0!==e?e:null===(t=this.image())||void 0===t?void 0:t.width}getHeight(){var e,t;return null!==(e=this.attrs.height)&&void 0!==e?e:null===(t=this.image())||void 0===t?void 0:t.height}static fromURL(e,t,n=null){var r=eJ.createImageElement();r.onload=function(){var e=new K0({image:r});t(e)},r.onerror=n,r.crossOrigin="Anonymous",r.src=e}}K0.prototype.className="Image",UQ(K0),dJ.addGetterSetter(K0,"image"),dJ.addComponentsGetterSetter(K0,"crop",["x","y","width","height"]),dJ.addGetterSetter(K0,"cropX",0,rJ()),dJ.addGetterSetter(K0,"cropY",0,rJ()),dJ.addGetterSetter(K0,"cropWidth",0,rJ()),dJ.addGetterSetter(K0,"cropHeight",0,rJ());var Q0=["fontFamily","fontSize","fontStyle","padding","lineHeight","text","width","height","pointerDirection","pointerWidth","pointerHeight"],J0="up",e1="right",t1="down",n1="left",r1=Q0.length;class o1 extends I0{constructor(e){super(e),this.on("add.konva",(function(e){this._addListeners(e.child),this._sync()}))}getText(){return this.find("Text")[0]}getTag(){return this.find("Tag")[0]}_addListeners(e){var t,n=this,r=function(){n._sync()};for(t=0;t<r1;t++)e.on(Q0[t]+"Change.konva",r)}getWidth(){return this.getText().width()}getHeight(){return this.getText().height()}_sync(){var e,t,n,r,o,i,a,s=this.getText(),l=this.getTag();if(s&&l){switch(e=s.width(),t=s.height(),n=l.pointerDirection(),r=l.pointerWidth(),a=l.pointerHeight(),o=0,i=0,n){case J0:o=e/2,i=-1*a;break;case e1:o=e+r,i=t/2;break;case t1:o=e/2,i=t+a;break;case n1:o=-1*r,i=t/2}l.setAttrs({x:-1*o,y:-1*i,width:e,height:t}),s.setAttrs({x:-1*o,y:-1*i})}}}o1.prototype.className="Label",UQ(o1);class i1 extends L0{_sceneFunc(e){var t=this.width(),n=this.height(),r=this.pointerDirection(),o=this.pointerWidth(),i=this.pointerHeight(),a=this.cornerRadius();let s=0,l=0,c=0,u=0;"number"==typeof a?s=l=c=u=Math.min(a,t/2,n/2):(s=Math.min(a[0]||0,t/2,n/2),l=Math.min(a[1]||0,t/2,n/2),u=Math.min(a[2]||0,t/2,n/2),c=Math.min(a[3]||0,t/2,n/2)),e.beginPath(),e.moveTo(s,0),r===J0&&(e.lineTo((t-o)/2,0),e.lineTo(t/2,-1*i),e.lineTo((t+o)/2,0)),e.lineTo(t-l,0),e.arc(t-l,l,l,3*Math.PI/2,0,!1),r===e1&&(e.lineTo(t,(n-i)/2),e.lineTo(t+o,n/2),e.lineTo(t,(n+i)/2)),e.lineTo(t,n-u),e.arc(t-u,n-u,u,0,Math.PI/2,!1),r===t1&&(e.lineTo((t+o)/2,n),e.lineTo(t/2,n+i),e.lineTo((t-o)/2,n)),e.lineTo(c,n),e.arc(c,n-c,c,Math.PI/2,Math.PI,!1),r===n1&&(e.lineTo(0,(n+i)/2),e.lineTo(-1*o,n/2),e.lineTo(0,(n-i)/2)),e.lineTo(0,s),e.arc(s,s,s,Math.PI,3*Math.PI/2,!1),e.closePath(),e.fillStrokeShape(this)}getSelfRect(){var e=0,t=0,n=this.pointerWidth(),r=this.pointerHeight(),o=this.pointerDirection(),i=this.width(),a=this.height();return o===J0?(t-=r,a+=r):o===t1?a+=r:o===n1?(e-=1.5*n,i+=n):o===e1&&(i+=1.5*n),{x:e,y:t,width:i,height:a}}}i1.prototype.className="Tag",UQ(i1),dJ.addGetterSetter(i1,"pointerDirection","none"),dJ.addGetterSetter(i1,"pointerWidth",0,rJ()),dJ.addGetterSetter(i1,"pointerHeight",0,rJ()),dJ.addGetterSetter(i1,"cornerRadius",0,oJ(4));class a1 extends L0{_sceneFunc(e){var t=this.cornerRadius(),n=this.width(),r=this.height();if(e.beginPath(),t){let o=0,i=0,a=0,s=0;"number"==typeof t?o=i=a=s=Math.min(t,n/2,r/2):(o=Math.min(t[0]||0,n/2,r/2),i=Math.min(t[1]||0,n/2,r/2),s=Math.min(t[2]||0,n/2,r/2),a=Math.min(t[3]||0,n/2,r/2)),e.moveTo(o,0),e.lineTo(n-i,0),e.arc(n-i,i,i,3*Math.PI/2,0,!1),e.lineTo(n,r-s),e.arc(n-s,r-s,s,0,Math.PI/2,!1),e.lineTo(a,r),e.arc(a,r-a,a,Math.PI/2,Math.PI,!1),e.lineTo(0,o),e.arc(o,o,o,Math.PI,3*Math.PI/2,!1)}else e.rect(0,0,n,r);e.closePath(),e.fillStrokeShape(this)}}a1.prototype.className="Rect",UQ(a1),dJ.addGetterSetter(a1,"cornerRadius",0,oJ(4));class s1 extends L0{_sceneFunc(e){const t=this._getPoints();e.beginPath(),e.moveTo(t[0].x,t[0].y);for(var n=1;n<t.length;n++)e.lineTo(t[n].x,t[n].y);e.closePath(),e.fillStrokeShape(this)}_getPoints(){const e=this.attrs.sides,t=this.attrs.radius||0,n=[];for(var r=0;r<e;r++)n.push({x:t*Math.sin(2*r*Math.PI/e),y:-1*t*Math.cos(2*r*Math.PI/e)});return n}getSelfRect(){const e=this._getPoints();var t=e[0].x,n=e[0].y,r=e[0].x,o=e[0].y;return e.forEach((e=>{t=Math.min(t,e.x),n=Math.max(n,e.x),r=Math.min(r,e.y),o=Math.max(o,e.y)})),{x:t,y:r,width:n-t,height:o-r}}getWidth(){return 2*this.radius()}getHeight(){return 2*this.radius()}setWidth(e){this.radius(e/2)}setHeight(e){this.radius(e/2)}}s1.prototype.className="RegularPolygon",s1.prototype._centroid=!0,s1.prototype._attrsAffectingSize=["radius"],UQ(s1),dJ.addGetterSetter(s1,"radius",0,rJ()),dJ.addGetterSetter(s1,"sides",0,rJ());var l1=2*Math.PI;class c1 extends L0{_sceneFunc(e){e.beginPath(),e.arc(0,0,this.innerRadius(),0,l1,!1),e.moveTo(this.outerRadius(),0),e.arc(0,0,this.outerRadius(),l1,0,!0),e.closePath(),e.fillStrokeShape(this)}getWidth(){return 2*this.outerRadius()}getHeight(){return 2*this.outerRadius()}setWidth(e){this.outerRadius(e/2)}setHeight(e){this.outerRadius(e/2)}}c1.prototype.className="Ring",c1.prototype._centroid=!0,c1.prototype._attrsAffectingSize=["innerRadius","outerRadius"],UQ(c1),dJ.addGetterSetter(c1,"innerRadius",0,rJ()),dJ.addGetterSetter(c1,"outerRadius",0,rJ());class u1 extends L0{constructor(e){super(e),this._updated=!0,this.anim=new N0((()=>{var e=this._updated;return this._updated=!1,e})),this.on("animationChange.konva",(function(){this.frameIndex(0)})),this.on("frameIndexChange.konva",(function(){this._updated=!0})),this.on("frameRateChange.konva",(function(){this.anim.isRunning()&&(clearInterval(this.interval),this._setInterval())}))}_sceneFunc(e){var t=this.animation(),n=this.frameIndex(),r=4*n,o=this.animations()[t],i=this.frameOffsets(),a=o[r+0],s=o[r+1],l=o[r+2],c=o[r+3],u=this.image();if((this.hasFill()||this.hasStroke())&&(e.beginPath(),e.rect(0,0,l,c),e.closePath(),e.fillStrokeShape(this)),u)if(i){var d=i[t],h=2*n;e.drawImage(u,a,s,l,c,d[h+0],d[h+1],l,c)}else e.drawImage(u,a,s,l,c,0,0,l,c)}_hitFunc(e){var t=this.animation(),n=this.frameIndex(),r=4*n,o=this.animations()[t],i=this.frameOffsets(),a=o[r+2],s=o[r+3];if(e.beginPath(),i){var l=i[t],c=2*n;e.rect(l[c+0],l[c+1],a,s)}else e.rect(0,0,a,s);e.closePath(),e.fillShape(this)}_useBufferCanvas(){return super._useBufferCanvas(!0)}_setInterval(){var e=this;this.interval=setInterval((function(){e._updateIndex()}),1e3/this.frameRate())}start(){if(!this.isRunning()){var e=this.getLayer();this.anim.setLayers(e),this._setInterval(),this.anim.start()}}stop(){this.anim.stop(),clearInterval(this.interval)}isRunning(){return this.anim.isRunning()}_updateIndex(){var e=this.frameIndex(),t=this.animation();e<this.animations()[t].length/4-1?this.frameIndex(e+1):this.frameIndex(0)}}u1.prototype.className="Sprite",UQ(u1),dJ.addGetterSetter(u1,"animation"),dJ.addGetterSetter(u1,"animations"),dJ.addGetterSetter(u1,"frameOffsets"),dJ.addGetterSetter(u1,"image"),dJ.addGetterSetter(u1,"frameIndex",0,rJ()),dJ.addGetterSetter(u1,"frameRate",17,rJ()),dJ.backCompat(u1,{index:"frameIndex",getIndex:"getFrameIndex",setIndex:"setFrameIndex"});class d1 extends L0{_sceneFunc(e){var t=this.innerRadius(),n=this.outerRadius(),r=this.numPoints();e.beginPath(),e.moveTo(0,0-n);for(var o=1;o<2*r;o++){var i=o%2==0?n:t,a=i*Math.sin(o*Math.PI/r),s=-1*i*Math.cos(o*Math.PI/r);e.lineTo(a,s)}e.closePath(),e.fillStrokeShape(this)}getWidth(){return 2*this.outerRadius()}getHeight(){return 2*this.outerRadius()}setWidth(e){this.outerRadius(e/2)}setHeight(e){this.outerRadius(e/2)}}function h1(e){return Array.from(e)}d1.prototype.className="Star",d1.prototype._centroid=!0,d1.prototype._attrsAffectingSize=["innerRadius","outerRadius"],UQ(d1),dJ.addGetterSetter(d1,"numPoints",5,rJ()),dJ.addGetterSetter(d1,"innerRadius",0,rJ()),dJ.addGetterSetter(d1,"outerRadius",0,rJ());var f1,p1="auto",g1="justify",m1="left",v1="middle",y1="normal",b1=" ",x1="none",w1=["fontFamily","fontSize","fontStyle","fontVariant","padding","align","verticalAlign","lineHeight","text","width","height","wrap","ellipsis","letterSpacing"],k1=w1.length;function S1(){return f1||(f1=eJ.createCanvasElement().getContext("2d"))}class C1 extends L0{constructor(e){super(function(e){return(e=e||{}).fillLinearGradientColorStops||e.fillRadialGradientColorStops||e.fillPatternImage||(e.fill=e.fill||"black"),e}(e)),this._partialTextX=0,this._partialTextY=0;for(var t=0;t<k1;t++)this.on(w1[t]+"Change.konva",this._setTextData);this._setTextData()}_sceneFunc(e){var t=this.textArr,n=t.length;if(this.text()){var r,o=this.padding(),i=this.fontSize(),a=this.lineHeight()*i,s=this.verticalAlign(),l=0,c=this.align(),u=this.getWidth(),d=this.letterSpacing(),h=this.fill(),f=this.textDecoration(),p=-1!==f.indexOf("underline"),g=-1!==f.indexOf("line-through"),m=0,v=(m=a/2,0),y=0;for(e.setAttr("font",this._getContextFont()),e.setAttr("textBaseline",v1),e.setAttr("textAlign",m1),s===v1?l=(this.getHeight()-n*a-2*o)/2:"bottom"===s&&(l=this.getHeight()-n*a-2*o),e.translate(o,l+o),r=0;r<n;r++){v=0,y=0;var b,x,w,k=t[r],S=k.text,C=k.width,_=k.lastInParagraph;if(e.save(),"right"===c?v+=u-C-2*o:"center"===c&&(v+=(u-C-2*o)/2),p&&(e.save(),e.beginPath(),e.moveTo(v,m+y+Math.round(i/2)),x=0===(b=S.split(" ").length-1),w=c===g1&&_&&!x?u-2*o:C,e.lineTo(v+Math.round(w),m+y+Math.round(i/2)),e.lineWidth=i/15,e.strokeStyle=h,e.stroke(),e.restore()),g&&(e.save(),e.beginPath(),e.moveTo(v,m+y),x=0===(b=S.split(" ").length-1),w=c===g1&&_&&!x?u-2*o:C,e.lineTo(v+Math.round(w),m+y),e.lineWidth=i/15,e.strokeStyle=h,e.stroke(),e.restore()),0!==d||c===g1){b=S.split(" ").length-1;for(var E=h1(S),P=0;P<E.length;P++){var L=E[P];" "!==L||_||c!==g1||(v+=(u-2*o-C)/b),this._partialTextX=v,this._partialTextY=m+y,this._partialText=L,e.fillStrokeShape(this),v+=this.measureSize(L).width+d}}else this._partialTextX=v,this._partialTextY=m+y,this._partialText=S,e.fillStrokeShape(this);e.restore(),n>1&&(m+=a)}}}_hitFunc(e){var t=this.getWidth(),n=this.getHeight();e.beginPath(),e.rect(0,0,t,n),e.closePath(),e.fillStrokeShape(this)}setText(e){var t=eJ._isString(e)?e:null==e?"":e+"";return this._setAttr("text",t),this}getWidth(){return this.attrs.width===p1||void 0===this.attrs.width?this.getTextWidth()+2*this.padding():this.attrs.width}getHeight(){return this.attrs.height===p1||void 0===this.attrs.height?this.fontSize()*this.textArr.length*this.lineHeight()+2*this.padding():this.attrs.height}getTextWidth(){return this.textWidth}getTextHeight(){return eJ.warn("text.getTextHeight() method is deprecated. Use text.height() - for full height and text.fontSize() - for one line height."),this.textHeight}measureSize(e){var t,n=S1(),r=this.fontSize();return n.save(),n.font=this._getContextFont(),t=n.measureText(e),n.restore(),{width:t.width,height:r}}_getContextFont(){return this.fontStyle()+b1+this.fontVariant()+b1+(this.fontSize()+"px ")+this.fontFamily().split(",").map((e=>{const t=(e=e.trim()).indexOf(" ")>=0,n=e.indexOf('"')>=0||e.indexOf("'")>=0;return t&&!n&&(e=`"${e}"`),e})).join(", ")}_addTextLine(e){this.align()===g1&&(e=e.trim());var t=this._getTextWidth(e);return this.textArr.push({text:e,width:t,lastInParagraph:!1})}_getTextWidth(e){var t=this.letterSpacing(),n=e.length;return S1().measureText(e).width+(n?t*(n-1):0)}_setTextData(){var e=this.text().split("\n"),t=+this.fontSize(),n=0,r=this.lineHeight()*t,o=this.attrs.width,i=this.attrs.height,a=o!==p1&&void 0!==o,s=i!==p1&&void 0!==i,l=this.padding(),c=o-2*l,u=i-2*l,d=0,h=this.wrap(),f="char"!==h&&h!==x1,p=this.ellipsis();this.textArr=[],S1().font=this._getContextFont();for(var g=p?this._getTextWidth("…"):0,m=0,v=e.length;m<v;++m){var y=e[m],b=this._getTextWidth(y);if(a&&b>c)for(;y.length>0;){for(var x=0,w=y.length,k="",S=0;x<w;){var C=x+w>>>1,_=y.slice(0,C+1),E=this._getTextWidth(_)+g;E<=c?(x=C+1,k=_,S=E):w=C}if(!k)break;if(f){var P,L=y[k.length];(P=(L===b1||"-"===L)&&S<=c?k.length:Math.max(k.lastIndexOf(b1),k.lastIndexOf("-"))+1)>0&&(x=P,k=k.slice(0,x),S=this._getTextWidth(k))}if(k=k.trimRight(),this._addTextLine(k),n=Math.max(n,S),d+=r,this._shouldHandleEllipsis(d)){this._tryToAddEllipsisToLastLine();break}if((y=(y=y.slice(x)).trimLeft()).length>0&&(b=this._getTextWidth(y))<=c){this._addTextLine(y),d+=r,n=Math.max(n,b);break}}else this._addTextLine(y),d+=r,n=Math.max(n,b),this._shouldHandleEllipsis(d)&&m<v-1&&this._tryToAddEllipsisToLastLine();if(s&&d+r>u)break;this.textArr[this.textArr.length-1]&&(this.textArr[this.textArr.length-1].lastInParagraph=!0)}this.textHeight=t,this.textWidth=n}_shouldHandleEllipsis(e){var t=+this.fontSize(),n=this.lineHeight()*t,r=this.attrs.height,o=r!==p1&&void 0!==r,i=r-2*this.padding();return!(this.wrap()!==x1)||o&&e+n>i}_tryToAddEllipsisToLastLine(){var e=this.attrs.width,t=e!==p1&&void 0!==e,n=e-2*this.padding(),r=this.ellipsis(),o=this.textArr[this.textArr.length-1];if(o&&r){if(t)this._getTextWidth(o.text+"…")<n||(o.text=o.text.slice(0,o.text.length-3));this.textArr.splice(this.textArr.length-1,1),this._addTextLine(o.text+"…")}}getStrokeScaleEnabled(){return!0}}C1.prototype._fillFunc=function(e){e.fillText(this._partialText,this._partialTextX,this._partialTextY)},C1.prototype._strokeFunc=function(e){e.strokeText(this._partialText,this._partialTextX,this._partialTextY)},C1.prototype.className="Text",C1.prototype._attrsAffectingSize=["text","fontSize","padding","wrap","lineHeight","letterSpacing"],UQ(C1),dJ.overWriteSetter(C1,"width",iJ()),dJ.overWriteSetter(C1,"height",iJ()),dJ.addGetterSetter(C1,"fontFamily","Arial"),dJ.addGetterSetter(C1,"fontSize",12,rJ()),dJ.addGetterSetter(C1,"fontStyle",y1),dJ.addGetterSetter(C1,"fontVariant",y1),dJ.addGetterSetter(C1,"padding",0,rJ()),dJ.addGetterSetter(C1,"align",m1),dJ.addGetterSetter(C1,"verticalAlign","top"),dJ.addGetterSetter(C1,"lineHeight",1,rJ()),dJ.addGetterSetter(C1,"wrap","word"),dJ.addGetterSetter(C1,"ellipsis",!1,lJ()),dJ.addGetterSetter(C1,"letterSpacing",0,rJ()),dJ.addGetterSetter(C1,"text","",aJ()),dJ.addGetterSetter(C1,"textDecoration","");var _1="normal";function E1(e){e.fillText(this.partialText,0,0)}function P1(e){e.strokeText(this.partialText,0,0)}class L1 extends L0{constructor(e){super(e),this.dummyCanvas=eJ.createCanvasElement(),this.dataArray=[],this.dataArray=q0.parsePathData(this.attrs.data),this.on("dataChange.konva",(function(){this.dataArray=q0.parsePathData(this.attrs.data),this._setTextData()})),this.on("textChange.konva alignChange.konva letterSpacingChange.konva kerningFuncChange.konva fontSizeChange.konva fontFamilyChange.konva",this._setTextData),this._setTextData()}_sceneFunc(e){e.setAttr("font",this._getContextFont()),e.setAttr("textBaseline",this.textBaseline()),e.setAttr("textAlign","left"),e.save();var t=this.textDecoration(),n=this.fill(),r=this.fontSize(),o=this.glyphInfo;"underline"===t&&e.beginPath();for(var i=0;i<o.length;i++){e.save();var a=o[i].p0;e.translate(a.x,a.y),e.rotate(o[i].rotation),this.partialText=o[i].text,e.fillStrokeShape(this),"underline"===t&&(0===i&&e.moveTo(0,r/2+1),e.lineTo(r,r/2+1)),e.restore()}"underline"===t&&(e.strokeStyle=n,e.lineWidth=r/20,e.stroke()),e.restore()}_hitFunc(e){e.beginPath();var t=this.glyphInfo;if(t.length>=1){var n=t[0].p0;e.moveTo(n.x,n.y)}for(var r=0;r<t.length;r++){var o=t[r].p1;e.lineTo(o.x,o.y)}e.setAttr("lineWidth",this.fontSize()),e.setAttr("strokeStyle",this.colorKey),e.stroke()}getTextWidth(){return this.textWidth}getTextHeight(){return eJ.warn("text.getTextHeight() method is deprecated. Use text.height() - for full height and text.fontSize() - for one line height."),this.textHeight}setText(e){return C1.prototype.setText.call(this,e)}_getContextFont(){return C1.prototype._getContextFont.call(this)}_getTextSize(e){var t=this.dummyCanvas.getContext("2d");t.save(),t.font=this._getContextFont();var n=t.measureText(e);return t.restore(),{width:n.width,height:parseInt(this.attrs.fontSize,10)}}_setTextData(){var e=this,t=this._getTextSize(this.attrs.text),n=this.letterSpacing(),r=this.align(),o=this.kerningFunc();this.textWidth=t.width,this.textHeight=t.height;var i=Math.max(this.textWidth+((this.attrs.text||"").length-1)*n,0);this.glyphInfo=[];for(var a=0,s=0;s<e.dataArray.length;s++)e.dataArray[s].pathLength>0&&(a+=e.dataArray[s].pathLength);var l=0;"center"===r&&(l=Math.max(0,a/2-i/2)),"right"===r&&(l=Math.max(0,a-i));for(var c,u,d,h=h1(this.text()),f=this.text().split(" ").length-1,p=-1,g=0,m=function(){g=0;for(var t=e.dataArray,n=p+1;n<t.length;n++){if(t[n].pathLength>0)return p=n,t[n];"M"===t[n].command&&(c={x:t[n].points[0],y:t[n].points[1]})}return{}},v=function(t){var o=e._getTextSize(t).width+n;" "===t&&"justify"===r&&(o+=(a-i)/f);var s=0,l=0;for(u=void 0;Math.abs(o-s)/o>.01&&l<20;){l++;for(var h=s;void 0===d;)(d=m())&&h+d.pathLength<o&&(h+=d.pathLength,d=void 0);if(0===Object.keys(d).length||void 0===c)return;var p=!1;switch(d.command){case"L":q0.getLineLength(c.x,c.y,d.points[0],d.points[1])>o?u=q0.getPointOnLine(o,c.x,c.y,d.points[0],d.points[1],c.x,c.y):d=void 0;break;case"A":var v=d.points[4],y=d.points[5],b=d.points[4]+y;0===g?g=v+1e-8:o>s?g+=Math.PI/180*y/Math.abs(y):g-=Math.PI/360*y/Math.abs(y),(y<0&&g<b||y>=0&&g>b)&&(g=b,p=!0),u=q0.getPointOnEllipticalArc(d.points[0],d.points[1],d.points[2],d.points[3],g,d.points[6]);break;case"C":0===g?g=o>d.pathLength?1e-8:o/d.pathLength:o>s?g+=(o-s)/d.pathLength/2:g=Math.max(g-(s-o)/d.pathLength/2,0),g>1&&(g=1,p=!0),u=q0.getPointOnCubicBezier(g,d.start.x,d.start.y,d.points[0],d.points[1],d.points[2],d.points[3],d.points[4],d.points[5]);break;case"Q":0===g?g=o/d.pathLength:o>s?g+=(o-s)/d.pathLength:g-=(s-o)/d.pathLength,g>1&&(g=1,p=!0),u=q0.getPointOnQuadraticBezier(g,d.start.x,d.start.y,d.points[0],d.points[1],d.points[2],d.points[3])}void 0!==u&&(s=q0.getLineLength(c.x,c.y,u.x,u.y)),p&&(p=!1,d=void 0)}},y=l/(e._getTextSize("C").width+n)-1,b=0;b<y&&(v("C"),void 0!==c&&void 0!==u);b++)c=u;for(var x=0;x<h.length&&(v(h[x]),void 0!==c&&void 0!==u);x++){var w=q0.getLineLength(c.x,c.y,u.x,u.y),k=0;if(o)try{k=o(h[x-1],h[x])*this.fontSize()}catch(B2){k=0}c.x+=k,u.x+=k,this.textWidth+=k;var S=q0.getPointOnLine(k+w/2,c.x,c.y,u.x,u.y),C=Math.atan2(u.y-c.y,u.x-c.x);this.glyphInfo.push({transposeX:S.x,transposeY:S.y,text:h[x],rotation:C,p0:c,p1:u}),c=u}}getSelfRect(){if(!this.glyphInfo.length)return{x:0,y:0,width:0,height:0};var e=[];this.glyphInfo.forEach((function(t){e.push(t.p0.x),e.push(t.p0.y),e.push(t.p1.x),e.push(t.p1.y)}));for(var t,n,r=e[0]||0,o=e[0]||0,i=e[1]||0,a=e[1]||0,s=0;s<e.length/2;s++)t=e[2*s],n=e[2*s+1],r=Math.min(r,t),o=Math.max(o,t),i=Math.min(i,n),a=Math.max(a,n);var l=this.fontSize();return{x:r-l/2,y:i-l/2,width:o-r+l,height:a-i+l}}destroy(){return eJ.releaseCanvas(this.dummyCanvas),super.destroy()}}L1.prototype._fillFunc=E1,L1.prototype._strokeFunc=P1,L1.prototype._fillFuncHit=E1,L1.prototype._strokeFuncHit=P1,L1.prototype.className="TextPath",L1.prototype._attrsAffectingSize=["text","fontSize","data"],UQ(L1),dJ.addGetterSetter(L1,"data"),dJ.addGetterSetter(L1,"fontFamily","Arial"),dJ.addGetterSetter(L1,"fontSize",12,rJ()),dJ.addGetterSetter(L1,"fontStyle",_1),dJ.addGetterSetter(L1,"align","left"),dJ.addGetterSetter(L1,"letterSpacing",0,rJ()),dJ.addGetterSetter(L1,"textBaseline","middle"),dJ.addGetterSetter(L1,"fontVariant",_1),dJ.addGetterSetter(L1,"text",""),dJ.addGetterSetter(L1,"textDecoration",null),dJ.addGetterSetter(L1,"kerningFunc",null);var O1="tr-konva",M1=["resizeEnabledChange","rotateAnchorOffsetChange","rotateEnabledChange","enabledAnchorsChange","anchorSizeChange","borderEnabledChange","borderStrokeChange","borderStrokeWidthChange","borderDashChange","anchorStrokeChange","anchorStrokeWidthChange","anchorFillChange","anchorCornerRadiusChange","ignoreStrokeChange"].map((e=>e+`.${O1}`)).join(" "),T1="nodesRect",A1=["widthChange","heightChange","scaleXChange","scaleYChange","skewXChange","skewYChange","rotationChange","offsetXChange","offsetYChange","transformsEnabledChange","strokeWidthChange"],I1={"top-left":-45,"top-center":0,"top-right":45,"middle-right":-90,"middle-left":90,"bottom-left":-135,"bottom-center":180,"bottom-right":135};const R1="ontouchstart"in $Q._global;var N1=["top-left","top-center","top-right","middle-right","middle-left","bottom-left","bottom-center","bottom-right"];function D1(e,t,n){const r=n.x+(e.x-n.x)*Math.cos(t)-(e.y-n.y)*Math.sin(t),o=n.y+(e.x-n.x)*Math.sin(t)+(e.y-n.y)*Math.cos(t);return Object.assign(Object.assign({},e),{rotation:e.rotation+t,x:r,y:o})}function z1(e,t){const n=function(e){return{x:e.x+e.width/2*Math.cos(e.rotation)+e.height/2*Math.sin(-e.rotation),y:e.y+e.height/2*Math.cos(e.rotation)+e.width/2*Math.sin(e.rotation)}}(e);return D1(e,t,n)}class B1 extends I0{constructor(e){super(e),this._transforming=!1,this._createElements(),this._handleMouseMove=this._handleMouseMove.bind(this),this._handleMouseUp=this._handleMouseUp.bind(this),this.update=this.update.bind(this),this.on(M1,this.update),this.getNode()&&this.update()}attachTo(e){return this.setNode(e),this}setNode(e){return eJ.warn("tr.setNode(shape), tr.node(shape) and tr.attachTo(shape) methods are deprecated. Please use tr.nodes(nodesArray) instead."),this.setNodes([e])}getNode(){return this._nodes&&this._nodes[0]}_getEventNamespace(){return O1+this._id}setNodes(e=[]){return this._nodes&&this._nodes.length&&this.detach(),this._nodes=e,1===e.length&&this.useSingleNodeRotation()?this.rotation(e[0].getAbsoluteRotation()):this.rotation(0),this._nodes.forEach((e=>{const t=()=>{1===this.nodes().length&&this.useSingleNodeRotation()&&this.rotation(this.nodes()[0].getAbsoluteRotation()),this._resetTransformCache(),this._transforming||this.isDragging()||this.update()},n=e._attrsAffectingSize.map((e=>e+"Change."+this._getEventNamespace())).join(" ");e.on(n,t),e.on(A1.map((e=>e+`.${this._getEventNamespace()}`)).join(" "),t),e.on(`absoluteTransformChange.${this._getEventNamespace()}`,t),this._proxyDrag(e)})),this._resetTransformCache(),!!this.findOne(".top-left")&&this.update(),this}_proxyDrag(e){let t;e.on(`dragstart.${this._getEventNamespace()}`,(n=>{t=e.getAbsolutePosition(),this.isDragging()||e===this.findOne(".back")||this.startDrag(n,!1)})),e.on(`dragmove.${this._getEventNamespace()}`,(n=>{if(!t)return;const r=e.getAbsolutePosition(),o=r.x-t.x,i=r.y-t.y;this.nodes().forEach((t=>{if(t===e)return;if(t.isDragging())return;const r=t.getAbsolutePosition();t.setAbsolutePosition({x:r.x+o,y:r.y+i}),t.startDrag(n)})),t=null}))}getNodes(){return this._nodes||[]}getActiveAnchor(){return this._movingAnchorName}detach(){this._nodes&&this._nodes.forEach((e=>{e.off("."+this._getEventNamespace())})),this._nodes=[],this._resetTransformCache()}_resetTransformCache(){this._clearCache(T1),this._clearCache("transform"),this._clearSelfAndDescendantCache("absoluteTransform")}_getNodeRect(){return this._getCache(T1,this.__getNodeRect)}__getNodeShape(e,t=this.rotation(),n){var r=e.getClientRect({skipTransform:!0,skipShadow:!0,skipStroke:this.ignoreStroke()}),o=e.getAbsoluteScale(n),i=e.getAbsolutePosition(n),a=r.x*o.x-e.offsetX()*o.x,s=r.y*o.y-e.offsetY()*o.y;const l=($Q.getAngle(e.getAbsoluteRotation())+2*Math.PI)%(2*Math.PI);return D1({x:i.x+a*Math.cos(l)+s*Math.sin(-l),y:i.y+s*Math.cos(l)+a*Math.sin(l),width:r.width*o.x,height:r.height*o.y,rotation:l},-$Q.getAngle(t),{x:0,y:0})}__getNodeRect(){if(!this.getNode())return{x:-1e8,y:-1e8,width:0,height:0,rotation:0};const e=[];this.nodes().map((t=>{const n=t.getClientRect({skipTransform:!0,skipShadow:!0,skipStroke:this.ignoreStroke()});var r=[{x:n.x,y:n.y},{x:n.x+n.width,y:n.y},{x:n.x+n.width,y:n.y+n.height},{x:n.x,y:n.y+n.height}],o=t.getAbsoluteTransform();r.forEach((function(t){var n=o.point(t);e.push(n)}))}));const t=new GQ;var n,r,o,i;t.rotate(-$Q.getAngle(this.rotation())),e.forEach((function(e){var a=t.point(e);void 0===n&&(n=o=a.x,r=i=a.y),n=Math.min(n,a.x),r=Math.min(r,a.y),o=Math.max(o,a.x),i=Math.max(i,a.y)})),t.invert();const a=t.point({x:n,y:r});return{x:a.x,y:a.y,width:o-n,height:i-r,rotation:$Q.getAngle(this.rotation())}}getX(){return this._getNodeRect().x}getY(){return this._getNodeRect().y}getWidth(){return this._getNodeRect().width}getHeight(){return this._getNodeRect().height}_createElements(){this._createBack(),N1.forEach(function(e){this._createAnchor(e)}.bind(this)),this._createAnchor("rotater")}_createAnchor(e){var t=new a1({stroke:"rgb(0, 161, 255)",fill:"white",strokeWidth:1,name:e+" _anchor",dragDistance:0,draggable:!0,hitStrokeWidth:R1?10:"auto"}),n=this;t.on("mousedown touchstart",(function(e){n._handleMouseDown(e)})),t.on("dragstart",(e=>{t.stopDrag(),e.cancelBubble=!0})),t.on("dragend",(e=>{e.cancelBubble=!0})),t.on("mouseenter",(()=>{var n=$Q.getAngle(this.rotation()),r=function(e,t){if("rotater"===e)return"crosshair";t+=eJ.degToRad(I1[e]||0);var n=(eJ.radToDeg(t)%360+360)%360;return eJ._inRange(n,337.5,360)||eJ._inRange(n,0,22.5)?"ns-resize":eJ._inRange(n,22.5,67.5)?"nesw-resize":eJ._inRange(n,67.5,112.5)?"ew-resize":eJ._inRange(n,112.5,157.5)?"nwse-resize":eJ._inRange(n,157.5,202.5)?"ns-resize":eJ._inRange(n,202.5,247.5)?"nesw-resize":eJ._inRange(n,247.5,292.5)?"ew-resize":eJ._inRange(n,292.5,337.5)?"nwse-resize":(eJ.error("Transformer has unknown angle for cursor detection: "+n),"pointer")}(e,n);t.getStage().content&&(t.getStage().content.style.cursor=r),this._cursorChange=!0})),t.on("mouseout",(()=>{t.getStage().content&&(t.getStage().content.style.cursor=""),this._cursorChange=!1})),this.add(t)}_createBack(){var e=new L0({name:"back",width:0,height:0,draggable:!0,sceneFunc(e){var t=this.getParent(),n=t.padding();e.beginPath(),e.rect(-n,-n,this.width()+2*n,this.height()+2*n),e.moveTo(this.width()/2,-n),t.rotateEnabled()&&e.lineTo(this.width()/2,-t.rotateAnchorOffset()*eJ._sign(this.height())-n),e.fillStrokeShape(this)},hitFunc:(e,t)=>{if(this.shouldOverdrawWholeArea()){var n=this.padding();e.beginPath(),e.rect(-n,-n,t.width()+2*n,t.height()+2*n),e.fillStrokeShape(t)}}});this.add(e),this._proxyDrag(e),e.on("dragstart",(e=>{e.cancelBubble=!0})),e.on("dragmove",(e=>{e.cancelBubble=!0})),e.on("dragend",(e=>{e.cancelBubble=!0})),this.on("dragmove",(e=>{this.update()}))}_handleMouseDown(e){this._movingAnchorName=e.target.name().split(" ")[0];var t=this._getNodeRect(),n=t.width,r=t.height,o=Math.sqrt(Math.pow(n,2)+Math.pow(r,2));this.sin=Math.abs(r/o),this.cos=Math.abs(n/o),"undefined"!=typeof window&&(window.addEventListener("mousemove",this._handleMouseMove),window.addEventListener("touchmove",this._handleMouseMove),window.addEventListener("mouseup",this._handleMouseUp,!0),window.addEventListener("touchend",this._handleMouseUp,!0)),this._transforming=!0;var i=e.target.getAbsolutePosition(),a=e.target.getStage().getPointerPosition();this._anchorDragOffset={x:a.x-i.x,y:a.y-i.y},this._fire("transformstart",{evt:e.evt,target:this.getNode()}),this._nodes.forEach((t=>{t._fire("transformstart",{evt:e.evt,target:t})}))}_handleMouseMove(e){var t,n,r,o=this.findOne("."+this._movingAnchorName),i=o.getStage();i.setPointersPositions(e);const a=i.getPointerPosition();let s={x:a.x-this._anchorDragOffset.x,y:a.y-this._anchorDragOffset.y};const l=o.getAbsolutePosition();this.anchorDragBoundFunc()&&(s=this.anchorDragBoundFunc()(l,s,e)),o.setAbsolutePosition(s);const c=o.getAbsolutePosition();if(l.x!==c.x||l.y!==c.y)if("rotater"!==this._movingAnchorName){var u=this.keepRatio()||e.shiftKey,d=this.centeredScaling()||e.altKey;if("top-left"===this._movingAnchorName){if(u){var h=d?{x:this.width()/2,y:this.height()/2}:{x:this.findOne(".bottom-right").x(),y:this.findOne(".bottom-right").y()};r=Math.sqrt(Math.pow(h.x-o.x(),2)+Math.pow(h.y-o.y(),2));var f=this.findOne(".top-left").x()>h.x?-1:1,p=this.findOne(".top-left").y()>h.y?-1:1;t=r*this.cos*f,n=r*this.sin*p,this.findOne(".top-left").x(h.x-t),this.findOne(".top-left").y(h.y-n)}}else if("top-center"===this._movingAnchorName)this.findOne(".top-left").y(o.y());else if("top-right"===this._movingAnchorName){if(u){h=d?{x:this.width()/2,y:this.height()/2}:{x:this.findOne(".bottom-left").x(),y:this.findOne(".bottom-left").y()};r=Math.sqrt(Math.pow(o.x()-h.x,2)+Math.pow(h.y-o.y(),2));f=this.findOne(".top-right").x()<h.x?-1:1,p=this.findOne(".top-right").y()>h.y?-1:1;t=r*this.cos*f,n=r*this.sin*p,this.findOne(".top-right").x(h.x+t),this.findOne(".top-right").y(h.y-n)}var g=o.position();this.findOne(".top-left").y(g.y),this.findOne(".bottom-right").x(g.x)}else if("middle-left"===this._movingAnchorName)this.findOne(".top-left").x(o.x());else if("middle-right"===this._movingAnchorName)this.findOne(".bottom-right").x(o.x());else if("bottom-left"===this._movingAnchorName){if(u){h=d?{x:this.width()/2,y:this.height()/2}:{x:this.findOne(".top-right").x(),y:this.findOne(".top-right").y()};r=Math.sqrt(Math.pow(h.x-o.x(),2)+Math.pow(o.y()-h.y,2));f=h.x<o.x()?-1:1,p=o.y()<h.y?-1:1;t=r*this.cos*f,n=r*this.sin*p,o.x(h.x-t),o.y(h.y+n)}g=o.position(),this.findOne(".top-left").x(g.x),this.findOne(".bottom-right").y(g.y)}else if("bottom-center"===this._movingAnchorName)this.findOne(".bottom-right").y(o.y());else if("bottom-right"===this._movingAnchorName){if(u){h=d?{x:this.width()/2,y:this.height()/2}:{x:this.findOne(".top-left").x(),y:this.findOne(".top-left").y()};r=Math.sqrt(Math.pow(o.x()-h.x,2)+Math.pow(o.y()-h.y,2));f=this.findOne(".bottom-right").x()<h.x?-1:1,p=this.findOne(".bottom-right").y()<h.y?-1:1;t=r*this.cos*f,n=r*this.sin*p,this.findOne(".bottom-right").x(h.x+t),this.findOne(".bottom-right").y(h.y+n)}}else console.error(new Error("Wrong position argument of selection resizer: "+this._movingAnchorName));if(d=this.centeredScaling()||e.altKey){var m=this.findOne(".top-left"),v=this.findOne(".bottom-right"),y=m.x(),b=m.y(),x=this.getWidth()-v.x(),w=this.getHeight()-v.y();v.move({x:-y,y:-b}),m.move({x:x,y:w})}var k=this.findOne(".top-left").getAbsolutePosition();t=k.x,n=k.y;var S=this.findOne(".bottom-right").x()-this.findOne(".top-left").x(),C=this.findOne(".bottom-right").y()-this.findOne(".top-left").y();this._fitNodesInto({x:t,y:n,width:S,height:C,rotation:$Q.getAngle(this.rotation())},e)}else{var _=this._getNodeRect();t=o.x()-_.width/2,n=-o.y()+_.height/2;let r=Math.atan2(-n,t)+Math.PI/2;_.height<0&&(r-=Math.PI);const i=$Q.getAngle(this.rotation())+r,a=$Q.getAngle(this.rotationSnapTolerance()),s=function(e,t,n){let r=t;for(let o=0;o<e.length;o++){const i=$Q.getAngle(e[o]),a=Math.abs(i-t)%(2*Math.PI);Math.min(a,2*Math.PI-a)<n&&(r=i)}return r}(this.rotationSnaps(),i,a),l=z1(_,s-_.rotation);this._fitNodesInto(l,e)}}_handleMouseUp(e){this._removeEvents(e)}getAbsoluteTransform(){return this.getTransform()}_removeEvents(e){if(this._transforming){this._transforming=!1,"undefined"!=typeof window&&(window.removeEventListener("mousemove",this._handleMouseMove),window.removeEventListener("touchmove",this._handleMouseMove),window.removeEventListener("mouseup",this._handleMouseUp,!0),window.removeEventListener("touchend",this._handleMouseUp,!0));var t=this.getNode();this._fire("transformend",{evt:e,target:t}),t&&this._nodes.forEach((t=>{t._fire("transformend",{evt:e,target:t})})),this._movingAnchorName=null}}_fitNodesInto(e,t){var n=this._getNodeRect();if(eJ._inRange(e.width,2*-this.padding()-1,1))return void this.update();if(eJ._inRange(e.height,2*-this.padding()-1,1))return void this.update();const r=this.flipEnabled();var o=new GQ;if(o.rotate($Q.getAngle(this.rotation())),this._movingAnchorName&&e.width<0&&this._movingAnchorName.indexOf("left")>=0){const t=o.point({x:2*-this.padding(),y:0});if(e.x+=t.x,e.y+=t.y,e.width+=2*this.padding(),this._movingAnchorName=this._movingAnchorName.replace("left","right"),this._anchorDragOffset.x-=t.x,this._anchorDragOffset.y-=t.y,!r)return void this.update()}else if(this._movingAnchorName&&e.width<0&&this._movingAnchorName.indexOf("right")>=0){const t=o.point({x:2*this.padding(),y:0});if(this._movingAnchorName=this._movingAnchorName.replace("right","left"),this._anchorDragOffset.x-=t.x,this._anchorDragOffset.y-=t.y,e.width+=2*this.padding(),!r)return void this.update()}if(this._movingAnchorName&&e.height<0&&this._movingAnchorName.indexOf("top")>=0){const t=o.point({x:0,y:2*-this.padding()});if(e.x+=t.x,e.y+=t.y,this._movingAnchorName=this._movingAnchorName.replace("top","bottom"),this._anchorDragOffset.x-=t.x,this._anchorDragOffset.y-=t.y,e.height+=2*this.padding(),!r)return void this.update()}else if(this._movingAnchorName&&e.height<0&&this._movingAnchorName.indexOf("bottom")>=0){const t=o.point({x:0,y:2*this.padding()});if(this._movingAnchorName=this._movingAnchorName.replace("bottom","top"),this._anchorDragOffset.x-=t.x,this._anchorDragOffset.y-=t.y,e.height+=2*this.padding(),!r)return void this.update()}if(this.boundBoxFunc()){const t=this.boundBoxFunc()(n,e);t?e=t:eJ.warn("boundBoxFunc returned falsy. You should return new bound rect from it!")}const i=1e7,a=new GQ;a.translate(n.x,n.y),a.rotate(n.rotation),a.scale(n.width/i,n.height/i);const s=new GQ;s.translate(e.x,e.y),s.rotate(e.rotation),s.scale(e.width/i,e.height/i);const l=s.multiply(a.invert());this._nodes.forEach((e=>{var n;const r=e.getParent().getAbsoluteTransform(),o=e.getTransform().copy();o.translate(e.offsetX(),e.offsetY());const i=new GQ;i.multiply(r.copy().invert()).multiply(l).multiply(r).multiply(o);const a=i.decompose();e.setAttrs(a),this._fire("transform",{evt:t,target:e}),e._fire("transform",{evt:t,target:e}),null===(n=e.getLayer())||void 0===n||n.batchDraw()})),this.rotation(eJ._getRotation(e.rotation)),this._resetTransformCache(),this.update(),this.getLayer().batchDraw()}forceUpdate(){this._resetTransformCache(),this.update()}_batchChangeChild(e,t){this.findOne(e).setAttrs(t)}update(){var e,t=this._getNodeRect();this.rotation(eJ._getRotation(t.rotation));var n=t.width,r=t.height,o=this.enabledAnchors(),i=this.resizeEnabled(),a=this.padding(),s=this.anchorSize();this.find("._anchor").forEach((e=>{e.setAttrs({width:s,height:s,offsetX:s/2,offsetY:s/2,stroke:this.anchorStroke(),strokeWidth:this.anchorStrokeWidth(),fill:this.anchorFill(),cornerRadius:this.anchorCornerRadius()})})),this._batchChangeChild(".top-left",{x:0,y:0,offsetX:s/2+a,offsetY:s/2+a,visible:i&&o.indexOf("top-left")>=0}),this._batchChangeChild(".top-center",{x:n/2,y:0,offsetY:s/2+a,visible:i&&o.indexOf("top-center")>=0}),this._batchChangeChild(".top-right",{x:n,y:0,offsetX:s/2-a,offsetY:s/2+a,visible:i&&o.indexOf("top-right")>=0}),this._batchChangeChild(".middle-left",{x:0,y:r/2,offsetX:s/2+a,visible:i&&o.indexOf("middle-left")>=0}),this._batchChangeChild(".middle-right",{x:n,y:r/2,offsetX:s/2-a,visible:i&&o.indexOf("middle-right")>=0}),this._batchChangeChild(".bottom-left",{x:0,y:r,offsetX:s/2+a,offsetY:s/2-a,visible:i&&o.indexOf("bottom-left")>=0}),this._batchChangeChild(".bottom-center",{x:n/2,y:r,offsetY:s/2-a,visible:i&&o.indexOf("bottom-center")>=0}),this._batchChangeChild(".bottom-right",{x:n,y:r,offsetX:s/2-a,offsetY:s/2-a,visible:i&&o.indexOf("bottom-right")>=0}),this._batchChangeChild(".rotater",{x:n/2,y:-this.rotateAnchorOffset()*eJ._sign(r)-a,visible:this.rotateEnabled()}),this._batchChangeChild(".back",{width:n,height:r,visible:this.borderEnabled(),stroke:this.borderStroke(),strokeWidth:this.borderStrokeWidth(),dash:this.borderDash(),x:0,y:0}),null===(e=this.getLayer())||void 0===e||e.batchDraw()}isTransforming(){return this._transforming}stopTransform(){if(this._transforming){this._removeEvents();var e=this.findOne("."+this._movingAnchorName);e&&e.stopDrag()}}destroy(){return this.getStage()&&this._cursorChange&&this.getStage().content&&(this.getStage().content.style.cursor=""),I0.prototype.destroy.call(this),this.detach(),this._removeEvents(),this}toObject(){return zJ.prototype.toObject.call(this)}getClientRect(){return this.nodes().length>0?super.getClientRect():{x:0,y:0,width:0,height:0}}}B1.prototype.className="Transformer",UQ(B1),dJ.addGetterSetter(B1,"enabledAnchors",N1,(function(e){return e instanceof Array||eJ.warn("enabledAnchors value should be an array"),e instanceof Array&&e.forEach((function(e){-1===N1.indexOf(e)&&eJ.warn("Unknown anchor name: "+e+". Available names are: "+N1.join(", "))})),e||[]})),dJ.addGetterSetter(B1,"flipEnabled",!0,lJ()),dJ.addGetterSetter(B1,"resizeEnabled",!0),dJ.addGetterSetter(B1,"anchorSize",10,rJ()),dJ.addGetterSetter(B1,"rotateEnabled",!0),dJ.addGetterSetter(B1,"rotationSnaps",[]),dJ.addGetterSetter(B1,"rotateAnchorOffset",50,rJ()),dJ.addGetterSetter(B1,"rotationSnapTolerance",5,rJ()),dJ.addGetterSetter(B1,"borderEnabled",!0),dJ.addGetterSetter(B1,"anchorStroke","rgb(0, 161, 255)"),dJ.addGetterSetter(B1,"anchorStrokeWidth",1,rJ()),dJ.addGetterSetter(B1,"anchorFill","white"),dJ.addGetterSetter(B1,"anchorCornerRadius",0,rJ()),dJ.addGetterSetter(B1,"borderStroke","rgb(0, 161, 255)"),dJ.addGetterSetter(B1,"borderStrokeWidth",1,rJ()),dJ.addGetterSetter(B1,"borderDash"),dJ.addGetterSetter(B1,"keepRatio",!0),dJ.addGetterSetter(B1,"centeredScaling",!1),dJ.addGetterSetter(B1,"ignoreStroke",!1),dJ.addGetterSetter(B1,"padding",0,rJ()),dJ.addGetterSetter(B1,"node"),dJ.addGetterSetter(B1,"nodes"),dJ.addGetterSetter(B1,"boundBoxFunc"),dJ.addGetterSetter(B1,"anchorDragBoundFunc"),dJ.addGetterSetter(B1,"shouldOverdrawWholeArea",!1),dJ.addGetterSetter(B1,"useSingleNodeRotation",!0),dJ.backCompat(B1,{lineEnabled:"borderEnabled",rotateHandlerOffset:"rotateAnchorOffset",enabledHandlers:"enabledAnchors"});class j1 extends L0{_sceneFunc(e){e.beginPath(),e.arc(0,0,this.radius(),0,$Q.getAngle(this.angle()),this.clockwise()),e.lineTo(0,0),e.closePath(),e.fillStrokeShape(this)}getWidth(){return 2*this.radius()}getHeight(){return 2*this.radius()}setWidth(e){this.radius(e/2)}setHeight(e){this.radius(e/2)}}function F1(){this.r=0,this.g=0,this.b=0,this.a=0,this.next=null}j1.prototype.className="Wedge",j1.prototype._centroid=!0,j1.prototype._attrsAffectingSize=["radius"],UQ(j1),dJ.addGetterSetter(j1,"radius",0,rJ()),dJ.addGetterSetter(j1,"angle",0,rJ()),dJ.addGetterSetter(j1,"clockwise",!1),dJ.backCompat(j1,{angleDeg:"angle",getAngleDeg:"getAngle",setAngleDeg:"setAngle"});var H1=[512,512,456,512,328,456,335,512,405,328,271,456,388,335,292,512,454,405,364,328,298,271,496,456,420,388,360,335,312,292,273,512,482,454,428,405,383,364,345,328,312,298,284,271,259,496,475,456,437,420,404,388,374,360,347,335,323,312,302,292,282,273,265,512,497,482,468,454,441,428,417,405,394,383,373,364,354,345,337,328,320,312,305,298,291,284,278,271,265,259,507,496,485,475,465,456,446,437,428,420,412,404,396,388,381,374,367,360,354,347,341,335,329,323,318,312,307,302,297,292,287,282,278,273,269,265,261,512,505,497,489,482,475,468,461,454,447,441,435,428,422,417,411,405,399,394,389,383,378,373,368,364,359,354,350,345,341,337,332,328,324,320,316,312,309,305,301,298,294,291,287,284,281,278,274,271,268,265,262,259,257,507,501,496,491,485,480,475,470,465,460,456,451,446,442,437,433,428,424,420,416,412,408,404,400,396,392,388,385,381,377,374,370,367,363,360,357,354,350,347,344,341,338,335,332,329,326,323,320,318,315,312,310,307,304,302,299,297,294,292,289,287,285,282,280,278,275,273,271,269,267,265,263,261,259],W1=[9,11,12,13,13,14,14,15,15,15,15,16,16,16,16,17,17,17,17,17,17,17,18,18,18,18,18,18,18,18,18,19,19,19,19,19,19,19,19,19,19,19,19,19,19,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24];dJ.addGetterSetter(zJ,"blurRadius",0,rJ(),dJ.afterSetFilter);dJ.addGetterSetter(zJ,"brightness",0,rJ(),dJ.afterSetFilter);dJ.addGetterSetter(zJ,"contrast",0,rJ(),dJ.afterSetFilter);function V1(e,t,n,r,o){var i=n-t,a=o-r;return 0===i?r+a/2:0===a?r:a*((e-t)/i)+r}dJ.addGetterSetter(zJ,"embossStrength",.5,rJ(),dJ.afterSetFilter),dJ.addGetterSetter(zJ,"embossWhiteLevel",.5,rJ(),dJ.afterSetFilter),dJ.addGetterSetter(zJ,"embossDirection","top-left",null,dJ.afterSetFilter),dJ.addGetterSetter(zJ,"embossBlend",!1,null,dJ.afterSetFilter);dJ.addGetterSetter(zJ,"enhance",0,rJ(),dJ.afterSetFilter);dJ.addGetterSetter(zJ,"hue",0,rJ(),dJ.afterSetFilter),dJ.addGetterSetter(zJ,"saturation",0,rJ(),dJ.afterSetFilter),dJ.addGetterSetter(zJ,"luminance",0,rJ(),dJ.afterSetFilter);dJ.addGetterSetter(zJ,"hue",0,rJ(),dJ.afterSetFilter),dJ.addGetterSetter(zJ,"saturation",0,rJ(),dJ.afterSetFilter),dJ.addGetterSetter(zJ,"value",0,rJ(),dJ.afterSetFilter);function $1(e,t,n){var r=4*(n*e.width+t),o=[];return o.push(e.data[r++],e.data[r++],e.data[r++],e.data[r++]),o}function U1(e,t){return Math.sqrt(Math.pow(e[0]-t[0],2)+Math.pow(e[1]-t[1],2)+Math.pow(e[2]-t[2],2))}dJ.addGetterSetter(zJ,"kaleidoscopePower",2,rJ(),dJ.afterSetFilter),dJ.addGetterSetter(zJ,"kaleidoscopeAngle",0,rJ(),dJ.afterSetFilter);dJ.addGetterSetter(zJ,"threshold",0,rJ(),dJ.afterSetFilter);dJ.addGetterSetter(zJ,"noise",.2,rJ(),dJ.afterSetFilter);dJ.addGetterSetter(zJ,"pixelSize",8,rJ(),dJ.afterSetFilter);dJ.addGetterSetter(zJ,"levels",.5,rJ(),dJ.afterSetFilter);dJ.addGetterSetter(zJ,"red",0,(function(e){return this._filterUpToDate=!1,e>255?255:e<0?0:Math.round(e)})),dJ.addGetterSetter(zJ,"green",0,(function(e){return this._filterUpToDate=!1,e>255?255:e<0?0:Math.round(e)})),dJ.addGetterSetter(zJ,"blue",0,nJ,dJ.afterSetFilter);dJ.addGetterSetter(zJ,"red",0,(function(e){return this._filterUpToDate=!1,e>255?255:e<0?0:Math.round(e)})),dJ.addGetterSetter(zJ,"green",0,(function(e){return this._filterUpToDate=!1,e>255?255:e<0?0:Math.round(e)})),dJ.addGetterSetter(zJ,"blue",0,nJ,dJ.afterSetFilter),dJ.addGetterSetter(zJ,"alpha",1,(function(e){return this._filterUpToDate=!1,e>1?1:e<0?0:e}));dJ.addGetterSetter(zJ,"threshold",.5,rJ(),dJ.afterSetFilter);const G1=W0.Util._assign(W0,{Arc:V0,Arrow:Y0,Circle:Z0,Ellipse:X0,Image:K0,Label:o1,Tag:i1,Line:G0,Path:q0,Rect:a1,RegularPolygon:s1,Ring:c1,Sprite:u1,Star:d1,Text:C1,TextPath:L1,Transformer:B1,Wedge:j1,Filters:{Blur:function(e){var t=Math.round(this.blurRadius());t>0&&function(e,t){var n,r,o,i,a,s,l,c,u,d,h,f,p,g,m,v,y,b,x,w,k,S,C,_,E=e.data,P=e.width,L=e.height,O=t+t+1,M=P-1,T=L-1,A=t+1,I=A*(A+1)/2,R=new F1,N=null,D=R,z=null,B=null,j=H1[t],F=W1[t];for(o=1;o<O;o++)D=D.next=new F1,o===A&&(N=D);for(D.next=R,l=s=0,r=0;r<L;r++){for(v=y=b=x=c=u=d=h=0,f=A*(w=E[s]),p=A*(k=E[s+1]),g=A*(S=E[s+2]),m=A*(C=E[s+3]),c+=I*w,u+=I*k,d+=I*S,h+=I*C,D=R,o=0;o<A;o++)D.r=w,D.g=k,D.b=S,D.a=C,D=D.next;for(o=1;o<A;o++)i=s+((M<o?M:o)<<2),c+=(D.r=w=E[i])*(_=A-o),u+=(D.g=k=E[i+1])*_,d+=(D.b=S=E[i+2])*_,h+=(D.a=C=E[i+3])*_,v+=w,y+=k,b+=S,x+=C,D=D.next;for(z=R,B=N,n=0;n<P;n++)E[s+3]=C=h*j>>F,0!==C?(C=255/C,E[s]=(c*j>>F)*C,E[s+1]=(u*j>>F)*C,E[s+2]=(d*j>>F)*C):E[s]=E[s+1]=E[s+2]=0,c-=f,u-=p,d-=g,h-=m,f-=z.r,p-=z.g,g-=z.b,m-=z.a,i=l+((i=n+t+1)<M?i:M)<<2,c+=v+=z.r=E[i],u+=y+=z.g=E[i+1],d+=b+=z.b=E[i+2],h+=x+=z.a=E[i+3],z=z.next,f+=w=B.r,p+=k=B.g,g+=S=B.b,m+=C=B.a,v-=w,y-=k,b-=S,x-=C,B=B.next,s+=4;l+=P}for(n=0;n<P;n++){for(y=b=x=v=u=d=h=c=0,f=A*(w=E[s=n<<2]),p=A*(k=E[s+1]),g=A*(S=E[s+2]),m=A*(C=E[s+3]),c+=I*w,u+=I*k,d+=I*S,h+=I*C,D=R,o=0;o<A;o++)D.r=w,D.g=k,D.b=S,D.a=C,D=D.next;for(a=P,o=1;o<=t;o++)s=a+n<<2,c+=(D.r=w=E[s])*(_=A-o),u+=(D.g=k=E[s+1])*_,d+=(D.b=S=E[s+2])*_,h+=(D.a=C=E[s+3])*_,v+=w,y+=k,b+=S,x+=C,D=D.next,o<T&&(a+=P);for(s=n,z=R,B=N,r=0;r<L;r++)E[3+(i=s<<2)]=C=h*j>>F,C>0?(C=255/C,E[i]=(c*j>>F)*C,E[i+1]=(u*j>>F)*C,E[i+2]=(d*j>>F)*C):E[i]=E[i+1]=E[i+2]=0,c-=f,u-=p,d-=g,h-=m,f-=z.r,p-=z.g,g-=z.b,m-=z.a,i=n+((i=r+A)<T?i:T)*P<<2,c+=v+=z.r=E[i],u+=y+=z.g=E[i+1],d+=b+=z.b=E[i+2],h+=x+=z.a=E[i+3],z=z.next,f+=w=B.r,p+=k=B.g,g+=S=B.b,m+=C=B.a,v-=w,y-=k,b-=S,x-=C,B=B.next,s+=P}}(e,t)},Brighten:function(e){var t,n=255*this.brightness(),r=e.data,o=r.length;for(t=0;t<o;t+=4)r[t]+=n,r[t+1]+=n,r[t+2]+=n},Contrast:function(e){var t,n=Math.pow((this.contrast()+100)/100,2),r=e.data,o=r.length,i=150,a=150,s=150;for(t=0;t<o;t+=4)i=r[t],a=r[t+1],s=r[t+2],i/=255,i-=.5,i*=n,i+=.5,a/=255,a-=.5,a*=n,a+=.5,s/=255,s-=.5,s*=n,s+=.5,i=(i*=255)<0?0:i>255?255:i,a=(a*=255)<0?0:a>255?255:a,s=(s*=255)<0?0:s>255?255:s,r[t]=i,r[t+1]=a,r[t+2]=s},Emboss:function(e){var t=10*this.embossStrength(),n=255*this.embossWhiteLevel(),r=this.embossDirection(),o=this.embossBlend(),i=0,a=0,s=e.data,l=e.width,c=e.height,u=4*l,d=c;switch(r){case"top-left":i=-1,a=-1;break;case"top":i=-1,a=0;break;case"top-right":i=-1,a=1;break;case"right":i=0,a=1;break;case"bottom-right":i=1,a=1;break;case"bottom":i=1,a=0;break;case"bottom-left":i=1,a=-1;break;case"left":i=0,a=-1;break;default:eJ.error("Unknown emboss direction: "+r)}do{var h=(d-1)*u,f=i;d+f<1&&(f=0),d+f>c&&(f=0);var p=(d-1+f)*l*4,g=l;do{var m=h+4*(g-1),v=a;g+v<1&&(v=0),g+v>l&&(v=0);var y=p+4*(g-1+v),b=s[m]-s[y],x=s[m+1]-s[y+1],w=s[m+2]-s[y+2],k=b,S=k>0?k:-k;if((x>0?x:-x)>S&&(k=x),(w>0?w:-w)>S&&(k=w),k*=t,o){var C=s[m]+k,_=s[m+1]+k,E=s[m+2]+k;s[m]=C>255?255:C<0?0:C,s[m+1]=_>255?255:_<0?0:_,s[m+2]=E>255?255:E<0?0:E}else{var P=n-k;P<0?P=0:P>255&&(P=255),s[m]=s[m+1]=s[m+2]=P}}while(--g)}while(--d)},Enhance:function(e){var t,n,r,o,i=e.data,a=i.length,s=i[0],l=s,c=i[1],u=c,d=i[2],h=d,f=this.enhance();if(0!==f){for(o=0;o<a;o+=4)(t=i[o+0])<s?s=t:t>l&&(l=t),(n=i[o+1])<c?c=n:n>u&&(u=n),(r=i[o+2])<d?d=r:r>h&&(h=r);var p,g,m,v,y,b,x,w,k;for(l===s&&(l=255,s=0),u===c&&(u=255,c=0),h===d&&(h=255,d=0),f>0?(g=l+f*(255-l),m=s-f*(s-0),y=u+f*(255-u),b=c-f*(c-0),w=h+f*(255-h),k=d-f*(d-0)):(g=l+f*(l-(p=.5*(l+s))),m=s+f*(s-p),y=u+f*(u-(v=.5*(u+c))),b=c+f*(c-v),w=h+f*(h-(x=.5*(h+d))),k=d+f*(d-x)),o=0;o<a;o+=4)i[o+0]=V1(i[o+0],s,l,m,g),i[o+1]=V1(i[o+1],c,u,b,y),i[o+2]=V1(i[o+2],d,h,k,w)}},Grayscale:function(e){var t,n,r=e.data,o=r.length;for(t=0;t<o;t+=4)n=.34*r[t]+.5*r[t+1]+.16*r[t+2],r[t]=n,r[t+1]=n,r[t+2]=n},HSL:function(e){var t,n,r,o,i,a=e.data,s=a.length,l=Math.pow(2,this.saturation()),c=Math.abs(this.hue()+360)%360,u=127*this.luminance(),d=1*l*Math.cos(c*Math.PI/180),h=1*l*Math.sin(c*Math.PI/180),f=.299+.701*d+.167*h,p=.587-.587*d+.33*h,g=.114-.114*d-.497*h,m=.299-.299*d-.328*h,v=.587+.413*d+.035*h,y=.114-.114*d+.293*h,b=.299-.3*d+1.25*h,x=.587-.586*d-1.05*h,w=.114+.886*d-.2*h;for(t=0;t<s;t+=4)n=a[t+0],r=a[t+1],o=a[t+2],i=a[t+3],a[t+0]=f*n+p*r+g*o+u,a[t+1]=m*n+v*r+y*o+u,a[t+2]=b*n+x*r+w*o+u,a[t+3]=i},HSV:function(e){var t,n,r,o,i,a=e.data,s=a.length,l=Math.pow(2,this.value()),c=Math.pow(2,this.saturation()),u=Math.abs(this.hue()+360)%360,d=l*c*Math.cos(u*Math.PI/180),h=l*c*Math.sin(u*Math.PI/180),f=.299*l+.701*d+.167*h,p=.587*l-.587*d+.33*h,g=.114*l-.114*d-.497*h,m=.299*l-.299*d-.328*h,v=.587*l+.413*d+.035*h,y=.114*l-.114*d+.293*h,b=.299*l-.3*d+1.25*h,x=.587*l-.586*d-1.05*h,w=.114*l+.886*d-.2*h;for(t=0;t<s;t+=4)n=a[t+0],r=a[t+1],o=a[t+2],i=a[t+3],a[t+0]=f*n+p*r+g*o,a[t+1]=m*n+v*r+y*o,a[t+2]=b*n+x*r+w*o,a[t+3]=i},Invert:function(e){var t,n=e.data,r=n.length;for(t=0;t<r;t+=4)n[t]=255-n[t],n[t+1]=255-n[t+1],n[t+2]=255-n[t+2]},Kaleidoscope:function(e){var t,n,r,o,i,a,s,l,c,u=e.width,d=e.height,h=Math.round(this.kaleidoscopePower()),f=Math.round(this.kaleidoscopeAngle()),p=Math.floor(u*(f%360)/360);if(!(h<1)){var g=eJ.createCanvasElement();g.width=u,g.height=d;var m=g.getContext("2d").getImageData(0,0,u,d);eJ.releaseCanvas(g),function(e,t,n){var r,o,i,a,s=e.data,l=t.data,c=e.width,u=e.height,d=n.polarCenterX||c/2,h=n.polarCenterY||u/2,f=0,p=0,g=0,m=0,v=Math.sqrt(d*d+h*h);o=c-d,i=u-h,v=(a=Math.sqrt(o*o+i*i))>v?a:v;var y,b,x,w,k=u,S=c,C=360/S*Math.PI/180;for(b=0;b<S;b+=1)for(x=Math.sin(b*C),w=Math.cos(b*C),y=0;y<k;y+=1)o=Math.floor(d+v*y/k*w),f=s[0+(r=4*((i=Math.floor(h+v*y/k*x))*c+o))],p=s[r+1],g=s[r+2],m=s[r+3],l[0+(r=4*(b+y*c))]=f,l[r+1]=p,l[r+2]=g,l[r+3]=m}(e,m,{polarCenterX:u/2,polarCenterY:d/2});for(var v=u/Math.pow(2,h);v<=8;)v*=2,h-=1;var y=v=Math.ceil(v),b=0,x=y,w=1;for(p+v>u&&(b=y,x=0,w=-1),n=0;n<d;n+=1)for(t=b;t!==x;t+=w)l=4*(u*n+Math.round(t+p)%u),o=m.data[l+0],i=m.data[l+1],a=m.data[l+2],s=m.data[l+3],c=4*(u*n+t),m.data[c+0]=o,m.data[c+1]=i,m.data[c+2]=a,m.data[c+3]=s;for(n=0;n<d;n+=1)for(y=Math.floor(v),r=0;r<h;r+=1){for(t=0;t<y+1;t+=1)l=4*(u*n+t),o=m.data[l+0],i=m.data[l+1],a=m.data[l+2],s=m.data[l+3],c=4*(u*n+2*y-t-1),m.data[c+0]=o,m.data[c+1]=i,m.data[c+2]=a,m.data[c+3]=s;y*=2}!function(e,t,n){var r,o,i,a,s,l,c=e.data,u=t.data,d=e.width,h=e.height,f=n.polarCenterX||d/2,p=n.polarCenterY||h/2,g=0,m=0,v=0,y=0,b=Math.sqrt(f*f+p*p);o=d-f,i=h-p,b=(l=Math.sqrt(o*o+i*i))>b?l:b;var x,w,k,S=h,C=d,_=n.polarRotation||0;for(o=0;o<d;o+=1)for(i=0;i<h;i+=1)a=o-f,s=i-p,x=Math.sqrt(a*a+s*s)*S/b,w=(w=(180*Math.atan2(s,a)/Math.PI+360+_)%360)*C/360,k=Math.floor(w),g=c[0+(r=4*(Math.floor(x)*d+k))],m=c[r+1],v=c[r+2],y=c[r+3],u[0+(r=4*(i*d+o))]=g,u[r+1]=m,u[r+2]=v,u[r+3]=y}(m,e,{polarRotation:0})}},Mask:function(e){var t=function(e,t){var n=$1(e,0,0),r=$1(e,e.width-1,0),o=$1(e,0,e.height-1),i=$1(e,e.width-1,e.height-1),a=t||10;if(U1(n,r)<a&&U1(r,i)<a&&U1(i,o)<a&&U1(o,n)<a){for(var s=function(e){for(var t=[0,0,0],n=0;n<e.length;n++)t[0]+=e[n][0],t[1]+=e[n][1],t[2]+=e[n][2];return t[0]/=e.length,t[1]/=e.length,t[2]/=e.length,t}([r,n,i,o]),l=[],c=0;c<e.width*e.height;c++){var u=U1(s,[e.data[4*c],e.data[4*c+1],e.data[4*c+2]]);l[c]=u<a?0:255}return l}}(e,this.threshold());return t&&(t=function(e,t,n){for(var r=[1,1,1,1,0,1,1,1,1],o=Math.round(Math.sqrt(r.length)),i=Math.floor(o/2),a=[],s=0;s<n;s++)for(var l=0;l<t;l++){for(var c=s*t+l,u=0,d=0;d<o;d++)for(var h=0;h<o;h++){var f=s+d-i,p=l+h-i;if(f>=0&&f<n&&p>=0&&p<t){var g=r[d*o+h];u+=e[f*t+p]*g}}a[c]=2040===u?255:0}return a}(t,e.width,e.height),t=function(e,t,n){for(var r=[1,1,1,1,1,1,1,1,1],o=Math.round(Math.sqrt(r.length)),i=Math.floor(o/2),a=[],s=0;s<n;s++)for(var l=0;l<t;l++){for(var c=s*t+l,u=0,d=0;d<o;d++)for(var h=0;h<o;h++){var f=s+d-i,p=l+h-i;if(f>=0&&f<n&&p>=0&&p<t){var g=r[d*o+h];u+=e[f*t+p]*g}}a[c]=u>=1020?255:0}return a}(t,e.width,e.height),t=function(e,t,n){for(var r=[1/9,1/9,1/9,1/9,1/9,1/9,1/9,1/9,1/9],o=Math.round(Math.sqrt(r.length)),i=Math.floor(o/2),a=[],s=0;s<n;s++)for(var l=0;l<t;l++){for(var c=s*t+l,u=0,d=0;d<o;d++)for(var h=0;h<o;h++){var f=s+d-i,p=l+h-i;if(f>=0&&f<n&&p>=0&&p<t){var g=r[d*o+h];u+=e[f*t+p]*g}}a[c]=u}return a}(t,e.width,e.height),function(e,t){for(var n=0;n<e.width*e.height;n++)e.data[4*n+3]=t[n]}(e,t)),e},Noise:function(e){var t,n=255*this.noise(),r=e.data,o=r.length,i=n/2;for(t=0;t<o;t+=4)r[t+0]+=i-2*i*Math.random(),r[t+1]+=i-2*i*Math.random(),r[t+2]+=i-2*i*Math.random()},Pixelate:function(e){var t,n,r,o,i,a,s,l,c,u,d,h,f,p,g=Math.ceil(this.pixelSize()),m=e.width,v=e.height,y=Math.ceil(m/g),b=Math.ceil(v/g),x=e.data;if(g<=0)eJ.error("pixelSize value can not be <= 0");else for(h=0;h<y;h+=1)for(f=0;f<b;f+=1){for(o=0,i=0,a=0,s=0,c=(l=h*g)+g,d=(u=f*g)+g,p=0,t=l;t<c;t+=1)if(!(t>=m))for(n=u;n<d;n+=1)n>=v||(o+=x[(r=4*(m*n+t))+0],i+=x[r+1],a+=x[r+2],s+=x[r+3],p+=1);for(o/=p,i/=p,a/=p,s/=p,t=l;t<c;t+=1)if(!(t>=m))for(n=u;n<d;n+=1)n>=v||(x[(r=4*(m*n+t))+0]=o,x[r+1]=i,x[r+2]=a,x[r+3]=s)}},Posterize:function(e){var t,n=Math.round(254*this.levels())+1,r=e.data,o=r.length,i=255/n;for(t=0;t<o;t+=1)r[t]=Math.floor(r[t]/i)*i},RGB:function(e){var 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t&&(this.parentNode.style.flexWrap=n),this.removeBase(e),r},t.prototype.bindEvents=function(){this.window&&(this.window.addEventListener("mouseup",this.onMouseUp),this.window.addEventListener("mousemove",this.onMouseMove),this.window.addEventListener("mouseleave",this.onMouseUp),this.window.addEventListener("touchmove",this.onMouseMove,{capture:!0,passive:!1}),this.window.addEventListener("touchend",this.onMouseUp))},t.prototype.unbindEvents=function(){this.window&&(this.window.removeEventListener("mouseup",this.onMouseUp),this.window.removeEventListener("mousemove",this.onMouseMove),this.window.removeEventListener("mouseleave",this.onMouseUp),this.window.removeEventListener("touchmove",this.onMouseMove,!0),this.window.removeEventListener("touchend",this.onMouseUp))},t.prototype.componentDidMount=function(){if(this.resizable&&this.window){var e=this.window.getComputedStyle(this.resizable);this.setState({width:this.state.width||this.size.width,height:this.state.height||this.size.height,flexBasis:"auto"!==e.flexBasis?e.flexBasis:void 0})}},t.prototype.componentWillUnmount=function(){this.window&&this.unbindEvents()},t.prototype.createSizeForCssProperty=function(e,t){var n=this.propsSize&&this.propsSize[t];return"auto"!==this.state[t]||this.state.original[t]!==e||void 0!==n&&"auto"!==n?e:"auto"},t.prototype.calculateNewMaxFromBoundary=function(e,t){var n,r,o=this.props.boundsByDirection,i=this.state.direction,a=o&&c8("left",i),s=o&&c8("top",i);if("parent"===this.props.bounds){var l=this.parentNode;l&&(n=a?this.resizableRight-this.parentLeft:l.offsetWidth+(this.parentLeft-this.resizableLeft),r=s?this.resizableBottom-this.parentTop:l.offsetHeight+(this.parentTop-this.resizableTop))}else"window"===this.props.bounds?this.window&&(n=a?this.resizableRight:this.window.innerWidth-this.resizableLeft,r=s?this.resizableBottom:this.window.innerHeight-this.resizableTop):this.props.bounds&&(n=a?this.resizableRight-this.targetLeft:this.props.bounds.offsetWidth+(this.targetLeft-this.resizableLeft),r=s?this.resizableBottom-this.targetTop:this.props.bounds.offsetHeight+(this.targetTop-this.resizableTop));return n&&Number.isFinite(n)&&(e=e&&e<n?e:n),r&&Number.isFinite(r)&&(t=t&&t<r?t:r),{maxWidth:e,maxHeight:t}},t.prototype.calculateNewSizeFromDirection=function(e,t){var n=this.props.scale||1,r=this.props.resizeRatio||1,o=this.state,i=o.direction,a=o.original,s=this.props,l=s.lockAspectRatio,c=s.lockAspectRatioExtraHeight,u=s.lockAspectRatioExtraWidth,d=a.width,h=a.height,f=c||0,p=u||0;return c8("right",i)&&(d=a.width+(e-a.x)*r/n,l&&(h=(d-p)/this.ratio+f)),c8("left",i)&&(d=a.width-(e-a.x)*r/n,l&&(h=(d-p)/this.ratio+f)),c8("bottom",i)&&(h=a.height+(t-a.y)*r/n,l&&(d=(h-f)*this.ratio+p)),c8("top",i)&&(h=a.height-(t-a.y)*r/n,l&&(d=(h-f)*this.ratio+p)),{newWidth:d,newHeight:h}},t.prototype.calculateNewSizeFromAspectRatio=function(e,t,n,r){var o=this.props,i=o.lockAspectRatio,a=o.lockAspectRatioExtraHeight,s=o.lockAspectRatioExtraWidth,l=void 0===r.width?10:r.width,c=void 0===n.width||n.width<0?e:n.width,u=void 0===r.height?10:r.height,d=void 0===n.height||n.height<0?t:n.height,h=a||0,f=s||0;if(i){var p=(u-h)*this.ratio+f,g=(d-h)*this.ratio+f,m=(l-f)/this.ratio+h,v=(c-f)/this.ratio+h,y=Math.max(l,p),b=Math.min(c,g),x=Math.max(u,m),w=Math.min(d,v);e=s8(e,y,b),t=s8(t,x,w)}else e=s8(e,l,c),t=s8(t,u,d);return{newWidth:e,newHeight:t}},t.prototype.setBoundingClientRect=function(){if("parent"===this.props.bounds){var e=this.parentNode;if(e){var t=e.getBoundingClientRect();this.parentLeft=t.left,this.parentTop=t.top}}if(this.props.bounds&&"string"!=typeof this.props.bounds){var n=this.props.bounds.getBoundingClientRect();this.targetLeft=n.left,this.targetTop=n.top}if(this.resizable){var r=this.resizable.getBoundingClientRect(),o=r.left,i=r.top,a=r.right,s=r.bottom;this.resizableLeft=o,this.resizableRight=a,this.resizableTop=i,this.resizableBottom=s}},t.prototype.onResizeStart=function(e,t){if(this.resizable&&this.window){var n,r=0,o=0;if(e.nativeEvent&&function(e){return Boolean((e.clientX||0===e.clientX)&&(e.clientY||0===e.clientY))}(e.nativeEvent)?(r=e.nativeEvent.clientX,o=e.nativeEvent.clientY):e.nativeEvent&&u8(e.nativeEvent)&&(r=e.nativeEvent.touches[0].clientX,o=e.nativeEvent.touches[0].clientY),this.props.onResizeStart)if(this.resizable)if(!1===this.props.onResizeStart(e,t,this.resizable))return;this.props.size&&(void 0!==this.props.size.height&&this.props.size.height!==this.state.height&&this.setState({height:this.props.size.height}),void 0!==this.props.size.width&&this.props.size.width!==this.state.width&&this.setState({width:this.props.size.width})),this.ratio="number"==typeof this.props.lockAspectRatio?this.props.lockAspectRatio:this.size.width/this.size.height;var i=this.window.getComputedStyle(this.resizable);if("auto"!==i.flexBasis){var a=this.parentNode;if(a){var s=this.window.getComputedStyle(a).flexDirection;this.flexDir=s.startsWith("row")?"row":"column",n=i.flexBasis}}this.setBoundingClientRect(),this.bindEvents();var l={original:{x:r,y:o,width:this.size.width,height:this.size.height},isResizing:!0,backgroundStyle:i8(i8({},this.state.backgroundStyle),{cursor:this.window.getComputedStyle(e.target).cursor||"auto"}),direction:t,flexBasis:n};this.setState(l)}},t.prototype.onMouseMove=function(e){var t=this;if(this.state.isResizing&&this.resizable&&this.window){if(this.window.TouchEvent&&u8(e))try{e.preventDefault(),e.stopPropagation()}catch(B2){}var n=this.props,r=n.maxWidth,o=n.maxHeight,i=n.minWidth,a=n.minHeight,s=u8(e)?e.touches[0].clientX:e.clientX,l=u8(e)?e.touches[0].clientY:e.clientY,c=this.state,u=c.direction,d=c.original,h=c.width,f=c.height,p=this.getParentSize(),g=function(e,t,n,r,o,i,a){return r=f8(r,e.width,t,n),o=f8(o,e.height,t,n),i=f8(i,e.width,t,n),a=f8(a,e.height,t,n),{maxWidth:void 0===r?void 0:Number(r),maxHeight:void 0===o?void 0:Number(o),minWidth:void 0===i?void 0:Number(i),minHeight:void 0===a?void 0:Number(a)}}(p,this.window.innerWidth,this.window.innerHeight,r,o,i,a);r=g.maxWidth,o=g.maxHeight,i=g.minWidth,a=g.minHeight;var m=this.calculateNewSizeFromDirection(s,l),v=m.newHeight,y=m.newWidth,b=this.calculateNewMaxFromBoundary(r,o);this.props.snap&&this.props.snap.x&&(y=d8(y,this.props.snap.x,this.props.snapGap)),this.props.snap&&this.props.snap.y&&(v=d8(v,this.props.snap.y,this.props.snapGap));var x=this.calculateNewSizeFromAspectRatio(y,v,{width:b.maxWidth,height:b.maxHeight},{width:i,height:a});if(y=x.newWidth,v=x.newHeight,this.props.grid){var w=l8(y,this.props.grid[0]),k=l8(v,this.props.grid[1]),S=this.props.snapGap||0;y=0===S||Math.abs(w-y)<=S?w:y,v=0===S||Math.abs(k-v)<=S?k:v}var C={width:y-d.width,height:v-d.height};if(h&&"string"==typeof h)if(h.endsWith("%"))y=y/p.width*100+"%";else if(h.endsWith("vw")){y=y/this.window.innerWidth*100+"vw"}else if(h.endsWith("vh")){y=y/this.window.innerHeight*100+"vh"}if(f&&"string"==typeof f)if(f.endsWith("%"))v=v/p.height*100+"%";else 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Ete=(e=>(e[e.PROMPT=0]="PROMPT",e[e.GALLERY=1]="GALLERY",e[e.OTHER=2]="OTHER",e[e.SEED=3]="SEED",e[e.VARIATIONS=4]="VARIATIONS",e[e.UPSCALE=5]="UPSCALE",e[e.FACE_CORRECTION=6]="FACE_CORRECTION",e[e.IMAGE_TO_IMAGE=7]="IMAGE_TO_IMAGE",e[e.BOUNDING_BOX=8]="BOUNDING_BOX",e[e.SEAM_CORRECTION=9]="SEAM_CORRECTION",e[e.INFILL_AND_SCALING=10]="INFILL_AND_SCALING",e))(Ete||{});const Pte=e=>{const t=(()=>{const{t:e}=FY();return a.exports.useMemo((()=>({0:{text:e("tooltip:feature.prompt"),href:"link/to/docs/feature3.html",guideImage:"asset/path.gif"},1:{text:e("tooltip:feature.gallery"),href:"link/to/docs/feature3.html",guideImage:"asset/path.gif"},2:{text:e("tooltip:feature.other"),href:"link/to/docs/feature3.html",guideImage:"asset/path.gif"},3:{text:e("tooltip:feature.seed"),href:"link/to/docs/feature3.html",guideImage:"asset/path.gif"},4:{text:e("tooltip:feature.variations"),href:"link/to/docs/feature3.html",guideImage:"asset/path.gif"},5:{text:e("tooltip:feature.upscale"),href:"link/to/docs/feature1.html",guideImage:"asset/path.gif"},6:{text:e("tooltip:feature.faceCorrection"),href:"link/to/docs/feature3.html",guideImage:"asset/path.gif"},7:{text:e("tooltip:feature.imageToImage"),href:"link/to/docs/feature3.html",guideImage:"asset/path.gif"},8:{text:e("tooltip:feature.boundingBox"),href:"link/to/docs/feature3.html",guideImage:"asset/path.gif"},9:{text:e("tooltip:feature.seamCorrection"),href:"link/to/docs/feature3.html",guideImage:"asset/path.gif"},10:{text:e("tooltip:feature.infillAndScaling"),href:"link/to/docs/feature3.html",guideImage:"asset/path.gif"}})),[e])})();return t[e]},Lte=uV((e=>e.options),(e=>({facetoolStrength:e.facetoolStrength,facetoolType:e.facetoolType,codeformerFidelity:e.codeformerFidelity})),{memoizeOptions:{resultEqualityCheck:MU.exports.isEqual}}),Ote=uV((e=>e.system),(e=>({isGFPGANAvailable:e.isGFPGANAvailable})),{memoizeOptions:{resultEqualityCheck:MU.exports.isEqual}}),Mte=()=>{const e=m5(),{facetoolStrength:t,facetoolType:n,codeformerFidelity:r}=v5(Lte),{isGFPGANAvailable:o}=v5(Ote),{t:i}=FY();return cd(CI,{direction:"column",gap:2,children:[ld(E3,{label:i("options:type"),validValues:jQ.concat(),value:n,onChange:t=>e(H$(t.target.value))}),ld(L3,{isDisabled:!o,label:i("options:strength"),step:.05,min:0,max:1,onChange:t=>e(F$(t)),value:t,width:"90px",isInteger:!1}),"codeformer"===n&&ld(L3,{isDisabled:!o,label:i("options:codeformerFidelity"),step:.05,min:0,max:1,onChange:t=>e(B$(t)),value:r,width:"90px",isInteger:!1})]})};function Tte(){const e=v5((e=>e.system.isGFPGANAvailable)),t=v5((e=>e.options.shouldRunFacetool)),n=m5();return ld(_3,{isDisabled:!e,isChecked:t,onChange:e=>n(gU(e.target.checked))})}function Ate(){const e=m5(),t=v5((e=>e.options.shouldFitToWidthHeight)),{t:n}=FY();return ld(_3,{label:n("options:imageFit"),isChecked:t,onChange:t=>e(lU(t.target.checked))})}function Ite(e){const[t,n]=a.exports.useState(!1),{label:r,value:o,min:i=1,max:s=100,step:l=1,onChange:c,width:u="100%",tooltipSuffix:d="",withSliderMarks:h=!1,sliderMarkLeftOffset:f=0,sliderMarkRightOffset:p=-7,withInput:g=!1,isInteger:m=!1,inputWidth:v="5.5rem",inputReadOnly:y=!0,withReset:b=!1,hideTooltip:x=!1,isCompact:w=!1,handleReset:k,isResetDisabled:S,isSliderDisabled:C,isInputDisabled:_,styleClass:E,sliderFormControlProps:P,sliderFormLabelProps:L,sliderMarkProps:O,sliderTrackProps:M,sliderThumbProps:T,sliderNumberInputProps:A,sliderNumberInputFieldProps:I,sliderNumberInputStepperProps:R,sliderTooltipProps:N,sliderIAIIconButtonProps:D,...z}=e,[B,j]=a.exports.useState(String(o)),F=a.exports.useMemo((()=>A?.max?A.max:s),[s,A?.max]);a.exports.useEffect((()=>{String(o)!==B&&""!==B&&j(String(o))}),[o,B,j]);const H=e=>{j(String(e)),c(Number(e))};return cd(GT,{className:E?`invokeai__slider-component 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Rte(e){const{t:t}=FY(),{label:n=`${t("options:strength")}`,styleClass:r}=e,o=v5((e=>e.options.img2imgStrength)),i=m5();return ld(Ite,{label:n,step:.01,min:.01,max:.99,onChange:e=>i($$(e)),value:o,isInteger:!1,styleClass:r,withInput:!0,withReset:!0,withSliderMarks:!0,inputWidth:"5.5rem",handleReset:()=>{i($$(.75))}})}const Nte=()=>{const e=m5(),t=v5((e=>e.options.seamless)),{t:n}=FY();return ld(CI,{gap:2,direction:"column",children:ld(_3,{label:n("options:seamlessTiling"),fontSize:"md",isChecked:t,onChange:t=>e(nU(t.target.checked))})})},Dte=()=>ld(CI,{gap:2,direction:"column",children:ld(Nte,{})});function zte(){const e=m5(),{t:t}=FY(),n=v5((e=>e.options.shouldRandomizeSeed));return ld(_3,{label:t("options:randomizeSeed"),isChecked:n,onChange:t=>e(fU(t.target.checked))})}function Bte(){const e=v5((e=>e.options.seed)),t=v5((e=>e.options.shouldRandomizeSeed)),n=v5((e=>e.options.shouldGenerateVariations)),{t:r}=FY(),o=m5();return ld(L3,{label:r("options:seed"),step:1,precision:0,flexGrow:1,min:0,max:BQ,isDisabled:t,isInvalid:e<0&&n,onChange:e=>o(aU(e)),value:e,width:"auto"})}function jte(){const e=m5(),t=v5((e=>e.options.shouldRandomizeSeed)),{t:n}=FY();return ld(RT,{size:"sm",isDisabled:t,onClick:()=>e(aU(HQ(0,BQ))),padding:"0 1.5rem",children:ld("p",{children:n("options:shuffle")})})}function Fte(){const e=m5(),t=v5((e=>e.options.threshold)),{t:n}=FY();return ld(L3,{label:n("options:noiseThreshold"),min:0,max:1e3,step:.1,onChange:t=>e(wU(t)),value:t,isInteger:!1})}function Hte(){const e=m5(),t=v5((e=>e.options.perlin)),{t:n}=FY();return ld(L3,{label:n("options:perlinNoise"),min:0,max:1,step:.05,onChange:t=>e(Q$(t)),value:t,isInteger:!1})}const Wte=()=>cd(CI,{gap:2,direction:"column",children:[ld(zte,{}),cd(CI,{gap:2,children:[ld(Bte,{}),ld(jte,{})]}),ld(CI,{gap:2,children:ld(Fte,{})}),ld(CI,{gap:2,children:ld(Hte,{})})]}),Vte=uV((e=>e.options),(e=>({upscalingLevel:e.upscalingLevel,upscalingStrength:e.upscalingStrength})),{memoizeOptions:{resultEqualityCheck:MU.exports.isEqual}}),$te=uV((e=>e.system),(e=>({isESRGANAvailable:e.isESRGANAvailable})),{memoizeOptions:{resultEqualityCheck:MU.exports.isEqual}}),Ute=()=>{const e=m5(),{upscalingLevel:t,upscalingStrength:n}=v5(Vte),{isESRGANAvailable:r}=v5($te),{t:o}=FY();return cd("div",{className:"upscale-options",children:[ld(E3,{isDisabled:!r,label:o("options:scale"),value:t,onChange:t=>e(SU(Number(t.target.value))),validValues:zQ}),ld(L3,{isDisabled:!r,label:o("options:strength"),step:.05,min:0,max:1,onChange:t=>e(CU(t)),value:n,isInteger:!1})]})};function Gte(){const e=v5((e=>e.system.isESRGANAvailable)),t=v5((e=>e.options.shouldRunESRGAN)),n=m5();return ld(_3,{isDisabled:!e,isChecked:t,onChange:e=>n(pU(e.target.checked))})}function qte(){const e=v5((e=>e.options.shouldGenerateVariations)),t=m5();return ld(_3,{isChecked:e,width:"auto",onChange:e=>t(cU(e.target.checked))})}function Yte(){const e=v5((e=>e.options.seedWeights)),t=v5((e=>e.options.shouldGenerateVariations)),{t:n}=FY(),r=m5();return ld(W3,{label:n("options:seedWeights"),value:e,isInvalid:t&&!(_$(e)||""===e),isDisabled:!t,onChange:e=>r(sU(e.target.value))})}function Zte(){const e=v5((e=>e.options.variationAmount)),t=v5((e=>e.options.shouldGenerateVariations)),{t:n}=FY(),r=m5();return ld(L3,{label:n("options:variationAmount"),value:e,step:.01,min:0,max:1,isDisabled:!t,onChange:e=>r(_U(e)),isInteger:!1})}const Xte=()=>cd(CI,{gap:2,direction:"column",children:[ld(Zte,{}),ld(Yte,{})]});function Kte(){const e=m5(),t=v5((e=>e.options.cfgScale)),{t:n}=FY();return ld(L3,{label:n("options:cfgScale"),step:.5,min:1.01,max:200,onChange:t=>e(z$(t)),value:t,width:"auto",styleClass:"main-option-block",textAlign:"center",isInteger:!1})}function Qte(){const e=v5((e=>e.options.height)),t=v5(T3),n=m5(),{t:r}=FY();return ld(E3,{isDisabled:"unifiedCanvas"===t,label:r("options:height"),value:e,flexGrow:1,onChange:e=>n(W$(Number(e.target.value))),validValues:DQ,styleClass:"main-option-block"})}const Jte=uV([e=>e.options],(e=>{const{iterations:t}=e;return{iterations:t}}),{memoizeOptions:{resultEqualityCheck:TU.isEqual}});function ene(){const e=m5(),{iterations:t}=v5(Jte),{t:n}=FY();return ld(L3,{label:n("options:images"),step:1,min:1,max:9999,onChange:t=>e(Y$(t)),value:t,width:"auto",labelFontSize:.5,styleClass:"main-option-block",textAlign:"center"})}function tne(){const e=v5((e=>e.options.sampler)),t=m5(),{t:n}=FY();return ld(E3,{label:n("options:sampler"),value:e,onChange:e=>t(eU(e.target.value)),validValues:RQ,styleClass:"main-option-block"})}function nne(){const e=m5(),t=v5((e=>e.options.steps)),{t:n}=FY();return ld(L3,{label:n("options:steps"),min:1,max:9999,step:1,onChange:t=>e(xU(t)),value:t,width:"auto",styleClass:"main-option-block",textAlign:"center"})}function rne(){const e=v5((e=>e.options.width)),t=v5(T3),{t:n}=FY(),r=m5();return ld(E3,{isDisabled:"unifiedCanvas"===t,label:n("options:width"),value:e,flexGrow:1,onChange:e=>r(EU(Number(e.target.value))),validValues:NQ,styleClass:"main-option-block"})}function one(){return ld("div",{className:"main-options",children:cd("div",{className:"main-options-list",children:[cd("div",{className:"main-options-row",children:[ld(ene,{}),ld(nne,{}),ld(Kte,{})]}),cd("div",{className:"main-options-row",children:[ld(rne,{}),ld(Qte,{}),ld(tne,{})]})]})})}const ine=uV((e=>e.system),(e=>e.shouldDisplayGuides)),ane=({children:e,feature:t})=>{const n=v5(ine),{text:r}=Pte(t);return n?cd(Aj,{trigger:"hover",children:[ld(Wj,{children:ld(mI,{children:e})}),cd(Fj,{className:"guide-popover-content",maxWidth:"400px",onClick:e=>e.preventDefault(),cursor:"initial",children:[ld(Ij,{className:"guide-popover-arrow"}),ld("div",{className:"guide-popover-guide-content",children:r})]})]}):null},sne=ok((({feature:e,icon:t=C9},n)=>ld(ane,{feature:e,children:ld(mI,{ref:n,children:ld(kk,{marginBottom:"-.15rem",as:t})})})));function lne(e){const{header:t,feature:n,content:r,additionalHeaderComponents:o}=e;return cd(lT,{className:"advanced-settings-item",children:[ld(nT,{className:"advanced-settings-header",children:cd(CI,{width:"100%",gap:"0.5rem",align:"center",children:[ld(mI,{flexGrow:1,textAlign:"left",children:t}),o,n&&ld(sne,{feature:n}),ld(sT,{})]})}),ld(cT,{className:"advanced-settings-panel",children:r})]})}const cne=e=>{const{accordionInfo:t}=e,n=v5((e=>e.system.openAccordions)),r=m5();return 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t=this,n=this.props.enter,r=this.context?this.context.isMounting:e,o=this.props.nodeRef?[r]:[Zu.findDOMNode(this),r],i=o[0],a=o[1],s=this.getTimeouts(),l=r?s.appear:s.enter;!e&&!n||_ne?this.safeSetState({status:Tne},(function(){t.props.onEntered(i)})):(this.props.onEnter(i,a),this.safeSetState({status:Mne},(function(){t.props.onEntering(i,a),t.onTransitionEnd(l,(function(){t.safeSetState({status:Tne},(function(){t.props.onEntered(i,a)}))}))})))},n.performExit=function(){var e=this,t=this.props.exit,n=this.getTimeouts(),r=this.props.nodeRef?void 0:Zu.findDOMNode(this);t&&!_ne?(this.props.onExit(r),this.safeSetState({status:Ane},(function(){e.props.onExiting(r),e.onTransitionEnd(n.exit,(function(){e.safeSetState({status:One},(function(){e.props.onExited(r)}))}))}))):this.safeSetState({status:One},(function(){e.props.onExited(r)}))},n.cancelNextCallback=function(){null!==this.nextCallback&&(this.nextCallback.cancel(),this.nextCallback=null)},n.safeSetState=function(e,t){t=this.setNextCallback(t),this.setState(e,t)},n.setNextCallback=function(e){var t=this,n=!0;return this.nextCallback=function(r){n&&(n=!1,t.nextCallback=null,e(r))},this.nextCallback.cancel=function(){n=!1},this.nextCallback},n.onTransitionEnd=function(e,t){this.setNextCallback(t);var n=this.props.nodeRef?this.props.nodeRef.current:Zu.findDOMNode(this),r=null==e&&!this.props.addEndListener;if(n&&!r){if(this.props.addEndListener){var 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Rne(){}Ine.contextType=Ene,Ine.propTypes={},Ine.defaultProps={in:!1,mountOnEnter:!1,unmountOnExit:!1,appear:!1,enter:!0,exit:!0,onEnter:Rne,onEntering:Rne,onEntered:Rne,onExit:Rne,onExiting:Rne,onExited:Rne},Ine.UNMOUNTED=Lne,Ine.EXITED=One,Ine.ENTERING=Mne,Ine.ENTERED=Tne,Ine.EXITING=Ane;const Nne=Ine;var Dne=function(e,t){return e&&t&&t.split(" ").forEach((function(t){return r=t,void((n=e).classList?n.classList.add(r):function(e,t){return e.classList?!!t&&e.classList.contains(t):-1!==(" "+(e.className.baseVal||e.className)+" ").indexOf(" "+t+" ")}(n,r)||("string"==typeof n.className?n.className=n.className+" "+r:n.setAttribute("class",(n.className&&n.className.baseVal||"")+" "+r)));var n,r}))},zne=function(e,t){return e&&t&&t.split(" ").forEach((function(t){return r=t,void((n=e).classList?n.classList.remove(r):"string"==typeof n.className?n.className=Cne(n.className,r):n.setAttribute("class",Cne(n.className&&n.className.baseVal||"",r)));var n,r}))},Bne=function(e){function 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n=t.resolveArguments(e)[0];t.removeClasses(n,"exit"),t.addClass(n,"exit","done"),t.props.onExited&&t.props.onExited(e)},t.resolveArguments=function(e,n){return t.props.nodeRef?[t.props.nodeRef.current,e]:[e,n]},t.getClassNames=function(e){var n=t.props.classNames,r="string"==typeof n,o=r?""+(r&&n?n+"-":"")+e:n[e];return{baseClassName:o,activeClassName:r?o+"-active":n[e+"Active"],doneClassName:r?o+"-done":n[e+"Done"]}},t}DD(t,e);var n=t.prototype;return n.addClass=function(e,t,n){var r=this.getClassNames(t)[n+"ClassName"],o=this.getClassNames("enter").doneClassName;"appear"===t&&"done"===n&&o&&(r+=" "+o),"active"===n&&e&&Pne(e),r&&(this.appliedClasses[t][n]=r,Dne(e,r))},n.removeClasses=function(e,t){var n=this.appliedClasses[t],r=n.base,o=n.active,i=n.done;this.appliedClasses[t]={},r&&zne(e,r),o&&zne(e,o),i&&zne(e,i)},n.render=function(){var e=this.props;e.classNames;var t=gD(e,["classNames"]);return ld(Nne,{...t,onEnter:this.onEnter,onEntered:this.onEntered,onEntering:this.onEntering,onExit:this.onExit,onExiting:this.onExiting,onExited:this.onExited})},t}(H.Component);Bne.defaultProps={classNames:""},Bne.propTypes={};const jne=Bne,Fne=uV((e=>e.options),(e=>{const{shouldShowOptionsPanel:t,shouldHoldOptionsPanelOpen:n,shouldPinOptionsPanel:r,optionsPanelScrollPosition:o}=e;return{shouldShowOptionsPanel:t,shouldHoldOptionsPanelOpen:n,shouldPinOptionsPanel:r,optionsPanelScrollPosition:o}}),{memoizeOptions:{resultEqualityCheck:TU.isEqual}}),Hne=e=>{const t=m5(),{shouldShowOptionsPanel:n,shouldHoldOptionsPanelOpen:r,shouldPinOptionsPanel:o}=v5(Fne),i=a.exports.useRef(null),s=a.exports.useRef(null),l=a.exports.useRef(null),{children:c}=e;gte("o",(()=>{t(vU(!n)),o&&setTimeout((()=>t(iX(!0))),400)}),[n,o]),gte("esc",(()=>{t(vU(!1))}),{enabled:()=>!o,preventDefault:!0},[o]),gte("shift+o",(()=>{h(),t(iX(!0))}),[o]);const u=a.exports.useCallback((()=>{o||(t(X$(s.current?s.current.scrollTop:0)),t(vU(!1)),t(uU(!1)))}),[t,o]),d=()=>{l.current&&window.clearTimeout(l.current)},h=()=>{t(hU(!o)),t(iX(!0))};return a.exports.useEffect((()=>{function e(e){i.current&&!i.current.contains(e.target)&&u()}return document.addEventListener("mousedown",e),()=>{document.removeEventListener("mousedown",e)}}),[u]),ld(jne,{nodeRef:i,in:n||r&&!o,unmountOnExit:!0,timeout:200,classNames:"options-panel-wrapper",children:ld("div",{className:"options-panel-wrapper","data-pinned":o,tabIndex:1,ref:i,onMouseEnter:o?void 0:d,onMouseOver:o?void 0:d,style:{borderRight:o?"":"0.3rem solid var(--tab-list-text-inactive)"},children:ld("div",{className:"options-panel-margin",children:cd("div",{className:"options-panel",ref:s,onMouseLeave:e=>{e.target!==s.current?d():!o&&(l.current=window.setTimeout((()=>u()),500))},children:[ld(YH,{label:"Pin Options 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a.exports.useEffect((()=>{if("img2img"===r){(()=>n(V$(!1)))()}}),[r,n]),cd(Hne,{children:[ld(wne,{}),ld(bne,{}),ld(one,{}),ld(Rte,{label:e("options:img2imgStrength"),styleClass:"main-option-block image-to-image-strength-main-option"}),ld(Ate,{}),ld(cne,{accordionInfo:t})]})}const Vne=a.exports.createContext(null),$ne=e=>{const{styleClass:t}=e,n=a.exports.useContext(Vne);return ld("div",{className:`image-uploader-button-outer ${t}`,onClick:()=>{n&&n()},children:cd("div",{className:"image-upload-button",children:[ld(y3,{}),ld(LI,{size:"lg",children:"Click or Drag and 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e=m5(),{isProcessing:t,isConnected:n,isGFPGANAvailable:r,isESRGANAvailable:o,upscalingLevel:i,facetoolStrength:a,shouldDisableToolbarButtons:s,shouldShowImageDetails:l,currentImage:c,isLightBoxOpen:u,activeTabName:d}=v5(qne),h=zH(),{t:f}=FY(),p=()=>{c&&(u&&e(q$(!1)),e(G$(c)),e(I$("img2img")))};gte("shift+i",(()=>{c?(p(),h({title:f("toast:sentToImageToImage"),status:"success",duration:2500,isClosable:!0})):h({title:f("toast:imageNotLoaded"),description:f("toast:imageNotLoadedDesc"),status:"error",duration:2500,isClosable:!0})}),[c]);const g=()=>{c&&(c.metadata&&e(N$(c.metadata)),"img2img"===c.metadata?.image.type?e(I$("img2img")):"txt2img"===c.metadata?.image.type&&e(I$("txt2img")))};gte("a",(()=>{["txt2img","img2img"].includes(c?.metadata?.image?.type)?(g(),h({title:f("toast:parametersSet"),status:"success",duration:2500,isClosable:!0})):h({title:f("toast:parametersNotSet"),description:f("toast:parametersNotSetDesc"),status:"error",duration:2500,isClosable:!0})}),[c]);const 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t=e,n=0,r=0;t;)n+=t.offsetLeft,r+=t.offsetTop,t=t.offsetParent;return{x:n,y:r}}(t),u=c.x,d=c.y,h=t.offsetWidth,f=t.offsetHeight,p=r.offsetWidth/h,g=r.offsetHeight/f,m=rse(n||Math.min(p,g),a,s,0,!1),v=(l.width-h*m)/2,y=(l.height-f*m)/2,b=ese((l.left-u)*m+v,(l.top-d)*m+y,Qae(e,m),i,0,0,r);return{positionX:b.x,positionY:b.y,scale:m}}(e,a,n);Kae(e,s,r,o)}}},Ose=function(e){return{instance:e,state:e.transformState,zoomIn:Sse(e),zoomOut:Cse(e),setTransform:_se(e),resetTransform:Ese(e),centerView:Pse(e),zoomToElement:Lse(e)}};function Mse(){try{return{get passive(){return!0,!1}}}catch(Hhe){return!1}}var Tse=function(e,t){var n=e.tagName.toUpperCase();return!!t.find((function(e){return e.toUpperCase()===n}))||!!t.find((function(t){return e.classList.contains(t)}))},Ase=function(e){e&&clearTimeout(e)},Ise=function(e,t,n){return"translate3d("+e+"px, "+t+"px, 0) scale("+n+")"},Rse=function(e,t,n){var 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Math.sqrt(Math.pow(e.touches[0].pageX-e.touches[1].pageX,2)+Math.pow(e.touches[0].pageY-e.touches[1].pageY,2))},Fse=function(e,t){var n=e.props,r=n.onWheelStart,o=n.onZoomStart;e.wheelStopEventTimer||(Zae(e),Gae(Ose(e),t,r),Gae(Ose(e),t,o))},Hse=function(e,t){var n=e.props,r=n.onWheel,o=n.onZoom,i=e.contentComponent,a=e.setup,s=e.transformState.scale,l=a.limitToBounds,c=a.centerZoomedOut,u=a.zoomAnimation,d=a.wheel,h=u.size,f=u.disabled,p=d.step;if(!i)throw new Error("Component not mounted");t.preventDefault(),t.stopPropagation();var g=function(e,t){var n,r,o=e?e.deltaY<0?1:-1:0;return r=o,"number"==typeof(n=t)?n:r}(t,null),m=function(e,t,n,r,o){var i=e.transformState.scale,a=e.wrapperComponent,s=e.setup,l=s.maxScale,c=s.minScale,u=s.zoomAnimation,d=u.size,h=u.disabled;if(!a)throw new Error("Wrapper is not mounted");var f=i+t*(i-i*n)*n;if(o)return f;var p=!r&&!h;return rse(Uae(f,3),c,l,d,p)}(e,g,p,!t.ctrlKey);if(s!==m){var v=Jae(e,m),y=Dse(t,i,s),b=l&&(f||0===h||c),x=nse(e,y.x,y.y,m,v,b),w=x.x,k=x.y;e.previousWheelEvent=t,e.setTransformState(m,w,k),Gae(Ose(e),t,r),Gae(Ose(e),t,o)}},Wse=function(e,t){var n=e.props,r=n.onWheelStop,o=n.onZoomStop;Ase(e.wheelAnimationTimer),e.wheelAnimationTimer=setTimeout((function(){e.mounted&&(fse(e,t.x,t.y),e.wheelAnimationTimer=null)}),100);var i=function(e,t){var n=e.previousWheelEvent,r=e.transformState.scale,o=e.setup,i=o.maxScale,a=o.minScale;return!!n&&(r<i||r>a||Math.sign(n.deltaY)!==Math.sign(t.deltaY)||n.deltaY>0&&n.deltaY<t.deltaY||n.deltaY<0&&n.deltaY>t.deltaY||Math.sign(n.deltaY)!==Math.sign(t.deltaY))}(e,t);i&&(Ase(e.wheelStopEventTimer),e.wheelStopEventTimer=setTimeout((function(){e.mounted&&(e.wheelStopEventTimer=null,Gae(Ose(e),t,r),Gae(Ose(e),t,o))}),160))},Vse=function(e,t){var n=jse(t);e.pinchStartDistance=n,e.lastDistance=n,e.pinchStartScale=e.transformState.scale,e.isPanning=!1,Zae(e)},$se=function(e,t){var n=e.contentComponent,r=e.pinchStartDistance,o=e.transformState.scale,i=e.setup,a=i.limitToBounds,s=i.centerZoomedOut,l=i.zoomAnimation,c=l.disabled,u=l.size;if(null!==r&&n){var d=function(e,t,n){var r=n.getBoundingClientRect(),o=e.touches,i=Uae(o[0].clientX-r.left,5),a=Uae(o[0].clientY-r.top,5);return{x:(i+Uae(o[1].clientX-r.left,5))/2/t,y:(a+Uae(o[1].clientY-r.top,5))/2/t}}(t,o,n);if(isFinite(d.x)&&isFinite(d.y)){var h=jse(t),f=function(e,t){var n=e.pinchStartScale,r=e.pinchStartDistance,o=e.setup,i=o.maxScale,a=o.minScale,s=o.zoomAnimation,l=s.size,c=s.disabled;if(!n||null===r||!t)throw new Error("Pinch touches distance was not provided");return t<0?e.transformState.scale:rse(Uae(t/r*n,2),a,i,l,!c)}(e,h);if(f!==o){var p=Jae(e,f),g=a&&(c||0===u||s),m=nse(e,d.x,d.y,f,p,g),v=m.x,y=m.y;e.pinchMidpoint=d,e.lastDistance=h,e.setTransformState(f,v,y)}}}},Use=function(e){var t=e.pinchMidpoint;e.velocity=null,e.lastDistance=null,e.pinchMidpoint=null,e.pinchStartScale=null,e.pinchStartDistance=null,fse(e,null==t?void 0:t.x,null==t?void 0:t.y)};function Gse(e,t){var n=e.setup.doubleClick,r=n.disabled,o=n.mode,i=n.step,a=n.animationTime,s=n.animationType;if(!r){if("reset"===o)return kse(e,a,s);var l=e.transformState.scale,c=e.contentComponent;if(!c)return console.error("No ContentComponent found");var u=xse(e,"zoomOut"===o?-1:1,i),d=Dse(t,c,l),h=pse(e,u,d.x,d.y);if(!h)return console.error("Error during zoom event. 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n=Mse();e.addEventListener("wheel",t.onWheelZoom,n),e.addEventListener("dblclick",t.onDoubleClick,n),e.addEventListener("touchstart",t.onTouchPanningStart,n),e.addEventListener("touchmove",t.onTouchPanning,n),e.addEventListener("touchend",t.onTouchPanningStop,n)},t.handleInitialize=function(){var e=t.setup.centerOnInit;t.applyTransformation(),t.forceUpdate(),e&&(setTimeout((function(){t.mounted&&t.setCenter()}),50),setTimeout((function(){t.mounted&&t.setCenter()}),100),setTimeout((function(){t.mounted&&t.setCenter()}),200))},t.onWheelZoom=function(e){t.setup.disabled||Nse(t,e)&&t.isPressingKeys(t.setup.wheel.activationKeys)&&(Fse(t,e),Hse(t,e),Wse(t,e))},t.onPanningStart=function(e){var n=t.setup.disabled,r=t.props.onPanningStart;n||ose(t,e)&&t.isPressingKeys(t.setup.panning.activationKeys)&&(e.preventDefault(),e.stopPropagation(),Zae(t),cse(t,e),Gae(Ose(t),e,r))},t.onPanning=function(e){var n=t.setup.disabled,r=t.props.onPanning;n||ise(t)&&t.isPressingKeys(t.setup.panning.activationKeys)&&(e.preventDefault(),e.stopPropagation(),use(t,e.clientX,e.clientY),Gae(Ose(t),e,r))},t.onPanningStop=function(e){var n=t.props.onPanningStop;t.isPanning&&(dse(t),Gae(Ose(t),e,n))},t.onPinchStart=function(e){var n=t.setup.disabled,r=t.props,o=r.onPinchingStart,i=r.onZoomStart;n||zse(t,e)&&(Vse(t,e),Zae(t),Gae(Ose(t),e,o),Gae(Ose(t),e,i))},t.onPinch=function(e){var n=t.setup.disabled,r=t.props,o=r.onPinching,i=r.onZoom;n||Bse(t)&&(e.preventDefault(),e.stopPropagation(),$se(t,e),Gae(Ose(t),e,o),Gae(Ose(t),e,i))},t.onPinchStop=function(e){var n=t.props,r=n.onPinchingStop,o=n.onZoomStop;t.pinchStartScale&&(Use(t),Gae(Ose(t),e,r),Gae(Ose(t),e,o))},t.onTouchPanningStart=function(e){var n=t.setup.disabled,r=t.props.onPanningStart;if(!n&&ose(t,e))if(t.lastTouch&&+new Date-t.lastTouch<200&&1===e.touches.length)t.onDoubleClick(e);else{t.lastTouch=+new Date,Zae(t);var o=e.touches,i=1===o.length,a=2===o.length;i&&(Zae(t),cse(t,e),Gae(Ose(t),e,r)),a&&t.onPinchStart(e)}},t.onTouchPanning=function(e){var n=t.setup.disabled,r=t.props.onPanning;if(t.isPanning&&1===e.touches.length){if(n)return;if(!ise(t))return;e.preventDefault(),e.stopPropagation();var o=e.touches[0];use(t,o.clientX,o.clientY),Gae(Ose(t),e,r)}else e.touches.length>1&&t.onPinch(e)},t.onTouchPanningStop=function(e){t.onPanningStop(e),t.onPinchStop(e)},t.onDoubleClick=function(e){t.setup.disabled||qse(t,e)&&Gse(t,e)},t.clearPanning=function(e){t.isPanning&&t.onPanningStop(e)},t.setKeyPressed=function(e){t.pressedKeys[e.key]=!0},t.setKeyUnPressed=function(e){t.pressedKeys[e.key]=!1},t.isPressingKeys=function(e){return!e.length||Boolean(e.find((function(e){return t.pressedKeys[e]})))},t.setComponents=function(e,n){t.wrapperComponent=e,t.contentComponent=n,Jae(t,t.transformState.scale),t.handleInitializeWrapperEvents(e),t.handleInitialize(),t.handleRef(),t.isInitialized=!0,Gae(Ose(t),void 0,t.props.onInit)},t.setTransformState=function(e,n,r){isNaN(e)||isNaN(n)||isNaN(r)?console.error("Detected NaN set state values"):(e!==t.transformState.scale&&(t.transformState.previousScale=t.transformState.scale,t.transformState.scale=e),t.transformState.positionX=n,t.transformState.positionY=r,t.applyTransformation())},t.setCenter=function(){if(t.wrapperComponent&&t.contentComponent){var e=Rse(t.transformState.scale,t.wrapperComponent,t.contentComponent);t.setTransformState(e.scale,e.positionX,e.positionY)}},t.applyTransformation=function(){if(t.mounted&&t.contentComponent){var e=t.transformState,n=e.scale,r=e.positionX,o=e.positionY,i=Ise(r,o,n);t.contentComponent.style.transform=i,t.handleRef()}},t.handleRef=function(){t.props.setRef(Ose(t))},t}return function(e,t){if("function"!=typeof t&&null!==t)throw new TypeError("Class extends value "+String(t)+" is not a constructor or null");function n(){this.constructor=e}Wae(e,t),e.prototype=null===t?Object.create(t):(n.prototype=t.prototype,new n)}(t,e),t.prototype.componentDidMount=function(){var e=Mse();window.addEventListener("mousedown",this.onPanningStart,e),window.addEventListener("mousemove",this.onPanning,e),window.addEventListener("mouseup",this.onPanningStop,e),document.addEventListener("mouseleave",this.clearPanning,e),window.addEventListener("keyup",this.setKeyUnPressed,e),window.addEventListener("keydown",this.setKeyPressed,e),this.handleRef()},t.prototype.componentWillUnmount=function(){var e=Mse();window.removeEventListener("mousedown",this.onPanningStart,e),window.removeEventListener("mousemove",this.onPanning,e),window.removeEventListener("mouseup",this.onPanningStop,e),window.removeEventListener("keyup",this.setKeyUnPressed,e),window.removeEventListener("keydown",this.setKeyPressed,e),Zae(this)},t.prototype.componentDidUpdate=function(e){e!==this.props&&(Jae(this,this.transformState.scale),this.setup=bse(this.props))},t.prototype.render=function(){var e=Ose(this),t=this.props.children,n="function"==typeof t?t(e):t;return ld(Yse.Provider,{value:Vae(Vae({},this.transformState),{setComponents:this.setComponents,contextInstance:this}),children:n})},t}(a.exports.Component),Xse=H.forwardRef((function(e,t){var n=a.exports.useState(null),r=n[0],o=n[1];return a.exports.useImperativeHandle(t,(function(){return r}),[r]),ld(Zse,{...Vae({},e,{setRef:o})})}));var Kse="transform-component-module_wrapper__1_Fgj",Qse="transform-component-module_content__2jYgh";!function(e,t){void 0===t&&(t={});var n=t.insertAt;if(e&&"undefined"!=typeof document){var r=document.head||document.getElementsByTagName("head")[0],o=document.createElement("style");o.type="text/css","top"===n&&r.firstChild?r.insertBefore(o,r.firstChild):r.appendChild(o),o.styleSheet?o.styleSheet.cssText=e:o.appendChild(document.createTextNode(e))}}(".transform-component-module_wrapper__1_Fgj {\n  position: relative;\n  width: -moz-fit-content;\n  width: fit-content;\n  height: -moz-fit-content;\n  height: fit-content;\n  overflow: hidden;\n  -webkit-touch-callout: none; /* iOS Safari */\n  -webkit-user-select: none; /* Safari */\n  -khtml-user-select: none; /* Konqueror HTML */\n  -moz-user-select: none; /* Firefox */\n  -ms-user-select: none; /* Internet Explorer/Edge */\n  user-select: none;\n  margin: 0;\n  padding: 0;\n}\n.transform-component-module_content__2jYgh {\n  display: flex;\n  flex-wrap: wrap;\n  width: -moz-fit-content;\n  width: fit-content;\n  height: -moz-fit-content;\n  height: 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a.getSnapshotBeforeUpdate&&(t.flags|=1024)):("function"!=typeof a.componentDidUpdate||s===e.memoizedProps&&h===e.memoizedState||(t.flags|=4),"function"!=typeof a.getSnapshotBeforeUpdate||s===e.memoizedProps&&h===e.memoizedState||(t.flags|=1024),t.memoizedProps=r,t.memoizedState=p),a.props=r,a.state=p,a.context=l,r=c):("function"!=typeof a.componentDidUpdate||s===e.memoizedProps&&h===e.memoizedState||(t.flags|=4),"function"!=typeof a.getSnapshotBeforeUpdate||s===e.memoizedProps&&h===e.memoizedState||(t.flags|=1024),r=!1)}return Go(e,t,n,r,i,o)}function Go(e,t,n,r,o,i){Vo(e,t);var a=0!=(128&t.flags);if(!r&&!a)return o&&wt(t,n,!1),li(e,t,i);r=t.stateNode,Do.current=t;var s=a&&"function"!=typeof n.getDerivedStateFromError?null:r.render();return t.flags|=1,null!==e&&a?(t.child=fr(t,e.child,null,i),t.child=fr(t,null,s,i)):Bo(e,t,s,i),t.memoizedState=r.state,o&&wt(t,n,!0),t.child}function qo(e){var t=e.stateNode;t.pendingContext?yt(0,t.pendingContext,t.pendingContext!==t.context):t.context&&yt(0,t.context,!1),xr(e,t.containerInfo)}function Yo(e,t,n,r,o){return Pn(),Ln(o),t.flags|=256,Bo(e,t,n,r),t.child}var Zo,Xo,Ko,Qo,Jo={dehydrated:null,treeContext:null,retryLane:0};function ei(e){return{baseLanes:e,cachePool:null,transitions:null}}function ti(e,t,n){var r,o=t.pendingProps,a=Cr.current,s=!1,l=0!=(128&t.flags);if((r=l)||(r=(null===e||null!==e.memoizedState)&&0!=(2&a)),r?(s=!0,t.flags&=-129):null!==e&&null===e.memoizedState||(a|=1),ut(Cr,1&a),null===e)return Sn(t),null!==(e=t.memoizedState)&&null!==(e=e.dehydrated)?(0==(1&t.mode)?t.lanes=1:Be(e)?t.lanes=8:t.lanes=1073741824,null):(l=o.children,e=o.fallback,s?(o=t.mode,s=t.child,l={mode:"hidden",children:l},0==(1&o)&&null!==s?(s.childLanes=0,s.pendingProps=l):s=ps(l,o,0,null),e=fs(e,o,n,null),s.return=t,e.return=t,s.sibling=e,t.child=s,t.child.memoizedState=ei(n),t.memoizedState=Jo,e):ni(t,l));if(null!==(a=e.memoizedState)&&null!==(r=a.dehydrated))return function(e,t,n,r,o,a,s){if(n)return 256&t.flags?(t.flags&=-257,ri(e,t,s,r=Lo(Error(i(422))))):null!==t.memoizedState?(t.child=e.child,t.flags|=128,null):(a=r.fallback,o=t.mode,r=ps({mode:"visible",children:r.children},o,0,null),(a=fs(a,o,s,null)).flags|=2,r.return=t,a.return=t,r.sibling=a,t.child=r,0!=(1&t.mode)&&fr(t,e.child,null,s),t.child.memoizedState=ei(s),t.memoizedState=Jo,a);if(0==(1&t.mode))return ri(e,t,s,null);if(Be(o))return r=je(o).digest,a=Error(i(419)),r=Lo(a,r,void 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0==(1&l)&&t.child!==a?((o=t.child).childLanes=0,o.pendingProps=c,t.deletions=null):(o=ds(a,c)).subtreeFlags=14680064&a.subtreeFlags,null!==r?s=ds(r,s):(s=fs(s,l,n,null)).flags|=2,s.return=t,o.return=t,o.sibling=s,t.child=o,o=s,s=t.child,l=null===(l=e.child.memoizedState)?ei(n):{baseLanes:l.baseLanes|n,cachePool:null,transitions:l.transitions},s.memoizedState=l,s.childLanes=e.childLanes&~n,t.memoizedState=Jo,o}return e=(s=e.child).sibling,o=ds(s,{mode:"visible",children:o.children}),0==(1&t.mode)&&(o.lanes=n),o.return=t,o.sibling=null,null!==e&&(null===(n=t.deletions)?(t.deletions=[e],t.flags|=16):n.push(e)),t.child=o,t.memoizedState=null,o}function ni(e,t){return(t=ps({mode:"visible",children:t},e.mode,0,null)).return=e,e.child=t}function ri(e,t,n,r){return null!==r&&Ln(r),fr(t,e.child,null,n),(e=ni(t,t.pendingProps.children)).flags|=2,t.memoizedState=null,e}function oi(e,t,n){e.lanes|=t;var r=e.alternate;null!==r&&(r.lanes|=t),Fn(e.return,t,n)}function ii(e,t,n,r,o){var 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ui(e,t){if(null!==e&&e.child===t.child)return!0;if(0!=(16&t.flags))return!1;for(e=t.child;null!==e;){if(0!=(12854&e.flags)||0!=(12854&e.subtreeFlags))return!1;e=e.sibling}return!0}if(Z)Zo=function(e,t){for(var n=t.child;null!==n;){if(5===n.tag||6===n.tag)j(e,n.stateNode);else if(4!==n.tag&&null!==n.child){n.child.return=n,n=n.child;continue}if(n===t)break;for(;null===n.sibling;){if(null===n.return||n.return===t)return;n=n.return}n.sibling.return=n.return,n=n.sibling}},Xo=function(){},Ko=function(e,t,n,r,o){if((e=e.memoizedProps)!==r){var i=t.stateNode,a=br(mr.current);n=H(i,n,e,r,o,a),(t.updateQueue=n)&&ci(t)}},Qo=function(e,t,n,r){n!==r&&ci(t)};else if(X){Zo=function(e,t,n,r){for(var o=t.child;null!==o;){if(5===o.tag){var i=o.stateNode;n&&r&&(i=Ae(i,o.type,o.memoizedProps,o)),j(e,i)}else if(6===o.tag)i=o.stateNode,n&&r&&(i=Ie(i,o.memoizedProps,o)),j(e,i);else if(4!==o.tag)if(22===o.tag&&null!==o.memoizedState)null!==(i=o.child)&&(i.return=o),Zo(e,o,!0,!0);else 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s=t.stateNode,l=br(mr.current),c=null;a!==r&&(c=H(s,n,a,r,o,l)),e&&null===c?t.stateNode=i:(i=Pe(i,c,n,a,r,t,e,s),F(i,n,r,o,l)&&ci(t),t.stateNode=i,e?ci(t):Zo(i,t,!1,!1))}},Qo=function(e,t,n,r){n!==r?(e=br(yr.current),n=br(mr.current),t.stateNode=V(r,e,n,t),ci(t)):t.stateNode=e.stateNode}}else Xo=function(){},Ko=function(){},Qo=function(){};function hi(e,t){if(!vn)switch(e.tailMode){case"hidden":t=e.tail;for(var n=null;null!==t;)null!==t.alternate&&(n=t),t=t.sibling;null===n?e.tail=null:n.sibling=null;break;case"collapsed":n=e.tail;for(var r=null;null!==n;)null!==n.alternate&&(r=n),n=n.sibling;null===r?t||null===e.tail?e.tail=null:e.tail.sibling=null:r.sibling=null}}function fi(e){var t=null!==e.alternate&&e.alternate.child===e.child,n=0,r=0;if(t)for(var o=e.child;null!==o;)n|=o.lanes|o.childLanes,r|=14680064&o.subtreeFlags,r|=14680064&o.flags,o.return=e,o=o.sibling;else for(o=e.child;null!==o;)n|=o.lanes|o.childLanes,r|=o.subtreeFlags,r|=o.flags,o.return=e,o=o.sibling;return e.subtreeFlags|=r,e.childLanes=n,t}function pi(e,t,n){var r=t.pendingProps;switch(pn(t),t.tag){case 2:case 16:case 15:case 0:case 11:case 7:case 8:case 12:case 9:case 14:return fi(t),null;case 1:case 17:return mt(t.type)&&vt(),fi(t),null;case 3:return n=t.stateNode,wr(),ct(ft),ct(ht),Pr(),n.pendingContext&&(n.context=n.pendingContext,n.pendingContext=null),null!==e&&null!==e.child||(_n(t)?ci(t):null===e||e.memoizedState.isDehydrated&&0==(256&t.flags)||(t.flags|=1024,null!==bn&&(ja(bn),bn=null))),Xo(e,t),fi(t),null;case 5:Sr(t),n=br(yr.current);var o=t.type;if(null!==e&&null!=t.stateNode)Ko(e,t,o,r,n),e.ref!==t.ref&&(t.flags|=512,t.flags|=2097152);else{if(!r){if(null===t.stateNode)throw Error(i(166));return fi(t),null}if(e=br(mr.current),_n(t)){if(!K)throw Error(i(175));e=Ue(t.stateNode,t.type,t.memoizedProps,n,e,t,!yn),t.updateQueue=e,null!==e&&ci(t)}else{var a=B(o,r,n,e,t);Zo(a,t,!1,!1),t.stateNode=a,F(a,o,r,n,e)&&ci(t)}null!==t.ref&&(t.flags|=512,t.flags|=2097152)}return fi(t),null;case 6:if(e&&null!=t.stateNode)Qo(e,t,e.memoizedProps,r);else{if("string"!=typeof r&&null===t.stateNode)throw Error(i(166));if(e=br(yr.current),n=br(mr.current),_n(t)){if(!K)throw Error(i(176));if(e=t.stateNode,n=t.memoizedProps,(r=Ge(e,n,t,!yn))&&null!==(o=gn))switch(o.tag){case 3:et(o.stateNode.containerInfo,e,n,0!=(1&o.mode));break;case 5:tt(o.type,o.memoizedProps,o.stateNode,e,n,0!=(1&o.mode))}r&&ci(t)}else t.stateNode=V(r,e,n,t)}return fi(t),null;case 13:if(ct(Cr),r=t.memoizedState,null===e||null!==e.memoizedState&&null!==e.memoizedState.dehydrated){if(vn&&null!==mn&&0!=(1&t.mode)&&0==(128&t.flags))En(),Pn(),t.flags|=98560,o=!1;else if(o=_n(t),null!==r&&null!==r.dehydrated){if(null===e){if(!o)throw Error(i(318));if(!K)throw Error(i(344));if(!(o=null!==(o=t.memoizedState)?o.dehydrated:null))throw Error(i(317));qe(o,t)}else Pn(),0==(128&t.flags)&&(t.memoizedState=null),t.flags|=4;fi(t),o=!1}else null!==bn&&(ja(bn),bn=null),o=!0;if(!o)return 65536&t.flags?t:null}return 0!=(128&t.flags)?(t.lanes=n,t):((n=null!==r)!==(null!==e&&null!==e.memoizedState)&&n&&(t.child.flags|=8192,0!=(1&t.mode)&&(null===e||0!=(1&Cr.current)?0===da&&(da=3):qa())),null!==t.updateQueue&&(t.flags|=4),fi(t),null);case 4:return wr(),Xo(e,t),null===e&&J(t.stateNode.containerInfo),fi(t),null;case 10:return jn(t.type._context),fi(t),null;case 19:if(ct(Cr),null===(o=t.memoizedState))return fi(t),null;if(r=0!=(128&t.flags),null===(a=o.rendering))if(r)hi(o,!1);else{if(0!==da||null!==e&&0!=(128&e.flags))for(e=t.child;null!==e;){if(null!==(a=_r(e))){for(t.flags|=128,hi(o,!1),null!==(e=a.updateQueue)&&(t.updateQueue=e,t.flags|=4),t.subtreeFlags=0,e=n,n=t.child;null!==n;)o=e,(r=n).flags&=14680066,null===(a=r.alternate)?(r.childLanes=0,r.lanes=o,r.child=null,r.subtreeFlags=0,r.memoizedProps=null,r.memoizedState=null,r.updateQueue=null,r.dependencies=null,r.stateNode=null):(r.childLanes=a.childLanes,r.lanes=a.lanes,r.child=a.child,r.subtreeFlags=0,r.deletions=null,r.memoizedProps=a.memoizedProps,r.memoizedState=a.memoizedState,r.updateQueue=a.updateQueue,r.type=a.type,o=a.dependencies,r.dependencies=null===o?null:{lanes:o.lanes,firstContext:o.firstContext}),n=n.sibling;return 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this.i18nFormat&&this.i18nFormat.getResource?this.i18nFormat.getResource(e,t,n,r):this.resourceStore.getResource(e,t,n,r)}}],[{key:"hasDefaultValue",value:function(e){var t="defaultValue";for(var n in e)if(Object.prototype.hasOwnProperty.call(e,n)&&t===n.substring(0,t.length)&&void 0!==e[n])return!0;return!1}}]),n}(mG);function HG(e){return e.charAt(0).toUpperCase()+e.slice(1)}var WG=function(){function e(t){JU(this,e),this.options=t,this.supportedLngs=this.options.supportedLngs||!1,this.logger=gG.create("languageUtils")}return nG(e,[{key:"getScriptPartFromCode",value:function(e){if(!e||e.indexOf("-")<0)return null;var t=e.split("-");return 2===t.length?null:(t.pop(),"x"===t[t.length-1].toLowerCase()?null:this.formatLanguageCode(t.join("-")))}},{key:"getLanguagePartFromCode",value:function(e){if(!e||e.indexOf("-")<0)return e;var t=e.split("-");return this.formatLanguageCode(t[0])}},{key:"formatLanguageCode",value:function(e){if("string"==typeof e&&e.indexOf("-")>-1){var t=["hans","hant","latn","cyrl","cans","mong","arab"],n=e.split("-");return this.options.lowerCaseLng?n=n.map((function(e){return e.toLowerCase()})):2===n.length?(n[0]=n[0].toLowerCase(),n[1]=n[1].toUpperCase(),t.indexOf(n[1].toLowerCase())>-1&&(n[1]=HG(n[1].toLowerCase()))):3===n.length&&(n[0]=n[0].toLowerCase(),2===n[1].length&&(n[1]=n[1].toUpperCase()),"sgn"!==n[0]&&2===n[2].length&&(n[2]=n[2].toUpperCase()),t.indexOf(n[1].toLowerCase())>-1&&(n[1]=HG(n[1].toLowerCase())),t.indexOf(n[2].toLowerCase())>-1&&(n[2]=HG(n[2].toLowerCase()))),n.join("-")}return this.options.cleanCode||this.options.lowerCaseLng?e.toLowerCase():e}},{key:"isSupportedCode",value:function(e){return("languageOnly"===this.options.load||this.options.nonExplicitSupportedLngs)&&(e=this.getLanguagePartFromCode(e)),!this.supportedLngs||!this.supportedLngs.length||this.supportedLngs.indexOf(e)>-1}},{key:"getBestMatchFromCodes",value:function(e){var t,n=this;return e?(e.forEach((function(e){if(!t){var 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Number(e>1)},2:function(e){return Number(1!=e)},3:function(e){return 0},4:function(e){return Number(e%10==1&&e%100!=11?0:e%10>=2&&e%10<=4&&(e%100<10||e%100>=20)?1:2)},5:function(e){return Number(0==e?0:1==e?1:2==e?2:e%100>=3&&e%100<=10?3:e%100>=11?4:5)},6:function(e){return Number(1==e?0:e>=2&&e<=4?1:2)},7:function(e){return Number(1==e?0:e%10>=2&&e%10<=4&&(e%100<10||e%100>=20)?1:2)},8:function(e){return Number(1==e?0:2==e?1:8!=e&&11!=e?2:3)},9:function(e){return Number(e>=2)},10:function(e){return Number(1==e?0:2==e?1:e<7?2:e<11?3:4)},11:function(e){return Number(1==e||11==e?0:2==e||12==e?1:e>2&&e<20?2:3)},12:function(e){return Number(e%10!=1||e%100==11)},13:function(e){return Number(0!==e)},14:function(e){return Number(1==e?0:2==e?1:3==e?2:3)},15:function(e){return Number(e%10==1&&e%100!=11?0:e%10>=2&&(e%100<10||e%100>=20)?1:2)},16:function(e){return Number(e%10==1&&e%100!=11?0:0!==e?1:2)},17:function(e){return Number(1==e||e%10==1&&e%100!=11?0:1)},18:function(e){return 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t="".concat(this.prefix).concat(this.unescapePrefix,"(.+?)").concat(this.unescapeSuffix).concat(this.suffix);this.regexpUnescape=new RegExp(t,"g");var n="".concat(this.nestingPrefix,"(.+?)").concat(this.nestingSuffix);this.nestingRegexp=new RegExp(n,"g")}},{key:"interpolate",value:function(e,t,n,r){var o,i,a,s=this,l=this.options&&this.options.interpolation&&this.options.interpolation.defaultVariables||{};function c(e){return e.replace(/\$/g,"$$$$")}var u=function(e){if(e.indexOf(s.formatSeparator)<0){var o=SG(t,l,e);return s.alwaysFormat?s.format(o,void 0,n,XG(XG(XG({},r),t),{},{interpolationkey:e})):o}var i=e.split(s.formatSeparator),a=i.shift().trim(),c=i.join(s.formatSeparator).trim();return s.format(SG(t,l,a),c,n,XG(XG(XG({},r),t),{},{interpolationkey:a}))};this.resetRegExp();var d=r&&r.missingInterpolationHandler||this.options.missingInterpolationHandler,h=r&&r.interpolation&&void 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t=uG(e.split(":")),r=t[0],o=t.slice(1).join(":").trim().replace(/^'+|'+$/g,"");n[r.trim()]||(n[r.trim()]=o),"false"===o&&(n[r.trim()]=!1),"true"===o&&(n[r.trim()]=!0),isNaN(o)||(n[r.trim()]=parseInt(o,10))}}))}return{formatName:t,formatOptions:n}}(t),a=i.formatName,s=i.formatOptions;if(o.formats[a]){var l=e;try{var c=r&&r.formatParams&&r.formatParams[r.interpolationkey]||{},u=c.locale||c.lng||r.locale||r.lng||n;l=o.formats[a](e,u,JG(JG(JG({},s),r),c))}catch(d){o.logger.warn(d)}return l}return o.logger.warn("there was no format function for ".concat(a)),e}),e);return i}}]),e}();function nq(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter((function(t){return Object.getOwnPropertyDescriptor(e,t).enumerable}))),n.push.apply(n,r)}return n}function rq(e){for(var t=1;t<arguments.length;t++){var n=null!=arguments[t]?arguments[t]:{};t%2?nq(Object(n),!0).forEach((function(t){lG(e,t,n[t])})):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(n)):nq(Object(n)).forEach((function(t){Object.defineProperty(e,t,Object.getOwnPropertyDescriptor(n,t))}))}return e}function oq(e){var t=function(){if("undefined"==typeof Reflect||!Reflect.construct)return!1;if(Reflect.construct.sham)return!1;if("function"==typeof Proxy)return!0;try{return Boolean.prototype.valueOf.call(Reflect.construct(Boolean,[],(function(){}))),!0}catch(B2){return!1}}();return function(){var n,r=sG(e);if(t){var o=sG(this).constructor;n=Reflect.construct(r,arguments,o)}else n=r.apply(this,arguments);return aG(this,n)}}var iq=function(e){iG(n,e);var t=oq(n);function n(e,r,o){var i,a=arguments.length>3&&void 0!==arguments[3]?arguments[3]:{};return JU(this,n),i=t.call(this),LG&&mG.call(rG(i)),i.backend=e,i.store=r,i.services=o,i.languageUtils=o.languageUtils,i.options=a,i.logger=gG.create("backendConnector"),i.waitingReads=[],i.maxParallelReads=a.maxParallelReads||10,i.readingCalls=0,i.maxRetries=a.maxRetries>=0?a.maxRetries:5,i.retryTimeout=a.retryTimeout>=1?a.retryTimeout:350,i.state={},i.queue=[],i.backend&&i.backend.init&&i.backend.init(o,a.backend,a),i}return nG(n,[{key:"queueLoad",value:function(e,t,n,r){var o=this,i={},a={},s={},l={};return e.forEach((function(e){var r=!0;t.forEach((function(t){var s="".concat(e,"|").concat(t);!n.reload&&o.store.hasResourceBundle(e,t)?o.state[s]=2:o.state[s]<0||(1===o.state[s]?void 0===a[s]&&(a[s]=!0):(o.state[s]=1,r=!1,void 0===a[s]&&(a[s]=!0),void 0===i[s]&&(i[s]=!0),void 0===l[t]&&(l[t]=!0)))})),r||(s[e]=!0)})),(Object.keys(i).length||Object.keys(a).length)&&this.queue.push({pending:a,pendingCount:Object.keys(a).length,loaded:{},errors:[],callback:r}),{toLoad:Object.keys(i),pending:Object.keys(a),toLoadLanguages:Object.keys(s),toLoadNamespaces:Object.keys(l)}}},{key:"loaded",value:function(e,t,n){var r=e.split("|"),o=r[0],i=r[1];t&&this.emit("failedLoading",o,i,t),n&&this.store.addResourceBundle(o,i,n),this.state[e]=t?-1:2;var a={};this.queue.forEach((function(n){!function(e,t,n,r){var o=xG(e,t,Object),i=o.obj,a=o.k;i[a]=i[a]||[],r&&(i[a]=i[a].concat(n)),r||i[a].push(n)}(n.loaded,[o],i),function(e,t){void 0!==e.pending[t]&&(delete e.pending[t],e.pendingCount--)}(n,e),t&&n.errors.push(t),0!==n.pendingCount||n.done||(Object.keys(n.loaded).forEach((function(e){a[e]||(a[e]={});var t=n.loaded[e];t.length&&t.forEach((function(t){void 0===a[e][t]&&(a[e][t]=!0)}))})),n.done=!0,n.errors.length?n.callback(n.errors):n.callback())})),this.emit("loaded",a),this.queue=this.queue.filter((function(e){return!e.done}))}},{key:"read",value:function(e,t,n){var r=this,o=arguments.length>3&&void 0!==arguments[3]?arguments[3]:0,i=arguments.length>4&&void 0!==arguments[4]?arguments[4]:this.retryTimeout,a=arguments.length>5?arguments[5]:void 0;if(!e.length)return a(null,{});if(this.readingCalls>=this.maxParallelReads)this.waitingReads.push({lng:e,ns:t,fcName:n,tried:o,wait:i,callback:a});else{this.readingCalls++;var s=function(s,l){if(r.readingCalls--,r.waitingReads.length>0){var c=r.waitingReads.shift();r.read(c.lng,c.ns,c.fcName,c.tried,c.wait,c.callback)}s&&l&&o<r.maxRetries?setTimeout((function(){r.read.call(r,e,t,n,o+1,2*i,a)}),i):a(s,l)},l=this.backend[n].bind(this.backend);if(2!==l.length)return l(e,t,s);try{var c=l(e,t);c&&"function"==typeof c.then?c.then((function(e){return s(null,e)})).catch(s):s(null,c)}catch(Vhe){s(Vhe)}}}},{key:"prepareLoading",value:function(e,t){var n=this,r=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{},o=arguments.length>3?arguments[3]:void 0;if(!this.backend)return this.logger.warn("No backend was added via i18next.use. Will not load resources."),o&&o();"string"==typeof e&&(e=this.languageUtils.toResolveHierarchy(e)),"string"==typeof t&&(t=[t]);var i=this.queueLoad(e,t,r,o);if(!i.toLoad.length)return i.pending.length||o(),null;i.toLoad.forEach((function(e){n.loadOne(e)}))}},{key:"load",value:function(e,t,n){this.prepareLoading(e,t,{},n)}},{key:"reload",value:function(e,t,n){this.prepareLoading(e,t,{reload:!0},n)}},{key:"loadOne",value:function(e){var t=this,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:"",r=e.split("|"),o=r[0],i=r[1];this.read(o,i,"read",void 0,void 0,(function(r,a){r&&t.logger.warn("".concat(n,"loading namespace ").concat(i," for language ").concat(o," failed"),r),!r&&a&&t.logger.log("".concat(n,"loaded namespace ").concat(i," for language ").concat(o),a),t.loaded(e,r,a)}))}},{key:"saveMissing",value:function(e,t,n,r,o){var i=arguments.length>5&&void 0!==arguments[5]?arguments[5]:{},a=arguments.length>6&&void 0!==arguments[6]?arguments[6]:function(){};if(this.services.utils&&this.services.utils.hasLoadedNamespace&&!this.services.utils.hasLoadedNamespace(t))this.logger.warn('did not save key "'.concat(n,'" as the namespace "').concat(t,'" was not yet loaded'),"This means something IS WRONG in your setup. You access the t function before i18next.init / i18next.loadNamespace / i18next.changeLanguage was done. Wait for the callback or Promise to resolve before accessing it!!!");else if(null!=n&&""!==n){if(this.backend&&this.backend.create){var s=rq(rq({},i),{},{isUpdate:o}),l=this.backend.create.bind(this.backend);if(l.length<6)try{var c;(c=5===l.length?l(e,t,n,r,s):l(e,t,n,r))&&"function"==typeof c.then?c.then((function(e){return a(null,e)})).catch(a):a(null,c)}catch(Vhe){a(Vhe)}else l(e,t,n,r,a,s)}e&&e[0]&&this.store.addResource(e[0],t,n,r)}}}]),n}(mG);function aq(){return{debug:!1,initImmediate:!0,ns:["translation"],defaultNS:["translation"],fallbackLng:["dev"],fallbackNS:!1,supportedLngs:!1,nonExplicitSupportedLngs:!1,load:"all",preload:!1,simplifyPluralSuffix:!0,keySeparator:".",nsSeparator:":",pluralSeparator:"_",contextSeparator:"_",partialBundledLanguages:!1,saveMissing:!1,updateMissing:!1,saveMissingTo:"fallback",saveMissingPlurals:!0,missingKeyHandler:!1,missingInterpolationHandler:!1,postProcess:!1,postProcessPassResolved:!1,returnNull:!0,returnEmptyString:!0,returnObjects:!1,joinArrays:!1,returnedObjectHandler:!1,parseMissingKeyHandler:!1,appendNamespaceToMissingKey:!1,appendNamespaceToCIMode:!1,overloadTranslationOptionHandler:function(e){var t={};if("object"===QU(e[1])&&(t=e[1]),"string"==typeof e[1]&&(t.defaultValue=e[1]),"string"==typeof e[2]&&(t.tDescription=e[2]),"object"===QU(e[2])||"object"===QU(e[3])){var n=e[3]||e[2];Object.keys(n).forEach((function(e){t[e]=n[e]}))}return t},interpolation:{escapeValue:!0,format:function(e,t,n,r){return e},prefix:"{{",suffix:"}}",formatSeparator:",",unescapePrefix:"-",nestingPrefix:"$t(",nestingSuffix:")",nestingOptionsSeparator:",",maxReplaces:1e3,skipOnVariables:!0}}}function sq(e){return"string"==typeof e.ns&&(e.ns=[e.ns]),"string"==typeof e.fallbackLng&&(e.fallbackLng=[e.fallbackLng]),"string"==typeof e.fallbackNS&&(e.fallbackNS=[e.fallbackNS]),e.supportedLngs&&e.supportedLngs.indexOf("cimode")<0&&(e.supportedLngs=e.supportedLngs.concat(["cimode"])),e}function lq(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter((function(t){return Object.getOwnPropertyDescriptor(e,t).enumerable}))),n.push.apply(n,r)}return n}function cq(e){for(var t=1;t<arguments.length;t++){var n=null!=arguments[t]?arguments[t]:{};t%2?lq(Object(n),!0).forEach((function(t){lG(e,t,n[t])})):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(n)):lq(Object(n)).forEach((function(t){Object.defineProperty(e,t,Object.getOwnPropertyDescriptor(n,t))}))}return e}function uq(e){var t=function(){if("undefined"==typeof Reflect||!Reflect.construct)return!1;if(Reflect.construct.sham)return!1;if("function"==typeof Proxy)return!0;try{return Boolean.prototype.valueOf.call(Reflect.construct(Boolean,[],(function(){}))),!0}catch(B2){return!1}}();return function(){var n,r=sG(e);if(t){var o=sG(this).constructor;n=Reflect.construct(r,arguments,o)}else n=r.apply(this,arguments);return aG(this,n)}}function dq(){}function hq(e){Object.getOwnPropertyNames(Object.getPrototypeOf(e)).forEach((function(t){"function"==typeof e[t]&&(e[t]=e[t].bind(e))}))}var fq=function(e){iG(n,e);var t=uq(n);function n(){var e,r=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{},o=arguments.length>1?arguments[1]:void 0;if(JU(this,n),e=t.call(this),LG&&mG.call(rG(e)),e.options=sq(r),e.services={},e.logger=gG,e.modules={external:[]},hq(rG(e)),o&&!e.isInitialized&&!r.isClone){if(!e.options.initImmediate)return e.init(r,o),aG(e,rG(e));setTimeout((function(){e.init(r,o)}),0)}return e}return nG(n,[{key:"init",value:function(){var e=this,t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{},n=arguments.length>1?arguments[1]:void 0;"function"==typeof t&&(n=t,t={}),!t.defaultNS&&!1!==t.defaultNS&&t.ns&&("string"==typeof t.ns?t.defaultNS=t.ns:t.ns.indexOf("translation")<0&&(t.defaultNS=t.ns[0]));var r=aq();function o(e){return e?"function"==typeof e?new e:e:null}if(this.options=cq(cq(cq({},r),this.options),sq(t)),"v1"!==this.options.compatibilityAPI&&(this.options.interpolation=cq(cq({},r.interpolation),this.options.interpolation)),void 0!==t.keySeparator&&(this.options.userDefinedKeySeparator=t.keySeparator),void 0!==t.nsSeparator&&(this.options.userDefinedNsSeparator=t.nsSeparator),!this.options.isClone){var i;this.modules.logger?gG.init(o(this.modules.logger),this.options):gG.init(null,this.options),this.modules.formatter?i=this.modules.formatter:"undefined"!=typeof Intl&&(i=tq);var a=new WG(this.options);this.store=new RG(this.options.resources,this.options);var s=this.services;s.logger=gG,s.resourceStore=this.store,s.languageUtils=a,s.pluralResolver=new YG(a,{prepend:this.options.pluralSeparator,compatibilityJSON:this.options.compatibilityJSON,simplifyPluralSuffix:this.options.simplifyPluralSuffix}),!i||this.options.interpolation.format&&this.options.interpolation.format!==r.interpolation.format||(s.formatter=o(i),s.formatter.init(s,this.options),this.options.interpolation.format=s.formatter.format.bind(s.formatter)),s.interpolator=new KG(this.options),s.utils={hasLoadedNamespace:this.hasLoadedNamespace.bind(this)},s.backendConnector=new iq(o(this.modules.backend),s.resourceStore,s,this.options),s.backendConnector.on("*",(function(t){for(var n=arguments.length,r=new Array(n>1?n-1:0),o=1;o<n;o++)r[o-1]=arguments[o];e.emit.apply(e,[t].concat(r))})),this.modules.languageDetector&&(s.languageDetector=o(this.modules.languageDetector),s.languageDetector.init&&s.languageDetector.init(s,this.options.detection,this.options)),this.modules.i18nFormat&&(s.i18nFormat=o(this.modules.i18nFormat),s.i18nFormat.init&&s.i18nFormat.init(this)),this.translator=new FG(this.services,this.options),this.translator.on("*",(function(t){for(var n=arguments.length,r=new Array(n>1?n-1:0),o=1;o<n;o++)r[o-1]=arguments[o];e.emit.apply(e,[t].concat(r))})),this.modules.external.forEach((function(t){t.init&&t.init(e)}))}if(this.format=this.options.interpolation.format,n||(n=dq),this.options.fallbackLng&&!this.services.languageDetector&&!this.options.lng){var l=this.services.languageUtils.getFallbackCodes(this.options.fallbackLng);l.length>0&&"dev"!==l[0]&&(this.options.lng=l[0])}this.services.languageDetector||this.options.lng||this.logger.warn("init: no languageDetector is used and no lng is defined");var c=["getResource","hasResourceBundle","getResourceBundle","getDataByLanguage"];c.forEach((function(t){e[t]=function(){var n;return(n=e.store)[t].apply(n,arguments)}}));var u=["addResource","addResources","addResourceBundle","removeResourceBundle"];u.forEach((function(t){e[t]=function(){var n;return(n=e.store)[t].apply(n,arguments),e}}));var d=vG(),h=function(){var t=function(t,r){e.isInitialized&&!e.initializedStoreOnce&&e.logger.warn("init: i18next is already initialized. 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Please check the object you are passing to i18next.use()");if(!e.type)throw new Error("You are passing a wrong module! Please check the object you are passing to i18next.use()");return"backend"===e.type&&(this.modules.backend=e),("logger"===e.type||e.log&&e.warn&&e.error)&&(this.modules.logger=e),"languageDetector"===e.type&&(this.modules.languageDetector=e),"i18nFormat"===e.type&&(this.modules.i18nFormat=e),"postProcessor"===e.type&&NG.addPostProcessor(e),"formatter"===e.type&&(this.modules.formatter=e),"3rdParty"===e.type&&this.modules.external.push(e),this}},{key:"setResolvedLanguage",value:function(e){if(e&&this.languages&&!(["cimode","dev"].indexOf(e)>-1))for(var t=0;t<this.languages.length;t++){var n=this.languages[t];if(!(["cimode","dev"].indexOf(n)>-1)&&this.store.hasLanguageSomeTranslations(n)){this.resolvedLanguage=n;break}}}},{key:"changeLanguage",value:function(e,t){var n=this;this.isLanguageChangingTo=e;var r=vG();this.emit("languageChanging",e);var o=function(e){n.language=e,n.languages=n.services.languageUtils.toResolveHierarchy(e),n.resolvedLanguage=void 0,n.setResolvedLanguage(e)},i=function(i){e||i||!n.services.languageDetector||(i=[]);var a="string"==typeof i?i:n.services.languageUtils.getBestMatchFromCodes(i);a&&(n.language||o(a),n.translator.language||n.translator.changeLanguage(a),n.services.languageDetector&&n.services.languageDetector.cacheUserLanguage&&n.services.languageDetector.cacheUserLanguage(a)),n.loadResources(a,(function(e){!function(e,i){i?(o(i),n.translator.changeLanguage(i),n.isLanguageChangingTo=void 0,n.emit("languageChanged",i),n.logger.log("languageChanged",i)):n.isLanguageChangingTo=void 0,r.resolve((function(){return n.t.apply(n,arguments)})),t&&t(e,(function(){return n.t.apply(n,arguments)}))}(e,a)}))};return e||!this.services.languageDetector||this.services.languageDetector.async?!e&&this.services.languageDetector&&this.services.languageDetector.async?0===this.services.languageDetector.detect.length?this.services.languageDetector.detect().then(i):this.services.languageDetector.detect(i):i(e):i(this.services.languageDetector.detect()),r}},{key:"getFixedT",value:function(e,t,n){var r=this,o=function e(t,o){var i;if("object"!==QU(o)){for(var a=arguments.length,s=new Array(a>2?a-2:0),l=2;l<a;l++)s[l-2]=arguments[l];i=r.options.overloadTranslationOptionHandler([t,o].concat(s))}else i=cq({},o);i.lng=i.lng||e.lng,i.lngs=i.lngs||e.lngs,i.ns=i.ns||e.ns,i.keyPrefix=i.keyPrefix||n||e.keyPrefix;var c=r.options.keySeparator||".",u=i.keyPrefix?"".concat(i.keyPrefix).concat(c).concat(t):t;return r.t(u,i)};return"string"==typeof e?o.lng=e:o.lngs=e,o.ns=t,o.keyPrefix=n,o}},{key:"t",value:function(){var e;return this.translator&&(e=this.translator).translate.apply(e,arguments)}},{key:"exists",value:function(){var e;return this.translator&&(e=this.translator).exists.apply(e,arguments)}},{key:"setDefaultNamespace",value:function(e){this.options.defaultNS=e}},{key:"hasLoadedNamespace",value:function(e){var t=this,n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};if(!this.isInitialized)return this.logger.warn("hasLoadedNamespace: i18next was not initialized",this.languages),!1;if(!this.languages||!this.languages.length)return this.logger.warn("hasLoadedNamespace: i18n.languages were undefined or empty",this.languages),!1;var r=this.resolvedLanguage||this.languages[0],o=!!this.options&&this.options.fallbackLng,i=this.languages[this.languages.length-1];if("cimode"===r.toLowerCase())return!0;var a=function(e,n){var r=t.services.backendConnector.state["".concat(e,"|").concat(n)];return-1===r||2===r};if(n.precheck){var s=n.precheck(this,a);if(void 0!==s)return s}return!!this.hasResourceBundle(r,e)||(!(this.services.backendConnector.backend&&(!this.options.resources||this.options.partialBundledLanguages))||!(!a(r,e)||o&&!a(i,e)))}},{key:"loadNamespaces",value:function(e,t){var n=this,r=vG();return this.options.ns?("string"==typeof e&&(e=[e]),e.forEach((function(e){n.options.ns.indexOf(e)<0&&n.options.ns.push(e)})),this.loadResources((function(e){r.resolve(),t&&t(e)})),r):(t&&t(),Promise.resolve())}},{key:"loadLanguages",value:function(e,t){var n=vG();"string"==typeof e&&(e=[e]);var r=this.options.preload||[],o=e.filter((function(e){return r.indexOf(e)<0}));return o.length?(this.options.preload=r.concat(o),this.loadResources((function(e){n.resolve(),t&&t(e)})),n):(t&&t(),Promise.resolve())}},{key:"dir",value:function(e){if(e||(e=this.resolvedLanguage||(this.languages&&this.languages.length>0?this.languages[0]:this.language)),!e)return"rtl";var t=this.services&&this.services.languageUtils||new WG(aq());return["ar","shu","sqr","ssh","xaa","yhd","yud","aao","abh","abv","acm","acq","acw","acx","acy","adf","ads","aeb","aec","afb","ajp","apc","apd","arb","arq","ars","ary","arz","auz","avl","ayh","ayl","ayn","ayp","bbz","pga","he","iw","ps","pbt","pbu","pst","prp","prd","ug","ur","ydd","yds","yih","ji","yi","hbo","men","xmn","fa","jpr","peo","pes","prs","dv","sam","ckb"].indexOf(t.getLanguagePartFromCode(e))>-1||e.toLowerCase().indexOf("-arab")>1?"rtl":"ltr"}},{key:"cloneInstance",value:function(){var e=this,t=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{},r=arguments.length>1&&void 0!==arguments[1]?arguments[1]:dq,o=cq(cq(cq({},this.options),t),{isClone:!0}),i=new n(o);void 0===t.debug&&void 0===t.prefix||(i.logger=i.logger.clone(t));var a=["store","services","language"];return a.forEach((function(t){i[t]=e[t]})),i.services=cq({},this.services),i.services.utils={hasLoadedNamespace:i.hasLoadedNamespace.bind(i)},i.translator=new FG(i.services,i.options),i.translator.on("*",(function(e){for(var t=arguments.length,n=new Array(t>1?t-1:0),r=1;r<t;r++)n[r-1]=arguments[r];i.emit.apply(i,[e].concat(n))})),i.init(o,r),i.translator.options=i.options,i.translator.backendConnector.services.utils={hasLoadedNamespace:i.hasLoadedNamespace.bind(i)},i}},{key:"toJSON",value:function(){return{options:this.options,store:this.store,language:this.language,languages:this.languages,resolvedLanguage:this.resolvedLanguage}}}]),n}(mG);lG(fq,"createInstance",(function(){var e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{},t=arguments.length>1?arguments[1]:void 0;return new fq(e,t)}));var pq=fq.createInstance();function gq(e,t){if(!(e instanceof t))throw new TypeError("Cannot call a class as a function")}function mq(e){return mq="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(e){return typeof e}:function(e){return e&&"function"==typeof Symbol&&e.constructor===Symbol&&e!==Symbol.prototype?"symbol":typeof e},mq(e)}function vq(e){var t=function(e,t){if("object"!==mq(e)||null===e)return e;var n=e[Symbol.toPrimitive];if(void 0!==n){var r=n.call(e,t||"default");if("object"!==mq(r))return r;throw new TypeError("@@toPrimitive must return a primitive value.")}return("string"===t?String:Number)(e)}(e,"string");return"symbol"===mq(t)?t:String(t)}function yq(e,t){for(var n=0;n<t.length;n++){var r=t[n];r.enumerable=r.enumerable||!1,r.configurable=!0,"value"in r&&(r.writable=!0),Object.defineProperty(e,vq(r.key),r)}}pq.createInstance=fq.createInstance,pq.createInstance,pq.dir,pq.init,pq.loadResources,pq.reloadResources,pq.use,pq.changeLanguage,pq.getFixedT,pq.t,pq.exists,pq.setDefaultNamespace,pq.hasLoadedNamespace,pq.loadNamespaces,pq.loadLanguages;var bq=[],xq=bq.forEach,wq=bq.slice;function kq(e){return xq.call(wq.call(arguments,1),(function(t){if(t)for(var n in t)void 0===e[n]&&(e[n]=t[n])})),e}var Sq=/^[\u0009\u0020-\u007e\u0080-\u00ff]+$/,Cq=function(e,t,n){var r=n||{};r.path=r.path||"/";var o=encodeURIComponent(t),i="".concat(e,"=").concat(o);if(r.maxAge>0){var a=r.maxAge-0;if(Number.isNaN(a))throw new Error("maxAge should be a Number");i+="; Max-Age=".concat(Math.floor(a))}if(r.domain){if(!Sq.test(r.domain))throw new TypeError("option domain is invalid");i+="; Domain=".concat(r.domain)}if(r.path){if(!Sq.test(r.path))throw new TypeError("option path is invalid");i+="; Path=".concat(r.path)}if(r.expires){if("function"!=typeof r.expires.toUTCString)throw new TypeError("option expires is invalid");i+="; Expires=".concat(r.expires.toUTCString())}if(r.httpOnly&&(i+="; HttpOnly"),r.secure&&(i+="; Secure"),r.sameSite)switch("string"==typeof r.sameSite?r.sameSite.toLowerCase():r.sameSite){case!0:i+="; SameSite=Strict";break;case"lax":i+="; SameSite=Lax";break;case"strict":i+="; SameSite=Strict";break;case"none":i+="; SameSite=None";break;default:throw new TypeError("option sameSite is invalid")}return i},_q=function(e,t,n,r){var o=arguments.length>4&&void 0!==arguments[4]?arguments[4]:{path:"/",sameSite:"strict"};n&&(o.expires=new Date,o.expires.setTime(o.expires.getTime()+60*n*1e3)),r&&(o.domain=r),document.cookie=Cq(e,encodeURIComponent(t),o)},Eq=function(e){for(var t="".concat(e,"="),n=document.cookie.split(";"),r=0;r<n.length;r++){for(var o=n[r];" "===o.charAt(0);)o=o.substring(1,o.length);if(0===o.indexOf(t))return o.substring(t.length,o.length)}return null},Pq={name:"cookie",lookup:function(e){var t;if(e.lookupCookie&&"undefined"!=typeof document){var n=Eq(e.lookupCookie);n&&(t=n)}return t},cacheUserLanguage:function(e,t){t.lookupCookie&&"undefined"!=typeof document&&_q(t.lookupCookie,e,t.cookieMinutes,t.cookieDomain,t.cookieOptions)}},Lq={name:"querystring",lookup:function(e){var t;if("undefined"!=typeof window){var n=window.location.search;!window.location.search&&window.location.hash&&window.location.hash.indexOf("?")>-1&&(n=window.location.hash.substring(window.location.hash.indexOf("?")));for(var r=n.substring(1).split("&"),o=0;o<r.length;o++){var i=r[o].indexOf("=");if(i>0)r[o].substring(0,i)===e.lookupQuerystring&&(t=r[o].substring(i+1))}}return t}},Oq=null,Mq=function(){if(null!==Oq)return Oq;try{Oq="undefined"!==window&&null!==window.localStorage;var e="i18next.translate.boo";window.localStorage.setItem(e,"foo"),window.localStorage.removeItem(e)}catch(B2){Oq=!1}return Oq},Tq={name:"localStorage",lookup:function(e){var t;if(e.lookupLocalStorage&&Mq()){var n=window.localStorage.getItem(e.lookupLocalStorage);n&&(t=n)}return t},cacheUserLanguage:function(e,t){t.lookupLocalStorage&&Mq()&&window.localStorage.setItem(t.lookupLocalStorage,e)}},Aq=null,Iq=function(){if(null!==Aq)return Aq;try{Aq="undefined"!==window&&null!==window.sessionStorage;var e="i18next.translate.boo";window.sessionStorage.setItem(e,"foo"),window.sessionStorage.removeItem(e)}catch(B2){Aq=!1}return Aq},Rq={name:"sessionStorage",lookup:function(e){var t;if(e.lookupSessionStorage&&Iq()){var n=window.sessionStorage.getItem(e.lookupSessionStorage);n&&(t=n)}return t},cacheUserLanguage:function(e,t){t.lookupSessionStorage&&Iq()&&window.sessionStorage.setItem(t.lookupSessionStorage,e)}},Nq={name:"navigator",lookup:function(e){var t=[];if("undefined"!=typeof navigator){if(navigator.languages)for(var n=0;n<navigator.languages.length;n++)t.push(navigator.languages[n]);navigator.userLanguage&&t.push(navigator.userLanguage),navigator.language&&t.push(navigator.language)}return t.length>0?t:void 0}},Dq={name:"htmlTag",lookup:function(e){var t,n=e.htmlTag||("undefined"!=typeof document?document.documentElement:null);return n&&"function"==typeof n.getAttribute&&(t=n.getAttribute("lang")),t}},zq={name:"path",lookup:function(e){var t;if("undefined"!=typeof window){var n=window.location.pathname.match(/\/([a-zA-Z-]*)/g);if(n instanceof Array)if("number"==typeof e.lookupFromPathIndex){if("string"!=typeof n[e.lookupFromPathIndex])return;t=n[e.lookupFromPathIndex].replace("/","")}else t=n[0].replace("/","")}return t}},Bq={name:"subdomain",lookup:function(e){var t="number"==typeof e.lookupFromSubdomainIndex?e.lookupFromSubdomainIndex+1:1,n="undefined"!=typeof window&&window.location&&window.location.hostname&&window.location.hostname.match(/^(\w{2,5})\.(([a-z0-9-]{1,63}\.[a-z]{2,6})|localhost)/i);if(n)return n[t]}};var jq=function(){function e(t){var n=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{};gq(this,e),this.type="languageDetector",this.detectors={},this.init(t,n)}var t,n,r;return t=e,n=[{key:"init",value:function(e){var t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{},n=arguments.length>2&&void 0!==arguments[2]?arguments[2]:{};this.services=e,this.options=kq(t,this.options||{},{order:["querystring","cookie","localStorage","sessionStorage","navigator","htmlTag"],lookupQuerystring:"lng",lookupCookie:"i18next",lookupLocalStorage:"i18nextLng",lookupSessionStorage:"i18nextLng",caches:["localStorage"],excludeCacheFor:["cimode"]}),this.options.lookupFromUrlIndex&&(this.options.lookupFromPathIndex=this.options.lookupFromUrlIndex),this.i18nOptions=n,this.addDetector(Pq),this.addDetector(Lq),this.addDetector(Tq),this.addDetector(Rq),this.addDetector(Nq),this.addDetector(Dq),this.addDetector(zq),this.addDetector(Bq)}},{key:"addDetector",value:function(e){this.detectors[e.name]=e}},{key:"detect",value:function(e){var t=this;e||(e=this.options.order);var n=[];return e.forEach((function(e){if(t.detectors[e]){var r=t.detectors[e].lookup(t.options);r&&"string"==typeof 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addEventListener&&(this.opts.closeOnBeforeunload&&(this.beforeunloadEventListener=()=>{this.transport&&(this.transport.removeAllListeners(),this.transport.close())},addEventListener("beforeunload",this.beforeunloadEventListener,!1)),"localhost"!==this.hostname&&(this.offlineEventListener=()=>{this.onClose("transport close",{description:"network connection lost"})},addEventListener("offline",this.offlineEventListener,!1))),this.open()}createTransport(e){const t=Object.assign({},this.opts.query);t.EIO=4,t.transport=e,this.id&&(t.sid=this.id);const n=Object.assign({},this.opts.transportOptions[e],this.opts,{query:t,socket:this,hostname:this.hostname,secure:this.secure,port:this.port});return new TK[e](n)}open(){let e;if(this.opts.rememberUpgrade&&NK.priorWebsocketSuccess&&-1!==this.transports.indexOf("websocket"))e="websocket";else{if(0===this.transports.length)return void this.setTimeoutFn((()=>{this.emitReserved("error","No transports 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t=this.upgrades.length;for(;e<t;e++)this.probe(this.upgrades[e])}}onPacket(e){if("opening"===this.readyState||"open"===this.readyState||"closing"===this.readyState)switch(this.emitReserved("packet",e),this.emitReserved("heartbeat"),e.type){case"open":this.onHandshake(JSON.parse(e.data));break;case"ping":this.resetPingTimeout(),this.sendPacket("pong"),this.emitReserved("ping"),this.emitReserved("pong");break;case"error":const t=new Error("server error");t.code=e.data,this.onError(t);break;case"message":this.emitReserved("data",e.data),this.emitReserved("message",e.data)}}onHandshake(e){this.emitReserved("handshake",e),this.id=e.sid,this.transport.query.sid=e.sid,this.upgrades=this.filterUpgrades(e.upgrades),this.pingInterval=e.pingInterval,this.pingTimeout=e.pingTimeout,this.maxPayload=e.maxPayload,this.onOpen(),"closed"!==this.readyState&&this.resetPingTimeout()}resetPingTimeout(){this.clearTimeoutFn(this.pingTimeoutTimer),this.pingTimeoutTimer=this.setTimeoutFn((()=>{this.onClose("ping timeout")}),this.pingInterval+this.pingTimeout),this.opts.autoUnref&&this.pingTimeoutTimer.unref()}onDrain(){this.writeBuffer.splice(0,this.prevBufferLen),this.prevBufferLen=0,0===this.writeBuffer.length?this.emitReserved("drain"):this.flush()}flush(){if("closed"!==this.readyState&&this.transport.writable&&!this.upgrading&&this.writeBuffer.length){const e=this.getWritablePackets();this.transport.send(e),this.prevBufferLen=e.length,this.emitReserved("flush")}}getWritablePackets(){if(!(this.maxPayload&&"polling"===this.transport.name&&this.writeBuffer.length>1))return this.writeBuffer;let e=1;for(let t=0;t<this.writeBuffer.length;t++){const n=this.writeBuffer[t].data;if(n&&(e+=uK(n)),t>0&&e>this.maxPayload)return this.writeBuffer.slice(0,t);e+=2}return this.writeBuffer}write(e,t,n){return this.sendPacket("message",e,t,n),this}send(e,t,n){return this.sendPacket("message",e,t,n),this}sendPacket(e,t,n,r){if("function"==typeof t&&(r=t,t=void 0),"function"==typeof n&&(r=n,n=null),"closing"===this.readyState||"closed"===this.readyState)return;(n=n||{}).compress=!1!==n.compress;const o={type:e,data:t,options:n};this.emitReserved("packetCreate",o),this.writeBuffer.push(o),r&&this.once("flush",r),this.flush()}close(){const e=()=>{this.onClose("forced close"),this.transport.close()},t=()=>{this.off("upgrade",t),this.off("upgradeError",t),e()},n=()=>{this.once("upgrade",t),this.once("upgradeError",t)};return"opening"!==this.readyState&&"open"!==this.readyState||(this.readyState="closing",this.writeBuffer.length?this.once("drain",(()=>{this.upgrading?n():e()})):this.upgrading?n():e()),this}onError(e){NK.priorWebsocketSuccess=!1,this.emitReserved("error",e),this.onClose("transport error",e)}onClose(e,t){"opening"!==this.readyState&&"open"!==this.readyState&&"closing"!==this.readyState||(this.clearTimeoutFn(this.pingTimeoutTimer),this.transport.removeAllListeners("close"),this.transport.close(),this.transport.removeAllListeners(),"function"==typeof removeEventListener&&(removeEventListener("beforeunload",this.beforeunloadEventListener,!1),removeEventListener("offline",this.offlineEventListener,!1)),this.readyState="closed",this.id=null,this.emitReserved("close",e,t),this.writeBuffer=[],this.prevBufferLen=0)}filterUpgrades(e){const t=[];let n=0;const r=e.length;for(;n<r;n++)~this.transports.indexOf(e[n])&&t.push(e[n]);return t}}NK.protocol=4;const DK="function"==typeof ArrayBuffer,zK=Object.prototype.toString,BK="function"==typeof Blob||"undefined"!=typeof Blob&&"[object BlobConstructor]"===zK.call(Blob),jK="function"==typeof File||"undefined"!=typeof File&&"[object FileConstructor]"===zK.call(File);function FK(e){return DK&&(e instanceof ArrayBuffer||(e=>"function"==typeof ArrayBuffer.isView?ArrayBuffer.isView(e):e.buffer instanceof ArrayBuffer)(e))||BK&&e instanceof Blob||jK&&e instanceof File}function HK(e,t){if(!e||"object"!=typeof e)return!1;if(Array.isArray(e)){for(let t=0,n=e.length;t<n;t++)if(HK(e[t]))return!0;return!1}if(FK(e))return!0;if(e.toJSON&&"function"==typeof e.toJSON&&1===arguments.length)return HK(e.toJSON(),!0);for(const n in e)if(Object.prototype.hasOwnProperty.call(e,n)&&HK(e[n]))return!0;return!1}function WK(e){const t=[],n=e.data,r=e;return r.data=VK(n,t),r.attachments=t.length,{packet:r,buffers:t}}function VK(e,t){if(!e)return e;if(FK(e)){const n={_placeholder:!0,num:t.length};return t.push(e),n}if(Array.isArray(e)){const n=new Array(e.length);for(let r=0;r<e.length;r++)n[r]=VK(e[r],t);return n}if("object"==typeof e&&!(e instanceof Date)){const n={};for(const r in e)Object.prototype.hasOwnProperty.call(e,r)&&(n[r]=VK(e[r],t));return n}return e}function $K(e,t){return e.data=UK(e.data,t),e.attachments=void 0,e}function UK(e,t){if(!e)return e;if(e&&!0===e._placeholder){if("number"==typeof e.num&&e.num>=0&&e.num<t.length)return t[e.num];throw new Error("illegal attachments")}if(Array.isArray(e))for(let n=0;n<e.length;n++)e[n]=UK(e[n],t);else if("object"==typeof e)for(const n in e)Object.prototype.hasOwnProperty.call(e,n)&&(e[n]=UK(e[n],t));return e}var GK;!function(e){e[e.CONNECT=0]="CONNECT",e[e.DISCONNECT=1]="DISCONNECT",e[e.EVENT=2]="EVENT",e[e.ACK=3]="ACK",e[e.CONNECT_ERROR=4]="CONNECT_ERROR",e[e.BINARY_EVENT=5]="BINARY_EVENT",e[e.BINARY_ACK=6]="BINARY_ACK"}(GK||(GK={}));class qK extends oK{constructor(e){super(),this.reviver=e}add(e){let t;if("string"==typeof e){if(this.reconstructor)throw new Error("got plaintext data when reconstructing a packet");t=this.decodeString(e),t.type===GK.BINARY_EVENT||t.type===GK.BINARY_ACK?(this.reconstructor=new YK(t),0===t.attachments&&super.emitReserved("decoded",t)):super.emitReserved("decoded",t)}else{if(!FK(e)&&!e.base64)throw new Error("Unknown type: "+e);if(!this.reconstructor)throw new Error("got binary data when not reconstructing a packet");t=this.reconstructor.takeBinaryData(e),t&&(this.reconstructor=null,super.emitReserved("decoded",t))}}decodeString(e){let t=0;const n={type:Number(e.charAt(0))};if(void 0===GK[n.type])throw new Error("unknown packet type "+n.type);if(n.type===GK.BINARY_EVENT||n.type===GK.BINARY_ACK){const r=t+1;for(;"-"!==e.charAt(++t)&&t!=e.length;);const o=e.substring(r,t);if(o!=Number(o)||"-"!==e.charAt(t))throw new Error("Illegal attachments");n.attachments=Number(o)}if("/"===e.charAt(t+1)){const r=t+1;for(;++t;){if(","===e.charAt(t))break;if(t===e.length)break}n.nsp=e.substring(r,t)}else n.nsp="/";const r=e.charAt(t+1);if(""!==r&&Number(r)==r){const r=t+1;for(;++t;){const n=e.charAt(t);if(null==n||Number(n)!=n){--t;break}if(t===e.length)break}n.id=Number(e.substring(r,t+1))}if(e.charAt(++t)){const r=this.tryParse(e.substr(t));if(!qK.isPayloadValid(n.type,r))throw new Error("invalid payload");n.data=r}return n}tryParse(e){try{return JSON.parse(e,this.reviver)}catch(B2){return!1}}static isPayloadValid(e,t){switch(e){case GK.CONNECT:return"object"==typeof t;case GK.DISCONNECT:return void 0===t;case GK.CONNECT_ERROR:return"string"==typeof t||"object"==typeof t;case GK.EVENT:case GK.BINARY_EVENT:return Array.isArray(t)&&t.length>0;case GK.ACK:case GK.BINARY_ACK:return Array.isArray(t)}}destroy(){this.reconstructor&&this.reconstructor.finishedReconstruction()}}class YK{constructor(e){this.packet=e,this.buffers=[],this.reconPack=e}takeBinaryData(e){if(this.buffers.push(e),this.buffers.length===this.reconPack.attachments){const e=$K(this.reconPack,this.buffers);return this.finishedReconstruction(),e}return null}finishedReconstruction(){this.reconPack=null,this.buffers=[]}}const ZK=Object.freeze(Object.defineProperty({__proto__:null,protocol:5,get PacketType(){return GK},Encoder:class{constructor(e){this.replacer=e}encode(e){return e.type!==GK.EVENT&&e.type!==GK.ACK||!HK(e)?[this.encodeAsString(e)]:(e.type=e.type===GK.EVENT?GK.BINARY_EVENT:GK.BINARY_ACK,this.encodeAsBinary(e))}encodeAsString(e){let t=""+e.type;return e.type!==GK.BINARY_EVENT&&e.type!==GK.BINARY_ACK||(t+=e.attachments+"-"),e.nsp&&"/"!==e.nsp&&(t+=e.nsp+","),null!=e.id&&(t+=e.id),null!=e.data&&(t+=JSON.stringify(e.data,this.replacer)),t}encodeAsBinary(e){const t=WK(e),n=this.encodeAsString(t.packet),r=t.buffers;return r.unshift(n),r}},Decoder:qK},Symbol.toStringTag,{value:"Module"}));function XK(e,t,n){return e.on(t,n),function(){e.off(t,n)}}const KK=Object.freeze({connect:1,connect_error:1,disconnect:1,disconnecting:1,newListener:1,removeListener:1});class QK extends oK{constructor(e,t,n){super(),this.connected=!1,this.receiveBuffer=[],this.sendBuffer=[],this.ids=0,this.acks={},this.flags={},this.io=e,this.nsp=t,n&&n.auth&&(this.auth=n.auth),this.io._autoConnect&&this.open()}get disconnected(){return!this.connected}subEvents(){if(this.subs)return;const e=this.io;this.subs=[XK(e,"open",this.onopen.bind(this)),XK(e,"packet",this.onpacket.bind(this)),XK(e,"error",this.onerror.bind(this)),XK(e,"close",this.onclose.bind(this))]}get active(){return!!this.subs}connect(){return this.connected||(this.subEvents(),this.io._reconnecting||this.io.open(),"open"===this.io._readyState&&this.onopen()),this}open(){return this.connect()}send(...e){return e.unshift("message"),this.emit.apply(this,e),this}emit(e,...t){if(KK.hasOwnProperty(e))throw new Error('"'+e.toString()+'" is a reserved event name');t.unshift(e);const n={type:GK.EVENT,data:t,options:{}};if(n.options.compress=!1!==this.flags.compress,"function"==typeof t[t.length-1]){const e=this.ids++,r=t.pop();this._registerAckCallback(e,r),n.id=e}const r=this.io.engine&&this.io.engine.transport&&this.io.engine.transport.writable;return this.flags.volatile&&(!r||!this.connected)||(this.connected?(this.notifyOutgoingListeners(n),this.packet(n)):this.sendBuffer.push(n)),this.flags={},this}_registerAckCallback(e,t){const n=this.flags.timeout;if(void 0===n)return void(this.acks[e]=t);const r=this.io.setTimeoutFn((()=>{delete this.acks[e];for(let t=0;t<this.sendBuffer.length;t++)this.sendBuffer[t].id===e&&this.sendBuffer.splice(t,1);t.call(this,new Error("operation has timed out"))}),n);this.acks[e]=(...e)=>{this.io.clearTimeoutFn(r),t.apply(this,[null,...e])}}packet(e){e.nsp=this.nsp,this.io._packet(e)}onopen(){"function"==typeof this.auth?this.auth((e=>{this.packet({type:GK.CONNECT,data:e})})):this.packet({type:GK.CONNECT,data:this.auth})}onerror(e){this.connected||this.emitReserved("connect_error",e)}onclose(e,t){this.connected=!1,delete this.id,this.emitReserved("disconnect",e,t)}onpacket(e){if(e.nsp===this.nsp)switch(e.type){case GK.CONNECT:if(e.data&&e.data.sid){const t=e.data.sid;this.onconnect(t)}else this.emitReserved("connect_error",new Error("It seems you are trying to reach a Socket.IO server in v2.x with a v3.x client, but they are not compatible (more information here: https://socket.io/docs/v3/migrating-from-2-x-to-3-0/)"));break;case GK.EVENT:case GK.BINARY_EVENT:this.onevent(e);break;case GK.ACK:case GK.BINARY_ACK:this.onack(e);break;case GK.DISCONNECT:this.ondisconnect();break;case GK.CONNECT_ERROR:this.destroy();const t=new Error(e.data.message);t.data=e.data.data,this.emitReserved("connect_error",t)}}onevent(e){const t=e.data||[];null!=e.id&&t.push(this.ack(e.id)),this.connected?this.emitEvent(t):this.receiveBuffer.push(Object.freeze(t))}emitEvent(e){if(this._anyListeners&&this._anyListeners.length){const t=this._anyListeners.slice();for(const n of t)n.apply(this,e)}super.emit.apply(this,e)}ack(e){const t=this;let n=!1;return function(...r){n||(n=!0,t.packet({type:GK.ACK,id:e,data:r}))}}onack(e){const t=this.acks[e.id];"function"==typeof t&&(t.apply(this,e.data),delete this.acks[e.id])}onconnect(e){this.id=e,this.connected=!0,this.emitBuffered(),this.emitReserved("connect")}emitBuffered(){this.receiveBuffer.forEach((e=>this.emitEvent(e))),this.receiveBuffer=[],this.sendBuffer.forEach((e=>{this.notifyOutgoingListeners(e),this.packet(e)})),this.sendBuffer=[]}ondisconnect(){this.destroy(),this.onclose("io server disconnect")}destroy(){this.subs&&(this.subs.forEach((e=>e())),this.subs=void 0),this.io._destroy(this)}disconnect(){return this.connected&&this.packet({type:GK.DISCONNECT}),this.destroy(),this.connected&&this.onclose("io client disconnect"),this}close(){return this.disconnect()}compress(e){return this.flags.compress=e,this}get volatile(){return this.flags.volatile=!0,this}timeout(e){return this.flags.timeout=e,this}onAny(e){return this._anyListeners=this._anyListeners||[],this._anyListeners.push(e),this}prependAny(e){return this._anyListeners=this._anyListeners||[],this._anyListeners.unshift(e),this}offAny(e){if(!this._anyListeners)return this;if(e){const t=this._anyListeners;for(let n=0;n<t.length;n++)if(e===t[n])return t.splice(n,1),this}else this._anyListeners=[];return this}listenersAny(){return this._anyListeners||[]}onAnyOutgoing(e){return this._anyOutgoingListeners=this._anyOutgoingListeners||[],this._anyOutgoingListeners.push(e),this}prependAnyOutgoing(e){return this._anyOutgoingListeners=this._anyOutgoingListeners||[],this._anyOutgoingListeners.unshift(e),this}offAnyOutgoing(e){if(!this._anyOutgoingListeners)return this;if(e){const t=this._anyOutgoingListeners;for(let n=0;n<t.length;n++)if(e===t[n])return t.splice(n,1),this}else this._anyOutgoingListeners=[];return this}listenersAnyOutgoing(){return this._anyOutgoingListeners||[]}notifyOutgoingListeners(e){if(this._anyOutgoingListeners&&this._anyOutgoingListeners.length){const t=this._anyOutgoingListeners.slice();for(const n of t)n.apply(this,e.data)}}}function JK(e){e=e||{},this.ms=e.min||100,this.max=e.max||1e4,this.factor=e.factor||2,this.jitter=e.jitter>0&&e.jitter<=1?e.jitter:0,this.attempts=0}JK.prototype.duration=function(){var e=this.ms*Math.pow(this.factor,this.attempts++);if(this.jitter){var t=Math.random(),n=Math.floor(t*this.jitter*e);e=0==(1&Math.floor(10*t))?e-n:e+n}return 0|Math.min(e,this.max)},JK.prototype.reset=function(){this.attempts=0},JK.prototype.setMin=function(e){this.ms=e},JK.prototype.setMax=function(e){this.max=e},JK.prototype.setJitter=function(e){this.jitter=e};class eQ extends oK{constructor(e,t){var n;super(),this.nsps={},this.subs=[],e&&"object"==typeof e&&(t=e,e=void 0),(t=t||{}).path=t.path||"/socket.io",this.opts=t,cK(this,t),this.reconnection(!1!==t.reconnection),this.reconnectionAttempts(t.reconnectionAttempts||1/0),this.reconnectionDelay(t.reconnectionDelay||1e3),this.reconnectionDelayMax(t.reconnectionDelayMax||5e3),this.randomizationFactor(null!==(n=t.randomizationFactor)&&void 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e=this.engine;this.subs.push(XK(e,"ping",this.onping.bind(this)),XK(e,"data",this.ondata.bind(this)),XK(e,"error",this.onerror.bind(this)),XK(e,"close",this.onclose.bind(this)),XK(this.decoder,"decoded",this.ondecoded.bind(this)))}onping(){this.emitReserved("ping")}ondata(e){try{this.decoder.add(e)}catch(B2){this.onclose("parse error",B2)}}ondecoded(e){LK((()=>{this.emitReserved("packet",e)}),this.setTimeoutFn)}onerror(e){this.emitReserved("error",e)}socket(e,t){let n=this.nsps[e];return n||(n=new QK(this,e,t),this.nsps[e]=n),n}_destroy(e){const t=Object.keys(this.nsps);for(const n of t){if(this.nsps[n].active)return}this._close()}_packet(e){const t=this.encoder.encode(e);for(let n=0;n<t.length;n++)this.engine.write(t[n],e.options)}cleanup(){this.subs.forEach((e=>e())),this.subs.length=0,this.decoder.destroy()}_close(){this.skipReconnect=!0,this._reconnecting=!1,this.onclose("forced close"),this.engine&&this.engine.close()}disconnect(){return this._close()}onclose(e,t){this.cleanup(),this.backoff.reset(),this._readyState="closed",this.emitReserved("close",e,t),this._reconnection&&!this.skipReconnect&&this.reconnect()}reconnect(){if(this._reconnecting||this.skipReconnect)return this;const e=this;if(this.backoff.attempts>=this._reconnectionAttempts)this.backoff.reset(),this.emitReserved("reconnect_failed"),this._reconnecting=!1;else{const t=this.backoff.duration();this._reconnecting=!0;const n=this.setTimeoutFn((()=>{e.skipReconnect||(this.emitReserved("reconnect_attempt",e.backoff.attempts),e.skipReconnect||e.open((t=>{t?(e._reconnecting=!1,e.reconnect(),this.emitReserved("reconnect_error",t)):e.onreconnect()})))}),t);this.opts.autoUnref&&n.unref(),this.subs.push((function(){clearTimeout(n)}))}}onreconnect(){const e=this.backoff.attempts;this._reconnecting=!1,this.backoff.reset(),this.emitReserved("reconnect",e)}}const tQ={};function nQ(e,t){"object"==typeof e&&(t=e,e=void 0);const n=function(e,t="",n){let r=e;n=n||"undefined"!=typeof location&&location,null==e&&(e=n.protocol+"//"+n.host),"string"==typeof e&&("/"===e.charAt(0)&&(e="/"===e.charAt(1)?n.protocol+e:n.host+e),/^(https?|wss?):\/\//.test(e)||(e=void 0!==n?n.protocol+"//"+e:"https://"+e),r=RK(e)),r.port||(/^(http|ws)$/.test(r.protocol)?r.port="80":/^(http|ws)s$/.test(r.protocol)&&(r.port="443")),r.path=r.path||"/";const o=-1!==r.host.indexOf(":")?"["+r.host+"]":r.host;return r.id=r.protocol+"://"+o+":"+r.port+t,r.href=r.protocol+"://"+o+(n&&n.port===r.port?"":":"+r.port),r}(e,(t=t||{}).path||"/socket.io"),r=n.source,o=n.id,i=n.path,a=tQ[o]&&i in tQ[o].nsps;let s;return t.forceNew||t["force new connection"]||!1===t.multiplex||a?s=new eQ(r,t):(tQ[o]||(tQ[o]=new eQ(r,t)),s=tQ[o]),n.query&&!t.query&&(t.query=n.queryKey),s.socket(n.path,t)}let rQ;Object.assign(nQ,{Manager:eQ,Socket:QK,io:nQ,connect:nQ});const oQ=new Uint8Array(16);function iQ(){if(!rQ&&(rQ="undefined"!=typeof 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",AJ="stage",IJ="transform",RJ="visible",NJ=["xChange.konva","yChange.konva","scaleXChange.konva","scaleYChange.konva","skewXChange.konva","skewYChange.konva","rotationChange.konva","offsetXChange.konva","offsetYChange.konva","transformsEnabledChange.konva"].join(TJ);let DJ=1;class zJ{constructor(e){this._id=DJ++,this.eventListeners={},this.attrs={},this.index=0,this._allEventListeners=null,this.parent=null,this._cache=new Map,this._attachedDepsListeners=new Map,this._lastPos=null,this._batchingTransformChange=!1,this._needClearTransformCache=!1,this._filterUpToDate=!1,this._isUnderCache=!1,this._dragEventId=null,this._shouldFireChangeEvents=!1,this.setAttrs(e),this._shouldFireChangeEvents=!0}hasChildren(){return!1}_clearCache(e){e!==IJ&&e!==CJ||!this._cache.get(e)?e?this._cache.delete(e):this._cache.clear():this._cache.get(e).dirty=!0}_getCache(e,t){var n=this._cache.get(e);return(void 0===n||(e===IJ||e===CJ)&&!0===n.dirty)&&(n=t.call(this),this._cache.set(e,n)),n}_calculate(e,t,n){if(!this._attachedDepsListeners.get(e)){const n=t.map((e=>e+"Change.konva")).join(TJ);this.on(n,(()=>{this._clearCache(e)})),this._attachedDepsListeners.set(e,!0)}return this._getCache(e,n)}_getCanvasCache(){return this._cache.get(EJ)}_clearSelfAndDescendantCache(e){this._clearCache(e),e===CJ&&this.fire("absoluteTransformChange")}clearCache(){if(this._cache.has(EJ)){const{scene:e,filter:t,hit:n}=this._cache.get(EJ);eJ.releaseCanvas(e,t,n),this._cache.delete(EJ)}return this._clearSelfAndDescendantCache(),this._requestDraw(),this}cache(e){var t=e||{},n={};void 0!==t.x&&void 0!==t.y&&void 0!==t.width&&void 0!==t.height||(n=this.getClientRect({skipTransform:!0,relativeTo:this.getParent()}));var r=Math.ceil(t.width||n.width),o=Math.ceil(t.height||n.height),i=t.pixelRatio,a=void 0===t.x?Math.floor(n.x):t.x,s=void 0===t.y?Math.floor(n.y):t.y,l=t.offset||0,c=t.drawBorder||!1,u=t.hitCanvasPixelRatio||1;if(r&&o){a-=l,s-=l;var d=new bJ({pixelRatio:i,width:r+=2*l+1,height:o+=2*l+1}),h=new bJ({pixelRatio:i,width:0,height:0}),f=new xJ({pixelRatio:u,width:r,height:o}),p=d.getContext(),g=f.getContext();return f.isCache=!0,d.isCache=!0,this._cache.delete(EJ),this._filterUpToDate=!1,!1===t.imageSmoothingEnabled&&(d.getContext()._context.imageSmoothingEnabled=!1,h.getContext()._context.imageSmoothingEnabled=!1),p.save(),g.save(),p.translate(-a,-s),g.translate(-a,-s),this._isUnderCache=!0,this._clearSelfAndDescendantCache(kJ),this._clearSelfAndDescendantCache(_J),this.drawScene(d,this),this.drawHit(f,this),this._isUnderCache=!1,p.restore(),g.restore(),c&&(p.save(),p.beginPath(),p.rect(0,0,r,o),p.closePath(),p.setAttr("strokeStyle","red"),p.setAttr("lineWidth",5),p.stroke(),p.restore()),this._cache.set(EJ,{scene:d,filter:h,hit:f,x:a,y:s}),this._requestDraw(),this}eJ.error("Can not cache the node. Width or height of the node equals 0. Caching is skipped.")}isCached(){return this._cache.has(EJ)}getClientRect(e){throw new Error('abstract "getClientRect" method call')}_transformedRect(e,t){var n,r,o,i,a=[{x:e.x,y:e.y},{x:e.x+e.width,y:e.y},{x:e.x+e.width,y:e.y+e.height},{x:e.x,y:e.y+e.height}],s=this.getAbsoluteTransform(t);return a.forEach((function(e){var t=s.point(e);void 0===n&&(n=o=t.x,r=i=t.y),n=Math.min(n,t.x),r=Math.min(r,t.y),o=Math.max(o,t.x),i=Math.max(i,t.y)})),{x:n,y:r,width:o-n,height:i-r}}_drawCachedSceneCanvas(e){e.save(),e._applyOpacity(this),e._applyGlobalCompositeOperation(this);const t=this._getCanvasCache();e.translate(t.x,t.y);var n=this._getCachedSceneCanvas(),r=n.pixelRatio;e.drawImage(n._canvas,0,0,n.width/r,n.height/r),e.restore()}_drawCachedHitCanvas(e){var t=this._getCanvasCache(),n=t.hit;e.save(),e.translate(t.x,t.y),e.drawImage(n._canvas,0,0,n.width/n.pixelRatio,n.height/n.pixelRatio),e.restore()}_getCachedSceneCanvas(){var e,t,n,r,o=this.filters(),i=this._getCanvasCache(),a=i.scene,s=i.filter,l=s.getContext();if(o){if(!this._filterUpToDate){var c=a.pixelRatio;s.setSize(a.width/a.pixelRatio,a.height/a.pixelRatio);try{for(e=o.length,l.clear(),l.drawImage(a._canvas,0,0,a.getWidth()/c,a.getHeight()/c),t=l.getImageData(0,0,s.getWidth(),s.getHeight()),n=0;n<e;n++)"function"==typeof(r=o[n])?(r.call(this,t),l.putImageData(t,0,0)):eJ.error("Filter should be type of function, but got "+typeof r+" instead. Please check correct filters")}catch(B2){eJ.error("Unable to apply filter. "+B2.message+" This post my help you https://konvajs.org/docs/posts/Tainted_Canvas.html.")}this._filterUpToDate=!0}return s}return a}on(e,t){if(this._cache&&this._cache.delete(SJ),3===arguments.length)return this._delegate.apply(this,arguments);var n,r,o,i,a=e.split(TJ),s=a.length;for(n=0;n<s;n++)o=(r=a[n].split("."))[0],i=r[1]||"",this.eventListeners[o]||(this.eventListeners[o]=[]),this.eventListeners[o].push({name:i,handler:t});return this}off(e,t){var n,r,o,i,a,s=(e||"").split(TJ),l=s.length;if(this._cache&&this._cache.delete(SJ),!e)for(r in this.eventListeners)this._off(r);for(n=0;n<l;n++)if(i=(o=s[n].split("."))[0],a=o[1],i)this.eventListeners[i]&&this._off(i,a,t);else for(r in this.eventListeners)this._off(r,a,t);return this}dispatchEvent(e){var t={target:this,type:e.type,evt:e};return this.fire(e.type,t),this}addEventListener(e,t){return this.on(e,(function(e){t.call(this,e.evt)})),this}removeEventListener(e){return this.off(e),this}_delegate(e,t,n){var r=this;this.on(e,(function(e){for(var o=e.target.findAncestors(t,!0,r),i=0;i<o.length;i++)(e=eJ.cloneObject(e)).currentTarget=o[i],n.call(o[i],e)}))}remove(){return this.isDragging()&&this.stopDrag(),wJ._dragElements.delete(this._id),this._remove(),this}_clearCaches(){this._clearSelfAndDescendantCache(CJ),this._clearSelfAndDescendantCache(kJ),this._clearSelfAndDescendantCache(_J),this._clearSelfAndDescendantCache(AJ),this._clearSelfAndDescendantCache(RJ),this._clearSelfAndDescendantCache(PJ)}_remove(){this._clearCaches();var e=this.getParent();e&&e.children&&(e.children.splice(this.index,1),e._setChildrenIndices(),this.parent=null)}destroy(){return this.remove(),this.clearCache(),this}getAttr(e){var t="get"+eJ._capitalize(e);return eJ._isFunction(this[t])?this[t]():this.attrs[e]}getAncestors(){for(var e=this.getParent(),t=[];e;)t.push(e),e=e.getParent();return t}getAttrs(){return this.attrs||{}}setAttrs(e){return this._batchTransformChanges((()=>{var t,n;if(!e)return this;for(t in e)"children"!==t&&(n="set"+eJ._capitalize(t),eJ._isFunction(this[n])?this[n](e[t]):this._setAttr(t,e[t]))})),this}isListening(){return this._getCache(PJ,this._isListening)}_isListening(e){if(!this.listening())return!1;const t=this.getParent();return!t||t===e||this===e||t._isListening(e)}isVisible(){return this._getCache(RJ,this._isVisible)}_isVisible(e){if(!this.visible())return!1;const t=this.getParent();return!t||t===e||this===e||t._isVisible(e)}shouldDrawHit(e,t=!1){if(e)return this._isVisible(e)&&this._isListening(e);var n=this.getLayer(),r=!1;wJ._dragElements.forEach((e=>{"dragging"===e.dragStatus&&("Stage"===e.node.nodeType||e.node.getLayer()===n)&&(r=!0)}));var o=!t&&!$Q.hitOnDragEnabled&&r;return this.isListening()&&this.isVisible()&&!o}show(){return this.visible(!0),this}hide(){return this.visible(!1),this}getZIndex(){return this.index||0}getAbsoluteZIndex(){var e,t,n,r,o=this.getDepth(),i=this,a=0;return"Stage"!==i.nodeType&&function s(l){for(e=[],t=l.length,n=0;n<t;n++)r=l[n],a++,r.nodeType!==MJ&&(e=e.concat(r.getChildren().slice())),r._id===i._id&&(n=t);e.length>0&&e[0].getDepth()<=o&&s(e)}(i.getStage().getChildren()),a}getDepth(){for(var e=0,t=this.parent;t;)e++,t=t.parent;return e}_batchTransformChanges(e){this._batchingTransformChange=!0,e(),this._batchingTransformChange=!1,this._needClearTransformCache&&(this._clearCache(IJ),this._clearSelfAndDescendantCache(CJ)),this._needClearTransformCache=!1}setPosition(e){return this._batchTransformChanges((()=>{this.x(e.x),this.y(e.y)})),this}getPosition(){return{x:this.x(),y:this.y()}}getRelativePointerPosition(){if(!this.getStage())return null;var e=this.getStage().getPointerPosition();if(!e)return null;var t=this.getAbsoluteTransform().copy();return t.invert(),t.point(e)}getAbsolutePosition(e){let t=!1,n=this.parent;for(;n;){if(n.isCached()){t=!0;break}n=n.parent}t&&!e&&(e=!0);var r=this.getAbsoluteTransform(e).getMatrix(),o=new GQ,i=this.offset();return o.m=r.slice(),o.translate(i.x,i.y),o.getTranslation()}setAbsolutePosition(e){var t=this._clearTransform();this.attrs.x=t.x,this.attrs.y=t.y,delete t.x,delete t.y,this._clearCache(IJ);var n=this._getAbsoluteTransform().copy();return n.invert(),n.translate(e.x,e.y),e={x:this.attrs.x+n.getTranslation().x,y:this.attrs.y+n.getTranslation().y},this._setTransform(t),this.setPosition({x:e.x,y:e.y}),this._clearCache(IJ),this._clearSelfAndDescendantCache(CJ),this}_setTransform(e){var t;for(t in e)this.attrs[t]=e[t]}_clearTransform(){var e={x:this.x(),y:this.y(),rotation:this.rotation(),scaleX:this.scaleX(),scaleY:this.scaleY(),offsetX:this.offsetX(),offsetY:this.offsetY(),skewX:this.skewX(),skewY:this.skewY()};return this.attrs.x=0,this.attrs.y=0,this.attrs.rotation=0,this.attrs.scaleX=1,this.attrs.scaleY=1,this.attrs.offsetX=0,this.attrs.offsetY=0,this.attrs.skewX=0,this.attrs.skewY=0,e}move(e){var t=e.x,n=e.y,r=this.x(),o=this.y();return void 0!==t&&(r+=t),void 0!==n&&(o+=n),this.setPosition({x:r,y:o}),this}_eachAncestorReverse(e,t){var n,r,o=[],i=this.getParent();if(!t||t._id!==this._id){for(o.unshift(this);i&&(!t||i._id!==t._id);)o.unshift(i),i=i.parent;for(n=o.length,r=0;r<n;r++)e(o[r])}}rotate(e){return this.rotation(this.rotation()+e),this}moveToTop(){if(!this.parent)return eJ.warn("Node has no parent. moveToTop function is ignored."),!1;var e=this.index;return e<this.parent.getChildren().length-1&&(this.parent.children.splice(e,1),this.parent.children.push(this),this.parent._setChildrenIndices(),!0)}moveUp(){if(!this.parent)return eJ.warn("Node has no parent. moveUp function is ignored."),!1;var e=this.index;return e<this.parent.getChildren().length-1&&(this.parent.children.splice(e,1),this.parent.children.splice(e+1,0,this),this.parent._setChildrenIndices(),!0)}moveDown(){if(!this.parent)return eJ.warn("Node has no parent. moveDown function is ignored."),!1;var e=this.index;return e>0&&(this.parent.children.splice(e,1),this.parent.children.splice(e-1,0,this),this.parent._setChildrenIndices(),!0)}moveToBottom(){if(!this.parent)return eJ.warn("Node has no parent. moveToBottom function is ignored."),!1;var e=this.index;return e>0&&(this.parent.children.splice(e,1),this.parent.children.unshift(this),this.parent._setChildrenIndices(),!0)}setZIndex(e){if(!this.parent)return eJ.warn("Node has no parent. zIndex parameter is ignored."),this;(e<0||e>=this.parent.children.length)&&eJ.warn("Unexpected value "+e+" for zIndex property. zIndex is just index of a node in children of its parent. Expected value is from 0 to "+(this.parent.children.length-1)+".");var t=this.index;return this.parent.children.splice(t,1),this.parent.children.splice(e,0,this),this.parent._setChildrenIndices(),this}getAbsoluteOpacity(){return this._getCache(kJ,this._getAbsoluteOpacity)}_getAbsoluteOpacity(){var e=this.opacity(),t=this.getParent();return t&&!t._isUnderCache&&(e*=t.getAbsoluteOpacity()),e}moveTo(e){return this.getParent()!==e&&(this._remove(),e.add(this)),this}toObject(){var e,t,n,r,o={},i=this.getAttrs();for(e in o.attrs={},i)t=i[e],eJ.isObject(t)&&!eJ._isPlainObject(t)&&!eJ._isArray(t)||(n="function"==typeof this[e]&&this[e],delete i[e],r=n?n.call(this):null,i[e]=t,r!==t&&(o.attrs[e]=t));return o.className=this.getClassName(),eJ._prepareToStringify(o)}toJSON(){return JSON.stringify(this.toObject())}getParent(){return this.parent}findAncestors(e,t,n){var r=[];t&&this._isMatch(e)&&r.push(this);for(var o=this.parent;o;){if(o===n)return r;o._isMatch(e)&&r.push(o),o=o.parent}return r}isAncestorOf(e){return!1}findAncestor(e,t,n){return this.findAncestors(e,t,n)[0]}_isMatch(e){if(!e)return!1;if("function"==typeof e)return e(this);var t,n,r=e.replace(/ /g,"").split(","),o=r.length;for(t=0;t<o;t++)if(n=r[t],eJ.isValidSelector(n)||(eJ.warn('Selector "'+n+'" is invalid. Allowed selectors examples are "#foo", ".bar" or "Group".'),eJ.warn('If you have a custom shape with such className, please change it to start with upper letter like "Triangle".'),eJ.warn("Konva is awesome, right?")),"#"===n.charAt(0)){if(this.id()===n.slice(1))return!0}else if("."===n.charAt(0)){if(this.hasName(n.slice(1)))return!0}else if(this.className===n||this.nodeType===n)return!0;return!1}getLayer(){var e=this.getParent();return e?e.getLayer():null}getStage(){return this._getCache(AJ,this._getStage)}_getStage(){var e=this.getParent();return e?e.getStage():void 0}fire(e,t={},n){return t.target=t.target||this,n?this._fireAndBubble(e,t):this._fire(e,t),this}getAbsoluteTransform(e){return e?this._getAbsoluteTransform(e):this._getCache(CJ,this._getAbsoluteTransform)}_getAbsoluteTransform(e){var t;if(e)return t=new GQ,this._eachAncestorReverse((function(e){var n=e.transformsEnabled();"all"===n?t.multiply(e.getTransform()):"position"===n&&t.translate(e.x()-e.offsetX(),e.y()-e.offsetY())}),e),t;t=this._cache.get(CJ)||new GQ,this.parent?this.parent.getAbsoluteTransform().copyInto(t):t.reset();var n=this.transformsEnabled();if("all"===n)t.multiply(this.getTransform());else if("position"===n){const e=this.attrs.x||0,n=this.attrs.y||0,r=this.attrs.offsetX||0,o=this.attrs.offsetY||0;t.translate(e-r,n-o)}return t.dirty=!1,t}getAbsoluteScale(e){for(var t=this;t;)t._isUnderCache&&(e=t),t=t.getParent();const n=this.getAbsoluteTransform(e).decompose();return{x:n.scaleX,y:n.scaleY}}getAbsoluteRotation(){return this.getAbsoluteTransform().decompose().rotation}getTransform(){return this._getCache(IJ,this._getTransform)}_getTransform(){var e,t,n=this._cache.get(IJ)||new GQ;n.reset();var r=this.x(),o=this.y(),i=$Q.getAngle(this.rotation()),a=null!==(e=this.attrs.scaleX)&&void 0!==e?e:1,s=null!==(t=this.attrs.scaleY)&&void 0!==t?t:1,l=this.attrs.skewX||0,c=this.attrs.skewY||0,u=this.attrs.offsetX||0,d=this.attrs.offsetY||0;return 0===r&&0===o||n.translate(r,o),0!==i&&n.rotate(i),0===l&&0===c||n.skew(l,c),1===a&&1===s||n.scale(a,s),0===u&&0===d||n.translate(-1*u,-1*d),n.dirty=!1,n}clone(e){var t,n,r,o,i,a=eJ.cloneObject(this.attrs);for(t in e)a[t]=e[t];var s=new this.constructor(a);for(t in this.eventListeners)for(r=(n=this.eventListeners[t]).length,o=0;o<r;o++)(i=n[o]).name.indexOf("konva")<0&&(s.eventListeners[t]||(s.eventListeners[t]=[]),s.eventListeners[t].push(i));return s}_toKonvaCanvas(e){e=e||{};var t=this.getClientRect(),n=this.getStage(),r=void 0!==e.x?e.x:Math.floor(t.x),o=void 0!==e.y?e.y:Math.floor(t.y),i=e.pixelRatio||1,a=new bJ({width:e.width||Math.ceil(t.width)||(n?n.width():0),height:e.height||Math.ceil(t.height)||(n?n.height():0),pixelRatio:i}),s=a.getContext();return!1===e.imageSmoothingEnabled&&(s._context.imageSmoothingEnabled=!1),s.save(),(r||o)&&s.translate(-1*r,-1*o),this.drawScene(a),s.restore(),a}toCanvas(e){return this._toKonvaCanvas(e)._canvas}toDataURL(e){var t=(e=e||{}).mimeType||null,n=e.quality||null,r=this._toKonvaCanvas(e).toDataURL(t,n);return e.callback&&e.callback(r),r}toImage(e){return new Promise(((t,n)=>{try{const n=null==e?void 0:e.callback;n&&delete e.callback,eJ._urlToImage(this.toDataURL(e),(function(e){t(e),null==n||n(e)}))}catch(Vhe){n(Vhe)}}))}toBlob(e){return new Promise(((t,n)=>{try{const n=null==e?void 0:e.callback;n&&delete e.callback,this.toCanvas(e).toBlob((e=>{t(e),null==n||n(e)}))}catch(Vhe){n(Vhe)}}))}setSize(e){return this.width(e.width),this.height(e.height),this}getSize(){return{width:this.width(),height:this.height()}}getClassName(){return this.className||this.nodeType}getType(){return this.nodeType}getDragDistance(){return void 0!==this.attrs.dragDistance?this.attrs.dragDistance:this.parent?this.parent.getDragDistance():$Q.dragDistance}_off(e,t,n){var r,o,i,a=this.eventListeners[e];for(r=0;r<a.length;r++)if(o=a[r].name,i=a[r].handler,!("konva"===o&&"konva"!==t||t&&o!==t||n&&n!==i)){if(a.splice(r,1),0===a.length){delete this.eventListeners[e];break}r--}}_fireChangeEvent(e,t,n){this._fire(e+"Change",{oldVal:t,newVal:n})}addName(e){if(!this.hasName(e)){var t=this.name(),n=t?t+" "+e:e;this.name(n)}return this}hasName(e){if(!e)return!1;const t=this.name();return!!t&&-1!==(t||"").split(/\s/g).indexOf(e)}removeName(e){var t=(this.name()||"").split(/\s/g),n=t.indexOf(e);return-1!==n&&(t.splice(n,1),this.name(t.join(" "))),this}setAttr(e,t){var n=this["set"+eJ._capitalize(e)];return eJ._isFunction(n)?n.call(this,t):this._setAttr(e,t),this}_requestDraw(){if($Q.autoDrawEnabled){const e=this.getLayer()||this.getStage();null==e||e.batchDraw()}}_setAttr(e,t){var n=this.attrs[e];(n!==t||eJ.isObject(t))&&(null==t?delete this.attrs[e]:this.attrs[e]=t,this._shouldFireChangeEvents&&this._fireChangeEvent(e,n,t),this._requestDraw())}_setComponentAttr(e,t,n){var r;void 0!==n&&((r=this.attrs[e])||(this.attrs[e]=this.getAttr(e)),this.attrs[e][t]=n,this._fireChangeEvent(e,r,n))}_fireAndBubble(e,t,n){if(t&&this.nodeType===MJ&&(t.target=this),!((e===LJ||e===OJ)&&(n&&(this===n||this.isAncestorOf&&this.isAncestorOf(n))||"Stage"===this.nodeType&&!n))){this._fire(e,t);var r=(e===LJ||e===OJ)&&n&&n.isAncestorOf&&n.isAncestorOf(this)&&!n.isAncestorOf(this.parent);(t&&!t.cancelBubble||!t)&&this.parent&&this.parent.isListening()&&!r&&(n&&n.parent?this._fireAndBubble.call(this.parent,e,t,n):this._fireAndBubble.call(this.parent,e,t))}}_getProtoListeners(e){let t=this._cache.get(SJ);if(!t){t={};let e=Object.getPrototypeOf(this);for(;e;)if(e.eventListeners){for(var n in e.eventListeners){const r=e.eventListeners[n],o=t[n]||[];t[n]=r.concat(o)}e=Object.getPrototypeOf(e)}else e=Object.getPrototypeOf(e);this._cache.set(SJ,t)}return t[e]}_fire(e,t){(t=t||{}).currentTarget=this,t.type=e;const n=this._getProtoListeners(e);if(n)for(var r=0;r<n.length;r++)n[r].handler.call(this,t);const o=this.eventListeners[e];if(o)for(r=0;r<o.length;r++)o[r].handler.call(this,t)}draw(){return this.drawScene(),this.drawHit(),this}_createDragElement(e){var t=e?e.pointerId:void 0,n=this.getStage(),r=this.getAbsolutePosition(),o=n._getPointerById(t)||n._changedPointerPositions[0]||r;wJ._dragElements.set(this._id,{node:this,startPointerPos:o,offset:{x:o.x-r.x,y:o.y-r.y},dragStatus:"ready",pointerId:t})}startDrag(e,t=!0){wJ._dragElements.has(this._id)||this._createDragElement(e);wJ._dragElements.get(this._id).dragStatus="dragging",this.fire("dragstart",{type:"dragstart",target:this,evt:e&&e.evt},t)}_setDragPosition(e,t){const n=this.getStage()._getPointerById(t.pointerId);if(n){var r={x:n.x-t.offset.x,y:n.y-t.offset.y},o=this.dragBoundFunc();if(void 0!==o){const t=o.call(this,r,e);t?r=t:eJ.warn("dragBoundFunc did not return any value. That is unexpected behavior. You must return new absolute position from dragBoundFunc.")}this._lastPos&&this._lastPos.x===r.x&&this._lastPos.y===r.y||(this.setAbsolutePosition(r),this._requestDraw()),this._lastPos=r}}stopDrag(e){const t=wJ._dragElements.get(this._id);t&&(t.dragStatus="stopped"),wJ._endDragBefore(e),wJ._endDragAfter(e)}setDraggable(e){this._setAttr("draggable",e),this._dragChange()}isDragging(){const e=wJ._dragElements.get(this._id);return!!e&&"dragging"===e.dragStatus}_listenDrag(){this._dragCleanup(),this.on("mousedown.konva touchstart.konva",(function(e){if((!(void 0!==e.evt.button)||$Q.dragButtons.indexOf(e.evt.button)>=0)&&!this.isDragging()){var t=!1;wJ._dragElements.forEach((e=>{this.isAncestorOf(e.node)&&(t=!0)})),t||this._createDragElement(e)}}))}_dragChange(){if(this.attrs.draggable)this._listenDrag();else{if(this._dragCleanup(),!this.getStage())return;const e=wJ._dragElements.get(this._id),t=e&&"dragging"===e.dragStatus,n=e&&"ready"===e.dragStatus;t?this.stopDrag():n&&wJ._dragElements.delete(this._id)}}_dragCleanup(){this.off("mousedown.konva"),this.off("touchstart.konva")}isClientRectOnScreen(e={x:0,y:0}){const t=this.getStage();if(!t)return!1;const n={x:-e.x,y:-e.y,width:t.width()+2*e.x,height:t.height()+2*e.y};return eJ.haveIntersection(n,this.getClientRect())}static create(e,t){return eJ._isString(e)&&(e=JSON.parse(e)),this._createNode(e,t)}static _createNode(e,t){var n,r,o,i=zJ.prototype.getClassName.call(e),a=e.children;t&&(e.attrs.container=t),$Q[i]||(eJ.warn('Can not find a node with class name "'+i+'". Fallback to "Shape".'),i="Shape");if(n=new(0,$Q[i])(e.attrs),a)for(r=a.length,o=0;o<r;o++)n.add(zJ._createNode(a[o]));return n}}zJ.prototype.nodeType="Node",zJ.prototype._attrsAffectingSize=[],zJ.prototype.eventListeners={},zJ.prototype.on.call(zJ.prototype,NJ,(function(){this._batchingTransformChange?this._needClearTransformCache=!0:(this._clearCache(IJ),this._clearSelfAndDescendantCache(CJ))})),zJ.prototype.on.call(zJ.prototype,"visibleChange.konva",(function(){this._clearSelfAndDescendantCache(RJ)})),zJ.prototype.on.call(zJ.prototype,"listeningChange.konva",(function(){this._clearSelfAndDescendantCache(PJ)})),zJ.prototype.on.call(zJ.prototype,"opacityChange.konva",(function(){this._clearSelfAndDescendantCache(kJ)}));const BJ=dJ.addGetterSetter;BJ(zJ,"zIndex"),BJ(zJ,"absolutePosition"),BJ(zJ,"position"),BJ(zJ,"x",0,rJ()),BJ(zJ,"y",0,rJ()),BJ(zJ,"globalCompositeOperation","source-over",aJ()),BJ(zJ,"opacity",1,rJ()),BJ(zJ,"name","",aJ()),BJ(zJ,"id","",aJ()),BJ(zJ,"rotation",0,rJ()),dJ.addComponentsGetterSetter(zJ,"scale",["x","y"]),BJ(zJ,"scaleX",1,rJ()),BJ(zJ,"scaleY",1,rJ()),dJ.addComponentsGetterSetter(zJ,"skew",["x","y"]),BJ(zJ,"skewX",0,rJ()),BJ(zJ,"skewY",0,rJ()),dJ.addComponentsGetterSetter(zJ,"offset",["x","y"]),BJ(zJ,"offsetX",0,rJ()),BJ(zJ,"offsetY",0,rJ()),BJ(zJ,"dragDistance",null,rJ()),BJ(zJ,"width",0,rJ()),BJ(zJ,"height",0,rJ()),BJ(zJ,"listening",!0,lJ()),BJ(zJ,"preventDefault",!0,lJ()),BJ(zJ,"filters",null,(function(e){return this._filterUpToDate=!1,e})),BJ(zJ,"visible",!0,lJ()),BJ(zJ,"transformsEnabled","all",aJ()),BJ(zJ,"size"),BJ(zJ,"dragBoundFunc"),BJ(zJ,"draggable",!1,lJ()),dJ.backCompat(zJ,{rotateDeg:"rotate",setRotationDeg:"setRotation",getRotationDeg:"getRotation"});class jJ extends zJ{constructor(){super(...arguments),this.children=[]}getChildren(e){if(!e)return this.children||[];const t=this.children||[];var n=[];return t.forEach((function(t){e(t)&&n.push(t)})),n}hasChildren(){return this.getChildren().length>0}removeChildren(){return this.getChildren().forEach((e=>{e.parent=null,e.index=0,e.remove()})),this.children=[],this._requestDraw(),this}destroyChildren(){return this.getChildren().forEach((e=>{e.parent=null,e.index=0,e.destroy()})),this.children=[],this._requestDraw(),this}add(...e){if(arguments.length>1){for(var t=0;t<arguments.length;t++)this.add(arguments[t]);return this}var n=e[0];return n.getParent()?(n.moveTo(this),this):(this._validateAdd(n),n.index=this.getChildren().length,n.parent=this,n._clearCaches(),this.getChildren().push(n),this._fire("add",{child:n}),this._requestDraw(),this)}destroy(){return this.hasChildren()&&this.destroyChildren(),super.destroy(),this}find(e){return this._generalFind(e,!1)}findOne(e){var t=this._generalFind(e,!0);return t.length>0?t[0]:void 0}_generalFind(e,t){var n=[];return this._descendants((r=>{const o=r._isMatch(e);return o&&n.push(r),!(!o||!t)})),n}_descendants(e){let t=!1;const n=this.getChildren();for(const r of n){if(t=e(r),t)return!0;if(r.hasChildren()&&(t=r._descendants(e),t))return!0}return!1}toObject(){var e=zJ.prototype.toObject.call(this);return e.children=[],this.getChildren().forEach((t=>{e.children.push(t.toObject())})),e}isAncestorOf(e){for(var t=e.getParent();t;){if(t._id===this._id)return!0;t=t.getParent()}return!1}clone(e){var t=zJ.prototype.clone.call(this,e);return this.getChildren().forEach((function(e){t.add(e.clone())})),t}getAllIntersections(e){var t=[];return this.find("Shape").forEach((function(n){n.isVisible()&&n.intersects(e)&&t.push(n)})),t}_clearSelfAndDescendantCache(e){var t;super._clearSelfAndDescendantCache(e),this.isCached()||null===(t=this.children)||void 0===t||t.forEach((function(t){t._clearSelfAndDescendantCache(e)}))}_setChildrenIndices(){var e;null===(e=this.children)||void 0===e||e.forEach((function(e,t){e.index=t})),this._requestDraw()}drawScene(e,t){var n=this.getLayer(),r=e||n&&n.getCanvas(),o=r&&r.getContext(),i=this._getCanvasCache(),a=i&&i.scene,s=r&&r.isCache;if(!this.isVisible()&&!s)return this;if(a){o.save();var l=this.getAbsoluteTransform(t).getMatrix();o.transform(l[0],l[1],l[2],l[3],l[4],l[5]),this._drawCachedSceneCanvas(o),o.restore()}else this._drawChildren("drawScene",r,t);return this}drawHit(e,t){if(!this.shouldDrawHit(t))return this;var n=this.getLayer(),r=e||n&&n.hitCanvas,o=r&&r.getContext(),i=this._getCanvasCache();if(i&&i.hit){o.save();var a=this.getAbsoluteTransform(t).getMatrix();o.transform(a[0],a[1],a[2],a[3],a[4],a[5]),this._drawCachedHitCanvas(o),o.restore()}else this._drawChildren("drawHit",r,t);return this}_drawChildren(e,t,n){var r,o=t&&t.getContext(),i=this.clipWidth(),a=this.clipHeight(),s=this.clipFunc(),l=i&&a||s;const c=n===this;if(l){o.save();var u=this.getAbsoluteTransform(n),d=u.getMatrix();if(o.transform(d[0],d[1],d[2],d[3],d[4],d[5]),o.beginPath(),s)s.call(this,o,this);else{var h=this.clipX(),f=this.clipY();o.rect(h,f,i,a)}o.clip(),d=u.copy().invert().getMatrix(),o.transform(d[0],d[1],d[2],d[3],d[4],d[5])}var p=!c&&"source-over"!==this.globalCompositeOperation()&&"drawScene"===e;p&&(o.save(),o._applyGlobalCompositeOperation(this)),null===(r=this.children)||void 0===r||r.forEach((function(r){r[e](t,n)})),p&&o.restore(),l&&o.restore()}getClientRect(e){var t,n,r,o,i,a=(e=e||{}).skipTransform,s=e.relativeTo,l={x:1/0,y:1/0,width:0,height:0},c=this;null===(t=this.children)||void 0===t||t.forEach((function(t){if(t.visible()){var a=t.getClientRect({relativeTo:c,skipShadow:e.skipShadow,skipStroke:e.skipStroke});0===a.width&&0===a.height||(void 0===n?(n=a.x,r=a.y,o=a.x+a.width,i=a.y+a.height):(n=Math.min(n,a.x),r=Math.min(r,a.y),o=Math.max(o,a.x+a.width),i=Math.max(i,a.y+a.height)))}}));for(var u=this.find("Shape"),d=!1,h=0;h<u.length;h++){if(u[h]._isVisible(this)){d=!0;break}}return l=d&&void 0!==n?{x:n,y:r,width:o-n,height:i-r}:{x:0,y:0,width:0,height:0},a?l:this._transformedRect(l,s)}}dJ.addComponentsGetterSetter(jJ,"clip",["x","y","width","height"]),dJ.addGetterSetter(jJ,"clipX",void 0,rJ()),dJ.addGetterSetter(jJ,"clipY",void 0,rJ()),dJ.addGetterSetter(jJ,"clipWidth",void 0,rJ()),dJ.addGetterSetter(jJ,"clipHeight",void 0,rJ()),dJ.addGetterSetter(jJ,"clipFunc");const FJ=new Map,HJ=void 0!==$Q._global.PointerEvent;function WJ(e){return FJ.get(e)}function VJ(e){return{evt:e,pointerId:e.pointerId}}function $J(e,t){return FJ.get(e)===t}function UJ(e,t){GJ(e);t.getStage()&&(FJ.set(e,t),HJ&&t._fire("gotpointercapture",VJ(new PointerEvent("gotpointercapture"))))}function GJ(e,t){const n=FJ.get(e);if(!n)return;const r=n.getStage();r&&r.content,FJ.delete(e),HJ&&n._fire("lostpointercapture",VJ(new PointerEvent("lostpointercapture")))}var qJ="mouseleave",YJ="mouseover",ZJ="mouseenter",XJ="mousemove",KJ="mousedown",QJ="mouseup",JJ="pointermove",e0="pointerdown",t0="pointerup",n0="pointercancel",r0="pointerout",o0="pointerleave",i0="pointerover",a0="pointerenter",s0="contextmenu",l0="touchstart",c0="touchend",u0="touchmove",d0="touchcancel",h0="wheel",f0=[[ZJ,"_pointerenter"],[KJ,"_pointerdown"],[XJ,"_pointermove"],[QJ,"_pointerup"],[qJ,"_pointerleave"],[l0,"_pointerdown"],[u0,"_pointermove"],[c0,"_pointerup"],[d0,"_pointercancel"],[YJ,"_pointerover"],[h0,"_wheel"],[s0,"_contextmenu"],[e0,"_pointerdown"],[JJ,"_pointermove"],[t0,"_pointerup"],[n0,"_pointercancel"],["lostpointercapture","_lostpointercapture"]];const p0={mouse:{[r0]:"mouseout",[o0]:qJ,[i0]:YJ,[a0]:ZJ,[JJ]:XJ,[e0]:KJ,[t0]:QJ,[n0]:"mousecancel",pointerclick:"click",pointerdblclick:"dblclick"},touch:{[r0]:"touchout",[o0]:"touchleave",[i0]:"touchover",[a0]:"touchenter",[JJ]:u0,[e0]:l0,[t0]:c0,[n0]:d0,pointerclick:"tap",pointerdblclick:"dbltap"},pointer:{[r0]:r0,[o0]:o0,[i0]:i0,[a0]:a0,[JJ]:JJ,[e0]:e0,[t0]:t0,[n0]:n0,pointerclick:"pointerclick",pointerdblclick:"pointerdblclick"}},g0=e=>e.indexOf("pointer")>=0?"pointer":e.indexOf("touch")>=0?"touch":"mouse",m0=e=>{const t=g0(e);return"pointer"===t?$Q.pointerEventsEnabled&&p0.pointer:"touch"===t?p0.touch:"mouse"===t?p0.mouse:void 0};function v0(e={}){return(e.clipFunc||e.clipWidth||e.clipHeight)&&eJ.warn("Stage does not support clipping. Please use clip for Layers or Groups."),e}const y0=[];class b0 extends jJ{constructor(e){super(v0(e)),this._pointerPositions=[],this._changedPointerPositions=[],this._buildDOM(),this._bindContentEvents(),y0.push(this),this.on("widthChange.konva heightChange.konva",this._resizeDOM),this.on("visibleChange.konva",this._checkVisibility),this.on("clipWidthChange.konva clipHeightChange.konva clipFuncChange.konva",(()=>{v0(this.attrs)})),this._checkVisibility()}_validateAdd(e){const t="Layer"===e.getType(),n="FastLayer"===e.getType();t||n||eJ.throw("You may only add layers to the stage.")}_checkVisibility(){if(!this.content)return;const e=this.visible()?"":"none";this.content.style.display=e}setContainer(e){if("string"==typeof e){if("."===e.charAt(0)){var t=e.slice(1);e=document.getElementsByClassName(t)[0]}else{var n;n="#"!==e.charAt(0)?e:e.slice(1),e=document.getElementById(n)}if(!e)throw"Can not find container in document with id "+n}return this._setAttr("container",e),this.content&&(this.content.parentElement&&this.content.parentElement.removeChild(this.content),e.appendChild(this.content)),this}shouldDrawHit(){return!0}clear(){var e,t=this.children,n=t.length;for(e=0;e<n;e++)t[e].clear();return this}clone(e){return e||(e={}),e.container="undefined"!=typeof document&&document.createElement("div"),jJ.prototype.clone.call(this,e)}destroy(){super.destroy();var e=this.content;e&&eJ._isInDocument(e)&&this.container().removeChild(e);var t=y0.indexOf(this);return t>-1&&y0.splice(t,1),eJ.releaseCanvas(this.bufferCanvas._canvas,this.bufferHitCanvas._canvas),this}getPointerPosition(){const e=this._pointerPositions[0]||this._changedPointerPositions[0];return e?{x:e.x,y:e.y}:(eJ.warn("Pointer position is missing and not registered by the stage. Looks like it is outside of the stage container. You can set it manually from event: stage.setPointersPositions(event);"),null)}_getPointerById(e){return this._pointerPositions.find((t=>t.id===e))}getPointersPositions(){return this._pointerPositions}getStage(){return this}getContent(){return this.content}_toKonvaCanvas(e){(e=e||{}).x=e.x||0,e.y=e.y||0,e.width=e.width||this.width(),e.height=e.height||this.height();var t=new bJ({width:e.width,height:e.height,pixelRatio:e.pixelRatio||1}),n=t.getContext()._context,r=this.children;return(e.x||e.y)&&n.translate(-1*e.x,-1*e.y),r.forEach((function(t){if(t.isVisible()){var r=t._toKonvaCanvas(e);n.drawImage(r._canvas,e.x,e.y,r.getWidth()/r.getPixelRatio(),r.getHeight()/r.getPixelRatio())}})),t}getIntersection(e){if(!e)return null;var t,n=this.children;for(t=n.length-1;t>=0;t--){const r=n[t].getIntersection(e);if(r)return r}return null}_resizeDOM(){var e=this.width(),t=this.height();this.content&&(this.content.style.width=e+"px",this.content.style.height=t+"px"),this.bufferCanvas.setSize(e,t),this.bufferHitCanvas.setSize(e,t),this.children.forEach((n=>{n.setSize({width:e,height:t}),n.draw()}))}add(e,...t){if(arguments.length>1){for(var n=0;n<arguments.length;n++)this.add(arguments[n]);return this}super.add(e);var r=this.children.length;return r>5&&eJ.warn("The stage has "+r+" layers. Recommended maximum number of layers is 3-5. Adding more layers into the stage may drop the performance. Rethink your tree structure, you can use Konva.Group."),e.setSize({width:this.width(),height:this.height()}),e.draw(),$Q.isBrowser&&this.content.appendChild(e.canvas._canvas),this}getParent(){return null}getLayer(){return null}hasPointerCapture(e){return $J(e,this)}setPointerCapture(e){UJ(e,this)}releaseCapture(e){GJ(e)}getLayers(){return this.children}_bindContentEvents(){$Q.isBrowser&&f0.forEach((([e,t])=>{this.content.addEventListener(e,(e=>{this[t](e)}),{passive:!1})}))}_pointerenter(e){this.setPointersPositions(e);const t=m0(e.type);this._fire(t.pointerenter,{evt:e,target:this,currentTarget:this})}_pointerover(e){this.setPointersPositions(e);const t=m0(e.type);this._fire(t.pointerover,{evt:e,target:this,currentTarget:this})}_getTargetShape(e){let t=this[e+"targetShape"];return t&&!t.getStage()&&(t=null),t}_pointerleave(e){const t=m0(e.type),n=g0(e.type);if(t){this.setPointersPositions(e);var r=this._getTargetShape(n),o=!wJ.isDragging||$Q.hitOnDragEnabled;r&&o?(r._fireAndBubble(t.pointerout,{evt:e}),r._fireAndBubble(t.pointerleave,{evt:e}),this._fire(t.pointerleave,{evt:e,target:this,currentTarget:this}),this[n+"targetShape"]=null):o&&(this._fire(t.pointerleave,{evt:e,target:this,currentTarget:this}),this._fire(t.pointerout,{evt:e,target:this,currentTarget:this})),this.pointerPos=void 0,this._pointerPositions=[]}}_pointerdown(e){const t=m0(e.type),n=g0(e.type);if(t){this.setPointersPositions(e);var r=!1;this._changedPointerPositions.forEach((o=>{var i=this.getIntersection(o);wJ.justDragged=!1,$Q["_"+n+"ListenClick"]=!0;if(!(i&&i.isListening()))return;$Q.capturePointerEventsEnabled&&i.setPointerCapture(o.id),this[n+"ClickStartShape"]=i,i._fireAndBubble(t.pointerdown,{evt:e,pointerId:o.id}),r=!0;const a=e.type.indexOf("touch")>=0;i.preventDefault()&&e.cancelable&&a&&e.preventDefault()})),r||this._fire(t.pointerdown,{evt:e,target:this,currentTarget:this,pointerId:this._pointerPositions[0].id})}}_pointermove(e){const t=m0(e.type),n=g0(e.type);if(!t)return;if(wJ.isDragging&&wJ.node.preventDefault()&&e.cancelable&&e.preventDefault(),this.setPointersPositions(e),!(!wJ.isDragging||$Q.hitOnDragEnabled))return;var r={};let o=!1;var i=this._getTargetShape(n);this._changedPointerPositions.forEach((a=>{const s=WJ(a.id)||this.getIntersection(a),l=a.id,c={evt:e,pointerId:l};var u=i!==s;if(u&&i&&(i._fireAndBubble(t.pointerout,Object.assign({},c),s),i._fireAndBubble(t.pointerleave,Object.assign({},c),s)),s){if(r[s._id])return;r[s._id]=!0}s&&s.isListening()?(o=!0,u&&(s._fireAndBubble(t.pointerover,Object.assign({},c),i),s._fireAndBubble(t.pointerenter,Object.assign({},c),i),this[n+"targetShape"]=s),s._fireAndBubble(t.pointermove,Object.assign({},c))):i&&(this._fire(t.pointerover,{evt:e,target:this,currentTarget:this,pointerId:l}),this[n+"targetShape"]=null)})),o||this._fire(t.pointermove,{evt:e,target:this,currentTarget:this,pointerId:this._changedPointerPositions[0].id})}_pointerup(e){const t=m0(e.type),n=g0(e.type);if(!t)return;this.setPointersPositions(e);const r=this[n+"ClickStartShape"],o=this[n+"ClickEndShape"];var i={};let a=!1;this._changedPointerPositions.forEach((s=>{const l=WJ(s.id)||this.getIntersection(s);if(l){if(l.releaseCapture(s.id),i[l._id])return;i[l._id]=!0}const c=s.id,u={evt:e,pointerId:c};let d=!1;$Q["_"+n+"InDblClickWindow"]?(d=!0,clearTimeout(this[n+"DblTimeout"])):wJ.justDragged||($Q["_"+n+"InDblClickWindow"]=!0,clearTimeout(this[n+"DblTimeout"])),this[n+"DblTimeout"]=setTimeout((function(){$Q["_"+n+"InDblClickWindow"]=!1}),$Q.dblClickWindow),l&&l.isListening()?(a=!0,this[n+"ClickEndShape"]=l,l._fireAndBubble(t.pointerup,Object.assign({},u)),$Q["_"+n+"ListenClick"]&&r&&r===l&&(l._fireAndBubble(t.pointerclick,Object.assign({},u)),d&&o&&o===l&&l._fireAndBubble(t.pointerdblclick,Object.assign({},u)))):(this[n+"ClickEndShape"]=null,$Q["_"+n+"ListenClick"]&&this._fire(t.pointerclick,{evt:e,target:this,currentTarget:this,pointerId:c}),d&&this._fire(t.pointerdblclick,{evt:e,target:this,currentTarget:this,pointerId:c}))})),a||this._fire(t.pointerup,{evt:e,target:this,currentTarget:this,pointerId:this._changedPointerPositions[0].id}),$Q["_"+n+"ListenClick"]=!1,e.cancelable&&"touch"!==n&&e.preventDefault()}_contextmenu(e){this.setPointersPositions(e);var t=this.getIntersection(this.getPointerPosition());t&&t.isListening()?t._fireAndBubble(s0,{evt:e}):this._fire(s0,{evt:e,target:this,currentTarget:this})}_wheel(e){this.setPointersPositions(e);var t=this.getIntersection(this.getPointerPosition());t&&t.isListening()?t._fireAndBubble(h0,{evt:e}):this._fire(h0,{evt:e,target:this,currentTarget:this})}_pointercancel(e){this.setPointersPositions(e);const t=WJ(e.pointerId)||this.getIntersection(this.getPointerPosition());t&&t._fireAndBubble(t0,VJ(e)),GJ(e.pointerId)}_lostpointercapture(e){GJ(e.pointerId)}setPointersPositions(e){var t=this._getContentPosition(),n=null,r=null;void 0!==(e=e||window.event).touches?(this._pointerPositions=[],this._changedPointerPositions=[],Array.prototype.forEach.call(e.touches,(e=>{this._pointerPositions.push({id:e.identifier,x:(e.clientX-t.left)/t.scaleX,y:(e.clientY-t.top)/t.scaleY})})),Array.prototype.forEach.call(e.changedTouches||e.touches,(e=>{this._changedPointerPositions.push({id:e.identifier,x:(e.clientX-t.left)/t.scaleX,y:(e.clientY-t.top)/t.scaleY})}))):(n=(e.clientX-t.left)/t.scaleX,r=(e.clientY-t.top)/t.scaleY,this.pointerPos={x:n,y:r},this._pointerPositions=[{x:n,y:r,id:eJ._getFirstPointerId(e)}],this._changedPointerPositions=[{x:n,y:r,id:eJ._getFirstPointerId(e)}])}_setPointerPosition(e){eJ.warn('Method _setPointerPosition is deprecated. Use "stage.setPointersPositions(event)" instead.'),this.setPointersPositions(e)}_getContentPosition(){if(!this.content||!this.content.getBoundingClientRect)return{top:0,left:0,scaleX:1,scaleY:1};var e=this.content.getBoundingClientRect();return{top:e.top,left:e.left,scaleX:e.width/this.content.clientWidth||1,scaleY:e.height/this.content.clientHeight||1}}_buildDOM(){if(this.bufferCanvas=new bJ({width:this.width(),height:this.height()}),this.bufferHitCanvas=new xJ({pixelRatio:1,width:this.width(),height:this.height()}),$Q.isBrowser){var e=this.container();if(!e)throw"Stage has no container. A container is required.";e.innerHTML="",this.content=document.createElement("div"),this.content.style.position="relative",this.content.style.userSelect="none",this.content.className="konvajs-content",this.content.setAttribute("role","presentation"),e.appendChild(this.content),this._resizeDOM()}}cache(){return eJ.warn("Cache function is not allowed for stage. You may use cache only for layers, groups and shapes."),this}clearCache(){return this}batchDraw(){return this.getChildren().forEach((function(e){e.batchDraw()})),this}}b0.prototype.nodeType="Stage",UQ(b0),dJ.addGetterSetter(b0,"container");var x0="hasShadow",w0="shadowRGBA",k0="patternImage",S0="linearGradient",C0="radialGradient";let _0;function E0(){return _0||(_0=eJ.createCanvasElement().getContext("2d"),_0)}const P0={};class L0 extends zJ{constructor(e){let t;for(super(e);t=eJ.getRandomColor(),!t||t in P0;);this.colorKey=t,P0[t]=this}getContext(){return eJ.warn("shape.getContext() method is deprecated. Please do not use it."),this.getLayer().getContext()}getCanvas(){return eJ.warn("shape.getCanvas() method is deprecated. Please do not use it."),this.getLayer().getCanvas()}getSceneFunc(){return this.attrs.sceneFunc||this._sceneFunc}getHitFunc(){return this.attrs.hitFunc||this._hitFunc}hasShadow(){return this._getCache(x0,this._hasShadow)}_hasShadow(){return this.shadowEnabled()&&0!==this.shadowOpacity()&&!!(this.shadowColor()||this.shadowBlur()||this.shadowOffsetX()||this.shadowOffsetY())}_getFillPattern(){return this._getCache(k0,this.__getFillPattern)}__getFillPattern(){if(this.fillPatternImage()){const e=E0().createPattern(this.fillPatternImage(),this.fillPatternRepeat()||"repeat");if(e&&e.setTransform){const t=new GQ;t.translate(this.fillPatternX(),this.fillPatternY()),t.rotate($Q.getAngle(this.fillPatternRotation())),t.scale(this.fillPatternScaleX(),this.fillPatternScaleY()),t.translate(-1*this.fillPatternOffsetX(),-1*this.fillPatternOffsetY());const n=t.getMatrix(),r="undefined"==typeof DOMMatrix?{a:n[0],b:n[1],c:n[2],d:n[3],e:n[4],f:n[5]}:new DOMMatrix(n);e.setTransform(r)}return e}}_getLinearGradient(){return this._getCache(S0,this.__getLinearGradient)}__getLinearGradient(){var e=this.fillLinearGradientColorStops();if(e){for(var t=E0(),n=this.fillLinearGradientStartPoint(),r=this.fillLinearGradientEndPoint(),o=t.createLinearGradient(n.x,n.y,r.x,r.y),i=0;i<e.length;i+=2)o.addColorStop(e[i],e[i+1]);return o}}_getRadialGradient(){return this._getCache(C0,this.__getRadialGradient)}__getRadialGradient(){var e=this.fillRadialGradientColorStops();if(e){for(var t=E0(),n=this.fillRadialGradientStartPoint(),r=this.fillRadialGradientEndPoint(),o=t.createRadialGradient(n.x,n.y,this.fillRadialGradientStartRadius(),r.x,r.y,this.fillRadialGradientEndRadius()),i=0;i<e.length;i+=2)o.addColorStop(e[i],e[i+1]);return o}}getShadowRGBA(){return this._getCache(w0,this._getShadowRGBA)}_getShadowRGBA(){if(this.hasShadow()){var e=eJ.colorToRGBA(this.shadowColor());return e?"rgba("+e.r+","+e.g+","+e.b+","+e.a*(this.shadowOpacity()||1)+")":void 0}}hasFill(){return this._calculate("hasFill",["fillEnabled","fill","fillPatternImage","fillLinearGradientColorStops","fillRadialGradientColorStops"],(()=>this.fillEnabled()&&!!(this.fill()||this.fillPatternImage()||this.fillLinearGradientColorStops()||this.fillRadialGradientColorStops())))}hasStroke(){return this._calculate("hasStroke",["strokeEnabled","strokeWidth","stroke","strokeLinearGradientColorStops"],(()=>this.strokeEnabled()&&this.strokeWidth()&&!(!this.stroke()&&!this.strokeLinearGradientColorStops())))}hasHitStroke(){const e=this.hitStrokeWidth();return"auto"===e?this.hasStroke():this.strokeEnabled()&&!!e}intersects(e){var t=this.getStage().bufferHitCanvas;return t.getContext().clear(),this.drawHit(t,null,!0),t.context.getImageData(Math.round(e.x),Math.round(e.y),1,1).data[3]>0}destroy(){return zJ.prototype.destroy.call(this),delete P0[this.colorKey],delete this.colorKey,this}_useBufferCanvas(e){var t;if(!this.getStage())return!1;if(!(null===(t=this.attrs.perfectDrawEnabled)||void 0===t||t))return!1;const n=e||this.hasFill(),r=this.hasStroke(),o=1!==this.getAbsoluteOpacity();if(n&&r&&o)return!0;const i=this.hasShadow(),a=this.shadowForStrokeEnabled();return!!(n&&r&&i&&a)}setStrokeHitEnabled(e){eJ.warn("strokeHitEnabled property is deprecated. Please use hitStrokeWidth instead."),e?this.hitStrokeWidth("auto"):this.hitStrokeWidth(0)}getStrokeHitEnabled(){return 0!==this.hitStrokeWidth()}getSelfRect(){var e=this.size();return{x:this._centroid?-e.width/2:0,y:this._centroid?-e.height/2:0,width:e.width,height:e.height}}getClientRect(e={}){const t=e.skipTransform,n=e.relativeTo,r=this.getSelfRect(),o=!e.skipStroke&&this.hasStroke()&&this.strokeWidth()||0,i=r.width+o,a=r.height+o,s=!e.skipShadow&&this.hasShadow(),l=s?this.shadowOffsetX():0,c=s?this.shadowOffsetY():0,u=i+Math.abs(l),d=a+Math.abs(c),h=s&&this.shadowBlur()||0,f={width:u+2*h,height:d+2*h,x:-(o/2+h)+Math.min(l,0)+r.x,y:-(o/2+h)+Math.min(c,0)+r.y};return t?f:this._transformedRect(f,n)}drawScene(e,t){var n,r,o=this.getLayer(),i=e||o.getCanvas(),a=i.getContext(),s=this._getCanvasCache(),l=this.getSceneFunc(),c=this.hasShadow(),u=i.isCache,d=t===this;if(!this.isVisible()&&!d)return this;if(s){a.save();var h=this.getAbsoluteTransform(t).getMatrix();return a.transform(h[0],h[1],h[2],h[3],h[4],h[5]),this._drawCachedSceneCanvas(a),a.restore(),this}if(!l)return this;if(a.save(),this._useBufferCanvas()&&!u){(r=(n=this.getStage().bufferCanvas).getContext()).clear(),r.save(),r._applyLineJoin(this);var f=this.getAbsoluteTransform(t).getMatrix();r.transform(f[0],f[1],f[2],f[3],f[4],f[5]),l.call(this,r,this),r.restore();var p=n.pixelRatio;c&&a._applyShadow(this),a._applyOpacity(this),a._applyGlobalCompositeOperation(this),a.drawImage(n._canvas,0,0,n.width/p,n.height/p)}else{if(a._applyLineJoin(this),!d){f=this.getAbsoluteTransform(t).getMatrix();a.transform(f[0],f[1],f[2],f[3],f[4],f[5]),a._applyOpacity(this),a._applyGlobalCompositeOperation(this)}c&&a._applyShadow(this),l.call(this,a,this)}return a.restore(),this}drawHit(e,t,n=!1){if(!this.shouldDrawHit(t,n))return this;var r=this.getLayer(),o=e||r.hitCanvas,i=o&&o.getContext(),a=this.hitFunc()||this.sceneFunc(),s=this._getCanvasCache(),l=s&&s.hit;if(this.colorKey||eJ.warn("Looks like your canvas has a destroyed shape in it. Do not reuse shape after you destroyed it. If you want to reuse shape you should call remove() instead of destroy()"),l){i.save();var c=this.getAbsoluteTransform(t).getMatrix();return i.transform(c[0],c[1],c[2],c[3],c[4],c[5]),this._drawCachedHitCanvas(i),i.restore(),this}if(!a)return this;i.save(),i._applyLineJoin(this);if(!(this===t)){var u=this.getAbsoluteTransform(t).getMatrix();i.transform(u[0],u[1],u[2],u[3],u[4],u[5])}return a.call(this,i,this),i.restore(),this}drawHitFromCache(e=0){var t,n,r,o,i,a=this._getCanvasCache(),s=this._getCachedSceneCanvas(),l=a.hit,c=l.getContext(),u=l.getWidth(),d=l.getHeight();c.clear(),c.drawImage(s._canvas,0,0,u,d);try{for(r=(n=(t=c.getImageData(0,0,u,d)).data).length,o=eJ._hexToRgb(this.colorKey),i=0;i<r;i+=4)n[i+3]>e?(n[i]=o.r,n[i+1]=o.g,n[i+2]=o.b,n[i+3]=255):n[i+3]=0;c.putImageData(t,0,0)}catch(B2){eJ.error("Unable to draw hit graph from cached scene canvas. "+B2.message)}return this}hasPointerCapture(e){return $J(e,this)}setPointerCapture(e){UJ(e,this)}releaseCapture(e){GJ(e)}}L0.prototype._fillFunc=function(e){e.fill()},L0.prototype._strokeFunc=function(e){e.stroke()},L0.prototype._fillFuncHit=function(e){e.fill()},L0.prototype._strokeFuncHit=function(e){e.stroke()},L0.prototype._centroid=!1,L0.prototype.nodeType="Shape",UQ(L0),L0.prototype.eventListeners={},L0.prototype.on.call(L0.prototype,"shadowColorChange.konva shadowBlurChange.konva shadowOffsetChange.konva shadowOpacityChange.konva shadowEnabledChange.konva",(function(){this._clearCache(x0)})),L0.prototype.on.call(L0.prototype,"shadowColorChange.konva shadowOpacityChange.konva shadowEnabledChange.konva",(function(){this._clearCache(w0)})),L0.prototype.on.call(L0.prototype,"fillPriorityChange.konva fillPatternImageChange.konva fillPatternRepeatChange.konva fillPatternScaleXChange.konva fillPatternScaleYChange.konva fillPatternOffsetXChange.konva fillPatternOffsetYChange.konva fillPatternXChange.konva fillPatternYChange.konva fillPatternRotationChange.konva",(function(){this._clearCache(k0)})),L0.prototype.on.call(L0.prototype,"fillPriorityChange.konva fillLinearGradientColorStopsChange.konva fillLinearGradientStartPointXChange.konva fillLinearGradientStartPointYChange.konva fillLinearGradientEndPointXChange.konva fillLinearGradientEndPointYChange.konva",(function(){this._clearCache(S0)})),L0.prototype.on.call(L0.prototype,"fillPriorityChange.konva fillRadialGradientColorStopsChange.konva fillRadialGradientStartPointXChange.konva fillRadialGradientStartPointYChange.konva fillRadialGradientEndPointXChange.konva fillRadialGradientEndPointYChange.konva fillRadialGradientStartRadiusChange.konva fillRadialGradientEndRadiusChange.konva",(function(){this._clearCache(C0)})),dJ.addGetterSetter(L0,"stroke",void 0,sJ()),dJ.addGetterSetter(L0,"strokeWidth",2,rJ()),dJ.addGetterSetter(L0,"fillAfterStrokeEnabled",!1),dJ.addGetterSetter(L0,"hitStrokeWidth","auto",iJ()),dJ.addGetterSetter(L0,"strokeHitEnabled",!0,lJ()),dJ.addGetterSetter(L0,"perfectDrawEnabled",!0,lJ()),dJ.addGetterSetter(L0,"shadowForStrokeEnabled",!0,lJ()),dJ.addGetterSetter(L0,"lineJoin"),dJ.addGetterSetter(L0,"lineCap"),dJ.addGetterSetter(L0,"sceneFunc"),dJ.addGetterSetter(L0,"hitFunc"),dJ.addGetterSetter(L0,"dash"),dJ.addGetterSetter(L0,"dashOffset",0,rJ()),dJ.addGetterSetter(L0,"shadowColor",void 0,aJ()),dJ.addGetterSetter(L0,"shadowBlur",0,rJ()),dJ.addGetterSetter(L0,"shadowOpacity",1,rJ()),dJ.addComponentsGetterSetter(L0,"shadowOffset",["x","y"]),dJ.addGetterSetter(L0,"shadowOffsetX",0,rJ()),dJ.addGetterSetter(L0,"shadowOffsetY",0,rJ()),dJ.addGetterSetter(L0,"fillPatternImage"),dJ.addGetterSetter(L0,"fill",void 0,sJ()),dJ.addGetterSetter(L0,"fillPatternX",0,rJ()),dJ.addGetterSetter(L0,"fillPatternY",0,rJ()),dJ.addGetterSetter(L0,"fillLinearGradientColorStops"),dJ.addGetterSetter(L0,"strokeLinearGradientColorStops"),dJ.addGetterSetter(L0,"fillRadialGradientStartRadius",0),dJ.addGetterSetter(L0,"fillRadialGradientEndRadius",0),dJ.addGetterSetter(L0,"fillRadialGradientColorStops"),dJ.addGetterSetter(L0,"fillPatternRepeat","repeat"),dJ.addGetterSetter(L0,"fillEnabled",!0),dJ.addGetterSetter(L0,"strokeEnabled",!0),dJ.addGetterSetter(L0,"shadowEnabled",!0),dJ.addGetterSetter(L0,"dashEnabled",!0),dJ.addGetterSetter(L0,"strokeScaleEnabled",!0),dJ.addGetterSetter(L0,"fillPriority","color"),dJ.addComponentsGetterSetter(L0,"fillPatternOffset",["x","y"]),dJ.addGetterSetter(L0,"fillPatternOffsetX",0,rJ()),dJ.addGetterSetter(L0,"fillPatternOffsetY",0,rJ()),dJ.addComponentsGetterSetter(L0,"fillPatternScale",["x","y"]),dJ.addGetterSetter(L0,"fillPatternScaleX",1,rJ()),dJ.addGetterSetter(L0,"fillPatternScaleY",1,rJ()),dJ.addComponentsGetterSetter(L0,"fillLinearGradientStartPoint",["x","y"]),dJ.addComponentsGetterSetter(L0,"strokeLinearGradientStartPoint",["x","y"]),dJ.addGetterSetter(L0,"fillLinearGradientStartPointX",0),dJ.addGetterSetter(L0,"strokeLinearGradientStartPointX",0),dJ.addGetterSetter(L0,"fillLinearGradientStartPointY",0),dJ.addGetterSetter(L0,"strokeLinearGradientStartPointY",0),dJ.addComponentsGetterSetter(L0,"fillLinearGradientEndPoint",["x","y"]),dJ.addComponentsGetterSetter(L0,"strokeLinearGradientEndPoint",["x","y"]),dJ.addGetterSetter(L0,"fillLinearGradientEndPointX",0),dJ.addGetterSetter(L0,"strokeLinearGradientEndPointX",0),dJ.addGetterSetter(L0,"fillLinearGradientEndPointY",0),dJ.addGetterSetter(L0,"strokeLinearGradientEndPointY",0),dJ.addComponentsGetterSetter(L0,"fillRadialGradientStartPoint",["x","y"]),dJ.addGetterSetter(L0,"fillRadialGradientStartPointX",0),dJ.addGetterSetter(L0,"fillRadialGradientStartPointY",0),dJ.addComponentsGetterSetter(L0,"fillRadialGradientEndPoint",["x","y"]),dJ.addGetterSetter(L0,"fillRadialGradientEndPointX",0),dJ.addGetterSetter(L0,"fillRadialGradientEndPointY",0),dJ.addGetterSetter(L0,"fillPatternRotation",0),dJ.backCompat(L0,{dashArray:"dash",getDashArray:"getDash",setDashArray:"getDash",drawFunc:"sceneFunc",getDrawFunc:"getSceneFunc",setDrawFunc:"setSceneFunc",drawHitFunc:"hitFunc",getDrawHitFunc:"getHitFunc",setDrawHitFunc:"setHitFunc"});var O0=[{x:0,y:0},{x:-1,y:-1},{x:1,y:-1},{x:1,y:1},{x:-1,y:1}],M0=O0.length;class T0 extends jJ{constructor(e){super(e),this.canvas=new bJ,this.hitCanvas=new xJ({pixelRatio:1}),this._waitingForDraw=!1,this.on("visibleChange.konva",this._checkVisibility),this._checkVisibility(),this.on("imageSmoothingEnabledChange.konva",this._setSmoothEnabled),this._setSmoothEnabled()}createPNGStream(){return this.canvas._canvas.createPNGStream()}getCanvas(){return this.canvas}getNativeCanvasElement(){return this.canvas._canvas}getHitCanvas(){return this.hitCanvas}getContext(){return this.getCanvas().getContext()}clear(e){return this.getContext().clear(e),this.getHitCanvas().getContext().clear(e),this}setZIndex(e){super.setZIndex(e);var t=this.getStage();return t&&t.content&&(t.content.removeChild(this.getNativeCanvasElement()),e<t.children.length-1?t.content.insertBefore(this.getNativeCanvasElement(),t.children[e+1].getCanvas()._canvas):t.content.appendChild(this.getNativeCanvasElement())),this}moveToTop(){zJ.prototype.moveToTop.call(this);var e=this.getStage();return e&&e.content&&(e.content.removeChild(this.getNativeCanvasElement()),e.content.appendChild(this.getNativeCanvasElement())),!0}moveUp(){if(!zJ.prototype.moveUp.call(this))return!1;var e=this.getStage();return!(!e||!e.content)&&(e.content.removeChild(this.getNativeCanvasElement()),this.index<e.children.length-1?e.content.insertBefore(this.getNativeCanvasElement(),e.children[this.index+1].getCanvas()._canvas):e.content.appendChild(this.getNativeCanvasElement()),!0)}moveDown(){if(zJ.prototype.moveDown.call(this)){var e=this.getStage();if(e){var t=e.children;e.content&&(e.content.removeChild(this.getNativeCanvasElement()),e.content.insertBefore(this.getNativeCanvasElement(),t[this.index+1].getCanvas()._canvas))}return!0}return!1}moveToBottom(){if(zJ.prototype.moveToBottom.call(this)){var e=this.getStage();if(e){var t=e.children;e.content&&(e.content.removeChild(this.getNativeCanvasElement()),e.content.insertBefore(this.getNativeCanvasElement(),t[1].getCanvas()._canvas))}return!0}return!1}getLayer(){return this}remove(){var e=this.getNativeCanvasElement();return zJ.prototype.remove.call(this),e&&e.parentNode&&eJ._isInDocument(e)&&e.parentNode.removeChild(e),this}getStage(){return this.parent}setSize({width:e,height:t}){return this.canvas.setSize(e,t),this.hitCanvas.setSize(e,t),this._setSmoothEnabled(),this}_validateAdd(e){var t=e.getType();"Group"!==t&&"Shape"!==t&&eJ.throw("You may only add groups and shapes to a layer.")}_toKonvaCanvas(e){return(e=e||{}).width=e.width||this.getWidth(),e.height=e.height||this.getHeight(),e.x=void 0!==e.x?e.x:this.x(),e.y=void 0!==e.y?e.y:this.y(),zJ.prototype._toKonvaCanvas.call(this,e)}_checkVisibility(){const e=this.visible();this.canvas._canvas.style.display=e?"block":"none"}_setSmoothEnabled(){this.getContext()._context.imageSmoothingEnabled=this.imageSmoothingEnabled()}getWidth(){if(this.parent)return this.parent.width()}setWidth(){eJ.warn('Can not change width of layer. Use "stage.width(value)" function instead.')}getHeight(){if(this.parent)return this.parent.height()}setHeight(){eJ.warn('Can not change height of layer. Use "stage.height(value)" function instead.')}batchDraw(){return this._waitingForDraw||(this._waitingForDraw=!0,eJ.requestAnimFrame((()=>{this.draw(),this._waitingForDraw=!1}))),this}getIntersection(e){if(!this.isListening()||!this.isVisible())return null;for(var t=1,n=!1;;){for(let r=0;r<M0;r++){const o=O0[r],i=this._getIntersection({x:e.x+o.x*t,y:e.y+o.y*t}),a=i.shape;if(a)return a;if(n=!!i.antialiased,!i.antialiased)break}if(!n)return null;t+=1}}_getIntersection(e){const t=this.hitCanvas.pixelRatio,n=this.hitCanvas.context.getImageData(Math.round(e.x*t),Math.round(e.y*t),1,1).data,r=n[3];if(255===r){const e=eJ._rgbToHex(n[0],n[1],n[2]),t=P0["#"+e];return t?{shape:t}:{antialiased:!0}}return r>0?{antialiased:!0}:{}}drawScene(e,t){var n=this.getLayer(),r=e||n&&n.getCanvas();return this._fire("beforeDraw",{node:this}),this.clearBeforeDraw()&&r.getContext().clear(),jJ.prototype.drawScene.call(this,r,t),this._fire("draw",{node:this}),this}drawHit(e,t){var n=this.getLayer(),r=e||n&&n.hitCanvas;return n&&n.clearBeforeDraw()&&n.getHitCanvas().getContext().clear(),jJ.prototype.drawHit.call(this,r,t),this}enableHitGraph(){return this.hitGraphEnabled(!0),this}disableHitGraph(){return this.hitGraphEnabled(!1),this}setHitGraphEnabled(e){eJ.warn("hitGraphEnabled method is deprecated. Please use layer.listening() instead."),this.listening(e)}getHitGraphEnabled(e){return eJ.warn("hitGraphEnabled method is deprecated. Please use layer.listening() instead."),this.listening()}toggleHitCanvas(){if(this.parent&&this.parent.content){var e=this.parent;!!this.hitCanvas._canvas.parentNode?e.content.removeChild(this.hitCanvas._canvas):e.content.appendChild(this.hitCanvas._canvas)}}destroy(){return eJ.releaseCanvas(this.getNativeCanvasElement(),this.getHitCanvas()._canvas),super.destroy()}}T0.prototype.nodeType="Layer",UQ(T0),dJ.addGetterSetter(T0,"imageSmoothingEnabled",!0),dJ.addGetterSetter(T0,"clearBeforeDraw",!0),dJ.addGetterSetter(T0,"hitGraphEnabled",!0,lJ());class A0 extends T0{constructor(e){super(e),this.listening(!1),eJ.warn('Konva.Fast layer is deprecated. Please use "new Konva.Layer({ listening: false })" instead.')}}A0.prototype.nodeType="FastLayer",UQ(A0);class I0 extends jJ{_validateAdd(e){var t=e.getType();"Group"!==t&&"Shape"!==t&&eJ.throw("You may only add groups and shapes to groups.")}}I0.prototype.nodeType="Group",UQ(I0);var R0=VQ.performance&&VQ.performance.now?function(){return VQ.performance.now()}:function(){return(new Date).getTime()};class N0{constructor(e,t){this.id=N0.animIdCounter++,this.frame={time:0,timeDiff:0,lastTime:R0(),frameRate:0},this.func=e,this.setLayers(t)}setLayers(e){var t=[];return t=e?e.length>0?e:[e]:[],this.layers=t,this}getLayers(){return this.layers}addLayer(e){var t,n=this.layers,r=n.length;for(t=0;t<r;t++)if(n[t]._id===e._id)return!1;return this.layers.push(e),!0}isRunning(){var e,t=N0.animations,n=t.length;for(e=0;e<n;e++)if(t[e].id===this.id)return!0;return!1}start(){return this.stop(),this.frame.timeDiff=0,this.frame.lastTime=R0(),N0._addAnimation(this),this}stop(){return N0._removeAnimation(this),this}_updateFrameObject(e){this.frame.timeDiff=e-this.frame.lastTime,this.frame.lastTime=e,this.frame.time+=this.frame.timeDiff,this.frame.frameRate=1e3/this.frame.timeDiff}static _addAnimation(e){this.animations.push(e),this._handleAnimation()}static _removeAnimation(e){var t,n=e.id,r=this.animations,o=r.length;for(t=0;t<o;t++)if(r[t].id===n){this.animations.splice(t,1);break}}static _runFrames(){var e,t,n,r,o,i,a,s,l={},c=this.animations;for(r=0;r<c.length;r++)if(t=(e=c[r]).layers,n=e.func,e._updateFrameObject(R0()),i=t.length,!n||!1!==n.call(e,e.frame))for(o=0;o<i;o++)void 0!==(a=t[o])._id&&(l[a._id]=a);for(s in l)l.hasOwnProperty(s)&&l[s].batchDraw()}static _animationLoop(){var e=N0;e.animations.length?(e._runFrames(),eJ.requestAnimFrame(e._animationLoop)):e.animRunning=!1}static _handleAnimation(){this.animRunning||(this.animRunning=!0,eJ.requestAnimFrame(this._animationLoop))}}N0.animations=[],N0.animIdCounter=0,N0.animRunning=!1;var D0={node:1,duration:1,easing:1,onFinish:1,yoyo:1},z0=0,B0=["fill","stroke","shadowColor"];class j0{constructor(e,t,n,r,o,i,a){this.prop=e,this.propFunc=t,this.begin=r,this._pos=r,this.duration=i,this._change=0,this.prevPos=0,this.yoyo=a,this._time=0,this._position=0,this._startTime=0,this._finish=0,this.func=n,this._change=o-this.begin,this.pause()}fire(e){var t=this[e];t&&t()}setTime(e){e>this.duration?this.yoyo?(this._time=this.duration,this.reverse()):this.finish():e<0?this.yoyo?(this._time=0,this.play()):this.reset():(this._time=e,this.update())}getTime(){return this._time}setPosition(e){this.prevPos=this._pos,this.propFunc(e),this._pos=e}getPosition(e){return void 0===e&&(e=this._time),this.func(e,this.begin,this._change,this.duration)}play(){this.state=2,this._startTime=this.getTimer()-this._time,this.onEnterFrame(),this.fire("onPlay")}reverse(){this.state=3,this._time=this.duration-this._time,this._startTime=this.getTimer()-this._time,this.onEnterFrame(),this.fire("onReverse")}seek(e){this.pause(),this._time=e,this.update(),this.fire("onSeek")}reset(){this.pause(),this._time=0,this.update(),this.fire("onReset")}finish(){this.pause(),this._time=this.duration,this.update(),this.fire("onFinish")}update(){this.setPosition(this.getPosition(this._time)),this.fire("onUpdate")}onEnterFrame(){var e=this.getTimer()-this._startTime;2===this.state?this.setTime(e):3===this.state&&this.setTime(this.duration-e)}pause(){this.state=1,this.fire("onPause")}getTimer(){return(new Date).getTime()}}class F0{constructor(e){var t,n,r=this,o=e.node,i=o._id,a=e.easing||H0.Linear,s=!!e.yoyo;t=void 0===e.duration?.3:0===e.duration?.001:e.duration,this.node=o,this._id=z0++;var l=o.getLayer()||(o instanceof $Q.Stage?o.getLayers():null);for(n in l||eJ.error("Tween constructor have `node` that is not in a layer. Please add node into layer first."),this.anim=new N0((function(){r.tween.onEnterFrame()}),l),this.tween=new j0(n,(function(e){r._tweenFunc(e)}),a,0,1,1e3*t,s),this._addListeners(),F0.attrs[i]||(F0.attrs[i]={}),F0.attrs[i][this._id]||(F0.attrs[i][this._id]={}),F0.tweens[i]||(F0.tweens[i]={}),e)void 0===D0[n]&&this._addAttr(n,e[n]);this.reset(),this.onFinish=e.onFinish,this.onReset=e.onReset,this.onUpdate=e.onUpdate}_addAttr(e,t){var n,r,o,i,a,s,l,c,u=this.node,d=u._id;if((o=F0.tweens[d][e])&&delete F0.attrs[d][o][e],n=u.getAttr(e),eJ._isArray(t))if(r=[],a=Math.max(t.length,n.length),"points"===e&&t.length!==n.length&&(t.length>n.length?(l=n,n=eJ._prepareArrayForTween(n,t,u.closed())):(s=t,t=eJ._prepareArrayForTween(t,n,u.closed()))),0===e.indexOf("fill"))for(i=0;i<a;i++)if(i%2==0)r.push(t[i]-n[i]);else{var h=eJ.colorToRGBA(n[i]);c=eJ.colorToRGBA(t[i]),n[i]=h,r.push({r:c.r-h.r,g:c.g-h.g,b:c.b-h.b,a:c.a-h.a})}else for(i=0;i<a;i++)r.push(t[i]-n[i]);else-1!==B0.indexOf(e)?(n=eJ.colorToRGBA(n),r={r:(c=eJ.colorToRGBA(t)).r-n.r,g:c.g-n.g,b:c.b-n.b,a:c.a-n.a}):r=t-n;F0.attrs[d][this._id][e]={start:n,diff:r,end:t,trueEnd:s,trueStart:l},F0.tweens[d][e]=this._id}_tweenFunc(e){var t,n,r,o,i,a,s,l,c=this.node,u=F0.attrs[c._id][this._id];for(t in u){if(r=(n=u[t]).start,o=n.diff,l=n.end,eJ._isArray(r))if(i=[],s=Math.max(r.length,l.length),0===t.indexOf("fill"))for(a=0;a<s;a++)a%2==0?i.push((r[a]||0)+o[a]*e):i.push("rgba("+Math.round(r[a].r+o[a].r*e)+","+Math.round(r[a].g+o[a].g*e)+","+Math.round(r[a].b+o[a].b*e)+","+(r[a].a+o[a].a*e)+")");else for(a=0;a<s;a++)i.push((r[a]||0)+o[a]*e);else i=-1!==B0.indexOf(t)?"rgba("+Math.round(r.r+o.r*e)+","+Math.round(r.g+o.g*e)+","+Math.round(r.b+o.b*e)+","+(r.a+o.a*e)+")":r+o*e;c.setAttr(t,i)}}_addListeners(){this.tween.onPlay=()=>{this.anim.start()},this.tween.onReverse=()=>{this.anim.start()},this.tween.onPause=()=>{this.anim.stop()},this.tween.onFinish=()=>{var e=this.node,t=F0.attrs[e._id][this._id];t.points&&t.points.trueEnd&&e.setAttr("points",t.points.trueEnd),this.onFinish&&this.onFinish.call(this)},this.tween.onReset=()=>{var e=this.node,t=F0.attrs[e._id][this._id];t.points&&t.points.trueStart&&e.points(t.points.trueStart),this.onReset&&this.onReset()},this.tween.onUpdate=()=>{this.onUpdate&&this.onUpdate.call(this)}}play(){return this.tween.play(),this}reverse(){return this.tween.reverse(),this}reset(){return this.tween.reset(),this}seek(e){return this.tween.seek(1e3*e),this}pause(){return this.tween.pause(),this}finish(){return this.tween.finish(),this}destroy(){var e,t=this.node._id,n=this._id,r=F0.tweens[t];for(e in this.pause(),r)delete F0.tweens[t][e];delete F0.attrs[t][n]}}F0.attrs={},F0.tweens={},zJ.prototype.to=function(e){var t=e.onFinish;e.node=this,e.onFinish=function(){this.destroy(),t&&t()},new F0(e).play()};const H0={BackEaseIn(e,t,n,r){var o=1.70158;return n*(e/=r)*e*((o+1)*e-o)+t},BackEaseOut(e,t,n,r){var o=1.70158;return n*((e=e/r-1)*e*((o+1)*e+o)+1)+t},BackEaseInOut(e,t,n,r){var o=1.70158;return(e/=r/2)<1?n/2*(e*e*((1+(o*=1.525))*e-o))+t:n/2*((e-=2)*e*((1+(o*=1.525))*e+o)+2)+t},ElasticEaseIn(e,t,n,r,o,i){var a=0;return 0===e?t:1==(e/=r)?t+n:(i||(i=.3*r),!o||o<Math.abs(n)?(o=n,a=i/4):a=i/(2*Math.PI)*Math.asin(n/o),-o*Math.pow(2,10*(e-=1))*Math.sin((e*r-a)*(2*Math.PI)/i)+t)},ElasticEaseOut(e,t,n,r,o,i){var a=0;return 0===e?t:1==(e/=r)?t+n:(i||(i=.3*r),!o||o<Math.abs(n)?(o=n,a=i/4):a=i/(2*Math.PI)*Math.asin(n/o),o*Math.pow(2,-10*e)*Math.sin((e*r-a)*(2*Math.PI)/i)+n+t)},ElasticEaseInOut(e,t,n,r,o,i){var a=0;return 0===e?t:2==(e/=r/2)?t+n:(i||(i=r*(.3*1.5)),!o||o<Math.abs(n)?(o=n,a=i/4):a=i/(2*Math.PI)*Math.asin(n/o),e<1?o*Math.pow(2,10*(e-=1))*Math.sin((e*r-a)*(2*Math.PI)/i)*-.5+t:o*Math.pow(2,-10*(e-=1))*Math.sin((e*r-a)*(2*Math.PI)/i)*.5+n+t)},BounceEaseOut:(e,t,n,r)=>(e/=r)<1/2.75?n*(7.5625*e*e)+t:e<2/2.75?n*(7.5625*(e-=1.5/2.75)*e+.75)+t:e<2.5/2.75?n*(7.5625*(e-=2.25/2.75)*e+.9375)+t:n*(7.5625*(e-=2.625/2.75)*e+.984375)+t,BounceEaseIn:(e,t,n,r)=>n-H0.BounceEaseOut(r-e,0,n,r)+t,BounceEaseInOut:(e,t,n,r)=>e<r/2?.5*H0.BounceEaseIn(2*e,0,n,r)+t:.5*H0.BounceEaseOut(2*e-r,0,n,r)+.5*n+t,EaseIn:(e,t,n,r)=>n*(e/=r)*e+t,EaseOut:(e,t,n,r)=>-n*(e/=r)*(e-2)+t,EaseInOut:(e,t,n,r)=>(e/=r/2)<1?n/2*e*e+t:-n/2*(--e*(e-2)-1)+t,StrongEaseIn:(e,t,n,r)=>n*(e/=r)*e*e*e*e+t,StrongEaseOut:(e,t,n,r)=>n*((e=e/r-1)*e*e*e*e+1)+t,StrongEaseInOut:(e,t,n,r)=>(e/=r/2)<1?n/2*e*e*e*e*e+t:n/2*((e-=2)*e*e*e*e+2)+t,Linear:(e,t,n,r)=>n*e/r+t},W0=eJ._assign($Q,{Util:eJ,Transform:GQ,Node:zJ,Container:jJ,Stage:b0,stages:y0,Layer:T0,FastLayer:A0,Group:I0,DD:wJ,Shape:L0,shapes:P0,Animation:N0,Tween:F0,Easings:H0,Context:pJ,Canvas:yJ});class V0 extends L0{_sceneFunc(e){var t=$Q.getAngle(this.angle()),n=this.clockwise();e.beginPath(),e.arc(0,0,this.outerRadius(),0,t,n),e.arc(0,0,this.innerRadius(),t,0,!n),e.closePath(),e.fillStrokeShape(this)}getWidth(){return 2*this.outerRadius()}getHeight(){return 2*this.outerRadius()}setWidth(e){this.outerRadius(e/2)}setHeight(e){this.outerRadius(e/2)}getSelfRect(){const e=this.innerRadius(),t=this.outerRadius(),n=this.clockwise(),r=$Q.getAngle(n?360-this.angle():this.angle()),o=Math.cos(Math.min(r,Math.PI)),i=Math.sin(Math.min(Math.max(Math.PI,r),3*Math.PI/2)),a=Math.sin(Math.min(r,Math.PI/2)),s=o*(o>0?e:t),l=i*(i>0?e:t),c=a*(a>0?t:e);return{x:s,y:n?-1*c:l,width:1*t-s,height:c-l}}}function $0(e,t,n,r,o,i,a){var s=Math.sqrt(Math.pow(n-e,2)+Math.pow(r-t,2)),l=Math.sqrt(Math.pow(o-n,2)+Math.pow(i-r,2)),c=a*s/(s+l),u=a*l/(s+l);return[n-c*(o-e),r-c*(i-t),n+u*(o-e),r+u*(i-t)]}function U0(e,t){var n,r,o=e.length,i=[];for(n=2;n<o-2;n+=2)r=$0(e[n-2],e[n-1],e[n],e[n+1],e[n+2],e[n+3],t),isNaN(r[0])||(i.push(r[0]),i.push(r[1]),i.push(e[n]),i.push(e[n+1]),i.push(r[2]),i.push(r[3]));return i}V0.prototype._centroid=!0,V0.prototype.className="Arc",V0.prototype._attrsAffectingSize=["innerRadius","outerRadius"],UQ(V0),dJ.addGetterSetter(V0,"innerRadius",0,rJ()),dJ.addGetterSetter(V0,"outerRadius",0,rJ()),dJ.addGetterSetter(V0,"angle",0,rJ()),dJ.addGetterSetter(V0,"clockwise",!1,lJ());class G0 extends L0{constructor(e){super(e),this.on("pointsChange.konva tensionChange.konva closedChange.konva bezierChange.konva",(function(){this._clearCache("tensionPoints")}))}_sceneFunc(e){var t,n,r,o=this.points(),i=o.length,a=this.tension(),s=this.closed(),l=this.bezier();if(i){if(e.beginPath(),e.moveTo(o[0],o[1]),0!==a&&i>4){for(n=(t=this.getTensionPoints()).length,r=s?0:4,s||e.quadraticCurveTo(t[0],t[1],t[2],t[3]);r<n-2;)e.bezierCurveTo(t[r++],t[r++],t[r++],t[r++],t[r++],t[r++]);s||e.quadraticCurveTo(t[n-2],t[n-1],o[i-2],o[i-1])}else if(l)for(r=2;r<i;)e.bezierCurveTo(o[r++],o[r++],o[r++],o[r++],o[r++],o[r++]);else for(r=2;r<i;r+=2)e.lineTo(o[r],o[r+1]);s?(e.closePath(),e.fillStrokeShape(this)):e.strokeShape(this)}}getTensionPoints(){return this._getCache("tensionPoints",this._getTensionPoints)}_getTensionPoints(){return this.closed()?this._getTensionPointsClosed():U0(this.points(),this.tension())}_getTensionPointsClosed(){var e=this.points(),t=e.length,n=this.tension(),r=$0(e[t-2],e[t-1],e[0],e[1],e[2],e[3],n),o=$0(e[t-4],e[t-3],e[t-2],e[t-1],e[0],e[1],n),i=U0(e,n);return[r[2],r[3]].concat(i).concat([o[0],o[1],e[t-2],e[t-1],o[2],o[3],r[0],r[1],e[0],e[1]])}getWidth(){return this.getSelfRect().width}getHeight(){return this.getSelfRect().height}getSelfRect(){var e=this.points();if(e.length<4)return{x:e[0]||0,y:e[1]||0,width:0,height:0};for(var t,n,r=(e=0!==this.tension()?[e[0],e[1],...this._getTensionPoints(),e[e.length-2],e[e.length-1]]:this.points())[0],o=e[0],i=e[1],a=e[1],s=0;s<e.length/2;s++)t=e[2*s],n=e[2*s+1],r=Math.min(r,t),o=Math.max(o,t),i=Math.min(i,n),a=Math.max(a,n);return{x:r,y:i,width:o-r,height:a-i}}}G0.prototype.className="Line",G0.prototype._attrsAffectingSize=["points","bezier","tension"],UQ(G0),dJ.addGetterSetter(G0,"closed",!1),dJ.addGetterSetter(G0,"bezier",!1),dJ.addGetterSetter(G0,"tension",0,rJ()),dJ.addGetterSetter(G0,"points",[],function(){if($Q.isUnminified)return function(e,t){const n=Int8Array?Object.getPrototypeOf(Int8Array):null;return n&&e instanceof n||(eJ._isArray(e)?e.forEach((function(e){eJ._isNumber(e)||eJ.warn('"'+t+'" attribute has non numeric element '+e+". Make sure that all elements are numbers.")})):eJ.warn(tJ(e)+' is a not valid value for "'+t+'" attribute. The value should be a array of numbers.')),e}}());class q0 extends L0{constructor(e){super(e),this.dataArray=[],this.pathLength=0,this.dataArray=q0.parsePathData(this.data()),this.pathLength=0;for(var t=0;t<this.dataArray.length;++t)this.pathLength+=this.dataArray[t].pathLength;this.on("dataChange.konva",(function(){this.dataArray=q0.parsePathData(this.data()),this.pathLength=0;for(var e=0;e<this.dataArray.length;++e)this.pathLength+=this.dataArray[e].pathLength}))}_sceneFunc(e){var t=this.dataArray;e.beginPath();for(var n=!1,r=0;r<t.length;r++){var o=t[r].command,i=t[r].points;switch(o){case"L":e.lineTo(i[0],i[1]);break;case"M":e.moveTo(i[0],i[1]);break;case"C":e.bezierCurveTo(i[0],i[1],i[2],i[3],i[4],i[5]);break;case"Q":e.quadraticCurveTo(i[0],i[1],i[2],i[3]);break;case"A":var a=i[0],s=i[1],l=i[2],c=i[3],u=i[4],d=i[5],h=i[6],f=i[7],p=l>c?l:c,g=l>c?1:l/c,m=l>c?c/l:1;e.translate(a,s),e.rotate(h),e.scale(g,m),e.arc(0,0,p,u,u+d,1-f),e.scale(1/g,1/m),e.rotate(-h),e.translate(-a,-s);break;case"z":n=!0,e.closePath()}}n||this.hasFill()?e.fillStrokeShape(this):e.strokeShape(this)}getSelfRect(){var e=[];this.dataArray.forEach((function(t){if("A"===t.command){var n=t.points[4],r=t.points[5],o=t.points[4]+r,i=Math.PI/180;if(Math.abs(n-o)<i&&(i=Math.abs(n-o)),r<0)for(let r=n-i;r>o;r-=i){const n=q0.getPointOnEllipticalArc(t.points[0],t.points[1],t.points[2],t.points[3],r,0);e.push(n.x,n.y)}else for(let r=n+i;r<o;r+=i){const n=q0.getPointOnEllipticalArc(t.points[0],t.points[1],t.points[2],t.points[3],r,0);e.push(n.x,n.y)}}else if("C"===t.command)for(let a=0;a<=1;a+=.01){const n=q0.getPointOnCubicBezier(a,t.start.x,t.start.y,t.points[0],t.points[1],t.points[2],t.points[3],t.points[4],t.points[5]);e.push(n.x,n.y)}else e=e.concat(t.points)}));for(var t,n,r=e[0],o=e[0],i=e[1],a=e[1],s=0;s<e.length/2;s++)t=e[2*s],n=e[2*s+1],isNaN(t)||(r=Math.min(r,t),o=Math.max(o,t)),isNaN(n)||(i=Math.min(i,n),a=Math.max(a,n));return{x:r,y:i,width:o-r,height:a-i}}getLength(){return this.pathLength}getPointAtLength(e){var t,n=0,r=this.dataArray.length;if(!r)return null;for(;n<r&&e>this.dataArray[n].pathLength;)e-=this.dataArray[n].pathLength,++n;if(n===r)return{x:(t=this.dataArray[n-1].points.slice(-2))[0],y:t[1]};if(e<.01)return{x:(t=this.dataArray[n].points.slice(0,2))[0],y:t[1]};var o=this.dataArray[n],i=o.points;switch(o.command){case"L":return q0.getPointOnLine(e,o.start.x,o.start.y,i[0],i[1]);case"C":return q0.getPointOnCubicBezier(e/o.pathLength,o.start.x,o.start.y,i[0],i[1],i[2],i[3],i[4],i[5]);case"Q":return q0.getPointOnQuadraticBezier(e/o.pathLength,o.start.x,o.start.y,i[0],i[1],i[2],i[3]);case"A":var a=i[0],s=i[1],l=i[2],c=i[3],u=i[4],d=i[5],h=i[6];return u+=d*e/o.pathLength,q0.getPointOnEllipticalArc(a,s,l,c,u,h)}return null}static getLineLength(e,t,n,r){return Math.sqrt((n-e)*(n-e)+(r-t)*(r-t))}static getPointOnLine(e,t,n,r,o,i,a){void 0===i&&(i=t),void 0===a&&(a=n);var s=(o-n)/(r-t+1e-8),l=Math.sqrt(e*e/(1+s*s));r<t&&(l*=-1);var c,u=s*l;if(r===t)c={x:i,y:a+u};else if((a-n)/(i-t+1e-8)===s)c={x:i+l,y:a+u};else{var d,h,f=this.getLineLength(t,n,r,o),p=(i-t)*(r-t)+(a-n)*(o-n);d=t+(p/=f*f)*(r-t),h=n+p*(o-n);var g=this.getLineLength(i,a,d,h),m=Math.sqrt(e*e-g*g);l=Math.sqrt(m*m/(1+s*s)),r<t&&(l*=-1),c={x:d+l,y:h+(u=s*l)}}return c}static getPointOnCubicBezier(e,t,n,r,o,i,a,s,l){function c(e){return e*e*e}function u(e){return 3*e*e*(1-e)}function d(e){return 3*e*(1-e)*(1-e)}function h(e){return(1-e)*(1-e)*(1-e)}return{x:s*c(e)+i*u(e)+r*d(e)+t*h(e),y:l*c(e)+a*u(e)+o*d(e)+n*h(e)}}static getPointOnQuadraticBezier(e,t,n,r,o,i,a){function s(e){return e*e}function l(e){return 2*e*(1-e)}function c(e){return(1-e)*(1-e)}return{x:i*s(e)+r*l(e)+t*c(e),y:a*s(e)+o*l(e)+n*c(e)}}static getPointOnEllipticalArc(e,t,n,r,o,i){var a=Math.cos(i),s=Math.sin(i),l=n*Math.cos(o),c=r*Math.sin(o);return{x:e+(l*a-c*s),y:t+(l*s+c*a)}}static parsePathData(e){if(!e)return[];var t=e,n=["m","M","l","L","v","V","h","H","z","Z","c","C","q","Q","t","T","s","S","a","A"];t=t.replace(new RegExp(" ","g"),",");for(var r=0;r<n.length;r++)t=t.replace(new RegExp(n[r],"g"),"|"+n[r]);var o,i=t.split("|"),a=[],s=[],l=0,c=0,u=/([-+]?((\d+\.\d+)|((\d+)|(\.\d+)))(?:e[-+]?\d+)?)/gi;for(r=1;r<i.length;r++){var d=i[r],h=d.charAt(0);for(d=d.slice(1),s.length=0;o=u.exec(d);)s.push(o[0]);for(var f=[],p=0,g=s.length;p<g;p++)if("00"!==s[p]){var m=parseFloat(s[p]);isNaN(m)?f.push(0):f.push(m)}else f.push(0,0);for(;f.length>0&&!isNaN(f[0]);){var v,y,b,x,w,k,S,C,_,E,P=null,L=[],O=l,M=c;switch(h){case"l":l+=f.shift(),c+=f.shift(),P="L",L.push(l,c);break;case"L":l=f.shift(),c=f.shift(),L.push(l,c);break;case"m":var T=f.shift(),A=f.shift();if(l+=T,c+=A,P="M",a.length>2&&"z"===a[a.length-1].command)for(var I=a.length-2;I>=0;I--)if("M"===a[I].command){l=a[I].points[0]+T,c=a[I].points[1]+A;break}L.push(l,c),h="l";break;case"M":l=f.shift(),c=f.shift(),P="M",L.push(l,c),h="L";break;case"h":l+=f.shift(),P="L",L.push(l,c);break;case"H":l=f.shift(),P="L",L.push(l,c);break;case"v":c+=f.shift(),P="L",L.push(l,c);break;case"V":c=f.shift(),P="L",L.push(l,c);break;case"C":L.push(f.shift(),f.shift(),f.shift(),f.shift()),l=f.shift(),c=f.shift(),L.push(l,c);break;case"c":L.push(l+f.shift(),c+f.shift(),l+f.shift(),c+f.shift()),l+=f.shift(),c+=f.shift(),P="C",L.push(l,c);break;case"S":y=l,b=c,"C"===(v=a[a.length-1]).command&&(y=l+(l-v.points[2]),b=c+(c-v.points[3])),L.push(y,b,f.shift(),f.shift()),l=f.shift(),c=f.shift(),P="C",L.push(l,c);break;case"s":y=l,b=c,"C"===(v=a[a.length-1]).command&&(y=l+(l-v.points[2]),b=c+(c-v.points[3])),L.push(y,b,l+f.shift(),c+f.shift()),l+=f.shift(),c+=f.shift(),P="C",L.push(l,c);break;case"Q":L.push(f.shift(),f.shift()),l=f.shift(),c=f.shift(),L.push(l,c);break;case"q":L.push(l+f.shift(),c+f.shift()),l+=f.shift(),c+=f.shift(),P="Q",L.push(l,c);break;case"T":y=l,b=c,"Q"===(v=a[a.length-1]).command&&(y=l+(l-v.points[0]),b=c+(c-v.points[1])),l=f.shift(),c=f.shift(),P="Q",L.push(y,b,l,c);break;case"t":y=l,b=c,"Q"===(v=a[a.length-1]).command&&(y=l+(l-v.points[0]),b=c+(c-v.points[1])),l+=f.shift(),c+=f.shift(),P="Q",L.push(y,b,l,c);break;case"A":x=f.shift(),w=f.shift(),k=f.shift(),S=f.shift(),C=f.shift(),_=l,E=c,l=f.shift(),c=f.shift(),P="A",L=this.convertEndpointToCenterParameterization(_,E,l,c,S,C,x,w,k);break;case"a":x=f.shift(),w=f.shift(),k=f.shift(),S=f.shift(),C=f.shift(),_=l,E=c,l+=f.shift(),c+=f.shift(),P="A",L=this.convertEndpointToCenterParameterization(_,E,l,c,S,C,x,w,k)}a.push({command:P||h,points:L,start:{x:O,y:M},pathLength:this.calcLength(O,M,P||h,L)})}"z"!==h&&"Z"!==h||a.push({command:"z",points:[],start:void 0,pathLength:0})}return a}static calcLength(e,t,n,r){var o,i,a,s,l=q0;switch(n){case"L":return l.getLineLength(e,t,r[0],r[1]);case"C":for(o=0,i=l.getPointOnCubicBezier(0,e,t,r[0],r[1],r[2],r[3],r[4],r[5]),s=.01;s<=1;s+=.01)a=l.getPointOnCubicBezier(s,e,t,r[0],r[1],r[2],r[3],r[4],r[5]),o+=l.getLineLength(i.x,i.y,a.x,a.y),i=a;return o;case"Q":for(o=0,i=l.getPointOnQuadraticBezier(0,e,t,r[0],r[1],r[2],r[3]),s=.01;s<=1;s+=.01)a=l.getPointOnQuadraticBezier(s,e,t,r[0],r[1],r[2],r[3]),o+=l.getLineLength(i.x,i.y,a.x,a.y),i=a;return o;case"A":o=0;var c=r[4],u=r[5],d=r[4]+u,h=Math.PI/180;if(Math.abs(c-d)<h&&(h=Math.abs(c-d)),i=l.getPointOnEllipticalArc(r[0],r[1],r[2],r[3],c,0),u<0)for(s=c-h;s>d;s-=h)a=l.getPointOnEllipticalArc(r[0],r[1],r[2],r[3],s,0),o+=l.getLineLength(i.x,i.y,a.x,a.y),i=a;else for(s=c+h;s<d;s+=h)a=l.getPointOnEllipticalArc(r[0],r[1],r[2],r[3],s,0),o+=l.getLineLength(i.x,i.y,a.x,a.y),i=a;return a=l.getPointOnEllipticalArc(r[0],r[1],r[2],r[3],d,0),o+=l.getLineLength(i.x,i.y,a.x,a.y)}return 0}static convertEndpointToCenterParameterization(e,t,n,r,o,i,a,s,l){var c=l*(Math.PI/180),u=Math.cos(c)*(e-n)/2+Math.sin(c)*(t-r)/2,d=-1*Math.sin(c)*(e-n)/2+Math.cos(c)*(t-r)/2,h=u*u/(a*a)+d*d/(s*s);h>1&&(a*=Math.sqrt(h),s*=Math.sqrt(h));var f=Math.sqrt((a*a*(s*s)-a*a*(d*d)-s*s*(u*u))/(a*a*(d*d)+s*s*(u*u)));o===i&&(f*=-1),isNaN(f)&&(f=0);var p=f*a*d/s,g=f*-s*u/a,m=(e+n)/2+Math.cos(c)*p-Math.sin(c)*g,v=(t+r)/2+Math.sin(c)*p+Math.cos(c)*g,y=function(e){return Math.sqrt(e[0]*e[0]+e[1]*e[1])},b=function(e,t){return(e[0]*t[0]+e[1]*t[1])/(y(e)*y(t))},x=function(e,t){return(e[0]*t[1]<e[1]*t[0]?-1:1)*Math.acos(b(e,t))},w=x([1,0],[(u-p)/a,(d-g)/s]),k=[(u-p)/a,(d-g)/s],S=[(-1*u-p)/a,(-1*d-g)/s],C=x(k,S);return b(k,S)<=-1&&(C=Math.PI),b(k,S)>=1&&(C=0),0===i&&C>0&&(C-=2*Math.PI),1===i&&C<0&&(C+=2*Math.PI),[m,v,a,s,w,C,c,i]}}q0.prototype.className="Path",q0.prototype._attrsAffectingSize=["data"],UQ(q0),dJ.addGetterSetter(q0,"data");class Y0 extends G0{_sceneFunc(e){super._sceneFunc(e);var t=2*Math.PI,n=this.points(),r=n,o=0!==this.tension()&&n.length>4;o&&(r=this.getTensionPoints());var i,a,s=this.pointerLength(),l=n.length;if(o){const e=[r[r.length-4],r[r.length-3],r[r.length-2],r[r.length-1],n[l-2],n[l-1]],t=q0.calcLength(r[r.length-4],r[r.length-3],"C",e),o=q0.getPointOnQuadraticBezier(Math.min(1,1-s/t),e[0],e[1],e[2],e[3],e[4],e[5]);i=n[l-2]-o.x,a=n[l-1]-o.y}else i=n[l-2]-n[l-4],a=n[l-1]-n[l-3];var c=(Math.atan2(a,i)+t)%t,u=this.pointerWidth();this.pointerAtEnding()&&(e.save(),e.beginPath(),e.translate(n[l-2],n[l-1]),e.rotate(c),e.moveTo(0,0),e.lineTo(-s,u/2),e.lineTo(-s,-u/2),e.closePath(),e.restore(),this.__fillStroke(e)),this.pointerAtBeginning()&&(e.save(),e.beginPath(),e.translate(n[0],n[1]),o?(i=(r[0]+r[2])/2-n[0],a=(r[1]+r[3])/2-n[1]):(i=n[2]-n[0],a=n[3]-n[1]),e.rotate((Math.atan2(-a,-i)+t)%t),e.moveTo(0,0),e.lineTo(-s,u/2),e.lineTo(-s,-u/2),e.closePath(),e.restore(),this.__fillStroke(e))}__fillStroke(e){var t=this.dashEnabled();t&&(this.attrs.dashEnabled=!1,e.setLineDash([])),e.fillStrokeShape(this),t&&(this.attrs.dashEnabled=!0)}getSelfRect(){const e=super.getSelfRect(),t=this.pointerWidth()/2;return{x:e.x-t,y:e.y-t,width:e.width+2*t,height:e.height+2*t}}}Y0.prototype.className="Arrow",UQ(Y0),dJ.addGetterSetter(Y0,"pointerLength",10,rJ()),dJ.addGetterSetter(Y0,"pointerWidth",10,rJ()),dJ.addGetterSetter(Y0,"pointerAtBeginning",!1),dJ.addGetterSetter(Y0,"pointerAtEnding",!0);class Z0 extends L0{_sceneFunc(e){e.beginPath(),e.arc(0,0,this.attrs.radius||0,0,2*Math.PI,!1),e.closePath(),e.fillStrokeShape(this)}getWidth(){return 2*this.radius()}getHeight(){return 2*this.radius()}setWidth(e){this.radius()!==e/2&&this.radius(e/2)}setHeight(e){this.radius()!==e/2&&this.radius(e/2)}}Z0.prototype._centroid=!0,Z0.prototype.className="Circle",Z0.prototype._attrsAffectingSize=["radius"],UQ(Z0),dJ.addGetterSetter(Z0,"radius",0,rJ());class X0 extends L0{_sceneFunc(e){var t=this.radiusX(),n=this.radiusY();e.beginPath(),e.save(),t!==n&&e.scale(1,n/t),e.arc(0,0,t,0,2*Math.PI,!1),e.restore(),e.closePath(),e.fillStrokeShape(this)}getWidth(){return 2*this.radiusX()}getHeight(){return 2*this.radiusY()}setWidth(e){this.radiusX(e/2)}setHeight(e){this.radiusY(e/2)}}X0.prototype.className="Ellipse",X0.prototype._centroid=!0,X0.prototype._attrsAffectingSize=["radiusX","radiusY"],UQ(X0),dJ.addComponentsGetterSetter(X0,"radius",["x","y"]),dJ.addGetterSetter(X0,"radiusX",0,rJ()),dJ.addGetterSetter(X0,"radiusY",0,rJ());class K0 extends L0{constructor(e){super(e),this.on("imageChange.konva",(()=>{this._setImageLoad()})),this._setImageLoad()}_setImageLoad(){const e=this.image();e&&e.complete||e&&4===e.readyState||e&&e.addEventListener&&e.addEventListener("load",(()=>{this._requestDraw()}))}_useBufferCanvas(){return super._useBufferCanvas(!0)}_sceneFunc(e){const t=this.getWidth(),n=this.getHeight(),r=this.attrs.image;let o;if(r){const e=this.attrs.cropWidth,i=this.attrs.cropHeight;o=e&&i?[r,this.cropX(),this.cropY(),e,i,0,0,t,n]:[r,0,0,t,n]}(this.hasFill()||this.hasStroke())&&(e.beginPath(),e.rect(0,0,t,n),e.closePath(),e.fillStrokeShape(this)),r&&e.drawImage.apply(e,o)}_hitFunc(e){var t=this.width(),n=this.height();e.beginPath(),e.rect(0,0,t,n),e.closePath(),e.fillStrokeShape(this)}getWidth(){var e,t;return null!==(e=this.attrs.width)&&void 0!==e?e:null===(t=this.image())||void 0===t?void 0:t.width}getHeight(){var e,t;return null!==(e=this.attrs.height)&&void 0!==e?e:null===(t=this.image())||void 0===t?void 0:t.height}static fromURL(e,t,n=null){var r=eJ.createImageElement();r.onload=function(){var e=new K0({image:r});t(e)},r.onerror=n,r.crossOrigin="Anonymous",r.src=e}}K0.prototype.className="Image",UQ(K0),dJ.addGetterSetter(K0,"image"),dJ.addComponentsGetterSetter(K0,"crop",["x","y","width","height"]),dJ.addGetterSetter(K0,"cropX",0,rJ()),dJ.addGetterSetter(K0,"cropY",0,rJ()),dJ.addGetterSetter(K0,"cropWidth",0,rJ()),dJ.addGetterSetter(K0,"cropHeight",0,rJ());var Q0=["fontFamily","fontSize","fontStyle","padding","lineHeight","text","width","height","pointerDirection","pointerWidth","pointerHeight"],J0="up",e1="right",t1="down",n1="left",r1=Q0.length;class o1 extends I0{constructor(e){super(e),this.on("add.konva",(function(e){this._addListeners(e.child),this._sync()}))}getText(){return this.find("Text")[0]}getTag(){return this.find("Tag")[0]}_addListeners(e){var t,n=this,r=function(){n._sync()};for(t=0;t<r1;t++)e.on(Q0[t]+"Change.konva",r)}getWidth(){return this.getText().width()}getHeight(){return this.getText().height()}_sync(){var e,t,n,r,o,i,a,s=this.getText(),l=this.getTag();if(s&&l){switch(e=s.width(),t=s.height(),n=l.pointerDirection(),r=l.pointerWidth(),a=l.pointerHeight(),o=0,i=0,n){case J0:o=e/2,i=-1*a;break;case e1:o=e+r,i=t/2;break;case t1:o=e/2,i=t+a;break;case n1:o=-1*r,i=t/2}l.setAttrs({x:-1*o,y:-1*i,width:e,height:t}),s.setAttrs({x:-1*o,y:-1*i})}}}o1.prototype.className="Label",UQ(o1);class i1 extends L0{_sceneFunc(e){var t=this.width(),n=this.height(),r=this.pointerDirection(),o=this.pointerWidth(),i=this.pointerHeight(),a=this.cornerRadius();let s=0,l=0,c=0,u=0;"number"==typeof a?s=l=c=u=Math.min(a,t/2,n/2):(s=Math.min(a[0]||0,t/2,n/2),l=Math.min(a[1]||0,t/2,n/2),u=Math.min(a[2]||0,t/2,n/2),c=Math.min(a[3]||0,t/2,n/2)),e.beginPath(),e.moveTo(s,0),r===J0&&(e.lineTo((t-o)/2,0),e.lineTo(t/2,-1*i),e.lineTo((t+o)/2,0)),e.lineTo(t-l,0),e.arc(t-l,l,l,3*Math.PI/2,0,!1),r===e1&&(e.lineTo(t,(n-i)/2),e.lineTo(t+o,n/2),e.lineTo(t,(n+i)/2)),e.lineTo(t,n-u),e.arc(t-u,n-u,u,0,Math.PI/2,!1),r===t1&&(e.lineTo((t+o)/2,n),e.lineTo(t/2,n+i),e.lineTo((t-o)/2,n)),e.lineTo(c,n),e.arc(c,n-c,c,Math.PI/2,Math.PI,!1),r===n1&&(e.lineTo(0,(n+i)/2),e.lineTo(-1*o,n/2),e.lineTo(0,(n-i)/2)),e.lineTo(0,s),e.arc(s,s,s,Math.PI,3*Math.PI/2,!1),e.closePath(),e.fillStrokeShape(this)}getSelfRect(){var e=0,t=0,n=this.pointerWidth(),r=this.pointerHeight(),o=this.pointerDirection(),i=this.width(),a=this.height();return o===J0?(t-=r,a+=r):o===t1?a+=r:o===n1?(e-=1.5*n,i+=n):o===e1&&(i+=1.5*n),{x:e,y:t,width:i,height:a}}}i1.prototype.className="Tag",UQ(i1),dJ.addGetterSetter(i1,"pointerDirection","none"),dJ.addGetterSetter(i1,"pointerWidth",0,rJ()),dJ.addGetterSetter(i1,"pointerHeight",0,rJ()),dJ.addGetterSetter(i1,"cornerRadius",0,oJ(4));class a1 extends L0{_sceneFunc(e){var t=this.cornerRadius(),n=this.width(),r=this.height();if(e.beginPath(),t){let o=0,i=0,a=0,s=0;"number"==typeof t?o=i=a=s=Math.min(t,n/2,r/2):(o=Math.min(t[0]||0,n/2,r/2),i=Math.min(t[1]||0,n/2,r/2),s=Math.min(t[2]||0,n/2,r/2),a=Math.min(t[3]||0,n/2,r/2)),e.moveTo(o,0),e.lineTo(n-i,0),e.arc(n-i,i,i,3*Math.PI/2,0,!1),e.lineTo(n,r-s),e.arc(n-s,r-s,s,0,Math.PI/2,!1),e.lineTo(a,r),e.arc(a,r-a,a,Math.PI/2,Math.PI,!1),e.lineTo(0,o),e.arc(o,o,o,Math.PI,3*Math.PI/2,!1)}else e.rect(0,0,n,r);e.closePath(),e.fillStrokeShape(this)}}a1.prototype.className="Rect",UQ(a1),dJ.addGetterSetter(a1,"cornerRadius",0,oJ(4));class s1 extends L0{_sceneFunc(e){const t=this._getPoints();e.beginPath(),e.moveTo(t[0].x,t[0].y);for(var n=1;n<t.length;n++)e.lineTo(t[n].x,t[n].y);e.closePath(),e.fillStrokeShape(this)}_getPoints(){const e=this.attrs.sides,t=this.attrs.radius||0,n=[];for(var r=0;r<e;r++)n.push({x:t*Math.sin(2*r*Math.PI/e),y:-1*t*Math.cos(2*r*Math.PI/e)});return n}getSelfRect(){const e=this._getPoints();var t=e[0].x,n=e[0].y,r=e[0].x,o=e[0].y;return e.forEach((e=>{t=Math.min(t,e.x),n=Math.max(n,e.x),r=Math.min(r,e.y),o=Math.max(o,e.y)})),{x:t,y:r,width:n-t,height:o-r}}getWidth(){return 2*this.radius()}getHeight(){return 2*this.radius()}setWidth(e){this.radius(e/2)}setHeight(e){this.radius(e/2)}}s1.prototype.className="RegularPolygon",s1.prototype._centroid=!0,s1.prototype._attrsAffectingSize=["radius"],UQ(s1),dJ.addGetterSetter(s1,"radius",0,rJ()),dJ.addGetterSetter(s1,"sides",0,rJ());var l1=2*Math.PI;class c1 extends L0{_sceneFunc(e){e.beginPath(),e.arc(0,0,this.innerRadius(),0,l1,!1),e.moveTo(this.outerRadius(),0),e.arc(0,0,this.outerRadius(),l1,0,!0),e.closePath(),e.fillStrokeShape(this)}getWidth(){return 2*this.outerRadius()}getHeight(){return 2*this.outerRadius()}setWidth(e){this.outerRadius(e/2)}setHeight(e){this.outerRadius(e/2)}}c1.prototype.className="Ring",c1.prototype._centroid=!0,c1.prototype._attrsAffectingSize=["innerRadius","outerRadius"],UQ(c1),dJ.addGetterSetter(c1,"innerRadius",0,rJ()),dJ.addGetterSetter(c1,"outerRadius",0,rJ());class u1 extends L0{constructor(e){super(e),this._updated=!0,this.anim=new N0((()=>{var e=this._updated;return this._updated=!1,e})),this.on("animationChange.konva",(function(){this.frameIndex(0)})),this.on("frameIndexChange.konva",(function(){this._updated=!0})),this.on("frameRateChange.konva",(function(){this.anim.isRunning()&&(clearInterval(this.interval),this._setInterval())}))}_sceneFunc(e){var t=this.animation(),n=this.frameIndex(),r=4*n,o=this.animations()[t],i=this.frameOffsets(),a=o[r+0],s=o[r+1],l=o[r+2],c=o[r+3],u=this.image();if((this.hasFill()||this.hasStroke())&&(e.beginPath(),e.rect(0,0,l,c),e.closePath(),e.fillStrokeShape(this)),u)if(i){var d=i[t],h=2*n;e.drawImage(u,a,s,l,c,d[h+0],d[h+1],l,c)}else e.drawImage(u,a,s,l,c,0,0,l,c)}_hitFunc(e){var t=this.animation(),n=this.frameIndex(),r=4*n,o=this.animations()[t],i=this.frameOffsets(),a=o[r+2],s=o[r+3];if(e.beginPath(),i){var l=i[t],c=2*n;e.rect(l[c+0],l[c+1],a,s)}else e.rect(0,0,a,s);e.closePath(),e.fillShape(this)}_useBufferCanvas(){return super._useBufferCanvas(!0)}_setInterval(){var e=this;this.interval=setInterval((function(){e._updateIndex()}),1e3/this.frameRate())}start(){if(!this.isRunning()){var e=this.getLayer();this.anim.setLayers(e),this._setInterval(),this.anim.start()}}stop(){this.anim.stop(),clearInterval(this.interval)}isRunning(){return this.anim.isRunning()}_updateIndex(){var e=this.frameIndex(),t=this.animation();e<this.animations()[t].length/4-1?this.frameIndex(e+1):this.frameIndex(0)}}u1.prototype.className="Sprite",UQ(u1),dJ.addGetterSetter(u1,"animation"),dJ.addGetterSetter(u1,"animations"),dJ.addGetterSetter(u1,"frameOffsets"),dJ.addGetterSetter(u1,"image"),dJ.addGetterSetter(u1,"frameIndex",0,rJ()),dJ.addGetterSetter(u1,"frameRate",17,rJ()),dJ.backCompat(u1,{index:"frameIndex",getIndex:"getFrameIndex",setIndex:"setFrameIndex"});class d1 extends L0{_sceneFunc(e){var t=this.innerRadius(),n=this.outerRadius(),r=this.numPoints();e.beginPath(),e.moveTo(0,0-n);for(var o=1;o<2*r;o++){var i=o%2==0?n:t,a=i*Math.sin(o*Math.PI/r),s=-1*i*Math.cos(o*Math.PI/r);e.lineTo(a,s)}e.closePath(),e.fillStrokeShape(this)}getWidth(){return 2*this.outerRadius()}getHeight(){return 2*this.outerRadius()}setWidth(e){this.outerRadius(e/2)}setHeight(e){this.outerRadius(e/2)}}function h1(e){return Array.from(e)}d1.prototype.className="Star",d1.prototype._centroid=!0,d1.prototype._attrsAffectingSize=["innerRadius","outerRadius"],UQ(d1),dJ.addGetterSetter(d1,"numPoints",5,rJ()),dJ.addGetterSetter(d1,"innerRadius",0,rJ()),dJ.addGetterSetter(d1,"outerRadius",0,rJ());var f1,p1="auto",g1="justify",m1="left",v1="middle",y1="normal",b1=" ",x1="none",w1=["fontFamily","fontSize","fontStyle","fontVariant","padding","align","verticalAlign","lineHeight","text","width","height","wrap","ellipsis","letterSpacing"],k1=w1.length;function S1(){return f1||(f1=eJ.createCanvasElement().getContext("2d"))}class C1 extends L0{constructor(e){super(function(e){return(e=e||{}).fillLinearGradientColorStops||e.fillRadialGradientColorStops||e.fillPatternImage||(e.fill=e.fill||"black"),e}(e)),this._partialTextX=0,this._partialTextY=0;for(var t=0;t<k1;t++)this.on(w1[t]+"Change.konva",this._setTextData);this._setTextData()}_sceneFunc(e){var t=this.textArr,n=t.length;if(this.text()){var r,o=this.padding(),i=this.fontSize(),a=this.lineHeight()*i,s=this.verticalAlign(),l=0,c=this.align(),u=this.getWidth(),d=this.letterSpacing(),h=this.fill(),f=this.textDecoration(),p=-1!==f.indexOf("underline"),g=-1!==f.indexOf("line-through"),m=0,v=(m=a/2,0),y=0;for(e.setAttr("font",this._getContextFont()),e.setAttr("textBaseline",v1),e.setAttr("textAlign",m1),s===v1?l=(this.getHeight()-n*a-2*o)/2:"bottom"===s&&(l=this.getHeight()-n*a-2*o),e.translate(o,l+o),r=0;r<n;r++){v=0,y=0;var b,x,w,k=t[r],S=k.text,C=k.width,_=k.lastInParagraph;if(e.save(),"right"===c?v+=u-C-2*o:"center"===c&&(v+=(u-C-2*o)/2),p&&(e.save(),e.beginPath(),e.moveTo(v,m+y+Math.round(i/2)),x=0===(b=S.split(" ").length-1),w=c===g1&&_&&!x?u-2*o:C,e.lineTo(v+Math.round(w),m+y+Math.round(i/2)),e.lineWidth=i/15,e.strokeStyle=h,e.stroke(),e.restore()),g&&(e.save(),e.beginPath(),e.moveTo(v,m+y),x=0===(b=S.split(" ").length-1),w=c===g1&&_&&!x?u-2*o:C,e.lineTo(v+Math.round(w),m+y),e.lineWidth=i/15,e.strokeStyle=h,e.stroke(),e.restore()),0!==d||c===g1){b=S.split(" ").length-1;for(var E=h1(S),P=0;P<E.length;P++){var L=E[P];" "!==L||_||c!==g1||(v+=(u-2*o-C)/b),this._partialTextX=v,this._partialTextY=m+y,this._partialText=L,e.fillStrokeShape(this),v+=this.measureSize(L).width+d}}else this._partialTextX=v,this._partialTextY=m+y,this._partialText=S,e.fillStrokeShape(this);e.restore(),n>1&&(m+=a)}}}_hitFunc(e){var t=this.getWidth(),n=this.getHeight();e.beginPath(),e.rect(0,0,t,n),e.closePath(),e.fillStrokeShape(this)}setText(e){var t=eJ._isString(e)?e:null==e?"":e+"";return this._setAttr("text",t),this}getWidth(){return this.attrs.width===p1||void 0===this.attrs.width?this.getTextWidth()+2*this.padding():this.attrs.width}getHeight(){return this.attrs.height===p1||void 0===this.attrs.height?this.fontSize()*this.textArr.length*this.lineHeight()+2*this.padding():this.attrs.height}getTextWidth(){return this.textWidth}getTextHeight(){return eJ.warn("text.getTextHeight() method is deprecated. Use text.height() - for full height and text.fontSize() - for one line height."),this.textHeight}measureSize(e){var t,n=S1(),r=this.fontSize();return n.save(),n.font=this._getContextFont(),t=n.measureText(e),n.restore(),{width:t.width,height:r}}_getContextFont(){return this.fontStyle()+b1+this.fontVariant()+b1+(this.fontSize()+"px ")+this.fontFamily().split(",").map((e=>{const t=(e=e.trim()).indexOf(" ")>=0,n=e.indexOf('"')>=0||e.indexOf("'")>=0;return t&&!n&&(e=`"${e}"`),e})).join(", ")}_addTextLine(e){this.align()===g1&&(e=e.trim());var t=this._getTextWidth(e);return this.textArr.push({text:e,width:t,lastInParagraph:!1})}_getTextWidth(e){var t=this.letterSpacing(),n=e.length;return S1().measureText(e).width+(n?t*(n-1):0)}_setTextData(){var e=this.text().split("\n"),t=+this.fontSize(),n=0,r=this.lineHeight()*t,o=this.attrs.width,i=this.attrs.height,a=o!==p1&&void 0!==o,s=i!==p1&&void 0!==i,l=this.padding(),c=o-2*l,u=i-2*l,d=0,h=this.wrap(),f="char"!==h&&h!==x1,p=this.ellipsis();this.textArr=[],S1().font=this._getContextFont();for(var g=p?this._getTextWidth("…"):0,m=0,v=e.length;m<v;++m){var y=e[m],b=this._getTextWidth(y);if(a&&b>c)for(;y.length>0;){for(var x=0,w=y.length,k="",S=0;x<w;){var C=x+w>>>1,_=y.slice(0,C+1),E=this._getTextWidth(_)+g;E<=c?(x=C+1,k=_,S=E):w=C}if(!k)break;if(f){var P,L=y[k.length];(P=(L===b1||"-"===L)&&S<=c?k.length:Math.max(k.lastIndexOf(b1),k.lastIndexOf("-"))+1)>0&&(x=P,k=k.slice(0,x),S=this._getTextWidth(k))}if(k=k.trimRight(),this._addTextLine(k),n=Math.max(n,S),d+=r,this._shouldHandleEllipsis(d)){this._tryToAddEllipsisToLastLine();break}if((y=(y=y.slice(x)).trimLeft()).length>0&&(b=this._getTextWidth(y))<=c){this._addTextLine(y),d+=r,n=Math.max(n,b);break}}else this._addTextLine(y),d+=r,n=Math.max(n,b),this._shouldHandleEllipsis(d)&&m<v-1&&this._tryToAddEllipsisToLastLine();if(s&&d+r>u)break;this.textArr[this.textArr.length-1]&&(this.textArr[this.textArr.length-1].lastInParagraph=!0)}this.textHeight=t,this.textWidth=n}_shouldHandleEllipsis(e){var t=+this.fontSize(),n=this.lineHeight()*t,r=this.attrs.height,o=r!==p1&&void 0!==r,i=r-2*this.padding();return!(this.wrap()!==x1)||o&&e+n>i}_tryToAddEllipsisToLastLine(){var e=this.attrs.width,t=e!==p1&&void 0!==e,n=e-2*this.padding(),r=this.ellipsis(),o=this.textArr[this.textArr.length-1];if(o&&r){if(t)this._getTextWidth(o.text+"…")<n||(o.text=o.text.slice(0,o.text.length-3));this.textArr.splice(this.textArr.length-1,1),this._addTextLine(o.text+"…")}}getStrokeScaleEnabled(){return!0}}C1.prototype._fillFunc=function(e){e.fillText(this._partialText,this._partialTextX,this._partialTextY)},C1.prototype._strokeFunc=function(e){e.strokeText(this._partialText,this._partialTextX,this._partialTextY)},C1.prototype.className="Text",C1.prototype._attrsAffectingSize=["text","fontSize","padding","wrap","lineHeight","letterSpacing"],UQ(C1),dJ.overWriteSetter(C1,"width",iJ()),dJ.overWriteSetter(C1,"height",iJ()),dJ.addGetterSetter(C1,"fontFamily","Arial"),dJ.addGetterSetter(C1,"fontSize",12,rJ()),dJ.addGetterSetter(C1,"fontStyle",y1),dJ.addGetterSetter(C1,"fontVariant",y1),dJ.addGetterSetter(C1,"padding",0,rJ()),dJ.addGetterSetter(C1,"align",m1),dJ.addGetterSetter(C1,"verticalAlign","top"),dJ.addGetterSetter(C1,"lineHeight",1,rJ()),dJ.addGetterSetter(C1,"wrap","word"),dJ.addGetterSetter(C1,"ellipsis",!1,lJ()),dJ.addGetterSetter(C1,"letterSpacing",0,rJ()),dJ.addGetterSetter(C1,"text","",aJ()),dJ.addGetterSetter(C1,"textDecoration","");var _1="normal";function E1(e){e.fillText(this.partialText,0,0)}function P1(e){e.strokeText(this.partialText,0,0)}class L1 extends L0{constructor(e){super(e),this.dummyCanvas=eJ.createCanvasElement(),this.dataArray=[],this.dataArray=q0.parsePathData(this.attrs.data),this.on("dataChange.konva",(function(){this.dataArray=q0.parsePathData(this.attrs.data),this._setTextData()})),this.on("textChange.konva alignChange.konva letterSpacingChange.konva kerningFuncChange.konva fontSizeChange.konva fontFamilyChange.konva",this._setTextData),this._setTextData()}_sceneFunc(e){e.setAttr("font",this._getContextFont()),e.setAttr("textBaseline",this.textBaseline()),e.setAttr("textAlign","left"),e.save();var t=this.textDecoration(),n=this.fill(),r=this.fontSize(),o=this.glyphInfo;"underline"===t&&e.beginPath();for(var i=0;i<o.length;i++){e.save();var a=o[i].p0;e.translate(a.x,a.y),e.rotate(o[i].rotation),this.partialText=o[i].text,e.fillStrokeShape(this),"underline"===t&&(0===i&&e.moveTo(0,r/2+1),e.lineTo(r,r/2+1)),e.restore()}"underline"===t&&(e.strokeStyle=n,e.lineWidth=r/20,e.stroke()),e.restore()}_hitFunc(e){e.beginPath();var t=this.glyphInfo;if(t.length>=1){var n=t[0].p0;e.moveTo(n.x,n.y)}for(var r=0;r<t.length;r++){var o=t[r].p1;e.lineTo(o.x,o.y)}e.setAttr("lineWidth",this.fontSize()),e.setAttr("strokeStyle",this.colorKey),e.stroke()}getTextWidth(){return this.textWidth}getTextHeight(){return eJ.warn("text.getTextHeight() method is deprecated. Use text.height() - for full height and text.fontSize() - for one line height."),this.textHeight}setText(e){return C1.prototype.setText.call(this,e)}_getContextFont(){return C1.prototype._getContextFont.call(this)}_getTextSize(e){var t=this.dummyCanvas.getContext("2d");t.save(),t.font=this._getContextFont();var n=t.measureText(e);return t.restore(),{width:n.width,height:parseInt(this.attrs.fontSize,10)}}_setTextData(){var e=this,t=this._getTextSize(this.attrs.text),n=this.letterSpacing(),r=this.align(),o=this.kerningFunc();this.textWidth=t.width,this.textHeight=t.height;var i=Math.max(this.textWidth+((this.attrs.text||"").length-1)*n,0);this.glyphInfo=[];for(var a=0,s=0;s<e.dataArray.length;s++)e.dataArray[s].pathLength>0&&(a+=e.dataArray[s].pathLength);var l=0;"center"===r&&(l=Math.max(0,a/2-i/2)),"right"===r&&(l=Math.max(0,a-i));for(var c,u,d,h=h1(this.text()),f=this.text().split(" ").length-1,p=-1,g=0,m=function(){g=0;for(var t=e.dataArray,n=p+1;n<t.length;n++){if(t[n].pathLength>0)return p=n,t[n];"M"===t[n].command&&(c={x:t[n].points[0],y:t[n].points[1]})}return{}},v=function(t){var o=e._getTextSize(t).width+n;" "===t&&"justify"===r&&(o+=(a-i)/f);var s=0,l=0;for(u=void 0;Math.abs(o-s)/o>.01&&l<20;){l++;for(var h=s;void 0===d;)(d=m())&&h+d.pathLength<o&&(h+=d.pathLength,d=void 0);if(0===Object.keys(d).length||void 0===c)return;var p=!1;switch(d.command){case"L":q0.getLineLength(c.x,c.y,d.points[0],d.points[1])>o?u=q0.getPointOnLine(o,c.x,c.y,d.points[0],d.points[1],c.x,c.y):d=void 0;break;case"A":var v=d.points[4],y=d.points[5],b=d.points[4]+y;0===g?g=v+1e-8:o>s?g+=Math.PI/180*y/Math.abs(y):g-=Math.PI/360*y/Math.abs(y),(y<0&&g<b||y>=0&&g>b)&&(g=b,p=!0),u=q0.getPointOnEllipticalArc(d.points[0],d.points[1],d.points[2],d.points[3],g,d.points[6]);break;case"C":0===g?g=o>d.pathLength?1e-8:o/d.pathLength:o>s?g+=(o-s)/d.pathLength/2:g=Math.max(g-(s-o)/d.pathLength/2,0),g>1&&(g=1,p=!0),u=q0.getPointOnCubicBezier(g,d.start.x,d.start.y,d.points[0],d.points[1],d.points[2],d.points[3],d.points[4],d.points[5]);break;case"Q":0===g?g=o/d.pathLength:o>s?g+=(o-s)/d.pathLength:g-=(s-o)/d.pathLength,g>1&&(g=1,p=!0),u=q0.getPointOnQuadraticBezier(g,d.start.x,d.start.y,d.points[0],d.points[1],d.points[2],d.points[3])}void 0!==u&&(s=q0.getLineLength(c.x,c.y,u.x,u.y)),p&&(p=!1,d=void 0)}},y=l/(e._getTextSize("C").width+n)-1,b=0;b<y&&(v("C"),void 0!==c&&void 0!==u);b++)c=u;for(var x=0;x<h.length&&(v(h[x]),void 0!==c&&void 0!==u);x++){var w=q0.getLineLength(c.x,c.y,u.x,u.y),k=0;if(o)try{k=o(h[x-1],h[x])*this.fontSize()}catch(B2){k=0}c.x+=k,u.x+=k,this.textWidth+=k;var S=q0.getPointOnLine(k+w/2,c.x,c.y,u.x,u.y),C=Math.atan2(u.y-c.y,u.x-c.x);this.glyphInfo.push({transposeX:S.x,transposeY:S.y,text:h[x],rotation:C,p0:c,p1:u}),c=u}}getSelfRect(){if(!this.glyphInfo.length)return{x:0,y:0,width:0,height:0};var e=[];this.glyphInfo.forEach((function(t){e.push(t.p0.x),e.push(t.p0.y),e.push(t.p1.x),e.push(t.p1.y)}));for(var t,n,r=e[0]||0,o=e[0]||0,i=e[1]||0,a=e[1]||0,s=0;s<e.length/2;s++)t=e[2*s],n=e[2*s+1],r=Math.min(r,t),o=Math.max(o,t),i=Math.min(i,n),a=Math.max(a,n);var l=this.fontSize();return{x:r-l/2,y:i-l/2,width:o-r+l,height:a-i+l}}destroy(){return eJ.releaseCanvas(this.dummyCanvas),super.destroy()}}L1.prototype._fillFunc=E1,L1.prototype._strokeFunc=P1,L1.prototype._fillFuncHit=E1,L1.prototype._strokeFuncHit=P1,L1.prototype.className="TextPath",L1.prototype._attrsAffectingSize=["text","fontSize","data"],UQ(L1),dJ.addGetterSetter(L1,"data"),dJ.addGetterSetter(L1,"fontFamily","Arial"),dJ.addGetterSetter(L1,"fontSize",12,rJ()),dJ.addGetterSetter(L1,"fontStyle",_1),dJ.addGetterSetter(L1,"align","left"),dJ.addGetterSetter(L1,"letterSpacing",0,rJ()),dJ.addGetterSetter(L1,"textBaseline","middle"),dJ.addGetterSetter(L1,"fontVariant",_1),dJ.addGetterSetter(L1,"text",""),dJ.addGetterSetter(L1,"textDecoration",null),dJ.addGetterSetter(L1,"kerningFunc",null);var O1="tr-konva",M1=["resizeEnabledChange","rotateAnchorOffsetChange","rotateEnabledChange","enabledAnchorsChange","anchorSizeChange","borderEnabledChange","borderStrokeChange","borderStrokeWidthChange","borderDashChange","anchorStrokeChange","anchorStrokeWidthChange","anchorFillChange","anchorCornerRadiusChange","ignoreStrokeChange"].map((e=>e+`.${O1}`)).join(" "),T1="nodesRect",A1=["widthChange","heightChange","scaleXChange","scaleYChange","skewXChange","skewYChange","rotationChange","offsetXChange","offsetYChange","transformsEnabledChange","strokeWidthChange"],I1={"top-left":-45,"top-center":0,"top-right":45,"middle-right":-90,"middle-left":90,"bottom-left":-135,"bottom-center":180,"bottom-right":135};const R1="ontouchstart"in $Q._global;var N1=["top-left","top-center","top-right","middle-right","middle-left","bottom-left","bottom-center","bottom-right"];function D1(e,t,n){const r=n.x+(e.x-n.x)*Math.cos(t)-(e.y-n.y)*Math.sin(t),o=n.y+(e.x-n.x)*Math.sin(t)+(e.y-n.y)*Math.cos(t);return Object.assign(Object.assign({},e),{rotation:e.rotation+t,x:r,y:o})}function z1(e,t){const n=function(e){return{x:e.x+e.width/2*Math.cos(e.rotation)+e.height/2*Math.sin(-e.rotation),y:e.y+e.height/2*Math.cos(e.rotation)+e.width/2*Math.sin(e.rotation)}}(e);return D1(e,t,n)}class B1 extends I0{constructor(e){super(e),this._transforming=!1,this._createElements(),this._handleMouseMove=this._handleMouseMove.bind(this),this._handleMouseUp=this._handleMouseUp.bind(this),this.update=this.update.bind(this),this.on(M1,this.update),this.getNode()&&this.update()}attachTo(e){return this.setNode(e),this}setNode(e){return eJ.warn("tr.setNode(shape), tr.node(shape) and tr.attachTo(shape) methods are deprecated. Please use tr.nodes(nodesArray) instead."),this.setNodes([e])}getNode(){return this._nodes&&this._nodes[0]}_getEventNamespace(){return O1+this._id}setNodes(e=[]){return this._nodes&&this._nodes.length&&this.detach(),this._nodes=e,1===e.length&&this.useSingleNodeRotation()?this.rotation(e[0].getAbsoluteRotation()):this.rotation(0),this._nodes.forEach((e=>{const t=()=>{1===this.nodes().length&&this.useSingleNodeRotation()&&this.rotation(this.nodes()[0].getAbsoluteRotation()),this._resetTransformCache(),this._transforming||this.isDragging()||this.update()},n=e._attrsAffectingSize.map((e=>e+"Change."+this._getEventNamespace())).join(" ");e.on(n,t),e.on(A1.map((e=>e+`.${this._getEventNamespace()}`)).join(" "),t),e.on(`absoluteTransformChange.${this._getEventNamespace()}`,t),this._proxyDrag(e)})),this._resetTransformCache(),!!this.findOne(".top-left")&&this.update(),this}_proxyDrag(e){let t;e.on(`dragstart.${this._getEventNamespace()}`,(n=>{t=e.getAbsolutePosition(),this.isDragging()||e===this.findOne(".back")||this.startDrag(n,!1)})),e.on(`dragmove.${this._getEventNamespace()}`,(n=>{if(!t)return;const r=e.getAbsolutePosition(),o=r.x-t.x,i=r.y-t.y;this.nodes().forEach((t=>{if(t===e)return;if(t.isDragging())return;const r=t.getAbsolutePosition();t.setAbsolutePosition({x:r.x+o,y:r.y+i}),t.startDrag(n)})),t=null}))}getNodes(){return this._nodes||[]}getActiveAnchor(){return this._movingAnchorName}detach(){this._nodes&&this._nodes.forEach((e=>{e.off("."+this._getEventNamespace())})),this._nodes=[],this._resetTransformCache()}_resetTransformCache(){this._clearCache(T1),this._clearCache("transform"),this._clearSelfAndDescendantCache("absoluteTransform")}_getNodeRect(){return this._getCache(T1,this.__getNodeRect)}__getNodeShape(e,t=this.rotation(),n){var r=e.getClientRect({skipTransform:!0,skipShadow:!0,skipStroke:this.ignoreStroke()}),o=e.getAbsoluteScale(n),i=e.getAbsolutePosition(n),a=r.x*o.x-e.offsetX()*o.x,s=r.y*o.y-e.offsetY()*o.y;const l=($Q.getAngle(e.getAbsoluteRotation())+2*Math.PI)%(2*Math.PI);return D1({x:i.x+a*Math.cos(l)+s*Math.sin(-l),y:i.y+s*Math.cos(l)+a*Math.sin(l),width:r.width*o.x,height:r.height*o.y,rotation:l},-$Q.getAngle(t),{x:0,y:0})}__getNodeRect(){if(!this.getNode())return{x:-1e8,y:-1e8,width:0,height:0,rotation:0};const e=[];this.nodes().map((t=>{const n=t.getClientRect({skipTransform:!0,skipShadow:!0,skipStroke:this.ignoreStroke()});var r=[{x:n.x,y:n.y},{x:n.x+n.width,y:n.y},{x:n.x+n.width,y:n.y+n.height},{x:n.x,y:n.y+n.height}],o=t.getAbsoluteTransform();r.forEach((function(t){var n=o.point(t);e.push(n)}))}));const t=new GQ;var n,r,o,i;t.rotate(-$Q.getAngle(this.rotation())),e.forEach((function(e){var a=t.point(e);void 0===n&&(n=o=a.x,r=i=a.y),n=Math.min(n,a.x),r=Math.min(r,a.y),o=Math.max(o,a.x),i=Math.max(i,a.y)})),t.invert();const a=t.point({x:n,y:r});return{x:a.x,y:a.y,width:o-n,height:i-r,rotation:$Q.getAngle(this.rotation())}}getX(){return this._getNodeRect().x}getY(){return this._getNodeRect().y}getWidth(){return this._getNodeRect().width}getHeight(){return this._getNodeRect().height}_createElements(){this._createBack(),N1.forEach(function(e){this._createAnchor(e)}.bind(this)),this._createAnchor("rotater")}_createAnchor(e){var t=new a1({stroke:"rgb(0, 161, 255)",fill:"white",strokeWidth:1,name:e+" _anchor",dragDistance:0,draggable:!0,hitStrokeWidth:R1?10:"auto"}),n=this;t.on("mousedown touchstart",(function(e){n._handleMouseDown(e)})),t.on("dragstart",(e=>{t.stopDrag(),e.cancelBubble=!0})),t.on("dragend",(e=>{e.cancelBubble=!0})),t.on("mouseenter",(()=>{var n=$Q.getAngle(this.rotation()),r=function(e,t){if("rotater"===e)return"crosshair";t+=eJ.degToRad(I1[e]||0);var n=(eJ.radToDeg(t)%360+360)%360;return eJ._inRange(n,337.5,360)||eJ._inRange(n,0,22.5)?"ns-resize":eJ._inRange(n,22.5,67.5)?"nesw-resize":eJ._inRange(n,67.5,112.5)?"ew-resize":eJ._inRange(n,112.5,157.5)?"nwse-resize":eJ._inRange(n,157.5,202.5)?"ns-resize":eJ._inRange(n,202.5,247.5)?"nesw-resize":eJ._inRange(n,247.5,292.5)?"ew-resize":eJ._inRange(n,292.5,337.5)?"nwse-resize":(eJ.error("Transformer has unknown angle for cursor detection: "+n),"pointer")}(e,n);t.getStage().content&&(t.getStage().content.style.cursor=r),this._cursorChange=!0})),t.on("mouseout",(()=>{t.getStage().content&&(t.getStage().content.style.cursor=""),this._cursorChange=!1})),this.add(t)}_createBack(){var e=new L0({name:"back",width:0,height:0,draggable:!0,sceneFunc(e){var t=this.getParent(),n=t.padding();e.beginPath(),e.rect(-n,-n,this.width()+2*n,this.height()+2*n),e.moveTo(this.width()/2,-n),t.rotateEnabled()&&e.lineTo(this.width()/2,-t.rotateAnchorOffset()*eJ._sign(this.height())-n),e.fillStrokeShape(this)},hitFunc:(e,t)=>{if(this.shouldOverdrawWholeArea()){var n=this.padding();e.beginPath(),e.rect(-n,-n,t.width()+2*n,t.height()+2*n),e.fillStrokeShape(t)}}});this.add(e),this._proxyDrag(e),e.on("dragstart",(e=>{e.cancelBubble=!0})),e.on("dragmove",(e=>{e.cancelBubble=!0})),e.on("dragend",(e=>{e.cancelBubble=!0})),this.on("dragmove",(e=>{this.update()}))}_handleMouseDown(e){this._movingAnchorName=e.target.name().split(" ")[0];var t=this._getNodeRect(),n=t.width,r=t.height,o=Math.sqrt(Math.pow(n,2)+Math.pow(r,2));this.sin=Math.abs(r/o),this.cos=Math.abs(n/o),"undefined"!=typeof window&&(window.addEventListener("mousemove",this._handleMouseMove),window.addEventListener("touchmove",this._handleMouseMove),window.addEventListener("mouseup",this._handleMouseUp,!0),window.addEventListener("touchend",this._handleMouseUp,!0)),this._transforming=!0;var i=e.target.getAbsolutePosition(),a=e.target.getStage().getPointerPosition();this._anchorDragOffset={x:a.x-i.x,y:a.y-i.y},this._fire("transformstart",{evt:e.evt,target:this.getNode()}),this._nodes.forEach((t=>{t._fire("transformstart",{evt:e.evt,target:t})}))}_handleMouseMove(e){var t,n,r,o=this.findOne("."+this._movingAnchorName),i=o.getStage();i.setPointersPositions(e);const a=i.getPointerPosition();let s={x:a.x-this._anchorDragOffset.x,y:a.y-this._anchorDragOffset.y};const l=o.getAbsolutePosition();this.anchorDragBoundFunc()&&(s=this.anchorDragBoundFunc()(l,s,e)),o.setAbsolutePosition(s);const c=o.getAbsolutePosition();if(l.x!==c.x||l.y!==c.y)if("rotater"!==this._movingAnchorName){var u=this.keepRatio()||e.shiftKey,d=this.centeredScaling()||e.altKey;if("top-left"===this._movingAnchorName){if(u){var h=d?{x:this.width()/2,y:this.height()/2}:{x:this.findOne(".bottom-right").x(),y:this.findOne(".bottom-right").y()};r=Math.sqrt(Math.pow(h.x-o.x(),2)+Math.pow(h.y-o.y(),2));var f=this.findOne(".top-left").x()>h.x?-1:1,p=this.findOne(".top-left").y()>h.y?-1:1;t=r*this.cos*f,n=r*this.sin*p,this.findOne(".top-left").x(h.x-t),this.findOne(".top-left").y(h.y-n)}}else if("top-center"===this._movingAnchorName)this.findOne(".top-left").y(o.y());else if("top-right"===this._movingAnchorName){if(u){h=d?{x:this.width()/2,y:this.height()/2}:{x:this.findOne(".bottom-left").x(),y:this.findOne(".bottom-left").y()};r=Math.sqrt(Math.pow(o.x()-h.x,2)+Math.pow(h.y-o.y(),2));f=this.findOne(".top-right").x()<h.x?-1:1,p=this.findOne(".top-right").y()>h.y?-1:1;t=r*this.cos*f,n=r*this.sin*p,this.findOne(".top-right").x(h.x+t),this.findOne(".top-right").y(h.y-n)}var g=o.position();this.findOne(".top-left").y(g.y),this.findOne(".bottom-right").x(g.x)}else if("middle-left"===this._movingAnchorName)this.findOne(".top-left").x(o.x());else if("middle-right"===this._movingAnchorName)this.findOne(".bottom-right").x(o.x());else if("bottom-left"===this._movingAnchorName){if(u){h=d?{x:this.width()/2,y:this.height()/2}:{x:this.findOne(".top-right").x(),y:this.findOne(".top-right").y()};r=Math.sqrt(Math.pow(h.x-o.x(),2)+Math.pow(o.y()-h.y,2));f=h.x<o.x()?-1:1,p=o.y()<h.y?-1:1;t=r*this.cos*f,n=r*this.sin*p,o.x(h.x-t),o.y(h.y+n)}g=o.position(),this.findOne(".top-left").x(g.x),this.findOne(".bottom-right").y(g.y)}else if("bottom-center"===this._movingAnchorName)this.findOne(".bottom-right").y(o.y());else if("bottom-right"===this._movingAnchorName){if(u){h=d?{x:this.width()/2,y:this.height()/2}:{x:this.findOne(".top-left").x(),y:this.findOne(".top-left").y()};r=Math.sqrt(Math.pow(o.x()-h.x,2)+Math.pow(o.y()-h.y,2));f=this.findOne(".bottom-right").x()<h.x?-1:1,p=this.findOne(".bottom-right").y()<h.y?-1:1;t=r*this.cos*f,n=r*this.sin*p,this.findOne(".bottom-right").x(h.x+t),this.findOne(".bottom-right").y(h.y+n)}}else console.error(new Error("Wrong position argument of selection resizer: "+this._movingAnchorName));if(d=this.centeredScaling()||e.altKey){var m=this.findOne(".top-left"),v=this.findOne(".bottom-right"),y=m.x(),b=m.y(),x=this.getWidth()-v.x(),w=this.getHeight()-v.y();v.move({x:-y,y:-b}),m.move({x:x,y:w})}var k=this.findOne(".top-left").getAbsolutePosition();t=k.x,n=k.y;var S=this.findOne(".bottom-right").x()-this.findOne(".top-left").x(),C=this.findOne(".bottom-right").y()-this.findOne(".top-left").y();this._fitNodesInto({x:t,y:n,width:S,height:C,rotation:$Q.getAngle(this.rotation())},e)}else{var _=this._getNodeRect();t=o.x()-_.width/2,n=-o.y()+_.height/2;let r=Math.atan2(-n,t)+Math.PI/2;_.height<0&&(r-=Math.PI);const i=$Q.getAngle(this.rotation())+r,a=$Q.getAngle(this.rotationSnapTolerance()),s=function(e,t,n){let r=t;for(let o=0;o<e.length;o++){const i=$Q.getAngle(e[o]),a=Math.abs(i-t)%(2*Math.PI);Math.min(a,2*Math.PI-a)<n&&(r=i)}return r}(this.rotationSnaps(),i,a),l=z1(_,s-_.rotation);this._fitNodesInto(l,e)}}_handleMouseUp(e){this._removeEvents(e)}getAbsoluteTransform(){return this.getTransform()}_removeEvents(e){if(this._transforming){this._transforming=!1,"undefined"!=typeof window&&(window.removeEventListener("mousemove",this._handleMouseMove),window.removeEventListener("touchmove",this._handleMouseMove),window.removeEventListener("mouseup",this._handleMouseUp,!0),window.removeEventListener("touchend",this._handleMouseUp,!0));var t=this.getNode();this._fire("transformend",{evt:e,target:t}),t&&this._nodes.forEach((t=>{t._fire("transformend",{evt:e,target:t})})),this._movingAnchorName=null}}_fitNodesInto(e,t){var n=this._getNodeRect();if(eJ._inRange(e.width,2*-this.padding()-1,1))return void this.update();if(eJ._inRange(e.height,2*-this.padding()-1,1))return void this.update();const r=this.flipEnabled();var o=new GQ;if(o.rotate($Q.getAngle(this.rotation())),this._movingAnchorName&&e.width<0&&this._movingAnchorName.indexOf("left")>=0){const t=o.point({x:2*-this.padding(),y:0});if(e.x+=t.x,e.y+=t.y,e.width+=2*this.padding(),this._movingAnchorName=this._movingAnchorName.replace("left","right"),this._anchorDragOffset.x-=t.x,this._anchorDragOffset.y-=t.y,!r)return void this.update()}else if(this._movingAnchorName&&e.width<0&&this._movingAnchorName.indexOf("right")>=0){const t=o.point({x:2*this.padding(),y:0});if(this._movingAnchorName=this._movingAnchorName.replace("right","left"),this._anchorDragOffset.x-=t.x,this._anchorDragOffset.y-=t.y,e.width+=2*this.padding(),!r)return void this.update()}if(this._movingAnchorName&&e.height<0&&this._movingAnchorName.indexOf("top")>=0){const t=o.point({x:0,y:2*-this.padding()});if(e.x+=t.x,e.y+=t.y,this._movingAnchorName=this._movingAnchorName.replace("top","bottom"),this._anchorDragOffset.x-=t.x,this._anchorDragOffset.y-=t.y,e.height+=2*this.padding(),!r)return void this.update()}else if(this._movingAnchorName&&e.height<0&&this._movingAnchorName.indexOf("bottom")>=0){const t=o.point({x:0,y:2*this.padding()});if(this._movingAnchorName=this._movingAnchorName.replace("bottom","top"),this._anchorDragOffset.x-=t.x,this._anchorDragOffset.y-=t.y,e.height+=2*this.padding(),!r)return void this.update()}if(this.boundBoxFunc()){const t=this.boundBoxFunc()(n,e);t?e=t:eJ.warn("boundBoxFunc returned falsy. You should return new bound rect from it!")}const i=1e7,a=new GQ;a.translate(n.x,n.y),a.rotate(n.rotation),a.scale(n.width/i,n.height/i);const s=new GQ;s.translate(e.x,e.y),s.rotate(e.rotation),s.scale(e.width/i,e.height/i);const l=s.multiply(a.invert());this._nodes.forEach((e=>{var n;const r=e.getParent().getAbsoluteTransform(),o=e.getTransform().copy();o.translate(e.offsetX(),e.offsetY());const i=new GQ;i.multiply(r.copy().invert()).multiply(l).multiply(r).multiply(o);const a=i.decompose();e.setAttrs(a),this._fire("transform",{evt:t,target:e}),e._fire("transform",{evt:t,target:e}),null===(n=e.getLayer())||void 0===n||n.batchDraw()})),this.rotation(eJ._getRotation(e.rotation)),this._resetTransformCache(),this.update(),this.getLayer().batchDraw()}forceUpdate(){this._resetTransformCache(),this.update()}_batchChangeChild(e,t){this.findOne(e).setAttrs(t)}update(){var e,t=this._getNodeRect();this.rotation(eJ._getRotation(t.rotation));var n=t.width,r=t.height,o=this.enabledAnchors(),i=this.resizeEnabled(),a=this.padding(),s=this.anchorSize();this.find("._anchor").forEach((e=>{e.setAttrs({width:s,height:s,offsetX:s/2,offsetY:s/2,stroke:this.anchorStroke(),strokeWidth:this.anchorStrokeWidth(),fill:this.anchorFill(),cornerRadius:this.anchorCornerRadius()})})),this._batchChangeChild(".top-left",{x:0,y:0,offsetX:s/2+a,offsetY:s/2+a,visible:i&&o.indexOf("top-left")>=0}),this._batchChangeChild(".top-center",{x:n/2,y:0,offsetY:s/2+a,visible:i&&o.indexOf("top-center")>=0}),this._batchChangeChild(".top-right",{x:n,y:0,offsetX:s/2-a,offsetY:s/2+a,visible:i&&o.indexOf("top-right")>=0}),this._batchChangeChild(".middle-left",{x:0,y:r/2,offsetX:s/2+a,visible:i&&o.indexOf("middle-left")>=0}),this._batchChangeChild(".middle-right",{x:n,y:r/2,offsetX:s/2-a,visible:i&&o.indexOf("middle-right")>=0}),this._batchChangeChild(".bottom-left",{x:0,y:r,offsetX:s/2+a,offsetY:s/2-a,visible:i&&o.indexOf("bottom-left")>=0}),this._batchChangeChild(".bottom-center",{x:n/2,y:r,offsetY:s/2-a,visible:i&&o.indexOf("bottom-center")>=0}),this._batchChangeChild(".bottom-right",{x:n,y:r,offsetX:s/2-a,offsetY:s/2-a,visible:i&&o.indexOf("bottom-right")>=0}),this._batchChangeChild(".rotater",{x:n/2,y:-this.rotateAnchorOffset()*eJ._sign(r)-a,visible:this.rotateEnabled()}),this._batchChangeChild(".back",{width:n,height:r,visible:this.borderEnabled(),stroke:this.borderStroke(),strokeWidth:this.borderStrokeWidth(),dash:this.borderDash(),x:0,y:0}),null===(e=this.getLayer())||void 0===e||e.batchDraw()}isTransforming(){return this._transforming}stopTransform(){if(this._transforming){this._removeEvents();var e=this.findOne("."+this._movingAnchorName);e&&e.stopDrag()}}destroy(){return this.getStage()&&this._cursorChange&&this.getStage().content&&(this.getStage().content.style.cursor=""),I0.prototype.destroy.call(this),this.detach(),this._removeEvents(),this}toObject(){return zJ.prototype.toObject.call(this)}getClientRect(){return this.nodes().length>0?super.getClientRect():{x:0,y:0,width:0,height:0}}}B1.prototype.className="Transformer",UQ(B1),dJ.addGetterSetter(B1,"enabledAnchors",N1,(function(e){return e instanceof Array||eJ.warn("enabledAnchors value should be an array"),e instanceof Array&&e.forEach((function(e){-1===N1.indexOf(e)&&eJ.warn("Unknown anchor name: "+e+". Available names are: "+N1.join(", "))})),e||[]})),dJ.addGetterSetter(B1,"flipEnabled",!0,lJ()),dJ.addGetterSetter(B1,"resizeEnabled",!0),dJ.addGetterSetter(B1,"anchorSize",10,rJ()),dJ.addGetterSetter(B1,"rotateEnabled",!0),dJ.addGetterSetter(B1,"rotationSnaps",[]),dJ.addGetterSetter(B1,"rotateAnchorOffset",50,rJ()),dJ.addGetterSetter(B1,"rotationSnapTolerance",5,rJ()),dJ.addGetterSetter(B1,"borderEnabled",!0),dJ.addGetterSetter(B1,"anchorStroke","rgb(0, 161, 255)"),dJ.addGetterSetter(B1,"anchorStrokeWidth",1,rJ()),dJ.addGetterSetter(B1,"anchorFill","white"),dJ.addGetterSetter(B1,"anchorCornerRadius",0,rJ()),dJ.addGetterSetter(B1,"borderStroke","rgb(0, 161, 255)"),dJ.addGetterSetter(B1,"borderStrokeWidth",1,rJ()),dJ.addGetterSetter(B1,"borderDash"),dJ.addGetterSetter(B1,"keepRatio",!0),dJ.addGetterSetter(B1,"centeredScaling",!1),dJ.addGetterSetter(B1,"ignoreStroke",!1),dJ.addGetterSetter(B1,"padding",0,rJ()),dJ.addGetterSetter(B1,"node"),dJ.addGetterSetter(B1,"nodes"),dJ.addGetterSetter(B1,"boundBoxFunc"),dJ.addGetterSetter(B1,"anchorDragBoundFunc"),dJ.addGetterSetter(B1,"shouldOverdrawWholeArea",!1),dJ.addGetterSetter(B1,"useSingleNodeRotation",!0),dJ.backCompat(B1,{lineEnabled:"borderEnabled",rotateHandlerOffset:"rotateAnchorOffset",enabledHandlers:"enabledAnchors"});class j1 extends L0{_sceneFunc(e){e.beginPath(),e.arc(0,0,this.radius(),0,$Q.getAngle(this.angle()),this.clockwise()),e.lineTo(0,0),e.closePath(),e.fillStrokeShape(this)}getWidth(){return 2*this.radius()}getHeight(){return 2*this.radius()}setWidth(e){this.radius(e/2)}setHeight(e){this.radius(e/2)}}function F1(){this.r=0,this.g=0,this.b=0,this.a=0,this.next=null}j1.prototype.className="Wedge",j1.prototype._centroid=!0,j1.prototype._attrsAffectingSize=["radius"],UQ(j1),dJ.addGetterSetter(j1,"radius",0,rJ()),dJ.addGetterSetter(j1,"angle",0,rJ()),dJ.addGetterSetter(j1,"clockwise",!1),dJ.backCompat(j1,{angleDeg:"angle",getAngleDeg:"getAngle",setAngleDeg:"setAngle"});var H1=[512,512,456,512,328,456,335,512,405,328,271,456,388,335,292,512,454,405,364,328,298,271,496,456,420,388,360,335,312,292,273,512,482,454,428,405,383,364,345,328,312,298,284,271,259,496,475,456,437,420,404,388,374,360,347,335,323,312,302,292,282,273,265,512,497,482,468,454,441,428,417,405,394,383,373,364,354,345,337,328,320,312,305,298,291,284,278,271,265,259,507,496,485,475,465,456,446,437,428,420,412,404,396,388,381,374,367,360,354,347,341,335,329,323,318,312,307,302,297,292,287,282,278,273,269,265,261,512,505,497,489,482,475,468,461,454,447,441,435,428,422,417,411,405,399,394,389,383,378,373,368,364,359,354,350,345,341,337,332,328,324,320,316,312,309,305,301,298,294,291,287,284,281,278,274,271,268,265,262,259,257,507,501,496,491,485,480,475,470,465,460,456,451,446,442,437,433,428,424,420,416,412,408,404,400,396,392,388,385,381,377,374,370,367,363,360,357,354,350,347,344,341,338,335,332,329,326,323,320,318,315,312,310,307,304,302,299,297,294,292,289,287,285,282,280,278,275,273,271,269,267,265,263,261,259],W1=[9,11,12,13,13,14,14,15,15,15,15,16,16,16,16,17,17,17,17,17,17,17,18,18,18,18,18,18,18,18,18,19,19,19,19,19,19,19,19,19,19,19,19,19,19,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,20,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,21,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,22,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,23,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24,24];dJ.addGetterSetter(zJ,"blurRadius",0,rJ(),dJ.afterSetFilter);dJ.addGetterSetter(zJ,"brightness",0,rJ(),dJ.afterSetFilter);dJ.addGetterSetter(zJ,"contrast",0,rJ(),dJ.afterSetFilter);function V1(e,t,n,r,o){var i=n-t,a=o-r;return 0===i?r+a/2:0===a?r:a*((e-t)/i)+r}dJ.addGetterSetter(zJ,"embossStrength",.5,rJ(),dJ.afterSetFilter),dJ.addGetterSetter(zJ,"embossWhiteLevel",.5,rJ(),dJ.afterSetFilter),dJ.addGetterSetter(zJ,"embossDirection","top-left",null,dJ.afterSetFilter),dJ.addGetterSetter(zJ,"embossBlend",!1,null,dJ.afterSetFilter);dJ.addGetterSetter(zJ,"enhance",0,rJ(),dJ.afterSetFilter);dJ.addGetterSetter(zJ,"hue",0,rJ(),dJ.afterSetFilter),dJ.addGetterSetter(zJ,"saturation",0,rJ(),dJ.afterSetFilter),dJ.addGetterSetter(zJ,"luminance",0,rJ(),dJ.afterSetFilter);dJ.addGetterSetter(zJ,"hue",0,rJ(),dJ.afterSetFilter),dJ.addGetterSetter(zJ,"saturation",0,rJ(),dJ.afterSetFilter),dJ.addGetterSetter(zJ,"value",0,rJ(),dJ.afterSetFilter);function $1(e,t,n){var r=4*(n*e.width+t),o=[];return o.push(e.data[r++],e.data[r++],e.data[r++],e.data[r++]),o}function U1(e,t){return Math.sqrt(Math.pow(e[0]-t[0],2)+Math.pow(e[1]-t[1],2)+Math.pow(e[2]-t[2],2))}dJ.addGetterSetter(zJ,"kaleidoscopePower",2,rJ(),dJ.afterSetFilter),dJ.addGetterSetter(zJ,"kaleidoscopeAngle",0,rJ(),dJ.afterSetFilter);dJ.addGetterSetter(zJ,"threshold",0,rJ(),dJ.afterSetFilter);dJ.addGetterSetter(zJ,"noise",.2,rJ(),dJ.afterSetFilter);dJ.addGetterSetter(zJ,"pixelSize",8,rJ(),dJ.afterSetFilter);dJ.addGetterSetter(zJ,"levels",.5,rJ(),dJ.afterSetFilter);dJ.addGetterSetter(zJ,"red",0,(function(e){return this._filterUpToDate=!1,e>255?255:e<0?0:Math.round(e)})),dJ.addGetterSetter(zJ,"green",0,(function(e){return this._filterUpToDate=!1,e>255?255:e<0?0:Math.round(e)})),dJ.addGetterSetter(zJ,"blue",0,nJ,dJ.afterSetFilter);dJ.addGetterSetter(zJ,"red",0,(function(e){return this._filterUpToDate=!1,e>255?255:e<0?0:Math.round(e)})),dJ.addGetterSetter(zJ,"green",0,(function(e){return this._filterUpToDate=!1,e>255?255:e<0?0:Math.round(e)})),dJ.addGetterSetter(zJ,"blue",0,nJ,dJ.afterSetFilter),dJ.addGetterSetter(zJ,"alpha",1,(function(e){return this._filterUpToDate=!1,e>1?1:e<0?0:e}));dJ.addGetterSetter(zJ,"threshold",.5,rJ(),dJ.afterSetFilter);const G1=W0.Util._assign(W0,{Arc:V0,Arrow:Y0,Circle:Z0,Ellipse:X0,Image:K0,Label:o1,Tag:i1,Line:G0,Path:q0,Rect:a1,RegularPolygon:s1,Ring:c1,Sprite:u1,Star:d1,Text:C1,TextPath:L1,Transformer:B1,Wedge:j1,Filters:{Blur:function(e){var t=Math.round(this.blurRadius());t>0&&function(e,t){var n,r,o,i,a,s,l,c,u,d,h,f,p,g,m,v,y,b,x,w,k,S,C,_,E=e.data,P=e.width,L=e.height,O=t+t+1,M=P-1,T=L-1,A=t+1,I=A*(A+1)/2,R=new F1,N=null,D=R,z=null,B=null,j=H1[t],F=W1[t];for(o=1;o<O;o++)D=D.next=new F1,o===A&&(N=D);for(D.next=R,l=s=0,r=0;r<L;r++){for(v=y=b=x=c=u=d=h=0,f=A*(w=E[s]),p=A*(k=E[s+1]),g=A*(S=E[s+2]),m=A*(C=E[s+3]),c+=I*w,u+=I*k,d+=I*S,h+=I*C,D=R,o=0;o<A;o++)D.r=w,D.g=k,D.b=S,D.a=C,D=D.next;for(o=1;o<A;o++)i=s+((M<o?M:o)<<2),c+=(D.r=w=E[i])*(_=A-o),u+=(D.g=k=E[i+1])*_,d+=(D.b=S=E[i+2])*_,h+=(D.a=C=E[i+3])*_,v+=w,y+=k,b+=S,x+=C,D=D.next;for(z=R,B=N,n=0;n<P;n++)E[s+3]=C=h*j>>F,0!==C?(C=255/C,E[s]=(c*j>>F)*C,E[s+1]=(u*j>>F)*C,E[s+2]=(d*j>>F)*C):E[s]=E[s+1]=E[s+2]=0,c-=f,u-=p,d-=g,h-=m,f-=z.r,p-=z.g,g-=z.b,m-=z.a,i=l+((i=n+t+1)<M?i:M)<<2,c+=v+=z.r=E[i],u+=y+=z.g=E[i+1],d+=b+=z.b=E[i+2],h+=x+=z.a=E[i+3],z=z.next,f+=w=B.r,p+=k=B.g,g+=S=B.b,m+=C=B.a,v-=w,y-=k,b-=S,x-=C,B=B.next,s+=4;l+=P}for(n=0;n<P;n++){for(y=b=x=v=u=d=h=c=0,f=A*(w=E[s=n<<2]),p=A*(k=E[s+1]),g=A*(S=E[s+2]),m=A*(C=E[s+3]),c+=I*w,u+=I*k,d+=I*S,h+=I*C,D=R,o=0;o<A;o++)D.r=w,D.g=k,D.b=S,D.a=C,D=D.next;for(a=P,o=1;o<=t;o++)s=a+n<<2,c+=(D.r=w=E[s])*(_=A-o),u+=(D.g=k=E[s+1])*_,d+=(D.b=S=E[s+2])*_,h+=(D.a=C=E[s+3])*_,v+=w,y+=k,b+=S,x+=C,D=D.next,o<T&&(a+=P);for(s=n,z=R,B=N,r=0;r<L;r++)E[3+(i=s<<2)]=C=h*j>>F,C>0?(C=255/C,E[i]=(c*j>>F)*C,E[i+1]=(u*j>>F)*C,E[i+2]=(d*j>>F)*C):E[i]=E[i+1]=E[i+2]=0,c-=f,u-=p,d-=g,h-=m,f-=z.r,p-=z.g,g-=z.b,m-=z.a,i=n+((i=r+A)<T?i:T)*P<<2,c+=v+=z.r=E[i],u+=y+=z.g=E[i+1],d+=b+=z.b=E[i+2],h+=x+=z.a=E[i+3],z=z.next,f+=w=B.r,p+=k=B.g,g+=S=B.b,m+=C=B.a,v-=w,y-=k,b-=S,x-=C,B=B.next,s+=P}}(e,t)},Brighten:function(e){var t,n=255*this.brightness(),r=e.data,o=r.length;for(t=0;t<o;t+=4)r[t]+=n,r[t+1]+=n,r[t+2]+=n},Contrast:function(e){var t,n=Math.pow((this.contrast()+100)/100,2),r=e.data,o=r.length,i=150,a=150,s=150;for(t=0;t<o;t+=4)i=r[t],a=r[t+1],s=r[t+2],i/=255,i-=.5,i*=n,i+=.5,a/=255,a-=.5,a*=n,a+=.5,s/=255,s-=.5,s*=n,s+=.5,i=(i*=255)<0?0:i>255?255:i,a=(a*=255)<0?0:a>255?255:a,s=(s*=255)<0?0:s>255?255:s,r[t]=i,r[t+1]=a,r[t+2]=s},Emboss:function(e){var t=10*this.embossStrength(),n=255*this.embossWhiteLevel(),r=this.embossDirection(),o=this.embossBlend(),i=0,a=0,s=e.data,l=e.width,c=e.height,u=4*l,d=c;switch(r){case"top-left":i=-1,a=-1;break;case"top":i=-1,a=0;break;case"top-right":i=-1,a=1;break;case"right":i=0,a=1;break;case"bottom-right":i=1,a=1;break;case"bottom":i=1,a=0;break;case"bottom-left":i=1,a=-1;break;case"left":i=0,a=-1;break;default:eJ.error("Unknown emboss direction: "+r)}do{var h=(d-1)*u,f=i;d+f<1&&(f=0),d+f>c&&(f=0);var p=(d-1+f)*l*4,g=l;do{var m=h+4*(g-1),v=a;g+v<1&&(v=0),g+v>l&&(v=0);var y=p+4*(g-1+v),b=s[m]-s[y],x=s[m+1]-s[y+1],w=s[m+2]-s[y+2],k=b,S=k>0?k:-k;if((x>0?x:-x)>S&&(k=x),(w>0?w:-w)>S&&(k=w),k*=t,o){var C=s[m]+k,_=s[m+1]+k,E=s[m+2]+k;s[m]=C>255?255:C<0?0:C,s[m+1]=_>255?255:_<0?0:_,s[m+2]=E>255?255:E<0?0:E}else{var P=n-k;P<0?P=0:P>255&&(P=255),s[m]=s[m+1]=s[m+2]=P}}while(--g)}while(--d)},Enhance:function(e){var t,n,r,o,i=e.data,a=i.length,s=i[0],l=s,c=i[1],u=c,d=i[2],h=d,f=this.enhance();if(0!==f){for(o=0;o<a;o+=4)(t=i[o+0])<s?s=t:t>l&&(l=t),(n=i[o+1])<c?c=n:n>u&&(u=n),(r=i[o+2])<d?d=r:r>h&&(h=r);var p,g,m,v,y,b,x,w,k;for(l===s&&(l=255,s=0),u===c&&(u=255,c=0),h===d&&(h=255,d=0),f>0?(g=l+f*(255-l),m=s-f*(s-0),y=u+f*(255-u),b=c-f*(c-0),w=h+f*(255-h),k=d-f*(d-0)):(g=l+f*(l-(p=.5*(l+s))),m=s+f*(s-p),y=u+f*(u-(v=.5*(u+c))),b=c+f*(c-v),w=h+f*(h-(x=.5*(h+d))),k=d+f*(d-x)),o=0;o<a;o+=4)i[o+0]=V1(i[o+0],s,l,m,g),i[o+1]=V1(i[o+1],c,u,b,y),i[o+2]=V1(i[o+2],d,h,k,w)}},Grayscale:function(e){var t,n,r=e.data,o=r.length;for(t=0;t<o;t+=4)n=.34*r[t]+.5*r[t+1]+.16*r[t+2],r[t]=n,r[t+1]=n,r[t+2]=n},HSL:function(e){var t,n,r,o,i,a=e.data,s=a.length,l=Math.pow(2,this.saturation()),c=Math.abs(this.hue()+360)%360,u=127*this.luminance(),d=1*l*Math.cos(c*Math.PI/180),h=1*l*Math.sin(c*Math.PI/180),f=.299+.701*d+.167*h,p=.587-.587*d+.33*h,g=.114-.114*d-.497*h,m=.299-.299*d-.328*h,v=.587+.413*d+.035*h,y=.114-.114*d+.293*h,b=.299-.3*d+1.25*h,x=.587-.586*d-1.05*h,w=.114+.886*d-.2*h;for(t=0;t<s;t+=4)n=a[t+0],r=a[t+1],o=a[t+2],i=a[t+3],a[t+0]=f*n+p*r+g*o+u,a[t+1]=m*n+v*r+y*o+u,a[t+2]=b*n+x*r+w*o+u,a[t+3]=i},HSV:function(e){var t,n,r,o,i,a=e.data,s=a.length,l=Math.pow(2,this.value()),c=Math.pow(2,this.saturation()),u=Math.abs(this.hue()+360)%360,d=l*c*Math.cos(u*Math.PI/180),h=l*c*Math.sin(u*Math.PI/180),f=.299*l+.701*d+.167*h,p=.587*l-.587*d+.33*h,g=.114*l-.114*d-.497*h,m=.299*l-.299*d-.328*h,v=.587*l+.413*d+.035*h,y=.114*l-.114*d+.293*h,b=.299*l-.3*d+1.25*h,x=.587*l-.586*d-1.05*h,w=.114*l+.886*d-.2*h;for(t=0;t<s;t+=4)n=a[t+0],r=a[t+1],o=a[t+2],i=a[t+3],a[t+0]=f*n+p*r+g*o,a[t+1]=m*n+v*r+y*o,a[t+2]=b*n+x*r+w*o,a[t+3]=i},Invert:function(e){var t,n=e.data,r=n.length;for(t=0;t<r;t+=4)n[t]=255-n[t],n[t+1]=255-n[t+1],n[t+2]=255-n[t+2]},Kaleidoscope:function(e){var t,n,r,o,i,a,s,l,c,u=e.width,d=e.height,h=Math.round(this.kaleidoscopePower()),f=Math.round(this.kaleidoscopeAngle()),p=Math.floor(u*(f%360)/360);if(!(h<1)){var g=eJ.createCanvasElement();g.width=u,g.height=d;var m=g.getContext("2d").getImageData(0,0,u,d);eJ.releaseCanvas(g),function(e,t,n){var r,o,i,a,s=e.data,l=t.data,c=e.width,u=e.height,d=n.polarCenterX||c/2,h=n.polarCenterY||u/2,f=0,p=0,g=0,m=0,v=Math.sqrt(d*d+h*h);o=c-d,i=u-h,v=(a=Math.sqrt(o*o+i*i))>v?a:v;var y,b,x,w,k=u,S=c,C=360/S*Math.PI/180;for(b=0;b<S;b+=1)for(x=Math.sin(b*C),w=Math.cos(b*C),y=0;y<k;y+=1)o=Math.floor(d+v*y/k*w),f=s[0+(r=4*((i=Math.floor(h+v*y/k*x))*c+o))],p=s[r+1],g=s[r+2],m=s[r+3],l[0+(r=4*(b+y*c))]=f,l[r+1]=p,l[r+2]=g,l[r+3]=m}(e,m,{polarCenterX:u/2,polarCenterY:d/2});for(var v=u/Math.pow(2,h);v<=8;)v*=2,h-=1;var y=v=Math.ceil(v),b=0,x=y,w=1;for(p+v>u&&(b=y,x=0,w=-1),n=0;n<d;n+=1)for(t=b;t!==x;t+=w)l=4*(u*n+Math.round(t+p)%u),o=m.data[l+0],i=m.data[l+1],a=m.data[l+2],s=m.data[l+3],c=4*(u*n+t),m.data[c+0]=o,m.data[c+1]=i,m.data[c+2]=a,m.data[c+3]=s;for(n=0;n<d;n+=1)for(y=Math.floor(v),r=0;r<h;r+=1){for(t=0;t<y+1;t+=1)l=4*(u*n+t),o=m.data[l+0],i=m.data[l+1],a=m.data[l+2],s=m.data[l+3],c=4*(u*n+2*y-t-1),m.data[c+0]=o,m.data[c+1]=i,m.data[c+2]=a,m.data[c+3]=s;y*=2}!function(e,t,n){var r,o,i,a,s,l,c=e.data,u=t.data,d=e.width,h=e.height,f=n.polarCenterX||d/2,p=n.polarCenterY||h/2,g=0,m=0,v=0,y=0,b=Math.sqrt(f*f+p*p);o=d-f,i=h-p,b=(l=Math.sqrt(o*o+i*i))>b?l:b;var x,w,k,S=h,C=d,_=n.polarRotation||0;for(o=0;o<d;o+=1)for(i=0;i<h;i+=1)a=o-f,s=i-p,x=Math.sqrt(a*a+s*s)*S/b,w=(w=(180*Math.atan2(s,a)/Math.PI+360+_)%360)*C/360,k=Math.floor(w),g=c[0+(r=4*(Math.floor(x)*d+k))],m=c[r+1],v=c[r+2],y=c[r+3],u[0+(r=4*(i*d+o))]=g,u[r+1]=m,u[r+2]=v,u[r+3]=y}(m,e,{polarRotation:0})}},Mask:function(e){var t=function(e,t){var n=$1(e,0,0),r=$1(e,e.width-1,0),o=$1(e,0,e.height-1),i=$1(e,e.width-1,e.height-1),a=t||10;if(U1(n,r)<a&&U1(r,i)<a&&U1(i,o)<a&&U1(o,n)<a){for(var s=function(e){for(var t=[0,0,0],n=0;n<e.length;n++)t[0]+=e[n][0],t[1]+=e[n][1],t[2]+=e[n][2];return t[0]/=e.length,t[1]/=e.length,t[2]/=e.length,t}([r,n,i,o]),l=[],c=0;c<e.width*e.height;c++){var u=U1(s,[e.data[4*c],e.data[4*c+1],e.data[4*c+2]]);l[c]=u<a?0:255}return l}}(e,this.threshold());return 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e?cd(CI,{flexDirection:"column",rowGap:"1rem",width:"100%",children:[ld(CI,{alignItems:"center",children:ld(VI,{fontSize:"lg",fontWeight:"bold",children:e})}),ld(CI,{flexDirection:"column",maxHeight:window.innerHeight-270,overflowY:"scroll",paddingRight:"2rem",children:ld(d9,{enableReinitialize:!0,initialValues:i,onSubmit:e=>{r(OQ(e))},children:({handleSubmit:e,errors:t,touched:r})=>ld("form",{onSubmit:e,children:cd(WI,{rowGap:"0.5rem",alignItems:"start",children:[cd(GT,{isInvalid:!!t.description&&r.description,isRequired:!0,children:[ld(eA,{htmlFor:"description",fontSize:"sm",children:o("modelmanager:description")}),cd(WI,{alignItems:"start",children:[ld(m9,{as:W3,id:"description",name:"description",type:"text",width:"lg"}),t.description&&r.description?ld(QT,{children:t.description}):ld(qT,{margin:0,children:o("modelmanager:descriptionValidationMsg")})]})]}),cd(GT,{isInvalid:!!t.repo_id&&r.repo_id,isRequired:!0,children:[ld(eA,{htmlFor:"config",fontSize:"sm",children:o("modelmanager:modelLocation")}),cd(WI,{alignItems:"start",children:[ld(m9,{as:W3,id:"repo_id",name:"repo_id",type:"text",width:"lg"}),t.repo_id&&r.repo_id?ld(QT,{children:t.repo_id}):ld(qT,{margin:0,children:o("modelmanager:modelLocationValidationMsg")})]})]}),cd(GT,{isInvalid:!!t.vae&&r.vae,children:[ld(eA,{htmlFor:"vae",fontSize:"sm",children:o("modelmanager:vaeLocation")}),cd(WI,{alignItems:"start",children:[ld(m9,{as:W3,id:"vae",name:"vae",type:"text",width:"lg"}),t.vae&&r.vae?ld(QT,{children:t.vae}):ld(qT,{margin:0,children:o("modelmanager:vaeLocationValidationMsg")})]})]}),ld(B3,{type:"submit",className:"modal-close-btn",isLoading:n,children:o("modelmanager:updateModel")})]})})})})]}):ld(CI,{width:"100%",height:"250px",justifyContent:"center",alignItems:"center",backgroundColor:"var(--background-color)",borderRadius:"0.5rem",children:ld(VI,{fontWeight:"bold",color:"var(--subtext-color-bright)",children:"Pick A Model To Edit"})})}function X9({children:e}){const{isOpen:t,onOpen:n,onClose:r}=QA(),o=v5((e=>e.system.model_list)),i=v5((e=>e.system.openModel)),{t:s}=FY();return cd(sd,{children:[a.exports.cloneElement(e,{onClick:n}),cd(RB,{isOpen:t,onClose:r,size:"6xl",children:[ld(GB,{}),cd(WB,{className:" modal",children:[ld(DB,{className:"modal-close-btn"}),ld($B,{children:s("modelmanager:modelManager")}),cd(CI,{padding:"0 1.5rem 1.5rem 1.5rem",width:"100%",columnGap:"2rem",children:[ld(q9,{}),i&&"diffusers"===o[i].format?ld(Z9,{}):ld(w9,{})]})]})]})]})}function K9(){const{t:e,i18n:t}=FY(),n={en:e("common:langEnglish"),ru:e("common:langRussian"),it:e("common:langItalian"),pt_br:e("common:langBrPortuguese"),de:e("common:langGerman"),pl:e("common:langPolish"),zh_cn:e("common:langSimplifiedChinese"),es:e("common:langSpanish")};return ld(z3,{trigger:"hover",triggerComponent:ld(k3,{"aria-label":e("common:languagePickerLabel"),tooltip:e("common:languagePickerLabel"),icon:ld(J5,{}),size:"sm",variant:"link","data-variant":"link",fontSize:26}),children:ld(WI,{children:(()=>{const e=[];return Object.keys(n).forEach((r=>{e.push(ld(B3,{"data-selected":localStorage.getItem("i18nextLng")===r,onClick:()=>t.changeLanguage(r),className:"modal-close-btn lang-select-btn","aria-label":n[r],tooltip:n[r],size:"sm",minWidth:"200px",children:n[r]},r))})),e})()})})}const Q9=()=>{const{t:e}=FY();return cd("div",{className:"site-header",children:[cd("div",{className:"site-header-left-side",children:[ld("img",{src:w3,alt:"invoke-ai-logo"}),cd("h1",{children:["invoke ",ld("strong",{children:"ai"})]})]}),cd("div",{className:"site-header-right-side",children:[ld(D3,{}),ld(H3,{}),ld(X9,{children:ld(k3,{"aria-label":e("modelmanager:modelManager"),tooltip:e("modelmanager:modelManager"),size:"sm",variant:"link","data-variant":"link",fontSize:20,icon:ld(H5,{})})}),ld(C3,{children:ld(k3,{"aria-label":e("common:hotkeysLabel"),tooltip:e("common:hotkeysLabel"),size:"sm",variant:"link","data-variant":"link",fontSize:20,icon:ld(Q5,{})})}),ld(j3,{}),ld(K9,{}),ld(k3,{"aria-label":e("common:reportBugLabel"),tooltip:e("common:reportBugLabel"),variant:"link","data-variant":"link",fontSize:20,size:"sm",icon:ld(MI,{isExternal:!0,href:"http://github.com/invoke-ai/InvokeAI/issues",children:ld(D5,{})})}),ld(k3,{"aria-label":e("common:githubLabel"),tooltip:e("common:githubLabel"),variant:"link","data-variant":"link",fontSize:20,size:"sm",icon:ld(MI,{isExternal:!0,href:"http://github.com/invoke-ai/InvokeAI",children:ld(L5,{})})}),ld(k3,{"aria-label":e("common:discordLabel"),tooltip:e("common:discordLabel"),variant:"link","data-variant":"link",fontSize:20,size:"sm",icon:ld(MI,{isExternal:!0,href:"https://discord.gg/ZmtBAhwWhy",children:ld(P5,{})})}),ld(R3,{children:ld(k3,{"aria-label":e("common:settingsLabel"),tooltip:e("common:settingsLabel"),variant:"link","data-variant":"link",fontSize:22,size:"sm",icon:ld(_9,{})})})]})]})};var J9=globalThis&&globalThis.__extends||function(){var e=function(t,n){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var n in t)Object.prototype.hasOwnProperty.call(t,n)&&(e[n]=t[n])},e(t,n)};return function(t,n){function r(){this.constructor=t}e(t,n),t.prototype=null===n?Object.create(n):(r.prototype=n.prototype,new r)}}(),e8=globalThis&&globalThis.__assign||function(){return e8=Object.assign||function(e){for(var t,n=1,r=arguments.length;n<r;n++)for(var o in t=arguments[n])Object.prototype.hasOwnProperty.call(t,o)&&(e[o]=t[o]);return e},e8.apply(this,arguments)},t8={width:"100%",height:"10px",top:"0px",left:"0px",cursor:"row-resize"},n8={width:"10px",height:"100%",top:"0px",left:"0px",cursor:"col-resize"},r8={width:"20px",height:"20px",position:"absolute"},o8={top:e8(e8({},t8),{top:"-5px"}),right:e8(e8({},n8),{left:void 0,right:"-5px"}),bottom:e8(e8({},t8),{top:void 0,bottom:"-5px"}),left:e8(e8({},n8),{left:"-5px"}),topRight:e8(e8({},r8),{right:"-10px",top:"-10px",cursor:"ne-resize"}),bottomRight:e8(e8({},r8),{right:"-10px",bottom:"-10px",cursor:"se-resize"}),bottomLeft:e8(e8({},r8),{left:"-10px",bottom:"-10px",cursor:"sw-resize"}),topLeft:e8(e8({},r8),{left:"-10px",top:"-10px",cursor:"nw-resize"})},i8=function(e){function t(){var t=null!==e&&e.apply(this,arguments)||this;return t.onMouseDown=function(e){t.props.onResizeStart(e,t.props.direction)},t.onTouchStart=function(e){t.props.onResizeStart(e,t.props.direction)},t}return J9(t,e),t.prototype.render=function(){return ld("div",{className:this.props.className||"",style:e8(e8({position:"absolute",userSelect:"none"},o8[this.props.direction]),this.props.replaceStyles||{}),onMouseDown:this.onMouseDown,onTouchStart:this.onTouchStart,children:this.props.children})},t}(a.exports.PureComponent),a8=globalThis&&globalThis.__extends||function(){var e=function(t,n){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var n in t)Object.prototype.hasOwnProperty.call(t,n)&&(e[n]=t[n])},e(t,n)};return function(t,n){function r(){this.constructor=t}e(t,n),t.prototype=null===n?Object.create(n):(r.prototype=n.prototype,new r)}}(),s8=globalThis&&globalThis.__assign||function(){return s8=Object.assign||function(e){for(var t,n=1,r=arguments.length;n<r;n++)for(var o in t=arguments[n])Object.prototype.hasOwnProperty.call(t,o)&&(e[o]=t[o]);return e},s8.apply(this,arguments)},l8={width:"auto",height:"auto"},c8=function(e,t,n){return Math.max(Math.min(e,n),t)},u8=function(e,t){return Math.round(e/t)*t},d8=function(e,t){return new RegExp(e,"i").test(t)},h8=function(e){return Boolean(e.touches&&e.touches.length)},f8=function(e,t,n){void 0===n&&(n=0);var r=t.reduce((function(n,r,o){return Math.abs(r-e)<Math.abs(t[n]-e)?o:n}),0),o=Math.abs(t[r]-e);return 0===n||o<n?t[r]:e},p8=function(e){return"auto"===(e=e.toString())||e.endsWith("px")||e.endsWith("%")||e.endsWith("vh")||e.endsWith("vw")||e.endsWith("vmax")||e.endsWith("vmin")?e:e+"px"},g8=function(e,t,n,r){if(e&&"string"==typeof e){if(e.endsWith("px"))return Number(e.replace("px",""));if(e.endsWith("%"))return t*(Number(e.replace("%",""))/100);if(e.endsWith("vw"))return n*(Number(e.replace("vw",""))/100);if(e.endsWith("vh"))return r*(Number(e.replace("vh",""))/100)}return e},m8=["as","style","className","grid","snap","bounds","boundsByDirection","size","defaultSize","minWidth","minHeight","maxWidth","maxHeight","lockAspectRatio","lockAspectRatioExtraWidth","lockAspectRatioExtraHeight","enable","handleStyles","handleClasses","handleWrapperStyle","handleWrapperClass","children","onResizeStart","onResize","onResizeStop","handleComponent","scale","resizeRatio","snapGap"],v8="__resizable_base__",y8=function(e){function t(t){var n=e.call(this,t)||this;return n.ratio=1,n.resizable=null,n.parentLeft=0,n.parentTop=0,n.resizableLeft=0,n.resizableRight=0,n.resizableTop=0,n.resizableBottom=0,n.targetLeft=0,n.targetTop=0,n.appendBase=function(){if(!n.resizable||!n.window)return null;var e=n.parentNode;if(!e)return null;var t=n.window.document.createElement("div");return t.style.width="100%",t.style.height="100%",t.style.position="absolute",t.style.transform="scale(0, 0)",t.style.left="0",t.style.flex="0 0 100%",t.classList?t.classList.add(v8):t.className+=v8,e.appendChild(t),t},n.removeBase=function(e){var t=n.parentNode;t&&t.removeChild(e)},n.ref=function(e){e&&(n.resizable=e)},n.state={isResizing:!1,width:void 0===(n.propsSize&&n.propsSize.width)?"auto":n.propsSize&&n.propsSize.width,height:void 0===(n.propsSize&&n.propsSize.height)?"auto":n.propsSize&&n.propsSize.height,direction:"right",original:{x:0,y:0,width:0,height:0},backgroundStyle:{height:"100%",width:"100%",backgroundColor:"rgba(0,0,0,0)",cursor:"auto",opacity:0,position:"fixed",zIndex:9999,top:"0",left:"0",bottom:"0",right:"0"},flexBasis:void 0},n.onResizeStart=n.onResizeStart.bind(n),n.onMouseMove=n.onMouseMove.bind(n),n.onMouseUp=n.onMouseUp.bind(n),n}return a8(t,e),Object.defineProperty(t.prototype,"parentNode",{get:function(){return this.resizable?this.resizable.parentNode:null},enumerable:!1,configurable:!0}),Object.defineProperty(t.prototype,"window",{get:function(){return this.resizable&&this.resizable.ownerDocument?this.resizable.ownerDocument.defaultView:null},enumerable:!1,configurable:!0}),Object.defineProperty(t.prototype,"propsSize",{get:function(){return this.props.size||this.props.defaultSize||l8},enumerable:!1,configurable:!0}),Object.defineProperty(t.prototype,"size",{get:function(){var e=0,t=0;if(this.resizable&&this.window){var n=this.resizable.offsetWidth,r=this.resizable.offsetHeight,o=this.resizable.style.position;"relative"!==o&&(this.resizable.style.position="relative"),e="auto"!==this.resizable.style.width?this.resizable.offsetWidth:n,t="auto"!==this.resizable.style.height?this.resizable.offsetHeight:r,this.resizable.style.position=o}return{width:e,height:t}},enumerable:!1,configurable:!0}),Object.defineProperty(t.prototype,"sizeStyle",{get:function(){var e=this,t=this.props.size,n=function(t){if(void 0===e.state[t]||"auto"===e.state[t])return"auto";if(e.propsSize&&e.propsSize[t]&&e.propsSize[t].toString().endsWith("%")){if(e.state[t].toString().endsWith("%"))return e.state[t].toString();var n=e.getParentSize();return Number(e.state[t].toString().replace("px",""))/n[t]*100+"%"}return p8(e.state[t])};return{width:t&&void 0!==t.width&&!this.state.isResizing?p8(t.width):n("width"),height:t&&void 0!==t.height&&!this.state.isResizing?p8(t.height):n("height")}},enumerable:!1,configurable:!0}),t.prototype.getParentSize=function(){if(!this.parentNode)return this.window?{width:this.window.innerWidth,height:this.window.innerHeight}:{width:0,height:0};var e=this.appendBase();if(!e)return{width:0,height:0};var t=!1,n=this.parentNode.style.flexWrap;"wrap"!==n&&(t=!0,this.parentNode.style.flexWrap="wrap"),e.style.position="relative",e.style.minWidth="100%",e.style.minHeight="100%";var r={width:e.offsetWidth,height:e.offsetHeight};return t&&(this.parentNode.style.flexWrap=n),this.removeBase(e),r},t.prototype.bindEvents=function(){this.window&&(this.window.addEventListener("mouseup",this.onMouseUp),this.window.addEventListener("mousemove",this.onMouseMove),this.window.addEventListener("mouseleave",this.onMouseUp),this.window.addEventListener("touchmove",this.onMouseMove,{capture:!0,passive:!1}),this.window.addEventListener("touchend",this.onMouseUp))},t.prototype.unbindEvents=function(){this.window&&(this.window.removeEventListener("mouseup",this.onMouseUp),this.window.removeEventListener("mousemove",this.onMouseMove),this.window.removeEventListener("mouseleave",this.onMouseUp),this.window.removeEventListener("touchmove",this.onMouseMove,!0),this.window.removeEventListener("touchend",this.onMouseUp))},t.prototype.componentDidMount=function(){if(this.resizable&&this.window){var e=this.window.getComputedStyle(this.resizable);this.setState({width:this.state.width||this.size.width,height:this.state.height||this.size.height,flexBasis:"auto"!==e.flexBasis?e.flexBasis:void 0})}},t.prototype.componentWillUnmount=function(){this.window&&this.unbindEvents()},t.prototype.createSizeForCssProperty=function(e,t){var n=this.propsSize&&this.propsSize[t];return"auto"!==this.state[t]||this.state.original[t]!==e||void 0!==n&&"auto"!==n?e:"auto"},t.prototype.calculateNewMaxFromBoundary=function(e,t){var n,r,o=this.props.boundsByDirection,i=this.state.direction,a=o&&d8("left",i),s=o&&d8("top",i);if("parent"===this.props.bounds){var l=this.parentNode;l&&(n=a?this.resizableRight-this.parentLeft:l.offsetWidth+(this.parentLeft-this.resizableLeft),r=s?this.resizableBottom-this.parentTop:l.offsetHeight+(this.parentTop-this.resizableTop))}else"window"===this.props.bounds?this.window&&(n=a?this.resizableRight:this.window.innerWidth-this.resizableLeft,r=s?this.resizableBottom:this.window.innerHeight-this.resizableTop):this.props.bounds&&(n=a?this.resizableRight-this.targetLeft:this.props.bounds.offsetWidth+(this.targetLeft-this.resizableLeft),r=s?this.resizableBottom-this.targetTop:this.props.bounds.offsetHeight+(this.targetTop-this.resizableTop));return n&&Number.isFinite(n)&&(e=e&&e<n?e:n),r&&Number.isFinite(r)&&(t=t&&t<r?t:r),{maxWidth:e,maxHeight:t}},t.prototype.calculateNewSizeFromDirection=function(e,t){var n=this.props.scale||1,r=this.props.resizeRatio||1,o=this.state,i=o.direction,a=o.original,s=this.props,l=s.lockAspectRatio,c=s.lockAspectRatioExtraHeight,u=s.lockAspectRatioExtraWidth,d=a.width,h=a.height,f=c||0,p=u||0;return d8("right",i)&&(d=a.width+(e-a.x)*r/n,l&&(h=(d-p)/this.ratio+f)),d8("left",i)&&(d=a.width-(e-a.x)*r/n,l&&(h=(d-p)/this.ratio+f)),d8("bottom",i)&&(h=a.height+(t-a.y)*r/n,l&&(d=(h-f)*this.ratio+p)),d8("top",i)&&(h=a.height-(t-a.y)*r/n,l&&(d=(h-f)*this.ratio+p)),{newWidth:d,newHeight:h}},t.prototype.calculateNewSizeFromAspectRatio=function(e,t,n,r){var o=this.props,i=o.lockAspectRatio,a=o.lockAspectRatioExtraHeight,s=o.lockAspectRatioExtraWidth,l=void 0===r.width?10:r.width,c=void 0===n.width||n.width<0?e:n.width,u=void 0===r.height?10:r.height,d=void 0===n.height||n.height<0?t:n.height,h=a||0,f=s||0;if(i){var p=(u-h)*this.ratio+f,g=(d-h)*this.ratio+f,m=(l-f)/this.ratio+h,v=(c-f)/this.ratio+h,y=Math.max(l,p),b=Math.min(c,g),x=Math.max(u,m),w=Math.min(d,v);e=c8(e,y,b),t=c8(t,x,w)}else e=c8(e,l,c),t=c8(t,u,d);return{newWidth:e,newHeight:t}},t.prototype.setBoundingClientRect=function(){if("parent"===this.props.bounds){var e=this.parentNode;if(e){var t=e.getBoundingClientRect();this.parentLeft=t.left,this.parentTop=t.top}}if(this.props.bounds&&"string"!=typeof this.props.bounds){var n=this.props.bounds.getBoundingClientRect();this.targetLeft=n.left,this.targetTop=n.top}if(this.resizable){var r=this.resizable.getBoundingClientRect(),o=r.left,i=r.top,a=r.right,s=r.bottom;this.resizableLeft=o,this.resizableRight=a,this.resizableTop=i,this.resizableBottom=s}},t.prototype.onResizeStart=function(e,t){if(this.resizable&&this.window){var n,r=0,o=0;if(e.nativeEvent&&function(e){return Boolean((e.clientX||0===e.clientX)&&(e.clientY||0===e.clientY))}(e.nativeEvent)?(r=e.nativeEvent.clientX,o=e.nativeEvent.clientY):e.nativeEvent&&h8(e.nativeEvent)&&(r=e.nativeEvent.touches[0].clientX,o=e.nativeEvent.touches[0].clientY),this.props.onResizeStart)if(this.resizable)if(!1===this.props.onResizeStart(e,t,this.resizable))return;this.props.size&&(void 0!==this.props.size.height&&this.props.size.height!==this.state.height&&this.setState({height:this.props.size.height}),void 0!==this.props.size.width&&this.props.size.width!==this.state.width&&this.setState({width:this.props.size.width})),this.ratio="number"==typeof this.props.lockAspectRatio?this.props.lockAspectRatio:this.size.width/this.size.height;var i=this.window.getComputedStyle(this.resizable);if("auto"!==i.flexBasis){var a=this.parentNode;if(a){var s=this.window.getComputedStyle(a).flexDirection;this.flexDir=s.startsWith("row")?"row":"column",n=i.flexBasis}}this.setBoundingClientRect(),this.bindEvents();var l={original:{x:r,y:o,width:this.size.width,height:this.size.height},isResizing:!0,backgroundStyle:s8(s8({},this.state.backgroundStyle),{cursor:this.window.getComputedStyle(e.target).cursor||"auto"}),direction:t,flexBasis:n};this.setState(l)}},t.prototype.onMouseMove=function(e){var t=this;if(this.state.isResizing&&this.resizable&&this.window){if(this.window.TouchEvent&&h8(e))try{e.preventDefault(),e.stopPropagation()}catch(B2){}var n=this.props,r=n.maxWidth,o=n.maxHeight,i=n.minWidth,a=n.minHeight,s=h8(e)?e.touches[0].clientX:e.clientX,l=h8(e)?e.touches[0].clientY:e.clientY,c=this.state,u=c.direction,d=c.original,h=c.width,f=c.height,p=this.getParentSize(),g=function(e,t,n,r,o,i,a){return r=g8(r,e.width,t,n),o=g8(o,e.height,t,n),i=g8(i,e.width,t,n),a=g8(a,e.height,t,n),{maxWidth:void 0===r?void 0:Number(r),maxHeight:void 0===o?void 0:Number(o),minWidth:void 0===i?void 0:Number(i),minHeight:void 0===a?void 0:Number(a)}}(p,this.window.innerWidth,this.window.innerHeight,r,o,i,a);r=g.maxWidth,o=g.maxHeight,i=g.minWidth,a=g.minHeight;var m=this.calculateNewSizeFromDirection(s,l),v=m.newHeight,y=m.newWidth,b=this.calculateNewMaxFromBoundary(r,o);this.props.snap&&this.props.snap.x&&(y=f8(y,this.props.snap.x,this.props.snapGap)),this.props.snap&&this.props.snap.y&&(v=f8(v,this.props.snap.y,this.props.snapGap));var x=this.calculateNewSizeFromAspectRatio(y,v,{width:b.maxWidth,height:b.maxHeight},{width:i,height:a});if(y=x.newWidth,v=x.newHeight,this.props.grid){var w=u8(y,this.props.grid[0]),k=u8(v,this.props.grid[1]),S=this.props.snapGap||0;y=0===S||Math.abs(w-y)<=S?w:y,v=0===S||Math.abs(k-v)<=S?k:v}var C={width:y-d.width,height:v-d.height};if(h&&"string"==typeof h)if(h.endsWith("%"))y=y/p.width*100+"%";else if(h.endsWith("vw")){y=y/this.window.innerWidth*100+"vw"}else if(h.endsWith("vh")){y=y/this.window.innerHeight*100+"vh"}if(f&&"string"==typeof f)if(f.endsWith("%"))v=v/p.height*100+"%";else 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Lte=(e=>(e[e.PROMPT=0]="PROMPT",e[e.GALLERY=1]="GALLERY",e[e.OTHER=2]="OTHER",e[e.SEED=3]="SEED",e[e.VARIATIONS=4]="VARIATIONS",e[e.UPSCALE=5]="UPSCALE",e[e.FACE_CORRECTION=6]="FACE_CORRECTION",e[e.IMAGE_TO_IMAGE=7]="IMAGE_TO_IMAGE",e[e.BOUNDING_BOX=8]="BOUNDING_BOX",e[e.SEAM_CORRECTION=9]="SEAM_CORRECTION",e[e.INFILL_AND_SCALING=10]="INFILL_AND_SCALING",e))(Lte||{});const Ote=e=>{const t=(()=>{const{t:e}=FY();return a.exports.useMemo((()=>({0:{text:e("tooltip:feature.prompt"),href:"link/to/docs/feature3.html",guideImage:"asset/path.gif"},1:{text:e("tooltip:feature.gallery"),href:"link/to/docs/feature3.html",guideImage:"asset/path.gif"},2:{text:e("tooltip:feature.other"),href:"link/to/docs/feature3.html",guideImage:"asset/path.gif"},3:{text:e("tooltip:feature.seed"),href:"link/to/docs/feature3.html",guideImage:"asset/path.gif"},4:{text:e("tooltip:feature.variations"),href:"link/to/docs/feature3.html",guideImage:"asset/path.gif"},5:{text:e("tooltip:feature.upscale"),href:"link/to/docs/feature1.html",guideImage:"asset/path.gif"},6:{text:e("tooltip:feature.faceCorrection"),href:"link/to/docs/feature3.html",guideImage:"asset/path.gif"},7:{text:e("tooltip:feature.imageToImage"),href:"link/to/docs/feature3.html",guideImage:"asset/path.gif"},8:{text:e("tooltip:feature.boundingBox"),href:"link/to/docs/feature3.html",guideImage:"asset/path.gif"},9:{text:e("tooltip:feature.seamCorrection"),href:"link/to/docs/feature3.html",guideImage:"asset/path.gif"},10:{text:e("tooltip:feature.infillAndScaling"),href:"link/to/docs/feature3.html",guideImage:"asset/path.gif"}})),[e])})();return t[e]},Mte=uV((e=>e.options),(e=>({facetoolStrength:e.facetoolStrength,facetoolType:e.facetoolType,codeformerFidelity:e.codeformerFidelity})),{memoizeOptions:{resultEqualityCheck:MU.exports.isEqual}}),Tte=uV((e=>e.system),(e=>({isGFPGANAvailable:e.isGFPGANAvailable})),{memoizeOptions:{resultEqualityCheck:MU.exports.isEqual}}),Ate=()=>{const e=m5(),{facetoolStrength:t,facetoolType:n,codeformerFidelity:r}=v5(Mte),{isGFPGANAvailable:o}=v5(Tte),{t:i}=FY();return cd(CI,{direction:"column",gap:2,children:[ld(E3,{label:i("options:type"),validValues:jQ.concat(),value:n,onChange:t=>e(H$(t.target.value))}),ld(L3,{isDisabled:!o,label:i("options:strength"),step:.05,min:0,max:1,onChange:t=>e(F$(t)),value:t,width:"90px",isInteger:!1}),"codeformer"===n&&ld(L3,{isDisabled:!o,label:i("options:codeformerFidelity"),step:.05,min:0,max:1,onChange:t=>e(B$(t)),value:r,width:"90px",isInteger:!1})]})};function Ite(){const e=v5((e=>e.system.isGFPGANAvailable)),t=v5((e=>e.options.shouldRunFacetool)),n=m5();return ld(_3,{isDisabled:!e,isChecked:t,onChange:e=>n(gU(e.target.checked))})}function Rte(){const e=m5(),t=v5((e=>e.options.shouldFitToWidthHeight)),{t:n}=FY();return ld(_3,{label:n("options:imageFit"),isChecked:t,onChange:t=>e(lU(t.target.checked))})}function Nte(e){const[t,n]=a.exports.useState(!1),{label:r,value:o,min:i=1,max:s=100,step:l=1,onChange:c,width:u="100%",tooltipSuffix:d="",withSliderMarks:h=!1,sliderMarkLeftOffset:f=0,sliderMarkRightOffset:p=-7,withInput:g=!1,isInteger:m=!1,inputWidth:v="5.5rem",inputReadOnly:y=!0,withReset:b=!1,hideTooltip:x=!1,isCompact:w=!1,handleReset:k,isResetDisabled:S,isSliderDisabled:C,isInputDisabled:_,styleClass:E,sliderFormControlProps:P,sliderFormLabelProps:L,sliderMarkProps:O,sliderTrackProps:M,sliderThumbProps:T,sliderNumberInputProps:A,sliderNumberInputFieldProps:I,sliderNumberInputStepperProps:R,sliderTooltipProps:N,sliderIAIIconButtonProps:D,...z}=e,[B,j]=a.exports.useState(String(o)),F=a.exports.useMemo((()=>A?.max?A.max:s),[s,A?.max]);a.exports.useEffect((()=>{String(o)!==B&&""!==B&&j(String(o))}),[o,B,j]);const H=e=>{j(String(e)),c(Number(e))};return cd(GT,{className:E?`invokeai__slider-component 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Dte(e){const{t:t}=FY(),{label:n=`${t("options:strength")}`,styleClass:r}=e,o=v5((e=>e.options.img2imgStrength)),i=m5();return ld(Nte,{label:n,step:.01,min:.01,max:.99,onChange:e=>i($$(e)),value:o,isInteger:!1,styleClass:r,withInput:!0,withReset:!0,withSliderMarks:!0,inputWidth:"5.5rem",handleReset:()=>{i($$(.75))}})}const zte=()=>{const e=m5(),t=v5((e=>e.options.seamless)),{t:n}=FY();return ld(CI,{gap:2,direction:"column",children:ld(_3,{label:n("options:seamlessTiling"),fontSize:"md",isChecked:t,onChange:t=>e(nU(t.target.checked))})})},Bte=()=>ld(CI,{gap:2,direction:"column",children:ld(zte,{})});function jte(){const e=m5(),{t:t}=FY(),n=v5((e=>e.options.shouldRandomizeSeed));return ld(_3,{label:t("options:randomizeSeed"),isChecked:n,onChange:t=>e(fU(t.target.checked))})}function Fte(){const e=v5((e=>e.options.seed)),t=v5((e=>e.options.shouldRandomizeSeed)),n=v5((e=>e.options.shouldGenerateVariations)),{t:r}=FY(),o=m5();return ld(L3,{label:r("options:seed"),step:1,precision:0,flexGrow:1,min:0,max:BQ,isDisabled:t,isInvalid:e<0&&n,onChange:e=>o(aU(e)),value:e,width:"auto"})}function Hte(){const e=m5(),t=v5((e=>e.options.shouldRandomizeSeed)),{t:n}=FY();return ld(RT,{size:"sm",isDisabled:t,onClick:()=>e(aU(HQ(0,BQ))),padding:"0 1.5rem",children:ld("p",{children:n("options:shuffle")})})}function Wte(){const e=m5(),t=v5((e=>e.options.threshold)),{t:n}=FY();return ld(L3,{label:n("options:noiseThreshold"),min:0,max:1e3,step:.1,onChange:t=>e(wU(t)),value:t,isInteger:!1})}function Vte(){const e=m5(),t=v5((e=>e.options.perlin)),{t:n}=FY();return ld(L3,{label:n("options:perlinNoise"),min:0,max:1,step:.05,onChange:t=>e(Q$(t)),value:t,isInteger:!1})}const $te=()=>cd(CI,{gap:2,direction:"column",children:[ld(jte,{}),cd(CI,{gap:2,children:[ld(Fte,{}),ld(Hte,{})]}),ld(CI,{gap:2,children:ld(Wte,{})}),ld(CI,{gap:2,children:ld(Vte,{})})]}),Ute=uV((e=>e.options),(e=>({upscalingLevel:e.upscalingLevel,upscalingStrength:e.upscalingStrength})),{memoizeOptions:{resultEqualityCheck:MU.exports.isEqual}}),Gte=uV((e=>e.system),(e=>({isESRGANAvailable:e.isESRGANAvailable})),{memoizeOptions:{resultEqualityCheck:MU.exports.isEqual}}),qte=()=>{const e=m5(),{upscalingLevel:t,upscalingStrength:n}=v5(Ute),{isESRGANAvailable:r}=v5(Gte),{t:o}=FY();return cd("div",{className:"upscale-options",children:[ld(E3,{isDisabled:!r,label:o("options:scale"),value:t,onChange:t=>e(SU(Number(t.target.value))),validValues:zQ}),ld(L3,{isDisabled:!r,label:o("options:strength"),step:.05,min:0,max:1,onChange:t=>e(CU(t)),value:n,isInteger:!1})]})};function Yte(){const e=v5((e=>e.system.isESRGANAvailable)),t=v5((e=>e.options.shouldRunESRGAN)),n=m5();return ld(_3,{isDisabled:!e,isChecked:t,onChange:e=>n(pU(e.target.checked))})}function Zte(){const e=v5((e=>e.options.shouldGenerateVariations)),t=m5();return ld(_3,{isChecked:e,width:"auto",onChange:e=>t(cU(e.target.checked))})}function Xte(){const e=v5((e=>e.options.seedWeights)),t=v5((e=>e.options.shouldGenerateVariations)),{t:n}=FY(),r=m5();return ld(W3,{label:n("options:seedWeights"),value:e,isInvalid:t&&!(_$(e)||""===e),isDisabled:!t,onChange:e=>r(sU(e.target.value))})}function Kte(){const e=v5((e=>e.options.variationAmount)),t=v5((e=>e.options.shouldGenerateVariations)),{t:n}=FY(),r=m5();return ld(L3,{label:n("options:variationAmount"),value:e,step:.01,min:0,max:1,isDisabled:!t,onChange:e=>r(_U(e)),isInteger:!1})}const Qte=()=>cd(CI,{gap:2,direction:"column",children:[ld(Kte,{}),ld(Xte,{})]});function Jte(){const e=m5(),t=v5((e=>e.options.cfgScale)),{t:n}=FY();return ld(L3,{label:n("options:cfgScale"),step:.5,min:1.01,max:200,onChange:t=>e(z$(t)),value:t,width:"auto",styleClass:"main-option-block",textAlign:"center",isInteger:!1})}function ene(){const e=v5((e=>e.options.height)),t=v5(T3),n=m5(),{t:r}=FY();return ld(E3,{isDisabled:"unifiedCanvas"===t,label:r("options:height"),value:e,flexGrow:1,onChange:e=>n(W$(Number(e.target.value))),validValues:DQ,styleClass:"main-option-block"})}const tne=uV([e=>e.options],(e=>{const{iterations:t}=e;return{iterations:t}}),{memoizeOptions:{resultEqualityCheck:TU.isEqual}});function nne(){const e=m5(),{iterations:t}=v5(tne),{t:n}=FY();return ld(L3,{label:n("options:images"),step:1,min:1,max:9999,onChange:t=>e(Y$(t)),value:t,width:"auto",labelFontSize:.5,styleClass:"main-option-block",textAlign:"center"})}function rne(){const e=v5((e=>e.options.sampler)),t=m5(),{t:n}=FY();return ld(E3,{label:n("options:sampler"),value:e,onChange:e=>t(eU(e.target.value)),validValues:RQ,styleClass:"main-option-block"})}function one(){const e=m5(),t=v5((e=>e.options.steps)),{t:n}=FY();return ld(L3,{label:n("options:steps"),min:1,max:9999,step:1,onChange:t=>e(xU(t)),value:t,width:"auto",styleClass:"main-option-block",textAlign:"center"})}function ine(){const e=v5((e=>e.options.width)),t=v5(T3),{t:n}=FY(),r=m5();return ld(E3,{isDisabled:"unifiedCanvas"===t,label:n("options:width"),value:e,flexGrow:1,onChange:e=>r(EU(Number(e.target.value))),validValues:NQ,styleClass:"main-option-block"})}function ane(){return ld("div",{className:"main-options",children:cd("div",{className:"main-options-list",children:[cd("div",{className:"main-options-row",children:[ld(nne,{}),ld(one,{}),ld(Jte,{})]}),cd("div",{className:"main-options-row",children:[ld(ine,{}),ld(ene,{}),ld(rne,{})]})]})})}const sne=uV((e=>e.system),(e=>e.shouldDisplayGuides)),lne=({children:e,feature:t})=>{const n=v5(sne),{text:r}=Ote(t);return 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yne(e){const{...t}=e,n=m5(),{isProcessing:r,isConnected:o,isCancelable:i}=v5(vne),a=()=>n(EQ()),{t:s}=FY();return vte("shift+x",(()=>{(o||r)&&i&&a()}),[o,r,i]),ld(k3,{icon:ld(O9,{}),tooltip:s("options:cancel"),"aria-label":s("options:cancel"),isDisabled:!o||!r||!i,onClick:a,styleClass:"cancel-btn",...t})}const bne=uV((e=>e.options),(e=>e.shouldLoopback)),xne=()=>{const e=m5(),t=v5(bne),{t:n}=FY();return ld(k3,{"aria-label":n("options:toggleLoopback"),tooltip:n("options:toggleLoopback"),styleClass:"loopback-btn",asCheckbox:!0,isChecked:t,icon:ld(l3,{}),onClick:()=>{e(dU(!t))}})},wne=()=>{const e=v5(T3);return cd("div",{className:"process-buttons",children:[ld(mne,{}),"img2img"===e&&ld(xne,{}),ld(yne,{})]})},kne=uV([e=>e.options,T3],((e,t)=>({prompt:e.prompt,activeTabName:t})),{memoizeOptions:{resultEqualityCheck:TU.isEqual}}),Sne=()=>{const e=m5(),{prompt:t,activeTabName:n}=v5(kne),{isReady:r}=v5(gne),o=a.exports.useRef(null),{t:i}=FY();vte("alt+a",(()=>{o.current?.focus()}),[]);return ld("div",{className:"prompt-bar",children:ld(GT,{isInvalid:0===t.length||Boolean(t.match(/^[\s\r\n]+$/)),children:ld(_H,{id:"prompt",name:"prompt",placeholder:i("options:promptPlaceholder"),size:"lg",value:t,onChange:t=>{e(J$(t.target.value))},onKeyDown:t=>{"Enter"===t.key&&!1===t.shiftKey&&r&&(t.preventDefault(),e(xQ(n)))},resize:"vertical",height:30,ref:o,_placeholder:{color:"var(--text-color-secondary)"}})})})};function Cne(e){return _5({tag:"svg",attr:{fill:"currentColor",viewBox:"0 0 16 16"},child:[{tag:"path",attr:{d:"M9.828.722a.5.5 0 0 1 .354.146l4.95 4.95a.5.5 0 0 1 0 .707c-.48.48-1.072.588-1.503.588-.177 0-.335-.018-.46-.039l-3.134 3.134a5.927 5.927 0 0 1 .16 1.013c.046.702-.032 1.687-.72 2.375a.5.5 0 0 1-.707 0l-2.829-2.828-3.182 3.182c-.195.195-1.219.902-1.414.707-.195-.195.512-1.22.707-1.414l3.182-3.182-2.828-2.829a.5.5 0 0 1 0-.707c.688-.688 1.673-.767 2.375-.72a5.922 5.922 0 0 1 1.013.16l3.134-3.133a2.772 2.772 0 0 1-.04-.461c0-.43.108-1.022.589-1.503a.5.5 0 0 1 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t(){for(var t,n=arguments.length,r=new Array(n),o=0;o<n;o++)r[o]=arguments[o];return(t=e.call.apply(e,[this].concat(r))||this).appliedClasses={appear:{},enter:{},exit:{}},t.onEnter=function(e,n){var r=t.resolveArguments(e,n),o=r[0],i=r[1];t.removeClasses(o,"exit"),t.addClass(o,i?"appear":"enter","base"),t.props.onEnter&&t.props.onEnter(e,n)},t.onEntering=function(e,n){var r=t.resolveArguments(e,n),o=r[0],i=r[1]?"appear":"enter";t.addClass(o,i,"active"),t.props.onEntering&&t.props.onEntering(e,n)},t.onEntered=function(e,n){var r=t.resolveArguments(e,n),o=r[0],i=r[1]?"appear":"enter";t.removeClasses(o,i),t.addClass(o,i,"done"),t.props.onEntered&&t.props.onEntered(e,n)},t.onExit=function(e){var n=t.resolveArguments(e)[0];t.removeClasses(n,"appear"),t.removeClasses(n,"enter"),t.addClass(n,"exit","base"),t.props.onExit&&t.props.onExit(e)},t.onExiting=function(e){var n=t.resolveArguments(e)[0];t.addClass(n,"exit","active"),t.props.onExiting&&t.props.onExiting(e)},t.onExited=function(e){var n=t.resolveArguments(e)[0];t.removeClasses(n,"exit"),t.addClass(n,"exit","done"),t.props.onExited&&t.props.onExited(e)},t.resolveArguments=function(e,n){return t.props.nodeRef?[t.props.nodeRef.current,e]:[e,n]},t.getClassNames=function(e){var n=t.props.classNames,r="string"==typeof n,o=r?""+(r&&n?n+"-":"")+e:n[e];return{baseClassName:o,activeClassName:r?o+"-active":n[e+"Active"],doneClassName:r?o+"-done":n[e+"Done"]}},t}DD(t,e);var n=t.prototype;return n.addClass=function(e,t,n){var r=this.getClassNames(t)[n+"ClassName"],o=this.getClassNames("enter").doneClassName;"appear"===t&&"done"===n&&o&&(r+=" "+o),"active"===n&&e&&One(e),r&&(this.appliedClasses[t][n]=r,Bne(e,r))},n.removeClasses=function(e,t){var n=this.appliedClasses[t],r=n.base,o=n.active,i=n.done;this.appliedClasses[t]={},r&&jne(e,r),o&&jne(e,o),i&&jne(e,i)},n.render=function(){var e=this.props;e.classNames;var t=gD(e,["classNames"]);return ld(zne,{...t,onEnter:this.onEnter,onEntered:this.onEntered,onEntering:this.onEntering,onExit:this.onExit,onExiting:this.onExiting,onExited:this.onExited})},t}(H.Component);Fne.defaultProps={classNames:""},Fne.propTypes={};const Hne=Fne,Wne=uV((e=>e.options),(e=>{const{shouldShowOptionsPanel:t,shouldHoldOptionsPanelOpen:n,shouldPinOptionsPanel:r,optionsPanelScrollPosition:o}=e;return{shouldShowOptionsPanel:t,shouldHoldOptionsPanelOpen:n,shouldPinOptionsPanel:r,optionsPanelScrollPosition:o}}),{memoizeOptions:{resultEqualityCheck:TU.isEqual}}),Vne=e=>{const t=m5(),{shouldShowOptionsPanel:n,shouldHoldOptionsPanelOpen:r,shouldPinOptionsPanel:o}=v5(Wne),i=a.exports.useRef(null),s=a.exports.useRef(null),l=a.exports.useRef(null),{children:c}=e;vte("o",(()=>{t(vU(!n)),o&&setTimeout((()=>t(iX(!0))),400)}),[n,o]),vte("esc",(()=>{t(vU(!1))}),{enabled:()=>!o,preventDefault:!0},[o]),vte("shift+o",(()=>{h(),t(iX(!0))}),[o]);const u=a.exports.useCallback((()=>{o||(t(X$(s.current?s.current.scrollTop:0)),t(vU(!1)),t(uU(!1)))}),[t,o]),d=()=>{l.current&&window.clearTimeout(l.current)},h=()=>{t(hU(!o)),t(iX(!0))};return a.exports.useEffect((()=>{function e(e){i.current&&!i.current.contains(e.target)&&u()}return document.addEventListener("mousedown",e),()=>{document.removeEventListener("mousedown",e)}}),[u]),ld(Hne,{nodeRef:i,in:n||r&&!o,unmountOnExit:!0,timeout:200,classNames:"options-panel-wrapper",children:ld("div",{className:"options-panel-wrapper","data-pinned":o,tabIndex:1,ref:i,onMouseEnter:o?void 0:d,onMouseOver:o?void 0:d,style:{borderRight:o?"":"0.3rem solid var(--tab-list-text-inactive)"},children:ld("div",{className:"options-panel-margin",children:cd("div",{className:"options-panel",ref:s,onMouseLeave:e=>{e.target!==s.current?d():!o&&(l.current=window.setTimeout((()=>u()),500))},children:[ld(YH,{label:"Pin Options Panel",children:ld("div",{className:"options-panel-pin-button","data-selected":o,onClick:h,children:ld(o?Cne:_ne,{})})}),!o&&cd("div",{className:"invoke-ai-logo-wrapper",children:[ld("img",{src:w3,alt:"invoke-ai-logo"}),cd("h1",{children:["invoke ",ld("strong",{children:"ai"})]})]}),c]})})})})};function $ne(){const{t:e}=FY(),t={seed:{header:`${e("options:seed")}`,feature:Lte.SEED,content:ld($te,{})},variations:{header:`${e("options:variations")}`,feature:Lte.VARIATIONS,content:ld(Qte,{}),additionalHeaderComponents:ld(Zte,{})},face_restore:{header:`${e("options:faceRestoration")}`,feature:Lte.FACE_CORRECTION,content:ld(Ate,{}),additionalHeaderComponents:ld(Ite,{})},upscale:{header:`${e("options:upscaling")}`,feature:Lte.UPSCALE,content:ld(qte,{}),additionalHeaderComponents:ld(Yte,{})},other:{header:`${e("options:otherOptions")}`,feature:Lte.OTHER,content:ld(Bte,{})}},n=m5(),r=v5(T3);return a.exports.useEffect((()=>{if("img2img"===r){(()=>n(V$(!1)))()}}),[r,n]),cd(Vne,{children:[ld(Sne,{}),ld(wne,{}),ld(ane,{}),ld(Dte,{label:e("options:img2imgStrength"),styleClass:"main-option-block image-to-image-strength-main-option"}),ld(Rte,{}),ld(dne,{accordionInfo:t})]})}const Une=a.exports.createContext(null),Gne=e=>{const{styleClass:t}=e,n=a.exports.useContext(Une);return ld("div",{className:`image-uploader-button-outer ${t}`,onClick:()=>{n&&n()},children:cd("div",{className:"image-upload-button",children:[ld(y3,{}),ld(LI,{size:"lg",children:"Click or Drag and 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e=m5(),{isProcessing:t,isConnected:n,isGFPGANAvailable:r,isESRGANAvailable:o,upscalingLevel:i,facetoolStrength:a,shouldDisableToolbarButtons:s,shouldShowImageDetails:l,currentImage:c,isLightBoxOpen:u,activeTabName:d}=v5(Zne),h=zH(),{t:f}=FY(),p=()=>{c&&(u&&e(q$(!1)),e(G$(c)),e(I$("img2img")))};vte("shift+i",(()=>{c?(p(),h({title:f("toast:sentToImageToImage"),status:"success",duration:2500,isClosable:!0})):h({title:f("toast:imageNotLoaded"),description:f("toast:imageNotLoadedDesc"),status:"error",duration:2500,isClosable:!0})}),[c]);const g=()=>{c&&(c.metadata&&e(N$(c.metadata)),"img2img"===c.metadata?.image.type?e(I$("img2img")):"txt2img"===c.metadata?.image.type&&e(I$("txt2img")))};vte("a",(()=>{["txt2img","img2img"].includes(c?.metadata?.image?.type)?(g(),h({title:f("toast:parametersSet"),status:"success",duration:2500,isClosable:!0})):h({title:f("toast:parametersNotSet"),description:f("toast:parametersNotSetDesc"),status:"error",duration:2500,isClosable:!0})}),[c]);const m=()=>{c?.metadata&&e(aU(c.metadata.image.seed))};vte("s",(()=>{c?.metadata?.image?.seed?(m(),h({title:f("toast:seedSet"),status:"success",duration:2500,isClosable:!0})):h({title:f("toast:seedNotSet"),description:f("toast:seedNotSetDesc"),status:"error",duration:2500,isClosable:!0})}),[c]);const v=()=>c?.metadata?.image?.prompt&&e(J$(c.metadata.image.prompt));vte("p",(()=>{c?.metadata?.image?.prompt?(v(),h({title:f("toast:promptSet"),status:"success",duration:2500,isClosable:!0})):h({title:f("toast:promptNotSet"),description:f("toast:promptNotSetDesc"),status:"error",duration:2500,isClosable:!0})}),[c]);const y=()=>{c&&e(wQ(c))};vte("Shift+U",(()=>{o&&!s&&n&&!t&&i?y():h({title:f("toast:upscalingFailed"),status:"error",duration:2500,isClosable:!0})}),[c,o,s,n,t,i]);const b=()=>{c&&e(kQ(c))};vte("Shift+R",(()=>{r&&!s&&n&&!t&&a?b():h({title:f("toast:faceRestoreFailed"),status:"error",duration:2500,isClosable:!0})}),[c,r,s,n,t,a]);const x=()=>e(mU(!l));vte("i",(()=>{c?x():h({title:f("toast:metadataLoadFailed"),status:"error",duration:2500,isClosable:!0})}),[c,l]);return cd("div",{className:"current-image-options",children:[cd(DT,{isAttached:!0,children:[ld(z3,{trigger:"hover",triggerComponent:ld(k3,{"aria-label":`${f("options:sendTo")}...`,icon:ld(h3,{})}),children:cd("div",{className:"current-image-send-to-popover",children:[ld(B3,{size:"sm",onClick:p,leftIcon:ld(f3,{}),children:f("options:sendToImg2Img")}),ld(B3,{size:"sm",onClick:()=>{c&&(u&&e(q$(!1)),e(aX(c)),e(iX(!0)),"unifiedCanvas"!==d&&e(I$("unifiedCanvas")),h({title:f("toast:sentToUnifiedCanvas"),status:"success",duration:2500,isClosable:!0}))},leftIcon:ld(f3,{}),children:f("options:sendToUnifiedCanvas")}),ld(B3,{size:"sm",onClick:()=>{navigator.clipboard.writeText(c?window.location.toString()+c.url:"").then((()=>{h({title:f("toast:imageLinkCopied"),status:"success",duration:2500,isClosable:!0})}))},leftIcon:ld(j5,{}),children:f("options:copyImageToLink")}),ld(B3,{leftIcon:ld(W5,{}),size:"sm",children:ld(MI,{download:!0,href:c?.url,children:f("options:downloadImage")})})]})}),ld(k3,{icon:ld(U5,{}),tooltip:u?`${f("options:closeViewer")} (Z)`:`${f("options:openInViewer")} (Z)`,"aria-label":u?`${f("options:closeViewer")} (Z)`:`${f("options:openInViewer")} (Z)`,"data-selected":u,onClick:()=>{e(q$(!u))}})]}),cd(DT,{isAttached:!0,children:[ld(k3,{icon:ld(s3,{}),tooltip:`${f("options:usePrompt")} (P)`,"aria-label":`${f("options:usePrompt")} (P)`,isDisabled:!c?.metadata?.image?.prompt,onClick:v}),ld(k3,{icon:ld(d3,{}),tooltip:`${f("options:useSeed")} (S)`,"aria-label":`${f("options:useSeed")} (S)`,isDisabled:!c?.metadata?.image?.seed,onClick:m}),ld(k3,{icon:ld(N5,{}),tooltip:`${f("options:useAll")} (A)`,"aria-label":`${f("options:useAll")} 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${e}`,icon:ld(j5,{}),size:"xs",variant:"ghost",fontSize:14,onClick:()=>navigator.clipboard.writeText(t.toString())})}),cd(CI,{direction:o?"column":"row",children:[cd(VI,{fontWeight:"semibold",whiteSpace:"pre-wrap",pr:2,children:[e,":"]}),r?cd(MI,{href:t.toString(),isExternal:!0,wordBreak:"break-all",children:[t.toString()," ",ld(V9,{mx:"2px"})]}):ld(VI,{overflowY:"scroll",wordBreak:"break-all",children:t.toString()})]})]}),Jne=a.exports.memo((({image:e,styleClass:t})=>{const n=m5();vte("esc",(()=>{n(mU(!1))}));const r=e?.metadata?.image||{},o=e?.dreamPrompt,{cfg_scale:i,fit:a,height:s,hires_fix:l,init_image_path:c,mask_image_path:u,orig_path:d,perlin:h,postprocessing:f,prompt:p,sampler:g,scale:m,seamless:v,seed:y,steps:b,strength:x,threshold:w,type:k,variations:S,width:C}=r,_=JSON.stringify(e.metadata,null,2);return ld("div",{className:`image-metadata-viewer ${t}`,children:cd(CI,{gap:1,direction:"column",width:"100%",children:[cd(CI,{gap:2,children:[ld(VI,{fontWeight:"semibold",children:"File:"}),cd(MI,{href:e.url,isExternal:!0,maxW:"calc(100% - 3rem)",children:[e.url.length>64?e.url.substring(0,64).concat("..."):e.url,ld(V9,{mx:"2px"})]})]}),Object.keys(r).length>0?cd(sd,{children:[k&&ld(Qne,{label:"Generation type",value:k}),e.metadata?.model_weights&&ld(Qne,{label:"Model",value:e.metadata.model_weights}),["esrgan","gfpgan"].includes(k)&&ld(Qne,{label:"Original image",value:d}),"gfpgan"===k&&void 0!==x&&ld(Qne,{label:"Fix faces strength",value:x,onClick:()=>n(F$(x))}),"esrgan"===k&&void 0!==m&&ld(Qne,{label:"Upscaling scale",value:m,onClick:()=>n(SU(m))}),"esrgan"===k&&void 0!==x&&ld(Qne,{label:"Upscaling strength",value:x,onClick:()=>n(CU(x))}),p&&ld(Qne,{label:"Prompt",labelPosition:"top",value:C$(p),onClick:()=>n(J$(p))}),void 0!==y&&ld(Qne,{label:"Seed",value:y,onClick:()=>n(aU(y))}),void 0!==w&&ld(Qne,{label:"Noise Threshold",value:w,onClick:()=>n(wU(w))}),void 0!==h&&ld(Qne,{label:"Perlin Noise",value:h,onClick:()=>n(Q$(h))}),g&&ld(Qne,{label:"Sampler",value:g,onClick:()=>n(eU(g))}),b&&ld(Qne,{label:"Steps",value:b,onClick:()=>n(xU(b))}),void 0!==i&&ld(Qne,{label:"CFG scale",value:i,onClick:()=>n(z$(i))}),S&&S.length>0&&ld(Qne,{label:"Seed-weight pairs",value:E$(S),onClick:()=>n(sU(E$(S)))}),v&&ld(Qne,{label:"Seamless",value:v,onClick:()=>n(nU(v))}),l&&ld(Qne,{label:"High Resolution Optimization",value:l,onClick:()=>n(V$(l))}),C&&ld(Qne,{label:"Width",value:C,onClick:()=>n(EU(C))}),s&&ld(Qne,{label:"Height",value:s,onClick:()=>n(W$(s))}),c&&ld(Qne,{label:"Initial image",value:c,isLink:!0,onClick:()=>n(G$(c))}),u&&ld(Qne,{label:"Mask image",value:u,isLink:!0,onClick:()=>n(Z$(u))}),"img2img"===k&&x&&ld(Qne,{label:"Image to image strength",value:x,onClick:()=>n($$(x))}),a&&ld(Qne,{label:"Image to image fit",value:a,onClick:()=>n(lU(a))}),f&&f.length>0&&cd(sd,{children:[ld(LI,{size:"sm",children:"Postprocessing"}),f.map(((e,t)=>{if("esrgan"===e.type){const{scale:r,strength:o}=e;return cd(CI,{pl:"2rem",gap:1,direction:"column",children:[ld(VI,{size:"md",children:`${t+1}: Upscale (ESRGAN)`}),ld(Qne,{label:"Scale",value:r,onClick:()=>n(SU(r))}),ld(Qne,{label:"Strength",value:o,onClick:()=>n(CU(o))})]},t)}if("gfpgan"===e.type){const{strength:r}=e;return cd(CI,{pl:"2rem",gap:1,direction:"column",children:[ld(VI,{size:"md",children:`${t+1}: Face restoration (GFPGAN)`}),ld(Qne,{label:"Strength",value:r,onClick:()=>{n(F$(r)),n(H$("gfpgan"))}})]},t)}if("codeformer"===e.type){const{strength:r,fidelity:o}=e;return cd(CI,{pl:"2rem",gap:1,direction:"column",children:[ld(VI,{size:"md",children:`${t+1}: Face restoration 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Hae(){const{t:e}=FY(),t={seed:{header:`${e("options:seed")}`,feature:Lte.SEED,content:ld($te,{})},variations:{header:`${e("options:variations")}`,feature:Lte.VARIATIONS,content:ld(Qte,{}),additionalHeaderComponents:ld(Zte,{})},face_restore:{header:`${e("options:faceRestoration")}`,feature:Lte.FACE_CORRECTION,content:ld(Ate,{}),additionalHeaderComponents:ld(Ite,{})},upscale:{header:`${e("options:upscaling")}`,feature:Lte.UPSCALE,content:ld(qte,{}),additionalHeaderComponents:ld(Yte,{})},other:{header:`${e("options:otherOptions")}`,feature:Lte.OTHER,content:ld(Fae,{})}};return cd(Vne,{children:[ld(Sne,{}),ld(wne,{}),ld(ane,{}),ld(dne,{accordionInfo:t})]})}const Wae=()=>ld("div",{className:"workarea-single-view",children:ld("div",{className:"text-to-image-area",children:ld(rre,{})})});function Vae(){return ld(zae,{optionsPanel:ld(Hae,{}),children:ld(Wae,{})})}var $ae=function(e,t){return $ae=Object.setPrototypeOf||{__proto__:[]}instanceof 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t=Ra(e),n=Gn(e,t);if(null!==n)Na(n,e,t,Ia());ks(e,t)}},t.attemptSynchronousHydration=function(e){switch(e.tag){case 3:var t=e.stateNode;if(t.current.memoizedState.isDehydrated){var n=Pt(t.pendingLanes);0!==n&&(Rt(t,1|n),Da(t,Ht()),0==(6&ia)&&(wa(),en()))}break;case 13:Wa((function(){var t=Gn(e,1);if(null!==t){var n=Ia();Na(t,e,1,n)}})),ks(e,1)}},t.batchedUpdates=function(e,t){var n=ia;ia|=1;try{return e(t)}finally{0===(ia=n)&&(wa(),Xt&&en())}},t.createComponentSelector=function(e){return{$$typeof:$i,value:e}},t.createContainer=function(e,t,n,r,o,i,a){return ys(e,t,!1,null,0,r,0,i,a)},t.createHasPseudoClassSelector=function(e){return{$$typeof:Ui,value:e}},t.createHydrationContainer=function(e,t,n,r,o,i,a,s,l){return(e=ys(n,r,!0,e,0,i,0,s,l)).context=bs(null),n=e.current,(i=Xn(r=Ia(),o=Ra(n))).callback=null!=t?t:null,Kn(n,i,o),e.current.lanes=o,It(e,o,r),Da(e,r),e},t.createPortal=function(e,t,n){var r=3<arguments.length&&void 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Nt},t.getFindAllNodesFailureDescription=function(e,t){if(!oe)throw Error(i(363));var n=0,r=[];e=[Xi(e),0];for(var o=0;o<e.length;){var a=e[o++],s=e[o++],l=t[s];if((5!==a.tag||!le(a))&&(Ki(a,l)&&(r.push(Qi(l)),++s>n&&(n=s)),s<t.length))for(a=a.child;null!==a;)e.push(a,s),a=a.sibling}if(n<t.length){for(e=[];n<t.length;n++)e.push(Qi(t[n]));return"findAllNodes was able to match part of the selector:\n  "+r.join(" > ")+"\n\nNo matching component was found for:\n  "+e.join(" > ")}return null},t.getPublicRootInstance=function(e){return(e=e.current).child?5===e.child.tag?I(e.child.stateNode):e.child.stateNode:null},t.injectIntoDevTools=function(e){if(e={bundleType:e.bundleType,version:e.version,rendererPackageName:e.rendererPackageName,rendererConfig:e.rendererConfig,overrideHookState:null,overrideHookStateDeletePath:null,overrideHookStateRenamePath:null,overrideProps:null,overridePropsDeletePath:null,overridePropsRenamePath:null,setErrorHandler:null,setSuspenseHandler:null,scheduleUpdate:null,currentDispatcherRef:s.ReactCurrentDispatcher,findHostInstanceByFiber:Ss,findFiberByHostInstance:e.findFiberByHostInstance||Cs,findHostInstancesForRefresh:null,scheduleRefresh:null,scheduleRoot:null,setRefreshHandler:null,getCurrentFiber:null,reconcilerVersion:"18.2.0"},"undefined"==typeof __REACT_DEVTOOLS_GLOBAL_HOOK__)e=!1;else{var t=__REACT_DEVTOOLS_GLOBAL_HOOK__;if(t.isDisabled||!t.supportsFiber)e=!0;else{try{Gt=t.inject(e),qt=t}catch(tue){}e=!!t.checkDCE}}return e},t.isAlreadyRendering=function(){return!1},t.observeVisibleRects=function(e,t,n,r){if(!oe)throw Error(i(363));e=ea(e,t);var o=de(e,n,r).disconnect;return{disconnect:function(){o()}}},t.registerMutableSourceForHydration=function(e,t){var n=t._getVersion;n=n(t._source),null==e.mutableSourceEagerHydrationData?e.mutableSourceEagerHydrationData=[t,n]:e.mutableSourceEagerHydrationData.push(t,n)},t.runWithPriority=function(e,t){var n=Nt;try{return Nt=e,t()}finally{Nt=n}},t.shouldError=function(){return null},t.shouldSuspend=function(){return!1},t.updateContainer=function(e,t,n,r){var o=t.current,i=Ia(),a=Ra(o);return n=bs(n),null===t.context?t.context=n:t.pendingContext=n,(t=Xn(i,a)).payload={element:e},null!==(r=void 0===r?null:r)&&(t.callback=r),null!==(e=Kn(o,t,a))&&(Na(e,o,a,i),Qn(e,o,a)),a},t};
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              * @license React
              * react-reconciler.production.min.js
@@ -35,7 +35,7 @@ var Y=a.exports,Z=G.exports;function X(e){for(var t="https://reactjs.org/docs/er
              *
              * This source code is licensed under the MIT license found in the
              * LICENSE file in the root directory of this source tree.
-             */!function(e){e.exports=ple}(fle);const gle=i(fle.exports);var mle={exports:{}},vle={
+             */!function(e){e.exports=mle}(gle);const vle=i(gle.exports);var yle={exports:{}},ble={
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              * @license React
              * react-reconciler-constants.production.min.js
@@ -45,4 +45,4 @@ var Y=a.exports,Z=G.exports;function X(e){for(var t="https://reactjs.org/docs/er
              * This source code is licensed under the MIT license found in the
              * LICENSE file in the root directory of this source tree.
              */
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jue=uV([une,dne,O3],((e,t,n)=>{const{isProcessing:r}=n,{tool:o}=e;return{tool:o,isStaging:t,isProcessing:r}}),{memoizeOptions:{resultEqualityCheck:TU.isEqual}}),Fue=()=>{const e=m5(),{t:t}=FY(),{tool:n,isStaging:r}=v5(jue);gte(["b"],(()=>{o()}),{enabled:()=>!r,preventDefault:!0},[]),gte(["e"],(()=>{i()}),{enabled:()=>!r,preventDefault:!0},[n]),gte(["c"],(()=>{a()}),{enabled:()=>!r,preventDefault:!0},[n]),gte(["shift+f"],(()=>{s()}),{enabled:()=>!r,preventDefault:!0}),gte(["delete","backspace"],(()=>{l()}),{enabled:()=>!r,preventDefault:!0});const o=()=>e(zX("brush")),i=()=>e(zX("eraser")),a=()=>e(zX("colorPicker")),s=()=>e(TZ()),l=()=>e(MZ());return cd(CI,{flexDirection:"column",gap:"0.5rem",children:[cd(DT,{children:[ld(k3,{"aria-label":`${t("unifiedcanvas:brush")} (B)`,tooltip:`${t("unifiedcanvas:brush")} (B)`,icon:ld(r3,{}),"data-selected":"brush"===n&&!r,onClick:o,isDisabled:r}),ld(k3,{"aria-label":`${t("unifiedcanvas:eraser")} (E)`,tooltip:`${t("unifiedcanvas:eraser")} 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ld(z3,{trigger:"hover",triggerComponent:ld(k3,{tooltip:t("unifiedcanvas:canvasSettings"),tooltipProps:{placement:"bottom"},"aria-label":t("unifiedcanvas:canvasSettings"),icon:ld(x3,{})}),children:cd(CI,{direction:"column",gap:"0.5rem",children:[ld(k9,{label:t("unifiedcanvas:showIntermediates"),isChecked:i,onChange:t=>e(MX(t.target.checked))}),ld(k9,{label:t("unifiedcanvas:autoSaveToGallery"),isChecked:n,onChange:t=>e(bX(t.target.checked))}),ld(k9,{label:t("unifiedcanvas:saveBoxRegionOnly"),isChecked:r,onChange:t=>e(xX(t.target.checked))}),ld(k9,{label:t("unifiedcanvas:showCanvasDebugInfo"),isChecked:o,onChange:t=>e(PX(t.target.checked))}),ld(Zce,{}),ld(Yce,{})]})})};function Vue(){const e=v5((e=>e.canvas.tool)),t=v5(dne),n=m5(),{t:r}=FY();gte(["v"],(()=>{o()}),{enabled:()=>!t,preventDefault:!0},[]);const o=()=>n(zX("move"));return ld(k3,{"aria-label":`${r("unifiedcanvas:move")} (V)`,tooltip:`${r("unifiedcanvas:move")} (V)`,icon:ld(R5,{}),"data-selected":"move"===e||t,onClick:o})}function $ue(){const e=Z1(),t=m5(),{t:n}=FY();gte(["r"],(()=>{r()}),{enabled:()=>!0,preventDefault:!0},[e]);const r=()=>{const e=Z1();if(!e)return;const n=e.getClientRect({skipTransform:!0});t(qZ({contentRect:n}))};return ld(k3,{"aria-label":`${n("unifiedcanvas:resetView")} (R)`,tooltip:`${n("unifiedcanvas:resetView")} (R)`,icon:ld(F5,{}),onClick:r})}function Uue(){const e=m5(),{t:t}=FY(),n=Z1(),r=v5(dne),o=v5((e=>e.system.isProcessing));gte(["shift+m"],(()=>{i()}),{enabled:()=>!r,preventDefault:!0},[n,o]);const i=()=>{e(Lue({cropVisible:!1,shouldSetAsInitialImage:!0}))};return ld(k3,{"aria-label":`${t("unifiedcanvas:mergeVisible")} (Shift+M)`,tooltip:`${t("unifiedcanvas:mergeVisible")} (Shift+M)`,icon:ld(e3,{}),onClick:i,isDisabled:r})}function Gue(){const e=v5(dne),t=Z1(),n=v5((e=>e.system.isProcessing)),r=v5((e=>e.canvas.shouldCropToBoundingBoxOnSave)),o=m5(),{t:i}=FY();gte(["shift+s"],(()=>{a()}),{enabled:()=>!e,preventDefault:!0},[t,n]);const a=()=>{o(Lue({cropVisible:!r,cropToBoundingBox:r,shouldSaveToGallery:!0}))};return ld(k3,{"aria-label":`${i("unifiedcanvas:saveToGallery")} (Shift+S)`,tooltip:`${i("unifiedcanvas:saveToGallery")} (Shift+S)`,icon:ld(u3,{}),onClick:a,isDisabled:e})}function que(){const e=v5(dne),t=Z1(),n=v5((e=>e.system.isProcessing)),r=v5((e=>e.canvas.shouldCropToBoundingBoxOnSave)),o=m5(),{t:i}=FY();gte(["meta+c","ctrl+c"],(()=>{a()}),{enabled:()=>!e,preventDefault:!0},[t,n]);const a=()=>{o(Lue({cropVisible:!r,cropToBoundingBox:r,shouldCopy:!0}))};return ld(k3,{"aria-label":`${i("unifiedcanvas:copyToClipboard")} (Cmd/Ctrl+C)`,tooltip:`${i("unifiedcanvas:copyToClipboard")} (Cmd/Ctrl+C)`,icon:ld(j5,{}),onClick:a,isDisabled:e})}function Yue(){const e=m5(),{t:t}=FY(),n=Z1(),r=v5(dne),o=v5((e=>e.system.isProcessing)),i=v5((e=>e.canvas.shouldCropToBoundingBoxOnSave));gte(["shift+d"],(()=>{a()}),{enabled:()=>!r,preventDefault:!0},[n,o]);const a=()=>{e(Lue({cropVisible:!i,cropToBoundingBox:i,shouldDownload:!0}))};return ld(k3,{"aria-label":`${t("unifiedcanvas:downloadAsImage")} (Shift+D)`,tooltip:`${t("unifiedcanvas:downloadAsImage")} (Shift+D)`,icon:ld(W5,{}),onClick:a,isDisabled:r})}function Zue(){const e=v5(dne),{openUploader:t}=Aue(),{t:n}=FY();return ld(k3,{"aria-label":n("common:upload"),tooltip:n("common:upload"),icon:ld(y3,{}),onClick:t,isDisabled:e})}function Xue(){const e=m5(),{t:t}=FY(),n=v5(dne);return ld(k3,{"aria-label":t("unifiedcanvas:clearCanvas"),tooltip:t("unifiedcanvas:clearCanvas"),icon:ld(m3,{}),onClick:()=>{e(UZ()),e(YZ())},style:{backgroundColor:"var(--btn-delete-image)"},isDisabled:n})}function Kue(){const e=v5((e=>e.options.shouldPinOptionsPanel)),t=m5(),{t:n}=FY();return cd(CI,{flexDirection:"column",gap:"0.5rem",children:[ld(k3,{tooltip:`${n("options:showOptionsPanel")} (O)`,tooltipProps:{placement:"top"},"aria-label":n("options:showOptionsPanel"),onClick:()=>{t(vU(!0)),e&&setTimeout((()=>t(iX(!0))),400)},children:ld(p3,{})}),ld(CI,{children:ld(pne,{iconButton:!0})}),ld(CI,{children:ld(mne,{width:"100%",height:"40px"})})]})}const Que=()=>{const e=v5((e=>e.options.shouldShowOptionsPanel));return cd(CI,{flexDirection:"column",rowGap:"0.5rem",width:"6rem",children:[ld(Bue,{}),ld(Fue,{}),cd(CI,{gap:"0.5rem",children:[ld(Vue,{}),ld($ue,{})]}),cd(CI,{columnGap:"0.5rem",children:[ld(Uue,{}),ld(Gue,{})]}),cd(CI,{columnGap:"0.5rem",children:[ld(que,{}),ld(Yue,{})]}),cd(CI,{gap:"0.5rem",children:[ld(Uce,{}),ld(qce,{})]}),cd(CI,{gap:"0.5rem",children:[ld(Zue,{}),ld(Xue,{})]}),ld(Wue,{}),!e&&ld(Kue,{})]})};function Jue(){const e=m5(),t=v5((e=>e.canvas.brushSize)),{t:n}=FY(),r=v5(dne);return gte(["BracketLeft"],(()=>{e(tX(Math.max(t-5,5)))}),{enabled:()=>!r,preventDefault:!0},[t]),gte(["BracketRight"],(()=>{e(tX(Math.min(t+5,500)))}),{enabled:()=>!r,preventDefault:!0},[t]),ld(Ite,{label:n("unifiedcanvas:brushSize"),value:t,withInput:!0,onChange:t=>e(tX(t)),sliderNumberInputProps:{max:500},inputReadOnly:!1,width:"100px",isCompact:!0})}const ede=uV([une,dne],((e,t)=>{const{brushColor:n,maskColor:r,layer:o}=e;return{brushColor:n,maskColor:r,layer:o,isStaging:t}}),{memoizeOptions:{resultEqualityCheck:TU.isEqual}});function tde(){const e=m5(),{brushColor:t,maskColor:n,layer:r,isStaging:o}=v5(ede);return gte(["shift+BracketLeft"],(()=>{e(eX({...t,a:TU.clamp(t.a-.05,.05,1)}))}),{enabled:()=>!o,preventDefault:!0},[t]),gte(["shift+BracketRight"],(()=>{e(eX({...t,a:TU.clamp(t.a+.05,.05,1)}))}),{enabled:()=>!o,preventDefault:!0},[t]),ld(z3,{trigger:"hover",triggerComponent:ld(mI,{style:{width:"30px",height:"30px",minWidth:"30px",minHeight:"30px",borderRadius:"99999999px",backgroundColor:"base"===r?`rgba(${t.r},${t.g},${t.b},${t.a})`:"mask"===r?`rgba(${n.r},${n.g},${n.b},${n.a})`:void 0,cursor:"pointer"}}),children:cd(CI,{minWidth:"15rem",direction:"column",gap:"1rem",width:"100%",children:["base"===r&&ld(Cue,{style:{width:"100%",paddingTop:"0.5rem",paddingBottom:"0.5rem"},color:t,onChange:t=>e(eX(t))}),"mask"===r&&ld(Cue,{style:{width:"100%",paddingTop:"0.5rem",paddingBottom:"0.5rem"},color:n,onChange:t=>e(vX(t))})]})})}function nde(){return cd(CI,{columnGap:"1rem",alignItems:"center",children:[ld(Jue,{}),ld(tde,{})]})}function rde(){const e=m5(),t=v5((e=>e.canvas.shouldRestrictStrokesToBox)),{t:n}=FY();return ld(k9,{label:n("unifiedcanvas:betaLimitToBox"),isChecked:t,onChange:t=>e(WX(t.target.checked))})}function ode(){return cd(CI,{gap:"1rem",alignItems:"center",children:[ld(nde,{}),ld(rde,{})]})}function ide(){const e=m5(),{t:t}=FY();return ld(B3,{size:"sm",leftIcon:ld(m3,{}),onClick:()=>e(DZ()),tooltip:`${t("unifiedcanvas:clearMask")} (Shift+C)`,children:t("unifiedcanvas:betaClear")})}function ade(){const e=v5((e=>e.canvas.isMaskEnabled)),t=m5(),{t:n}=FY();return ld(k9,{label:`${n("unifiedcanvas:enableMask")} (H)`,isChecked:e,onChange:()=>t(cX(!e))})}function sde(){const e=m5(),{t:t}=FY(),n=v5((e=>e.canvas.shouldPreserveMaskedArea));return ld(k9,{label:t("unifiedcanvas:betaPreserveMasked"),isChecked:n,onChange:t=>e(SX(t.target.checked))})}function lde(){return cd(CI,{gap:"1rem",alignItems:"center",children:[ld(nde,{}),ld(ade,{}),ld(sde,{}),ld(ide,{})]})}function cde(){const e=v5((e=>e.canvas.shouldDarkenOutsideBoundingBox)),t=m5(),{t:n}=FY();return ld(k9,{label:n("unifiedcanvas:betaDarkenOutside"),isChecked:e,onChange:e=>t(wX(e.target.checked))})}function ude(){const e=v5((e=>e.canvas.shouldShowGrid)),t=m5(),{t:n}=FY();return ld(k9,{label:n("unifiedcanvas:showGrid"),isChecked:e,onChange:e=>t(OX(e.target.checked))})}function dde(){const e=v5((e=>e.canvas.shouldSnapToGrid)),t=m5(),{t:n}=FY();return ld(k9,{label:`${n("unifiedcanvas:snapToGrid")} (N)`,isChecked:e,onChange:e=>t(IX(e.target.checked))})}function hde(){return cd(CI,{alignItems:"center",gap:"1rem",children:[ld(ude,{}),ld(dde,{}),ld(cde,{})]})}const fde=uV([une],(e=>{const{tool:t,layer:n}=e;return{tool:t,layer:n}}),{memoizeOptions:{resultEqualityCheck:TU.isEqual}});function pde(){const{tool:e,layer:t}=v5(fde);return cd(CI,{height:"2rem",minHeight:"2rem",maxHeight:"2rem",alignItems:"center",children:["base"==t&&["brush","eraser","colorPicker"].includes(e)&&ld(ode,{}),"mask"==t&&["brush","eraser","colorPicker"].includes(e)&&ld(lde,{}),"move"==e&&ld(hde,{})]})}const gde=uV([une],(e=>{const{doesCanvasNeedScaling:t}=e;return{doesCanvasNeedScaling:t}}),{memoizeOptions:{resultEqualityCheck:TU.isEqual}}),mde=()=>{const e=m5(),{doesCanvasNeedScaling:t}=v5(gde);return a.exports.useLayoutEffect((()=>{e(iX(!0));const t=TU.debounce((()=>{e(iX(!0))}),250);return window.addEventListener("resize",t),()=>window.removeEventListener("resize",t)}),[e]),ld("div",{className:"workarea-single-view",children:cd(CI,{flexDirection:"row",width:"100%",height:"100%",columnGap:"1rem",padding:"1rem",children:[ld(Que,{}),cd(CI,{width:"100%",height:"100%",flexDirection:"column",rowGap:"1rem",children:[ld(pde,{}),ld(t?hle:Vce,{})]})]})})};function vde(){const e=v5((e=>e.options.shouldUseCanvasBetaLayout));return 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g="inherit";("none"===p&&(i<512||a<512)||"manual"===p&&s*l<262144)&&(g="var(--status-working-color)");return{activeLayerColor:"mask"===f?"var(--status-working-color)":"inherit",activeLayerString:f.charAt(0).toUpperCase()+f.slice(1),boundingBoxColor:g,boundingBoxCoordinatesString:`(${Ice(c)}, 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qce(){const e=m5(),{t:t}=FY(),{canUndo:n,activeTabName:r}=v5(Gce),o=()=>{e(FX())};return vte(["meta+z","ctrl+z"],(()=>{o()}),{enabled:()=>n,preventDefault:!0},[r,n]),ld(k3,{"aria-label":`${t("unifiedcanvas:undo")} (Ctrl+Z)`,tooltip:`${t("unifiedcanvas:undo")} (Ctrl+Z)`,icon:ld(v3,{}),onClick:o,isDisabled:!n})}const Yce=uV([hne,T3,O3],((e,t,n)=>{const{futureLayerStates:r}=e;return{canRedo:r.length>0&&!n.isProcessing,activeTabName:t}}),{memoizeOptions:{resultEqualityCheck:TU.isEqual}});function Zce(){const e=m5(),{canRedo:t,activeTabName:n}=v5(Yce),{t:r}=FY(),o=()=>{e($Z())};return vte(["meta+shift+z","ctrl+shift+z","control+y","meta+y"],(()=>{o()}),{enabled:()=>t,preventDefault:!0},[n,t]),ld(k3,{"aria-label":`${r("unifiedcanvas:redo")} (Ctrl+Shift+Z)`,tooltip:`${r("unifiedcanvas:redo")} (Ctrl+Shift+Z)`,icon:ld(c3,{}),onClick:o,isDisabled:!t})}const Xce=()=>{const e=v5(fne),t=m5(),{t:n}=FY();return cd($9,{title:n("unifiedcanvas:emptyTempImageFolder"),acceptCallback:()=>{t(IQ()),t(UZ()),t(NZ())},acceptButtonText:n("unifiedcanvas:emptyFolder"),triggerComponent:ld(B3,{leftIcon:ld(m3,{}),size:"sm",isDisabled:e,children:n("unifiedcanvas:emptyTempImageFolder")}),children:[ld("p",{children:n("unifiedcanvas:emptyTempImagesFolderMessage")}),ld("br",{}),ld("p",{children:n("unifiedcanvas:emptyTempImagesFolderConfirm")})]})},Kce=()=>{const e=v5(fne),t=m5(),{t:n}=FY();return cd($9,{title:n("unifiedcanvas:clearCanvasHistory"),acceptCallback:()=>t(NZ()),acceptButtonText:n("unifiedcanvas:clearHistory"),triggerComponent:ld(B3,{size:"sm",leftIcon:ld(m3,{}),isDisabled:e,children:n("unifiedcanvas:clearCanvasHistory")}),children:[ld("p",{children:n("unifiedcanvas:clearCanvasHistoryMessage")}),ld("br",{}),ld("p",{children:n("unifiedcanvas:clearCanvasHistoryConfirm")})]})},Qce=uV([hne],(e=>{const{shouldAutoSave:t,shouldCropToBoundingBoxOnSave:n,shouldDarkenOutsideBoundingBox:r,shouldShowCanvasDebugInfo:o,shouldShowGrid:i,shouldShowIntermediates:a,shouldSnapToGrid:s,shouldRestrictStrokesToBox:l}=e;return{shouldAutoSave:t,shouldCropToBoundingBoxOnSave:n,shouldDarkenOutsideBoundingBox:r,shouldShowCanvasDebugInfo:o,shouldShowGrid:i,shouldShowIntermediates:a,shouldSnapToGrid:s,shouldRestrictStrokesToBox:l}}),{memoizeOptions:{resultEqualityCheck:TU.isEqual}}),Jce=()=>{const e=m5(),{t:t}=FY(),{shouldAutoSave:n,shouldCropToBoundingBoxOnSave:r,shouldDarkenOutsideBoundingBox:o,shouldShowCanvasDebugInfo:i,shouldShowGrid:a,shouldShowIntermediates:s,shouldSnapToGrid:l,shouldRestrictStrokesToBox:c}=v5(Qce);vte(["n"],(()=>{e(IX(!l))}),{enabled:!0,preventDefault:!0},[l]);return ld(z3,{trigger:"hover",triggerComponent:ld(k3,{tooltip:t("unifiedcanvas:canvasSettings"),"aria-label":t("unifiedcanvas:canvasSettings"),icon:ld(x3,{})}),children:cd(CI,{direction:"column",gap:"0.5rem",children:[ld(k9,{label:t("unifiedcanvas:showIntermediates"),isChecked:s,onChange:t=>e(MX(t.target.checked))}),ld(k9,{label:t("unifiedcanvas:showGrid"),isChecked:a,onChange:t=>e(OX(t.target.checked))}),ld(k9,{label:t("unifiedcanvas:snapToGrid"),isChecked:l,onChange:t=>e(IX(t.target.checked))}),ld(k9,{label:t("unifiedcanvas:darkenOutsideSelection"),isChecked:o,onChange:t=>e(wX(t.target.checked))}),ld(k9,{label:t("unifiedcanvas:autoSaveToGallery"),isChecked:n,onChange:t=>e(bX(t.target.checked))}),ld(k9,{label:t("unifiedcanvas:saveBoxRegionOnly"),isChecked:r,onChange:t=>e(xX(t.target.checked))}),ld(k9,{label:t("unifiedcanvas:limitStrokesToBox"),isChecked:c,onChange:t=>e(WX(t.target.checked))}),ld(k9,{label:t("unifiedcanvas:showCanvasDebugInfo"),isChecked:i,onChange:t=>e(PX(t.target.checked))}),ld(Kce,{}),ld(Xce,{})]})})};function 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Hue=uV([hne,fne,O3],((e,t,n)=>{const{isProcessing:r}=n,{tool:o}=e;return{tool:o,isStaging:t,isProcessing:r}}),{memoizeOptions:{resultEqualityCheck:TU.isEqual}}),Wue=()=>{const e=m5(),{t:t}=FY(),{tool:n,isStaging:r}=v5(Hue);vte(["b"],(()=>{o()}),{enabled:()=>!r,preventDefault:!0},[]),vte(["e"],(()=>{i()}),{enabled:()=>!r,preventDefault:!0},[n]),vte(["c"],(()=>{a()}),{enabled:()=>!r,preventDefault:!0},[n]),vte(["shift+f"],(()=>{s()}),{enabled:()=>!r,preventDefault:!0}),vte(["delete","backspace"],(()=>{l()}),{enabled:()=>!r,preventDefault:!0});const o=()=>e(zX("brush")),i=()=>e(zX("eraser")),a=()=>e(zX("colorPicker")),s=()=>e(TZ()),l=()=>e(MZ());return cd(CI,{flexDirection:"column",gap:"0.5rem",children:[cd(DT,{children:[ld(k3,{"aria-label":`${t("unifiedcanvas:brush")} (B)`,tooltip:`${t("unifiedcanvas:brush")} (B)`,icon:ld(r3,{}),"data-selected":"brush"===n&&!r,onClick:o,isDisabled:r}),ld(k3,{"aria-label":`${t("unifiedcanvas:eraser")} (E)`,tooltip:`${t("unifiedcanvas:eraser")} (B)`,icon:ld(V5,{}),"data-selected":"eraser"===n&&!r,isDisabled:r,onClick:i})]}),cd(DT,{children:[ld(k3,{"aria-label":`${t("unifiedcanvas:fillBoundingBox")} (Shift+F)`,tooltip:`${t("unifiedcanvas:fillBoundingBox")} (Shift+F)`,icon:ld(Z5,{}),isDisabled:r,onClick:s}),ld(k3,{"aria-label":`${t("unifiedcanvas:eraseBoundingBox")} (Del/Backspace)`,tooltip:`${t("unifiedcanvas:eraseBoundingBox")} (Del/Backspace)`,icon:ld(a3,{style:{transform:"rotate(45deg)"}}),isDisabled:r,onClick:l})]}),ld(k3,{"aria-label":`${t("unifiedcanvas:colorPicker")} (C)`,tooltip:`${t("unifiedcanvas:colorPicker")} (C)`,icon:ld(G5,{}),"data-selected":"colorPicker"===n&&!r,isDisabled:r,onClick:a,width:"max-content"})]})},Vue=uV([hne],(e=>{const{shouldAutoSave:t,shouldCropToBoundingBoxOnSave:n,shouldShowCanvasDebugInfo:r,shouldShowIntermediates:o}=e;return{shouldAutoSave:t,shouldCropToBoundingBoxOnSave:n,shouldShowCanvasDebugInfo:r,shouldShowIntermediates:o}}),{memoizeOptions:{resultEqualityCheck:TU.isEqual}}),$ue=()=>{const e=m5(),{t:t}=FY(),{shouldAutoSave:n,shouldCropToBoundingBoxOnSave:r,shouldShowCanvasDebugInfo:o,shouldShowIntermediates:i}=v5(Vue);return ld(z3,{trigger:"hover",triggerComponent:ld(k3,{tooltip:t("unifiedcanvas:canvasSettings"),tooltipProps:{placement:"bottom"},"aria-label":t("unifiedcanvas:canvasSettings"),icon:ld(x3,{})}),children:cd(CI,{direction:"column",gap:"0.5rem",children:[ld(k9,{label:t("unifiedcanvas:showIntermediates"),isChecked:i,onChange:t=>e(MX(t.target.checked))}),ld(k9,{label:t("unifiedcanvas:autoSaveToGallery"),isChecked:n,onChange:t=>e(bX(t.target.checked))}),ld(k9,{label:t("unifiedcanvas:saveBoxRegionOnly"),isChecked:r,onChange:t=>e(xX(t.target.checked))}),ld(k9,{label:t("unifiedcanvas:showCanvasDebugInfo"),isChecked:o,onChange:t=>e(PX(t.target.checked))}),ld(Kce,{}),ld(Xce,{})]})})};function Uue(){const e=v5((e=>e.canvas.tool)),t=v5(fne),n=m5(),{t:r}=FY();vte(["v"],(()=>{o()}),{enabled:()=>!t,preventDefault:!0},[]);const o=()=>n(zX("move"));return ld(k3,{"aria-label":`${r("unifiedcanvas:move")} (V)`,tooltip:`${r("unifiedcanvas:move")} (V)`,icon:ld(R5,{}),"data-selected":"move"===e||t,onClick:o})}function Gue(){const e=Z1(),t=m5(),{t:n}=FY();vte(["r"],(()=>{r()}),{enabled:()=>!0,preventDefault:!0},[e]);const r=()=>{const e=Z1();if(!e)return;const n=e.getClientRect({skipTransform:!0});t(qZ({contentRect:n}))};return ld(k3,{"aria-label":`${n("unifiedcanvas:resetView")} (R)`,tooltip:`${n("unifiedcanvas:resetView")} (R)`,icon:ld(F5,{}),onClick:r})}function que(){const e=m5(),{t:t}=FY(),n=Z1(),r=v5(fne),o=v5((e=>e.system.isProcessing));vte(["shift+m"],(()=>{i()}),{enabled:()=>!r,preventDefault:!0},[n,o]);const i=()=>{e(Mue({cropVisible:!1,shouldSetAsInitialImage:!0}))};return ld(k3,{"aria-label":`${t("unifiedcanvas:mergeVisible")} (Shift+M)`,tooltip:`${t("unifiedcanvas:mergeVisible")} (Shift+M)`,icon:ld(e3,{}),onClick:i,isDisabled:r})}function Yue(){const e=v5(fne),t=Z1(),n=v5((e=>e.system.isProcessing)),r=v5((e=>e.canvas.shouldCropToBoundingBoxOnSave)),o=m5(),{t:i}=FY();vte(["shift+s"],(()=>{a()}),{enabled:()=>!e,preventDefault:!0},[t,n]);const a=()=>{o(Mue({cropVisible:!r,cropToBoundingBox:r,shouldSaveToGallery:!0}))};return ld(k3,{"aria-label":`${i("unifiedcanvas:saveToGallery")} (Shift+S)`,tooltip:`${i("unifiedcanvas:saveToGallery")} (Shift+S)`,icon:ld(u3,{}),onClick:a,isDisabled:e})}function Zue(){const e=v5(fne),t=Z1(),n=v5((e=>e.system.isProcessing)),r=v5((e=>e.canvas.shouldCropToBoundingBoxOnSave)),o=m5(),{t:i}=FY();vte(["meta+c","ctrl+c"],(()=>{a()}),{enabled:()=>!e,preventDefault:!0},[t,n]);const a=()=>{o(Mue({cropVisible:!r,cropToBoundingBox:r,shouldCopy:!0}))};return ld(k3,{"aria-label":`${i("unifiedcanvas:copyToClipboard")} (Cmd/Ctrl+C)`,tooltip:`${i("unifiedcanvas:copyToClipboard")} (Cmd/Ctrl+C)`,icon:ld(j5,{}),onClick:a,isDisabled:e})}function Xue(){const e=m5(),{t:t}=FY(),n=Z1(),r=v5(fne),o=v5((e=>e.system.isProcessing)),i=v5((e=>e.canvas.shouldCropToBoundingBoxOnSave));vte(["shift+d"],(()=>{a()}),{enabled:()=>!r,preventDefault:!0},[n,o]);const a=()=>{e(Mue({cropVisible:!i,cropToBoundingBox:i,shouldDownload:!0}))};return ld(k3,{"aria-label":`${t("unifiedcanvas:downloadAsImage")} (Shift+D)`,tooltip:`${t("unifiedcanvas:downloadAsImage")} (Shift+D)`,icon:ld(W5,{}),onClick:a,isDisabled:r})}function Kue(){const e=v5(fne),{openUploader:t}=Rue(),{t:n}=FY();return ld(k3,{"aria-label":n("common:upload"),tooltip:n("common:upload"),icon:ld(y3,{}),onClick:t,isDisabled:e})}function Que(){const e=m5(),{t:t}=FY(),n=v5(fne);return ld(k3,{"aria-label":t("unifiedcanvas:clearCanvas"),tooltip:t("unifiedcanvas:clearCanvas"),icon:ld(m3,{}),onClick:()=>{e(UZ()),e(YZ())},style:{backgroundColor:"var(--btn-delete-image)"},isDisabled:n})}function Jue(){const e=v5((e=>e.options.shouldPinOptionsPanel)),t=m5(),{t:n}=FY();return cd(CI,{flexDirection:"column",gap:"0.5rem",children:[ld(k3,{tooltip:`${n("options:showOptionsPanel")} (O)`,tooltipProps:{placement:"top"},"aria-label":n("options:showOptionsPanel"),onClick:()=>{t(vU(!0)),e&&setTimeout((()=>t(iX(!0))),400)},children:ld(p3,{})}),ld(CI,{children:ld(mne,{iconButton:!0})}),ld(CI,{children:ld(yne,{width:"100%",height:"40px"})})]})}const ede=()=>{const e=v5((e=>e.options.shouldShowOptionsPanel));return cd(CI,{flexDirection:"column",rowGap:"0.5rem",width:"6rem",children:[ld(Fue,{}),ld(Wue,{}),cd(CI,{gap:"0.5rem",children:[ld(Uue,{}),ld(Gue,{})]}),cd(CI,{columnGap:"0.5rem",children:[ld(que,{}),ld(Yue,{})]}),cd(CI,{columnGap:"0.5rem",children:[ld(Zue,{}),ld(Xue,{})]}),cd(CI,{gap:"0.5rem",children:[ld(qce,{}),ld(Zce,{})]}),cd(CI,{gap:"0.5rem",children:[ld(Kue,{}),ld(Que,{})]}),ld($ue,{}),!e&&ld(Jue,{})]})};function tde(){const e=m5(),t=v5((e=>e.canvas.brushSize)),{t:n}=FY(),r=v5(fne);return vte(["BracketLeft"],(()=>{e(tX(Math.max(t-5,5)))}),{enabled:()=>!r,preventDefault:!0},[t]),vte(["BracketRight"],(()=>{e(tX(Math.min(t+5,500)))}),{enabled:()=>!r,preventDefault:!0},[t]),ld(Nte,{label:n("unifiedcanvas:brushSize"),value:t,withInput:!0,onChange:t=>e(tX(t)),sliderNumberInputProps:{max:500},inputReadOnly:!1,width:"100px",isCompact:!0})}const nde=uV([hne,fne],((e,t)=>{const{brushColor:n,maskColor:r,layer:o}=e;return{brushColor:n,maskColor:r,layer:o,isStaging:t}}),{memoizeOptions:{resultEqualityCheck:TU.isEqual}});function rde(){const e=m5(),{brushColor:t,maskColor:n,layer:r,isStaging:o}=v5(nde);return vte(["shift+BracketLeft"],(()=>{e(eX({...t,a:TU.clamp(t.a-.05,.05,1)}))}),{enabled:()=>!o,preventDefault:!0},[t]),vte(["shift+BracketRight"],(()=>{e(eX({...t,a:TU.clamp(t.a+.05,.05,1)}))}),{enabled:()=>!o,preventDefault:!0},[t]),ld(z3,{trigger:"hover",triggerComponent:ld(mI,{style:{width:"30px",height:"30px",minWidth:"30px",minHeight:"30px",borderRadius:"99999999px",backgroundColor:"base"===r?`rgba(${t.r},${t.g},${t.b},${t.a})`:"mask"===r?`rgba(${n.r},${n.g},${n.b},${n.a})`:void 0,cursor:"pointer"}}),children:cd(CI,{minWidth:"15rem",direction:"column",gap:"1rem",width:"100%",children:["base"===r&&ld(Eue,{style:{width:"100%",paddingTop:"0.5rem",paddingBottom:"0.5rem"},color:t,onChange:t=>e(eX(t))}),"mask"===r&&ld(Eue,{style:{width:"100%",paddingTop:"0.5rem",paddingBottom:"0.5rem"},color:n,onChange:t=>e(vX(t))})]})})}function ode(){return cd(CI,{columnGap:"1rem",alignItems:"center",children:[ld(tde,{}),ld(rde,{})]})}function ide(){const e=m5(),t=v5((e=>e.canvas.shouldRestrictStrokesToBox)),{t:n}=FY();return ld(k9,{label:n("unifiedcanvas:betaLimitToBox"),isChecked:t,onChange:t=>e(WX(t.target.checked))})}function ade(){return cd(CI,{gap:"1rem",alignItems:"center",children:[ld(ode,{}),ld(ide,{})]})}function sde(){const e=m5(),{t:t}=FY();return ld(B3,{size:"sm",leftIcon:ld(m3,{}),onClick:()=>e(DZ()),tooltip:`${t("unifiedcanvas:clearMask")} (Shift+C)`,children:t("unifiedcanvas:betaClear")})}function lde(){const e=v5((e=>e.canvas.isMaskEnabled)),t=m5(),{t:n}=FY();return ld(k9,{label:`${n("unifiedcanvas:enableMask")} (H)`,isChecked:e,onChange:()=>t(cX(!e))})}function cde(){const e=m5(),{t:t}=FY(),n=v5((e=>e.canvas.shouldPreserveMaskedArea));return ld(k9,{label:t("unifiedcanvas:betaPreserveMasked"),isChecked:n,onChange:t=>e(SX(t.target.checked))})}function ude(){return cd(CI,{gap:"1rem",alignItems:"center",children:[ld(ode,{}),ld(lde,{}),ld(cde,{}),ld(sde,{})]})}function dde(){const e=v5((e=>e.canvas.shouldDarkenOutsideBoundingBox)),t=m5(),{t:n}=FY();return ld(k9,{label:n("unifiedcanvas:betaDarkenOutside"),isChecked:e,onChange:e=>t(wX(e.target.checked))})}function hde(){const e=v5((e=>e.canvas.shouldShowGrid)),t=m5(),{t:n}=FY();return ld(k9,{label:n("unifiedcanvas:showGrid"),isChecked:e,onChange:e=>t(OX(e.target.checked))})}function fde(){const e=v5((e=>e.canvas.shouldSnapToGrid)),t=m5(),{t:n}=FY();return ld(k9,{label:`${n("unifiedcanvas:snapToGrid")} (N)`,isChecked:e,onChange:e=>t(IX(e.target.checked))})}function pde(){return cd(CI,{alignItems:"center",gap:"1rem",children:[ld(hde,{}),ld(fde,{}),ld(dde,{})]})}const gde=uV([hne],(e=>{const{tool:t,layer:n}=e;return{tool:t,layer:n}}),{memoizeOptions:{resultEqualityCheck:TU.isEqual}});function mde(){const{tool:e,layer:t}=v5(gde);return cd(CI,{height:"2rem",minHeight:"2rem",maxHeight:"2rem",alignItems:"center",children:["base"==t&&["brush","eraser","colorPicker"].includes(e)&&ld(ade,{}),"mask"==t&&["brush","eraser","colorPicker"].includes(e)&&ld(ude,{}),"move"==e&&ld(pde,{})]})}const vde=uV([hne],(e=>{const{doesCanvasNeedScaling:t}=e;return{doesCanvasNeedScaling:t}}),{memoizeOptions:{resultEqualityCheck:TU.isEqual}}),yde=()=>{const e=m5(),{doesCanvasNeedScaling:t}=v5(vde);return a.exports.useLayoutEffect((()=>{e(iX(!0));const t=TU.debounce((()=>{e(iX(!0))}),250);return window.addEventListener("resize",t),()=>window.removeEventListener("resize",t)}),[e]),ld("div",{className:"workarea-single-view",children:cd(CI,{flexDirection:"row",width:"100%",height:"100%",columnGap:"1rem",padding:"1rem",children:[ld(ede,{}),cd(CI,{width:"100%",height:"100%",flexDirection:"column",rowGap:"1rem",children:[ld(mde,{}),ld(t?ple:Uce,{})]})]})})};function bde(){const e=v5((e=>e.options.shouldUseCanvasBetaLayout));return ld(zae,{optionsPanel:ld(hle,{}),styleClass:"inpainting-workarea-overrides",children:ld(e?yde:Bue,{})})}const xde=Sk({displayName:"UnifiedCanvasIcon",viewBox:"0 0 3544 3544",path:ld("path",{fill:"currentColor",fillRule:"evenodd",clipRule:"evenodd",d:"M3543.31,770.787C3543.31,515.578 3336.11,308.38 3080.9,308.38L462.407,308.38C207.197,308.38 -0,515.578 -0,770.787L-0,2766.03C-0,3021.24 207.197,3228.44 462.407,3228.44L3080.9,3228.44C3336.11,3228.44 3543.31,3021.24 3543.31,2766.03C3543.31,2766.03 3543.31,770.787 3543.31,770.787ZM922.933,3113.01L462.407,3113.01C437.741,3113.01 413.674,3110.43 390.453,3105.52L390.453,2899.11L922.933,2899.11L922.933,3113.01ZM947.933,2899.11L947.933,3113.01L1480.41,3113.01L1480.41,2899.11L947.933,2899.11ZM1505.41,2899.11L1505.41,3113.01L2037.89,3113.01L2037.89,2899.11L1505.41,2899.11ZM2062.89,2899.11L2062.89,3113.01L2595.37,3113.01L2595.37,2899.11L2062.89,2899.11ZM3152.85,3105.52C3129.63,3110.43 3105.57,3113.01 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2411.11,1225.13 2411.11,1339.59C2411.11,1454.05 2318.18,1546.98 2203.72,1546.98C2089.26,1546.98 1996.33,1454.05 1996.33,1339.59C1996.33,1225.13 2089.26,1132.2 2203.72,1132.2ZM2782.24,1541.41L2782.24,1441.41L2732.24,1441.41L2732.24,1541.41L2782.24,1541.41ZM761.068,1336.12L761.068,1436.12L811.068,1436.12L811.068,1336.12L761.068,1336.12ZM2782.24,1391.41L2782.24,1291.41L2732.24,1291.41L2732.24,1391.41L2782.24,1391.41ZM761.068,1186.12L761.068,1286.12L811.068,1286.12L811.068,1186.12L761.068,1186.12ZM2732.24,1197.98L2732.24,1241.41L2782.24,1241.41L2782.24,1172.98L2781.03,1172.98C2780.06,1162.82 2778.49,1152.83 2776.36,1143.04L2771.04,1118.62L2722.18,1129.24L2727.5,1153.67C2730.61,1167.95 2732.24,1182.78 2732.24,1197.98ZM3412.74,669.193L3412.89,669.694C3414.66,675.5 3416.28,681.348 3417.73,687.238C3420.46,698.265 3422.65,709.427 3424.28,720.67C3425.85,731.554 3426.91,742.513 3427.45,753.497C3427.74,759.256 3427.87,765.021 3427.88,770.787L3427.88,1201.67L3177.85,1201.67L3177.85,669.193L3412.74,669.193ZM115.431,1201.67L115.431,770.787C115.436,765.021 115.572,759.256 115.855,753.497C116.395,742.513 117.453,731.554 119.031,720.67C120.66,709.427 122.844,698.265 125.574,687.238C127.032,681.348 128.65,675.5 130.414,669.694L130.567,669.193L365.453,669.193L365.453,1201.67L115.431,1201.67ZM804.386,1055C794.186,1070.26 785.572,1086.67 778.777,1103.99L769.647,1127.26L816.194,1145.52L825.324,1122.25C830.797,1108.3 837.738,1095.08 845.955,1082.79L859.848,1062L818.279,1034.21L804.386,1055ZM2730.5,1043.14C2719.39,1028.39 2706.73,1014.86 2692.77,1002.81L2673.84,986.48L2641.17,1024.34L2660.1,1040.67C2671.37,1050.39 2681.59,1061.31 2690.56,1073.22L2705.6,1093.19L2745.54,1063.11L2730.5,1043.14ZM933.266,954.821C915.698,961.006 898.998,969.041 883.402,978.694L862.144,991.851L888.457,1034.37L909.715,1021.21C922.275,1013.44 935.723,1006.96 949.871,1001.98L973.452,993.681L956.848,946.518L933.266,954.821ZM2596.18,950.378C2578.71,945.327 2560.49,942.072 2541.72,940.832L2516.78,939.183L2513.48,989.074L2538.43,990.723C2553.54,991.722 2568.22,994.341 2582.28,998.409L2606.3,1005.36L2620.19,957.325L2596.18,950.378ZM2165.09,940.265L2065.09,940.265L2065.09,990.265L2165.09,990.265L2165.09,940.265ZM1865.08,940.265L1765.08,940.265L1765.08,990.265L1865.08,990.265L1865.08,940.265ZM1115.08,940.265L1015.08,940.265L1015.08,990.265L1115.08,990.265L1115.08,940.265ZM2015.09,940.265L1915.09,940.265L1915.09,990.265L2015.09,990.265L2015.09,940.265ZM2315.09,940.265L2215.09,940.265L2215.09,990.265L2315.09,990.265L2315.09,940.265ZM1265.08,940.265L1165.08,940.265L1165.08,990.265L1265.08,990.265L1265.08,940.265ZM1415.08,940.265L1315.08,940.265L1315.08,990.265L1415.08,990.265L1415.08,940.265ZM1565.08,940.265L1465.08,940.265L1465.08,990.265L1565.08,990.265L1565.08,940.265ZM1715.08,940.265L1615.08,940.265L1615.08,990.265L1715.08,990.265L1715.08,940.265ZM2465.09,940.265L2365.09,940.265L2365.09,990.265L2465.09,990.265L2465.09,940.265ZM365.453,437.562L365.453,644.193L139.286,644.193C178.303,544.782 261.917,467.677 365.453,437.562ZM922.933,423.812L922.933,644.193L390.453,644.193L390.453,431.295C413.674,426.391 437.741,423.812 462.407,423.812L922.933,423.812ZM947.933,423.812L947.933,644.193L1480.41,644.193L1480.41,423.812L947.933,423.812ZM1505.41,423.812L1505.41,644.193L2037.89,644.193L2037.89,423.812L1505.41,423.812ZM2062.89,423.812L2062.89,644.193L2595.37,644.193L2595.37,423.812L2062.89,423.812ZM2620.37,423.812L3080.9,423.812C3105.57,423.812 3129.63,426.391 3152.85,431.295L3152.85,644.193L2620.37,644.193L2620.37,423.812ZM3177.85,437.562C3281.38,467.669 3365,544.774 3404.02,644.193L3177.85,644.193L3177.85,437.562Z"})});function wde(){const{t:e}=FY();return cd("div",{className:"work-in-progress nodes-work-in-progress",children:[ld("h1",{children:e("common:training")}),cd("p",{children:[e("common:trainingDesc1"),ld("br",{}),ld("br",{}),e("common:trainingDesc2")]})]})}const kde=Sk({displayName:"TrainingIcon",viewBox:"0 0 3544 3544",path:ld("path",{fill:"currentColor",fillRule:"evenodd",clipRule:"evenodd",d:"M0,768.593L0,2774.71C0,2930.6 78.519,3068.3 198.135,3150.37C273.059,3202.68 364.177,3233.38 462.407,3233.38C462.407,3233.38 3080.9,3233.38 3080.9,3233.38C3179.13,3233.38 3270.25,3202.68 3345.17,3150.37C3464.79,3068.3 3543.31,2930.6 3543.31,2774.71L3543.31,768.593C3543.31,517.323 3339.31,313.324 3088.04,313.324L455.269,313.324C203.999,313.324 0,517.323 0,768.593ZM3427.88,775.73L3427.88,2770.97C3427.88,2962.47 3272.4,3117.95 3080.9,3117.95L462.407,3117.95C270.906,3117.95 115.431,2962.47 115.431,2770.97C115.431,2770.97 115.431,775.73 115.431,775.73C115.431,584.229 270.906,428.755 462.407,428.755C462.407,428.755 3080.9,428.755 3080.9,428.755C3272.4,428.755 3427.88,584.229 3427.88,775.73ZM796.24,1322.76L796.24,1250.45C796.24,1199.03 836.16,1157.27 885.331,1157.27C885.331,1157.27 946.847,1157.27 946.847,1157.27C996.017,1157.27 1035.94,1199.03 1035.94,1250.45L1035.94,1644.81L2507.37,1644.81L2507.37,1250.45C2507.37,1199.03 2547.29,1157.27 2596.46,1157.27C2596.46,1157.27 2657.98,1157.27 2657.98,1157.27C2707.15,1157.27 2747.07,1199.03 2747.07,1250.45L2747.07,1322.76C2756.66,1319.22 2767.02,1317.29 2777.83,1317.29C2777.83,1317.29 2839.34,1317.29 2839.34,1317.29C2888.51,1317.29 2928.43,1357.21 2928.43,1406.38L2928.43,1527.32C2933.51,1526.26 2938.77,1525.71 2944.16,1525.71L2995.3,1525.71C3036.18,1525.71 3069.37,1557.59 3069.37,1596.86C3069.37,1596.86 3069.37,1946.44 3069.37,1946.44C3069.37,1985.72 3036.18,2017.6 2995.3,2017.6C2995.3,2017.6 2944.16,2017.6 2944.16,2017.6C2938.77,2017.6 2933.51,2017.04 2928.43,2015.99L2928.43,2136.92C2928.43,2186.09 2888.51,2226.01 2839.34,2226.01L2777.83,2226.01C2767.02,2226.01 2756.66,2224.08 2747.07,2220.55L2747.07,2292.85C2747.07,2344.28 2707.15,2386.03 2657.98,2386.03C2657.98,2386.03 2596.46,2386.03 2596.46,2386.03C2547.29,2386.03 2507.37,2344.28 2507.37,2292.85L2507.37,1898.5L1035.94,1898.5L1035.94,2292.85C1035.94,2344.28 996.017,2386.03 946.847,2386.03C946.847,2386.03 885.331,2386.03 885.331,2386.03C836.16,2386.03 796.24,2344.28 796.24,2292.85L796.24,2220.55C786.651,2224.08 776.29,2226.01 765.482,2226.01L703.967,2226.01C654.796,2226.01 614.876,2186.09 614.876,2136.92L614.876,2015.99C609.801,2017.04 604.539,2017.6 599.144,2017.6C599.144,2017.6 548.003,2017.6 548.003,2017.6C507.125,2017.6 473.937,1985.72 473.937,1946.44C473.937,1946.44 473.937,1596.86 473.937,1596.86C473.937,1557.59 507.125,1525.71 548.003,1525.71L599.144,1525.71C604.539,1525.71 609.801,1526.26 614.876,1527.32L614.876,1406.38C614.876,1357.21 654.796,1317.29 703.967,1317.29C703.967,1317.29 765.482,1317.29 765.482,1317.29C776.29,1317.29 786.651,1319.22 796.24,1322.76ZM977.604,1250.45C977.604,1232.7 963.822,1218.29 946.847,1218.29L885.331,1218.29C868.355,1218.29 854.573,1232.7 854.573,1250.45L854.573,2292.85C854.573,2310.61 868.355,2325.02 885.331,2325.02L946.847,2325.02C963.822,2325.02 977.604,2310.61 977.604,2292.85L977.604,1250.45ZM2565.7,1250.45C2565.7,1232.7 2579.49,1218.29 2596.46,1218.29L2657.98,1218.29C2674.95,1218.29 2688.73,1232.7 2688.73,1250.45L2688.73,2292.85C2688.73,2310.61 2674.95,2325.02 2657.98,2325.02L2596.46,2325.02C2579.49,2325.02 2565.7,2310.61 2565.7,2292.85L2565.7,1250.45ZM673.209,1406.38L673.209,2136.92C673.209,2153.9 686.991,2167.68 703.967,2167.68L765.482,2167.68C782.458,2167.68 796.24,2153.9 796.24,2136.92L796.24,1406.38C796.24,1389.41 782.458,1375.63 765.482,1375.63L703.967,1375.63C686.991,1375.63 673.209,1389.41 673.209,1406.38ZM2870.1,1406.38L2870.1,2136.92C2870.1,2153.9 2856.32,2167.68 2839.34,2167.68L2777.83,2167.68C2760.85,2167.68 2747.07,2153.9 2747.07,2136.92L2747.07,1406.38C2747.07,1389.41 2760.85,1375.63 2777.83,1375.63L2839.34,1375.63C2856.32,1375.63 2870.1,1389.41 2870.1,1406.38ZM614.876,1577.5C610.535,1574.24 605.074,1572.3 599.144,1572.3L548.003,1572.3C533.89,1572.3 522.433,1583.3 522.433,1596.86L522.433,1946.44C522.433,1960 533.89,1971.01 548.003,1971.01L599.144,1971.01C605.074,1971.01 610.535,1969.07 614.876,1965.81L614.876,1577.5ZM2928.43,1965.81L2928.43,1577.5C2932.77,1574.24 2938.23,1572.3 2944.16,1572.3L2995.3,1572.3C3009.42,1572.3 3020.87,1583.3 3020.87,1596.86L3020.87,1946.44C3020.87,1960 3009.42,1971.01 2995.3,1971.01L2944.16,1971.01C2938.23,1971.01 2932.77,1969.07 2928.43,1965.81ZM2507.37,1703.14L1035.94,1703.14L1035.94,1840.16L2507.37,1840.16L2507.37,1898.38L2507.37,1659.46L2507.37,1703.14Z"})});const Sde={txt2img:{title:ld(Pte,{fill:"black",boxSize:"2.5rem"}),workarea:ld(Vae,{}),tooltip:"Text To Image"},img2img:{title:ld(Cte,{fill:"black",boxSize:"2.5rem"}),workarea:ld(Bae,{}),tooltip:"Image To Image"},unifiedCanvas:{title:ld(xde,{fill:"black",boxSize:"2.5rem"}),workarea:ld(bde,{}),tooltip:"Unified Canvas"},nodes:{title:ld(_te,{fill:"black",boxSize:"2.5rem"}),workarea:ld(kte,{}),tooltip:"Nodes"},postprocess:{title:ld(Ete,{fill:"black",boxSize:"2.5rem"}),workarea:ld(Ste,{}),tooltip:"Post Processing"},training:{title:ld(kde,{fill:"black",boxSize:"2.5rem"}),workarea:ld(wde,{}),tooltip:"Training"}};function Cde(){Sde.txt2img.tooltip=pq.t("common:text2img"),Sde.img2img.tooltip=pq.t("common:img2img"),Sde.unifiedCanvas.tooltip=pq.t("common:unifiedCanvas"),Sde.nodes.tooltip=pq.t("common:nodes"),Sde.postprocess.tooltip=pq.t("common:postProcessing"),Sde.training.tooltip=pq.t("common:training")}function _de(){const e=v5((e=>e.options.activeTab)),t=v5((e=>e.options.isLightBoxOpen));!function(e){const{i18n:t}=FY(),n=localStorage.getItem("i18nextLng");H.useEffect((()=>{e()}),[e]),H.useEffect((()=>{t.on("languageChanged",(()=>{e()}))}),[e,t,n])}(Cde);const n=m5();vte("1",(()=>{n(I$(0))})),vte("2",(()=>{n(I$(1))})),vte("3",(()=>{n(I$(2))})),vte("4",(()=>{n(I$(3))})),vte("5",(()=>{n(I$(4))})),vte("6",(()=>{n(I$(5))})),vte("z",(()=>{n(q$(!t))}),[t]);return cd(vH,{isLazy:!0,className:"app-tabs",variant:"unstyled",defaultIndex:e,index:e,onChange:e=>{n(I$(e))},children:[ld("div",{className:"app-tabs-list",children:(()=>{const e=[];return Object.keys(Sde).forEach((t=>{e.push(ld(YH,{hasArrow:!0,label:Sde[t].tooltip,placement:"right",children:ld(kH,{children:Sde[t].title})},t))})),e})()}),ld(wH,{className:"app-tabs-panels",children:t?ld(rle,{}):(()=>{const e=[];return Object.keys(Sde).forEach((t=>{e.push(ld(xH,{className:"app-tabs-panel",children:Sde[t].workarea},t))})),e})()})]})}var Ede=new 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diff --git a/frontend/dist/assets/index.ec2d89c6.js b/frontend/dist/assets/index.1b59e83a.js
similarity index 65%
rename from frontend/dist/assets/index.ec2d89c6.js
rename to frontend/dist/assets/index.1b59e83a.js
index acfa5f1f0b3..6d724298249 100644
--- a/frontend/dist/assets/index.ec2d89c6.js
+++ b/frontend/dist/assets/index.1b59e83a.js
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  *
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  * react-jsx-runtime.production.min.js
  *
@@ -37,14 +37,14 @@ Error generating stack: `+o.message+`
  *
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e(this.toString())},e.prototype.lighten=function(t){t===void 0&&(t=10);var n=this.toHsl();return n.l+=t/100,n.l=K3(n.l),new e(n)},e.prototype.brighten=function(t){t===void 0&&(t=10);var n=this.toRgb();return n.r=Math.max(0,Math.min(255,n.r-Math.round(255*-(t/100)))),n.g=Math.max(0,Math.min(255,n.g-Math.round(255*-(t/100)))),n.b=Math.max(0,Math.min(255,n.b-Math.round(255*-(t/100)))),new e(n)},e.prototype.darken=function(t){t===void 0&&(t=10);var n=this.toHsl();return n.l-=t/100,n.l=K3(n.l),new e(n)},e.prototype.tint=function(t){return t===void 0&&(t=10),this.mix("white",t)},e.prototype.shade=function(t){return t===void 0&&(t=10),this.mix("black",t)},e.prototype.desaturate=function(t){t===void 0&&(t=10);var n=this.toHsl();return n.s-=t/100,n.s=K3(n.s),new e(n)},e.prototype.saturate=function(t){t===void 0&&(t=10);var n=this.toHsl();return n.s+=t/100,n.s=K3(n.s),new e(n)},e.prototype.greyscale=function(){return this.desaturate(100)},e.prototype.spin=function(t){var n=this.toHsl(),r=(n.h+t)%360;return n.h=r<0?360+r:r,new e(n)},e.prototype.mix=function(t,n){n===void 0&&(n=50);var r=this.toRgb(),i=new e(t).toRgb(),o=n/100,a={r:(i.r-r.r)*o+r.r,g:(i.g-r.g)*o+r.g,b:(i.b-r.b)*o+r.b,a:(i.a-r.a)*o+r.a};return new e(a)},e.prototype.analogous=function(t,n){t===void 0&&(t=6),n===void 0&&(n=30);var r=this.toHsl(),i=360/n,o=[this];for(r.h=(r.h-(i*t>>1)+720)%360;--t;)r.h=(r.h+i)%360,o.push(new e(r));return o},e.prototype.complement=function(){var t=this.toHsl();return t.h=(t.h+180)%360,new e(t)},e.prototype.monochromatic=function(t){t===void 0&&(t=6);for(var n=this.toHsv(),r=n.h,i=n.s,o=n.v,a=[],s=1/t;t--;)a.push(new e({h:r,s:i,v:o})),o=(o+s)%1;return a},e.prototype.splitcomplement=function(){var t=this.toHsl(),n=t.h;return[this,new e({h:(n+72)%360,s:t.s,l:t.l}),new e({h:(n+216)%360,s:t.s,l:t.l})]},e.prototype.onBackground=function(t){var n=this.toRgb(),r=new e(t).toRgb();return new e({r:r.r+(n.r-r.r)*n.a,g:r.g+(n.g-r.g)*n.a,b:r.b+(n.b-r.b)*n.a})},e.prototype.triad=function(){return this.polyad(3)},e.prototype.tetrad=function(){return this.polyad(4)},e.prototype.polyad=function(t){for(var n=this.toHsl(),r=n.h,i=[this],o=360/t,a=1;a<t;a++)i.push(new e({h:(r+a*o)%360,s:n.s,l:n.l}));return i},e.prototype.equals=function(t){return this.toRgbString()===new e(t).toRgbString()},e}();function FF(e){if(e===void 0&&(e={}),e.count!==void 0&&e.count!==null){var t=e.count,n=[];for(e.count=void 0;t>n.length;)e.count=null,e.seed&&(e.seed+=1),n.push(FF(e));return e.count=t,n}var r=Foe(e.hue,e.seed),i=zoe(r,e),o=joe(r,i,e),a={h:r,s:i,v:o};return e.alpha!==void 0&&(a.a=e.alpha),new dy(a)}function Foe(e,t){var n=Voe(e),r=a5(n,t);return r<0&&(r=360+r),r}function zoe(e,t){if(t.hue==="monochrome")return 0;if(t.luminosity==="random")return a5([0,100],t.seed);var n=zF(e).saturationRange,r=n[0],i=n[1];switch(t.luminosity){case"bright":r=55;break;case"dark":r=i-10;break;case"light":i=55;break}return a5([r,i],t.seed)}function joe(e,t,n){var r=Hoe(e,t),i=100;switch(n.luminosity){case"dark":i=r+20;break;case"light":r=(i+r)/2;break;case"random":r=0,i=100;break}return a5([r,i],n.seed)}function Hoe(e,t){for(var n=zF(e).lowerBounds,r=0;r<n.length-1;r++){var i=n[r][0],o=n[r][1],a=n[r+1][0],s=n[r+1][1];if(t>=i&&t<=a){var l=(s-o)/(a-i),u=o-l*i;return l*t+u}}return 0}function Voe(e){var t=parseInt(e,10);if(!Number.isNaN(t)&&t<360&&t>0)return[t,t];if(typeof e=="string"){var n=HF.find(function(a){return a.name===e});if(n){var r=jF(n);if(r.hueRange)return r.hueRange}var i=new dy(e);if(i.isValid){var o=i.toHsv().h;return[o,o]}}return[0,360]}function zF(e){e>=334&&e<=360&&(e-=360);for(var t=0,n=HF;t<n.length;t++){var r=n[t],i=jF(r);if(i.hueRange&&e>=i.hueRange[0]&&e<=i.hueRange[1])return i}throw Error("Color not found")}function a5(e,t){if(t===void 0)return 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HF=[{name:"monochrome",hueRange:null,lowerBounds:[[0,0],[100,0]]},{name:"red",hueRange:[-26,18],lowerBounds:[[20,100],[30,92],[40,89],[50,85],[60,78],[70,70],[80,60],[90,55],[100,50]]},{name:"orange",hueRange:[19,46],lowerBounds:[[20,100],[30,93],[40,88],[50,86],[60,85],[70,70],[100,70]]},{name:"yellow",hueRange:[47,62],lowerBounds:[[25,100],[40,94],[50,89],[60,86],[70,84],[80,82],[90,80],[100,75]]},{name:"green",hueRange:[63,178],lowerBounds:[[30,100],[40,90],[50,85],[60,81],[70,74],[80,64],[90,50],[100,40]]},{name:"blue",hueRange:[179,257],lowerBounds:[[20,100],[30,86],[40,80],[50,74],[60,60],[70,52],[80,44],[90,39],[100,35]]},{name:"purple",hueRange:[258,282],lowerBounds:[[20,100],[30,87],[40,79],[50,70],[60,65],[70,59],[80,52],[90,45],[100,42]]},{name:"pink",hueRange:[283,334],lowerBounds:[[20,100],[30,90],[40,86],[60,84],[80,80],[90,75],[100,73]]}];function Uoe(e,t,n,r,i){for(t=t.split?t.split("."):t,r=0;r<t.length;r++)e=e?e[t[r]]:i;return e===i?n:e}var Woe=e=>Object.keys(e).length===0,Eo=(e,t,n)=>{const r=Uoe(e,`colors.${t}`,t),{isValid:i}=new dy(r);return i?r:n},Goe=e=>t=>{const n=Eo(t,e);return new dy(n).isDark()?"dark":"light"},qoe=e=>t=>Goe(e)(t)==="dark",Yg=(e,t)=>n=>{const r=Eo(n,e);return new dy(r).setAlpha(t).toRgbString()};function OA(e="1rem",t="rgba(255, 255, 255, 0.15)"){return{backgroundImage:`linear-gradient(
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a}startAnimation(t){const{drag:n,dragMomentum:r,dragElastic:i,dragTransition:o,dragSnapToOrigin:a,onDragTransitionEnd:s}=this.getProps(),l=this.constraints||{},u=$l(f=>{var p;if(!sb(f,n,this.currentDirection))return;let m=(p=l==null?void 0:l[f])!==null&&p!==void 0?p:{};a&&(m={min:0,max:0});const v=i?200:1e6,b=i?40:1e7,x={type:"inertia",velocity:r?t[f]:0,bounceStiffness:v,bounceDamping:b,timeConstant:750,restDelta:1,restSpeed:10,...o,...m};return this.startAxisValueAnimation(f,x)});return Promise.all(u).then(s)}startAxisValueAnimation(t,n){const r=this.getAxisMotionValue(t);return Ak(t,r,0,n)}stopAnimation(){$l(t=>this.getAxisMotionValue(t).stop())}getAxisMotionValue(t){var n,r;const i="_drag"+t.toUpperCase(),o=this.visualElement.getProps()[i];return o||this.visualElement.getValue(t,(r=(n=this.visualElement.getProps().initial)===null||n===void 0?void 0:n[t])!==null&&r!==void 0?r:0)}snapToCursor(t){$l(n=>{const{drag:r}=this.getProps();if(!sb(n,r,this.currentDirection))return;const{projection:i}=this.visualElement,o=this.getAxisMotionValue(n);if(i&&i.layout){const{min:a,max:s}=i.layout.layoutBox[n];o.set(t[n]-Dr(a,s,.5))}})}scalePositionWithinConstraints(){var t;if(!this.visualElement.current)return;const{drag:n,dragConstraints:r}=this.getProps(),{projection:i}=this.visualElement;if(!J0(r)||!i||!this.constraints)return;this.stopAnimation();const o={x:0,y:0};$l(s=>{const l=this.getAxisMotionValue(s);if(l){const u=l.get();o[s]=f0e({min:u,max:u},this.constraints[s])}});const{transformTemplate:a}=this.visualElement.getProps();this.visualElement.current.style.transform=a?a({},""):"none",(t=i.root)===null||t===void 0||t.updateScroll(),i.updateLayout(),this.resolveConstraints(),$l(s=>{if(!sb(s,n,null))return;const l=this.getAxisMotionValue(s),{min:u,max:f}=this.constraints[s];l.set(Dr(u,f,o[s]))})}addListeners(){var t;if(!this.visualElement.current)return;x0e.set(this.visualElement,this);const n=this.visualElement.current,r=Eg(n,"pointerdown",u=>{const{drag:f,dragListener:p=!0}=this.getProps();f&&p&&this.start(u)}),i=()=>{const{dragConstraints:u}=this.getProps();J0(u)&&(this.constraints=this.resolveRefConstraints())},{projection:o}=this.visualElement,a=o.addEventListener("measure",i);o&&!o.layout&&((t=o.root)===null||t===void 0||t.updateScroll(),o.updateLayout()),i();const s=qS(window,"resize",()=>this.scalePositionWithinConstraints()),l=o.addEventListener("didUpdate",({delta:u,hasLayoutChanged:f})=>{this.isDragging&&f&&($l(p=>{const m=this.getAxisMotionValue(p);!m||(this.originPoint[p]+=u[p].translate,m.set(m.get()+u[p].translate))}),this.visualElement.render())});return()=>{s(),r(),a(),l==null||l()}}getProps(){const t=this.visualElement.getProps(),{drag:n=!1,dragDirectionLock:r=!1,dragPropagation:i=!1,dragConstraints:o=!1,dragElastic:a=h9,dragMomentum:s=!0}=t;return{...t,drag:n,dragDirectionLock:r,dragPropagation:i,dragConstraints:o,dragElastic:a,dragMomentum:s}}}function sb(e,t,n){return(t===!0||t===e)&&(n===null||n===e)}function C0e(e,t=10){let n=null;return Math.abs(e.y)>t?n="y":Math.abs(e.x)>t&&(n="x"),n}function _0e(e){const{dragControls:t,visualElement:n}=e,r=GS(()=>new w0e(n));C.exports.useEffect(()=>t&&t.subscribe(r),[r,t]),C.exports.useEffect(()=>r.addListeners(),[r])}function k0e({onPan:e,onPanStart:t,onPanEnd:n,onPanSessionStart:r,visualElement:i}){const o=e||t||n||r,a=C.exports.useRef(null),{transformPagePoint:s}=C.exports.useContext(lk),l={onSessionStart:r,onStart:t,onMove:e,onEnd:(f,p)=>{a.current=null,n&&n(f,p)}};C.exports.useEffect(()=>{a.current!==null&&a.current.updateHandlers(l)});function u(f){a.current=new cj(f,l,{transformPagePoint:s})}g5(i,"pointerdown",o&&u),yk(()=>a.current&&a.current.end())}const E0e={pan:Ad(k0e),drag:Ad(_0e)};function m9(e){return typeof e=="string"&&e.startsWith("var(--")}const mj=/var\((--[a-zA-Z0-9-_]+),? ?([a-zA-Z0-9 ()%#.,-]+)?\)/;function P0e(e){const t=mj.exec(e);if(!t)return[,];const[,n,r]=t;return[n,r]}function v9(e,t,n=1){const[r,i]=P0e(e);if(!r)return;const o=window.getComputedStyle(t).getPropertyValue(r);return o?o.trim():m9(i)?v9(i,t,n+1):i}function T0e(e,{...t},n){const r=e.current;if(!(r instanceof Element))return{target:t,transitionEnd:n};n&&(n={...n}),e.values.forEach(i=>{const o=i.get();if(!m9(o))return;const a=v9(o,r);a&&i.set(a)});for(const i in t){const o=t[i];if(!m9(o))continue;const a=v9(o,r);!a||(t[i]=a,n&&n[i]===void 0&&(n[i]=o))}return{target:t,transitionEnd:n}}const L0e=new Set(["width","height","top","left","right","bottom","x","y"]),vj=e=>L0e.has(e),A0e=e=>Object.keys(e).some(vj),yj=(e,t)=>{e.set(t,!1),e.set(t)},FO=e=>e===lp||e===Ot;var 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r=t.measureViewportBox(),i=t.current,o=getComputedStyle(i),{display:a}=o,s={};a==="none"&&t.setStaticValue("display",e.display||"block"),n.forEach(u=>{s[u]=VO[u](r,o)}),t.render();const l=t.measureViewportBox();return n.forEach(u=>{const f=t.getValue(u);yj(f,s[u]),e[u]=VO[u](l,o)}),e},D0e=(e,t,n={},r={})=>{t={...t},r={...r};const i=Object.keys(t).filter(vj);let o=[],a=!1;const s=[];if(i.forEach(l=>{const u=e.getValue(l);if(!e.hasValue(l))return;let f=n[l],p=Km(f);const m=t[l];let v;if($2(m)){const b=m.length,x=m[0]===null?1:0;f=m[x],p=Km(f);for(let E=x;E<b;E++)v?m5(Km(m[E])===v):v=Km(m[E])}else v=Km(m);if(p!==v)if(FO(p)&&FO(v)){const b=u.get();typeof b=="string"&&u.set(parseFloat(b)),typeof m=="string"?t[l]=parseFloat(m):Array.isArray(m)&&v===Ot&&(t[l]=m.map(parseFloat))}else(p==null?void 0:p.transform)&&(v==null?void 0:v.transform)&&(f===0||m===0)?f===0?u.set(v.transform(f)):t[l]=p.transform(m):(a||(o=R0e(e),a=!0),s.push(l),r[l]=r[l]!==void 0?r[l]:t[l],yj(u,m))}),s.length){const l=s.indexOf("height")>=0?window.pageYOffset:null,u=I0e(t,e,s);return o.length&&o.forEach(([f,p])=>{e.getValue(f).set(p)}),e.render(),sp&&l!==null&&window.scrollTo({top:l}),{target:u,transitionEnd:r}}else return{target:t,transitionEnd:r}};function N0e(e,t,n,r){return A0e(t)?D0e(e,t,n,r):{target:t,transitionEnd:r}}const B0e=(e,t,n,r)=>{const i=T0e(e,t,r);return t=i.target,r=i.transitionEnd,N0e(e,t,n,r)},y9={current:null},bj={current:!1};function $0e(){if(bj.current=!0,!!sp)if(window.matchMedia){const e=window.matchMedia("(prefers-reduced-motion)"),t=()=>y9.current=e.matches;e.addListener(t),t()}else y9.current=!1}function F0e(e,t,n){const{willChange:r}=t;for(const i in t){const o=t[i],a=n[i];if(lu(o))e.addValue(i,o),x5(r)&&r.add(i);else if(lu(a))e.addValue(i,Kg(o)),x5(r)&&r.remove(i);else if(a!==o)if(e.hasValue(i)){const s=e.getValue(i);!s.hasAnimated&&s.set(o)}else{const s=e.getStaticValue(i);e.addValue(i,Kg(s!==void 0?s:o))}}for(const i in n)t[i]===void 0&&e.removeValue(i);return t}const Sj=Object.keys(N2),z0e=Sj.length,UO=["AnimationStart","AnimationComplete","Update","Unmount","BeforeLayoutMeasure","LayoutMeasure","LayoutAnimationStart","LayoutAnimationComplete"];class j0e{constructor({parent:t,props:n,reducedMotionConfig:r,visualState:i},o={}){this.current=null,this.children=new Set,this.isVariantNode=!1,this.isControllingVariants=!1,this.shouldReduceMotion=null,this.values=new Map,this.isPresent=!0,this.valueSubscriptions=new Map,this.prevMotionValues={},this.events={},this.propEventSubscriptions={},this.notifyUpdate=()=>this.notify("Update",this.latestValues),this.render=()=>{!this.current||(this.triggerBuild(),this.renderInstance(this.current,this.renderState,this.props.style,this.projection))},this.scheduleRender=()=>Xs.render(this.render,!1,!0);const{latestValues:a,renderState:s}=i;this.latestValues=a,this.baseTarget={...a},this.initialValues=n.initial?{...a}:{},this.renderState=s,this.parent=t,this.props=n,this.depth=t?t.depth+1:0,this.reducedMotionConfig=r,this.options=o,this.isControllingVariants=WS(n),this.isVariantNode=iz(n),this.isVariantNode&&(this.variantChildren=new Set),this.manuallyAnimateOnMount=Boolean(t&&t.current);const{willChange:l,...u}=this.scrapeMotionValuesFromProps(n);for(const f in u){const p=u[f];a[f]!==void 0&&lu(p)&&(p.set(a[f],!1),x5(l)&&l.add(f))}}scrapeMotionValuesFromProps(t){return{}}mount(t){var n;this.current=t,this.projection&&this.projection.mount(t),this.parent&&this.isVariantNode&&!this.isControllingVariants&&(this.removeFromVariantTree=(n=this.parent)===null||n===void 0?void 0:n.addVariantChild(this)),this.values.forEach((r,i)=>this.bindToMotionValue(i,r)),bj.current||$0e(),this.shouldReduceMotion=this.reducedMotionConfig==="never"?!1:this.reducedMotionConfig==="always"?!0:y9.current,this.parent&&this.parent.children.add(this),this.setProps(this.props)}unmount(){var t,n,r;(t=this.projection)===null||t===void 0||t.unmount(),Zh.update(this.notifyUpdate),Zh.render(this.render),this.valueSubscriptions.forEach(i=>i()),(n=this.removeFromVariantTree)===null||n===void 0||n.call(this),(r=this.parent)===null||r===void 0||r.children.delete(this);for(const i in this.events)this.events[i].clear();this.current=null}bindToMotionValue(t,n){const r=g1.has(t),i=n.onChange(a=>{this.latestValues[t]=a,this.props.onUpdate&&Xs.update(this.notifyUpdate,!1,!0),r&&this.projection&&(this.projection.isProjectionDirty=!0)}),o=n.onRenderRequest(this.scheduleRender);this.valueSubscriptions.set(t,()=>{i(),o()})}sortNodePosition(t){return!this.current||!this.sortInstanceNodePosition||this.type!==t.type?0:this.sortInstanceNodePosition(this.current,t.current)}loadFeatures(t,n,r,i,o,a){const s=[];for(let l=0;l<z0e;l++){const u=Sj[l],{isEnabled:f,Component:p}=N2[u];f(t)&&p&&s.push(C.exports.createElement(p,{key:u,...t,visualElement:this}))}if(!this.projection&&o){this.projection=new o(i,this.latestValues,this.parent&&this.parent.projection);const{layoutId:l,layout:u,drag:f,dragConstraints:p,layoutScroll:m}=t;this.projection.setOptions({layoutId:l,layout:u,alwaysMeasureLayout:Boolean(f)||p&&J0(p),visualElement:this,scheduleRender:()=>this.scheduleRender(),animationType:typeof u=="string"?u:"both",initialPromotionConfig:a,layoutScroll:m})}return s}triggerBuild(){this.build(this.renderState,this.latestValues,this.options,this.props)}measureViewportBox(){return this.current?this.measureInstanceViewportBox(this.current,this.props):fi()}getStaticValue(t){return this.latestValues[t]}setStaticValue(t,n){this.latestValues[t]=n}makeTargetAnimatable(t,n=!0){return this.makeTargetAnimatableFromInstance(t,this.props,n)}setProps(t){(t.transformTemplate||this.props.transformTemplate)&&this.scheduleRender(),this.props=t;for(let n=0;n<UO.length;n++){const r=UO[n];this.propEventSubscriptions[r]&&(this.propEventSubscriptions[r](),delete this.propEventSubscriptions[r]);const i=t["on"+r];i&&(this.propEventSubscriptions[r]=this.on(r,i))}this.prevMotionValues=F0e(this,this.scrapeMotionValuesFromProps(t),this.prevMotionValues)}getProps(){return this.props}getVariant(t){var n;return(n=this.props.variants)===null||n===void 0?void 0:n[t]}getDefaultTransition(){return this.props.transition}getTransformPagePoint(){return this.props.transformPagePoint}getClosestVariantNode(){var t;return this.isVariantNode?this:(t=this.parent)===null||t===void 0?void 0:t.getClosestVariantNode()}getVariantContext(t=!1){var n,r;if(t)return(n=this.parent)===null||n===void 0?void 0:n.getVariantContext();if(!this.isControllingVariants){const o=((r=this.parent)===null||r===void 0?void 0:r.getVariantContext())||{};return this.props.initial!==void 0&&(o.initial=this.props.initial),o}const i={};for(let o=0;o<H0e;o++){const a=xj[o],s=this.props[a];(D2(s)||s===!1)&&(i[a]=s)}return i}addVariantChild(t){var n;const r=this.getClosestVariantNode();if(r)return(n=r.variantChildren)===null||n===void 0||n.add(t),()=>r.variantChildren.delete(t)}addValue(t,n){this.hasValue(t)&&this.removeValue(t),this.values.set(t,n),this.latestValues[t]=n.get(),this.bindToMotionValue(t,n)}removeValue(t){var n;this.values.delete(t),(n=this.valueSubscriptions.get(t))===null||n===void 0||n(),this.valueSubscriptions.delete(t),delete 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j0e{sortInstanceNodePosition(t,n){return t.compareDocumentPosition(n)&2?1:-1}getBaseTargetFromProps(t,n){var r;return(r=t.style)===null||r===void 0?void 0:r[n]}removeValueFromRenderState(t,{vars:n,style:r}){delete n[t],delete r[t]}makeTargetAnimatableFromInstance({transition:t,transitionEnd:n,...r},{transformValues:i},o){let a=Ype(r,t||{},this);if(i&&(n&&(n=i(n)),r&&(r=i(r)),a&&(a=i(a))),o){Gpe(this,r,a);const s=B0e(this,r,a,n);n=s.transitionEnd,r=s.target}return{transition:t,transitionEnd:n,...r}}}function V0e(e){return window.getComputedStyle(e)}class U0e extends wj{readValueFromInstance(t,n){if(g1.has(n)){const r=Pk(n);return r&&r.default||0}else{const r=V0e(t),i=(sz(n)?r.getPropertyValue(n):r[n])||0;return typeof i=="string"?i.trim():i}}measureInstanceViewportBox(t,{transformPagePoint:n}){return gj(t,n)}build(t,n,r,i){fk(t,n,r,i.transformTemplate)}scrapeMotionValuesFromProps(t){return gk(t)}renderInstance(t,n,r,i){Sz(t,n,r,i)}}class W0e extends wj{getBaseTargetFromProps(t,n){return t[n]}readValueFromInstance(t,n){var r;return g1.has(n)?((r=Pk(n))===null||r===void 0?void 0:r.default)||0:(n=xz.has(n)?n:bz(n),t.getAttribute(n))}measureInstanceViewportBox(){return fi()}scrapeMotionValuesFromProps(t){return Cz(t)}build(t,n,r,i){pk(t,n,r,i.transformTemplate)}renderInstance(t,n,r,i){wz(t,n,r,i)}}const G0e=(e,t)=>ck(e)?new W0e(t,{enableHardwareAcceleration:!1}):new U0e(t,{enableHardwareAcceleration:!0});function WO(e,t){return t.max===t.min?0:e/(t.max-t.min)*100}const Xm={correct:(e,t)=>{if(!t.target)return e;if(typeof e=="string")if(Ot.test(e))e=parseFloat(e);else return e;const n=WO(e,t.target.x),r=WO(e,t.target.y);return`${n}% ${r}%`}},GO="_$css",q0e={correct:(e,{treeScale:t,projectionDelta:n})=>{const r=e,i=e.includes("var("),o=[];i&&(e=e.replace(mj,v=>(o.push(v),GO)));const a=ac.parse(e);if(a.length>5)return r;const s=ac.createTransformer(e),l=typeof a[0]!="number"?1:0,u=n.x.scale*t.x,f=n.y.scale*t.y;a[0+l]/=u,a[1+l]/=f;const p=Dr(u,f,.5);typeof a[2+l]=="number"&&(a[2+l]/=p),typeof a[3+l]=="number"&&(a[3+l]/=p);let m=s(a);if(i){let v=0;m=m.replace(GO,()=>{const b=o[v];return v++,b})}return m}};class Y0e extends oe.Component{componentDidMount(){const{visualElement:t,layoutGroup:n,switchLayoutGroup:r,layoutId:i}=this.props,{projection:o}=t;afe(X0e),o&&(n.group&&n.group.add(o),r&&r.register&&i&&r.register(o),o.root.didUpdate(),o.addEventListener("animationComplete",()=>{this.safeToRemove()}),o.setOptions({...o.options,onExitComplete:()=>this.safeToRemove()})),qv.hasEverUpdated=!0}getSnapshotBeforeUpdate(t){const{layoutDependency:n,visualElement:r,drag:i,isPresent:o}=this.props,a=r.projection;return a&&(a.isPresent=o,i||t.layoutDependency!==n||n===void 0?a.willUpdate():this.safeToRemove(),t.isPresent!==o&&(o?a.promote():a.relegate()||Xs.postRender(()=>{var s;!((s=a.getStack())===null||s===void 0)&&s.members.length||this.safeToRemove()}))),null}componentDidUpdate(){const{projection:t}=this.props.visualElement;t&&(t.root.didUpdate(),!t.currentAnimation&&t.isLead()&&this.safeToRemove())}componentWillUnmount(){const{visualElement:t,layoutGroup:n,switchLayoutGroup:r}=this.props,{projection:i}=t;i&&(i.scheduleCheckAfterUnmount(),n!=null&&n.group&&n.group.remove(i),r!=null&&r.deregister&&r.deregister(i))}safeToRemove(){const{safeToRemove:t}=this.props;t==null||t()}render(){return null}}function K0e(e){const[t,n]=Ek(),r=C.exports.useContext(uk);return y(Y0e,{...e,layoutGroup:r,switchLayoutGroup:C.exports.useContext(oz),isPresent:t,safeToRemove:n})}const X0e={borderRadius:{...Xm,applyTo:["borderTopLeftRadius","borderTopRightRadius","borderBottomLeftRadius","borderBottomRightRadius"]},borderTopLeftRadius:Xm,borderTopRightRadius:Xm,borderBottomLeftRadius:Xm,borderBottomRightRadius:Xm,boxShadow:q0e},Z0e={measureLayout:K0e};function Q0e(e,t,n={}){const r=lu(e)?e:Kg(e);return Ak("",r,t,n),{stop:()=>r.stop(),isAnimating:()=>r.isAnimating()}}const Cj=["TopLeft","TopRight","BottomLeft","BottomRight"],J0e=Cj.length,qO=e=>typeof e=="string"?parseFloat(e):e,YO=e=>typeof e=="number"||Ot.test(e);function ege(e,t,n,r,i,o){var a,s,l,u;i?(e.opacity=Dr(0,(a=n.opacity)!==null&&a!==void 0?a:1,tge(r)),e.opacityExit=Dr((s=t.opacity)!==null&&s!==void 0?s:1,0,nge(r))):o&&(e.opacity=Dr((l=t.opacity)!==null&&l!==void 0?l:1,(u=n.opacity)!==null&&u!==void 0?u:1,r));for(let f=0;f<J0e;f++){const p=`border${Cj[f]}Radius`;let m=KO(t,p),v=KO(n,p);if(m===void 0&&v===void 0)continue;m||(m=0),v||(v=0),m===0||v===0||YO(m)===YO(v)?(e[p]=Math.max(Dr(qO(m),qO(v),r),0),(tu.test(v)||tu.test(m))&&(e[p]+="%")):e[p]=v}(t.rotate||n.rotate)&&(e.rotate=Dr(t.rotate||0,n.rotate||0,r))}function KO(e,t){var n;return(n=e[t])!==null&&n!==void 0?n:e.borderRadius}const tge=_j(0,.5,Ck),nge=_j(.5,.95,xk);function _j(e,t,n){return r=>r<e?0:r>t?1:n(F2(e,t,r))}function XO(e,t){e.min=t.min,e.max=t.max}function Ns(e,t){XO(e.x,t.x),XO(e.y,t.y)}function ZO(e,t,n,r,i){return e-=t,e=w5(e,1/n,r),i!==void 0&&(e=w5(e,1/i,r)),e}function rge(e,t=0,n=1,r=.5,i,o=e,a=e){if(tu.test(t)&&(t=parseFloat(t),t=Dr(a.min,a.max,t/100)-a.min),typeof t!="number")return;let s=Dr(o.min,o.max,r);e===o&&(s-=t),e.min=ZO(e.min,t,n,s,i),e.max=ZO(e.max,t,n,s,i)}function QO(e,t,[n,r,i],o,a){rge(e,t[n],t[r],t[i],t.scale,o,a)}const ige=["x","scaleX","originX"],oge=["y","scaleY","originY"];function JO(e,t,n,r){QO(e.x,t,ige,n==null?void 0:n.x,r==null?void 0:r.x),QO(e.y,t,oge,n==null?void 0:n.y,r==null?void 0:r.y)}function eM(e){return e.translate===0&&e.scale===1}function kj(e){return eM(e.x)&&eM(e.y)}function Ej(e,t){return e.x.min===t.x.min&&e.x.max===t.x.max&&e.y.min===t.y.min&&e.y.max===t.y.max}function tM(e){return Ra(e.x)/Ra(e.y)}function age(e,t,n=.1){return kk(e,t)<=n}class sge{constructor(){this.members=[]}add(t){Ok(this.members,t),t.scheduleRender()}remove(t){if(Mk(this.members,t),t===this.prevLead&&(this.prevLead=void 0),t===this.lead){const n=this.members[this.members.length-1];n&&this.promote(n)}}relegate(t){const n=this.members.findIndex(i=>t===i);if(n===0)return!1;let r;for(let i=n;i>=0;i--){const o=this.members[i];if(o.isPresent!==!1){r=o;break}}return r?(this.promote(r),!0):!1}promote(t,n){var r;const i=this.lead;if(t!==i&&(this.prevLead=i,this.lead=t,t.show(),i)){i.instance&&i.scheduleRender(),t.scheduleRender(),t.resumeFrom=i,n&&(t.resumeFrom.preserveOpacity=!0),i.snapshot&&(t.snapshot=i.snapshot,t.snapshot.latestValues=i.animationValues||i.latestValues),!((r=t.root)===null||r===void 0)&&r.isUpdating&&(t.isLayoutDirty=!0);const{crossfade:o}=t.options;o===!1&&i.hide()}}exitAnimationComplete(){this.members.forEach(t=>{var n,r,i,o,a;(r=(n=t.options).onExitComplete)===null||r===void 0||r.call(n),(a=(i=t.resumingFrom)===null||i===void 0?void 0:(o=i.options).onExitComplete)===null||a===void 0||a.call(o)})}scheduleRender(){this.members.forEach(t=>{t.instance&&t.scheduleRender(!1)})}removeLeadSnapshot(){this.lead&&this.lead.snapshot&&(this.lead.snapshot=void 0)}}function nM(e,t,n){let r="";const i=e.x.translate/t.x,o=e.y.translate/t.y;if((i||o)&&(r=`translate3d(${i}px, ${o}px, 0) `),(t.x!==1||t.y!==1)&&(r+=`scale(${1/t.x}, ${1/t.y}) `),n){const{rotate:l,rotateX:u,rotateY:f}=n;l&&(r+=`rotate(${l}deg) `),u&&(r+=`rotateX(${u}deg) `),f&&(r+=`rotateY(${f}deg) `)}const a=e.x.scale*t.x,s=e.y.scale*t.y;return(a!==1||s!==1)&&(r+=`scale(${a}, ${s})`),r||"none"}const lge=(e,t)=>e.depth-t.depth;class uge{constructor(){this.children=[],this.isDirty=!1}add(t){Ok(this.children,t),this.isDirty=!0}remove(t){Mk(this.children,t),this.isDirty=!0}forEach(t){this.isDirty&&this.children.sort(lge),this.isDirty=!1,this.children.forEach(t)}}const rM=["","X","Y","Z"],iM=1e3;let cge=0;function Pj({attachResizeListener:e,defaultParent:t,measureScroll:n,checkIsScrollRoot:r,resetTransform:i}){return class{constructor(a,s={},l=t==null?void 0:t()){this.id=cge++,this.animationId=0,this.children=new Set,this.options={},this.isTreeAnimating=!1,this.isAnimationBlocked=!1,this.isLayoutDirty=!1,this.isProjectionDirty=!1,this.updateManuallyBlocked=!1,this.updateBlockedByResize=!1,this.isUpdating=!1,this.isSVG=!1,this.needsReset=!1,this.shouldResetTransform=!1,this.treeScale={x:1,y:1},this.eventHandlers=new Map,this.potentialNodes=new Map,this.checkUpdateFailed=()=>{this.isUpdating&&(this.isUpdating=!1,this.clearAllSnapshots())},this.updateProjection=()=>{this.nodes.forEach(gge),this.nodes.forEach(mge)},this.hasProjected=!1,this.isVisible=!0,this.animationProgress=0,this.sharedNodes=new Map,this.elementId=a,this.latestValues=s,this.root=l?l.root||l:this,this.path=l?[...l.path,l]:[],this.parent=l,this.depth=l?l.depth+1:0,a&&this.root.registerPotentialNode(a,this);for(let u=0;u<this.path.length;u++)this.path[u].shouldResetTransform=!0;this.root===this&&(this.nodes=new uge)}addEventListener(a,s){return this.eventHandlers.has(a)||this.eventHandlers.set(a,new Xv),this.eventHandlers.get(a).add(s)}notifyListeners(a,...s){const l=this.eventHandlers.get(a);l==null||l.notify(...s)}hasListeners(a){return this.eventHandlers.has(a)}registerPotentialNode(a,s){this.potentialNodes.set(a,s)}mount(a,s=!1){var l;if(this.instance)return;this.isSVG=a instanceof SVGElement&&a.tagName!=="svg",this.instance=a;const{layoutId:u,layout:f,visualElement:p}=this.options;if(p&&!p.current&&p.mount(a),this.root.nodes.add(this),(l=this.parent)===null||l===void 0||l.children.add(this),this.elementId&&this.root.potentialNodes.delete(this.elementId),s&&(f||u)&&(this.isLayoutDirty=!0),e){let m;const v=()=>this.root.updateBlockedByResize=!1;e(a,()=>{this.root.updateBlockedByResize=!0,m&&m(),m=aj(v,250),qv.hasAnimatedSinceResize&&(qv.hasAnimatedSinceResize=!1,this.nodes.forEach(aM))})}u&&this.root.registerSharedNode(u,this),this.options.animate!==!1&&p&&(u||f)&&this.addEventListener("didUpdate",({delta:m,hasLayoutChanged:v,hasRelativeTargetChanged:b,layout:x})=>{var E,k,_,T,A;if(this.isTreeAnimationBlocked()){this.target=void 0,this.relativeTarget=void 0;return}const R=(k=(E=this.options.transition)!==null&&E!==void 0?E:p.getDefaultTransition())!==null&&k!==void 0?k:xge,{onLayoutAnimationStart:I,onLayoutAnimationComplete:D}=p.getProps(),B=!this.targetLayout||!Ej(this.targetLayout,x)||b,z=!v&&b;if(((_=this.resumeFrom)===null||_===void 0?void 0:_.instance)||z||v&&(B||!this.currentAnimation)){this.resumeFrom&&(this.resumingFrom=this.resumeFrom,this.resumingFrom.resumingFrom=void 0),this.setAnimationOrigin(m,z);const j={...Lk(R,"layout"),onPlay:I,onComplete:D};p.shouldReduceMotion&&(j.delay=0,j.type=!1),this.startAnimation(j)}else!v&&this.animationProgress===0&&aM(this),this.isLead()&&((A=(T=this.options).onExitComplete)===null||A===void 0||A.call(T));this.targetLayout=x})}unmount(){var a,s;this.options.layoutId&&this.willUpdate(),this.root.nodes.remove(this),(a=this.getStack())===null||a===void 0||a.remove(this),(s=this.parent)===null||s===void 0||s.children.delete(this),this.instance=void 0,Zh.preRender(this.updateProjection)}blockUpdate(){this.updateManuallyBlocked=!0}unblockUpdate(){this.updateManuallyBlocked=!1}isUpdateBlocked(){return this.updateManuallyBlocked||this.updateBlockedByResize}isTreeAnimationBlocked(){var a;return this.isAnimationBlocked||((a=this.parent)===null||a===void 0?void 0:a.isTreeAnimationBlocked())||!1}startUpdate(){var a;this.isUpdateBlocked()||(this.isUpdating=!0,(a=this.nodes)===null||a===void 0||a.forEach(vge),this.animationId++)}willUpdate(a=!0){var s,l,u;if(this.root.isUpdateBlocked()){(l=(s=this.options).onExitComplete)===null||l===void 0||l.call(s);return}if(!this.root.isUpdating&&this.root.startUpdate(),this.isLayoutDirty)return;this.isLayoutDirty=!0;for(let v=0;v<this.path.length;v++){const b=this.path[v];b.shouldResetTransform=!0,b.updateScroll("snapshot")}const{layoutId:f,layout:p}=this.options;if(f===void 0&&!p)return;const m=(u=this.options.visualElement)===null||u===void 0?void 0:u.getProps().transformTemplate;this.prevTransformTemplateValue=m==null?void 0:m(this.latestValues,""),this.updateSnapshot(),a&&this.notifyListeners("willUpdate")}didUpdate(){if(this.isUpdateBlocked()){this.unblockUpdate(),this.clearAllSnapshots(),this.nodes.forEach(oM);return}!this.isUpdating||(this.isUpdating=!1,this.potentialNodes.size&&(this.potentialNodes.forEach(wge),this.potentialNodes.clear()),this.nodes.forEach(pge),this.nodes.forEach(dge),this.nodes.forEach(fge),this.clearAllSnapshots(),M6.update(),M6.preRender(),M6.render())}clearAllSnapshots(){this.nodes.forEach(hge),this.sharedNodes.forEach(yge)}scheduleUpdateProjection(){Xs.preRender(this.updateProjection,!1,!0)}scheduleCheckAfterUnmount(){Xs.postRender(()=>{this.isLayoutDirty?this.root.didUpdate():this.root.checkUpdateFailed()})}updateSnapshot(){this.snapshot||!this.instance||(this.snapshot=this.measure())}updateLayout(){var a;if(!this.instance||(this.updateScroll(),!(this.options.alwaysMeasureLayout&&this.isLead())&&!this.isLayoutDirty))return;if(this.resumeFrom&&!this.resumeFrom.instance)for(let l=0;l<this.path.length;l++)this.path[l].updateScroll();const s=this.layout;this.layout=this.measure(!1),this.layoutCorrected=fi(),this.isLayoutDirty=!1,this.projectionDelta=void 0,this.notifyListeners("measure",this.layout.layoutBox),(a=this.options.visualElement)===null||a===void 0||a.notify("LayoutMeasure",this.layout.layoutBox,s==null?void 0:s.layoutBox)}updateScroll(a="measure"){let s=Boolean(this.options.layoutScroll&&this.instance);this.scroll&&this.scroll.animationId===this.root.animationId&&this.scroll.phase===a&&(s=!1),s&&(this.scroll={animationId:this.root.animationId,phase:a,isRoot:r(this.instance),offset:n(this.instance)})}resetTransform(){var a;if(!i)return;const s=this.isLayoutDirty||this.shouldResetTransform,l=this.projectionDelta&&!kj(this.projectionDelta),u=(a=this.options.visualElement)===null||a===void 0?void 0:a.getProps().transformTemplate,f=u==null?void 0:u(this.latestValues,""),p=f!==this.prevTransformTemplateValue;s&&(l||vh(this.latestValues)||p)&&(i(this.instance,f),this.shouldResetTransform=!1,this.scheduleRender())}measure(a=!0){var s;const l=this.measurePageBox();let u=this.removeElementScroll(l);a&&(u=this.removeTransform(u)),Cge(u);const f=(s=this.options.visualElement)===null||s===void 0?void 0:s.readValue("position"),p=f==="fixed"||f==="sticky"?f:"static";return{animationId:this.root.animationId,measuredBox:l,layoutBox:u,latestValues:{},source:this.id,position:p}}measurePageBox(){const{visualElement:a}=this.options;if(!a)return fi();const s=a.measureViewportBox(),{scroll:l}=this.root;return l&&(bd(s.x,l.offset.x),bd(s.y,l.offset.y)),s}removeElementScroll(a){const s=fi();Ns(s,a);for(let l=0;l<this.path.length;l++){const u=this.path[l],{scroll:f,options:p}=u;if(u!==this.root&&f&&p.layoutScroll){if(f.isRoot){Ns(s,a);const{scroll:m}=this.root;m&&(bd(s.x,-m.offset.x),bd(s.y,-m.offset.y))}bd(s.x,f.offset.x),bd(s.y,f.offset.y)}}return s}applyTransform(a,s=!1){const l=fi();Ns(l,a);for(let u=0;u<this.path.length;u++){const f=this.path[u];!s&&f.options.layoutScroll&&f.scroll&&f!==f.root&&eg(l,{x:-f.scroll.offset.x,y:-f.scroll.offset.y}),vh(f.latestValues)&&eg(l,f.latestValues)}return vh(this.latestValues)&&eg(l,this.latestValues),l}removeTransform(a){var s;const l=fi();Ns(l,a);for(let u=0;u<this.path.length;u++){const f=this.path[u];if(!f.instance||!vh(f.latestValues))continue;p9(f.latestValues)&&f.updateSnapshot();const p=fi(),m=f.measurePageBox();Ns(p,m),JO(l,f.latestValues,(s=f.snapshot)===null||s===void 0?void 0:s.layoutBox,p)}return vh(this.latestValues)&&JO(l,this.latestValues),l}setTargetDelta(a){this.targetDelta=a,this.isProjectionDirty=!0,this.root.scheduleUpdateProjection()}setOptions(a){this.options={...this.options,...a,crossfade:a.crossfade!==void 0?a.crossfade:!0}}clearMeasurements(){this.scroll=void 0,this.layout=void 0,this.snapshot=void 0,this.prevTransformTemplateValue=void 0,this.targetDelta=void 0,this.target=void 0,this.isLayoutDirty=!1}resolveTargetDelta(){var a;if(this.isProjectionDirty||(this.isProjectionDirty=this.getLead().isProjectionDirty||Boolean(this.parent&&this.parent.isProjectionDirty)),!this.isProjectionDirty)return;const{layout:s,layoutId:l}=this.options;if(!(!this.layout||!(s||l))){if(!this.targetDelta&&!this.relativeTarget){const u=this.getClosestProjectingParent();u&&u.layout?(this.relativeParent=u,this.relativeTarget=fi(),this.relativeTargetOrigin=fi(),Qv(this.relativeTargetOrigin,this.layout.layoutBox,u.layout.layoutBox),Ns(this.relativeTarget,this.relativeTargetOrigin)):this.relativeParent=this.relativeTarget=void 0}if(!(!this.relativeTarget&&!this.targetDelta)&&(this.target||(this.target=fi(),this.targetWithTransforms=fi()),this.relativeTarget&&this.relativeTargetOrigin&&((a=this.relativeParent)===null||a===void 0?void 0:a.target)?l0e(this.target,this.relativeTarget,this.relativeParent.target):this.targetDelta?(Boolean(this.resumingFrom)?this.target=this.applyTransform(this.layout.layoutBox):Ns(this.target,this.layout.layoutBox),pj(this.target,this.targetDelta)):Ns(this.target,this.layout.layoutBox),this.attemptToResolveRelativeTarget)){this.attemptToResolveRelativeTarget=!1;const u=this.getClosestProjectingParent();u&&Boolean(u.resumingFrom)===Boolean(this.resumingFrom)&&!u.options.layoutScroll&&u.target?(this.relativeParent=u,this.relativeTarget=fi(),this.relativeTargetOrigin=fi(),Qv(this.relativeTargetOrigin,this.target,u.target),Ns(this.relativeTarget,this.relativeTargetOrigin)):this.relativeParent=this.relativeTarget=void 0}}}getClosestProjectingParent(){if(!(!this.parent||p9(this.parent.latestValues)||hj(this.parent.latestValues)))return(this.parent.relativeTarget||this.parent.targetDelta)&&this.parent.layout?this.parent:this.parent.getClosestProjectingParent()}calcProjection(){var a;if(!this.isProjectionDirty)return;this.isProjectionDirty=!1;const{layout:s,layoutId:l}=this.options;if(this.isTreeAnimating=Boolean(((a=this.parent)===null||a===void 0?void 0:a.isTreeAnimating)||this.currentAnimation||this.pendingAnimation),this.isTreeAnimating||(this.targetDelta=this.relativeTarget=void 0),!this.layout||!(s||l))return;const u=this.getLead();Ns(this.layoutCorrected,this.layout.layoutBox),v0e(this.layoutCorrected,this.treeScale,this.path,Boolean(this.resumingFrom)||this!==u);const{target:f}=u;if(!f)return;this.projectionDelta||(this.projectionDelta=Jv(),this.projectionDeltaWithTransform=Jv());const p=this.treeScale.x,m=this.treeScale.y,v=this.projectionTransform;Zv(this.projectionDelta,this.layoutCorrected,f,this.latestValues),this.projectionTransform=nM(this.projectionDelta,this.treeScale),(this.projectionTransform!==v||this.treeScale.x!==p||this.treeScale.y!==m)&&(this.hasProjected=!0,this.scheduleRender(),this.notifyListeners("projectionUpdate",f))}hide(){this.isVisible=!1}show(){this.isVisible=!0}scheduleRender(a=!0){var s,l,u;(l=(s=this.options).scheduleRender)===null||l===void 0||l.call(s),a&&((u=this.getStack())===null||u===void 0||u.scheduleRender()),this.resumingFrom&&!this.resumingFrom.instance&&(this.resumingFrom=void 0)}setAnimationOrigin(a,s=!1){var l,u;const f=this.snapshot,p=(f==null?void 0:f.latestValues)||{},m={...this.latestValues},v=Jv();this.relativeTarget=this.relativeTargetOrigin=void 0,this.attemptToResolveRelativeTarget=!s;const b=fi(),x=(f==null?void 0:f.source)!==((l=this.layout)===null||l===void 0?void 0:l.source),E=(((u=this.getStack())===null||u===void 0?void 0:u.members.length)||0)<=1,k=Boolean(x&&!E&&this.options.crossfade===!0&&!this.path.some(Sge));this.animationProgress=0,this.mixTargetDelta=_=>{var T;const A=_/1e3;sM(v.x,a.x,A),sM(v.y,a.y,A),this.setTargetDelta(v),this.relativeTarget&&this.relativeTargetOrigin&&this.layout&&((T=this.relativeParent)===null||T===void 0?void 0:T.layout)&&(Qv(b,this.layout.layoutBox,this.relativeParent.layout.layoutBox),bge(this.relativeTarget,this.relativeTargetOrigin,b,A)),x&&(this.animationValues=m,ege(m,p,this.latestValues,A,k,E)),this.root.scheduleUpdateProjection(),this.scheduleRender(),this.animationProgress=A},this.mixTargetDelta(0)}startAnimation(a){var s,l;this.notifyListeners("animationStart"),(s=this.currentAnimation)===null||s===void 0||s.stop(),this.resumingFrom&&((l=this.resumingFrom.currentAnimation)===null||l===void 0||l.stop()),this.pendingAnimation&&(Zh.update(this.pendingAnimation),this.pendingAnimation=void 0),this.pendingAnimation=Xs.update(()=>{qv.hasAnimatedSinceResize=!0,this.currentAnimation=Q0e(0,iM,{...a,onUpdate:u=>{var f;this.mixTargetDelta(u),(f=a.onUpdate)===null||f===void 0||f.call(a,u)},onComplete:()=>{var u;(u=a.onComplete)===null||u===void 0||u.call(a),this.completeAnimation()}}),this.resumingFrom&&(this.resumingFrom.currentAnimation=this.currentAnimation),this.pendingAnimation=void 0})}completeAnimation(){var a;this.resumingFrom&&(this.resumingFrom.currentAnimation=void 0,this.resumingFrom.preserveOpacity=void 0),(a=this.getStack())===null||a===void 0||a.exitAnimationComplete(),this.resumingFrom=this.currentAnimation=this.animationValues=void 0,this.notifyListeners("animationComplete")}finishAnimation(){var a;this.currentAnimation&&((a=this.mixTargetDelta)===null||a===void 0||a.call(this,iM),this.currentAnimation.stop()),this.completeAnimation()}applyTransformsToTarget(){const a=this.getLead();let{targetWithTransforms:s,target:l,layout:u,latestValues:f}=a;if(!(!s||!l||!u)){if(this!==a&&this.layout&&u&&Tj(this.options.animationType,this.layout.layoutBox,u.layoutBox)){l=this.target||fi();const p=Ra(this.layout.layoutBox.x);l.x.min=a.target.x.min,l.x.max=l.x.min+p;const m=Ra(this.layout.layoutBox.y);l.y.min=a.target.y.min,l.y.max=l.y.min+m}Ns(s,l),eg(s,f),Zv(this.projectionDeltaWithTransform,this.layoutCorrected,s,f)}}registerSharedNode(a,s){var l,u,f;this.sharedNodes.has(a)||this.sharedNodes.set(a,new sge),this.sharedNodes.get(a).add(s),s.promote({transition:(l=s.options.initialPromotionConfig)===null||l===void 0?void 0:l.transition,preserveFollowOpacity:(f=(u=s.options.initialPromotionConfig)===null||u===void 0?void 0:u.shouldPreserveFollowOpacity)===null||f===void 0?void 0:f.call(u,s)})}isLead(){const a=this.getStack();return a?a.lead===this:!0}getLead(){var a;const{layoutId:s}=this.options;return s?((a=this.getStack())===null||a===void 0?void 0:a.lead)||this:this}getPrevLead(){var a;const{layoutId:s}=this.options;return s?(a=this.getStack())===null||a===void 0?void 0:a.prevLead:void 0}getStack(){const{layoutId:a}=this.options;if(a)return this.root.sharedNodes.get(a)}promote({needsReset:a,transition:s,preserveFollowOpacity:l}={}){const u=this.getStack();u&&u.promote(this,l),a&&(this.projectionDelta=void 0,this.needsReset=!0),s&&this.setOptions({transition:s})}relegate(){const a=this.getStack();return a?a.relegate(this):!1}resetRotation(){const{visualElement:a}=this.options;if(!a)return;let s=!1;const{latestValues:l}=a;if((l.rotate||l.rotateX||l.rotateY||l.rotateZ)&&(s=!0),!s)return;const u={};for(let f=0;f<rM.length;f++){const p="rotate"+rM[f];l[p]&&(u[p]=l[p],a.setStaticValue(p,0))}a==null||a.render();for(const f in u)a.setStaticValue(f,u[f]);a.scheduleRender()}getProjectionStyles(a={}){var s,l,u;const f={};if(!this.instance||this.isSVG)return f;if(this.isVisible)f.visibility="";else return{visibility:"hidden"};const p=(s=this.options.visualElement)===null||s===void 0?void 0:s.getProps().transformTemplate;if(this.needsReset)return this.needsReset=!1,f.opacity="",f.pointerEvents=d4(a.pointerEvents)||"",f.transform=p?p(this.latestValues,""):"none",f;const m=this.getLead();if(!this.projectionDelta||!this.layout||!m.target){const E={};return this.options.layoutId&&(E.opacity=this.latestValues.opacity!==void 0?this.latestValues.opacity:1,E.pointerEvents=d4(a.pointerEvents)||""),this.hasProjected&&!vh(this.latestValues)&&(E.transform=p?p({},""):"none",this.hasProjected=!1),E}const v=m.animationValues||m.latestValues;this.applyTransformsToTarget(),f.transform=nM(this.projectionDeltaWithTransform,this.treeScale,v),p&&(f.transform=p(v,f.transform));const{x:b,y:x}=this.projectionDelta;f.transformOrigin=`${b.origin*100}% ${x.origin*100}% 0`,m.animationValues?f.opacity=m===this?(u=(l=v.opacity)!==null&&l!==void 0?l:this.latestValues.opacity)!==null&&u!==void 0?u:1:this.preserveOpacity?this.latestValues.opacity:v.opacityExit:f.opacity=m===this?v.opacity!==void 0?v.opacity:"":v.opacityExit!==void 0?v.opacityExit:0;for(const E in f5){if(v[E]===void 0)continue;const{correct:k,applyTo:_}=f5[E],T=k(v[E],m);if(_){const A=_.length;for(let R=0;R<A;R++)f[_[R]]=T}else f[E]=T}return this.options.layoutId&&(f.pointerEvents=m===this?d4(a.pointerEvents)||"":"none"),f}clearSnapshot(){this.resumeFrom=this.snapshot=void 0}resetTree(){this.root.nodes.forEach(a=>{var s;return(s=a.currentAnimation)===null||s===void 0?void 0:s.stop()}),this.root.nodes.forEach(oM),this.root.sharedNodes.clear()}}}function dge(e){e.updateLayout()}function fge(e){var t,n,r;const i=((t=e.resumeFrom)===null||t===void 0?void 0:t.snapshot)||e.snapshot;if(e.isLead()&&e.layout&&i&&e.hasListeners("didUpdate")){const{layoutBox:o,measuredBox:a}=e.layout,{animationType:s}=e.options,l=i.source!==e.layout.source;s==="size"?$l(v=>{const b=l?i.measuredBox[v]:i.layoutBox[v],x=Ra(b);b.min=o[v].min,b.max=b.min+x}):Tj(s,i.layoutBox,o)&&$l(v=>{const b=l?i.measuredBox[v]:i.layoutBox[v],x=Ra(o[v]);b.max=b.min+x});const u=Jv();Zv(u,o,i.layoutBox);const f=Jv();l?Zv(f,e.applyTransform(a,!0),i.measuredBox):Zv(f,o,i.layoutBox);const p=!kj(u);let m=!1;if(!e.resumeFrom){const v=e.getClosestProjectingParent();if(v&&!v.resumeFrom){const{snapshot:b,layout:x}=v;if(b&&x){const E=fi();Qv(E,i.layoutBox,b.layoutBox);const k=fi();Qv(k,o,x.layoutBox),Ej(E,k)||(m=!0)}}}e.notifyListeners("didUpdate",{layout:o,snapshot:i,delta:f,layoutDelta:u,hasLayoutChanged:p,hasRelativeTargetChanged:m})}else e.isLead()&&((r=(n=e.options).onExitComplete)===null||r===void 0||r.call(n));e.options.transition=void 0}function hge(e){e.clearSnapshot()}function oM(e){e.clearMeasurements()}function pge(e){const{visualElement:t}=e.options;t!=null&&t.getProps().onBeforeLayoutMeasure&&t.notify("BeforeLayoutMeasure"),e.resetTransform()}function aM(e){e.finishAnimation(),e.targetDelta=e.relativeTarget=e.target=void 0}function gge(e){e.resolveTargetDelta()}function mge(e){e.calcProjection()}function vge(e){e.resetRotation()}function yge(e){e.removeLeadSnapshot()}function sM(e,t,n){e.translate=Dr(t.translate,0,n),e.scale=Dr(t.scale,1,n),e.origin=t.origin,e.originPoint=t.originPoint}function lM(e,t,n,r){e.min=Dr(t.min,n.min,r),e.max=Dr(t.max,n.max,r)}function bge(e,t,n,r){lM(e.x,t.x,n.x,r),lM(e.y,t.y,n.y,r)}function Sge(e){return e.animationValues&&e.animationValues.opacityExit!==void 0}const xge={duration:.45,ease:[.4,0,.1,1]};function wge(e,t){let n=e.root;for(let o=e.path.length-1;o>=0;o--)if(Boolean(e.path[o].instance)){n=e.path[o];break}const i=(n&&n!==e.root?n.instance:document).querySelector(`[data-projection-id="${t}"]`);i&&e.mount(i,!0)}function uM(e){e.min=Math.round(e.min),e.max=Math.round(e.max)}function Cge(e){uM(e.x),uM(e.y)}function Tj(e,t,n){return e==="position"||e==="preserve-aspect"&&!age(tM(t),tM(n),.2)}const _ge=Pj({attachResizeListener:(e,t)=>qS(e,"resize",t),measureScroll:()=>({x:document.documentElement.scrollLeft||document.body.scrollLeft,y:document.documentElement.scrollTop||document.body.scrollTop}),checkIsScrollRoot:()=>!0}),N6={current:void 0},kge=Pj({measureScroll:e=>({x:e.scrollLeft,y:e.scrollTop}),defaultParent:()=>{if(!N6.current){const e=new _ge(0,{});e.mount(window),e.setOptions({layoutScroll:!0}),N6.current=e}return N6.current},resetTransform:(e,t)=>{e.style.transform=t!==void 0?t:"none"},checkIsScrollRoot:e=>Boolean(window.getComputedStyle(e).position==="fixed")}),Ege={...o0e,...gpe,...E0e,...Z0e},gu=ife((e,t)=>Vfe(e,t,Ege,G0e,kge));function Lj(){const e=C.exports.useRef(!1);return d5(()=>(e.current=!0,()=>{e.current=!1}),[]),e}function Pge(){const e=Lj(),[t,n]=C.exports.useState(0),r=C.exports.useCallback(()=>{e.current&&n(t+1)},[t]);return[C.exports.useCallback(()=>Xs.postRender(r),[r]),t]}class Tge extends C.exports.Component{getSnapshotBeforeUpdate(t){const n=this.props.childRef.current;if(n&&t.isPresent&&!this.props.isPresent){const r=this.props.sizeRef.current;r.height=n.offsetHeight||0,r.width=n.offsetWidth||0,r.top=n.offsetTop,r.left=n.offsetLeft}return null}componentDidUpdate(){}render(){return this.props.children}}function Lge({children:e,isPresent:t}){const n=C.exports.useId(),r=C.exports.useRef(null),i=C.exports.useRef({width:0,height:0,top:0,left:0});return C.exports.useInsertionEffect(()=>{const{width:o,height:a,top:s,left:l}=i.current;if(t||!r.current||!o||!a)return;r.current.dataset.motionPopId=n;const u=document.createElement("style");return document.head.appendChild(u),u.sheet&&u.sheet.insertRule(`
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0:i.call(r,e).container,control:wue(e)}}),_ue={md:l4({control:{w:"4",h:"4"},label:{fontSize:"md"}}),lg:l4({control:{w:"5",h:"5"},label:{fontSize:"lg"}}),sm:l4({control:{width:"3",height:"3"},label:{fontSize:"sm"}})},kue=xue({baseStyle:Cue,sizes:_ue,defaultProps:{size:"md",colorScheme:"blue"}}),{defineMultiStyleConfig:Eue,definePartsStyle:Pue}=lr(boe.keys),J3=jn("select-bg"),zA,Tue={...(zA=Cn.baseStyle)==null?void 0:zA.field,appearance:"none",paddingBottom:"1px",lineHeight:"normal",bg:J3.reference,[J3.variable]:"colors.white",_dark:{[J3.variable]:"colors.gray.700"},"> option, > optgroup":{bg:J3.reference}},Lue={width:"6",height:"100%",insetEnd:"2",position:"relative",color:"currentColor",fontSize:"xl",_disabled:{opacity:.5}},Aue=Pue({field:Tue,icon:Lue}),eb={paddingInlineEnd:"8"},jA,HA,VA,UA,WA,GA,qA,YA,Oue={lg:{...(jA=Cn.sizes)==null?void 0:jA.lg,field:{...(HA=Cn.sizes)==null?void 0:HA.lg.field,...eb}},md:{...(VA=Cn.sizes)==null?void 0:VA.md,field:{...(UA=Cn.sizes)==null?void 0:UA.md.field,...eb}},sm:{...(WA=Cn.sizes)==null?void 0:WA.sm,field:{...(GA=Cn.sizes)==null?void 0:GA.sm.field,...eb}},xs:{...(qA=Cn.sizes)==null?void 0:qA.xs,field:{...(YA=Cn.sizes)==null?void 0:YA.xs.field,...eb},icon:{insetEnd:"1"}}},Mue=Eue({baseStyle:Aue,sizes:Oue,variants:Cn.variants,defaultProps:Cn.defaultProps}),C6=jn("skeleton-start-color"),_6=jn("skeleton-end-color"),Rue={[C6.variable]:"colors.gray.100",[_6.variable]:"colors.gray.400",_dark:{[C6.variable]:"colors.gray.800",[_6.variable]:"colors.gray.600"},background:C6.reference,borderColor:_6.reference,opacity:.7,borderRadius:"sm"},Iue={baseStyle:Rue},k6=jn("skip-link-bg"),Due={borderRadius:"md",fontWeight:"semibold",_focusVisible:{boxShadow:"outline",padding:"4",position:"fixed",top:"6",insetStart:"6",[k6.variable]:"colors.white",_dark:{[k6.variable]:"colors.gray.700"},bg:k6.reference}},Nue={baseStyle:Due},{defineMultiStyleConfig:Bue,definePartsStyle:jS}=lr(Soe.keys),R2=jn("slider-thumb-size"),I2=jn("slider-track-size"),Cd=jn("slider-bg"),$ue=e=>{const{orientation:t}=e;return{display:"inline-block",position:"relative",cursor:"pointer",_disabled:{opacity:.6,cursor:"default",pointerEvents:"none"},...sk({orientation:t,vertical:{h:"100%"},horizontal:{w:"100%"}})}},Fue=e=>({...sk({orientation:e.orientation,horizontal:{h:I2.reference},vertical:{w:I2.reference}}),overflow:"hidden",borderRadius:"sm",[Cd.variable]:"colors.gray.200",_dark:{[Cd.variable]:"colors.whiteAlpha.200"},_disabled:{[Cd.variable]:"colors.gray.300",_dark:{[Cd.variable]:"colors.whiteAlpha.300"}},bg:Cd.reference}),zue=e=>{const{orientation:t}=e;return{...sk({orientation:t,vertical:{left:"50%",transform:"translateX(-50%)",_active:{transform:"translateX(-50%) scale(1.15)"}},horizontal:{top:"50%",transform:"translateY(-50%)",_active:{transform:"translateY(-50%) scale(1.15)"}}}),w:R2.reference,h:R2.reference,display:"flex",alignItems:"center",justifyContent:"center",position:"absolute",outline:0,zIndex:1,borderRadius:"full",bg:"white",boxShadow:"base",border:"1px solid",borderColor:"transparent",transitionProperty:"transform",transitionDuration:"normal",_focusVisible:{boxShadow:"outline"},_disabled:{bg:"gray.300"}}},jue=e=>{const{colorScheme:t}=e;return{width:"inherit",height:"inherit",[Cd.variable]:`colors.${t}.500`,_dark:{[Cd.variable]:`colors.${t}.200`},bg:Cd.reference}},Hue=jS(e=>({container:$ue(e),track:Fue(e),thumb:zue(e),filledTrack:jue(e)})),Vue=jS({container:{[R2.variable]:"sizes.4",[I2.variable]:"sizes.1"}}),Uue=jS({container:{[R2.variable]:"sizes.3.5",[I2.variable]:"sizes.1"}}),Wue=jS({container:{[R2.variable]:"sizes.2.5",[I2.variable]:"sizes.0.5"}}),Gue={lg:Vue,md:Uue,sm:Wue},que=Bue({baseStyle:Hue,sizes:Gue,defaultProps:{size:"md",colorScheme:"blue"}}),_h=yi("spinner-size"),Yue={width:[_h.reference],height:[_h.reference]},Kue={xs:{[_h.variable]:"sizes.3"},sm:{[_h.variable]:"sizes.4"},md:{[_h.variable]:"sizes.6"},lg:{[_h.variable]:"sizes.8"},xl:{[_h.variable]:"sizes.12"}},Xue={baseStyle:Yue,sizes:Kue,defaultProps:{size:"md"}},{defineMultiStyleConfig:Zue,definePartsStyle:QF}=lr(xoe.keys),Que={fontWeight:"medium"},Jue={opacity:.8,marginBottom:"2"},ece={verticalAlign:"baseline",fontWeight:"semibold"},tce={marginEnd:1,w:"3.5",h:"3.5",verticalAlign:"middle"},nce=QF({container:{},label:Que,helpText:Jue,number:ece,icon:tce}),rce={md:QF({label:{fontSize:"sm"},helpText:{fontSize:"sm"},number:{fontSize:"2xl"}})},ice=Zue({baseStyle:nce,sizes:rce,defaultProps:{size:"md"}}),{defineMultiStyleConfig:oce,definePartsStyle:u4}=lr(woe.keys),Gv=yi("switch-track-width"),$h=yi("switch-track-height"),E6=yi("switch-track-diff"),ace=qu.subtract(Gv,$h),e9=yi("switch-thumb-x"),Ym=yi("switch-bg"),sce=e=>{const{colorScheme:t}=e;return{borderRadius:"full",p:"0.5",width:[Gv.reference],height:[$h.reference],transitionProperty:"common",transitionDuration:"fast",[Ym.variable]:"colors.gray.300",_dark:{[Ym.variable]:"colors.whiteAlpha.400"},_focusVisible:{boxShadow:"outline"},_disabled:{opacity:.4,cursor:"not-allowed"},_checked:{[Ym.variable]:`colors.${t}.500`,_dark:{[Ym.variable]:`colors.${t}.200`}},bg:Ym.reference}},lce={bg:"white",transitionProperty:"transform",transitionDuration:"normal",borderRadius:"inherit",width:[$h.reference],height:[$h.reference],_checked:{transform:`translateX(${e9.reference})`}},uce=u4(e=>({container:{[E6.variable]:ace,[e9.variable]:E6.reference,_rtl:{[e9.variable]:qu(E6).negate().toString()}},track:sce(e),thumb:lce})),cce={sm:u4({container:{[Gv.variable]:"1.375rem",[$h.variable]:"sizes.3"}}),md:u4({container:{[Gv.variable]:"1.875rem",[$h.variable]:"sizes.4"}}),lg:u4({container:{[Gv.variable]:"2.875rem",[$h.variable]:"sizes.6"}})},dce=oce({baseStyle:uce,sizes:cce,defaultProps:{size:"md",colorScheme:"blue"}}),{defineMultiStyleConfig:fce,definePartsStyle:kg}=lr(Coe.keys),hce=kg({table:{fontVariantNumeric:"lining-nums tabular-nums",borderCollapse:"collapse",width:"full"},th:{fontFamily:"heading",fontWeight:"bold",textTransform:"uppercase",letterSpacing:"wider",textAlign:"start"},td:{textAlign:"start"},caption:{mt:4,fontFamily:"heading",textAlign:"center",fontWeight:"medium"}}),u5={"&[data-is-numeric=true]":{textAlign:"end"}},pce=kg(e=>{const{colorScheme:t}=e;return{th:{color:Tt("gray.600","gray.400")(e),borderBottom:"1px",borderColor:Tt(`${t}.100`,`${t}.700`)(e),...u5},td:{borderBottom:"1px",borderColor:Tt(`${t}.100`,`${t}.700`)(e),...u5},caption:{color:Tt("gray.600","gray.100")(e)},tfoot:{tr:{"&:last-of-type":{th:{borderBottomWidth:0}}}}}}),gce=kg(e=>{const{colorScheme:t}=e;return{th:{color:Tt("gray.600","gray.400")(e),borderBottom:"1px",borderColor:Tt(`${t}.100`,`${t}.700`)(e),...u5},td:{borderBottom:"1px",borderColor:Tt(`${t}.100`,`${t}.700`)(e),...u5},caption:{color:Tt("gray.600","gray.100")(e)},tbody:{tr:{"&:nth-of-type(odd)":{"th, td":{borderBottomWidth:"1px",borderColor:Tt(`${t}.100`,`${t}.700`)(e)},td:{background:Tt(`${t}.100`,`${t}.700`)(e)}}}},tfoot:{tr:{"&:last-of-type":{th:{borderBottomWidth:0}}}}}}),mce={simple:pce,striped:gce,unstyled:{}},vce={sm:kg({th:{px:"4",py:"1",lineHeight:"4",fontSize:"xs"},td:{px:"4",py:"2",fontSize:"sm",lineHeight:"4"},caption:{px:"4",py:"2",fontSize:"xs"}}),md:kg({th:{px:"6",py:"3",lineHeight:"4",fontSize:"xs"},td:{px:"6",py:"4",lineHeight:"5"},caption:{px:"6",py:"2",fontSize:"sm"}}),lg:kg({th:{px:"8",py:"4",lineHeight:"5",fontSize:"sm"},td:{px:"8",py:"5",lineHeight:"6"},caption:{px:"6",py:"2",fontSize:"md"}})},yce=fce({baseStyle:hce,variants:mce,sizes:vce,defaultProps:{variant:"simple",size:"md",colorScheme:"gray"}}),Xo=jn("tabs-color"),Ws=jn("tabs-bg"),tb=jn("tabs-border-color"),{defineMultiStyleConfig:bce,definePartsStyle:eu}=lr(_oe.keys),Sce=e=>{const{orientation:t}=e;return{display:t==="vertical"?"flex":"block"}},xce=e=>{const{isFitted:t}=e;return{flex:t?1:void 0,transitionProperty:"common",transitionDuration:"normal",_focusVisible:{zIndex:1,boxShadow:"outline"},_disabled:{cursor:"not-allowed",opacity:.4}}},wce=e=>{const{align:t="start",orientation:n}=e;return{justifyContent:{end:"flex-end",center:"center",start:"flex-start"}[t],flexDirection:n==="vertical"?"column":"row"}},Cce={p:4},_ce=eu(e=>({root:Sce(e),tab:xce(e),tablist:wce(e),tabpanel:Cce})),kce={sm:eu({tab:{py:1,px:4,fontSize:"sm"}}),md:eu({tab:{fontSize:"md",py:2,px:4}}),lg:eu({tab:{fontSize:"lg",py:3,px:4}})},Ece=eu(e=>{const{colorScheme:t,orientation:n}=e,r=n==="vertical",i=n==="vertical"?"borderStart":"borderBottom",o=r?"marginStart":"marginBottom";return{tablist:{[i]:"2px solid",borderColor:"inherit"},tab:{[i]:"2px solid",borderColor:"transparent",[o]:"-2px",_selected:{[Xo.variable]:`colors.${t}.600`,_dark:{[Xo.variable]:`colors.${t}.300`},borderColor:"currentColor"},_active:{[Ws.variable]:"colors.gray.200",_dark:{[Ws.variable]:"colors.whiteAlpha.300"}},_disabled:{_active:{bg:"none"}},color:Xo.reference,bg:Ws.reference}}}),Pce=eu(e=>{const{colorScheme:t}=e;return{tab:{borderTopRadius:"md",border:"1px solid",borderColor:"transparent",mb:"-1px",[tb.reference]:"transparent",_selected:{[Xo.variable]:`colors.${t}.600`,[tb.variable]:"colors.white",_dark:{[Xo.variable]:`colors.${t}.300`,[tb.variable]:"colors.gray.800"},borderColor:"inherit",borderBottomColor:tb.reference},color:Xo.reference},tablist:{mb:"-1px",borderBottom:"1px solid",borderColor:"inherit"}}}),Tce=eu(e=>{const{colorScheme:t}=e;return{tab:{border:"1px 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r=t.measureViewportBox(),i=t.current,o=getComputedStyle(i),{display:a}=o,s={};a==="none"&&t.setStaticValue("display",e.display||"block"),n.forEach(u=>{s[u]=UO[u](r,o)}),t.render();const l=t.measureViewportBox();return n.forEach(u=>{const f=t.getValue(u);yj(f,s[u]),e[u]=UO[u](l,o)}),e},D0e=(e,t,n={},r={})=>{t={...t},r={...r};const i=Object.keys(t).filter(vj);let o=[],a=!1;const s=[];if(i.forEach(l=>{const u=e.getValue(l);if(!e.hasValue(l))return;let f=n[l],p=Km(f);const m=t[l];let v;if($2(m)){const b=m.length,x=m[0]===null?1:0;f=m[x],p=Km(f);for(let E=x;E<b;E++)v?m5(Km(m[E])===v):v=Km(m[E])}else v=Km(m);if(p!==v)if(zO(p)&&zO(v)){const b=u.get();typeof b=="string"&&u.set(parseFloat(b)),typeof m=="string"?t[l]=parseFloat(m):Array.isArray(m)&&v===Ot&&(t[l]=m.map(parseFloat))}else(p==null?void 0:p.transform)&&(v==null?void 0:v.transform)&&(f===0||m===0)?f===0?u.set(v.transform(f)):t[l]=p.transform(m):(a||(o=R0e(e),a=!0),s.push(l),r[l]=r[l]!==void 0?r[l]:t[l],yj(u,m))}),s.length){const l=s.indexOf("height")>=0?window.pageYOffset:null,u=I0e(t,e,s);return o.length&&o.forEach(([f,p])=>{e.getValue(f).set(p)}),e.render(),sp&&l!==null&&window.scrollTo({top:l}),{target:u,transitionEnd:r}}else return{target:t,transitionEnd:r}};function N0e(e,t,n,r){return A0e(t)?D0e(e,t,n,r):{target:t,transitionEnd:r}}const B0e=(e,t,n,r)=>{const i=T0e(e,t,r);return t=i.target,r=i.transitionEnd,N0e(e,t,n,r)},x9={current:null},bj={current:!1};function $0e(){if(bj.current=!0,!!sp)if(window.matchMedia){const e=window.matchMedia("(prefers-reduced-motion)"),t=()=>x9.current=e.matches;e.addListener(t),t()}else x9.current=!1}function F0e(e,t,n){const{willChange:r}=t;for(const i in t){const o=t[i],a=n[i];if(lu(o))e.addValue(i,o),x5(r)&&r.add(i);else if(lu(a))e.addValue(i,Kg(o)),x5(r)&&r.remove(i);else if(a!==o)if(e.hasValue(i)){const s=e.getValue(i);!s.hasAnimated&&s.set(o)}else{const s=e.getStaticValue(i);e.addValue(i,Kg(s!==void 0?s:o))}}for(const i in n)t[i]===void 0&&e.removeValue(i);return t}const Sj=Object.keys(N2),z0e=Sj.length,WO=["AnimationStart","AnimationComplete","Update","Unmount","BeforeLayoutMeasure","LayoutMeasure","LayoutAnimationStart","LayoutAnimationComplete"];class j0e{constructor({parent:t,props:n,reducedMotionConfig:r,visualState:i},o={}){this.current=null,this.children=new Set,this.isVariantNode=!1,this.isControllingVariants=!1,this.shouldReduceMotion=null,this.values=new Map,this.isPresent=!0,this.valueSubscriptions=new Map,this.prevMotionValues={},this.events={},this.propEventSubscriptions={},this.notifyUpdate=()=>this.notify("Update",this.latestValues),this.render=()=>{!this.current||(this.triggerBuild(),this.renderInstance(this.current,this.renderState,this.props.style,this.projection))},this.scheduleRender=()=>Qs.render(this.render,!1,!0);const{latestValues:a,renderState:s}=i;this.latestValues=a,this.baseTarget={...a},this.initialValues=n.initial?{...a}:{},this.renderState=s,this.parent=t,this.props=n,this.depth=t?t.depth+1:0,this.reducedMotionConfig=r,this.options=o,this.isControllingVariants=WS(n),this.isVariantNode=iz(n),this.isVariantNode&&(this.variantChildren=new Set),this.manuallyAnimateOnMount=Boolean(t&&t.current);const{willChange:l,...u}=this.scrapeMotionValuesFromProps(n);for(const f in u){const p=u[f];a[f]!==void 0&&lu(p)&&(p.set(a[f],!1),x5(l)&&l.add(f))}}scrapeMotionValuesFromProps(t){return{}}mount(t){var n;this.current=t,this.projection&&this.projection.mount(t),this.parent&&this.isVariantNode&&!this.isControllingVariants&&(this.removeFromVariantTree=(n=this.parent)===null||n===void 0?void 0:n.addVariantChild(this)),this.values.forEach((r,i)=>this.bindToMotionValue(i,r)),bj.current||$0e(),this.shouldReduceMotion=this.reducedMotionConfig==="never"?!1:this.reducedMotionConfig==="always"?!0:x9.current,this.parent&&this.parent.children.add(this),this.setProps(this.props)}unmount(){var t,n,r;(t=this.projection)===null||t===void 0||t.unmount(),Zh.update(this.notifyUpdate),Zh.render(this.render),this.valueSubscriptions.forEach(i=>i()),(n=this.removeFromVariantTree)===null||n===void 0||n.call(this),(r=this.parent)===null||r===void 0||r.children.delete(this);for(const i in this.events)this.events[i].clear();this.current=null}bindToMotionValue(t,n){const r=g1.has(t),i=n.onChange(a=>{this.latestValues[t]=a,this.props.onUpdate&&Qs.update(this.notifyUpdate,!1,!0),r&&this.projection&&(this.projection.isProjectionDirty=!0)}),o=n.onRenderRequest(this.scheduleRender);this.valueSubscriptions.set(t,()=>{i(),o()})}sortNodePosition(t){return!this.current||!this.sortInstanceNodePosition||this.type!==t.type?0:this.sortInstanceNodePosition(this.current,t.current)}loadFeatures(t,n,r,i,o,a){const s=[];for(let l=0;l<z0e;l++){const u=Sj[l],{isEnabled:f,Component:p}=N2[u];f(t)&&p&&s.push(C.exports.createElement(p,{key:u,...t,visualElement:this}))}if(!this.projection&&o){this.projection=new o(i,this.latestValues,this.parent&&this.parent.projection);const{layoutId:l,layout:u,drag:f,dragConstraints:p,layoutScroll:m}=t;this.projection.setOptions({layoutId:l,layout:u,alwaysMeasureLayout:Boolean(f)||p&&J0(p),visualElement:this,scheduleRender:()=>this.scheduleRender(),animationType:typeof u=="string"?u:"both",initialPromotionConfig:a,layoutScroll:m})}return s}triggerBuild(){this.build(this.renderState,this.latestValues,this.options,this.props)}measureViewportBox(){return this.current?this.measureInstanceViewportBox(this.current,this.props):hi()}getStaticValue(t){return this.latestValues[t]}setStaticValue(t,n){this.latestValues[t]=n}makeTargetAnimatable(t,n=!0){return this.makeTargetAnimatableFromInstance(t,this.props,n)}setProps(t){(t.transformTemplate||this.props.transformTemplate)&&this.scheduleRender(),this.props=t;for(let n=0;n<WO.length;n++){const r=WO[n];this.propEventSubscriptions[r]&&(this.propEventSubscriptions[r](),delete this.propEventSubscriptions[r]);const i=t["on"+r];i&&(this.propEventSubscriptions[r]=this.on(r,i))}this.prevMotionValues=F0e(this,this.scrapeMotionValuesFromProps(t),this.prevMotionValues)}getProps(){return this.props}getVariant(t){var n;return(n=this.props.variants)===null||n===void 0?void 0:n[t]}getDefaultTransition(){return this.props.transition}getTransformPagePoint(){return this.props.transformPagePoint}getClosestVariantNode(){var t;return this.isVariantNode?this:(t=this.parent)===null||t===void 0?void 0:t.getClosestVariantNode()}getVariantContext(t=!1){var n,r;if(t)return(n=this.parent)===null||n===void 0?void 0:n.getVariantContext();if(!this.isControllingVariants){const o=((r=this.parent)===null||r===void 0?void 0:r.getVariantContext())||{};return this.props.initial!==void 0&&(o.initial=this.props.initial),o}const i={};for(let o=0;o<H0e;o++){const a=xj[o],s=this.props[a];(D2(s)||s===!1)&&(i[a]=s)}return i}addVariantChild(t){var n;const r=this.getClosestVariantNode();if(r)return(n=r.variantChildren)===null||n===void 0||n.add(t),()=>r.variantChildren.delete(t)}addValue(t,n){this.hasValue(t)&&this.removeValue(t),this.values.set(t,n),this.latestValues[t]=n.get(),this.bindToMotionValue(t,n)}removeValue(t){var n;this.values.delete(t),(n=this.valueSubscriptions.get(t))===null||n===void 0||n(),this.valueSubscriptions.delete(t),delete 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j0e{sortInstanceNodePosition(t,n){return t.compareDocumentPosition(n)&2?1:-1}getBaseTargetFromProps(t,n){var r;return(r=t.style)===null||r===void 0?void 0:r[n]}removeValueFromRenderState(t,{vars:n,style:r}){delete n[t],delete r[t]}makeTargetAnimatableFromInstance({transition:t,transitionEnd:n,...r},{transformValues:i},o){let a=Ype(r,t||{},this);if(i&&(n&&(n=i(n)),r&&(r=i(r)),a&&(a=i(a))),o){Gpe(this,r,a);const s=B0e(this,r,a,n);n=s.transitionEnd,r=s.target}return{transition:t,transitionEnd:n,...r}}}function V0e(e){return window.getComputedStyle(e)}class U0e extends wj{readValueFromInstance(t,n){if(g1.has(n)){const r=Ak(n);return r&&r.default||0}else{const r=V0e(t),i=(sz(n)?r.getPropertyValue(n):r[n])||0;return typeof i=="string"?i.trim():i}}measureInstanceViewportBox(t,{transformPagePoint:n}){return gj(t,n)}build(t,n,r,i){gk(t,n,r,i.transformTemplate)}scrapeMotionValuesFromProps(t){return yk(t)}renderInstance(t,n,r,i){Sz(t,n,r,i)}}class W0e extends wj{getBaseTargetFromProps(t,n){return t[n]}readValueFromInstance(t,n){var r;return g1.has(n)?((r=Ak(n))===null||r===void 0?void 0:r.default)||0:(n=xz.has(n)?n:bz(n),t.getAttribute(n))}measureInstanceViewportBox(){return hi()}scrapeMotionValuesFromProps(t){return Cz(t)}build(t,n,r,i){vk(t,n,r,i.transformTemplate)}renderInstance(t,n,r,i){wz(t,n,r,i)}}const G0e=(e,t)=>hk(e)?new W0e(t,{enableHardwareAcceleration:!1}):new U0e(t,{enableHardwareAcceleration:!0});function GO(e,t){return t.max===t.min?0:e/(t.max-t.min)*100}const Xm={correct:(e,t)=>{if(!t.target)return e;if(typeof e=="string")if(Ot.test(e))e=parseFloat(e);else return e;const n=GO(e,t.target.x),r=GO(e,t.target.y);return`${n}% ${r}%`}},qO="_$css",q0e={correct:(e,{treeScale:t,projectionDelta:n})=>{const r=e,i=e.includes("var("),o=[];i&&(e=e.replace(mj,v=>(o.push(v),qO)));const a=ac.parse(e);if(a.length>5)return r;const s=ac.createTransformer(e),l=typeof a[0]!="number"?1:0,u=n.x.scale*t.x,f=n.y.scale*t.y;a[0+l]/=u,a[1+l]/=f;const p=$r(u,f,.5);typeof a[2+l]=="number"&&(a[2+l]/=p),typeof a[3+l]=="number"&&(a[3+l]/=p);let m=s(a);if(i){let v=0;m=m.replace(qO,()=>{const b=o[v];return v++,b})}return m}};class Y0e extends oe.Component{componentDidMount(){const{visualElement:t,layoutGroup:n,switchLayoutGroup:r,layoutId:i}=this.props,{projection:o}=t;afe(X0e),o&&(n.group&&n.group.add(o),r&&r.register&&i&&r.register(o),o.root.didUpdate(),o.addEventListener("animationComplete",()=>{this.safeToRemove()}),o.setOptions({...o.options,onExitComplete:()=>this.safeToRemove()})),qv.hasEverUpdated=!0}getSnapshotBeforeUpdate(t){const{layoutDependency:n,visualElement:r,drag:i,isPresent:o}=this.props,a=r.projection;return a&&(a.isPresent=o,i||t.layoutDependency!==n||n===void 0?a.willUpdate():this.safeToRemove(),t.isPresent!==o&&(o?a.promote():a.relegate()||Qs.postRender(()=>{var s;!((s=a.getStack())===null||s===void 0)&&s.members.length||this.safeToRemove()}))),null}componentDidUpdate(){const{projection:t}=this.props.visualElement;t&&(t.root.didUpdate(),!t.currentAnimation&&t.isLead()&&this.safeToRemove())}componentWillUnmount(){const{visualElement:t,layoutGroup:n,switchLayoutGroup:r}=this.props,{projection:i}=t;i&&(i.scheduleCheckAfterUnmount(),n!=null&&n.group&&n.group.remove(i),r!=null&&r.deregister&&r.deregister(i))}safeToRemove(){const{safeToRemove:t}=this.props;t==null||t()}render(){return null}}function K0e(e){const[t,n]=Lk(),r=C.exports.useContext(fk);return y(Y0e,{...e,layoutGroup:r,switchLayoutGroup:C.exports.useContext(oz),isPresent:t,safeToRemove:n})}const X0e={borderRadius:{...Xm,applyTo:["borderTopLeftRadius","borderTopRightRadius","borderBottomLeftRadius","borderBottomRightRadius"]},borderTopLeftRadius:Xm,borderTopRightRadius:Xm,borderBottomLeftRadius:Xm,borderBottomRightRadius:Xm,boxShadow:q0e},Z0e={measureLayout:K0e};function Q0e(e,t,n={}){const r=lu(e)?e:Kg(e);return Rk("",r,t,n),{stop:()=>r.stop(),isAnimating:()=>r.isAnimating()}}const Cj=["TopLeft","TopRight","BottomLeft","BottomRight"],J0e=Cj.length,YO=e=>typeof e=="string"?parseFloat(e):e,KO=e=>typeof e=="number"||Ot.test(e);function ege(e,t,n,r,i,o){var a,s,l,u;i?(e.opacity=$r(0,(a=n.opacity)!==null&&a!==void 0?a:1,tge(r)),e.opacityExit=$r((s=t.opacity)!==null&&s!==void 0?s:1,0,nge(r))):o&&(e.opacity=$r((l=t.opacity)!==null&&l!==void 0?l:1,(u=n.opacity)!==null&&u!==void 0?u:1,r));for(let f=0;f<J0e;f++){const p=`border${Cj[f]}Radius`;let m=XO(t,p),v=XO(n,p);if(m===void 0&&v===void 0)continue;m||(m=0),v||(v=0),m===0||v===0||KO(m)===KO(v)?(e[p]=Math.max($r(YO(m),YO(v),r),0),(tu.test(v)||tu.test(m))&&(e[p]+="%")):e[p]=v}(t.rotate||n.rotate)&&(e.rotate=$r(t.rotate||0,n.rotate||0,r))}function XO(e,t){var n;return(n=e[t])!==null&&n!==void 0?n:e.borderRadius}const tge=_j(0,.5,Ek),nge=_j(.5,.95,_k);function _j(e,t,n){return r=>r<e?0:r>t?1:n(F2(e,t,r))}function ZO(e,t){e.min=t.min,e.max=t.max}function $s(e,t){ZO(e.x,t.x),ZO(e.y,t.y)}function QO(e,t,n,r,i){return e-=t,e=w5(e,1/n,r),i!==void 0&&(e=w5(e,1/i,r)),e}function rge(e,t=0,n=1,r=.5,i,o=e,a=e){if(tu.test(t)&&(t=parseFloat(t),t=$r(a.min,a.max,t/100)-a.min),typeof t!="number")return;let s=$r(o.min,o.max,r);e===o&&(s-=t),e.min=QO(e.min,t,n,s,i),e.max=QO(e.max,t,n,s,i)}function JO(e,t,[n,r,i],o,a){rge(e,t[n],t[r],t[i],t.scale,o,a)}const ige=["x","scaleX","originX"],oge=["y","scaleY","originY"];function eM(e,t,n,r){JO(e.x,t,ige,n==null?void 0:n.x,r==null?void 0:r.x),JO(e.y,t,oge,n==null?void 0:n.y,r==null?void 0:r.y)}function tM(e){return e.translate===0&&e.scale===1}function kj(e){return tM(e.x)&&tM(e.y)}function Ej(e,t){return e.x.min===t.x.min&&e.x.max===t.x.max&&e.y.min===t.y.min&&e.y.max===t.y.max}function nM(e){return Ia(e.x)/Ia(e.y)}function age(e,t,n=.1){return Tk(e,t)<=n}class sge{constructor(){this.members=[]}add(t){Ik(this.members,t),t.scheduleRender()}remove(t){if(Dk(this.members,t),t===this.prevLead&&(this.prevLead=void 0),t===this.lead){const n=this.members[this.members.length-1];n&&this.promote(n)}}relegate(t){const n=this.members.findIndex(i=>t===i);if(n===0)return!1;let r;for(let i=n;i>=0;i--){const o=this.members[i];if(o.isPresent!==!1){r=o;break}}return r?(this.promote(r),!0):!1}promote(t,n){var r;const i=this.lead;if(t!==i&&(this.prevLead=i,this.lead=t,t.show(),i)){i.instance&&i.scheduleRender(),t.scheduleRender(),t.resumeFrom=i,n&&(t.resumeFrom.preserveOpacity=!0),i.snapshot&&(t.snapshot=i.snapshot,t.snapshot.latestValues=i.animationValues||i.latestValues),!((r=t.root)===null||r===void 0)&&r.isUpdating&&(t.isLayoutDirty=!0);const{crossfade:o}=t.options;o===!1&&i.hide()}}exitAnimationComplete(){this.members.forEach(t=>{var n,r,i,o,a;(r=(n=t.options).onExitComplete)===null||r===void 0||r.call(n),(a=(i=t.resumingFrom)===null||i===void 0?void 0:(o=i.options).onExitComplete)===null||a===void 0||a.call(o)})}scheduleRender(){this.members.forEach(t=>{t.instance&&t.scheduleRender(!1)})}removeLeadSnapshot(){this.lead&&this.lead.snapshot&&(this.lead.snapshot=void 0)}}function rM(e,t,n){let r="";const i=e.x.translate/t.x,o=e.y.translate/t.y;if((i||o)&&(r=`translate3d(${i}px, ${o}px, 0) `),(t.x!==1||t.y!==1)&&(r+=`scale(${1/t.x}, ${1/t.y}) `),n){const{rotate:l,rotateX:u,rotateY:f}=n;l&&(r+=`rotate(${l}deg) `),u&&(r+=`rotateX(${u}deg) `),f&&(r+=`rotateY(${f}deg) `)}const a=e.x.scale*t.x,s=e.y.scale*t.y;return(a!==1||s!==1)&&(r+=`scale(${a}, ${s})`),r||"none"}const lge=(e,t)=>e.depth-t.depth;class uge{constructor(){this.children=[],this.isDirty=!1}add(t){Ik(this.children,t),this.isDirty=!0}remove(t){Dk(this.children,t),this.isDirty=!0}forEach(t){this.isDirty&&this.children.sort(lge),this.isDirty=!1,this.children.forEach(t)}}const iM=["","X","Y","Z"],oM=1e3;let cge=0;function Pj({attachResizeListener:e,defaultParent:t,measureScroll:n,checkIsScrollRoot:r,resetTransform:i}){return class{constructor(a,s={},l=t==null?void 0:t()){this.id=cge++,this.animationId=0,this.children=new Set,this.options={},this.isTreeAnimating=!1,this.isAnimationBlocked=!1,this.isLayoutDirty=!1,this.isProjectionDirty=!1,this.updateManuallyBlocked=!1,this.updateBlockedByResize=!1,this.isUpdating=!1,this.isSVG=!1,this.needsReset=!1,this.shouldResetTransform=!1,this.treeScale={x:1,y:1},this.eventHandlers=new Map,this.potentialNodes=new Map,this.checkUpdateFailed=()=>{this.isUpdating&&(this.isUpdating=!1,this.clearAllSnapshots())},this.updateProjection=()=>{this.nodes.forEach(gge),this.nodes.forEach(mge)},this.hasProjected=!1,this.isVisible=!0,this.animationProgress=0,this.sharedNodes=new Map,this.elementId=a,this.latestValues=s,this.root=l?l.root||l:this,this.path=l?[...l.path,l]:[],this.parent=l,this.depth=l?l.depth+1:0,a&&this.root.registerPotentialNode(a,this);for(let u=0;u<this.path.length;u++)this.path[u].shouldResetTransform=!0;this.root===this&&(this.nodes=new uge)}addEventListener(a,s){return this.eventHandlers.has(a)||this.eventHandlers.set(a,new Xv),this.eventHandlers.get(a).add(s)}notifyListeners(a,...s){const l=this.eventHandlers.get(a);l==null||l.notify(...s)}hasListeners(a){return this.eventHandlers.has(a)}registerPotentialNode(a,s){this.potentialNodes.set(a,s)}mount(a,s=!1){var l;if(this.instance)return;this.isSVG=a instanceof SVGElement&&a.tagName!=="svg",this.instance=a;const{layoutId:u,layout:f,visualElement:p}=this.options;if(p&&!p.current&&p.mount(a),this.root.nodes.add(this),(l=this.parent)===null||l===void 0||l.children.add(this),this.elementId&&this.root.potentialNodes.delete(this.elementId),s&&(f||u)&&(this.isLayoutDirty=!0),e){let m;const v=()=>this.root.updateBlockedByResize=!1;e(a,()=>{this.root.updateBlockedByResize=!0,m&&m(),m=aj(v,250),qv.hasAnimatedSinceResize&&(qv.hasAnimatedSinceResize=!1,this.nodes.forEach(sM))})}u&&this.root.registerSharedNode(u,this),this.options.animate!==!1&&p&&(u||f)&&this.addEventListener("didUpdate",({delta:m,hasLayoutChanged:v,hasRelativeTargetChanged:b,layout:x})=>{var E,k,_,T,A;if(this.isTreeAnimationBlocked()){this.target=void 0,this.relativeTarget=void 0;return}const R=(k=(E=this.options.transition)!==null&&E!==void 0?E:p.getDefaultTransition())!==null&&k!==void 0?k:xge,{onLayoutAnimationStart:I,onLayoutAnimationComplete:D}=p.getProps(),B=!this.targetLayout||!Ej(this.targetLayout,x)||b,z=!v&&b;if(((_=this.resumeFrom)===null||_===void 0?void 0:_.instance)||z||v&&(B||!this.currentAnimation)){this.resumeFrom&&(this.resumingFrom=this.resumeFrom,this.resumingFrom.resumingFrom=void 0),this.setAnimationOrigin(m,z);const j={...Mk(R,"layout"),onPlay:I,onComplete:D};p.shouldReduceMotion&&(j.delay=0,j.type=!1),this.startAnimation(j)}else!v&&this.animationProgress===0&&sM(this),this.isLead()&&((A=(T=this.options).onExitComplete)===null||A===void 0||A.call(T));this.targetLayout=x})}unmount(){var a,s;this.options.layoutId&&this.willUpdate(),this.root.nodes.remove(this),(a=this.getStack())===null||a===void 0||a.remove(this),(s=this.parent)===null||s===void 0||s.children.delete(this),this.instance=void 0,Zh.preRender(this.updateProjection)}blockUpdate(){this.updateManuallyBlocked=!0}unblockUpdate(){this.updateManuallyBlocked=!1}isUpdateBlocked(){return this.updateManuallyBlocked||this.updateBlockedByResize}isTreeAnimationBlocked(){var a;return this.isAnimationBlocked||((a=this.parent)===null||a===void 0?void 0:a.isTreeAnimationBlocked())||!1}startUpdate(){var a;this.isUpdateBlocked()||(this.isUpdating=!0,(a=this.nodes)===null||a===void 0||a.forEach(vge),this.animationId++)}willUpdate(a=!0){var s,l,u;if(this.root.isUpdateBlocked()){(l=(s=this.options).onExitComplete)===null||l===void 0||l.call(s);return}if(!this.root.isUpdating&&this.root.startUpdate(),this.isLayoutDirty)return;this.isLayoutDirty=!0;for(let v=0;v<this.path.length;v++){const b=this.path[v];b.shouldResetTransform=!0,b.updateScroll("snapshot")}const{layoutId:f,layout:p}=this.options;if(f===void 0&&!p)return;const m=(u=this.options.visualElement)===null||u===void 0?void 0:u.getProps().transformTemplate;this.prevTransformTemplateValue=m==null?void 0:m(this.latestValues,""),this.updateSnapshot(),a&&this.notifyListeners("willUpdate")}didUpdate(){if(this.isUpdateBlocked()){this.unblockUpdate(),this.clearAllSnapshots(),this.nodes.forEach(aM);return}!this.isUpdating||(this.isUpdating=!1,this.potentialNodes.size&&(this.potentialNodes.forEach(wge),this.potentialNodes.clear()),this.nodes.forEach(pge),this.nodes.forEach(dge),this.nodes.forEach(fge),this.clearAllSnapshots(),I6.update(),I6.preRender(),I6.render())}clearAllSnapshots(){this.nodes.forEach(hge),this.sharedNodes.forEach(yge)}scheduleUpdateProjection(){Qs.preRender(this.updateProjection,!1,!0)}scheduleCheckAfterUnmount(){Qs.postRender(()=>{this.isLayoutDirty?this.root.didUpdate():this.root.checkUpdateFailed()})}updateSnapshot(){this.snapshot||!this.instance||(this.snapshot=this.measure())}updateLayout(){var a;if(!this.instance||(this.updateScroll(),!(this.options.alwaysMeasureLayout&&this.isLead())&&!this.isLayoutDirty))return;if(this.resumeFrom&&!this.resumeFrom.instance)for(let l=0;l<this.path.length;l++)this.path[l].updateScroll();const s=this.layout;this.layout=this.measure(!1),this.layoutCorrected=hi(),this.isLayoutDirty=!1,this.projectionDelta=void 0,this.notifyListeners("measure",this.layout.layoutBox),(a=this.options.visualElement)===null||a===void 0||a.notify("LayoutMeasure",this.layout.layoutBox,s==null?void 0:s.layoutBox)}updateScroll(a="measure"){let s=Boolean(this.options.layoutScroll&&this.instance);this.scroll&&this.scroll.animationId===this.root.animationId&&this.scroll.phase===a&&(s=!1),s&&(this.scroll={animationId:this.root.animationId,phase:a,isRoot:r(this.instance),offset:n(this.instance)})}resetTransform(){var a;if(!i)return;const s=this.isLayoutDirty||this.shouldResetTransform,l=this.projectionDelta&&!kj(this.projectionDelta),u=(a=this.options.visualElement)===null||a===void 0?void 0:a.getProps().transformTemplate,f=u==null?void 0:u(this.latestValues,""),p=f!==this.prevTransformTemplateValue;s&&(l||vh(this.latestValues)||p)&&(i(this.instance,f),this.shouldResetTransform=!1,this.scheduleRender())}measure(a=!0){var s;const l=this.measurePageBox();let u=this.removeElementScroll(l);a&&(u=this.removeTransform(u)),Cge(u);const f=(s=this.options.visualElement)===null||s===void 0?void 0:s.readValue("position"),p=f==="fixed"||f==="sticky"?f:"static";return{animationId:this.root.animationId,measuredBox:l,layoutBox:u,latestValues:{},source:this.id,position:p}}measurePageBox(){const{visualElement:a}=this.options;if(!a)return hi();const s=a.measureViewportBox(),{scroll:l}=this.root;return l&&(bd(s.x,l.offset.x),bd(s.y,l.offset.y)),s}removeElementScroll(a){const s=hi();$s(s,a);for(let l=0;l<this.path.length;l++){const u=this.path[l],{scroll:f,options:p}=u;if(u!==this.root&&f&&p.layoutScroll){if(f.isRoot){$s(s,a);const{scroll:m}=this.root;m&&(bd(s.x,-m.offset.x),bd(s.y,-m.offset.y))}bd(s.x,f.offset.x),bd(s.y,f.offset.y)}}return s}applyTransform(a,s=!1){const l=hi();$s(l,a);for(let u=0;u<this.path.length;u++){const f=this.path[u];!s&&f.options.layoutScroll&&f.scroll&&f!==f.root&&eg(l,{x:-f.scroll.offset.x,y:-f.scroll.offset.y}),vh(f.latestValues)&&eg(l,f.latestValues)}return vh(this.latestValues)&&eg(l,this.latestValues),l}removeTransform(a){var s;const l=hi();$s(l,a);for(let u=0;u<this.path.length;u++){const f=this.path[u];if(!f.instance||!vh(f.latestValues))continue;v9(f.latestValues)&&f.updateSnapshot();const p=hi(),m=f.measurePageBox();$s(p,m),eM(l,f.latestValues,(s=f.snapshot)===null||s===void 0?void 0:s.layoutBox,p)}return vh(this.latestValues)&&eM(l,this.latestValues),l}setTargetDelta(a){this.targetDelta=a,this.isProjectionDirty=!0,this.root.scheduleUpdateProjection()}setOptions(a){this.options={...this.options,...a,crossfade:a.crossfade!==void 0?a.crossfade:!0}}clearMeasurements(){this.scroll=void 0,this.layout=void 0,this.snapshot=void 0,this.prevTransformTemplateValue=void 0,this.targetDelta=void 0,this.target=void 0,this.isLayoutDirty=!1}resolveTargetDelta(){var a;if(this.isProjectionDirty||(this.isProjectionDirty=this.getLead().isProjectionDirty||Boolean(this.parent&&this.parent.isProjectionDirty)),!this.isProjectionDirty)return;const{layout:s,layoutId:l}=this.options;if(!(!this.layout||!(s||l))){if(!this.targetDelta&&!this.relativeTarget){const u=this.getClosestProjectingParent();u&&u.layout?(this.relativeParent=u,this.relativeTarget=hi(),this.relativeTargetOrigin=hi(),Qv(this.relativeTargetOrigin,this.layout.layoutBox,u.layout.layoutBox),$s(this.relativeTarget,this.relativeTargetOrigin)):this.relativeParent=this.relativeTarget=void 0}if(!(!this.relativeTarget&&!this.targetDelta)&&(this.target||(this.target=hi(),this.targetWithTransforms=hi()),this.relativeTarget&&this.relativeTargetOrigin&&((a=this.relativeParent)===null||a===void 0?void 0:a.target)?l0e(this.target,this.relativeTarget,this.relativeParent.target):this.targetDelta?(Boolean(this.resumingFrom)?this.target=this.applyTransform(this.layout.layoutBox):$s(this.target,this.layout.layoutBox),pj(this.target,this.targetDelta)):$s(this.target,this.layout.layoutBox),this.attemptToResolveRelativeTarget)){this.attemptToResolveRelativeTarget=!1;const u=this.getClosestProjectingParent();u&&Boolean(u.resumingFrom)===Boolean(this.resumingFrom)&&!u.options.layoutScroll&&u.target?(this.relativeParent=u,this.relativeTarget=hi(),this.relativeTargetOrigin=hi(),Qv(this.relativeTargetOrigin,this.target,u.target),$s(this.relativeTarget,this.relativeTargetOrigin)):this.relativeParent=this.relativeTarget=void 0}}}getClosestProjectingParent(){if(!(!this.parent||v9(this.parent.latestValues)||hj(this.parent.latestValues)))return(this.parent.relativeTarget||this.parent.targetDelta)&&this.parent.layout?this.parent:this.parent.getClosestProjectingParent()}calcProjection(){var a;if(!this.isProjectionDirty)return;this.isProjectionDirty=!1;const{layout:s,layoutId:l}=this.options;if(this.isTreeAnimating=Boolean(((a=this.parent)===null||a===void 0?void 0:a.isTreeAnimating)||this.currentAnimation||this.pendingAnimation),this.isTreeAnimating||(this.targetDelta=this.relativeTarget=void 0),!this.layout||!(s||l))return;const u=this.getLead();$s(this.layoutCorrected,this.layout.layoutBox),v0e(this.layoutCorrected,this.treeScale,this.path,Boolean(this.resumingFrom)||this!==u);const{target:f}=u;if(!f)return;this.projectionDelta||(this.projectionDelta=Jv(),this.projectionDeltaWithTransform=Jv());const p=this.treeScale.x,m=this.treeScale.y,v=this.projectionTransform;Zv(this.projectionDelta,this.layoutCorrected,f,this.latestValues),this.projectionTransform=rM(this.projectionDelta,this.treeScale),(this.projectionTransform!==v||this.treeScale.x!==p||this.treeScale.y!==m)&&(this.hasProjected=!0,this.scheduleRender(),this.notifyListeners("projectionUpdate",f))}hide(){this.isVisible=!1}show(){this.isVisible=!0}scheduleRender(a=!0){var s,l,u;(l=(s=this.options).scheduleRender)===null||l===void 0||l.call(s),a&&((u=this.getStack())===null||u===void 0||u.scheduleRender()),this.resumingFrom&&!this.resumingFrom.instance&&(this.resumingFrom=void 0)}setAnimationOrigin(a,s=!1){var l,u;const f=this.snapshot,p=(f==null?void 0:f.latestValues)||{},m={...this.latestValues},v=Jv();this.relativeTarget=this.relativeTargetOrigin=void 0,this.attemptToResolveRelativeTarget=!s;const b=hi(),x=(f==null?void 0:f.source)!==((l=this.layout)===null||l===void 0?void 0:l.source),E=(((u=this.getStack())===null||u===void 0?void 0:u.members.length)||0)<=1,k=Boolean(x&&!E&&this.options.crossfade===!0&&!this.path.some(Sge));this.animationProgress=0,this.mixTargetDelta=_=>{var T;const A=_/1e3;lM(v.x,a.x,A),lM(v.y,a.y,A),this.setTargetDelta(v),this.relativeTarget&&this.relativeTargetOrigin&&this.layout&&((T=this.relativeParent)===null||T===void 0?void 0:T.layout)&&(Qv(b,this.layout.layoutBox,this.relativeParent.layout.layoutBox),bge(this.relativeTarget,this.relativeTargetOrigin,b,A)),x&&(this.animationValues=m,ege(m,p,this.latestValues,A,k,E)),this.root.scheduleUpdateProjection(),this.scheduleRender(),this.animationProgress=A},this.mixTargetDelta(0)}startAnimation(a){var s,l;this.notifyListeners("animationStart"),(s=this.currentAnimation)===null||s===void 0||s.stop(),this.resumingFrom&&((l=this.resumingFrom.currentAnimation)===null||l===void 0||l.stop()),this.pendingAnimation&&(Zh.update(this.pendingAnimation),this.pendingAnimation=void 0),this.pendingAnimation=Qs.update(()=>{qv.hasAnimatedSinceResize=!0,this.currentAnimation=Q0e(0,oM,{...a,onUpdate:u=>{var f;this.mixTargetDelta(u),(f=a.onUpdate)===null||f===void 0||f.call(a,u)},onComplete:()=>{var u;(u=a.onComplete)===null||u===void 0||u.call(a),this.completeAnimation()}}),this.resumingFrom&&(this.resumingFrom.currentAnimation=this.currentAnimation),this.pendingAnimation=void 0})}completeAnimation(){var a;this.resumingFrom&&(this.resumingFrom.currentAnimation=void 0,this.resumingFrom.preserveOpacity=void 0),(a=this.getStack())===null||a===void 0||a.exitAnimationComplete(),this.resumingFrom=this.currentAnimation=this.animationValues=void 0,this.notifyListeners("animationComplete")}finishAnimation(){var a;this.currentAnimation&&((a=this.mixTargetDelta)===null||a===void 0||a.call(this,oM),this.currentAnimation.stop()),this.completeAnimation()}applyTransformsToTarget(){const a=this.getLead();let{targetWithTransforms:s,target:l,layout:u,latestValues:f}=a;if(!(!s||!l||!u)){if(this!==a&&this.layout&&u&&Tj(this.options.animationType,this.layout.layoutBox,u.layoutBox)){l=this.target||hi();const p=Ia(this.layout.layoutBox.x);l.x.min=a.target.x.min,l.x.max=l.x.min+p;const m=Ia(this.layout.layoutBox.y);l.y.min=a.target.y.min,l.y.max=l.y.min+m}$s(s,l),eg(s,f),Zv(this.projectionDeltaWithTransform,this.layoutCorrected,s,f)}}registerSharedNode(a,s){var l,u,f;this.sharedNodes.has(a)||this.sharedNodes.set(a,new sge),this.sharedNodes.get(a).add(s),s.promote({transition:(l=s.options.initialPromotionConfig)===null||l===void 0?void 0:l.transition,preserveFollowOpacity:(f=(u=s.options.initialPromotionConfig)===null||u===void 0?void 0:u.shouldPreserveFollowOpacity)===null||f===void 0?void 0:f.call(u,s)})}isLead(){const a=this.getStack();return a?a.lead===this:!0}getLead(){var a;const{layoutId:s}=this.options;return s?((a=this.getStack())===null||a===void 0?void 0:a.lead)||this:this}getPrevLead(){var a;const{layoutId:s}=this.options;return s?(a=this.getStack())===null||a===void 0?void 0:a.prevLead:void 0}getStack(){const{layoutId:a}=this.options;if(a)return this.root.sharedNodes.get(a)}promote({needsReset:a,transition:s,preserveFollowOpacity:l}={}){const u=this.getStack();u&&u.promote(this,l),a&&(this.projectionDelta=void 0,this.needsReset=!0),s&&this.setOptions({transition:s})}relegate(){const a=this.getStack();return a?a.relegate(this):!1}resetRotation(){const{visualElement:a}=this.options;if(!a)return;let s=!1;const{latestValues:l}=a;if((l.rotate||l.rotateX||l.rotateY||l.rotateZ)&&(s=!0),!s)return;const u={};for(let f=0;f<iM.length;f++){const p="rotate"+iM[f];l[p]&&(u[p]=l[p],a.setStaticValue(p,0))}a==null||a.render();for(const f in u)a.setStaticValue(f,u[f]);a.scheduleRender()}getProjectionStyles(a={}){var s,l,u;const f={};if(!this.instance||this.isSVG)return f;if(this.isVisible)f.visibility="";else return{visibility:"hidden"};const p=(s=this.options.visualElement)===null||s===void 0?void 0:s.getProps().transformTemplate;if(this.needsReset)return this.needsReset=!1,f.opacity="",f.pointerEvents=d4(a.pointerEvents)||"",f.transform=p?p(this.latestValues,""):"none",f;const m=this.getLead();if(!this.projectionDelta||!this.layout||!m.target){const E={};return this.options.layoutId&&(E.opacity=this.latestValues.opacity!==void 0?this.latestValues.opacity:1,E.pointerEvents=d4(a.pointerEvents)||""),this.hasProjected&&!vh(this.latestValues)&&(E.transform=p?p({},""):"none",this.hasProjected=!1),E}const v=m.animationValues||m.latestValues;this.applyTransformsToTarget(),f.transform=rM(this.projectionDeltaWithTransform,this.treeScale,v),p&&(f.transform=p(v,f.transform));const{x:b,y:x}=this.projectionDelta;f.transformOrigin=`${b.origin*100}% ${x.origin*100}% 0`,m.animationValues?f.opacity=m===this?(u=(l=v.opacity)!==null&&l!==void 0?l:this.latestValues.opacity)!==null&&u!==void 0?u:1:this.preserveOpacity?this.latestValues.opacity:v.opacityExit:f.opacity=m===this?v.opacity!==void 0?v.opacity:"":v.opacityExit!==void 0?v.opacityExit:0;for(const E in f5){if(v[E]===void 0)continue;const{correct:k,applyTo:_}=f5[E],T=k(v[E],m);if(_){const A=_.length;for(let R=0;R<A;R++)f[_[R]]=T}else f[E]=T}return this.options.layoutId&&(f.pointerEvents=m===this?d4(a.pointerEvents)||"":"none"),f}clearSnapshot(){this.resumeFrom=this.snapshot=void 0}resetTree(){this.root.nodes.forEach(a=>{var s;return(s=a.currentAnimation)===null||s===void 0?void 0:s.stop()}),this.root.nodes.forEach(aM),this.root.sharedNodes.clear()}}}function dge(e){e.updateLayout()}function fge(e){var t,n,r;const i=((t=e.resumeFrom)===null||t===void 0?void 0:t.snapshot)||e.snapshot;if(e.isLead()&&e.layout&&i&&e.hasListeners("didUpdate")){const{layoutBox:o,measuredBox:a}=e.layout,{animationType:s}=e.options,l=i.source!==e.layout.source;s==="size"?$l(v=>{const b=l?i.measuredBox[v]:i.layoutBox[v],x=Ia(b);b.min=o[v].min,b.max=b.min+x}):Tj(s,i.layoutBox,o)&&$l(v=>{const b=l?i.measuredBox[v]:i.layoutBox[v],x=Ia(o[v]);b.max=b.min+x});const u=Jv();Zv(u,o,i.layoutBox);const f=Jv();l?Zv(f,e.applyTransform(a,!0),i.measuredBox):Zv(f,o,i.layoutBox);const p=!kj(u);let m=!1;if(!e.resumeFrom){const v=e.getClosestProjectingParent();if(v&&!v.resumeFrom){const{snapshot:b,layout:x}=v;if(b&&x){const E=hi();Qv(E,i.layoutBox,b.layoutBox);const k=hi();Qv(k,o,x.layoutBox),Ej(E,k)||(m=!0)}}}e.notifyListeners("didUpdate",{layout:o,snapshot:i,delta:f,layoutDelta:u,hasLayoutChanged:p,hasRelativeTargetChanged:m})}else e.isLead()&&((r=(n=e.options).onExitComplete)===null||r===void 0||r.call(n));e.options.transition=void 0}function hge(e){e.clearSnapshot()}function aM(e){e.clearMeasurements()}function pge(e){const{visualElement:t}=e.options;t!=null&&t.getProps().onBeforeLayoutMeasure&&t.notify("BeforeLayoutMeasure"),e.resetTransform()}function sM(e){e.finishAnimation(),e.targetDelta=e.relativeTarget=e.target=void 0}function gge(e){e.resolveTargetDelta()}function mge(e){e.calcProjection()}function vge(e){e.resetRotation()}function yge(e){e.removeLeadSnapshot()}function lM(e,t,n){e.translate=$r(t.translate,0,n),e.scale=$r(t.scale,1,n),e.origin=t.origin,e.originPoint=t.originPoint}function uM(e,t,n,r){e.min=$r(t.min,n.min,r),e.max=$r(t.max,n.max,r)}function bge(e,t,n,r){uM(e.x,t.x,n.x,r),uM(e.y,t.y,n.y,r)}function Sge(e){return e.animationValues&&e.animationValues.opacityExit!==void 0}const xge={duration:.45,ease:[.4,0,.1,1]};function wge(e,t){let n=e.root;for(let o=e.path.length-1;o>=0;o--)if(Boolean(e.path[o].instance)){n=e.path[o];break}const i=(n&&n!==e.root?n.instance:document).querySelector(`[data-projection-id="${t}"]`);i&&e.mount(i,!0)}function cM(e){e.min=Math.round(e.min),e.max=Math.round(e.max)}function Cge(e){cM(e.x),cM(e.y)}function Tj(e,t,n){return e==="position"||e==="preserve-aspect"&&!age(nM(t),nM(n),.2)}const _ge=Pj({attachResizeListener:(e,t)=>qS(e,"resize",t),measureScroll:()=>({x:document.documentElement.scrollLeft||document.body.scrollLeft,y:document.documentElement.scrollTop||document.body.scrollTop}),checkIsScrollRoot:()=>!0}),$6={current:void 0},kge=Pj({measureScroll:e=>({x:e.scrollLeft,y:e.scrollTop}),defaultParent:()=>{if(!$6.current){const e=new _ge(0,{});e.mount(window),e.setOptions({layoutScroll:!0}),$6.current=e}return 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r=["hans","hant","latn","cyrl","cans","mong","arab"],i=n.split("-");return this.options.lowerCaseLng?i=i.map(function(o){return o.toLowerCase()}):i.length===2?(i[0]=i[0].toLowerCase(),i[1]=i[1].toUpperCase(),r.indexOf(i[1].toLowerCase())>-1&&(i[1]=uC(i[1].toLowerCase()))):i.length===3&&(i[0]=i[0].toLowerCase(),i[1].length===2&&(i[1]=i[1].toUpperCase()),i[0]!=="sgn"&&i[2].length===2&&(i[2]=i[2].toUpperCase()),r.indexOf(i[1].toLowerCase())>-1&&(i[1]=uC(i[1].toLowerCase())),r.indexOf(i[2].toLowerCase())>-1&&(i[2]=uC(i[2].toLowerCase()))),i.join("-")}return this.options.cleanCode||this.options.lowerCaseLng?n.toLowerCase():n}},{key:"isSupportedCode",value:function(n){return(this.options.load==="languageOnly"||this.options.nonExplicitSupportedLngs)&&(n=this.getLanguagePartFromCode(n)),!this.supportedLngs||!this.supportedLngs.length||this.supportedLngs.indexOf(n)>-1}},{key:"getBestMatchFromCodes",value:function(n){var r=this;if(!n)return null;var i;return n.forEach(function(o){if(!i){var a=r.formatLanguageCode(o);(!r.options.supportedLngs||r.isSupportedCode(a))&&(i=a)}}),!i&&this.options.supportedLngs&&n.forEach(function(o){if(!i){var a=r.getLanguagePartFromCode(o);if(r.isSupportedCode(a))return i=a;i=r.options.supportedLngs.find(function(s){if(s.indexOf(a)===0)return s})}}),i||(i=this.getFallbackCodes(this.options.fallbackLng)[0]),i}},{key:"getFallbackCodes",value:function(n,r){if(!n)return[];if(typeof n=="function"&&(n=n(r)),typeof n=="string"&&(n=[n]),Object.prototype.toString.apply(n)==="[object Array]")return n;if(!r)return n.default||[];var i=n[r];return i||(i=n[this.getScriptPartFromCode(r)]),i||(i=n[this.formatLanguageCode(r)]),i||(i=n[this.getLanguagePartFromCode(r)]),i||(i=n.default),i||[]}},{key:"toResolveHierarchy",value:function(n,r){var i=this,o=this.getFallbackCodes(r||this.options.fallbackLng||[],n),a=[],s=function(u){!u||(i.isSupportedCode(u)?a.push(u):i.logger.warn("rejecting language code not found in supportedLngs: ".concat(u)))};return typeof 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r="".concat(this.prefix).concat(this.unescapePrefix,"(.+?)").concat(this.unescapeSuffix).concat(this.suffix);this.regexpUnescape=new RegExp(r,"g");var i="".concat(this.nestingPrefix,"(.+?)").concat(this.nestingSuffix);this.nestingRegexp=new RegExp(i,"g")}},{key:"interpolate",value:function(n,r,i,o){var a=this,s,l,u,f=this.options&&this.options.interpolation&&this.options.interpolation.defaultVariables||{};function p(E){return E.replace(/\$/g,"$$$$")}var m=function(k){if(k.indexOf(a.formatSeparator)<0){var _=LR(r,f,k);return a.alwaysFormat?a.format(_,void 0,i,Bs(Bs(Bs({},o),r),{},{interpolationkey:k})):_}var T=k.split(a.formatSeparator),A=T.shift().trim(),R=T.join(a.formatSeparator).trim();return a.format(LR(r,f,A),R,i,Bs(Bs(Bs({},o),r),{},{interpolationkey:A}))};this.resetRegExp();var v=o&&o.missingInterpolationHandler||this.options.missingInterpolationHandler,b=o&&o.interpolation&&o.interpolation.skipOnVariables!==void 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0?arguments[3]:0,u=arguments.length>4&&arguments[4]!==void 0?arguments[4]:this.retryTimeout,f=arguments.length>5?arguments[5]:void 0;if(!i.length)return f(null,{});if(this.readingCalls>=this.maxParallelReads){this.waitingReads.push({lng:i,ns:o,fcName:a,tried:l,wait:u,callback:f});return}this.readingCalls++;var p=function(x,E){if(s.readingCalls--,s.waitingReads.length>0){var k=s.waitingReads.shift();s.read(k.lng,k.ns,k.fcName,k.tried,k.wait,k.callback)}if(x&&E&&l<s.maxRetries){setTimeout(function(){s.read.call(s,i,o,a,l+1,u*2,f)},u);return}f(x,E)},m=this.backend[a].bind(this.backend);if(m.length===2){try{var v=m(i,o);v&&typeof v.then=="function"?v.then(function(b){return p(null,b)}).catch(p):p(null,v)}catch(b){p(b)}return}return m(i,o,p)}},{key:"prepareLoading",value:function(i,o){var a=this,s=arguments.length>2&&arguments[2]!==void 0?arguments[2]:{},l=arguments.length>3?arguments[3]:void 0;if(!this.backend)return this.logger.warn("No backend was added via i18next.use. 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zR(){return{debug:!1,initImmediate:!0,ns:["translation"],defaultNS:["translation"],fallbackLng:["dev"],fallbackNS:!1,supportedLngs:!1,nonExplicitSupportedLngs:!1,load:"all",preload:!1,simplifyPluralSuffix:!0,keySeparator:".",nsSeparator:":",pluralSeparator:"_",contextSeparator:"_",partialBundledLanguages:!1,saveMissing:!1,updateMissing:!1,saveMissingTo:"fallback",saveMissingPlurals:!0,missingKeyHandler:!1,missingInterpolationHandler:!1,postProcess:!1,postProcessPassResolved:!1,returnNull:!0,returnEmptyString:!0,returnObjects:!1,joinArrays:!1,returnedObjectHandler:!1,parseMissingKeyHandler:!1,appendNamespaceToMissingKey:!1,appendNamespaceToCIMode:!1,overloadTranslationOptionHandler:function(t){var n={};if(Qs(t[1])==="object"&&(n=t[1]),typeof t[1]=="string"&&(n.defaultValue=t[1]),typeof t[2]=="string"&&(n.tDescription=t[2]),Qs(t[2])==="object"||Qs(t[3])==="object"){var r=t[3]||t[2];Object.keys(r).forEach(function(i){n[i]=r[i]})}return 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n=arguments[t]!=null?arguments[t]:{};t%2?HR(Object(n),!0).forEach(function(r){pf(e,r,n[r])}):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(n)):HR(Object(n)).forEach(function(r){Object.defineProperty(e,r,Object.getOwnPropertyDescriptor(n,r))})}return e}function exe(e){var t=txe();return function(){var r=cu(e),i;if(t){var o=cu(this).constructor;i=Reflect.construct(r,arguments,o)}else i=r.apply(this,arguments);return Ay(this,i)}}function txe(){if(typeof Reflect>"u"||!Reflect.construct||Reflect.construct.sham)return!1;if(typeof Proxy=="function")return!0;try{return Boolean.prototype.valueOf.call(Reflect.construct(Boolean,[],function(){})),!0}catch(e){return!1}}function vb(){}function nxe(e){var t=Object.getOwnPropertyNames(Object.getPrototypeOf(e));t.forEach(function(n){typeof e[n]=="function"&&(e[n]=e[n].bind(e))})}var $5=function(e){vx(n,e);var t=exe(n);function n(){var r,i=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{},o=arguments.length>1?arguments[1]:void 0;if(vu(this,n),r=t.call(this),yx&&tf.call(Ud(r)),r.options=jR(i),r.services={},r.logger=Kl,r.modules={external:[]},nxe(Ud(r)),o&&!r.isInitialized&&!i.isClone){if(!r.options.initImmediate)return r.init(i,o),Ay(r,Ud(r));setTimeout(function(){r.init(i,o)},0)}return r}return yu(n,[{key:"init",value:function(){var i=this,o=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{},a=arguments.length>1?arguments[1]:void 0;typeof o=="function"&&(a=o,o={}),!o.defaultNS&&o.defaultNS!==!1&&o.ns&&(typeof o.ns=="string"?o.defaultNS=o.ns:o.ns.indexOf("translation")<0&&(o.defaultNS=o.ns[0]));var s=zR();this.options=Dl(Dl(Dl({},s),this.options),jR(o)),this.options.compatibilityAPI!=="v1"&&(this.options.interpolation=Dl(Dl({},s.interpolation),this.options.interpolation)),o.keySeparator!==void 0&&(this.options.userDefinedKeySeparator=o.keySeparator),o.nsSeparator!==void 0&&(this.options.userDefinedNsSeparator=o.nsSeparator);function l(k){return k?typeof k=="function"?new k:k:null}if(!this.options.isClone){this.modules.logger?Kl.init(l(this.modules.logger),this.options):Kl.init(null,this.options);var u;this.modules.formatter?u=this.modules.formatter:typeof Intl<"u"&&(u=KSe);var f=new IR(this.options);this.store=new FSe(this.options.resources,this.options);var p=this.services;p.logger=Kl,p.resourceStore=this.store,p.languageUtils=f,p.pluralResolver=new GSe(f,{prepend:this.options.pluralSeparator,compatibilityJSON:this.options.compatibilityJSON,simplifyPluralSuffix:this.options.simplifyPluralSuffix}),u&&(!this.options.interpolation.format||this.options.interpolation.format===s.interpolation.format)&&(p.formatter=l(u),p.formatter.init(p,this.options),this.options.interpolation.format=p.formatter.format.bind(p.formatter)),p.interpolator=new qSe(this.options),p.utils={hasLoadedNamespace:this.hasLoadedNamespace.bind(this)},p.backendConnector=new JSe(l(this.modules.backend),p.resourceStore,p,this.options),p.backendConnector.on("*",function(k){for(var _=arguments.length,T=new Array(_>1?_-1:0),A=1;A<_;A++)T[A-1]=arguments[A];i.emit.apply(i,[k].concat(T))}),this.modules.languageDetector&&(p.languageDetector=l(this.modules.languageDetector),p.languageDetector.init&&p.languageDetector.init(p,this.options.detection,this.options)),this.modules.i18nFormat&&(p.i18nFormat=l(this.modules.i18nFormat),p.i18nFormat.init&&p.i18nFormat.init(this)),this.translator=new RR(this.services,this.options),this.translator.on("*",function(k){for(var _=arguments.length,T=new Array(_>1?_-1:0),A=1;A<_;A++)T[A-1]=arguments[A];i.emit.apply(i,[k].concat(T))}),this.modules.external.forEach(function(k){k.init&&k.init(i)})}if(this.format=this.options.interpolation.format,a||(a=vb),this.options.fallbackLng&&!this.services.languageDetector&&!this.options.lng){var m=this.services.languageUtils.getFallbackCodes(this.options.fallbackLng);m.length>0&&m[0]!=="dev"&&(this.options.lng=m[0])}!this.services.languageDetector&&!this.options.lng&&this.logger.warn("init: no languageDetector is used and no lng is defined");var v=["getResource","hasResourceBundle","getResourceBundle","getDataByLanguage"];v.forEach(function(k){i[k]=function(){var _;return(_=i.store)[k].apply(_,arguments)}});var b=["addResource","addResources","addResourceBundle","removeResourceBundle"];b.forEach(function(k){i[k]=function(){var _;return(_=i.store)[k].apply(_,arguments),i}});var x=tv(),E=function(){var _=function(A,R){i.isInitialized&&!i.initializedStoreOnce&&i.logger.warn("init: i18next is already initialized. 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Please check the object you are passing to i18next.use()");if(!i.type)throw new Error("You are passing a wrong module! Please check the object you are passing to i18next.use()");return i.type==="backend"&&(this.modules.backend=i),(i.type==="logger"||i.log&&i.warn&&i.error)&&(this.modules.logger=i),i.type==="languageDetector"&&(this.modules.languageDetector=i),i.type==="i18nFormat"&&(this.modules.i18nFormat=i),i.type==="postProcessor"&&HU.addPostProcessor(i),i.type==="formatter"&&(this.modules.formatter=i),i.type==="3rdParty"&&this.modules.external.push(i),this}},{key:"setResolvedLanguage",value:function(i){if(!(!i||!this.languages)&&!(["cimode","dev"].indexOf(i)>-1))for(var o=0;o<this.languages.length;o++){var a=this.languages[o];if(!(["cimode","dev"].indexOf(a)>-1)&&this.store.hasLanguageSomeTranslations(a)){this.resolvedLanguage=a;break}}}},{key:"changeLanguage",value:function(i,o){var a=this;this.isLanguageChangingTo=i;var s=tv();this.emit("languageChanging",i);var l=function(m){a.language=m,a.languages=a.services.languageUtils.toResolveHierarchy(m),a.resolvedLanguage=void 0,a.setResolvedLanguage(m)},u=function(m,v){v?(l(v),a.translator.changeLanguage(v),a.isLanguageChangingTo=void 0,a.emit("languageChanged",v),a.logger.log("languageChanged",v)):a.isLanguageChangingTo=void 0,s.resolve(function(){return a.t.apply(a,arguments)}),o&&o(m,function(){return a.t.apply(a,arguments)})},f=function(m){!i&&!m&&a.services.languageDetector&&(m=[]);var v=typeof m=="string"?m:a.services.languageUtils.getBestMatchFromCodes(m);v&&(a.language||l(v),a.translator.language||a.translator.changeLanguage(v),a.services.languageDetector&&a.services.languageDetector.cacheUserLanguage&&a.services.languageDetector.cacheUserLanguage(v)),a.loadResources(v,function(b){u(b,v)})};return!i&&this.services.languageDetector&&!this.services.languageDetector.async?f(this.services.languageDetector.detect()):!i&&this.services.languageDetector&&this.services.languageDetector.async?this.services.languageDetector.detect.length===0?this.services.languageDetector.detect().then(f):this.services.languageDetector.detect(f):f(i),s}},{key:"getFixedT",value:function(i,o,a){var s=this,l=function u(f,p){var m;if(Qs(p)!=="object"){for(var v=arguments.length,b=new Array(v>2?v-2:0),x=2;x<v;x++)b[x-2]=arguments[x];m=s.options.overloadTranslationOptionHandler([f,p].concat(b))}else m=Dl({},p);m.lng=m.lng||u.lng,m.lngs=m.lngs||u.lngs,m.ns=m.ns||u.ns,m.keyPrefix=m.keyPrefix||a||u.keyPrefix;var E=s.options.keySeparator||".",k=m.keyPrefix?"".concat(m.keyPrefix).concat(E).concat(f):f;return s.t(k,m)};return typeof i=="string"?l.lng=i:l.lngs=i,l.ns=o,l.keyPrefix=a,l}},{key:"t",value:function(){var i;return this.translator&&(i=this.translator).translate.apply(i,arguments)}},{key:"exists",value:function(){var i;return this.translator&&(i=this.translator).exists.apply(i,arguments)}},{key:"setDefaultNamespace",value:function(i){this.options.defaultNS=i}},{key:"hasLoadedNamespace",value:function(i){var o=this,a=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{};if(!this.isInitialized)return this.logger.warn("hasLoadedNamespace: i18next was not initialized",this.languages),!1;if(!this.languages||!this.languages.length)return this.logger.warn("hasLoadedNamespace: i18n.languages were undefined or empty",this.languages),!1;var s=this.resolvedLanguage||this.languages[0],l=this.options?this.options.fallbackLng:!1,u=this.languages[this.languages.length-1];if(s.toLowerCase()==="cimode")return!0;var f=function(v,b){var x=o.services.backendConnector.state["".concat(v,"|").concat(b)];return x===-1||x===2};if(a.precheck){var p=a.precheck(this,f);if(p!==void 0)return p}return!!(this.hasResourceBundle(s,i)||!this.services.backendConnector.backend||this.options.resources&&!this.options.partialBundledLanguages||f(s,i)&&(!l||f(u,i)))}},{key:"loadNamespaces",value:function(i,o){var a=this,s=tv();return this.options.ns?(typeof i=="string"&&(i=[i]),i.forEach(function(l){a.options.ns.indexOf(l)<0&&a.options.ns.push(l)}),this.loadResources(function(l){s.resolve(),o&&o(l)}),s):(o&&o(),Promise.resolve())}},{key:"loadLanguages",value:function(i,o){var a=tv();typeof i=="string"&&(i=[i]);var s=this.options.preload||[],l=i.filter(function(u){return s.indexOf(u)<0});return l.length?(this.options.preload=s.concat(l),this.loadResources(function(u){a.resolve(),o&&o(u)}),a):(o&&o(),Promise.resolve())}},{key:"dir",value:function(i){if(i||(i=this.resolvedLanguage||(this.languages&&this.languages.length>0?this.languages[0]:this.language)),!i)return"rtl";var o=["ar","shu","sqr","ssh","xaa","yhd","yud","aao","abh","abv","acm","acq","acw","acx","acy","adf","ads","aeb","aec","afb","ajp","apc","apd","arb","arq","ars","ary","arz","auz","avl","ayh","ayl","ayn","ayp","bbz","pga","he","iw","ps","pbt","pbu","pst","prp","prd","ug","ur","ydd","yds","yih","ji","yi","hbo","men","xmn","fa","jpr","peo","pes","prs","dv","sam","ckb"],a=this.services&&this.services.languageUtils||new IR(zR());return o.indexOf(a.getLanguagePartFromCode(i))>-1||i.toLowerCase().indexOf("-arab")>1?"rtl":"ltr"}},{key:"cloneInstance",value:function(){var i=this,o=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{},a=arguments.length>1&&arguments[1]!==void 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i=location.port;i||(i=r?"443":"80"),this.xd=typeof location<"u"&&t.hostname!==location.hostname||i!==t.port,this.xs=t.secure!==r}const n=t&&t.forceBase64;this.supportsBinary=g6e&&!n}get name(){return"polling"}doOpen(){this.poll()}pause(t){this.readyState="pausing";const n=()=>{this.readyState="paused",t()};if(this.polling||!this.writable){let r=0;this.polling&&(r++,this.once("pollComplete",function(){--r||n()})),this.writable||(r++,this.once("drain",function(){--r||n()}))}else n()}poll(){this.polling=!0,this.doPoll(),this.emitReserved("poll")}onData(t){const n=r=>{if(this.readyState==="opening"&&r.type==="open"&&this.onOpen(),r.type==="close")return this.onClose({description:"transport closed by the server"}),!1;this.onPacket(r)};r6e(t,this.socket.binaryType).forEach(n),this.readyState!=="closed"&&(this.polling=!1,this.emitReserved("pollComplete"),this.readyState==="open"&&this.poll())}doClose(){const t=()=>{this.write([{type:"close"}])};this.readyState==="open"?t():this.once("open",t)}write(t){this.writable=!1,n6e(t,n=>{this.doWrite(n,()=>{this.writable=!0,this.emitReserved("drain")})})}uri(){let t=this.query||{};const n=this.opts.secure?"https":"http";let r="";this.opts.timestampRequests!==!1&&(t[this.opts.timestampParam]=AW()),!this.supportsBinary&&!t.sid&&(t.b64=1),this.opts.port&&(n==="https"&&Number(this.opts.port)!==443||n==="http"&&Number(this.opts.port)!==80)&&(r=":"+this.opts.port);const i=OW(t),o=this.opts.hostname.indexOf(":")!==-1;return n+"://"+(o?"["+this.opts.hostname+"]":this.opts.hostname)+r+this.opts.path+(i.length?"?"+i:"")}request(t={}){return Object.assign(t,{xd:this.xd,xs:this.xs},this.opts),new ou(this.uri(),t)}doWrite(t,n){const r=this.request({method:"POST",data:t});r.on("success",n),r.on("error",(i,o)=>{this.onError("xhr post error",i,o)})}doPoll(){const t=this.request();t.on("data",this.onData.bind(this)),t.on("error",(n,r)=>{this.onError("xhr poll error",n,r)}),this.pollXhr=t}}class ou extends oi{constructor(t,n){super(),xx(this,n),this.opts=n,this.method=n.method||"GET",this.uri=t,this.async=n.async!==!1,this.data=n.data!==void 0?n.data:null,this.create()}create(){const t=PW(this.opts,"agent","pfx","key","passphrase","cert","ca","ciphers","rejectUnauthorized","autoUnref");t.xdomain=!!this.opts.xd,t.xscheme=!!this.opts.xs;const n=this.xhr=new RW(t);try{n.open(this.method,this.uri,this.async);try{if(this.opts.extraHeaders){n.setDisableHeaderCheck&&n.setDisableHeaderCheck(!0);for(let r in this.opts.extraHeaders)this.opts.extraHeaders.hasOwnProperty(r)&&n.setRequestHeader(r,this.opts.extraHeaders[r])}}catch(r){}if(this.method==="POST")try{n.setRequestHeader("Content-type","text/plain;charset=UTF-8")}catch(r){}try{n.setRequestHeader("Accept","*/*")}catch(r){}"withCredentials"in n&&(n.withCredentials=this.opts.withCredentials),this.opts.requestTimeout&&(n.timeout=this.opts.requestTimeout),n.onreadystatechange=()=>{n.readyState===4&&(n.status===200||n.status===1223?this.onLoad():this.setTimeoutFn(()=>{this.onError(typeof n.status=="number"?n.status:0)},0))},n.send(this.data)}catch(r){this.setTimeoutFn(()=>{this.onError(r)},0);return}typeof document<"u"&&(this.index=ou.requestsCount++,ou.requests[this.index]=this)}onError(t){this.emitReserved("error",t,this.xhr),this.cleanup(!0)}cleanup(t){if(!(typeof this.xhr>"u"||this.xhr===null)){if(this.xhr.onreadystatechange=p6e,t)try{this.xhr.abort()}catch(n){}typeof document<"u"&&delete ou.requests[this.index],this.xhr=null}}onLoad(){const t=this.xhr.responseText;t!==null&&(this.emitReserved("data",t),this.emitReserved("success"),this.cleanup())}abort(){this.cleanup()}}ou.requestsCount=0;ou.requests={};if(typeof document<"u"){if(typeof attachEvent=="function")attachEvent("onunload",yI);else if(typeof addEventListener=="function"){const e="onpagehide"in Md?"pagehide":"unload";addEventListener(e,yI,!1)}}function yI(){for(let e in ou.requests)ou.requests.hasOwnProperty(e)&&ou.requests[e].abort()}const IW=(()=>typeof Promise=="function"&&typeof Promise.resolve=="function"?t=>Promise.resolve().then(t):(t,n)=>n(t,0))(),Cb=Md.WebSocket||Md.MozWebSocket,bI=!0,v6e="arraybuffer",SI=typeof navigator<"u"&&typeof navigator.product=="string"&&navigator.product.toLowerCase()==="reactnative";class y6e extends TW{constructor(t){super(t),this.supportsBinary=!t.forceBase64}get name(){return"websocket"}doOpen(){if(!this.check())return;const t=this.uri(),n=this.opts.protocols,r=SI?{}:PW(this.opts,"agent","perMessageDeflate","pfx","key","passphrase","cert","ca","ciphers","rejectUnauthorized","localAddress","protocolVersion","origin","maxPayload","family","checkServerIdentity");this.opts.extraHeaders&&(r.headers=this.opts.extraHeaders);try{this.ws=bI&&!SI?n?new Cb(t,n):new Cb(t):new Cb(t,n,r)}catch(i){return this.emitReserved("error",i)}this.ws.binaryType=this.socket.binaryType||v6e,this.addEventListeners()}addEventListeners(){this.ws.onopen=()=>{this.opts.autoUnref&&this.ws._socket.unref(),this.onOpen()},this.ws.onclose=t=>this.onClose({description:"websocket connection closed",context:t}),this.ws.onmessage=t=>this.onData(t.data),this.ws.onerror=t=>this.onError("websocket error",t)}write(t){this.writable=!1;for(let n=0;n<t.length;n++){const r=t[n],i=n===t.length-1;wW(r,this.supportsBinary,o=>{const a={};try{bI&&this.ws.send(o)}catch(s){}i&&IW(()=>{this.writable=!0,this.emitReserved("drain")},this.setTimeoutFn)})}}doClose(){typeof this.ws<"u"&&(this.ws.close(),this.ws=null)}uri(){let t=this.query||{};const n=this.opts.secure?"wss":"ws";let r="";this.opts.port&&(n==="wss"&&Number(this.opts.port)!==443||n==="ws"&&Number(this.opts.port)!==80)&&(r=":"+this.opts.port),this.opts.timestampRequests&&(t[this.opts.timestampParam]=AW()),this.supportsBinary||(t.b64=1);const i=OW(t),o=this.opts.hostname.indexOf(":")!==-1;return n+"://"+(o?"["+this.opts.hostname+"]":this.opts.hostname)+r+this.opts.path+(i.length?"?"+i:"")}check(){return!!Cb}}const b6e={websocket:y6e,polling:m6e},S6e=/^(?:(?![^:@]+:[^:@\/]*@)(http|https|ws|wss):\/\/)?((?:(([^:@]*)(?::([^:@]*))?)?@)?((?:[a-f0-9]{0,4}:){2,7}[a-f0-9]{0,4}|[^:\/?#]*)(?::(\d*))?)(((\/(?:[^?#](?![^?#\/]*\.[^?#\/.]+(?:[?#]|$)))*\/?)?([^?#\/]*))(?:\?([^#]*))?(?:#(.*))?)/,x6e=["source","protocol","authority","userInfo","user","password","host","port","relative","path","directory","file","query","anchor"];function o8(e){const t=e,n=e.indexOf("["),r=e.indexOf("]");n!=-1&&r!=-1&&(e=e.substring(0,n)+e.substring(n,r).replace(/:/g,";")+e.substring(r,e.length));let i=S6e.exec(e||""),o={},a=14;for(;a--;)o[x6e[a]]=i[a]||"";return n!=-1&&r!=-1&&(o.source=t,o.host=o.host.substring(1,o.host.length-1).replace(/;/g,":"),o.authority=o.authority.replace("[","").replace("]","").replace(/;/g,":"),o.ipv6uri=!0),o.pathNames=w6e(o,o.path),o.queryKey=C6e(o,o.query),o}function w6e(e,t){const n=/\/{2,9}/g,r=t.replace(n,"/").split("/");return(t.slice(0,1)=="/"||t.length===0)&&r.splice(0,1),t.slice(-1)=="/"&&r.splice(r.length-1,1),r}function C6e(e,t){const n={};return t.replace(/(?:^|&)([^&=]*)=?([^&]*)/g,function(r,i,o){i&&(n[i]=o)}),n}class kd extends oi{constructor(t,n={}){super(),t&&typeof t=="object"&&(n=t,t=null),t?(t=o8(t),n.hostname=t.host,n.secure=t.protocol==="https"||t.protocol==="wss",n.port=t.port,t.query&&(n.query=t.query)):n.host&&(n.hostname=o8(n.host).host),xx(this,n),this.secure=n.secure!=null?n.secure:typeof location<"u"&&location.protocol==="https:",n.hostname&&!n.port&&(n.port=this.secure?"443":"80"),this.hostname=n.hostname||(typeof location<"u"?location.hostname:"localhost"),this.port=n.port||(typeof location<"u"&&location.port?location.port:this.secure?"443":"80"),this.transports=n.transports||["polling","websocket"],this.readyState="",this.writeBuffer=[],this.prevBufferLen=0,this.opts=Object.assign({path:"/engine.io",agent:!1,withCredentials:!1,upgrade:!0,timestampParam:"t",rememberUpgrade:!1,rejectUnauthorized:!0,perMessageDeflate:{threshold:1024},transportOptions:{},closeOnBeforeunload:!0},n),this.opts.path=this.opts.path.replace(/\/$/,"")+"/",typeof this.opts.query=="string"&&(this.opts.query=f6e(this.opts.query)),this.id=null,this.upgrades=null,this.pingInterval=null,this.pingTimeout=null,this.pingTimeoutTimer=null,typeof addEventListener=="function"&&(this.opts.closeOnBeforeunload&&(this.beforeunloadEventListener=()=>{this.transport&&(this.transport.removeAllListeners(),this.transport.close())},addEventListener("beforeunload",this.beforeunloadEventListener,!1)),this.hostname!=="localhost"&&(this.offlineEventListener=()=>{this.onClose("transport close",{description:"network connection lost"})},addEventListener("offline",this.offlineEventListener,!1))),this.open()}createTransport(t){const n=Object.assign({},this.opts.query);n.EIO=EW,n.transport=t,this.id&&(n.sid=this.id);const r=Object.assign({},this.opts.transportOptions[t],this.opts,{query:n,socket:this,hostname:this.hostname,secure:this.secure,port:this.port});return new b6e[t](r)}open(){let t;if(this.opts.rememberUpgrade&&kd.priorWebsocketSuccess&&this.transports.indexOf("websocket")!==-1)t="websocket";else if(this.transports.length===0){this.setTimeoutFn(()=>{this.emitReserved("error","No transports available")},0);return}else t=this.transports[0];this.readyState="opening";try{t=this.createTransport(t)}catch(n){this.transports.shift(),this.open();return}t.open(),this.setTransport(t)}setTransport(t){this.transport&&this.transport.removeAllListeners(),this.transport=t,t.on("drain",this.onDrain.bind(this)).on("packet",this.onPacket.bind(this)).on("error",this.onError.bind(this)).on("close",n=>this.onClose("transport close",n))}probe(t){let n=this.createTransport(t),r=!1;kd.priorWebsocketSuccess=!1;const i=()=>{r||(n.send([{type:"ping",data:"probe"}]),n.once("packet",p=>{if(!r)if(p.type==="pong"&&p.data==="probe"){if(this.upgrading=!0,this.emitReserved("upgrading",n),!n)return;kd.priorWebsocketSuccess=n.name==="websocket",this.transport.pause(()=>{r||this.readyState!=="closed"&&(f(),this.setTransport(n),n.send([{type:"upgrade"}]),this.emitReserved("upgrade",n),n=null,this.upgrading=!1,this.flush())})}else{const m=new Error("probe error");m.transport=n.name,this.emitReserved("upgradeError",m)}}))};function o(){r||(r=!0,f(),n.close(),n=null)}const a=p=>{const m=new Error("probe error: "+p);m.transport=n.name,o(),this.emitReserved("upgradeError",m)};function s(){a("transport closed")}function l(){a("socket closed")}function u(p){n&&p.name!==n.name&&o()}const f=()=>{n.removeListener("open",i),n.removeListener("error",a),n.removeListener("close",s),this.off("close",l),this.off("upgrading",u)};n.once("open",i),n.once("error",a),n.once("close",s),this.once("close",l),this.once("upgrading",u),n.open()}onOpen(){if(this.readyState="open",kd.priorWebsocketSuccess=this.transport.name==="websocket",this.emitReserved("open"),this.flush(),this.readyState==="open"&&this.opts.upgrade&&this.transport.pause){let t=0;const n=this.upgrades.length;for(;t<n;t++)this.probe(this.upgrades[t])}}onPacket(t){if(this.readyState==="opening"||this.readyState==="open"||this.readyState==="closing")switch(this.emitReserved("packet",t),this.emitReserved("heartbeat"),t.type){case"open":this.onHandshake(JSON.parse(t.data));break;case"ping":this.resetPingTimeout(),this.sendPacket("pong"),this.emitReserved("ping"),this.emitReserved("pong");break;case"error":const n=new Error("server error");n.code=t.data,this.onError(n);break;case"message":this.emitReserved("data",t.data),this.emitReserved("message",t.data);break}}onHandshake(t){this.emitReserved("handshake",t),this.id=t.sid,this.transport.query.sid=t.sid,this.upgrades=this.filterUpgrades(t.upgrades),this.pingInterval=t.pingInterval,this.pingTimeout=t.pingTimeout,this.maxPayload=t.maxPayload,this.onOpen(),this.readyState!=="closed"&&this.resetPingTimeout()}resetPingTimeout(){this.clearTimeoutFn(this.pingTimeoutTimer),this.pingTimeoutTimer=this.setTimeoutFn(()=>{this.onClose("ping timeout")},this.pingInterval+this.pingTimeout),this.opts.autoUnref&&this.pingTimeoutTimer.unref()}onDrain(){this.writeBuffer.splice(0,this.prevBufferLen),this.prevBufferLen=0,this.writeBuffer.length===0?this.emitReserved("drain"):this.flush()}flush(){if(this.readyState!=="closed"&&this.transport.writable&&!this.upgrading&&this.writeBuffer.length){const t=this.getWritablePackets();this.transport.send(t),this.prevBufferLen=t.length,this.emitReserved("flush")}}getWritablePackets(){if(!(this.maxPayload&&this.transport.name==="polling"&&this.writeBuffer.length>1))return this.writeBuffer;let n=1;for(let r=0;r<this.writeBuffer.length;r++){const i=this.writeBuffer[r].data;if(i&&(n+=l6e(i)),r>0&&n>this.maxPayload)return this.writeBuffer.slice(0,r);n+=2}return this.writeBuffer}write(t,n,r){return this.sendPacket("message",t,n,r),this}send(t,n,r){return this.sendPacket("message",t,n,r),this}sendPacket(t,n,r,i){if(typeof n=="function"&&(i=n,n=void 0),typeof r=="function"&&(i=r,r=null),this.readyState==="closing"||this.readyState==="closed")return;r=r||{},r.compress=r.compress!==!1;const o={type:t,data:n,options:r};this.emitReserved("packetCreate",o),this.writeBuffer.push(o),i&&this.once("flush",i),this.flush()}close(){const t=()=>{this.onClose("forced close"),this.transport.close()},n=()=>{this.off("upgrade",n),this.off("upgradeError",n),t()},r=()=>{this.once("upgrade",n),this.once("upgradeError",n)};return(this.readyState==="opening"||this.readyState==="open")&&(this.readyState="closing",this.writeBuffer.length?this.once("drain",()=>{this.upgrading?r():t()}):this.upgrading?r():t()),this}onError(t){kd.priorWebsocketSuccess=!1,this.emitReserved("error",t),this.onClose("transport error",t)}onClose(t,n){(this.readyState==="opening"||this.readyState==="open"||this.readyState==="closing")&&(this.clearTimeoutFn(this.pingTimeoutTimer),this.transport.removeAllListeners("close"),this.transport.close(),this.transport.removeAllListeners(),typeof removeEventListener=="function"&&(removeEventListener("beforeunload",this.beforeunloadEventListener,!1),removeEventListener("offline",this.offlineEventListener,!1)),this.readyState="closed",this.id=null,this.emitReserved("close",t,n),this.writeBuffer=[],this.prevBufferLen=0)}filterUpgrades(t){const n=[];let r=0;const i=t.length;for(;r<i;r++)~this.transports.indexOf(t[r])&&n.push(t[r]);return n}}kd.protocol=EW;function _6e(e,t="",n){let r=e;n=n||typeof location<"u"&&location,e==null&&(e=n.protocol+"//"+n.host),typeof e=="string"&&(e.charAt(0)==="/"&&(e.charAt(1)==="/"?e=n.protocol+e:e=n.host+e),/^(https?|wss?):\/\//.test(e)||(typeof n<"u"?e=n.protocol+"//"+e:e="https://"+e),r=o8(e)),r.port||(/^(http|ws)$/.test(r.protocol)?r.port="80":/^(http|ws)s$/.test(r.protocol)&&(r.port="443")),r.path=r.path||"/";const o=r.host.indexOf(":")!==-1?"["+r.host+"]":r.host;return r.id=r.protocol+"://"+o+":"+r.port+t,r.href=r.protocol+"://"+o+(n&&n.port===r.port?"":":"+r.port),r}const k6e=typeof ArrayBuffer=="function",E6e=e=>typeof ArrayBuffer.isView=="function"?ArrayBuffer.isView(e):e.buffer instanceof ArrayBuffer,DW=Object.prototype.toString,P6e=typeof Blob=="function"||typeof Blob<"u"&&DW.call(Blob)==="[object BlobConstructor]",T6e=typeof File=="function"||typeof File<"u"&&DW.call(File)==="[object FileConstructor]";function YE(e){return k6e&&(e instanceof ArrayBuffer||E6e(e))||P6e&&e instanceof Blob||T6e&&e instanceof File}function E4(e,t){if(!e||typeof e!="object")return!1;if(Array.isArray(e)){for(let n=0,r=e.length;n<r;n++)if(E4(e[n]))return!0;return!1}if(YE(e))return!0;if(e.toJSON&&typeof e.toJSON=="function"&&arguments.length===1)return E4(e.toJSON(),!0);for(const n in e)if(Object.prototype.hasOwnProperty.call(e,n)&&E4(e[n]))return!0;return!1}function L6e(e){const t=[],n=e.data,r=e;return r.data=a8(n,t),r.attachments=t.length,{packet:r,buffers:t}}function a8(e,t){if(!e)return e;if(YE(e)){const n={_placeholder:!0,num:t.length};return t.push(e),n}else if(Array.isArray(e)){const n=new Array(e.length);for(let r=0;r<e.length;r++)n[r]=a8(e[r],t);return n}else if(typeof e=="object"&&!(e instanceof Date)){const n={};for(const r in e)Object.prototype.hasOwnProperty.call(e,r)&&(n[r]=a8(e[r],t));return n}return e}function A6e(e,t){return e.data=s8(e.data,t),e.attachments=void 0,e}function s8(e,t){if(!e)return e;if(e&&e._placeholder===!0){if(typeof e.num=="number"&&e.num>=0&&e.num<t.length)return t[e.num];throw new Error("illegal attachments")}else if(Array.isArray(e))for(let n=0;n<e.length;n++)e[n]=s8(e[n],t);else if(typeof e=="object")for(const n in e)Object.prototype.hasOwnProperty.call(e,n)&&(e[n]=s8(e[n],t));return e}const O6e=5;var dn;(function(e){e[e.CONNECT=0]="CONNECT",e[e.DISCONNECT=1]="DISCONNECT",e[e.EVENT=2]="EVENT",e[e.ACK=3]="ACK",e[e.CONNECT_ERROR=4]="CONNECT_ERROR",e[e.BINARY_EVENT=5]="BINARY_EVENT",e[e.BINARY_ACK=6]="BINARY_ACK"})(dn||(dn={}));class M6e{constructor(t){this.replacer=t}encode(t){return(t.type===dn.EVENT||t.type===dn.ACK)&&E4(t)?(t.type=t.type===dn.EVENT?dn.BINARY_EVENT:dn.BINARY_ACK,this.encodeAsBinary(t)):[this.encodeAsString(t)]}encodeAsString(t){let n=""+t.type;return(t.type===dn.BINARY_EVENT||t.type===dn.BINARY_ACK)&&(n+=t.attachments+"-"),t.nsp&&t.nsp!=="/"&&(n+=t.nsp+","),t.id!=null&&(n+=t.id),t.data!=null&&(n+=JSON.stringify(t.data,this.replacer)),n}encodeAsBinary(t){const n=L6e(t),r=this.encodeAsString(n.packet),i=n.buffers;return i.unshift(r),i}}class KE extends oi{constructor(t){super(),this.reviver=t}add(t){let n;if(typeof t=="string"){if(this.reconstructor)throw new Error("got plaintext data when reconstructing a packet");n=this.decodeString(t),n.type===dn.BINARY_EVENT||n.type===dn.BINARY_ACK?(this.reconstructor=new R6e(n),n.attachments===0&&super.emitReserved("decoded",n)):super.emitReserved("decoded",n)}else if(YE(t)||t.base64)if(this.reconstructor)n=this.reconstructor.takeBinaryData(t),n&&(this.reconstructor=null,super.emitReserved("decoded",n));else throw new Error("got binary data when not reconstructing a packet");else throw new Error("Unknown type: "+t)}decodeString(t){let n=0;const r={type:Number(t.charAt(0))};if(dn[r.type]===void 0)throw new Error("unknown packet type "+r.type);if(r.type===dn.BINARY_EVENT||r.type===dn.BINARY_ACK){const o=n+1;for(;t.charAt(++n)!=="-"&&n!=t.length;);const a=t.substring(o,n);if(a!=Number(a)||t.charAt(n)!=="-")throw new Error("Illegal attachments");r.attachments=Number(a)}if(t.charAt(n+1)==="/"){const o=n+1;for(;++n&&!(t.charAt(n)===","||n===t.length););r.nsp=t.substring(o,n)}else r.nsp="/";const i=t.charAt(n+1);if(i!==""&&Number(i)==i){const o=n+1;for(;++n;){const a=t.charAt(n);if(a==null||Number(a)!=a){--n;break}if(n===t.length)break}r.id=Number(t.substring(o,n+1))}if(t.charAt(++n)){const o=this.tryParse(t.substr(n));if(KE.isPayloadValid(r.type,o))r.data=o;else throw new Error("invalid payload")}return r}tryParse(t){try{return JSON.parse(t,this.reviver)}catch(n){return!1}}static isPayloadValid(t,n){switch(t){case dn.CONNECT:return typeof n=="object";case dn.DISCONNECT:return n===void 0;case dn.CONNECT_ERROR:return typeof n=="string"||typeof n=="object";case dn.EVENT:case dn.BINARY_EVENT:return Array.isArray(n)&&n.length>0;case dn.ACK:case dn.BINARY_ACK:return Array.isArray(n)}}destroy(){this.reconstructor&&this.reconstructor.finishedReconstruction()}}class R6e{constructor(t){this.packet=t,this.buffers=[],this.reconPack=t}takeBinaryData(t){if(this.buffers.push(t),this.buffers.length===this.reconPack.attachments){const n=A6e(this.reconPack,this.buffers);return this.finishedReconstruction(),n}return null}finishedReconstruction(){this.reconPack=null,this.buffers=[]}}const I6e=Object.freeze(Object.defineProperty({__proto__:null,protocol:O6e,get PacketType(){return dn},Encoder:M6e,Decoder:KE},Symbol.toStringTag,{value:"Module"}));function js(e,t,n){return e.on(t,n),function(){e.off(t,n)}}const D6e=Object.freeze({connect:1,connect_error:1,disconnect:1,disconnecting:1,newListener:1,removeListener:1});class NW extends oi{constructor(t,n,r){super(),this.connected=!1,this.receiveBuffer=[],this.sendBuffer=[],this.ids=0,this.acks={},this.flags={},this.io=t,this.nsp=n,r&&r.auth&&(this.auth=r.auth),this.io._autoConnect&&this.open()}get disconnected(){return!this.connected}subEvents(){if(this.subs)return;const t=this.io;this.subs=[js(t,"open",this.onopen.bind(this)),js(t,"packet",this.onpacket.bind(this)),js(t,"error",this.onerror.bind(this)),js(t,"close",this.onclose.bind(this))]}get active(){return!!this.subs}connect(){return this.connected?this:(this.subEvents(),this.io._reconnecting||this.io.open(),this.io._readyState==="open"&&this.onopen(),this)}open(){return this.connect()}send(...t){return t.unshift("message"),this.emit.apply(this,t),this}emit(t,...n){if(D6e.hasOwnProperty(t))throw new Error('"'+t.toString()+'" is a reserved event name');n.unshift(t);const r={type:dn.EVENT,data:n};if(r.options={},r.options.compress=this.flags.compress!==!1,typeof n[n.length-1]=="function"){const a=this.ids++,s=n.pop();this._registerAckCallback(a,s),r.id=a}const i=this.io.engine&&this.io.engine.transport&&this.io.engine.transport.writable;return this.flags.volatile&&(!i||!this.connected)||(this.connected?(this.notifyOutgoingListeners(r),this.packet(r)):this.sendBuffer.push(r)),this.flags={},this}_registerAckCallback(t,n){const r=this.flags.timeout;if(r===void 0){this.acks[t]=n;return}const i=this.io.setTimeoutFn(()=>{delete this.acks[t];for(let o=0;o<this.sendBuffer.length;o++)this.sendBuffer[o].id===t&&this.sendBuffer.splice(o,1);n.call(this,new Error("operation has timed out"))},r);this.acks[t]=(...o)=>{this.io.clearTimeoutFn(i),n.apply(this,[null,...o])}}packet(t){t.nsp=this.nsp,this.io._packet(t)}onopen(){typeof this.auth=="function"?this.auth(t=>{this.packet({type:dn.CONNECT,data:t})}):this.packet({type:dn.CONNECT,data:this.auth})}onerror(t){this.connected||this.emitReserved("connect_error",t)}onclose(t,n){this.connected=!1,delete this.id,this.emitReserved("disconnect",t,n)}onpacket(t){if(t.nsp===this.nsp)switch(t.type){case dn.CONNECT:if(t.data&&t.data.sid){const i=t.data.sid;this.onconnect(i)}else this.emitReserved("connect_error",new Error("It seems you are trying to reach a Socket.IO server in v2.x with a v3.x client, but they are not compatible (more information here: https://socket.io/docs/v3/migrating-from-2-x-to-3-0/)"));break;case dn.EVENT:case dn.BINARY_EVENT:this.onevent(t);break;case dn.ACK:case dn.BINARY_ACK:this.onack(t);break;case dn.DISCONNECT:this.ondisconnect();break;case dn.CONNECT_ERROR:this.destroy();const r=new Error(t.data.message);r.data=t.data.data,this.emitReserved("connect_error",r);break}}onevent(t){const n=t.data||[];t.id!=null&&n.push(this.ack(t.id)),this.connected?this.emitEvent(n):this.receiveBuffer.push(Object.freeze(n))}emitEvent(t){if(this._anyListeners&&this._anyListeners.length){const n=this._anyListeners.slice();for(const r of n)r.apply(this,t)}super.emit.apply(this,t)}ack(t){const n=this;let r=!1;return function(...i){r||(r=!0,n.packet({type:dn.ACK,id:t,data:i}))}}onack(t){const n=this.acks[t.id];typeof n=="function"&&(n.apply(this,t.data),delete this.acks[t.id])}onconnect(t){this.id=t,this.connected=!0,this.emitBuffered(),this.emitReserved("connect")}emitBuffered(){this.receiveBuffer.forEach(t=>this.emitEvent(t)),this.receiveBuffer=[],this.sendBuffer.forEach(t=>{this.notifyOutgoingListeners(t),this.packet(t)}),this.sendBuffer=[]}ondisconnect(){this.destroy(),this.onclose("io server disconnect")}destroy(){this.subs&&(this.subs.forEach(t=>t()),this.subs=void 0),this.io._destroy(this)}disconnect(){return this.connected&&this.packet({type:dn.DISCONNECT}),this.destroy(),this.connected&&this.onclose("io client disconnect"),this}close(){return this.disconnect()}compress(t){return this.flags.compress=t,this}get volatile(){return this.flags.volatile=!0,this}timeout(t){return this.flags.timeout=t,this}onAny(t){return this._anyListeners=this._anyListeners||[],this._anyListeners.push(t),this}prependAny(t){return this._anyListeners=this._anyListeners||[],this._anyListeners.unshift(t),this}offAny(t){if(!this._anyListeners)return this;if(t){const n=this._anyListeners;for(let r=0;r<n.length;r++)if(t===n[r])return n.splice(r,1),this}else this._anyListeners=[];return this}listenersAny(){return this._anyListeners||[]}onAnyOutgoing(t){return this._anyOutgoingListeners=this._anyOutgoingListeners||[],this._anyOutgoingListeners.push(t),this}prependAnyOutgoing(t){return this._anyOutgoingListeners=this._anyOutgoingListeners||[],this._anyOutgoingListeners.unshift(t),this}offAnyOutgoing(t){if(!this._anyOutgoingListeners)return this;if(t){const n=this._anyOutgoingListeners;for(let r=0;r<n.length;r++)if(t===n[r])return n.splice(r,1),this}else this._anyOutgoingListeners=[];return this}listenersAnyOutgoing(){return this._anyOutgoingListeners||[]}notifyOutgoingListeners(t){if(this._anyOutgoingListeners&&this._anyOutgoingListeners.length){const n=this._anyOutgoingListeners.slice();for(const r of n)r.apply(this,t.data)}}}function _1(e){e=e||{},this.ms=e.min||100,this.max=e.max||1e4,this.factor=e.factor||2,this.jitter=e.jitter>0&&e.jitter<=1?e.jitter:0,this.attempts=0}_1.prototype.duration=function(){var e=this.ms*Math.pow(this.factor,this.attempts++);if(this.jitter){var t=Math.random(),n=Math.floor(t*this.jitter*e);e=(Math.floor(t*10)&1)==0?e-n:e+n}return Math.min(e,this.max)|0};_1.prototype.reset=function(){this.attempts=0};_1.prototype.setMin=function(e){this.ms=e};_1.prototype.setMax=function(e){this.max=e};_1.prototype.setJitter=function(e){this.jitter=e};class l8 extends oi{constructor(t,n){var r;super(),this.nsps={},this.subs=[],t&&typeof t=="object"&&(n=t,t=void 0),n=n||{},n.path=n.path||"/socket.io",this.opts=n,xx(this,n),this.reconnection(n.reconnection!==!1),this.reconnectionAttempts(n.reconnectionAttempts||1/0),this.reconnectionDelay(n.reconnectionDelay||1e3),this.reconnectionDelayMax(n.reconnectionDelayMax||5e3),this.randomizationFactor((r=n.randomizationFactor)!==null&&r!==void 0?r:.5),this.backoff=new _1({min:this.reconnectionDelay(),max:this.reconnectionDelayMax(),jitter:this.randomizationFactor()}),this.timeout(n.timeout==null?2e4:n.timeout),this._readyState="closed",this.uri=t;const i=n.parser||I6e;this.encoder=new i.Encoder,this.decoder=new i.Decoder,this._autoConnect=n.autoConnect!==!1,this._autoConnect&&this.open()}reconnection(t){return arguments.length?(this._reconnection=!!t,this):this._reconnection}reconnectionAttempts(t){return t===void 0?this._reconnectionAttempts:(this._reconnectionAttempts=t,this)}reconnectionDelay(t){var n;return t===void 0?this._reconnectionDelay:(this._reconnectionDelay=t,(n=this.backoff)===null||n===void 0||n.setMin(t),this)}randomizationFactor(t){var n;return t===void 0?this._randomizationFactor:(this._randomizationFactor=t,(n=this.backoff)===null||n===void 0||n.setJitter(t),this)}reconnectionDelayMax(t){var n;return t===void 0?this._reconnectionDelayMax:(this._reconnectionDelayMax=t,(n=this.backoff)===null||n===void 0||n.setMax(t),this)}timeout(t){return arguments.length?(this._timeout=t,this):this._timeout}maybeReconnectOnOpen(){!this._reconnecting&&this._reconnection&&this.backoff.attempts===0&&this.reconnect()}open(t){if(~this._readyState.indexOf("open"))return this;this.engine=new kd(this.uri,this.opts);const n=this.engine,r=this;this._readyState="opening",this.skipReconnect=!1;const i=js(n,"open",function(){r.onopen(),t&&t()}),o=js(n,"error",a=>{r.cleanup(),r._readyState="closed",this.emitReserved("error",a),t?t(a):r.maybeReconnectOnOpen()});if(this._timeout!==!1){const a=this._timeout;a===0&&i();const s=this.setTimeoutFn(()=>{i(),n.close(),n.emit("error",new Error("timeout"))},a);this.opts.autoUnref&&s.unref(),this.subs.push(function(){clearTimeout(s)})}return this.subs.push(i),this.subs.push(o),this}connect(t){return this.open(t)}onopen(){this.cleanup(),this._readyState="open",this.emitReserved("open");const t=this.engine;this.subs.push(js(t,"ping",this.onping.bind(this)),js(t,"data",this.ondata.bind(this)),js(t,"error",this.onerror.bind(this)),js(t,"close",this.onclose.bind(this)),js(this.decoder,"decoded",this.ondecoded.bind(this)))}onping(){this.emitReserved("ping")}ondata(t){try{this.decoder.add(t)}catch(n){this.onclose("parse error",n)}}ondecoded(t){IW(()=>{this.emitReserved("packet",t)},this.setTimeoutFn)}onerror(t){this.emitReserved("error",t)}socket(t,n){let r=this.nsps[t];return r||(r=new NW(this,t,n),this.nsps[t]=r),r}_destroy(t){const n=Object.keys(this.nsps);for(const r of n)if(this.nsps[r].active)return;this._close()}_packet(t){const n=this.encoder.encode(t);for(let r=0;r<n.length;r++)this.engine.write(n[r],t.options)}cleanup(){this.subs.forEach(t=>t()),this.subs.length=0,this.decoder.destroy()}_close(){this.skipReconnect=!0,this._reconnecting=!1,this.onclose("forced close"),this.engine&&this.engine.close()}disconnect(){return this._close()}onclose(t,n){this.cleanup(),this.backoff.reset(),this._readyState="closed",this.emitReserved("close",t,n),this._reconnection&&!this.skipReconnect&&this.reconnect()}reconnect(){if(this._reconnecting||this.skipReconnect)return this;const t=this;if(this.backoff.attempts>=this._reconnectionAttempts)this.backoff.reset(),this.emitReserved("reconnect_failed"),this._reconnecting=!1;else{const n=this.backoff.duration();this._reconnecting=!0;const r=this.setTimeoutFn(()=>{t.skipReconnect||(this.emitReserved("reconnect_attempt",t.backoff.attempts),!t.skipReconnect&&t.open(i=>{i?(t._reconnecting=!1,t.reconnect(),this.emitReserved("reconnect_error",i)):t.onreconnect()}))},n);this.opts.autoUnref&&r.unref(),this.subs.push(function(){clearTimeout(r)})}}onreconnect(){const t=this.backoff.attempts;this._reconnecting=!1,this.backoff.reset(),this.emitReserved("reconnect",t)}}const ov={};function P4(e,t){typeof e=="object"&&(t=e,e=void 0),t=t||{};const n=_6e(e,t.path||"/socket.io"),r=n.source,i=n.id,o=n.path,a=ov[i]&&o in ov[i].nsps,s=t.forceNew||t["force new connection"]||t.multiplex===!1||a;let l;return s?l=new l8(r,t):(ov[i]||(ov[i]=new l8(r,t)),l=ov[i]),n.query&&!t.query&&(t.query=n.queryKey),l.socket(n.path,t)}Object.assign(P4,{Manager:l8,Socket:NW,io:P4,connect:P4});let _b;const N6e=new Uint8Array(16);function B6e(){if(!_b&&(_b=typeof crypto<"u"&&crypto.getRandomValues&&crypto.getRandomValues.bind(crypto),!_b))throw new Error("crypto.getRandomValues() not supported. See https://github.com/uuidjs/uuid#getrandomvalues-not-supported");return _b(N6e)}const zi=[];for(let e=0;e<256;++e)zi.push((e+256).toString(16).slice(1));function $6e(e,t=0){return(zi[e[t+0]]+zi[e[t+1]]+zi[e[t+2]]+zi[e[t+3]]+"-"+zi[e[t+4]]+zi[e[t+5]]+"-"+zi[e[t+6]]+zi[e[t+7]]+"-"+zi[e[t+8]]+zi[e[t+9]]+"-"+zi[e[t+10]]+zi[e[t+11]]+zi[e[t+12]]+zi[e[t+13]]+zi[e[t+14]]+zi[e[t+15]]).toLowerCase()}const F6e=typeof crypto<"u"&&crypto.randomUUID&&crypto.randomUUID.bind(crypto),xI={randomUUID:F6e};function lg(e,t,n){if(xI.randomUUID&&!t&&!e)return xI.randomUUID();e=e||{};const r=e.random||(e.rng||B6e)();if(r[6]=r[6]&15|64,r[8]=r[8]&63|128,t){n=n||0;for(let i=0;i<16;++i)t[n+i]=r[i];return t}return $6e(r)}var z6e=/d{1,4}|D{3,4}|m{1,4}|yy(?:yy)?|([HhMsTt])\1?|W{1,2}|[LlopSZN]|"[^"]*"|'[^']*'/g,j6e=/\b(?:[A-Z]{1,3}[A-Z][TC])(?:[-+]\d{4})?|((?:Australian )?(?:Pacific|Mountain|Central|Eastern|Atlantic) (?:Standard|Daylight|Prevailing) Time)\b/g,H6e=/[^-+\dA-Z]/g;function to(e,t,n,r){if(arguments.length===1&&typeof e=="string"&&!/\d/.test(e)&&(t=e,e=void 0),e=e||e===0?e:new Date,e instanceof Date||(e=new Date(e)),isNaN(e))throw TypeError("Invalid date");t=String(wI[t]||t||wI.default);var i=t.slice(0,4);(i==="UTC:"||i==="GMT:")&&(t=t.slice(4),n=!0,i==="GMT:"&&(r=!0));var o=function(){return n?"getUTC":"get"},a=function(){return e[o()+"Date"]()},s=function(){return e[o()+"Day"]()},l=function(){return e[o()+"Month"]()},u=function(){return e[o()+"FullYear"]()},f=function(){return e[o()+"Hours"]()},p=function(){return e[o()+"Minutes"]()},m=function(){return e[o()+"Seconds"]()},v=function(){return e[o()+"Milliseconds"]()},b=function(){return n?0:e.getTimezoneOffset()},x=function(){return V6e(e)},E=function(){return U6e(e)},k={d:function(){return a()},dd:function(){return Ca(a())},ddd:function(){return Ho.dayNames[s()]},DDD:function(){return CI({y:u(),m:l(),d:a(),_:o(),dayName:Ho.dayNames[s()],short:!0})},dddd:function(){return 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r?"GMT":n?"UTC":W6e(e)},o:function(){return(b()>0?"-":"+")+Ca(Math.floor(Math.abs(b())/60)*100+Math.abs(b())%60,4)},p:function(){return(b()>0?"-":"+")+Ca(Math.floor(Math.abs(b())/60),2)+":"+Ca(Math.floor(Math.abs(b())%60),2)},S:function(){return["th","st","nd","rd"][a()%10>3?0:(a()%100-a()%10!=10)*a()%10]},W:function(){return x()},WW:function(){return Ca(x())},N:function(){return E()}};return t.replace(z6e,function(_){return _ in k?k[_]():_.slice(1,_.length-1)})}var wI={default:"ddd mmm dd yyyy HH:MM:ss",shortDate:"m/d/yy",paddedShortDate:"mm/dd/yyyy",mediumDate:"mmm d, yyyy",longDate:"mmmm d, yyyy",fullDate:"dddd, mmmm d, yyyy",shortTime:"h:MM TT",mediumTime:"h:MM:ss TT",longTime:"h:MM:ss TT Z",isoDate:"yyyy-mm-dd",isoTime:"HH:MM:ss",isoDateTime:"yyyy-mm-dd'T'HH:MM:sso",isoUtcDateTime:"UTC:yyyy-mm-dd'T'HH:MM:ss'Z'",expiresHeaderFormat:"ddd, dd mmm yyyy HH:MM:ss Z"},Ho={dayNames:["Sun","Mon","Tue","Wed","Thu","Fri","Sat","Sunday","Monday","Tuesday","Wednesday","Thursday","Friday","Saturday"],monthNames:["Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec","January","February","March","April","May","June","July","August","September","October","November","December"],timeNames:["a","p","am","pm","A","P","AM","PM"]},Ca=function(t){var n=arguments.length>1&&arguments[1]!==void 0?arguments[1]:2;return String(t).padStart(n,"0")},CI=function(t){var n=t.y,r=t.m,i=t.d,o=t._,a=t.dayName,s=t.short,l=s===void 0?!1:s,u=new Date,f=new Date;f.setDate(f[o+"Date"]()-1);var p=new Date;p.setDate(p[o+"Date"]()+1);var m=function(){return u[o+"Date"]()},v=function(){return u[o+"Month"]()},b=function(){return u[o+"FullYear"]()},x=function(){return f[o+"Date"]()},E=function(){return f[o+"Month"]()},k=function(){return f[o+"FullYear"]()},_=function(){return p[o+"Date"]()},T=function(){return p[o+"Month"]()},A=function(){return p[o+"FullYear"]()};return b()===n&&v()===r&&m()===i?l?"Tdy":"Today":k()===n&&E()===r&&x()===i?l?"Ysd":"Yesterday":A()===n&&T()===r&&_()===i?l?"Tmw":"Tomorrow":a},V6e=function(t){var n=new Date(t.getFullYear(),t.getMonth(),t.getDate());n.setDate(n.getDate()-(n.getDay()+6)%7+3);var r=new Date(n.getFullYear(),0,4);r.setDate(r.getDate()-(r.getDay()+6)%7+3);var i=n.getTimezoneOffset()-r.getTimezoneOffset();n.setHours(n.getHours()-i);var o=(n-r)/(864e5*7);return 1+Math.floor(o)},U6e=function(t){var n=t.getDay();return n===0&&(n=7),n},W6e=function(t){return(String(t).match(j6e)||[""]).pop().replace(H6e,"").replace(/GMT\+0000/g,"UTC")};const 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"+i.message+" For more info read https://konvajs.org/docs/posts/Tainted_Canvas.html."),""}}}}ee.addGetterSetter(Cx,"pixelRatio",void 0,Ge());class Fg extends Cx{constructor(t={width:0,height:0}){super(t),this.context=new RCe(this),this.setSize(t.width,t.height)}}class eP extends Cx{constructor(t={width:0,height:0}){super(t),this.hitCanvas=!0,this.context=new ICe(this),this.setSize(t.width,t.height)}}const yn={get isDragging(){var e=!1;return yn._dragElements.forEach(t=>{t.dragStatus==="dragging"&&(e=!0)}),e},justDragged:!1,get node(){var e;return yn._dragElements.forEach(t=>{e=t.node}),e},_dragElements:new Map,_drag(e){const t=[];yn._dragElements.forEach((n,r)=>{const{node:i}=n,o=i.getStage();o.setPointersPositions(e),n.pointerId===void 0&&(n.pointerId=he._getFirstPointerId(e));const a=o._changedPointerPositions.find(u=>u.id===n.pointerId);if(!!a){if(n.dragStatus!=="dragging"){var s=i.dragDistance(),l=Math.max(Math.abs(a.x-n.startPointerPos.x),Math.abs(a.y-n.startPointerPos.y));if(l<s||(i.startDrag({evt:e}),!i.isDragging()))return}i._setDragPosition(e,n),t.push(i)}}),t.forEach(n=>{n.fire("dragmove",{type:"dragmove",target:n,evt:e},!0)})},_endDragBefore(e){const t=[];yn._dragElements.forEach(n=>{const{node:r}=n,i=r.getStage();if(e&&i.setPointersPositions(e),!i._changedPointerPositions.find(s=>s.id===n.pointerId))return;(n.dragStatus==="dragging"||n.dragStatus==="stopped")&&(yn.justDragged=!0,gt._mouseListenClick=!1,gt._touchListenClick=!1,gt._pointerListenClick=!1,n.dragStatus="stopped");const a=n.node.getLayer()||n.node instanceof gt.Stage&&n.node;a&&t.indexOf(a)===-1&&t.push(a)}),t.forEach(n=>{n.draw()})},_endDragAfter(e){yn._dragElements.forEach((t,n)=>{t.dragStatus==="stopped"&&t.node.fire("dragend",{type:"dragend",target:t.node,evt:e},!0),t.dragStatus!=="dragging"&&yn._dragElements.delete(n)})}};gt.isBrowser&&(window.addEventListener("mouseup",yn._endDragBefore,!0),window.addEventListener("touchend",yn._endDragBefore,!0),window.addEventListener("mousemove",yn._drag),window.addEventListener("touchmove",yn._drag),window.addEventListener("mouseup",yn._endDragAfter,!1),window.addEventListener("touchend",yn._endDragAfter,!1));var T4="absoluteOpacity",Pb="allEventListeners",Uu="absoluteTransform",LI="absoluteScale",dh="canvas",NCe="Change",BCe="children",$Ce="konva",d8="listening",AI="mouseenter",OI="mouseleave",MI="set",RI="Shape",L4=" ",II="stage",md="transform",FCe="Stage",f8="visible",zCe=["xChange.konva","yChange.konva","scaleXChange.konva","scaleYChange.konva","skewXChange.konva","skewYChange.konva","rotationChange.konva","offsetXChange.konva","offsetYChange.konva","transformsEnabledChange.konva"].join(L4);let jCe=1;class qe{constructor(t){this._id=jCe++,this.eventListeners={},this.attrs={},this.index=0,this._allEventListeners=null,this.parent=null,this._cache=new Map,this._attachedDepsListeners=new Map,this._lastPos=null,this._batchingTransformChange=!1,this._needClearTransformCache=!1,this._filterUpToDate=!1,this._isUnderCache=!1,this._dragEventId=null,this._shouldFireChangeEvents=!1,this.setAttrs(t),this._shouldFireChangeEvents=!0}hasChildren(){return!1}_clearCache(t){(t===md||t===Uu)&&this._cache.get(t)?this._cache.get(t).dirty=!0:t?this._cache.delete(t):this._cache.clear()}_getCache(t,n){var r=this._cache.get(t),i=t===md||t===Uu,o=r===void 0||i&&r.dirty===!0;return o&&(r=n.call(this),this._cache.set(t,r)),r}_calculate(t,n,r){if(!this._attachedDepsListeners.get(t)){const i=n.map(o=>o+"Change.konva").join(L4);this.on(i,()=>{this._clearCache(t)}),this._attachedDepsListeners.set(t,!0)}return this._getCache(t,r)}_getCanvasCache(){return this._cache.get(dh)}_clearSelfAndDescendantCache(t){this._clearCache(t),t===Uu&&this.fire("absoluteTransformChange")}clearCache(){if(this._cache.has(dh)){const{scene:t,filter:n,hit:r}=this._cache.get(dh);he.releaseCanvas(t,n,r),this._cache.delete(dh)}return this._clearSelfAndDescendantCache(),this._requestDraw(),this}cache(t){var n=t||{},r={};(n.x===void 0||n.y===void 0||n.width===void 0||n.height===void 0)&&(r=this.getClientRect({skipTransform:!0,relativeTo:this.getParent()}));var i=Math.ceil(n.width||r.width),o=Math.ceil(n.height||r.height),a=n.pixelRatio,s=n.x===void 0?Math.floor(r.x):n.x,l=n.y===void 0?Math.floor(r.y):n.y,u=n.offset||0,f=n.drawBorder||!1,p=n.hitCanvasPixelRatio||1;if(!i||!o){he.error("Can not cache the node. 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Caching is skipped.");return}i+=u*2+1,o+=u*2+1,s-=u,l-=u;var m=new Fg({pixelRatio:a,width:i,height:o}),v=new Fg({pixelRatio:a,width:0,height:0}),b=new eP({pixelRatio:p,width:i,height:o}),x=m.getContext(),E=b.getContext();return b.isCache=!0,m.isCache=!0,this._cache.delete(dh),this._filterUpToDate=!1,n.imageSmoothingEnabled===!1&&(m.getContext()._context.imageSmoothingEnabled=!1,v.getContext()._context.imageSmoothingEnabled=!1),x.save(),E.save(),x.translate(-s,-l),E.translate(-s,-l),this._isUnderCache=!0,this._clearSelfAndDescendantCache(T4),this._clearSelfAndDescendantCache(LI),this.drawScene(m,this),this.drawHit(b,this),this._isUnderCache=!1,x.restore(),E.restore(),f&&(x.save(),x.beginPath(),x.rect(0,0,i,o),x.closePath(),x.setAttr("strokeStyle","red"),x.setAttr("lineWidth",5),x.stroke(),x.restore()),this._cache.set(dh,{scene:m,filter:v,hit:b,x:s,y:l}),this._requestDraw(),this}isCached(){return this._cache.has(dh)}getClientRect(t){throw new Error('abstract "getClientRect" method call')}_transformedRect(t,n){var r=[{x:t.x,y:t.y},{x:t.x+t.width,y:t.y},{x:t.x+t.width,y:t.y+t.height},{x:t.x,y:t.y+t.height}],i,o,a,s,l=this.getAbsoluteTransform(n);return r.forEach(function(u){var f=l.point(u);i===void 0&&(i=a=f.x,o=s=f.y),i=Math.min(i,f.x),o=Math.min(o,f.y),a=Math.max(a,f.x),s=Math.max(s,f.y)}),{x:i,y:o,width:a-i,height:s-o}}_drawCachedSceneCanvas(t){t.save(),t._applyOpacity(this),t._applyGlobalCompositeOperation(this);const n=this._getCanvasCache();t.translate(n.x,n.y);var r=this._getCachedSceneCanvas(),i=r.pixelRatio;t.drawImage(r._canvas,0,0,r.width/i,r.height/i),t.restore()}_drawCachedHitCanvas(t){var n=this._getCanvasCache(),r=n.hit;t.save(),t.translate(n.x,n.y),t.drawImage(r._canvas,0,0,r.width/r.pixelRatio,r.height/r.pixelRatio),t.restore()}_getCachedSceneCanvas(){var t=this.filters(),n=this._getCanvasCache(),r=n.scene,i=n.filter,o=i.getContext(),a,s,l,u;if(t){if(!this._filterUpToDate){var f=r.pixelRatio;i.setSize(r.width/r.pixelRatio,r.height/r.pixelRatio);try{for(a=t.length,o.clear(),o.drawImage(r._canvas,0,0,r.getWidth()/f,r.getHeight()/f),s=o.getImageData(0,0,i.getWidth(),i.getHeight()),l=0;l<a;l++){if(u=t[l],typeof u!="function"){he.error("Filter should be type of function, but got "+typeof u+" instead. Please check correct filters");continue}u.call(this,s),o.putImageData(s,0,0)}}catch(p){he.error("Unable to apply filter. "+p.message+" This post my help you https://konvajs.org/docs/posts/Tainted_Canvas.html.")}this._filterUpToDate=!0}return i}return r}on(t,n){if(this._cache&&this._cache.delete(Pb),arguments.length===3)return this._delegate.apply(this,arguments);var r=t.split(L4),i=r.length,o,a,s,l,u;for(o=0;o<i;o++)a=r[o],s=a.split("."),l=s[0],u=s[1]||"",this.eventListeners[l]||(this.eventListeners[l]=[]),this.eventListeners[l].push({name:u,handler:n});return this}off(t,n){var r=(t||"").split(L4),i=r.length,o,a,s,l,u,f;if(this._cache&&this._cache.delete(Pb),!t)for(a in this.eventListeners)this._off(a);for(o=0;o<i;o++)if(s=r[o],l=s.split("."),u=l[0],f=l[1],u)this.eventListeners[u]&&this._off(u,f,n);else for(a in this.eventListeners)this._off(a,f,n);return this}dispatchEvent(t){var n={target:this,type:t.type,evt:t};return this.fire(t.type,n),this}addEventListener(t,n){return this.on(t,function(r){n.call(this,r.evt)}),this}removeEventListener(t){return this.off(t),this}_delegate(t,n,r){var i=this;this.on(t,function(o){for(var a=o.target.findAncestors(n,!0,i),s=0;s<a.length;s++)o=he.cloneObject(o),o.currentTarget=a[s],r.call(a[s],o)})}remove(){return this.isDragging()&&this.stopDrag(),yn._dragElements.delete(this._id),this._remove(),this}_clearCaches(){this._clearSelfAndDescendantCache(Uu),this._clearSelfAndDescendantCache(T4),this._clearSelfAndDescendantCache(LI),this._clearSelfAndDescendantCache(II),this._clearSelfAndDescendantCache(f8),this._clearSelfAndDescendantCache(d8)}_remove(){this._clearCaches();var t=this.getParent();t&&t.children&&(t.children.splice(this.index,1),t._setChildrenIndices(),this.parent=null)}destroy(){return this.remove(),this.clearCache(),this}getAttr(t){var n="get"+he._capitalize(t);return he._isFunction(this[n])?this[n]():this.attrs[t]}getAncestors(){for(var t=this.getParent(),n=[];t;)n.push(t),t=t.getParent();return n}getAttrs(){return this.attrs||{}}setAttrs(t){return this._batchTransformChanges(()=>{var n,r;if(!t)return this;for(n in t)n!==BCe&&(r=MI+he._capitalize(n),he._isFunction(this[r])?this[r](t[n]):this._setAttr(n,t[n]))}),this}isListening(){return this._getCache(d8,this._isListening)}_isListening(t){if(!this.listening())return!1;const r=this.getParent();return r&&r!==t&&this!==t?r._isListening(t):!0}isVisible(){return this._getCache(f8,this._isVisible)}_isVisible(t){if(!this.visible())return!1;const r=this.getParent();return r&&r!==t&&this!==t?r._isVisible(t):!0}shouldDrawHit(t,n=!1){if(t)return this._isVisible(t)&&this._isListening(t);var r=this.getLayer(),i=!1;yn._dragElements.forEach(a=>{a.dragStatus==="dragging"&&(a.node.nodeType==="Stage"||a.node.getLayer()===r)&&(i=!0)});var o=!n&&!gt.hitOnDragEnabled&&i;return this.isListening()&&this.isVisible()&&!o}show(){return this.visible(!0),this}hide(){return this.visible(!1),this}getZIndex(){return this.index||0}getAbsoluteZIndex(){var t=this.getDepth(),n=this,r=0,i,o,a,s;function l(u){for(i=[],o=u.length,a=0;a<o;a++)s=u[a],r++,s.nodeType!==RI&&(i=i.concat(s.getChildren().slice())),s._id===n._id&&(a=o);i.length>0&&i[0].getDepth()<=t&&l(i)}return n.nodeType!==FCe&&l(n.getStage().getChildren()),r}getDepth(){for(var t=0,n=this.parent;n;)t++,n=n.parent;return t}_batchTransformChanges(t){this._batchingTransformChange=!0,t(),this._batchingTransformChange=!1,this._needClearTransformCache&&(this._clearCache(md),this._clearSelfAndDescendantCache(Uu)),this._needClearTransformCache=!1}setPosition(t){return this._batchTransformChanges(()=>{this.x(t.x),this.y(t.y)}),this}getPosition(){return{x:this.x(),y:this.y()}}getRelativePointerPosition(){if(!this.getStage())return null;var t=this.getStage().getPointerPosition();if(!t)return null;var n=this.getAbsoluteTransform().copy();return n.invert(),n.point(t)}getAbsolutePosition(t){let n=!1,r=this.parent;for(;r;){if(r.isCached()){n=!0;break}r=r.parent}n&&!t&&(t=!0);var i=this.getAbsoluteTransform(t).getMatrix(),o=new Ea,a=this.offset();return o.m=i.slice(),o.translate(a.x,a.y),o.getTranslation()}setAbsolutePosition(t){var n=this._clearTransform();this.attrs.x=n.x,this.attrs.y=n.y,delete n.x,delete n.y,this._clearCache(md);var r=this._getAbsoluteTransform().copy();return r.invert(),r.translate(t.x,t.y),t={x:this.attrs.x+r.getTranslation().x,y:this.attrs.y+r.getTranslation().y},this._setTransform(n),this.setPosition({x:t.x,y:t.y}),this._clearCache(md),this._clearSelfAndDescendantCache(Uu),this}_setTransform(t){var n;for(n in t)this.attrs[n]=t[n]}_clearTransform(){var t={x:this.x(),y:this.y(),rotation:this.rotation(),scaleX:this.scaleX(),scaleY:this.scaleY(),offsetX:this.offsetX(),offsetY:this.offsetY(),skewX:this.skewX(),skewY:this.skewY()};return this.attrs.x=0,this.attrs.y=0,this.attrs.rotation=0,this.attrs.scaleX=1,this.attrs.scaleY=1,this.attrs.offsetX=0,this.attrs.offsetY=0,this.attrs.skewX=0,this.attrs.skewY=0,t}move(t){var n=t.x,r=t.y,i=this.x(),o=this.y();return n!==void 0&&(i+=n),r!==void 0&&(o+=r),this.setPosition({x:i,y:o}),this}_eachAncestorReverse(t,n){var r=[],i=this.getParent(),o,a;if(!(n&&n._id===this._id)){for(r.unshift(this);i&&(!n||i._id!==n._id);)r.unshift(i),i=i.parent;for(o=r.length,a=0;a<o;a++)t(r[a])}}rotate(t){return this.rotation(this.rotation()+t),this}moveToTop(){if(!this.parent)return he.warn("Node has no parent. moveToTop function is ignored."),!1;var t=this.index,n=this.parent.getChildren().length;return t<n-1?(this.parent.children.splice(t,1),this.parent.children.push(this),this.parent._setChildrenIndices(),!0):!1}moveUp(){if(!this.parent)return he.warn("Node has no parent. moveUp function is ignored."),!1;var t=this.index,n=this.parent.getChildren().length;return t<n-1?(this.parent.children.splice(t,1),this.parent.children.splice(t+1,0,this),this.parent._setChildrenIndices(),!0):!1}moveDown(){if(!this.parent)return he.warn("Node has no parent. moveDown function is ignored."),!1;var t=this.index;return t>0?(this.parent.children.splice(t,1),this.parent.children.splice(t-1,0,this),this.parent._setChildrenIndices(),!0):!1}moveToBottom(){if(!this.parent)return he.warn("Node has no parent. moveToBottom function is ignored."),!1;var t=this.index;return t>0?(this.parent.children.splice(t,1),this.parent.children.unshift(this),this.parent._setChildrenIndices(),!0):!1}setZIndex(t){if(!this.parent)return he.warn("Node has no parent. zIndex parameter is ignored."),this;(t<0||t>=this.parent.children.length)&&he.warn("Unexpected value "+t+" for zIndex property. zIndex is just index of a node in children of its parent. Expected value is from 0 to "+(this.parent.children.length-1)+".");var n=this.index;return this.parent.children.splice(n,1),this.parent.children.splice(t,0,this),this.parent._setChildrenIndices(),this}getAbsoluteOpacity(){return this._getCache(T4,this._getAbsoluteOpacity)}_getAbsoluteOpacity(){var t=this.opacity(),n=this.getParent();return n&&!n._isUnderCache&&(t*=n.getAbsoluteOpacity()),t}moveTo(t){return this.getParent()!==t&&(this._remove(),t.add(this)),this}toObject(){var t={},n=this.getAttrs(),r,i,o,a,s;t.attrs={};for(r in n)i=n[r],s=he.isObject(i)&&!he._isPlainObject(i)&&!he._isArray(i),!s&&(o=typeof this[r]=="function"&&this[r],delete n[r],a=o?o.call(this):null,n[r]=i,a!==i&&(t.attrs[r]=i));return t.className=this.getClassName(),he._prepareToStringify(t)}toJSON(){return JSON.stringify(this.toObject())}getParent(){return this.parent}findAncestors(t,n,r){var i=[];n&&this._isMatch(t)&&i.push(this);for(var o=this.parent;o;){if(o===r)return i;o._isMatch(t)&&i.push(o),o=o.parent}return i}isAncestorOf(t){return!1}findAncestor(t,n,r){return this.findAncestors(t,n,r)[0]}_isMatch(t){if(!t)return!1;if(typeof t=="function")return t(this);var n=t.replace(/ /g,"").split(","),r=n.length,i,o;for(i=0;i<r;i++)if(o=n[i],he.isValidSelector(o)||(he.warn('Selector "'+o+'" is invalid. Allowed selectors examples are "#foo", ".bar" or "Group".'),he.warn('If you have a custom shape with such className, please change it to start with upper letter like "Triangle".'),he.warn("Konva is awesome, right?")),o.charAt(0)==="#"){if(this.id()===o.slice(1))return!0}else if(o.charAt(0)==="."){if(this.hasName(o.slice(1)))return!0}else if(this.className===o||this.nodeType===o)return!0;return!1}getLayer(){var t=this.getParent();return t?t.getLayer():null}getStage(){return this._getCache(II,this._getStage)}_getStage(){var t=this.getParent();if(t)return t.getStage()}fire(t,n={},r){return n.target=n.target||this,r?this._fireAndBubble(t,n):this._fire(t,n),this}getAbsoluteTransform(t){return t?this._getAbsoluteTransform(t):this._getCache(Uu,this._getAbsoluteTransform)}_getAbsoluteTransform(t){var n;if(t)return n=new Ea,this._eachAncestorReverse(function(i){var o=i.transformsEnabled();o==="all"?n.multiply(i.getTransform()):o==="position"&&n.translate(i.x()-i.offsetX(),i.y()-i.offsetY())},t),n;n=this._cache.get(Uu)||new Ea,this.parent?this.parent.getAbsoluteTransform().copyInto(n):n.reset();var r=this.transformsEnabled();if(r==="all")n.multiply(this.getTransform());else if(r==="position"){const i=this.attrs.x||0,o=this.attrs.y||0,a=this.attrs.offsetX||0,s=this.attrs.offsetY||0;n.translate(i-a,o-s)}return n.dirty=!1,n}getAbsoluteScale(t){for(var n=this;n;)n._isUnderCache&&(t=n),n=n.getParent();const i=this.getAbsoluteTransform(t).decompose();return{x:i.scaleX,y:i.scaleY}}getAbsoluteRotation(){return this.getAbsoluteTransform().decompose().rotation}getTransform(){return this._getCache(md,this._getTransform)}_getTransform(){var t,n,r=this._cache.get(md)||new Ea;r.reset();var i=this.x(),o=this.y(),a=gt.getAngle(this.rotation()),s=(t=this.attrs.scaleX)!==null&&t!==void 0?t:1,l=(n=this.attrs.scaleY)!==null&&n!==void 0?n:1,u=this.attrs.skewX||0,f=this.attrs.skewY||0,p=this.attrs.offsetX||0,m=this.attrs.offsetY||0;return(i!==0||o!==0)&&r.translate(i,o),a!==0&&r.rotate(a),(u!==0||f!==0)&&r.skew(u,f),(s!==1||l!==1)&&r.scale(s,l),(p!==0||m!==0)&&r.translate(-1*p,-1*m),r.dirty=!1,r}clone(t){var n=he.cloneObject(this.attrs),r,i,o,a,s;for(r in t)n[r]=t[r];var l=new this.constructor(n);for(r in this.eventListeners)for(i=this.eventListeners[r],o=i.length,a=0;a<o;a++)s=i[a],s.name.indexOf($Ce)<0&&(l.eventListeners[r]||(l.eventListeners[r]=[]),l.eventListeners[r].push(s));return l}_toKonvaCanvas(t){t=t||{};var n=this.getClientRect(),r=this.getStage(),i=t.x!==void 0?t.x:Math.floor(n.x),o=t.y!==void 0?t.y:Math.floor(n.y),a=t.pixelRatio||1,s=new Fg({width:t.width||Math.ceil(n.width)||(r?r.width():0),height:t.height||Math.ceil(n.height)||(r?r.height():0),pixelRatio:a}),l=s.getContext();return t.imageSmoothingEnabled===!1&&(l._context.imageSmoothingEnabled=!1),l.save(),(i||o)&&l.translate(-1*i,-1*o),this.drawScene(s),l.restore(),s}toCanvas(t){return this._toKonvaCanvas(t)._canvas}toDataURL(t){t=t||{};var n=t.mimeType||null,r=t.quality||null,i=this._toKonvaCanvas(t).toDataURL(n,r);return t.callback&&t.callback(i),i}toImage(t){return new Promise((n,r)=>{try{const i=t==null?void 0:t.callback;i&&delete t.callback,he._urlToImage(this.toDataURL(t),function(o){n(o),i==null||i(o)})}catch(i){r(i)}})}toBlob(t){return new Promise((n,r)=>{try{const i=t==null?void 0:t.callback;i&&delete t.callback,this.toCanvas(t).toBlob(o=>{n(o),i==null||i(o)})}catch(i){r(i)}})}setSize(t){return this.width(t.width),this.height(t.height),this}getSize(){return{width:this.width(),height:this.height()}}getClassName(){return this.className||this.nodeType}getType(){return this.nodeType}getDragDistance(){return this.attrs.dragDistance!==void 0?this.attrs.dragDistance:this.parent?this.parent.getDragDistance():gt.dragDistance}_off(t,n,r){var i=this.eventListeners[t],o,a,s;for(o=0;o<i.length;o++)if(a=i[o].name,s=i[o].handler,(a!=="konva"||n==="konva")&&(!n||a===n)&&(!r||r===s)){if(i.splice(o,1),i.length===0){delete this.eventListeners[t];break}o--}}_fireChangeEvent(t,n,r){this._fire(t+NCe,{oldVal:n,newVal:r})}addName(t){if(!this.hasName(t)){var n=this.name(),r=n?n+" "+t:t;this.name(r)}return this}hasName(t){if(!t)return!1;const n=this.name();if(!n)return!1;var r=(n||"").split(/\s/g);return r.indexOf(t)!==-1}removeName(t){var n=(this.name()||"").split(/\s/g),r=n.indexOf(t);return r!==-1&&(n.splice(r,1),this.name(n.join(" "))),this}setAttr(t,n){var r=this[MI+he._capitalize(t)];return he._isFunction(r)?r.call(this,n):this._setAttr(t,n),this}_requestDraw(){if(gt.autoDrawEnabled){const t=this.getLayer()||this.getStage();t==null||t.batchDraw()}}_setAttr(t,n){var r=this.attrs[t];r===n&&!he.isObject(n)||(n==null?delete this.attrs[t]:this.attrs[t]=n,this._shouldFireChangeEvents&&this._fireChangeEvent(t,r,n),this._requestDraw())}_setComponentAttr(t,n,r){var i;r!==void 0&&(i=this.attrs[t],i||(this.attrs[t]=this.getAttr(t)),this.attrs[t][n]=r,this._fireChangeEvent(t,i,r))}_fireAndBubble(t,n,r){n&&this.nodeType===RI&&(n.target=this);var i=(t===AI||t===OI)&&(r&&(this===r||this.isAncestorOf&&this.isAncestorOf(r))||this.nodeType==="Stage"&&!r);if(!i){this._fire(t,n);var o=(t===AI||t===OI)&&r&&r.isAncestorOf&&r.isAncestorOf(this)&&!r.isAncestorOf(this.parent);(n&&!n.cancelBubble||!n)&&this.parent&&this.parent.isListening()&&!o&&(r&&r.parent?this._fireAndBubble.call(this.parent,t,n,r):this._fireAndBubble.call(this.parent,t,n))}}_getProtoListeners(t){let n=this._cache.get(Pb);if(!n){n={};let i=Object.getPrototypeOf(this);for(;i;){if(!i.eventListeners){i=Object.getPrototypeOf(i);continue}for(var r in i.eventListeners){const o=i.eventListeners[r],a=n[r]||[];n[r]=o.concat(a)}i=Object.getPrototypeOf(i)}this._cache.set(Pb,n)}return n[t]}_fire(t,n){n=n||{},n.currentTarget=this,n.type=t;const r=this._getProtoListeners(t);if(r)for(var i=0;i<r.length;i++)r[i].handler.call(this,n);const o=this.eventListeners[t];if(o)for(var i=0;i<o.length;i++)o[i].handler.call(this,n)}draw(){return this.drawScene(),this.drawHit(),this}_createDragElement(t){var n=t?t.pointerId:void 0,r=this.getStage(),i=this.getAbsolutePosition(),o=r._getPointerById(n)||r._changedPointerPositions[0]||i;yn._dragElements.set(this._id,{node:this,startPointerPos:o,offset:{x:o.x-i.x,y:o.y-i.y},dragStatus:"ready",pointerId:n})}startDrag(t,n=!0){yn._dragElements.has(this._id)||this._createDragElement(t);const r=yn._dragElements.get(this._id);r.dragStatus="dragging",this.fire("dragstart",{type:"dragstart",target:this,evt:t&&t.evt},n)}_setDragPosition(t,n){const r=this.getStage()._getPointerById(n.pointerId);if(!!r){var i={x:r.x-n.offset.x,y:r.y-n.offset.y},o=this.dragBoundFunc();if(o!==void 0){const a=o.call(this,i,t);a?i=a:he.warn("dragBoundFunc did not return any value. That is unexpected behavior. You must return new absolute position from dragBoundFunc.")}(!this._lastPos||this._lastPos.x!==i.x||this._lastPos.y!==i.y)&&(this.setAbsolutePosition(i),this._requestDraw()),this._lastPos=i}}stopDrag(t){const n=yn._dragElements.get(this._id);n&&(n.dragStatus="stopped"),yn._endDragBefore(t),yn._endDragAfter(t)}setDraggable(t){this._setAttr("draggable",t),this._dragChange()}isDragging(){const t=yn._dragElements.get(this._id);return t?t.dragStatus==="dragging":!1}_listenDrag(){this._dragCleanup(),this.on("mousedown.konva touchstart.konva",function(t){var n=t.evt.button!==void 0,r=!n||gt.dragButtons.indexOf(t.evt.button)>=0;if(!!r&&!this.isDragging()){var i=!1;yn._dragElements.forEach(o=>{this.isAncestorOf(o.node)&&(i=!0)}),i||this._createDragElement(t)}})}_dragChange(){if(this.attrs.draggable)this._listenDrag();else{this._dragCleanup();var t=this.getStage();if(!t)return;const n=yn._dragElements.get(this._id),r=n&&n.dragStatus==="dragging",i=n&&n.dragStatus==="ready";r?this.stopDrag():i&&yn._dragElements.delete(this._id)}}_dragCleanup(){this.off("mousedown.konva"),this.off("touchstart.konva")}isClientRectOnScreen(t={x:0,y:0}){const n=this.getStage();if(!n)return!1;const r={x:-t.x,y:-t.y,width:n.width()+2*t.x,height:n.height()+2*t.y};return he.haveIntersection(r,this.getClientRect())}static create(t,n){return he._isString(t)&&(t=JSON.parse(t)),this._createNode(t,n)}static _createNode(t,n){var r=qe.prototype.getClassName.call(t),i=t.children,o,a,s;n&&(t.attrs.container=n),gt[r]||(he.warn('Can not find a node with class name "'+r+'". Fallback to "Shape".'),r="Shape");const l=gt[r];if(o=new l(t.attrs),i)for(a=i.length,s=0;s<a;s++)o.add(qe._createNode(i[s]));return o}}qe.prototype.nodeType="Node";qe.prototype._attrsAffectingSize=[];qe.prototype.eventListeners={};qe.prototype.on.call(qe.prototype,zCe,function(){if(this._batchingTransformChange){this._needClearTransformCache=!0;return}this._clearCache(md),this._clearSelfAndDescendantCache(Uu)});qe.prototype.on.call(qe.prototype,"visibleChange.konva",function(){this._clearSelfAndDescendantCache(f8)});qe.prototype.on.call(qe.prototype,"listeningChange.konva",function(){this._clearSelfAndDescendantCache(d8)});qe.prototype.on.call(qe.prototype,"opacityChange.konva",function(){this._clearSelfAndDescendantCache(T4)});const qn=ee.addGetterSetter;qn(qe,"zIndex");qn(qe,"absolutePosition");qn(qe,"position");qn(qe,"x",0,Ge());qn(qe,"y",0,Ge());qn(qe,"globalCompositeOperation","source-over",k1());qn(qe,"opacity",1,Ge());qn(qe,"name","",k1());qn(qe,"id","",k1());qn(qe,"rotation",0,Ge());ee.addComponentsGetterSetter(qe,"scale",["x","y"]);qn(qe,"scaleX",1,Ge());qn(qe,"scaleY",1,Ge());ee.addComponentsGetterSetter(qe,"skew",["x","y"]);qn(qe,"skewX",0,Ge());qn(qe,"skewY",0,Ge());ee.addComponentsGetterSetter(qe,"offset",["x","y"]);qn(qe,"offsetX",0,Ge());qn(qe,"offsetY",0,Ge());qn(qe,"dragDistance",null,Ge());qn(qe,"width",0,Ge());qn(qe,"height",0,Ge());qn(qe,"listening",!0,il());qn(qe,"preventDefault",!0,il());qn(qe,"filters",null,function(e){return this._filterUpToDate=!1,e});qn(qe,"visible",!0,il());qn(qe,"transformsEnabled","all",k1());qn(qe,"size");qn(qe,"dragBoundFunc");qn(qe,"draggable",!1,il());ee.backCompat(qe,{rotateDeg:"rotate",setRotationDeg:"setRotation",getRotationDeg:"getRotation"});class Oa extends qe{constructor(){super(...arguments),this.children=[]}getChildren(t){if(!t)return this.children||[];const n=this.children||[];var r=[];return n.forEach(function(i){t(i)&&r.push(i)}),r}hasChildren(){return this.getChildren().length>0}removeChildren(){return this.getChildren().forEach(t=>{t.parent=null,t.index=0,t.remove()}),this.children=[],this._requestDraw(),this}destroyChildren(){return this.getChildren().forEach(t=>{t.parent=null,t.index=0,t.destroy()}),this.children=[],this._requestDraw(),this}add(...t){if(arguments.length>1){for(var n=0;n<arguments.length;n++)this.add(arguments[n]);return this}var r=t[0];return r.getParent()?(r.moveTo(this),this):(this._validateAdd(r),r.index=this.getChildren().length,r.parent=this,r._clearCaches(),this.getChildren().push(r),this._fire("add",{child:r}),this._requestDraw(),this)}destroy(){return this.hasChildren()&&this.destroyChildren(),super.destroy(),this}find(t){return this._generalFind(t,!1)}findOne(t){var n=this._generalFind(t,!0);return n.length>0?n[0]:void 0}_generalFind(t,n){var r=[];return this._descendants(i=>{const o=i._isMatch(t);return o&&r.push(i),!!(o&&n)}),r}_descendants(t){let n=!1;const r=this.getChildren();for(const i of r){if(n=t(i),n)return!0;if(!!i.hasChildren()&&(n=i._descendants(t),n))return!0}return!1}toObject(){var t=qe.prototype.toObject.call(this);return t.children=[],this.getChildren().forEach(n=>{t.children.push(n.toObject())}),t}isAncestorOf(t){for(var n=t.getParent();n;){if(n._id===this._id)return!0;n=n.getParent()}return!1}clone(t){var n=qe.prototype.clone.call(this,t);return this.getChildren().forEach(function(r){n.add(r.clone())}),n}getAllIntersections(t){var n=[];return this.find("Shape").forEach(function(r){r.isVisible()&&r.intersects(t)&&n.push(r)}),n}_clearSelfAndDescendantCache(t){var n;super._clearSelfAndDescendantCache(t),!this.isCached()&&((n=this.children)===null||n===void 0||n.forEach(function(r){r._clearSelfAndDescendantCache(t)}))}_setChildrenIndices(){var t;(t=this.children)===null||t===void 0||t.forEach(function(n,r){n.index=r}),this._requestDraw()}drawScene(t,n){var r=this.getLayer(),i=t||r&&r.getCanvas(),o=i&&i.getContext(),a=this._getCanvasCache(),s=a&&a.scene,l=i&&i.isCache;if(!this.isVisible()&&!l)return this;if(s){o.save();var u=this.getAbsoluteTransform(n).getMatrix();o.transform(u[0],u[1],u[2],u[3],u[4],u[5]),this._drawCachedSceneCanvas(o),o.restore()}else this._drawChildren("drawScene",i,n);return this}drawHit(t,n){if(!this.shouldDrawHit(n))return this;var r=this.getLayer(),i=t||r&&r.hitCanvas,o=i&&i.getContext(),a=this._getCanvasCache(),s=a&&a.hit;if(s){o.save();var l=this.getAbsoluteTransform(n).getMatrix();o.transform(l[0],l[1],l[2],l[3],l[4],l[5]),this._drawCachedHitCanvas(o),o.restore()}else this._drawChildren("drawHit",i,n);return this}_drawChildren(t,n,r){var i,o=n&&n.getContext(),a=this.clipWidth(),s=this.clipHeight(),l=this.clipFunc(),u=a&&s||l;const f=r===this;if(u){o.save();var p=this.getAbsoluteTransform(r),m=p.getMatrix();if(o.transform(m[0],m[1],m[2],m[3],m[4],m[5]),o.beginPath(),l)l.call(this,o,this);else{var v=this.clipX(),b=this.clipY();o.rect(v,b,a,s)}o.clip(),m=p.copy().invert().getMatrix(),o.transform(m[0],m[1],m[2],m[3],m[4],m[5])}var x=!f&&this.globalCompositeOperation()!=="source-over"&&t==="drawScene";x&&(o.save(),o._applyGlobalCompositeOperation(this)),(i=this.children)===null||i===void 0||i.forEach(function(E){E[t](n,r)}),x&&o.restore(),u&&o.restore()}getClientRect(t){var n;t=t||{};var r=t.skipTransform,i=t.relativeTo,o,a,s,l,u={x:1/0,y:1/0,width:0,height:0},f=this;(n=this.children)===null||n===void 0||n.forEach(function(x){if(!!x.visible()){var E=x.getClientRect({relativeTo:f,skipShadow:t.skipShadow,skipStroke:t.skipStroke});E.width===0&&E.height===0||(o===void 0?(o=E.x,a=E.y,s=E.x+E.width,l=E.y+E.height):(o=Math.min(o,E.x),a=Math.min(a,E.y),s=Math.max(s,E.x+E.width),l=Math.max(l,E.y+E.height)))}});for(var p=this.find("Shape"),m=!1,v=0;v<p.length;v++){var b=p[v];if(b._isVisible(this)){m=!0;break}}return m&&o!==void 0?u={x:o,y:a,width:s-o,height:l-a}:u={x:0,y:0,width:0,height:0},r?u:this._transformedRect(u,i)}}ee.addComponentsGetterSetter(Oa,"clip",["x","y","width","height"]);ee.addGetterSetter(Oa,"clipX",void 0,Ge());ee.addGetterSetter(Oa,"clipY",void 0,Ge());ee.addGetterSetter(Oa,"clipWidth",void 0,Ge());ee.addGetterSetter(Oa,"clipHeight",void 0,Ge());ee.addGetterSetter(Oa,"clipFunc");const Q2=new Map,HW=gt._global.PointerEvent!==void 0;function vC(e){return Q2.get(e)}function tP(e){return{evt:e,pointerId:e.pointerId}}function VW(e,t){return Q2.get(e)===t}function UW(e,t){n2(e),t.getStage()&&(Q2.set(e,t),HW&&t._fire("gotpointercapture",tP(new PointerEvent("gotpointercapture"))))}function n2(e,t){const n=Q2.get(e);if(!n)return;const r=n.getStage();r&&r.content,Q2.delete(e),HW&&n._fire("lostpointercapture",tP(new PointerEvent("lostpointercapture")))}var HCe="Stage",VCe="string",DI="px",UCe="mouseout",WW="mouseleave",GW="mouseover",qW="mouseenter",YW="mousemove",KW="mousedown",XW="mouseup",Ev="pointermove",Pv="pointerdown",ug="pointerup",Tv="pointercancel",WCe="lostpointercapture",Tb="pointerout",Lb="pointerleave",Ab="pointerover",Ob="pointerenter",h8="contextmenu",ZW="touchstart",QW="touchend",JW="touchmove",eG="touchcancel",p8="wheel",GCe=5,qCe=[[qW,"_pointerenter"],[KW,"_pointerdown"],[YW,"_pointermove"],[XW,"_pointerup"],[WW,"_pointerleave"],[ZW,"_pointerdown"],[JW,"_pointermove"],[QW,"_pointerup"],[eG,"_pointercancel"],[GW,"_pointerover"],[p8,"_wheel"],[h8,"_contextmenu"],[Pv,"_pointerdown"],[Ev,"_pointermove"],[ug,"_pointerup"],[Tv,"_pointercancel"],[WCe,"_lostpointercapture"]];const yC={mouse:{[Tb]:UCe,[Lb]:WW,[Ab]:GW,[Ob]:qW,[Ev]:YW,[Pv]:KW,[ug]:XW,[Tv]:"mousecancel",pointerclick:"click",pointerdblclick:"dblclick"},touch:{[Tb]:"touchout",[Lb]:"touchleave",[Ab]:"touchover",[Ob]:"touchenter",[Ev]:JW,[Pv]:ZW,[ug]:QW,[Tv]:eG,pointerclick:"tap",pointerdblclick:"dbltap"},pointer:{[Tb]:Tb,[Lb]:Lb,[Ab]:Ab,[Ob]:Ob,[Ev]:Ev,[Pv]:Pv,[ug]:ug,[Tv]:Tv,pointerclick:"pointerclick",pointerdblclick:"pointerdblclick"}},Lv=e=>e.indexOf("pointer")>=0?"pointer":e.indexOf("touch")>=0?"touch":"mouse",I0=e=>{const t=Lv(e);if(t==="pointer")return gt.pointerEventsEnabled&&yC.pointer;if(t==="touch")return yC.touch;if(t==="mouse")return yC.mouse};function NI(e={}){return(e.clipFunc||e.clipWidth||e.clipHeight)&&he.warn("Stage does not support clipping. Please use clip for Layers or Groups."),e}const YCe="Pointer position is missing and not registered by the stage. Looks like it is outside of the stage container. You can set it manually from event: stage.setPointersPositions(event);",A4=[];class _x extends Oa{constructor(t){super(NI(t)),this._pointerPositions=[],this._changedPointerPositions=[],this._buildDOM(),this._bindContentEvents(),A4.push(this),this.on("widthChange.konva heightChange.konva",this._resizeDOM),this.on("visibleChange.konva",this._checkVisibility),this.on("clipWidthChange.konva clipHeightChange.konva clipFuncChange.konva",()=>{NI(this.attrs)}),this._checkVisibility()}_validateAdd(t){const n=t.getType()==="Layer",r=t.getType()==="FastLayer";n||r||he.throw("You may only add layers to the stage.")}_checkVisibility(){if(!this.content)return;const t=this.visible()?"":"none";this.content.style.display=t}setContainer(t){if(typeof t===VCe){if(t.charAt(0)==="."){var n=t.slice(1);t=document.getElementsByClassName(n)[0]}else{var r;t.charAt(0)!=="#"?r=t:r=t.slice(1),t=document.getElementById(r)}if(!t)throw"Can not find container in document with id "+r}return this._setAttr("container",t),this.content&&(this.content.parentElement&&this.content.parentElement.removeChild(this.content),t.appendChild(this.content)),this}shouldDrawHit(){return!0}clear(){var t=this.children,n=t.length,r;for(r=0;r<n;r++)t[r].clear();return this}clone(t){return t||(t={}),t.container=typeof document<"u"&&document.createElement("div"),Oa.prototype.clone.call(this,t)}destroy(){super.destroy();var t=this.content;t&&he._isInDocument(t)&&this.container().removeChild(t);var n=A4.indexOf(this);return n>-1&&A4.splice(n,1),he.releaseCanvas(this.bufferCanvas._canvas,this.bufferHitCanvas._canvas),this}getPointerPosition(){const t=this._pointerPositions[0]||this._changedPointerPositions[0];return t?{x:t.x,y:t.y}:(he.warn(YCe),null)}_getPointerById(t){return this._pointerPositions.find(n=>n.id===t)}getPointersPositions(){return this._pointerPositions}getStage(){return this}getContent(){return this.content}_toKonvaCanvas(t){t=t||{},t.x=t.x||0,t.y=t.y||0,t.width=t.width||this.width(),t.height=t.height||this.height();var n=new Fg({width:t.width,height:t.height,pixelRatio:t.pixelRatio||1}),r=n.getContext()._context,i=this.children;return(t.x||t.y)&&r.translate(-1*t.x,-1*t.y),i.forEach(function(o){if(!!o.isVisible()){var a=o._toKonvaCanvas(t);r.drawImage(a._canvas,t.x,t.y,a.getWidth()/a.getPixelRatio(),a.getHeight()/a.getPixelRatio())}}),n}getIntersection(t){if(!t)return null;var n=this.children,r=n.length,i=r-1,o;for(o=i;o>=0;o--){const a=n[o].getIntersection(t);if(a)return a}return null}_resizeDOM(){var t=this.width(),n=this.height();this.content&&(this.content.style.width=t+DI,this.content.style.height=n+DI),this.bufferCanvas.setSize(t,n),this.bufferHitCanvas.setSize(t,n),this.children.forEach(r=>{r.setSize({width:t,height:n}),r.draw()})}add(t,...n){if(arguments.length>1){for(var r=0;r<arguments.length;r++)this.add(arguments[r]);return this}super.add(t);var i=this.children.length;return i>GCe&&he.warn("The stage has "+i+" layers. Recommended maximum number of layers is 3-5. Adding more layers into the stage may drop the performance. Rethink your tree structure, you can use Konva.Group."),t.setSize({width:this.width(),height:this.height()}),t.draw(),gt.isBrowser&&this.content.appendChild(t.canvas._canvas),this}getParent(){return null}getLayer(){return null}hasPointerCapture(t){return VW(t,this)}setPointerCapture(t){UW(t,this)}releaseCapture(t){n2(t)}getLayers(){return this.children}_bindContentEvents(){!gt.isBrowser||qCe.forEach(([t,n])=>{this.content.addEventListener(t,r=>{this[n](r)},{passive:!1})})}_pointerenter(t){this.setPointersPositions(t);const n=I0(t.type);this._fire(n.pointerenter,{evt:t,target:this,currentTarget:this})}_pointerover(t){this.setPointersPositions(t);const n=I0(t.type);this._fire(n.pointerover,{evt:t,target:this,currentTarget:this})}_getTargetShape(t){let n=this[t+"targetShape"];return n&&!n.getStage()&&(n=null),n}_pointerleave(t){const n=I0(t.type),r=Lv(t.type);if(!!n){this.setPointersPositions(t);var i=this._getTargetShape(r),o=!yn.isDragging||gt.hitOnDragEnabled;i&&o?(i._fireAndBubble(n.pointerout,{evt:t}),i._fireAndBubble(n.pointerleave,{evt:t}),this._fire(n.pointerleave,{evt:t,target:this,currentTarget:this}),this[r+"targetShape"]=null):o&&(this._fire(n.pointerleave,{evt:t,target:this,currentTarget:this}),this._fire(n.pointerout,{evt:t,target:this,currentTarget:this})),this.pointerPos=void 0,this._pointerPositions=[]}}_pointerdown(t){const n=I0(t.type),r=Lv(t.type);if(!!n){this.setPointersPositions(t);var i=!1;this._changedPointerPositions.forEach(o=>{var a=this.getIntersection(o);if(yn.justDragged=!1,gt["_"+r+"ListenClick"]=!0,!(a&&a.isListening()))return;gt.capturePointerEventsEnabled&&a.setPointerCapture(o.id),this[r+"ClickStartShape"]=a,a._fireAndBubble(n.pointerdown,{evt:t,pointerId:o.id}),i=!0;const l=t.type.indexOf("touch")>=0;a.preventDefault()&&t.cancelable&&l&&t.preventDefault()}),i||this._fire(n.pointerdown,{evt:t,target:this,currentTarget:this,pointerId:this._pointerPositions[0].id})}}_pointermove(t){const n=I0(t.type),r=Lv(t.type);if(!n)return;yn.isDragging&&yn.node.preventDefault()&&t.cancelable&&t.preventDefault(),this.setPointersPositions(t);var i=!yn.isDragging||gt.hitOnDragEnabled;if(!i)return;var o={};let a=!1;var s=this._getTargetShape(r);this._changedPointerPositions.forEach(l=>{const u=vC(l.id)||this.getIntersection(l),f=l.id,p={evt:t,pointerId:f};var m=s!==u;if(m&&s&&(s._fireAndBubble(n.pointerout,Object.assign({},p),u),s._fireAndBubble(n.pointerleave,Object.assign({},p),u)),u){if(o[u._id])return;o[u._id]=!0}u&&u.isListening()?(a=!0,m&&(u._fireAndBubble(n.pointerover,Object.assign({},p),s),u._fireAndBubble(n.pointerenter,Object.assign({},p),s),this[r+"targetShape"]=u),u._fireAndBubble(n.pointermove,Object.assign({},p))):s&&(this._fire(n.pointerover,{evt:t,target:this,currentTarget:this,pointerId:f}),this[r+"targetShape"]=null)}),a||this._fire(n.pointermove,{evt:t,target:this,currentTarget:this,pointerId:this._changedPointerPositions[0].id})}_pointerup(t){const n=I0(t.type),r=Lv(t.type);if(!n)return;this.setPointersPositions(t);const i=this[r+"ClickStartShape"],o=this[r+"ClickEndShape"];var a={};let s=!1;this._changedPointerPositions.forEach(l=>{const u=vC(l.id)||this.getIntersection(l);if(u){if(u.releaseCapture(l.id),a[u._id])return;a[u._id]=!0}const f=l.id,p={evt:t,pointerId:f};let m=!1;gt["_"+r+"InDblClickWindow"]?(m=!0,clearTimeout(this[r+"DblTimeout"])):yn.justDragged||(gt["_"+r+"InDblClickWindow"]=!0,clearTimeout(this[r+"DblTimeout"])),this[r+"DblTimeout"]=setTimeout(function(){gt["_"+r+"InDblClickWindow"]=!1},gt.dblClickWindow),u&&u.isListening()?(s=!0,this[r+"ClickEndShape"]=u,u._fireAndBubble(n.pointerup,Object.assign({},p)),gt["_"+r+"ListenClick"]&&i&&i===u&&(u._fireAndBubble(n.pointerclick,Object.assign({},p)),m&&o&&o===u&&u._fireAndBubble(n.pointerdblclick,Object.assign({},p)))):(this[r+"ClickEndShape"]=null,gt["_"+r+"ListenClick"]&&this._fire(n.pointerclick,{evt:t,target:this,currentTarget:this,pointerId:f}),m&&this._fire(n.pointerdblclick,{evt:t,target:this,currentTarget:this,pointerId:f}))}),s||this._fire(n.pointerup,{evt:t,target:this,currentTarget:this,pointerId:this._changedPointerPositions[0].id}),gt["_"+r+"ListenClick"]=!1,t.cancelable&&r!=="touch"&&t.preventDefault()}_contextmenu(t){this.setPointersPositions(t);var n=this.getIntersection(this.getPointerPosition());n&&n.isListening()?n._fireAndBubble(h8,{evt:t}):this._fire(h8,{evt:t,target:this,currentTarget:this})}_wheel(t){this.setPointersPositions(t);var n=this.getIntersection(this.getPointerPosition());n&&n.isListening()?n._fireAndBubble(p8,{evt:t}):this._fire(p8,{evt:t,target:this,currentTarget:this})}_pointercancel(t){this.setPointersPositions(t);const n=vC(t.pointerId)||this.getIntersection(this.getPointerPosition());n&&n._fireAndBubble(ug,tP(t)),n2(t.pointerId)}_lostpointercapture(t){n2(t.pointerId)}setPointersPositions(t){var n=this._getContentPosition(),r=null,i=null;t=t||window.event,t.touches!==void 0?(this._pointerPositions=[],this._changedPointerPositions=[],Array.prototype.forEach.call(t.touches,o=>{this._pointerPositions.push({id:o.identifier,x:(o.clientX-n.left)/n.scaleX,y:(o.clientY-n.top)/n.scaleY})}),Array.prototype.forEach.call(t.changedTouches||t.touches,o=>{this._changedPointerPositions.push({id:o.identifier,x:(o.clientX-n.left)/n.scaleX,y:(o.clientY-n.top)/n.scaleY})})):(r=(t.clientX-n.left)/n.scaleX,i=(t.clientY-n.top)/n.scaleY,this.pointerPos={x:r,y:i},this._pointerPositions=[{x:r,y:i,id:he._getFirstPointerId(t)}],this._changedPointerPositions=[{x:r,y:i,id:he._getFirstPointerId(t)}])}_setPointerPosition(t){he.warn('Method _setPointerPosition is deprecated. Use "stage.setPointersPositions(event)" instead.'),this.setPointersPositions(t)}_getContentPosition(){if(!this.content||!this.content.getBoundingClientRect)return{top:0,left:0,scaleX:1,scaleY:1};var t=this.content.getBoundingClientRect();return{top:t.top,left:t.left,scaleX:t.width/this.content.clientWidth||1,scaleY:t.height/this.content.clientHeight||1}}_buildDOM(){if(this.bufferCanvas=new Fg({width:this.width(),height:this.height()}),this.bufferHitCanvas=new eP({pixelRatio:1,width:this.width(),height:this.height()}),!!gt.isBrowser){var t=this.container();if(!t)throw"Stage has no container. A container is required.";t.innerHTML="",this.content=document.createElement("div"),this.content.style.position="relative",this.content.style.userSelect="none",this.content.className="konvajs-content",this.content.setAttribute("role","presentation"),t.appendChild(this.content),this._resizeDOM()}}cache(){return he.warn("Cache function is not allowed for stage. You may use cache only for layers, groups and shapes."),this}clearCache(){return this}batchDraw(){return this.getChildren().forEach(function(t){t.batchDraw()}),this}}_x.prototype.nodeType=HCe;Cr(_x);ee.addGetterSetter(_x,"container");var tG="hasShadow",nG="shadowRGBA",rG="patternImage",iG="linearGradient",oG="radialGradient";let Mb;function bC(){return Mb||(Mb=he.createCanvasElement().getContext("2d"),Mb)}const r2={};function KCe(e){e.fill()}function XCe(e){e.stroke()}function ZCe(e){e.fill()}function QCe(e){e.stroke()}function JCe(){this._clearCache(tG)}function e7e(){this._clearCache(nG)}function t7e(){this._clearCache(rG)}function n7e(){this._clearCache(iG)}function r7e(){this._clearCache(oG)}class $e extends qe{constructor(t){super(t);let n;for(;n=he.getRandomColor(),!(n&&!(n in r2)););this.colorKey=n,r2[n]=this}getContext(){return he.warn("shape.getContext() method is deprecated. Please do not use it."),this.getLayer().getContext()}getCanvas(){return he.warn("shape.getCanvas() method is deprecated. Please do not use it."),this.getLayer().getCanvas()}getSceneFunc(){return this.attrs.sceneFunc||this._sceneFunc}getHitFunc(){return this.attrs.hitFunc||this._hitFunc}hasShadow(){return this._getCache(tG,this._hasShadow)}_hasShadow(){return this.shadowEnabled()&&this.shadowOpacity()!==0&&!!(this.shadowColor()||this.shadowBlur()||this.shadowOffsetX()||this.shadowOffsetY())}_getFillPattern(){return this._getCache(rG,this.__getFillPattern)}__getFillPattern(){if(this.fillPatternImage()){var t=bC();const n=t.createPattern(this.fillPatternImage(),this.fillPatternRepeat()||"repeat");if(n&&n.setTransform){const r=new Ea;r.translate(this.fillPatternX(),this.fillPatternY()),r.rotate(gt.getAngle(this.fillPatternRotation())),r.scale(this.fillPatternScaleX(),this.fillPatternScaleY()),r.translate(-1*this.fillPatternOffsetX(),-1*this.fillPatternOffsetY());const i=r.getMatrix(),o=typeof DOMMatrix>"u"?{a:i[0],b:i[1],c:i[2],d:i[3],e:i[4],f:i[5]}:new DOMMatrix(i);n.setTransform(o)}return n}}_getLinearGradient(){return this._getCache(iG,this.__getLinearGradient)}__getLinearGradient(){var t=this.fillLinearGradientColorStops();if(t){for(var n=bC(),r=this.fillLinearGradientStartPoint(),i=this.fillLinearGradientEndPoint(),o=n.createLinearGradient(r.x,r.y,i.x,i.y),a=0;a<t.length;a+=2)o.addColorStop(t[a],t[a+1]);return o}}_getRadialGradient(){return this._getCache(oG,this.__getRadialGradient)}__getRadialGradient(){var t=this.fillRadialGradientColorStops();if(t){for(var n=bC(),r=this.fillRadialGradientStartPoint(),i=this.fillRadialGradientEndPoint(),o=n.createRadialGradient(r.x,r.y,this.fillRadialGradientStartRadius(),i.x,i.y,this.fillRadialGradientEndRadius()),a=0;a<t.length;a+=2)o.addColorStop(t[a],t[a+1]);return o}}getShadowRGBA(){return this._getCache(nG,this._getShadowRGBA)}_getShadowRGBA(){if(!!this.hasShadow()){var t=he.colorToRGBA(this.shadowColor());if(t)return"rgba("+t.r+","+t.g+","+t.b+","+t.a*(this.shadowOpacity()||1)+")"}}hasFill(){return this._calculate("hasFill",["fillEnabled","fill","fillPatternImage","fillLinearGradientColorStops","fillRadialGradientColorStops"],()=>this.fillEnabled()&&!!(this.fill()||this.fillPatternImage()||this.fillLinearGradientColorStops()||this.fillRadialGradientColorStops()))}hasStroke(){return this._calculate("hasStroke",["strokeEnabled","strokeWidth","stroke","strokeLinearGradientColorStops"],()=>this.strokeEnabled()&&this.strokeWidth()&&!!(this.stroke()||this.strokeLinearGradientColorStops()))}hasHitStroke(){const t=this.hitStrokeWidth();return t==="auto"?this.hasStroke():this.strokeEnabled()&&!!t}intersects(t){var n=this.getStage(),r=n.bufferHitCanvas,i;return r.getContext().clear(),this.drawHit(r,null,!0),i=r.context.getImageData(Math.round(t.x),Math.round(t.y),1,1).data,i[3]>0}destroy(){return qe.prototype.destroy.call(this),delete r2[this.colorKey],delete this.colorKey,this}_useBufferCanvas(t){var n;if(!this.getStage()||!((n=this.attrs.perfectDrawEnabled)!==null&&n!==void 0?n:!0))return!1;const i=t||this.hasFill(),o=this.hasStroke(),a=this.getAbsoluteOpacity()!==1;if(i&&o&&a)return!0;const s=this.hasShadow(),l=this.shadowForStrokeEnabled();return!!(i&&o&&s&&l)}setStrokeHitEnabled(t){he.warn("strokeHitEnabled property is deprecated. Please use hitStrokeWidth instead."),t?this.hitStrokeWidth("auto"):this.hitStrokeWidth(0)}getStrokeHitEnabled(){return this.hitStrokeWidth()!==0}getSelfRect(){var t=this.size();return{x:this._centroid?-t.width/2:0,y:this._centroid?-t.height/2:0,width:t.width,height:t.height}}getClientRect(t={}){const n=t.skipTransform,r=t.relativeTo,i=this.getSelfRect(),a=!t.skipStroke&&this.hasStroke()&&this.strokeWidth()||0,s=i.width+a,l=i.height+a,u=!t.skipShadow&&this.hasShadow(),f=u?this.shadowOffsetX():0,p=u?this.shadowOffsetY():0,m=s+Math.abs(f),v=l+Math.abs(p),b=u&&this.shadowBlur()||0,x=m+b*2,E=v+b*2,k={width:x,height:E,x:-(a/2+b)+Math.min(f,0)+i.x,y:-(a/2+b)+Math.min(p,0)+i.y};return n?k:this._transformedRect(k,r)}drawScene(t,n){var r=this.getLayer(),i=t||r.getCanvas(),o=i.getContext(),a=this._getCanvasCache(),s=this.getSceneFunc(),l=this.hasShadow(),u,f,p,m=i.isCache,v=n===this;if(!this.isVisible()&&!v)return this;if(a){o.save();var b=this.getAbsoluteTransform(n).getMatrix();return o.transform(b[0],b[1],b[2],b[3],b[4],b[5]),this._drawCachedSceneCanvas(o),o.restore(),this}if(!s)return this;if(o.save(),this._useBufferCanvas()&&!m){u=this.getStage(),f=u.bufferCanvas,p=f.getContext(),p.clear(),p.save(),p._applyLineJoin(this);var x=this.getAbsoluteTransform(n).getMatrix();p.transform(x[0],x[1],x[2],x[3],x[4],x[5]),s.call(this,p,this),p.restore();var E=f.pixelRatio;l&&o._applyShadow(this),o._applyOpacity(this),o._applyGlobalCompositeOperation(this),o.drawImage(f._canvas,0,0,f.width/E,f.height/E)}else{if(o._applyLineJoin(this),!v){var x=this.getAbsoluteTransform(n).getMatrix();o.transform(x[0],x[1],x[2],x[3],x[4],x[5]),o._applyOpacity(this),o._applyGlobalCompositeOperation(this)}l&&o._applyShadow(this),s.call(this,o,this)}return o.restore(),this}drawHit(t,n,r=!1){if(!this.shouldDrawHit(n,r))return this;var i=this.getLayer(),o=t||i.hitCanvas,a=o&&o.getContext(),s=this.hitFunc()||this.sceneFunc(),l=this._getCanvasCache(),u=l&&l.hit;if(this.colorKey||he.warn("Looks like your canvas has a destroyed shape in it. Do not reuse shape after you destroyed it. If you want to reuse shape you should call remove() instead of destroy()"),u){a.save();var f=this.getAbsoluteTransform(n).getMatrix();return a.transform(f[0],f[1],f[2],f[3],f[4],f[5]),this._drawCachedHitCanvas(a),a.restore(),this}if(!s)return this;if(a.save(),a._applyLineJoin(this),!(this===n)){var m=this.getAbsoluteTransform(n).getMatrix();a.transform(m[0],m[1],m[2],m[3],m[4],m[5])}return s.call(this,a,this),a.restore(),this}drawHitFromCache(t=0){var n=this._getCanvasCache(),r=this._getCachedSceneCanvas(),i=n.hit,o=i.getContext(),a=i.getWidth(),s=i.getHeight(),l,u,f,p,m,v;o.clear(),o.drawImage(r._canvas,0,0,a,s);try{for(l=o.getImageData(0,0,a,s),u=l.data,f=u.length,p=he._hexToRgb(this.colorKey),m=0;m<f;m+=4)v=u[m+3],v>t?(u[m]=p.r,u[m+1]=p.g,u[m+2]=p.b,u[m+3]=255):u[m+3]=0;o.putImageData(l,0,0)}catch(b){he.error("Unable to draw hit graph from cached scene canvas. "+b.message)}return this}hasPointerCapture(t){return VW(t,this)}setPointerCapture(t){UW(t,this)}releaseCapture(t){n2(t)}}$e.prototype._fillFunc=KCe;$e.prototype._strokeFunc=XCe;$e.prototype._fillFuncHit=ZCe;$e.prototype._strokeFuncHit=QCe;$e.prototype._centroid=!1;$e.prototype.nodeType="Shape";Cr($e);$e.prototype.eventListeners={};$e.prototype.on.call($e.prototype,"shadowColorChange.konva shadowBlurChange.konva shadowOffsetChange.konva shadowOpacityChange.konva shadowEnabledChange.konva",JCe);$e.prototype.on.call($e.prototype,"shadowColorChange.konva shadowOpacityChange.konva shadowEnabledChange.konva",e7e);$e.prototype.on.call($e.prototype,"fillPriorityChange.konva fillPatternImageChange.konva fillPatternRepeatChange.konva fillPatternScaleXChange.konva fillPatternScaleYChange.konva fillPatternOffsetXChange.konva fillPatternOffsetYChange.konva fillPatternXChange.konva fillPatternYChange.konva fillPatternRotationChange.konva",t7e);$e.prototype.on.call($e.prototype,"fillPriorityChange.konva fillLinearGradientColorStopsChange.konva fillLinearGradientStartPointXChange.konva fillLinearGradientStartPointYChange.konva fillLinearGradientEndPointXChange.konva fillLinearGradientEndPointYChange.konva",n7e);$e.prototype.on.call($e.prototype,"fillPriorityChange.konva fillRadialGradientColorStopsChange.konva fillRadialGradientStartPointXChange.konva fillRadialGradientStartPointYChange.konva fillRadialGradientEndPointXChange.konva fillRadialGradientEndPointYChange.konva fillRadialGradientStartRadiusChange.konva fillRadialGradientEndRadiusChange.konva",r7e);ee.addGetterSetter($e,"stroke",void 0,jW());ee.addGetterSetter($e,"strokeWidth",2,Ge());ee.addGetterSetter($e,"fillAfterStrokeEnabled",!1);ee.addGetterSetter($e,"hitStrokeWidth","auto",JE());ee.addGetterSetter($e,"strokeHitEnabled",!0,il());ee.addGetterSetter($e,"perfectDrawEnabled",!0,il());ee.addGetterSetter($e,"shadowForStrokeEnabled",!0,il());ee.addGetterSetter($e,"lineJoin");ee.addGetterSetter($e,"lineCap");ee.addGetterSetter($e,"sceneFunc");ee.addGetterSetter($e,"hitFunc");ee.addGetterSetter($e,"dash");ee.addGetterSetter($e,"dashOffset",0,Ge());ee.addGetterSetter($e,"shadowColor",void 0,k1());ee.addGetterSetter($e,"shadowBlur",0,Ge());ee.addGetterSetter($e,"shadowOpacity",1,Ge());ee.addComponentsGetterSetter($e,"shadowOffset",["x","y"]);ee.addGetterSetter($e,"shadowOffsetX",0,Ge());ee.addGetterSetter($e,"shadowOffsetY",0,Ge());ee.addGetterSetter($e,"fillPatternImage");ee.addGetterSetter($e,"fill",void 0,jW());ee.addGetterSetter($e,"fillPatternX",0,Ge());ee.addGetterSetter($e,"fillPatternY",0,Ge());ee.addGetterSetter($e,"fillLinearGradientColorStops");ee.addGetterSetter($e,"strokeLinearGradientColorStops");ee.addGetterSetter($e,"fillRadialGradientStartRadius",0);ee.addGetterSetter($e,"fillRadialGradientEndRadius",0);ee.addGetterSetter($e,"fillRadialGradientColorStops");ee.addGetterSetter($e,"fillPatternRepeat","repeat");ee.addGetterSetter($e,"fillEnabled",!0);ee.addGetterSetter($e,"strokeEnabled",!0);ee.addGetterSetter($e,"shadowEnabled",!0);ee.addGetterSetter($e,"dashEnabled",!0);ee.addGetterSetter($e,"strokeScaleEnabled",!0);ee.addGetterSetter($e,"fillPriority","color");ee.addComponentsGetterSetter($e,"fillPatternOffset",["x","y"]);ee.addGetterSetter($e,"fillPatternOffsetX",0,Ge());ee.addGetterSetter($e,"fillPatternOffsetY",0,Ge());ee.addComponentsGetterSetter($e,"fillPatternScale",["x","y"]);ee.addGetterSetter($e,"fillPatternScaleX",1,Ge());ee.addGetterSetter($e,"fillPatternScaleY",1,Ge());ee.addComponentsGetterSetter($e,"fillLinearGradientStartPoint",["x","y"]);ee.addComponentsGetterSetter($e,"strokeLinearGradientStartPoint",["x","y"]);ee.addGetterSetter($e,"fillLinearGradientStartPointX",0);ee.addGetterSetter($e,"strokeLinearGradientStartPointX",0);ee.addGetterSetter($e,"fillLinearGradientStartPointY",0);ee.addGetterSetter($e,"strokeLinearGradientStartPointY",0);ee.addComponentsGetterSetter($e,"fillLinearGradientEndPoint",["x","y"]);ee.addComponentsGetterSetter($e,"strokeLinearGradientEndPoint",["x","y"]);ee.addGetterSetter($e,"fillLinearGradientEndPointX",0);ee.addGetterSetter($e,"strokeLinearGradientEndPointX",0);ee.addGetterSetter($e,"fillLinearGradientEndPointY",0);ee.addGetterSetter($e,"strokeLinearGradientEndPointY",0);ee.addComponentsGetterSetter($e,"fillRadialGradientStartPoint",["x","y"]);ee.addGetterSetter($e,"fillRadialGradientStartPointX",0);ee.addGetterSetter($e,"fillRadialGradientStartPointY",0);ee.addComponentsGetterSetter($e,"fillRadialGradientEndPoint",["x","y"]);ee.addGetterSetter($e,"fillRadialGradientEndPointX",0);ee.addGetterSetter($e,"fillRadialGradientEndPointY",0);ee.addGetterSetter($e,"fillPatternRotation",0);ee.backCompat($e,{dashArray:"dash",getDashArray:"getDash",setDashArray:"getDash",drawFunc:"sceneFunc",getDrawFunc:"getSceneFunc",setDrawFunc:"setSceneFunc",drawHitFunc:"hitFunc",getDrawHitFunc:"getHitFunc",setDrawHitFunc:"setHitFunc"});var i7e="#",o7e="beforeDraw",a7e="draw",aG=[{x:0,y:0},{x:-1,y:-1},{x:1,y:-1},{x:1,y:1},{x:-1,y:1}],s7e=aG.length;class hp extends Oa{constructor(t){super(t),this.canvas=new Fg,this.hitCanvas=new eP({pixelRatio:1}),this._waitingForDraw=!1,this.on("visibleChange.konva",this._checkVisibility),this._checkVisibility(),this.on("imageSmoothingEnabledChange.konva",this._setSmoothEnabled),this._setSmoothEnabled()}createPNGStream(){return this.canvas._canvas.createPNGStream()}getCanvas(){return this.canvas}getNativeCanvasElement(){return this.canvas._canvas}getHitCanvas(){return this.hitCanvas}getContext(){return this.getCanvas().getContext()}clear(t){return this.getContext().clear(t),this.getHitCanvas().getContext().clear(t),this}setZIndex(t){super.setZIndex(t);var n=this.getStage();return n&&n.content&&(n.content.removeChild(this.getNativeCanvasElement()),t<n.children.length-1?n.content.insertBefore(this.getNativeCanvasElement(),n.children[t+1].getCanvas()._canvas):n.content.appendChild(this.getNativeCanvasElement())),this}moveToTop(){qe.prototype.moveToTop.call(this);var t=this.getStage();return t&&t.content&&(t.content.removeChild(this.getNativeCanvasElement()),t.content.appendChild(this.getNativeCanvasElement())),!0}moveUp(){var t=qe.prototype.moveUp.call(this);if(!t)return!1;var n=this.getStage();return!n||!n.content?!1:(n.content.removeChild(this.getNativeCanvasElement()),this.index<n.children.length-1?n.content.insertBefore(this.getNativeCanvasElement(),n.children[this.index+1].getCanvas()._canvas):n.content.appendChild(this.getNativeCanvasElement()),!0)}moveDown(){if(qe.prototype.moveDown.call(this)){var t=this.getStage();if(t){var n=t.children;t.content&&(t.content.removeChild(this.getNativeCanvasElement()),t.content.insertBefore(this.getNativeCanvasElement(),n[this.index+1].getCanvas()._canvas))}return!0}return!1}moveToBottom(){if(qe.prototype.moveToBottom.call(this)){var t=this.getStage();if(t){var n=t.children;t.content&&(t.content.removeChild(this.getNativeCanvasElement()),t.content.insertBefore(this.getNativeCanvasElement(),n[1].getCanvas()._canvas))}return!0}return!1}getLayer(){return this}remove(){var t=this.getNativeCanvasElement();return qe.prototype.remove.call(this),t&&t.parentNode&&he._isInDocument(t)&&t.parentNode.removeChild(t),this}getStage(){return this.parent}setSize({width:t,height:n}){return this.canvas.setSize(t,n),this.hitCanvas.setSize(t,n),this._setSmoothEnabled(),this}_validateAdd(t){var n=t.getType();n!=="Group"&&n!=="Shape"&&he.throw("You may only add groups and shapes to a layer.")}_toKonvaCanvas(t){return t=t||{},t.width=t.width||this.getWidth(),t.height=t.height||this.getHeight(),t.x=t.x!==void 0?t.x:this.x(),t.y=t.y!==void 0?t.y:this.y(),qe.prototype._toKonvaCanvas.call(this,t)}_checkVisibility(){this.visible()?this.canvas._canvas.style.display="block":this.canvas._canvas.style.display="none"}_setSmoothEnabled(){this.getContext()._context.imageSmoothingEnabled=this.imageSmoothingEnabled()}getWidth(){if(this.parent)return this.parent.width()}setWidth(){he.warn('Can not change width of layer. Use "stage.width(value)" function instead.')}getHeight(){if(this.parent)return this.parent.height()}setHeight(){he.warn('Can not change height of layer. Use "stage.height(value)" function instead.')}batchDraw(){return this._waitingForDraw||(this._waitingForDraw=!0,he.requestAnimFrame(()=>{this.draw(),this._waitingForDraw=!1})),this}getIntersection(t){if(!this.isListening()||!this.isVisible())return null;for(var n=1,r=!1;;){for(let i=0;i<s7e;i++){const o=aG[i],a=this._getIntersection({x:t.x+o.x*n,y:t.y+o.y*n}),s=a.shape;if(s)return s;if(r=!!a.antialiased,!a.antialiased)break}if(r)n+=1;else return null}}_getIntersection(t){const n=this.hitCanvas.pixelRatio,r=this.hitCanvas.context.getImageData(Math.round(t.x*n),Math.round(t.y*n),1,1).data,i=r[3];if(i===255){const o=he._rgbToHex(r[0],r[1],r[2]),a=r2[i7e+o];return a?{shape:a}:{antialiased:!0}}else if(i>0)return{antialiased:!0};return{}}drawScene(t,n){var r=this.getLayer(),i=t||r&&r.getCanvas();return this._fire(o7e,{node:this}),this.clearBeforeDraw()&&i.getContext().clear(),Oa.prototype.drawScene.call(this,i,n),this._fire(a7e,{node:this}),this}drawHit(t,n){var r=this.getLayer(),i=t||r&&r.hitCanvas;return r&&r.clearBeforeDraw()&&r.getHitCanvas().getContext().clear(),Oa.prototype.drawHit.call(this,i,n),this}enableHitGraph(){return this.hitGraphEnabled(!0),this}disableHitGraph(){return this.hitGraphEnabled(!1),this}setHitGraphEnabled(t){he.warn("hitGraphEnabled method is deprecated. Please use layer.listening() instead."),this.listening(t)}getHitGraphEnabled(t){return he.warn("hitGraphEnabled method is deprecated. Please use layer.listening() instead."),this.listening()}toggleHitCanvas(){if(!(!this.parent||!this.parent.content)){var t=this.parent,n=!!this.hitCanvas._canvas.parentNode;n?t.content.removeChild(this.hitCanvas._canvas):t.content.appendChild(this.hitCanvas._canvas)}}destroy(){return he.releaseCanvas(this.getNativeCanvasElement(),this.getHitCanvas()._canvas),super.destroy()}}hp.prototype.nodeType="Layer";Cr(hp);ee.addGetterSetter(hp,"imageSmoothingEnabled",!0);ee.addGetterSetter(hp,"clearBeforeDraw",!0);ee.addGetterSetter(hp,"hitGraphEnabled",!0,il());class nP extends hp{constructor(t){super(t),this.listening(!1),he.warn('Konva.Fast layer is deprecated. Please use "new Konva.Layer({ listening: false })" instead.')}}nP.prototype.nodeType="FastLayer";Cr(nP);class n1 extends Oa{_validateAdd(t){var n=t.getType();n!=="Group"&&n!=="Shape"&&he.throw("You may only add groups and shapes to groups.")}}n1.prototype.nodeType="Group";Cr(n1);var SC=function(){return $g.performance&&$g.performance.now?function(){return $g.performance.now()}:function(){return new Date().getTime()}}();class ns{constructor(t,n){this.id=ns.animIdCounter++,this.frame={time:0,timeDiff:0,lastTime:SC(),frameRate:0},this.func=t,this.setLayers(n)}setLayers(t){var n=[];return t?t.length>0?n=t:n=[t]:n=[],this.layers=n,this}getLayers(){return this.layers}addLayer(t){var n=this.layers,r=n.length,i;for(i=0;i<r;i++)if(n[i]._id===t._id)return!1;return this.layers.push(t),!0}isRunning(){var t=ns,n=t.animations,r=n.length,i;for(i=0;i<r;i++)if(n[i].id===this.id)return!0;return!1}start(){return this.stop(),this.frame.timeDiff=0,this.frame.lastTime=SC(),ns._addAnimation(this),this}stop(){return ns._removeAnimation(this),this}_updateFrameObject(t){this.frame.timeDiff=t-this.frame.lastTime,this.frame.lastTime=t,this.frame.time+=this.frame.timeDiff,this.frame.frameRate=1e3/this.frame.timeDiff}static _addAnimation(t){this.animations.push(t),this._handleAnimation()}static _removeAnimation(t){var n=t.id,r=this.animations,i=r.length,o;for(o=0;o<i;o++)if(r[o].id===n){this.animations.splice(o,1);break}}static _runFrames(){var t={},n=this.animations,r,i,o,a,s,l,u,f,p;for(a=0;a<n.length;a++)if(r=n[a],i=r.layers,o=r.func,r._updateFrameObject(SC()),l=i.length,o?p=o.call(r,r.frame)!==!1:p=!0,!!p)for(s=0;s<l;s++)u=i[s],u._id!==void 0&&(t[u._id]=u);for(f in t)!t.hasOwnProperty(f)||t[f].batchDraw()}static _animationLoop(){var t=ns;t.animations.length?(t._runFrames(),he.requestAnimFrame(t._animationLoop)):t.animRunning=!1}static _handleAnimation(){this.animRunning||(this.animRunning=!0,he.requestAnimFrame(this._animationLoop))}}ns.animations=[];ns.animIdCounter=0;ns.animRunning=!1;var l7e={node:1,duration:1,easing:1,onFinish:1,yoyo:1},u7e=1,BI=2,$I=3,c7e=0,FI=["fill","stroke","shadowColor"];class d7e{constructor(t,n,r,i,o,a,s){this.prop=t,this.propFunc=n,this.begin=i,this._pos=i,this.duration=a,this._change=0,this.prevPos=0,this.yoyo=s,this._time=0,this._position=0,this._startTime=0,this._finish=0,this.func=r,this._change=o-this.begin,this.pause()}fire(t){var n=this[t];n&&n()}setTime(t){t>this.duration?this.yoyo?(this._time=this.duration,this.reverse()):this.finish():t<0?this.yoyo?(this._time=0,this.play()):this.reset():(this._time=t,this.update())}getTime(){return this._time}setPosition(t){this.prevPos=this._pos,this.propFunc(t),this._pos=t}getPosition(t){return t===void 0&&(t=this._time),this.func(t,this.begin,this._change,this.duration)}play(){this.state=BI,this._startTime=this.getTimer()-this._time,this.onEnterFrame(),this.fire("onPlay")}reverse(){this.state=$I,this._time=this.duration-this._time,this._startTime=this.getTimer()-this._time,this.onEnterFrame(),this.fire("onReverse")}seek(t){this.pause(),this._time=t,this.update(),this.fire("onSeek")}reset(){this.pause(),this._time=0,this.update(),this.fire("onReset")}finish(){this.pause(),this._time=this.duration,this.update(),this.fire("onFinish")}update(){this.setPosition(this.getPosition(this._time)),this.fire("onUpdate")}onEnterFrame(){var t=this.getTimer()-this._startTime;this.state===BI?this.setTime(t):this.state===$I&&this.setTime(this.duration-t)}pause(){this.state=u7e,this.fire("onPause")}getTimer(){return new Date().getTime()}}class Wr{constructor(t){var n=this,r=t.node,i=r._id,o,a=t.easing||i2.Linear,s=!!t.yoyo,l;typeof t.duration>"u"?o=.3:t.duration===0?o=.001:o=t.duration,this.node=r,this._id=c7e++;var u=r.getLayer()||(r instanceof gt.Stage?r.getLayers():null);u||he.error("Tween constructor have `node` that is not in a layer. Please add node into layer first."),this.anim=new ns(function(){n.tween.onEnterFrame()},u),this.tween=new d7e(l,function(f){n._tweenFunc(f)},a,0,1,o*1e3,s),this._addListeners(),Wr.attrs[i]||(Wr.attrs[i]={}),Wr.attrs[i][this._id]||(Wr.attrs[i][this._id]={}),Wr.tweens[i]||(Wr.tweens[i]={});for(l in t)l7e[l]===void 0&&this._addAttr(l,t[l]);this.reset(),this.onFinish=t.onFinish,this.onReset=t.onReset,this.onUpdate=t.onUpdate}_addAttr(t,n){var r=this.node,i=r._id,o,a,s,l,u,f,p,m;if(s=Wr.tweens[i][t],s&&delete Wr.attrs[i][s][t],o=r.getAttr(t),he._isArray(n))if(a=[],u=Math.max(n.length,o.length),t==="points"&&n.length!==o.length&&(n.length>o.length?(p=o,o=he._prepareArrayForTween(o,n,r.closed())):(f=n,n=he._prepareArrayForTween(n,o,r.closed()))),t.indexOf("fill")===0)for(l=0;l<u;l++)if(l%2===0)a.push(n[l]-o[l]);else{var v=he.colorToRGBA(o[l]);m=he.colorToRGBA(n[l]),o[l]=v,a.push({r:m.r-v.r,g:m.g-v.g,b:m.b-v.b,a:m.a-v.a})}else for(l=0;l<u;l++)a.push(n[l]-o[l]);else FI.indexOf(t)!==-1?(o=he.colorToRGBA(o),m=he.colorToRGBA(n),a={r:m.r-o.r,g:m.g-o.g,b:m.b-o.b,a:m.a-o.a}):a=n-o;Wr.attrs[i][this._id][t]={start:o,diff:a,end:n,trueEnd:f,trueStart:p},Wr.tweens[i][t]=this._id}_tweenFunc(t){var n=this.node,r=Wr.attrs[n._id][this._id],i,o,a,s,l,u,f,p;for(i in r){if(o=r[i],a=o.start,s=o.diff,p=o.end,he._isArray(a))if(l=[],f=Math.max(a.length,p.length),i.indexOf("fill")===0)for(u=0;u<f;u++)u%2===0?l.push((a[u]||0)+s[u]*t):l.push("rgba("+Math.round(a[u].r+s[u].r*t)+","+Math.round(a[u].g+s[u].g*t)+","+Math.round(a[u].b+s[u].b*t)+","+(a[u].a+s[u].a*t)+")");else for(u=0;u<f;u++)l.push((a[u]||0)+s[u]*t);else FI.indexOf(i)!==-1?l="rgba("+Math.round(a.r+s.r*t)+","+Math.round(a.g+s.g*t)+","+Math.round(a.b+s.b*t)+","+(a.a+s.a*t)+")":l=a+s*t;n.setAttr(i,l)}}_addListeners(){this.tween.onPlay=()=>{this.anim.start()},this.tween.onReverse=()=>{this.anim.start()},this.tween.onPause=()=>{this.anim.stop()},this.tween.onFinish=()=>{var t=this.node,n=Wr.attrs[t._id][this._id];n.points&&n.points.trueEnd&&t.setAttr("points",n.points.trueEnd),this.onFinish&&this.onFinish.call(this)},this.tween.onReset=()=>{var t=this.node,n=Wr.attrs[t._id][this._id];n.points&&n.points.trueStart&&t.points(n.points.trueStart),this.onReset&&this.onReset()},this.tween.onUpdate=()=>{this.onUpdate&&this.onUpdate.call(this)}}play(){return this.tween.play(),this}reverse(){return this.tween.reverse(),this}reset(){return this.tween.reset(),this}seek(t){return this.tween.seek(t*1e3),this}pause(){return this.tween.pause(),this}finish(){return this.tween.finish(),this}destroy(){var t=this.node._id,n=this._id,r=Wr.tweens[t],i;this.pause();for(i in r)delete Wr.tweens[t][i];delete Wr.attrs[t][n]}}Wr.attrs={};Wr.tweens={};qe.prototype.to=function(e){var t=e.onFinish;e.node=this,e.onFinish=function(){this.destroy(),t&&t()};var n=new Wr(e);n.play()};const i2={BackEaseIn(e,t,n,r){var i=1.70158;return n*(e/=r)*e*((i+1)*e-i)+t},BackEaseOut(e,t,n,r){var i=1.70158;return n*((e=e/r-1)*e*((i+1)*e+i)+1)+t},BackEaseInOut(e,t,n,r){var i=1.70158;return(e/=r/2)<1?n/2*(e*e*(((i*=1.525)+1)*e-i))+t:n/2*((e-=2)*e*(((i*=1.525)+1)*e+i)+2)+t},ElasticEaseIn(e,t,n,r,i,o){var a=0;return e===0?t:(e/=r)===1?t+n:(o||(o=r*.3),!i||i<Math.abs(n)?(i=n,a=o/4):a=o/(2*Math.PI)*Math.asin(n/i),-(i*Math.pow(2,10*(e-=1))*Math.sin((e*r-a)*(2*Math.PI)/o))+t)},ElasticEaseOut(e,t,n,r,i,o){var a=0;return e===0?t:(e/=r)===1?t+n:(o||(o=r*.3),!i||i<Math.abs(n)?(i=n,a=o/4):a=o/(2*Math.PI)*Math.asin(n/i),i*Math.pow(2,-10*e)*Math.sin((e*r-a)*(2*Math.PI)/o)+n+t)},ElasticEaseInOut(e,t,n,r,i,o){var a=0;return e===0?t:(e/=r/2)===2?t+n:(o||(o=r*(.3*1.5)),!i||i<Math.abs(n)?(i=n,a=o/4):a=o/(2*Math.PI)*Math.asin(n/i),e<1?-.5*(i*Math.pow(2,10*(e-=1))*Math.sin((e*r-a)*(2*Math.PI)/o))+t:i*Math.pow(2,-10*(e-=1))*Math.sin((e*r-a)*(2*Math.PI)/o)*.5+n+t)},BounceEaseOut(e,t,n,r){return(e/=r)<1/2.75?n*(7.5625*e*e)+t:e<2/2.75?n*(7.5625*(e-=1.5/2.75)*e+.75)+t:e<2.5/2.75?n*(7.5625*(e-=2.25/2.75)*e+.9375)+t:n*(7.5625*(e-=2.625/2.75)*e+.984375)+t},BounceEaseIn(e,t,n,r){return n-i2.BounceEaseOut(r-e,0,n,r)+t},BounceEaseInOut(e,t,n,r){return e<r/2?i2.BounceEaseIn(e*2,0,n,r)*.5+t:i2.BounceEaseOut(e*2-r,0,n,r)*.5+n*.5+t},EaseIn(e,t,n,r){return n*(e/=r)*e+t},EaseOut(e,t,n,r){return-n*(e/=r)*(e-2)+t},EaseInOut(e,t,n,r){return(e/=r/2)<1?n/2*e*e+t:-n/2*(--e*(e-2)-1)+t},StrongEaseIn(e,t,n,r){return n*(e/=r)*e*e*e*e+t},StrongEaseOut(e,t,n,r){return n*((e=e/r-1)*e*e*e*e+1)+t},StrongEaseInOut(e,t,n,r){return(e/=r/2)<1?n/2*e*e*e*e*e+t:n/2*((e-=2)*e*e*e*e+2)+t},Linear(e,t,n,r){return n*e/r+t}},rp=he._assign(gt,{Util:he,Transform:Ea,Node:qe,Container:Oa,Stage:_x,stages:A4,Layer:hp,FastLayer:nP,Group:n1,DD:yn,Shape:$e,shapes:r2,Animation:ns,Tween:Wr,Easings:i2,Context:wx,Canvas:Cx});class gc extends $e{_sceneFunc(t){var n=gt.getAngle(this.angle()),r=this.clockwise();t.beginPath(),t.arc(0,0,this.outerRadius(),0,n,r),t.arc(0,0,this.innerRadius(),n,0,!r),t.closePath(),t.fillStrokeShape(this)}getWidth(){return this.outerRadius()*2}getHeight(){return this.outerRadius()*2}setWidth(t){this.outerRadius(t/2)}setHeight(t){this.outerRadius(t/2)}getSelfRect(){const t=this.innerRadius(),n=this.outerRadius(),r=this.clockwise(),i=gt.getAngle(r?360-this.angle():this.angle()),o=Math.cos(Math.min(i,Math.PI)),a=1,s=Math.sin(Math.min(Math.max(Math.PI,i),3*Math.PI/2)),l=Math.sin(Math.min(i,Math.PI/2)),u=o*(o>0?t:n),f=a*n,p=s*(s>0?t:n),m=l*(l>0?n:t);return{x:u,y:r?-1*m:p,width:f-u,height:m-p}}}gc.prototype._centroid=!0;gc.prototype.className="Arc";gc.prototype._attrsAffectingSize=["innerRadius","outerRadius"];Cr(gc);ee.addGetterSetter(gc,"innerRadius",0,Ge());ee.addGetterSetter(gc,"outerRadius",0,Ge());ee.addGetterSetter(gc,"angle",0,Ge());ee.addGetterSetter(gc,"clockwise",!1,il());function g8(e,t,n,r,i,o,a){var s=Math.sqrt(Math.pow(n-e,2)+Math.pow(r-t,2)),l=Math.sqrt(Math.pow(i-n,2)+Math.pow(o-r,2)),u=a*s/(s+l),f=a*l/(s+l),p=n-u*(i-e),m=r-u*(o-t),v=n+f*(i-e),b=r+f*(o-t);return[p,m,v,b]}function zI(e,t){var n=e.length,r=[],i,o;for(i=2;i<n-2;i+=2)o=g8(e[i-2],e[i-1],e[i],e[i+1],e[i+2],e[i+3],t),!isNaN(o[0])&&(r.push(o[0]),r.push(o[1]),r.push(e[i]),r.push(e[i+1]),r.push(o[2]),r.push(o[3]));return r}class mc extends $e{constructor(t){super(t),this.on("pointsChange.konva tensionChange.konva closedChange.konva bezierChange.konva",function(){this._clearCache("tensionPoints")})}_sceneFunc(t){var n=this.points(),r=n.length,i=this.tension(),o=this.closed(),a=this.bezier(),s,l,u;if(!!r){if(t.beginPath(),t.moveTo(n[0],n[1]),i!==0&&r>4){for(s=this.getTensionPoints(),l=s.length,u=o?0:4,o||t.quadraticCurveTo(s[0],s[1],s[2],s[3]);u<l-2;)t.bezierCurveTo(s[u++],s[u++],s[u++],s[u++],s[u++],s[u++]);o||t.quadraticCurveTo(s[l-2],s[l-1],n[r-2],n[r-1])}else if(a)for(u=2;u<r;)t.bezierCurveTo(n[u++],n[u++],n[u++],n[u++],n[u++],n[u++]);else for(u=2;u<r;u+=2)t.lineTo(n[u],n[u+1]);o?(t.closePath(),t.fillStrokeShape(this)):t.strokeShape(this)}}getTensionPoints(){return this._getCache("tensionPoints",this._getTensionPoints)}_getTensionPoints(){return this.closed()?this._getTensionPointsClosed():zI(this.points(),this.tension())}_getTensionPointsClosed(){var t=this.points(),n=t.length,r=this.tension(),i=g8(t[n-2],t[n-1],t[0],t[1],t[2],t[3],r),o=g8(t[n-4],t[n-3],t[n-2],t[n-1],t[0],t[1],r),a=zI(t,r),s=[i[2],i[3]].concat(a).concat([o[0],o[1],t[n-2],t[n-1],o[2],o[3],i[0],i[1],t[0],t[1]]);return s}getWidth(){return this.getSelfRect().width}getHeight(){return this.getSelfRect().height}getSelfRect(){var t=this.points();if(t.length<4)return{x:t[0]||0,y:t[1]||0,width:0,height:0};this.tension()!==0?t=[t[0],t[1],...this._getTensionPoints(),t[t.length-2],t[t.length-1]]:t=this.points();for(var n=t[0],r=t[0],i=t[1],o=t[1],a,s,l=0;l<t.length/2;l++)a=t[l*2],s=t[l*2+1],n=Math.min(n,a),r=Math.max(r,a),i=Math.min(i,s),o=Math.max(o,s);return{x:n,y:i,width:r-n,height:o-i}}}mc.prototype.className="Line";mc.prototype._attrsAffectingSize=["points","bezier","tension"];Cr(mc);ee.addGetterSetter(mc,"closed",!1);ee.addGetterSetter(mc,"bezier",!1);ee.addGetterSetter(mc,"tension",0,Ge());ee.addGetterSetter(mc,"points",[],xCe());class zn extends $e{constructor(t){super(t),this.dataArray=[],this.pathLength=0,this.dataArray=zn.parsePathData(this.data()),this.pathLength=0;for(var n=0;n<this.dataArray.length;++n)this.pathLength+=this.dataArray[n].pathLength;this.on("dataChange.konva",function(){this.dataArray=zn.parsePathData(this.data()),this.pathLength=0;for(var r=0;r<this.dataArray.length;++r)this.pathLength+=this.dataArray[r].pathLength})}_sceneFunc(t){var n=this.dataArray;t.beginPath();for(var r=!1,i=0;i<n.length;i++){var o=n[i].command,a=n[i].points;switch(o){case"L":t.lineTo(a[0],a[1]);break;case"M":t.moveTo(a[0],a[1]);break;case"C":t.bezierCurveTo(a[0],a[1],a[2],a[3],a[4],a[5]);break;case"Q":t.quadraticCurveTo(a[0],a[1],a[2],a[3]);break;case"A":var s=a[0],l=a[1],u=a[2],f=a[3],p=a[4],m=a[5],v=a[6],b=a[7],x=u>f?u:f,E=u>f?1:u/f,k=u>f?f/u:1;t.translate(s,l),t.rotate(v),t.scale(E,k),t.arc(0,0,x,p,p+m,1-b),t.scale(1/E,1/k),t.rotate(-v),t.translate(-s,-l);break;case"z":r=!0,t.closePath();break}}!r&&!this.hasFill()?t.strokeShape(this):t.fillStrokeShape(this)}getSelfRect(){var t=[];this.dataArray.forEach(function(u){if(u.command==="A"){var f=u.points[4],p=u.points[5],m=u.points[4]+p,v=Math.PI/180;if(Math.abs(f-m)<v&&(v=Math.abs(f-m)),p<0)for(let b=f-v;b>m;b-=v){const x=zn.getPointOnEllipticalArc(u.points[0],u.points[1],u.points[2],u.points[3],b,0);t.push(x.x,x.y)}else for(let b=f+v;b<m;b+=v){const x=zn.getPointOnEllipticalArc(u.points[0],u.points[1],u.points[2],u.points[3],b,0);t.push(x.x,x.y)}}else if(u.command==="C")for(let b=0;b<=1;b+=.01){const x=zn.getPointOnCubicBezier(b,u.start.x,u.start.y,u.points[0],u.points[1],u.points[2],u.points[3],u.points[4],u.points[5]);t.push(x.x,x.y)}else t=t.concat(u.points)});for(var n=t[0],r=t[0],i=t[1],o=t[1],a,s,l=0;l<t.length/2;l++)a=t[l*2],s=t[l*2+1],isNaN(a)||(n=Math.min(n,a),r=Math.max(r,a)),isNaN(s)||(i=Math.min(i,s),o=Math.max(o,s));return{x:n,y:i,width:r-n,height:o-i}}getLength(){return this.pathLength}getPointAtLength(t){var n,r=0,i=this.dataArray.length;if(!i)return null;for(;r<i&&t>this.dataArray[r].pathLength;)t-=this.dataArray[r].pathLength,++r;if(r===i)return n=this.dataArray[r-1].points.slice(-2),{x:n[0],y:n[1]};if(t<.01)return n=this.dataArray[r].points.slice(0,2),{x:n[0],y:n[1]};var o=this.dataArray[r],a=o.points;switch(o.command){case"L":return zn.getPointOnLine(t,o.start.x,o.start.y,a[0],a[1]);case"C":return zn.getPointOnCubicBezier(t/o.pathLength,o.start.x,o.start.y,a[0],a[1],a[2],a[3],a[4],a[5]);case"Q":return zn.getPointOnQuadraticBezier(t/o.pathLength,o.start.x,o.start.y,a[0],a[1],a[2],a[3]);case"A":var s=a[0],l=a[1],u=a[2],f=a[3],p=a[4],m=a[5],v=a[6];return p+=m*t/o.pathLength,zn.getPointOnEllipticalArc(s,l,u,f,p,v)}return null}static getLineLength(t,n,r,i){return Math.sqrt((r-t)*(r-t)+(i-n)*(i-n))}static getPointOnLine(t,n,r,i,o,a,s){a===void 0&&(a=n),s===void 0&&(s=r);var l=(o-r)/(i-n+1e-8),u=Math.sqrt(t*t/(1+l*l));i<n&&(u*=-1);var f=l*u,p;if(i===n)p={x:a,y:s+f};else if((s-r)/(a-n+1e-8)===l)p={x:a+u,y:s+f};else{var m,v,b=this.getLineLength(n,r,i,o),x=(a-n)*(i-n)+(s-r)*(o-r);x=x/(b*b),m=n+x*(i-n),v=r+x*(o-r);var E=this.getLineLength(a,s,m,v),k=Math.sqrt(t*t-E*E);u=Math.sqrt(k*k/(1+l*l)),i<n&&(u*=-1),f=l*u,p={x:m+u,y:v+f}}return p}static getPointOnCubicBezier(t,n,r,i,o,a,s,l,u){function f(E){return E*E*E}function p(E){return 3*E*E*(1-E)}function m(E){return 3*E*(1-E)*(1-E)}function v(E){return(1-E)*(1-E)*(1-E)}var b=l*f(t)+a*p(t)+i*m(t)+n*v(t),x=u*f(t)+s*p(t)+o*m(t)+r*v(t);return{x:b,y:x}}static getPointOnQuadraticBezier(t,n,r,i,o,a,s){function l(v){return v*v}function u(v){return 2*v*(1-v)}function f(v){return(1-v)*(1-v)}var p=a*l(t)+i*u(t)+n*f(t),m=s*l(t)+o*u(t)+r*f(t);return{x:p,y:m}}static getPointOnEllipticalArc(t,n,r,i,o,a){var s=Math.cos(a),l=Math.sin(a),u={x:r*Math.cos(o),y:i*Math.sin(o)};return{x:t+(u.x*s-u.y*l),y:n+(u.x*l+u.y*s)}}static parsePathData(t){if(!t)return[];var n=t,r=["m","M","l","L","v","V","h","H","z","Z","c","C","q","Q","t","T","s","S","a","A"];n=n.replace(new RegExp(" ","g"),",");for(var i=0;i<r.length;i++)n=n.replace(new RegExp(r[i],"g"),"|"+r[i]);var o=n.split("|"),a=[],s=[],l=0,u=0,f=/([-+]?((\d+\.\d+)|((\d+)|(\.\d+)))(?:e[-+]?\d+)?)/gi,p;for(i=1;i<o.length;i++){var m=o[i],v=m.charAt(0);for(m=m.slice(1),s.length=0;p=f.exec(m);)s.push(p[0]);for(var b=[],x=0,E=s.length;x<E;x++){if(s[x]==="00"){b.push(0,0);continue}var k=parseFloat(s[x]);isNaN(k)?b.push(0):b.push(k)}for(;b.length>0&&!isNaN(b[0]);){var _=null,T=[],A=l,R=u,I,D,B,z,j,Y,te,G,$,V;switch(v){case"l":l+=b.shift(),u+=b.shift(),_="L",T.push(l,u);break;case"L":l=b.shift(),u=b.shift(),T.push(l,u);break;case"m":var J=b.shift(),Z=b.shift();if(l+=J,u+=Z,_="M",a.length>2&&a[a.length-1].command==="z"){for(var U=a.length-2;U>=0;U--)if(a[U].command==="M"){l=a[U].points[0]+J,u=a[U].points[1]+Z;break}}T.push(l,u),v="l";break;case"M":l=b.shift(),u=b.shift(),_="M",T.push(l,u),v="L";break;case"h":l+=b.shift(),_="L",T.push(l,u);break;case"H":l=b.shift(),_="L",T.push(l,u);break;case"v":u+=b.shift(),_="L",T.push(l,u);break;case"V":u=b.shift(),_="L",T.push(l,u);break;case"C":T.push(b.shift(),b.shift(),b.shift(),b.shift()),l=b.shift(),u=b.shift(),T.push(l,u);break;case"c":T.push(l+b.shift(),u+b.shift(),l+b.shift(),u+b.shift()),l+=b.shift(),u+=b.shift(),_="C",T.push(l,u);break;case"S":D=l,B=u,I=a[a.length-1],I.command==="C"&&(D=l+(l-I.points[2]),B=u+(u-I.points[3])),T.push(D,B,b.shift(),b.shift()),l=b.shift(),u=b.shift(),_="C",T.push(l,u);break;case"s":D=l,B=u,I=a[a.length-1],I.command==="C"&&(D=l+(l-I.points[2]),B=u+(u-I.points[3])),T.push(D,B,l+b.shift(),u+b.shift()),l+=b.shift(),u+=b.shift(),_="C",T.push(l,u);break;case"Q":T.push(b.shift(),b.shift()),l=b.shift(),u=b.shift(),T.push(l,u);break;case"q":T.push(l+b.shift(),u+b.shift()),l+=b.shift(),u+=b.shift(),_="Q",T.push(l,u);break;case"T":D=l,B=u,I=a[a.length-1],I.command==="Q"&&(D=l+(l-I.points[0]),B=u+(u-I.points[1])),l=b.shift(),u=b.shift(),_="Q",T.push(D,B,l,u);break;case"t":D=l,B=u,I=a[a.length-1],I.command==="Q"&&(D=l+(l-I.points[0]),B=u+(u-I.points[1])),l+=b.shift(),u+=b.shift(),_="Q",T.push(D,B,l,u);break;case"A":z=b.shift(),j=b.shift(),Y=b.shift(),te=b.shift(),G=b.shift(),$=l,V=u,l=b.shift(),u=b.shift(),_="A",T=this.convertEndpointToCenterParameterization($,V,l,u,te,G,z,j,Y);break;case"a":z=b.shift(),j=b.shift(),Y=b.shift(),te=b.shift(),G=b.shift(),$=l,V=u,l+=b.shift(),u+=b.shift(),_="A",T=this.convertEndpointToCenterParameterization($,V,l,u,te,G,z,j,Y);break}a.push({command:_||v,points:T,start:{x:A,y:R},pathLength:this.calcLength(A,R,_||v,T)})}(v==="z"||v==="Z")&&a.push({command:"z",points:[],start:void 0,pathLength:0})}return a}static calcLength(t,n,r,i){var o,a,s,l,u=zn;switch(r){case"L":return u.getLineLength(t,n,i[0],i[1]);case"C":for(o=0,a=u.getPointOnCubicBezier(0,t,n,i[0],i[1],i[2],i[3],i[4],i[5]),l=.01;l<=1;l+=.01)s=u.getPointOnCubicBezier(l,t,n,i[0],i[1],i[2],i[3],i[4],i[5]),o+=u.getLineLength(a.x,a.y,s.x,s.y),a=s;return o;case"Q":for(o=0,a=u.getPointOnQuadraticBezier(0,t,n,i[0],i[1],i[2],i[3]),l=.01;l<=1;l+=.01)s=u.getPointOnQuadraticBezier(l,t,n,i[0],i[1],i[2],i[3]),o+=u.getLineLength(a.x,a.y,s.x,s.y),a=s;return o;case"A":o=0;var f=i[4],p=i[5],m=i[4]+p,v=Math.PI/180;if(Math.abs(f-m)<v&&(v=Math.abs(f-m)),a=u.getPointOnEllipticalArc(i[0],i[1],i[2],i[3],f,0),p<0)for(l=f-v;l>m;l-=v)s=u.getPointOnEllipticalArc(i[0],i[1],i[2],i[3],l,0),o+=u.getLineLength(a.x,a.y,s.x,s.y),a=s;else for(l=f+v;l<m;l+=v)s=u.getPointOnEllipticalArc(i[0],i[1],i[2],i[3],l,0),o+=u.getLineLength(a.x,a.y,s.x,s.y),a=s;return s=u.getPointOnEllipticalArc(i[0],i[1],i[2],i[3],m,0),o+=u.getLineLength(a.x,a.y,s.x,s.y),o}return 0}static convertEndpointToCenterParameterization(t,n,r,i,o,a,s,l,u){var f=u*(Math.PI/180),p=Math.cos(f)*(t-r)/2+Math.sin(f)*(n-i)/2,m=-1*Math.sin(f)*(t-r)/2+Math.cos(f)*(n-i)/2,v=p*p/(s*s)+m*m/(l*l);v>1&&(s*=Math.sqrt(v),l*=Math.sqrt(v));var b=Math.sqrt((s*s*(l*l)-s*s*(m*m)-l*l*(p*p))/(s*s*(m*m)+l*l*(p*p)));o===a&&(b*=-1),isNaN(b)&&(b=0);var x=b*s*m/l,E=b*-l*p/s,k=(t+r)/2+Math.cos(f)*x-Math.sin(f)*E,_=(n+i)/2+Math.sin(f)*x+Math.cos(f)*E,T=function(j){return Math.sqrt(j[0]*j[0]+j[1]*j[1])},A=function(j,Y){return(j[0]*Y[0]+j[1]*Y[1])/(T(j)*T(Y))},R=function(j,Y){return(j[0]*Y[1]<j[1]*Y[0]?-1:1)*Math.acos(A(j,Y))},I=R([1,0],[(p-x)/s,(m-E)/l]),D=[(p-x)/s,(m-E)/l],B=[(-1*p-x)/s,(-1*m-E)/l],z=R(D,B);return A(D,B)<=-1&&(z=Math.PI),A(D,B)>=1&&(z=0),a===0&&z>0&&(z=z-2*Math.PI),a===1&&z<0&&(z=z+2*Math.PI),[k,_,s,l,I,z,f,a]}}zn.prototype.className="Path";zn.prototype._attrsAffectingSize=["data"];Cr(zn);ee.addGetterSetter(zn,"data");class pp extends mc{_sceneFunc(t){super._sceneFunc(t);var n=Math.PI*2,r=this.points(),i=r,o=this.tension()!==0&&r.length>4;o&&(i=this.getTensionPoints());var a=this.pointerLength(),s=r.length,l,u;if(o){const m=[i[i.length-4],i[i.length-3],i[i.length-2],i[i.length-1],r[s-2],r[s-1]],v=zn.calcLength(i[i.length-4],i[i.length-3],"C",m),b=zn.getPointOnQuadraticBezier(Math.min(1,1-a/v),m[0],m[1],m[2],m[3],m[4],m[5]);l=r[s-2]-b.x,u=r[s-1]-b.y}else l=r[s-2]-r[s-4],u=r[s-1]-r[s-3];var f=(Math.atan2(u,l)+n)%n,p=this.pointerWidth();this.pointerAtEnding()&&(t.save(),t.beginPath(),t.translate(r[s-2],r[s-1]),t.rotate(f),t.moveTo(0,0),t.lineTo(-a,p/2),t.lineTo(-a,-p/2),t.closePath(),t.restore(),this.__fillStroke(t)),this.pointerAtBeginning()&&(t.save(),t.beginPath(),t.translate(r[0],r[1]),o?(l=(i[0]+i[2])/2-r[0],u=(i[1]+i[3])/2-r[1]):(l=r[2]-r[0],u=r[3]-r[1]),t.rotate((Math.atan2(-u,-l)+n)%n),t.moveTo(0,0),t.lineTo(-a,p/2),t.lineTo(-a,-p/2),t.closePath(),t.restore(),this.__fillStroke(t))}__fillStroke(t){var n=this.dashEnabled();n&&(this.attrs.dashEnabled=!1,t.setLineDash([])),t.fillStrokeShape(this),n&&(this.attrs.dashEnabled=!0)}getSelfRect(){const t=super.getSelfRect(),n=this.pointerWidth()/2;return{x:t.x-n,y:t.y-n,width:t.width+n*2,height:t.height+n*2}}}pp.prototype.className="Arrow";Cr(pp);ee.addGetterSetter(pp,"pointerLength",10,Ge());ee.addGetterSetter(pp,"pointerWidth",10,Ge());ee.addGetterSetter(pp,"pointerAtBeginning",!1);ee.addGetterSetter(pp,"pointerAtEnding",!0);class E1 extends $e{_sceneFunc(t){t.beginPath(),t.arc(0,0,this.attrs.radius||0,0,Math.PI*2,!1),t.closePath(),t.fillStrokeShape(this)}getWidth(){return this.radius()*2}getHeight(){return this.radius()*2}setWidth(t){this.radius()!==t/2&&this.radius(t/2)}setHeight(t){this.radius()!==t/2&&this.radius(t/2)}}E1.prototype._centroid=!0;E1.prototype.className="Circle";E1.prototype._attrsAffectingSize=["radius"];Cr(E1);ee.addGetterSetter(E1,"radius",0,Ge());class mf extends $e{_sceneFunc(t){var n=this.radiusX(),r=this.radiusY();t.beginPath(),t.save(),n!==r&&t.scale(1,r/n),t.arc(0,0,n,0,Math.PI*2,!1),t.restore(),t.closePath(),t.fillStrokeShape(this)}getWidth(){return this.radiusX()*2}getHeight(){return this.radiusY()*2}setWidth(t){this.radiusX(t/2)}setHeight(t){this.radiusY(t/2)}}mf.prototype.className="Ellipse";mf.prototype._centroid=!0;mf.prototype._attrsAffectingSize=["radiusX","radiusY"];Cr(mf);ee.addComponentsGetterSetter(mf,"radius",["x","y"]);ee.addGetterSetter(mf,"radiusX",0,Ge());ee.addGetterSetter(mf,"radiusY",0,Ge());class ol extends $e{constructor(t){super(t),this.on("imageChange.konva",()=>{this._setImageLoad()}),this._setImageLoad()}_setImageLoad(){const t=this.image();t&&t.complete||t&&t.readyState===4||t&&t.addEventListener&&t.addEventListener("load",()=>{this._requestDraw()})}_useBufferCanvas(){return super._useBufferCanvas(!0)}_sceneFunc(t){const n=this.getWidth(),r=this.getHeight(),i=this.attrs.image;let o;if(i){const a=this.attrs.cropWidth,s=this.attrs.cropHeight;a&&s?o=[i,this.cropX(),this.cropY(),a,s,0,0,n,r]:o=[i,0,0,n,r]}(this.hasFill()||this.hasStroke())&&(t.beginPath(),t.rect(0,0,n,r),t.closePath(),t.fillStrokeShape(this)),i&&t.drawImage.apply(t,o)}_hitFunc(t){var n=this.width(),r=this.height();t.beginPath(),t.rect(0,0,n,r),t.closePath(),t.fillStrokeShape(this)}getWidth(){var t,n;return(t=this.attrs.width)!==null&&t!==void 0?t:(n=this.image())===null||n===void 0?void 0:n.width}getHeight(){var t,n;return(t=this.attrs.height)!==null&&t!==void 0?t:(n=this.image())===null||n===void 0?void 0:n.height}static fromURL(t,n,r=null){var i=he.createImageElement();i.onload=function(){var o=new ol({image:i});n(o)},i.onerror=r,i.crossOrigin="Anonymous",i.src=t}}ol.prototype.className="Image";Cr(ol);ee.addGetterSetter(ol,"image");ee.addComponentsGetterSetter(ol,"crop",["x","y","width","height"]);ee.addGetterSetter(ol,"cropX",0,Ge());ee.addGetterSetter(ol,"cropY",0,Ge());ee.addGetterSetter(ol,"cropWidth",0,Ge());ee.addGetterSetter(ol,"cropHeight",0,Ge());var sG=["fontFamily","fontSize","fontStyle","padding","lineHeight","text","width","height","pointerDirection","pointerWidth","pointerHeight"],f7e="Change.konva",h7e="none",m8="up",v8="right",y8="down",b8="left",p7e=sG.length;class rP extends n1{constructor(t){super(t),this.on("add.konva",function(n){this._addListeners(n.child),this._sync()})}getText(){return this.find("Text")[0]}getTag(){return this.find("Tag")[0]}_addListeners(t){var n=this,r,i=function(){n._sync()};for(r=0;r<p7e;r++)t.on(sG[r]+f7e,i)}getWidth(){return this.getText().width()}getHeight(){return this.getText().height()}_sync(){var t=this.getText(),n=this.getTag(),r,i,o,a,s,l,u;if(t&&n){switch(r=t.width(),i=t.height(),o=n.pointerDirection(),a=n.pointerWidth(),u=n.pointerHeight(),s=0,l=0,o){case m8:s=r/2,l=-1*u;break;case v8:s=r+a,l=i/2;break;case y8:s=r/2,l=i+u;break;case b8:s=-1*a,l=i/2;break}n.setAttrs({x:-1*s,y:-1*l,width:r,height:i}),t.setAttrs({x:-1*s,y:-1*l})}}}rP.prototype.className="Label";Cr(rP);class gp extends $e{_sceneFunc(t){var n=this.width(),r=this.height(),i=this.pointerDirection(),o=this.pointerWidth(),a=this.pointerHeight(),s=this.cornerRadius();let l=0,u=0,f=0,p=0;typeof s=="number"?l=u=f=p=Math.min(s,n/2,r/2):(l=Math.min(s[0]||0,n/2,r/2),u=Math.min(s[1]||0,n/2,r/2),p=Math.min(s[2]||0,n/2,r/2),f=Math.min(s[3]||0,n/2,r/2)),t.beginPath(),t.moveTo(l,0),i===m8&&(t.lineTo((n-o)/2,0),t.lineTo(n/2,-1*a),t.lineTo((n+o)/2,0)),t.lineTo(n-u,0),t.arc(n-u,u,u,Math.PI*3/2,0,!1),i===v8&&(t.lineTo(n,(r-a)/2),t.lineTo(n+o,r/2),t.lineTo(n,(r+a)/2)),t.lineTo(n,r-p),t.arc(n-p,r-p,p,0,Math.PI/2,!1),i===y8&&(t.lineTo((n+o)/2,r),t.lineTo(n/2,r+a),t.lineTo((n-o)/2,r)),t.lineTo(f,r),t.arc(f,r-f,f,Math.PI/2,Math.PI,!1),i===b8&&(t.lineTo(0,(r+a)/2),t.lineTo(-1*o,r/2),t.lineTo(0,(r-a)/2)),t.lineTo(0,l),t.arc(l,l,l,Math.PI,Math.PI*3/2,!1),t.closePath(),t.fillStrokeShape(this)}getSelfRect(){var t=0,n=0,r=this.pointerWidth(),i=this.pointerHeight(),o=this.pointerDirection(),a=this.width(),s=this.height();return o===m8?(n-=i,s+=i):o===y8?s+=i:o===b8?(t-=r*1.5,a+=r):o===v8&&(a+=r*1.5),{x:t,y:n,width:a,height:s}}}gp.prototype.className="Tag";Cr(gp);ee.addGetterSetter(gp,"pointerDirection",h7e);ee.addGetterSetter(gp,"pointerWidth",0,Ge());ee.addGetterSetter(gp,"pointerHeight",0,Ge());ee.addGetterSetter(gp,"cornerRadius",0,zW(4));class My extends $e{_sceneFunc(t){var n=this.cornerRadius(),r=this.width(),i=this.height();if(t.beginPath(),!n)t.rect(0,0,r,i);else{let o=0,a=0,s=0,l=0;typeof n=="number"?o=a=s=l=Math.min(n,r/2,i/2):(o=Math.min(n[0]||0,r/2,i/2),a=Math.min(n[1]||0,r/2,i/2),l=Math.min(n[2]||0,r/2,i/2),s=Math.min(n[3]||0,r/2,i/2)),t.moveTo(o,0),t.lineTo(r-a,0),t.arc(r-a,a,a,Math.PI*3/2,0,!1),t.lineTo(r,i-l),t.arc(r-l,i-l,l,0,Math.PI/2,!1),t.lineTo(s,i),t.arc(s,i-s,s,Math.PI/2,Math.PI,!1),t.lineTo(0,o),t.arc(o,o,o,Math.PI,Math.PI*3/2,!1)}t.closePath(),t.fillStrokeShape(this)}}My.prototype.className="Rect";Cr(My);ee.addGetterSetter(My,"cornerRadius",0,zW(4));class mp extends $e{_sceneFunc(t){const n=this._getPoints();t.beginPath(),t.moveTo(n[0].x,n[0].y);for(var r=1;r<n.length;r++)t.lineTo(n[r].x,n[r].y);t.closePath(),t.fillStrokeShape(this)}_getPoints(){const t=this.attrs.sides,n=this.attrs.radius||0,r=[];for(var i=0;i<t;i++)r.push({x:n*Math.sin(i*2*Math.PI/t),y:-1*n*Math.cos(i*2*Math.PI/t)});return r}getSelfRect(){const t=this._getPoints();var n=t[0].x,r=t[0].y,i=t[0].x,o=t[0].y;return t.forEach(a=>{n=Math.min(n,a.x),r=Math.max(r,a.x),i=Math.min(i,a.y),o=Math.max(o,a.y)}),{x:n,y:i,width:r-n,height:o-i}}getWidth(){return this.radius()*2}getHeight(){return this.radius()*2}setWidth(t){this.radius(t/2)}setHeight(t){this.radius(t/2)}}mp.prototype.className="RegularPolygon";mp.prototype._centroid=!0;mp.prototype._attrsAffectingSize=["radius"];Cr(mp);ee.addGetterSetter(mp,"radius",0,Ge());ee.addGetterSetter(mp,"sides",0,Ge());var jI=Math.PI*2;class vp extends $e{_sceneFunc(t){t.beginPath(),t.arc(0,0,this.innerRadius(),0,jI,!1),t.moveTo(this.outerRadius(),0),t.arc(0,0,this.outerRadius(),jI,0,!0),t.closePath(),t.fillStrokeShape(this)}getWidth(){return this.outerRadius()*2}getHeight(){return this.outerRadius()*2}setWidth(t){this.outerRadius(t/2)}setHeight(t){this.outerRadius(t/2)}}vp.prototype.className="Ring";vp.prototype._centroid=!0;vp.prototype._attrsAffectingSize=["innerRadius","outerRadius"];Cr(vp);ee.addGetterSetter(vp,"innerRadius",0,Ge());ee.addGetterSetter(vp,"outerRadius",0,Ge());class bu extends $e{constructor(t){super(t),this._updated=!0,this.anim=new ns(()=>{var n=this._updated;return this._updated=!1,n}),this.on("animationChange.konva",function(){this.frameIndex(0)}),this.on("frameIndexChange.konva",function(){this._updated=!0}),this.on("frameRateChange.konva",function(){!this.anim.isRunning()||(clearInterval(this.interval),this._setInterval())})}_sceneFunc(t){var n=this.animation(),r=this.frameIndex(),i=r*4,o=this.animations()[n],a=this.frameOffsets(),s=o[i+0],l=o[i+1],u=o[i+2],f=o[i+3],p=this.image();if((this.hasFill()||this.hasStroke())&&(t.beginPath(),t.rect(0,0,u,f),t.closePath(),t.fillStrokeShape(this)),p)if(a){var m=a[n],v=r*2;t.drawImage(p,s,l,u,f,m[v+0],m[v+1],u,f)}else t.drawImage(p,s,l,u,f,0,0,u,f)}_hitFunc(t){var n=this.animation(),r=this.frameIndex(),i=r*4,o=this.animations()[n],a=this.frameOffsets(),s=o[i+2],l=o[i+3];if(t.beginPath(),a){var u=a[n],f=r*2;t.rect(u[f+0],u[f+1],s,l)}else t.rect(0,0,s,l);t.closePath(),t.fillShape(this)}_useBufferCanvas(){return super._useBufferCanvas(!0)}_setInterval(){var t=this;this.interval=setInterval(function(){t._updateIndex()},1e3/this.frameRate())}start(){if(!this.isRunning()){var t=this.getLayer();this.anim.setLayers(t),this._setInterval(),this.anim.start()}}stop(){this.anim.stop(),clearInterval(this.interval)}isRunning(){return this.anim.isRunning()}_updateIndex(){var t=this.frameIndex(),n=this.animation(),r=this.animations(),i=r[n],o=i.length/4;t<o-1?this.frameIndex(t+1):this.frameIndex(0)}}bu.prototype.className="Sprite";Cr(bu);ee.addGetterSetter(bu,"animation");ee.addGetterSetter(bu,"animations");ee.addGetterSetter(bu,"frameOffsets");ee.addGetterSetter(bu,"image");ee.addGetterSetter(bu,"frameIndex",0,Ge());ee.addGetterSetter(bu,"frameRate",17,Ge());ee.backCompat(bu,{index:"frameIndex",getIndex:"getFrameIndex",setIndex:"setFrameIndex"});class vf extends $e{_sceneFunc(t){var n=this.innerRadius(),r=this.outerRadius(),i=this.numPoints();t.beginPath(),t.moveTo(0,0-r);for(var o=1;o<i*2;o++){var a=o%2===0?r:n,s=a*Math.sin(o*Math.PI/i),l=-1*a*Math.cos(o*Math.PI/i);t.lineTo(s,l)}t.closePath(),t.fillStrokeShape(this)}getWidth(){return this.outerRadius()*2}getHeight(){return this.outerRadius()*2}setWidth(t){this.outerRadius(t/2)}setHeight(t){this.outerRadius(t/2)}}vf.prototype.className="Star";vf.prototype._centroid=!0;vf.prototype._attrsAffectingSize=["innerRadius","outerRadius"];Cr(vf);ee.addGetterSetter(vf,"numPoints",5,Ge());ee.addGetterSetter(vf,"innerRadius",0,Ge());ee.addGetterSetter(vf,"outerRadius",0,Ge());function lG(e){return Array.from(e)}var D0="auto",g7e="center",lv="justify",m7e="Change.konva",v7e="2d",HI="-",uG="left",y7e="text",b7e="Text",S7e="top",x7e="bottom",VI="middle",cG="normal",w7e="px ",Rb=" ",C7e="right",_7e="word",k7e="char",UI="none",xC="\u2026",dG=["fontFamily","fontSize","fontStyle","fontVariant","padding","align","verticalAlign","lineHeight","text","width","height","wrap","ellipsis","letterSpacing"],E7e=dG.length;function P7e(e){return e.split(",").map(t=>{t=t.trim();const n=t.indexOf(" ")>=0,r=t.indexOf('"')>=0||t.indexOf("'")>=0;return n&&!r&&(t=`"${t}"`),t}).join(", ")}var Ib;function wC(){return Ib||(Ib=he.createCanvasElement().getContext(v7e),Ib)}function T7e(e){e.fillText(this._partialText,this._partialTextX,this._partialTextY)}function L7e(e){e.strokeText(this._partialText,this._partialTextX,this._partialTextY)}function A7e(e){return e=e||{},!e.fillLinearGradientColorStops&&!e.fillRadialGradientColorStops&&!e.fillPatternImage&&(e.fill=e.fill||"black"),e}class wr extends $e{constructor(t){super(A7e(t)),this._partialTextX=0,this._partialTextY=0;for(var n=0;n<E7e;n++)this.on(dG[n]+m7e,this._setTextData);this._setTextData()}_sceneFunc(t){var n=this.textArr,r=n.length;if(!!this.text()){var i=this.padding(),o=this.fontSize(),a=this.lineHeight()*o,s=this.verticalAlign(),l=0,u=this.align(),f=this.getWidth(),p=this.letterSpacing(),m=this.fill(),v=this.textDecoration(),b=v.indexOf("underline")!==-1,x=v.indexOf("line-through")!==-1,E,k=0,k=a/2,_=0,T=0;for(t.setAttr("font",this._getContextFont()),t.setAttr("textBaseline",VI),t.setAttr("textAlign",uG),s===VI?l=(this.getHeight()-r*a-i*2)/2:s===x7e&&(l=this.getHeight()-r*a-i*2),t.translate(i,l+i),E=0;E<r;E++){var _=0,T=0,A=n[E],R=A.text,I=A.width,D=A.lastInParagraph,B,z,j;if(t.save(),u===C7e?_+=f-I-i*2:u===g7e&&(_+=(f-I-i*2)/2),b&&(t.save(),t.beginPath(),t.moveTo(_,k+T+Math.round(o/2)),B=R.split(" ").length-1,z=B===0,j=u===lv&&D&&!z?f-i*2:I,t.lineTo(_+Math.round(j),k+T+Math.round(o/2)),t.lineWidth=o/15,t.strokeStyle=m,t.stroke(),t.restore()),x&&(t.save(),t.beginPath(),t.moveTo(_,k+T),B=R.split(" ").length-1,z=B===0,j=u===lv&&D&&!z?f-i*2:I,t.lineTo(_+Math.round(j),k+T),t.lineWidth=o/15,t.strokeStyle=m,t.stroke(),t.restore()),p!==0||u===lv){B=R.split(" ").length-1;for(var Y=lG(R),te=0;te<Y.length;te++){var G=Y[te];G===" "&&!D&&u===lv&&(_+=(f-i*2-I)/B),this._partialTextX=_,this._partialTextY=k+T,this._partialText=G,t.fillStrokeShape(this),_+=this.measureSize(G).width+p}}else this._partialTextX=_,this._partialTextY=k+T,this._partialText=R,t.fillStrokeShape(this);t.restore(),r>1&&(k+=a)}}}_hitFunc(t){var n=this.getWidth(),r=this.getHeight();t.beginPath(),t.rect(0,0,n,r),t.closePath(),t.fillStrokeShape(this)}setText(t){var n=he._isString(t)?t:t==null?"":t+"";return this._setAttr(y7e,n),this}getWidth(){var t=this.attrs.width===D0||this.attrs.width===void 0;return t?this.getTextWidth()+this.padding()*2:this.attrs.width}getHeight(){var t=this.attrs.height===D0||this.attrs.height===void 0;return t?this.fontSize()*this.textArr.length*this.lineHeight()+this.padding()*2:this.attrs.height}getTextWidth(){return this.textWidth}getTextHeight(){return he.warn("text.getTextHeight() method is deprecated. Use text.height() - for full height and text.fontSize() - for one line height."),this.textHeight}measureSize(t){var n=wC(),r=this.fontSize(),i;return n.save(),n.font=this._getContextFont(),i=n.measureText(t),n.restore(),{width:i.width,height:r}}_getContextFont(){return this.fontStyle()+Rb+this.fontVariant()+Rb+(this.fontSize()+w7e)+P7e(this.fontFamily())}_addTextLine(t){this.align()===lv&&(t=t.trim());var n=this._getTextWidth(t);return this.textArr.push({text:t,width:n,lastInParagraph:!1})}_getTextWidth(t){var n=this.letterSpacing(),r=t.length;return wC().measureText(t).width+(r?n*(r-1):0)}_setTextData(){var t=this.text().split(`
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te=t.dataArray,G=x+1;G<te.length;G++){if(te[G].pathLength>0)return x=G,te[G];te[G].command==="M"&&(m={x:te[G].points[0],y:te[G].points[1]})}return{}},_=function(te){var G=t._getTextSize(te).width+r;te===" "&&i==="justify"&&(G+=(s-a)/p);var $=0,V=0;for(v=void 0;Math.abs(G-$)/G>.01&&V<20;){V++;for(var J=$;b===void 0;)b=k(),b&&J+b.pathLength<G&&(J+=b.pathLength,b=void 0);if(Object.keys(b).length===0||m===void 0)return;var Z=!1;switch(b.command){case"L":zn.getLineLength(m.x,m.y,b.points[0],b.points[1])>G?v=zn.getPointOnLine(G,m.x,m.y,b.points[0],b.points[1],m.x,m.y):b=void 0;break;case"A":var 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B=zn.getLineLength(m.x,m.y,v.x,v.y),z=0;if(o)try{z=o(f[D-1],f[D])*this.fontSize()}catch(te){z=0}m.x+=z,v.x+=z,this.textWidth+=z;var j=zn.getPointOnLine(z+B/2,m.x,m.y,v.x,v.y),Y=Math.atan2(v.y-m.y,v.x-m.x);this.glyphInfo.push({transposeX:j.x,transposeY:j.y,text:f[D],rotation:Y,p0:m,p1:v}),m=v}}getSelfRect(){if(!this.glyphInfo.length)return{x:0,y:0,width:0,height:0};var t=[];this.glyphInfo.forEach(function(f){t.push(f.p0.x),t.push(f.p0.y),t.push(f.p1.x),t.push(f.p1.y)});for(var n=t[0]||0,r=t[0]||0,i=t[1]||0,o=t[1]||0,a,s,l=0;l<t.length/2;l++)a=t[l*2],s=t[l*2+1],n=Math.min(n,a),r=Math.max(r,a),i=Math.min(i,s),o=Math.max(o,s);var u=this.fontSize();return{x:n-u/2,y:i-u/2,width:r-n+u,height:o-i+u}}destroy(){return he.releaseCanvas(this.dummyCanvas),super.destroy()}}bi.prototype._fillFunc=hG;bi.prototype._strokeFunc=pG;bi.prototype._fillFuncHit=hG;bi.prototype._strokeFuncHit=pG;bi.prototype.className="TextPath";bi.prototype._attrsAffectingSize=["text","fontSize","data"];Cr(bi);ee.addGetterSetter(bi,"data");ee.addGetterSetter(bi,"fontFamily","Arial");ee.addGetterSetter(bi,"fontSize",12,Ge());ee.addGetterSetter(bi,"fontStyle",fG);ee.addGetterSetter(bi,"align","left");ee.addGetterSetter(bi,"letterSpacing",0,Ge());ee.addGetterSetter(bi,"textBaseline","middle");ee.addGetterSetter(bi,"fontVariant",fG);ee.addGetterSetter(bi,"text",O7e);ee.addGetterSetter(bi,"textDecoration",null);ee.addGetterSetter(bi,"kerningFunc",null);var gG="tr-konva",M7e=["resizeEnabledChange","rotateAnchorOffsetChange","rotateEnabledChange","enabledAnchorsChange","anchorSizeChange","borderEnabledChange","borderStrokeChange","borderStrokeWidthChange","borderDashChange","anchorStrokeChange","anchorStrokeWidthChange","anchorFillChange","anchorCornerRadiusChange","ignoreStrokeChange"].map(e=>e+`.${gG}`).join(" "),WI="nodesRect",R7e=["widthChange","heightChange","scaleXChange","scaleYChange","skewXChange","skewYChange","rotationChange","offsetXChange","offsetYChange","transformsEnabledChange","strokeWidthChange"],I7e={"top-left":-45,"top-center":0,"top-right":45,"middle-right":-90,"middle-left":90,"bottom-left":-135,"bottom-center":180,"bottom-right":135};const D7e="ontouchstart"in gt._global;function N7e(e,t){if(e==="rotater")return"crosshair";t+=he.degToRad(I7e[e]||0);var n=(he.radToDeg(t)%360+360)%360;return he._inRange(n,315+22.5,360)||he._inRange(n,0,22.5)?"ns-resize":he._inRange(n,45-22.5,45+22.5)?"nesw-resize":he._inRange(n,90-22.5,90+22.5)?"ew-resize":he._inRange(n,135-22.5,135+22.5)?"nwse-resize":he._inRange(n,180-22.5,180+22.5)?"ns-resize":he._inRange(n,225-22.5,225+22.5)?"nesw-resize":he._inRange(n,270-22.5,270+22.5)?"ew-resize":he._inRange(n,315-22.5,315+22.5)?"nwse-resize":(he.error("Transformer has unknown angle for cursor detection: "+n),"pointer")}var G5=["top-left","top-center","top-right","middle-right","middle-left","bottom-left","bottom-center","bottom-right"],GI=1e8;function B7e(e){return{x:e.x+e.width/2*Math.cos(e.rotation)+e.height/2*Math.sin(-e.rotation),y:e.y+e.height/2*Math.cos(e.rotation)+e.width/2*Math.sin(e.rotation)}}function mG(e,t,n){const r=n.x+(e.x-n.x)*Math.cos(t)-(e.y-n.y)*Math.sin(t),i=n.y+(e.x-n.x)*Math.sin(t)+(e.y-n.y)*Math.cos(t);return Object.assign(Object.assign({},e),{rotation:e.rotation+t,x:r,y:i})}function $7e(e,t){const n=B7e(e);return mG(e,t,n)}function F7e(e,t,n){let r=t;for(let i=0;i<e.length;i++){const o=gt.getAngle(e[i]),a=Math.abs(o-t)%(Math.PI*2);Math.min(a,Math.PI*2-a)<n&&(r=o)}return r}class On extends n1{constructor(t){super(t),this._transforming=!1,this._createElements(),this._handleMouseMove=this._handleMouseMove.bind(this),this._handleMouseUp=this._handleMouseUp.bind(this),this.update=this.update.bind(this),this.on(M7e,this.update),this.getNode()&&this.update()}attachTo(t){return this.setNode(t),this}setNode(t){return he.warn("tr.setNode(shape), tr.node(shape) and tr.attachTo(shape) methods are deprecated. Please use tr.nodes(nodesArray) instead."),this.setNodes([t])}getNode(){return this._nodes&&this._nodes[0]}_getEventNamespace(){return gG+this._id}setNodes(t=[]){this._nodes&&this._nodes.length&&this.detach(),this._nodes=t,t.length===1&&this.useSingleNodeRotation()?this.rotation(t[0].getAbsoluteRotation()):this.rotation(0),this._nodes.forEach(r=>{const i=()=>{this.nodes().length===1&&this.useSingleNodeRotation()&&this.rotation(this.nodes()[0].getAbsoluteRotation()),this._resetTransformCache(),!this._transforming&&!this.isDragging()&&this.update()},o=r._attrsAffectingSize.map(a=>a+"Change."+this._getEventNamespace()).join(" ");r.on(o,i),r.on(R7e.map(a=>a+`.${this._getEventNamespace()}`).join(" "),i),r.on(`absoluteTransformChange.${this._getEventNamespace()}`,i),this._proxyDrag(r)}),this._resetTransformCache();var n=!!this.findOne(".top-left");return n&&this.update(),this}_proxyDrag(t){let n;t.on(`dragstart.${this._getEventNamespace()}`,r=>{n=t.getAbsolutePosition(),!this.isDragging()&&t!==this.findOne(".back")&&this.startDrag(r,!1)}),t.on(`dragmove.${this._getEventNamespace()}`,r=>{if(!n)return;const i=t.getAbsolutePosition(),o=i.x-n.x,a=i.y-n.y;this.nodes().forEach(s=>{if(s===t||s.isDragging())return;const l=s.getAbsolutePosition();s.setAbsolutePosition({x:l.x+o,y:l.y+a}),s.startDrag(r)}),n=null})}getNodes(){return this._nodes||[]}getActiveAnchor(){return this._movingAnchorName}detach(){this._nodes&&this._nodes.forEach(t=>{t.off("."+this._getEventNamespace())}),this._nodes=[],this._resetTransformCache()}_resetTransformCache(){this._clearCache(WI),this._clearCache("transform"),this._clearSelfAndDescendantCache("absoluteTransform")}_getNodeRect(){return this._getCache(WI,this.__getNodeRect)}__getNodeShape(t,n=this.rotation(),r){var i=t.getClientRect({skipTransform:!0,skipShadow:!0,skipStroke:this.ignoreStroke()}),o=t.getAbsoluteScale(r),a=t.getAbsolutePosition(r),s=i.x*o.x-t.offsetX()*o.x,l=i.y*o.y-t.offsetY()*o.y;const u=(gt.getAngle(t.getAbsoluteRotation())+Math.PI*2)%(Math.PI*2),f={x:a.x+s*Math.cos(u)+l*Math.sin(-u),y:a.y+l*Math.cos(u)+s*Math.sin(u),width:i.width*o.x,height:i.height*o.y,rotation:u};return mG(f,-gt.getAngle(n),{x:0,y:0})}__getNodeRect(){var t=this.getNode();if(!t)return{x:-GI,y:-GI,width:0,height:0,rotation:0};const n=[];this.nodes().map(u=>{const f=u.getClientRect({skipTransform:!0,skipShadow:!0,skipStroke:this.ignoreStroke()});var p=[{x:f.x,y:f.y},{x:f.x+f.width,y:f.y},{x:f.x+f.width,y:f.y+f.height},{x:f.x,y:f.y+f.height}],m=u.getAbsoluteTransform();p.forEach(function(v){var b=m.point(v);n.push(b)})});const r=new Ea;r.rotate(-gt.getAngle(this.rotation()));var i,o,a,s;n.forEach(function(u){var f=r.point(u);i===void 0&&(i=a=f.x,o=s=f.y),i=Math.min(i,f.x),o=Math.min(o,f.y),a=Math.max(a,f.x),s=Math.max(s,f.y)}),r.invert();const l=r.point({x:i,y:o});return{x:l.x,y:l.y,width:a-i,height:s-o,rotation:gt.getAngle(this.rotation())}}getX(){return this._getNodeRect().x}getY(){return this._getNodeRect().y}getWidth(){return this._getNodeRect().width}getHeight(){return this._getNodeRect().height}_createElements(){this._createBack(),G5.forEach(function(t){this._createAnchor(t)}.bind(this)),this._createAnchor("rotater")}_createAnchor(t){var n=new My({stroke:"rgb(0, 161, 255)",fill:"white",strokeWidth:1,name:t+" _anchor",dragDistance:0,draggable:!0,hitStrokeWidth:D7e?10:"auto"}),r=this;n.on("mousedown touchstart",function(i){r._handleMouseDown(i)}),n.on("dragstart",i=>{n.stopDrag(),i.cancelBubble=!0}),n.on("dragend",i=>{i.cancelBubble=!0}),n.on("mouseenter",()=>{var i=gt.getAngle(this.rotation()),o=N7e(t,i);n.getStage().content&&(n.getStage().content.style.cursor=o),this._cursorChange=!0}),n.on("mouseout",()=>{n.getStage().content&&(n.getStage().content.style.cursor=""),this._cursorChange=!1}),this.add(n)}_createBack(){var t=new $e({name:"back",width:0,height:0,draggable:!0,sceneFunc(n){var r=this.getParent(),i=r.padding();n.beginPath(),n.rect(-i,-i,this.width()+i*2,this.height()+i*2),n.moveTo(this.width()/2,-i),r.rotateEnabled()&&n.lineTo(this.width()/2,-r.rotateAnchorOffset()*he._sign(this.height())-i),n.fillStrokeShape(this)},hitFunc:(n,r)=>{if(!!this.shouldOverdrawWholeArea()){var i=this.padding();n.beginPath(),n.rect(-i,-i,r.width()+i*2,r.height()+i*2),n.fillStrokeShape(r)}}});this.add(t),this._proxyDrag(t),t.on("dragstart",n=>{n.cancelBubble=!0}),t.on("dragmove",n=>{n.cancelBubble=!0}),t.on("dragend",n=>{n.cancelBubble=!0}),this.on("dragmove",n=>{this.update()})}_handleMouseDown(t){this._movingAnchorName=t.target.name().split(" ")[0];var n=this._getNodeRect(),r=n.width,i=n.height,o=Math.sqrt(Math.pow(r,2)+Math.pow(i,2));this.sin=Math.abs(i/o),this.cos=Math.abs(r/o),typeof window<"u"&&(window.addEventListener("mousemove",this._handleMouseMove),window.addEventListener("touchmove",this._handleMouseMove),window.addEventListener("mouseup",this._handleMouseUp,!0),window.addEventListener("touchend",this._handleMouseUp,!0)),this._transforming=!0;var a=t.target.getAbsolutePosition(),s=t.target.getStage().getPointerPosition();this._anchorDragOffset={x:s.x-a.x,y:s.y-a.y},this._fire("transformstart",{evt:t.evt,target:this.getNode()}),this._nodes.forEach(l=>{l._fire("transformstart",{evt:t.evt,target:l})})}_handleMouseMove(t){var n,r,i,o=this.findOne("."+this._movingAnchorName),a=o.getStage();a.setPointersPositions(t);const s=a.getPointerPosition();let l={x:s.x-this._anchorDragOffset.x,y:s.y-this._anchorDragOffset.y};const u=o.getAbsolutePosition();this.anchorDragBoundFunc()&&(l=this.anchorDragBoundFunc()(u,l,t)),o.setAbsolutePosition(l);const f=o.getAbsolutePosition();if(!(u.x===f.x&&u.y===f.y)){if(this._movingAnchorName==="rotater"){var p=this._getNodeRect();n=o.x()-p.width/2,r=-o.y()+p.height/2;let te=Math.atan2(-r,n)+Math.PI/2;p.height<0&&(te-=Math.PI);var m=gt.getAngle(this.rotation());const G=m+te,$=gt.getAngle(this.rotationSnapTolerance()),J=F7e(this.rotationSnaps(),G,$)-p.rotation,Z=$7e(p,J);this._fitNodesInto(Z,t);return}var v=this.keepRatio()||t.shiftKey,_=this.centeredScaling()||t.altKey;if(this._movingAnchorName==="top-left"){if(v){var b=_?{x:this.width()/2,y:this.height()/2}:{x:this.findOne(".bottom-right").x(),y:this.findOne(".bottom-right").y()};i=Math.sqrt(Math.pow(b.x-o.x(),2)+Math.pow(b.y-o.y(),2));var x=this.findOne(".top-left").x()>b.x?-1:1,E=this.findOne(".top-left").y()>b.y?-1:1;n=i*this.cos*x,r=i*this.sin*E,this.findOne(".top-left").x(b.x-n),this.findOne(".top-left").y(b.y-r)}}else if(this._movingAnchorName==="top-center")this.findOne(".top-left").y(o.y());else if(this._movingAnchorName==="top-right"){if(v){var b=_?{x:this.width()/2,y:this.height()/2}:{x:this.findOne(".bottom-left").x(),y:this.findOne(".bottom-left").y()};i=Math.sqrt(Math.pow(o.x()-b.x,2)+Math.pow(b.y-o.y(),2));var x=this.findOne(".top-right").x()<b.x?-1:1,E=this.findOne(".top-right").y()>b.y?-1:1;n=i*this.cos*x,r=i*this.sin*E,this.findOne(".top-right").x(b.x+n),this.findOne(".top-right").y(b.y-r)}var k=o.position();this.findOne(".top-left").y(k.y),this.findOne(".bottom-right").x(k.x)}else if(this._movingAnchorName==="middle-left")this.findOne(".top-left").x(o.x());else if(this._movingAnchorName==="middle-right")this.findOne(".bottom-right").x(o.x());else if(this._movingAnchorName==="bottom-left"){if(v){var b=_?{x:this.width()/2,y:this.height()/2}:{x:this.findOne(".top-right").x(),y:this.findOne(".top-right").y()};i=Math.sqrt(Math.pow(b.x-o.x(),2)+Math.pow(o.y()-b.y,2));var x=b.x<o.x()?-1:1,E=o.y()<b.y?-1:1;n=i*this.cos*x,r=i*this.sin*E,o.x(b.x-n),o.y(b.y+r)}k=o.position(),this.findOne(".top-left").x(k.x),this.findOne(".bottom-right").y(k.y)}else if(this._movingAnchorName==="bottom-center")this.findOne(".bottom-right").y(o.y());else if(this._movingAnchorName==="bottom-right"){if(v){var b=_?{x:this.width()/2,y:this.height()/2}:{x:this.findOne(".top-left").x(),y:this.findOne(".top-left").y()};i=Math.sqrt(Math.pow(o.x()-b.x,2)+Math.pow(o.y()-b.y,2));var x=this.findOne(".bottom-right").x()<b.x?-1:1,E=this.findOne(".bottom-right").y()<b.y?-1:1;n=i*this.cos*x,r=i*this.sin*E,this.findOne(".bottom-right").x(b.x+n),this.findOne(".bottom-right").y(b.y+r)}}else console.error(new Error("Wrong position argument of selection resizer: "+this._movingAnchorName));var _=this.centeredScaling()||t.altKey;if(_){var T=this.findOne(".top-left"),A=this.findOne(".bottom-right"),R=T.x(),I=T.y(),D=this.getWidth()-A.x(),B=this.getHeight()-A.y();A.move({x:-R,y:-I}),T.move({x:D,y:B})}var z=this.findOne(".top-left").getAbsolutePosition();n=z.x,r=z.y;var j=this.findOne(".bottom-right").x()-this.findOne(".top-left").x(),Y=this.findOne(".bottom-right").y()-this.findOne(".top-left").y();this._fitNodesInto({x:n,y:r,width:j,height:Y,rotation:gt.getAngle(this.rotation())},t)}}_handleMouseUp(t){this._removeEvents(t)}getAbsoluteTransform(){return this.getTransform()}_removeEvents(t){if(this._transforming){this._transforming=!1,typeof window<"u"&&(window.removeEventListener("mousemove",this._handleMouseMove),window.removeEventListener("touchmove",this._handleMouseMove),window.removeEventListener("mouseup",this._handleMouseUp,!0),window.removeEventListener("touchend",this._handleMouseUp,!0));var n=this.getNode();this._fire("transformend",{evt:t,target:n}),n&&this._nodes.forEach(r=>{r._fire("transformend",{evt:t,target:r})}),this._movingAnchorName=null}}_fitNodesInto(t,n){var r=this._getNodeRect();const i=1;if(he._inRange(t.width,-this.padding()*2-i,i)){this.update();return}if(he._inRange(t.height,-this.padding()*2-i,i)){this.update();return}const o=this.flipEnabled();var a=new Ea;if(a.rotate(gt.getAngle(this.rotation())),this._movingAnchorName&&t.width<0&&this._movingAnchorName.indexOf("left")>=0){const p=a.point({x:-this.padding()*2,y:0});if(t.x+=p.x,t.y+=p.y,t.width+=this.padding()*2,this._movingAnchorName=this._movingAnchorName.replace("left","right"),this._anchorDragOffset.x-=p.x,this._anchorDragOffset.y-=p.y,!o){this.update();return}}else if(this._movingAnchorName&&t.width<0&&this._movingAnchorName.indexOf("right")>=0){const p=a.point({x:this.padding()*2,y:0});if(this._movingAnchorName=this._movingAnchorName.replace("right","left"),this._anchorDragOffset.x-=p.x,this._anchorDragOffset.y-=p.y,t.width+=this.padding()*2,!o){this.update();return}}if(this._movingAnchorName&&t.height<0&&this._movingAnchorName.indexOf("top")>=0){const p=a.point({x:0,y:-this.padding()*2});if(t.x+=p.x,t.y+=p.y,this._movingAnchorName=this._movingAnchorName.replace("top","bottom"),this._anchorDragOffset.x-=p.x,this._anchorDragOffset.y-=p.y,t.height+=this.padding()*2,!o){this.update();return}}else if(this._movingAnchorName&&t.height<0&&this._movingAnchorName.indexOf("bottom")>=0){const p=a.point({x:0,y:this.padding()*2});if(this._movingAnchorName=this._movingAnchorName.replace("bottom","top"),this._anchorDragOffset.x-=p.x,this._anchorDragOffset.y-=p.y,t.height+=this.padding()*2,!o){this.update();return}}if(this.boundBoxFunc()){const p=this.boundBoxFunc()(r,t);p?t=p:he.warn("boundBoxFunc returned falsy. You should return new bound rect from it!")}const s=1e7,l=new Ea;l.translate(r.x,r.y),l.rotate(r.rotation),l.scale(r.width/s,r.height/s);const u=new Ea;u.translate(t.x,t.y),u.rotate(t.rotation),u.scale(t.width/s,t.height/s);const f=u.multiply(l.invert());this._nodes.forEach(p=>{var m;const v=p.getParent().getAbsoluteTransform(),b=p.getTransform().copy();b.translate(p.offsetX(),p.offsetY());const x=new Ea;x.multiply(v.copy().invert()).multiply(f).multiply(v).multiply(b);const E=x.decompose();p.setAttrs(E),this._fire("transform",{evt:n,target:p}),p._fire("transform",{evt:n,target:p}),(m=p.getLayer())===null||m===void 0||m.batchDraw()}),this.rotation(he._getRotation(t.rotation)),this._resetTransformCache(),this.update(),this.getLayer().batchDraw()}forceUpdate(){this._resetTransformCache(),this.update()}_batchChangeChild(t,n){this.findOne(t).setAttrs(n)}update(){var t,n=this._getNodeRect();this.rotation(he._getRotation(n.rotation));var r=n.width,i=n.height,o=this.enabledAnchors(),a=this.resizeEnabled(),s=this.padding(),l=this.anchorSize();this.find("._anchor").forEach(u=>{u.setAttrs({width:l,height:l,offsetX:l/2,offsetY:l/2,stroke:this.anchorStroke(),strokeWidth:this.anchorStrokeWidth(),fill:this.anchorFill(),cornerRadius:this.anchorCornerRadius()})}),this._batchChangeChild(".top-left",{x:0,y:0,offsetX:l/2+s,offsetY:l/2+s,visible:a&&o.indexOf("top-left")>=0}),this._batchChangeChild(".top-center",{x:r/2,y:0,offsetY:l/2+s,visible:a&&o.indexOf("top-center")>=0}),this._batchChangeChild(".top-right",{x:r,y:0,offsetX:l/2-s,offsetY:l/2+s,visible:a&&o.indexOf("top-right")>=0}),this._batchChangeChild(".middle-left",{x:0,y:i/2,offsetX:l/2+s,visible:a&&o.indexOf("middle-left")>=0}),this._batchChangeChild(".middle-right",{x:r,y:i/2,offsetX:l/2-s,visible:a&&o.indexOf("middle-right")>=0}),this._batchChangeChild(".bottom-left",{x:0,y:i,offsetX:l/2+s,offsetY:l/2-s,visible:a&&o.indexOf("bottom-left")>=0}),this._batchChangeChild(".bottom-center",{x:r/2,y:i,offsetY:l/2-s,visible:a&&o.indexOf("bottom-center")>=0}),this._batchChangeChild(".bottom-right",{x:r,y:i,offsetX:l/2-s,offsetY:l/2-s,visible:a&&o.indexOf("bottom-right")>=0}),this._batchChangeChild(".rotater",{x:r/2,y:-this.rotateAnchorOffset()*he._sign(i)-s,visible:this.rotateEnabled()}),this._batchChangeChild(".back",{width:r,height:i,visible:this.borderEnabled(),stroke:this.borderStroke(),strokeWidth:this.borderStrokeWidth(),dash:this.borderDash(),x:0,y:0}),(t=this.getLayer())===null||t===void 0||t.batchDraw()}isTransforming(){return this._transforming}stopTransform(){if(this._transforming){this._removeEvents();var t=this.findOne("."+this._movingAnchorName);t&&t.stopDrag()}}destroy(){return this.getStage()&&this._cursorChange&&this.getStage().content&&(this.getStage().content.style.cursor=""),n1.prototype.destroy.call(this),this.detach(),this._removeEvents(),this}toObject(){return qe.prototype.toObject.call(this)}getClientRect(){return this.nodes().length>0?super.getClientRect():{x:0,y:0,width:0,height:0}}}function z7e(e){return e instanceof Array||he.warn("enabledAnchors value should be an array"),e instanceof Array&&e.forEach(function(t){G5.indexOf(t)===-1&&he.warn("Unknown anchor name: "+t+". Available names are: "+G5.join(", "))}),e||[]}On.prototype.className="Transformer";Cr(On);ee.addGetterSetter(On,"enabledAnchors",G5,z7e);ee.addGetterSetter(On,"flipEnabled",!0,il());ee.addGetterSetter(On,"resizeEnabled",!0);ee.addGetterSetter(On,"anchorSize",10,Ge());ee.addGetterSetter(On,"rotateEnabled",!0);ee.addGetterSetter(On,"rotationSnaps",[]);ee.addGetterSetter(On,"rotateAnchorOffset",50,Ge());ee.addGetterSetter(On,"rotationSnapTolerance",5,Ge());ee.addGetterSetter(On,"borderEnabled",!0);ee.addGetterSetter(On,"anchorStroke","rgb(0, 161, 255)");ee.addGetterSetter(On,"anchorStrokeWidth",1,Ge());ee.addGetterSetter(On,"anchorFill","white");ee.addGetterSetter(On,"anchorCornerRadius",0,Ge());ee.addGetterSetter(On,"borderStroke","rgb(0, 161, 255)");ee.addGetterSetter(On,"borderStrokeWidth",1,Ge());ee.addGetterSetter(On,"borderDash");ee.addGetterSetter(On,"keepRatio",!0);ee.addGetterSetter(On,"centeredScaling",!1);ee.addGetterSetter(On,"ignoreStroke",!1);ee.addGetterSetter(On,"padding",0,Ge());ee.addGetterSetter(On,"node");ee.addGetterSetter(On,"nodes");ee.addGetterSetter(On,"boundBoxFunc");ee.addGetterSetter(On,"anchorDragBoundFunc");ee.addGetterSetter(On,"shouldOverdrawWholeArea",!1);ee.addGetterSetter(On,"useSingleNodeRotation",!0);ee.backCompat(On,{lineEnabled:"borderEnabled",rotateHandlerOffset:"rotateAnchorOffset",enabledHandlers:"enabledAnchors"});class vc extends $e{_sceneFunc(t){t.beginPath(),t.arc(0,0,this.radius(),0,gt.getAngle(this.angle()),this.clockwise()),t.lineTo(0,0),t.closePath(),t.fillStrokeShape(this)}getWidth(){return this.radius()*2}getHeight(){return this.radius()*2}setWidth(t){this.radius(t/2)}setHeight(t){this.radius(t/2)}}vc.prototype.className="Wedge";vc.prototype._centroid=!0;vc.prototype._attrsAffectingSize=["radius"];Cr(vc);ee.addGetterSetter(vc,"radius",0,Ge());ee.addGetterSetter(vc,"angle",0,Ge());ee.addGetterSetter(vc,"clockwise",!1);ee.backCompat(vc,{angleDeg:"angle",getAngleDeg:"getAngle",setAngleDeg:"setAngle"});function qI(){this.r=0,this.g=0,this.b=0,this.a=0,this.next=null}var 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Either set keySeparator: false on init or make sure your translations are published in nested format.")}var De=[],Je=this.languageUtils.getFallbackCodes(this.options.fallbackLng,o.lng||this.language);if(this.options.saveMissingTo==="fallback"&&Je&&Je[0])for(var Ke=0;Ke<Je.length;Ke++)De.push(Je[Ke]);else this.options.saveMissingTo==="all"?De=this.languageUtils.toResolveHierarchy(o.lng||this.language):De.push(o.lng||this.language);var mt=function(dt,vt,xt){var Le=X&&xt!==_?xt:Ae;s.options.missingKeyHandler?s.options.missingKeyHandler(dt,v,vt,Le,be,o):s.backendConnector&&s.backendConnector.saveMissing&&s.backendConnector.saveMissing(dt,v,vt,Le,be,o),s.emit("missingKey",dt,v,vt,_)};this.options.saveMissing&&(this.options.saveMissingPlurals&&U?De.forEach(function(_t){s.pluralResolver.getSuffixes(_t,o).forEach(function(dt){mt([_t],p+dt,o["defaultValue".concat(dt)]||fe)})}):mt(De,p,fe))}_=this.extendTranslation(_,i,o,k,a),Z&&_===p&&this.options.appendNamespaceToMissingKey&&(_="".concat(v,":").concat(p)),(Z||J)&&this.options.parseMissingKeyHandler&&(this.options.compatibilityAPI!=="v1"?_=this.options.parseMissingKeyHandler(this.options.appendNamespaceToMissingKey?"".concat(v,":").concat(p):p,J?_:void 0):_=this.options.parseMissingKeyHandler(_))}return l?(k.res=_,k):_}},{key:"extendTranslation",value:function(i,o,a,s,l){var u=this;if(this.i18nFormat&&this.i18nFormat.parse)i=this.i18nFormat.parse(i,xo(xo({},this.options.interpolation.defaultVariables),a),s.usedLng,s.usedNS,s.usedKey,{resolved:s});else if(!a.skipInterpolation){a.interpolation&&this.interpolator.init(xo(xo({},a),{interpolation:xo(xo({},this.options.interpolation),a.interpolation)}));var f=typeof i=="string"&&(a&&a.interpolation&&a.interpolation.skipOnVariables!==void 0?a.interpolation.skipOnVariables:this.options.interpolation.skipOnVariables),p;if(f){var m=i.match(this.interpolator.nestingRegexp);p=m&&m.length}var v=a.replace&&typeof a.replace!="string"?a.replace:a;if(this.options.interpolation.defaultVariables&&(v=xo(xo({},this.options.interpolation.defaultVariables),v)),i=this.interpolator.interpolate(i,v,a.lng||this.language,a),f){var b=i.match(this.interpolator.nestingRegexp),x=b&&b.length;p<x&&(a.nest=!1)}a.nest!==!1&&(i=this.interpolator.nest(i,function(){for(var _=arguments.length,T=new Array(_),A=0;A<_;A++)T[A]=arguments[A];return l&&l[0]===T[0]&&!a.context?(u.logger.warn("It seems you are nesting recursively key: ".concat(T[0]," in key: ").concat(o[0])),null):u.translate.apply(u,T.concat([o]))},a)),a.interpolation&&this.interpolator.reset()}var E=a.postProcess||this.options.postProcess,k=typeof E=="string"?[E]:E;return i!=null&&k&&k.length&&a.applyPostProcessor!==!1&&(i=HU.handle(k,i,o,this.options&&this.options.postProcessPassResolved?xo({i18nResolved:s},a):a,this)),i}},{key:"resolve",value:function(i){var o=this,a=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{},s,l,u,f,p;return typeof i=="string"&&(i=[i]),i.forEach(function(m){if(!o.isValidLookup(s)){var v=o.extractFromKey(m,a),b=v.key;l=b;var x=v.namespaces;o.options.fallbackNS&&(x=x.concat(o.options.fallbackNS));var E=a.count!==void 0&&typeof a.count!="string",k=E&&!a.ordinal&&a.count===0&&o.pluralResolver.shouldUseIntlApi(),_=a.context!==void 0&&(typeof a.context=="string"||typeof a.context=="number")&&a.context!=="",T=a.lngs?a.lngs:o.languageUtils.toResolveHierarchy(a.lng||o.language,a.fallbackLng);x.forEach(function(A){o.isValidLookup(s)||(p=A,!RR["".concat(T[0],"-").concat(A)]&&o.utils&&o.utils.hasLoadedNamespace&&!o.utils.hasLoadedNamespace(p)&&(RR["".concat(T[0],"-").concat(A)]=!0,o.logger.warn('key "'.concat(l,'" for languages "').concat(T.join(", "),`" won't get resolved as namespace "`).concat(p,'" was not yet loaded'),"This means something IS WRONG in your setup. You access the t function before i18next.init / i18next.loadNamespace / i18next.changeLanguage was done. 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this.i18nFormat&&this.i18nFormat.getResource?this.i18nFormat.getResource(i,o,a,s):this.resourceStore.getResource(i,o,a,s)}}],[{key:"hasDefaultValue",value:function(i){var o="defaultValue";for(var a in i)if(Object.prototype.hasOwnProperty.call(i,a)&&o===a.substring(0,o.length)&&i[a]!==void 0)return!0;return!1}}]),n}(tf);function dC(e){return e.charAt(0).toUpperCase()+e.slice(1)}var DR=function(){function e(t){vu(this,e),this.options=t,this.supportedLngs=this.options.supportedLngs||!1,this.logger=Kl.create("languageUtils")}return yu(e,[{key:"getScriptPartFromCode",value:function(n){if(!n||n.indexOf("-")<0)return null;var r=n.split("-");return r.length===2||(r.pop(),r[r.length-1].toLowerCase()==="x")?null:this.formatLanguageCode(r.join("-"))}},{key:"getLanguagePartFromCode",value:function(n){if(!n||n.indexOf("-")<0)return n;var r=n.split("-");return this.formatLanguageCode(r[0])}},{key:"formatLanguageCode",value:function(n){if(typeof n=="string"&&n.indexOf("-")>-1){var r=["hans","hant","latn","cyrl","cans","mong","arab"],i=n.split("-");return this.options.lowerCaseLng?i=i.map(function(o){return o.toLowerCase()}):i.length===2?(i[0]=i[0].toLowerCase(),i[1]=i[1].toUpperCase(),r.indexOf(i[1].toLowerCase())>-1&&(i[1]=dC(i[1].toLowerCase()))):i.length===3&&(i[0]=i[0].toLowerCase(),i[1].length===2&&(i[1]=i[1].toUpperCase()),i[0]!=="sgn"&&i[2].length===2&&(i[2]=i[2].toUpperCase()),r.indexOf(i[1].toLowerCase())>-1&&(i[1]=dC(i[1].toLowerCase())),r.indexOf(i[2].toLowerCase())>-1&&(i[2]=dC(i[2].toLowerCase()))),i.join("-")}return this.options.cleanCode||this.options.lowerCaseLng?n.toLowerCase():n}},{key:"isSupportedCode",value:function(n){return(this.options.load==="languageOnly"||this.options.nonExplicitSupportedLngs)&&(n=this.getLanguagePartFromCode(n)),!this.supportedLngs||!this.supportedLngs.length||this.supportedLngs.indexOf(n)>-1}},{key:"getBestMatchFromCodes",value:function(n){var r=this;if(!n)return null;var i;return n.forEach(function(o){if(!i){var a=r.formatLanguageCode(o);(!r.options.supportedLngs||r.isSupportedCode(a))&&(i=a)}}),!i&&this.options.supportedLngs&&n.forEach(function(o){if(!i){var a=r.getLanguagePartFromCode(o);if(r.isSupportedCode(a))return i=a;i=r.options.supportedLngs.find(function(s){if(s.indexOf(a)===0)return s})}}),i||(i=this.getFallbackCodes(this.options.fallbackLng)[0]),i}},{key:"getFallbackCodes",value:function(n,r){if(!n)return[];if(typeof n=="function"&&(n=n(r)),typeof n=="string"&&(n=[n]),Object.prototype.toString.apply(n)==="[object Array]")return n;if(!r)return n.default||[];var i=n[r];return i||(i=n[this.getScriptPartFromCode(r)]),i||(i=n[this.formatLanguageCode(r)]),i||(i=n[this.getLanguagePartFromCode(r)]),i||(i=n.default),i||[]}},{key:"toResolveHierarchy",value:function(n,r){var i=this,o=this.getFallbackCodes(r||this.options.fallbackLng||[],n),a=[],s=function(u){!u||(i.isSupportedCode(u)?a.push(u):i.logger.warn("rejecting language code not found in supportedLngs: ".concat(u)))};return typeof 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n=="string"&&s(this.formatLanguageCode(n)),o.forEach(function(l){a.indexOf(l)<0&&s(i.formatLanguageCode(l))}),a}}]),e}(),HSe=[{lngs:["ach","ak","am","arn","br","fil","gun","ln","mfe","mg","mi","oc","pt","pt-BR","tg","tl","ti","tr","uz","wa"],nr:[1,2],fc:1},{lngs:["af","an","ast","az","bg","bn","ca","da","de","dev","el","en","eo","es","et","eu","fi","fo","fur","fy","gl","gu","ha","hi","hu","hy","ia","it","kk","kn","ku","lb","mai","ml","mn","mr","nah","nap","nb","ne","nl","nn","no","nso","pa","pap","pms","ps","pt-PT","rm","sco","se","si","so","son","sq","sv","sw","ta","te","tk","ur","yo"],nr:[1,2],fc:2},{lngs:["ay","bo","cgg","fa","ht","id","ja","jbo","ka","km","ko","ky","lo","ms","sah","su","th","tt","ug","vi","wo","zh"],nr:[1],fc:3},{lngs:["be","bs","cnr","dz","hr","ru","sr","uk"],nr:[1,2,5],fc:4},{lngs:["ar"],nr:[0,1,2,3,11,100],fc:5},{lngs:["cs","sk"],nr:[1,2,5],fc:6},{lngs:["csb","pl"],nr:[1,2,5],fc:7},{lngs:["cy"],nr:[1,2,3,8],fc:8},{lngs:["fr"],nr:[1,2],fc:9},{lngs:["ga"],nr:[1,2,3,7,11],fc:10},{lngs:["gd"],nr:[1,2,3,20],fc:11},{lngs:["is"],nr:[1,2],fc:12},{lngs:["jv"],nr:[0,1],fc:13},{lngs:["kw"],nr:[1,2,3,4],fc:14},{lngs:["lt"],nr:[1,2,10],fc:15},{lngs:["lv"],nr:[1,2,0],fc:16},{lngs:["mk"],nr:[1,2],fc:17},{lngs:["mnk"],nr:[0,1,2],fc:18},{lngs:["mt"],nr:[1,2,11,20],fc:19},{lngs:["or"],nr:[2,1],fc:2},{lngs:["ro"],nr:[1,2,20],fc:20},{lngs:["sl"],nr:[5,1,2,3],fc:21},{lngs:["he","iw"],nr:[1,2,20,21],fc:22}],VSe={1:function(t){return Number(t>1)},2:function(t){return Number(t!=1)},3:function(t){return 0},4:function(t){return Number(t%10==1&&t%100!=11?0:t%10>=2&&t%10<=4&&(t%100<10||t%100>=20)?1:2)},5:function(t){return Number(t==0?0:t==1?1:t==2?2:t%100>=3&&t%100<=10?3:t%100>=11?4:5)},6:function(t){return Number(t==1?0:t>=2&&t<=4?1:2)},7:function(t){return Number(t==1?0:t%10>=2&&t%10<=4&&(t%100<10||t%100>=20)?1:2)},8:function(t){return Number(t==1?0:t==2?1:t!=8&&t!=11?2:3)},9:function(t){return Number(t>=2)},10:function(t){return Number(t==1?0:t==2?1:t<7?2:t<11?3:4)},11:function(t){return Number(t==1||t==11?0:t==2||t==12?1:t>2&&t<20?2:3)},12:function(t){return Number(t%10!=1||t%100==11)},13:function(t){return Number(t!==0)},14:function(t){return Number(t==1?0:t==2?1:t==3?2:3)},15:function(t){return Number(t%10==1&&t%100!=11?0:t%10>=2&&(t%100<10||t%100>=20)?1:2)},16:function(t){return Number(t%10==1&&t%100!=11?0:t!==0?1:2)},17:function(t){return Number(t==1||t%10==1&&t%100!=11?0:1)},18:function(t){return 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r="".concat(this.prefix).concat(this.unescapePrefix,"(.+?)").concat(this.unescapeSuffix).concat(this.suffix);this.regexpUnescape=new RegExp(r,"g");var i="".concat(this.nestingPrefix,"(.+?)").concat(this.nestingSuffix);this.nestingRegexp=new RegExp(i,"g")}},{key:"interpolate",value:function(n,r,i,o){var a=this,s,l,u,f=this.options&&this.options.interpolation&&this.options.interpolation.defaultVariables||{};function p(E){return E.replace(/\$/g,"$$$$")}var m=function(k){if(k.indexOf(a.formatSeparator)<0){var _=AR(r,f,k);return a.alwaysFormat?a.format(_,void 0,i,Fs(Fs(Fs({},o),r),{},{interpolationkey:k})):_}var T=k.split(a.formatSeparator),A=T.shift().trim(),R=T.join(a.formatSeparator).trim();return a.format(AR(r,f,A),R,i,Fs(Fs(Fs({},o),r),{},{interpolationkey:A}))};this.resetRegExp();var v=o&&o.missingInterpolationHandler||this.options.missingInterpolationHandler,b=o&&o.interpolation&&o.interpolation.skipOnVariables!==void 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n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter(function(i){return Object.getOwnPropertyDescriptor(e,i).enumerable})),n.push.apply(n,r)}return n}function zR(e){for(var t=1;t<arguments.length;t++){var n=arguments[t]!=null?arguments[t]:{};t%2?FR(Object(n),!0).forEach(function(r){pf(e,r,n[r])}):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(n)):FR(Object(n)).forEach(function(r){Object.defineProperty(e,r,Object.getOwnPropertyDescriptor(n,r))})}return e}function XSe(e){var t=ZSe();return function(){var r=cu(e),i;if(t){var o=cu(this).constructor;i=Reflect.construct(r,arguments,o)}else i=r.apply(this,arguments);return Ay(this,i)}}function ZSe(){if(typeof Reflect>"u"||!Reflect.construct||Reflect.construct.sham)return!1;if(typeof Proxy=="function")return!0;try{return Boolean.prototype.valueOf.call(Reflect.construct(Boolean,[],function(){})),!0}catch(e){return!1}}function QSe(e,t){e.pending[t]!==void 0&&(delete e.pending[t],e.pendingCount--)}var JSe=function(e){vx(n,e);var t=XSe(n);function n(r,i,o){var a,s=arguments.length>3&&arguments[3]!==void 0?arguments[3]:{};return vu(this,n),a=t.call(this),yx&&tf.call(Ud(a)),a.backend=r,a.store=i,a.services=o,a.languageUtils=o.languageUtils,a.options=s,a.logger=Kl.create("backendConnector"),a.waitingReads=[],a.maxParallelReads=s.maxParallelReads||10,a.readingCalls=0,a.maxRetries=s.maxRetries>=0?s.maxRetries:5,a.retryTimeout=s.retryTimeout>=1?s.retryTimeout:350,a.state={},a.queue=[],a.backend&&a.backend.init&&a.backend.init(o,s.backend,s),a}return yu(n,[{key:"queueLoad",value:function(i,o,a,s){var l=this,u={},f={},p={},m={};return i.forEach(function(v){var b=!0;o.forEach(function(x){var E="".concat(v,"|").concat(x);!a.reload&&l.store.hasResourceBundle(v,x)?l.state[E]=2:l.state[E]<0||(l.state[E]===1?f[E]===void 0&&(f[E]=!0):(l.state[E]=1,b=!1,f[E]===void 0&&(f[E]=!0),u[E]===void 0&&(u[E]=!0),m[x]===void 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0?arguments[3]:0,u=arguments.length>4&&arguments[4]!==void 0?arguments[4]:this.retryTimeout,f=arguments.length>5?arguments[5]:void 0;if(!i.length)return f(null,{});if(this.readingCalls>=this.maxParallelReads){this.waitingReads.push({lng:i,ns:o,fcName:a,tried:l,wait:u,callback:f});return}this.readingCalls++;var p=function(x,E){if(s.readingCalls--,s.waitingReads.length>0){var k=s.waitingReads.shift();s.read(k.lng,k.ns,k.fcName,k.tried,k.wait,k.callback)}if(x&&E&&l<s.maxRetries){setTimeout(function(){s.read.call(s,i,o,a,l+1,u*2,f)},u);return}f(x,E)},m=this.backend[a].bind(this.backend);if(m.length===2){try{var v=m(i,o);v&&typeof v.then=="function"?v.then(function(b){return p(null,b)}).catch(p):p(null,v)}catch(b){p(b)}return}return m(i,o,p)}},{key:"prepareLoading",value:function(i,o){var a=this,s=arguments.length>2&&arguments[2]!==void 0?arguments[2]:{},l=arguments.length>3?arguments[3]:void 0;if(!this.backend)return this.logger.warn("No backend was added via i18next.use. Will not load resources."),l&&l();typeof i=="string"&&(i=this.languageUtils.toResolveHierarchy(i)),typeof o=="string"&&(o=[o]);var u=this.queueLoad(i,o,s,l);if(!u.toLoad.length)return u.pending.length||l(),null;u.toLoad.forEach(function(f){a.loadOne(f)})}},{key:"load",value:function(i,o,a){this.prepareLoading(i,o,{},a)}},{key:"reload",value:function(i,o,a){this.prepareLoading(i,o,{reload:!0},a)}},{key:"loadOne",value:function(i){var o=this,a=arguments.length>1&&arguments[1]!==void 0?arguments[1]:"",s=i.split("|"),l=s[0],u=s[1];this.read(l,u,"read",void 0,void 0,function(f,p){f&&o.logger.warn("".concat(a,"loading namespace ").concat(u," for language ").concat(l," failed"),f),!f&&p&&o.logger.log("".concat(a,"loaded namespace ").concat(u," for language ").concat(l),p),o.loaded(i,f,p)})}},{key:"saveMissing",value:function(i,o,a,s,l){var u=arguments.length>5&&arguments[5]!==void 0?arguments[5]:{},f=arguments.length>6&&arguments[6]!==void 0?arguments[6]:function(){};if(this.services.utils&&this.services.utils.hasLoadedNamespace&&!this.services.utils.hasLoadedNamespace(o)){this.logger.warn('did not save key "'.concat(a,'" as the namespace "').concat(o,'" was not yet loaded'),"This means something IS WRONG in your setup. 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jR(){return{debug:!1,initImmediate:!0,ns:["translation"],defaultNS:["translation"],fallbackLng:["dev"],fallbackNS:!1,supportedLngs:!1,nonExplicitSupportedLngs:!1,load:"all",preload:!1,simplifyPluralSuffix:!0,keySeparator:".",nsSeparator:":",pluralSeparator:"_",contextSeparator:"_",partialBundledLanguages:!1,saveMissing:!1,updateMissing:!1,saveMissingTo:"fallback",saveMissingPlurals:!0,missingKeyHandler:!1,missingInterpolationHandler:!1,postProcess:!1,postProcessPassResolved:!1,returnNull:!0,returnEmptyString:!0,returnObjects:!1,joinArrays:!1,returnedObjectHandler:!1,parseMissingKeyHandler:!1,appendNamespaceToMissingKey:!1,appendNamespaceToCIMode:!1,overloadTranslationOptionHandler:function(t){var n={};if(el(t[1])==="object"&&(n=t[1]),typeof t[1]=="string"&&(n.defaultValue=t[1]),typeof t[2]=="string"&&(n.tDescription=t[2]),el(t[2])==="object"||el(t[3])==="object"){var r=t[3]||t[2];Object.keys(r).forEach(function(i){n[i]=r[i]})}return n},interpolation:{escapeValue:!0,format:function(t,n,r,i){return t},prefix:"{{",suffix:"}}",formatSeparator:",",unescapePrefix:"-",nestingPrefix:"$t(",nestingSuffix:")",nestingOptionsSeparator:",",maxReplaces:1e3,skipOnVariables:!0}}}function HR(e){return typeof e.ns=="string"&&(e.ns=[e.ns]),typeof e.fallbackLng=="string"&&(e.fallbackLng=[e.fallbackLng]),typeof e.fallbackNS=="string"&&(e.fallbackNS=[e.fallbackNS]),e.supportedLngs&&e.supportedLngs.indexOf("cimode")<0&&(e.supportedLngs=e.supportedLngs.concat(["cimode"])),e}function VR(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter(function(i){return Object.getOwnPropertyDescriptor(e,i).enumerable})),n.push.apply(n,r)}return n}function Dl(e){for(var t=1;t<arguments.length;t++){var n=arguments[t]!=null?arguments[t]:{};t%2?VR(Object(n),!0).forEach(function(r){pf(e,r,n[r])}):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(n)):VR(Object(n)).forEach(function(r){Object.defineProperty(e,r,Object.getOwnPropertyDescriptor(n,r))})}return e}function exe(e){var t=txe();return function(){var r=cu(e),i;if(t){var o=cu(this).constructor;i=Reflect.construct(r,arguments,o)}else i=r.apply(this,arguments);return Ay(this,i)}}function txe(){if(typeof Reflect>"u"||!Reflect.construct||Reflect.construct.sham)return!1;if(typeof Proxy=="function")return!0;try{return Boolean.prototype.valueOf.call(Reflect.construct(Boolean,[],function(){})),!0}catch(e){return!1}}function vb(){}function nxe(e){var t=Object.getOwnPropertyNames(Object.getPrototypeOf(e));t.forEach(function(n){typeof e[n]=="function"&&(e[n]=e[n].bind(e))})}var $5=function(e){vx(n,e);var t=exe(n);function n(){var r,i=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{},o=arguments.length>1?arguments[1]:void 0;if(vu(this,n),r=t.call(this),yx&&tf.call(Ud(r)),r.options=HR(i),r.services={},r.logger=Kl,r.modules={external:[]},nxe(Ud(r)),o&&!r.isInitialized&&!i.isClone){if(!r.options.initImmediate)return r.init(i,o),Ay(r,Ud(r));setTimeout(function(){r.init(i,o)},0)}return r}return yu(n,[{key:"init",value:function(){var i=this,o=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{},a=arguments.length>1?arguments[1]:void 0;typeof o=="function"&&(a=o,o={}),!o.defaultNS&&o.defaultNS!==!1&&o.ns&&(typeof o.ns=="string"?o.defaultNS=o.ns:o.ns.indexOf("translation")<0&&(o.defaultNS=o.ns[0]));var s=jR();this.options=Dl(Dl(Dl({},s),this.options),HR(o)),this.options.compatibilityAPI!=="v1"&&(this.options.interpolation=Dl(Dl({},s.interpolation),this.options.interpolation)),o.keySeparator!==void 0&&(this.options.userDefinedKeySeparator=o.keySeparator),o.nsSeparator!==void 0&&(this.options.userDefinedNsSeparator=o.nsSeparator);function l(k){return k?typeof k=="function"?new k:k:null}if(!this.options.isClone){this.modules.logger?Kl.init(l(this.modules.logger),this.options):Kl.init(null,this.options);var u;this.modules.formatter?u=this.modules.formatter:typeof Intl<"u"&&(u=KSe);var f=new DR(this.options);this.store=new FSe(this.options.resources,this.options);var p=this.services;p.logger=Kl,p.resourceStore=this.store,p.languageUtils=f,p.pluralResolver=new GSe(f,{prepend:this.options.pluralSeparator,compatibilityJSON:this.options.compatibilityJSON,simplifyPluralSuffix:this.options.simplifyPluralSuffix}),u&&(!this.options.interpolation.format||this.options.interpolation.format===s.interpolation.format)&&(p.formatter=l(u),p.formatter.init(p,this.options),this.options.interpolation.format=p.formatter.format.bind(p.formatter)),p.interpolator=new qSe(this.options),p.utils={hasLoadedNamespace:this.hasLoadedNamespace.bind(this)},p.backendConnector=new JSe(l(this.modules.backend),p.resourceStore,p,this.options),p.backendConnector.on("*",function(k){for(var _=arguments.length,T=new Array(_>1?_-1:0),A=1;A<_;A++)T[A-1]=arguments[A];i.emit.apply(i,[k].concat(T))}),this.modules.languageDetector&&(p.languageDetector=l(this.modules.languageDetector),p.languageDetector.init&&p.languageDetector.init(p,this.options.detection,this.options)),this.modules.i18nFormat&&(p.i18nFormat=l(this.modules.i18nFormat),p.i18nFormat.init&&p.i18nFormat.init(this)),this.translator=new IR(this.services,this.options),this.translator.on("*",function(k){for(var _=arguments.length,T=new Array(_>1?_-1:0),A=1;A<_;A++)T[A-1]=arguments[A];i.emit.apply(i,[k].concat(T))}),this.modules.external.forEach(function(k){k.init&&k.init(i)})}if(this.format=this.options.interpolation.format,a||(a=vb),this.options.fallbackLng&&!this.services.languageDetector&&!this.options.lng){var m=this.services.languageUtils.getFallbackCodes(this.options.fallbackLng);m.length>0&&m[0]!=="dev"&&(this.options.lng=m[0])}!this.services.languageDetector&&!this.options.lng&&this.logger.warn("init: no languageDetector is used and no lng is defined");var v=["getResource","hasResourceBundle","getResourceBundle","getDataByLanguage"];v.forEach(function(k){i[k]=function(){var _;return(_=i.store)[k].apply(_,arguments)}});var b=["addResource","addResources","addResourceBundle","removeResourceBundle"];b.forEach(function(k){i[k]=function(){var _;return(_=i.store)[k].apply(_,arguments),i}});var x=tv(),E=function(){var _=function(A,R){i.isInitialized&&!i.initializedStoreOnce&&i.logger.warn("init: i18next is already initialized. You should call init just once!"),i.isInitialized=!0,i.options.isClone||i.logger.log("initialized",i.options),i.emit("initialized",i.options),x.resolve(R),a(A,R)};if(i.languages&&i.options.compatibilityAPI!=="v1"&&!i.isInitialized)return _(null,i.t.bind(i));i.changeLanguage(i.options.lng,_)};return this.options.resources||!this.options.initImmediate?E():setTimeout(E,0),x}},{key:"loadResources",value:function(i){var o=this,a=arguments.length>1&&arguments[1]!==void 0?arguments[1]:vb,s=a,l=typeof i=="string"?i:this.language;if(typeof i=="function"&&(s=i),!this.options.resources||this.options.partialBundledLanguages){if(l&&l.toLowerCase()==="cimode")return s();var u=[],f=function(v){if(!!v){var b=o.services.languageUtils.toResolveHierarchy(v);b.forEach(function(x){u.indexOf(x)<0&&u.push(x)})}};if(l)f(l);else{var p=this.services.languageUtils.getFallbackCodes(this.options.fallbackLng);p.forEach(function(m){return f(m)})}this.options.preload&&this.options.preload.forEach(function(m){return f(m)}),this.services.backendConnector.load(u,this.options.ns,function(m){!m&&!o.resolvedLanguage&&o.language&&o.setResolvedLanguage(o.language),s(m)})}else s(null)}},{key:"reloadResources",value:function(i,o,a){var s=tv();return i||(i=this.languages),o||(o=this.options.ns),a||(a=vb),this.services.backendConnector.reload(i,o,function(l){s.resolve(),a(l)}),s}},{key:"use",value:function(i){if(!i)throw new Error("You are passing an undefined module! Please check the object you are passing to i18next.use()");if(!i.type)throw new Error("You are passing a wrong module! Please check the object you are passing to i18next.use()");return i.type==="backend"&&(this.modules.backend=i),(i.type==="logger"||i.log&&i.warn&&i.error)&&(this.modules.logger=i),i.type==="languageDetector"&&(this.modules.languageDetector=i),i.type==="i18nFormat"&&(this.modules.i18nFormat=i),i.type==="postProcessor"&&HU.addPostProcessor(i),i.type==="formatter"&&(this.modules.formatter=i),i.type==="3rdParty"&&this.modules.external.push(i),this}},{key:"setResolvedLanguage",value:function(i){if(!(!i||!this.languages)&&!(["cimode","dev"].indexOf(i)>-1))for(var o=0;o<this.languages.length;o++){var a=this.languages[o];if(!(["cimode","dev"].indexOf(a)>-1)&&this.store.hasLanguageSomeTranslations(a)){this.resolvedLanguage=a;break}}}},{key:"changeLanguage",value:function(i,o){var a=this;this.isLanguageChangingTo=i;var s=tv();this.emit("languageChanging",i);var l=function(m){a.language=m,a.languages=a.services.languageUtils.toResolveHierarchy(m),a.resolvedLanguage=void 0,a.setResolvedLanguage(m)},u=function(m,v){v?(l(v),a.translator.changeLanguage(v),a.isLanguageChangingTo=void 0,a.emit("languageChanged",v),a.logger.log("languageChanged",v)):a.isLanguageChangingTo=void 0,s.resolve(function(){return a.t.apply(a,arguments)}),o&&o(m,function(){return a.t.apply(a,arguments)})},f=function(m){!i&&!m&&a.services.languageDetector&&(m=[]);var v=typeof m=="string"?m:a.services.languageUtils.getBestMatchFromCodes(m);v&&(a.language||l(v),a.translator.language||a.translator.changeLanguage(v),a.services.languageDetector&&a.services.languageDetector.cacheUserLanguage&&a.services.languageDetector.cacheUserLanguage(v)),a.loadResources(v,function(b){u(b,v)})};return!i&&this.services.languageDetector&&!this.services.languageDetector.async?f(this.services.languageDetector.detect()):!i&&this.services.languageDetector&&this.services.languageDetector.async?this.services.languageDetector.detect.length===0?this.services.languageDetector.detect().then(f):this.services.languageDetector.detect(f):f(i),s}},{key:"getFixedT",value:function(i,o,a){var s=this,l=function u(f,p){var m;if(el(p)!=="object"){for(var v=arguments.length,b=new Array(v>2?v-2:0),x=2;x<v;x++)b[x-2]=arguments[x];m=s.options.overloadTranslationOptionHandler([f,p].concat(b))}else m=Dl({},p);m.lng=m.lng||u.lng,m.lngs=m.lngs||u.lngs,m.ns=m.ns||u.ns,m.keyPrefix=m.keyPrefix||a||u.keyPrefix;var E=s.options.keySeparator||".",k=m.keyPrefix?"".concat(m.keyPrefix).concat(E).concat(f):f;return s.t(k,m)};return typeof i=="string"?l.lng=i:l.lngs=i,l.ns=o,l.keyPrefix=a,l}},{key:"t",value:function(){var i;return this.translator&&(i=this.translator).translate.apply(i,arguments)}},{key:"exists",value:function(){var i;return this.translator&&(i=this.translator).exists.apply(i,arguments)}},{key:"setDefaultNamespace",value:function(i){this.options.defaultNS=i}},{key:"hasLoadedNamespace",value:function(i){var o=this,a=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{};if(!this.isInitialized)return this.logger.warn("hasLoadedNamespace: i18next was not initialized",this.languages),!1;if(!this.languages||!this.languages.length)return this.logger.warn("hasLoadedNamespace: i18n.languages were undefined or empty",this.languages),!1;var s=this.resolvedLanguage||this.languages[0],l=this.options?this.options.fallbackLng:!1,u=this.languages[this.languages.length-1];if(s.toLowerCase()==="cimode")return!0;var f=function(v,b){var x=o.services.backendConnector.state["".concat(v,"|").concat(b)];return x===-1||x===2};if(a.precheck){var p=a.precheck(this,f);if(p!==void 0)return p}return!!(this.hasResourceBundle(s,i)||!this.services.backendConnector.backend||this.options.resources&&!this.options.partialBundledLanguages||f(s,i)&&(!l||f(u,i)))}},{key:"loadNamespaces",value:function(i,o){var a=this,s=tv();return this.options.ns?(typeof i=="string"&&(i=[i]),i.forEach(function(l){a.options.ns.indexOf(l)<0&&a.options.ns.push(l)}),this.loadResources(function(l){s.resolve(),o&&o(l)}),s):(o&&o(),Promise.resolve())}},{key:"loadLanguages",value:function(i,o){var a=tv();typeof i=="string"&&(i=[i]);var s=this.options.preload||[],l=i.filter(function(u){return s.indexOf(u)<0});return 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ai.prototype)e[t]=ai.prototype[t];return e}ai.prototype.on=ai.prototype.addEventListener=function(e,t){return this._callbacks=this._callbacks||{},(this._callbacks["$"+e]=this._callbacks["$"+e]||[]).push(t),this};ai.prototype.once=function(e,t){function n(){this.off(e,n),t.apply(this,arguments)}return n.fn=t,this.on(e,n),this};ai.prototype.off=ai.prototype.removeListener=ai.prototype.removeAllListeners=ai.prototype.removeEventListener=function(e,t){if(this._callbacks=this._callbacks||{},arguments.length==0)return this._callbacks={},this;var n=this._callbacks["$"+e];if(!n)return this;if(arguments.length==1)return delete this._callbacks["$"+e],this;for(var r,i=0;i<n.length;i++)if(r=n[i],r===t||r.fn===t){n.splice(i,1);break}return n.length===0&&delete this._callbacks["$"+e],this};ai.prototype.emit=function(e){this._callbacks=this._callbacks||{};for(var t=new Array(arguments.length-1),n=this._callbacks["$"+e],r=1;r<arguments.length;r++)t[r-1]=arguments[r];if(n){n=n.slice(0);for(var r=0,i=n.length;r<i;++r)n[r].apply(this,t)}return this};ai.prototype.emitReserved=ai.prototype.emit;ai.prototype.listeners=function(e){return this._callbacks=this._callbacks||{},this._callbacks["$"+e]||[]};ai.prototype.hasListeners=function(e){return!!this.listeners(e).length};const Md=(()=>typeof self<"u"?self:typeof window<"u"?window:Function("return this")())();function PW(e,...t){return t.reduce((n,r)=>(e.hasOwnProperty(r)&&(n[r]=e[r]),n),{})}const o6e=setTimeout,a6e=clearTimeout;function xx(e,t){t.useNativeTimers?(e.setTimeoutFn=o6e.bind(Md),e.clearTimeoutFn=a6e.bind(Md)):(e.setTimeoutFn=setTimeout.bind(Md),e.clearTimeoutFn=clearTimeout.bind(Md))}const s6e=1.33;function l6e(e){return typeof e=="string"?u6e(e):Math.ceil((e.byteLength||e.size)*s6e)}function u6e(e){let t=0,n=0;for(let r=0,i=e.length;r<i;r++)t=e.charCodeAt(r),t<128?n+=1:t<2048?n+=2:t<55296||t>=57344?n+=3:(r++,n+=4);return n}class c6e extends Error{constructor(t,n,r){super(t),this.description=n,this.context=r,this.type="TransportError"}}class TW extends ai{constructor(t){super(),this.writable=!1,xx(this,t),this.opts=t,this.query=t.query,this.readyState="",this.socket=t.socket}onError(t,n,r){return super.emitReserved("error",new c6e(t,n,r)),this}open(){return(this.readyState==="closed"||this.readyState==="")&&(this.readyState="opening",this.doOpen()),this}close(){return(this.readyState==="opening"||this.readyState==="open")&&(this.doClose(),this.onClose()),this}send(t){this.readyState==="open"&&this.write(t)}onOpen(){this.readyState="open",this.writable=!0,super.emitReserved("open")}onData(t){const n=CW(t,this.socket.binaryType);this.onPacket(n)}onPacket(t){super.emitReserved("packet",t)}onClose(t){this.readyState="closed",super.emitReserved("close",t)}}const LW="0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz-_".split(""),s8=64,d6e={};let mI=0,wb=0,vI;function yI(e){let t="";do t=LW[e%s8]+t,e=Math.floor(e/s8);while(e>0);return t}function AW(){const e=yI(+new Date);return e!==vI?(mI=0,vI=e):e+"."+yI(mI++)}for(;wb<s8;wb++)d6e[LW[wb]]=wb;function OW(e){let t="";for(let n in e)e.hasOwnProperty(n)&&(t.length&&(t+="&"),t+=encodeURIComponent(n)+"="+encodeURIComponent(e[n]));return t}function f6e(e){let t={},n=e.split("&");for(let r=0,i=n.length;r<i;r++){let o=n[r].split("=");t[decodeURIComponent(o[0])]=decodeURIComponent(o[1])}return t}let MW=!1;try{MW=typeof XMLHttpRequest<"u"&&"withCredentials"in new XMLHttpRequest}catch(e){}const h6e=MW;function RW(e){const t=e.xdomain;try{if(typeof XMLHttpRequest<"u"&&(!t||h6e))return new XMLHttpRequest}catch(n){}if(!t)try{return new Md[["Active"].concat("Object").join("X")]("Microsoft.XMLHTTP")}catch(n){}}function p6e(){}const g6e=function(){return new RW({xdomain:!1}).responseType!=null}();class m6e extends TW{constructor(t){if(super(t),this.polling=!1,typeof location<"u"){const r=location.protocol==="https:";let i=location.port;i||(i=r?"443":"80"),this.xd=typeof location<"u"&&t.hostname!==location.hostname||i!==t.port,this.xs=t.secure!==r}const n=t&&t.forceBase64;this.supportsBinary=g6e&&!n}get name(){return"polling"}doOpen(){this.poll()}pause(t){this.readyState="pausing";const n=()=>{this.readyState="paused",t()};if(this.polling||!this.writable){let r=0;this.polling&&(r++,this.once("pollComplete",function(){--r||n()})),this.writable||(r++,this.once("drain",function(){--r||n()}))}else n()}poll(){this.polling=!0,this.doPoll(),this.emitReserved("poll")}onData(t){const n=r=>{if(this.readyState==="opening"&&r.type==="open"&&this.onOpen(),r.type==="close")return this.onClose({description:"transport closed by the server"}),!1;this.onPacket(r)};r6e(t,this.socket.binaryType).forEach(n),this.readyState!=="closed"&&(this.polling=!1,this.emitReserved("pollComplete"),this.readyState==="open"&&this.poll())}doClose(){const t=()=>{this.write([{type:"close"}])};this.readyState==="open"?t():this.once("open",t)}write(t){this.writable=!1,n6e(t,n=>{this.doWrite(n,()=>{this.writable=!0,this.emitReserved("drain")})})}uri(){let t=this.query||{};const n=this.opts.secure?"https":"http";let r="";this.opts.timestampRequests!==!1&&(t[this.opts.timestampParam]=AW()),!this.supportsBinary&&!t.sid&&(t.b64=1),this.opts.port&&(n==="https"&&Number(this.opts.port)!==443||n==="http"&&Number(this.opts.port)!==80)&&(r=":"+this.opts.port);const i=OW(t),o=this.opts.hostname.indexOf(":")!==-1;return n+"://"+(o?"["+this.opts.hostname+"]":this.opts.hostname)+r+this.opts.path+(i.length?"?"+i:"")}request(t={}){return Object.assign(t,{xd:this.xd,xs:this.xs},this.opts),new ou(this.uri(),t)}doWrite(t,n){const r=this.request({method:"POST",data:t});r.on("success",n),r.on("error",(i,o)=>{this.onError("xhr post error",i,o)})}doPoll(){const t=this.request();t.on("data",this.onData.bind(this)),t.on("error",(n,r)=>{this.onError("xhr poll 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n&&(n.withCredentials=this.opts.withCredentials),this.opts.requestTimeout&&(n.timeout=this.opts.requestTimeout),n.onreadystatechange=()=>{n.readyState===4&&(n.status===200||n.status===1223?this.onLoad():this.setTimeoutFn(()=>{this.onError(typeof n.status=="number"?n.status:0)},0))},n.send(this.data)}catch(r){this.setTimeoutFn(()=>{this.onError(r)},0);return}typeof document<"u"&&(this.index=ou.requestsCount++,ou.requests[this.index]=this)}onError(t){this.emitReserved("error",t,this.xhr),this.cleanup(!0)}cleanup(t){if(!(typeof this.xhr>"u"||this.xhr===null)){if(this.xhr.onreadystatechange=p6e,t)try{this.xhr.abort()}catch(n){}typeof document<"u"&&delete ou.requests[this.index],this.xhr=null}}onLoad(){const t=this.xhr.responseText;t!==null&&(this.emitReserved("data",t),this.emitReserved("success"),this.cleanup())}abort(){this.cleanup()}}ou.requestsCount=0;ou.requests={};if(typeof document<"u"){if(typeof attachEvent=="function")attachEvent("onunload",bI);else if(typeof addEventListener=="function"){const e="onpagehide"in Md?"pagehide":"unload";addEventListener(e,bI,!1)}}function bI(){for(let e in ou.requests)ou.requests.hasOwnProperty(e)&&ou.requests[e].abort()}const IW=(()=>typeof Promise=="function"&&typeof Promise.resolve=="function"?t=>Promise.resolve().then(t):(t,n)=>n(t,0))(),Cb=Md.WebSocket||Md.MozWebSocket,SI=!0,v6e="arraybuffer",xI=typeof navigator<"u"&&typeof navigator.product=="string"&&navigator.product.toLowerCase()==="reactnative";class y6e extends TW{constructor(t){super(t),this.supportsBinary=!t.forceBase64}get name(){return"websocket"}doOpen(){if(!this.check())return;const t=this.uri(),n=this.opts.protocols,r=xI?{}:PW(this.opts,"agent","perMessageDeflate","pfx","key","passphrase","cert","ca","ciphers","rejectUnauthorized","localAddress","protocolVersion","origin","maxPayload","family","checkServerIdentity");this.opts.extraHeaders&&(r.headers=this.opts.extraHeaders);try{this.ws=SI&&!xI?n?new Cb(t,n):new Cb(t):new Cb(t,n,r)}catch(i){return this.emitReserved("error",i)}this.ws.binaryType=this.socket.binaryType||v6e,this.addEventListeners()}addEventListeners(){this.ws.onopen=()=>{this.opts.autoUnref&&this.ws._socket.unref(),this.onOpen()},this.ws.onclose=t=>this.onClose({description:"websocket connection closed",context:t}),this.ws.onmessage=t=>this.onData(t.data),this.ws.onerror=t=>this.onError("websocket error",t)}write(t){this.writable=!1;for(let n=0;n<t.length;n++){const r=t[n],i=n===t.length-1;wW(r,this.supportsBinary,o=>{const a={};try{SI&&this.ws.send(o)}catch(s){}i&&IW(()=>{this.writable=!0,this.emitReserved("drain")},this.setTimeoutFn)})}}doClose(){typeof this.ws<"u"&&(this.ws.close(),this.ws=null)}uri(){let t=this.query||{};const n=this.opts.secure?"wss":"ws";let r="";this.opts.port&&(n==="wss"&&Number(this.opts.port)!==443||n==="ws"&&Number(this.opts.port)!==80)&&(r=":"+this.opts.port),this.opts.timestampRequests&&(t[this.opts.timestampParam]=AW()),this.supportsBinary||(t.b64=1);const i=OW(t),o=this.opts.hostname.indexOf(":")!==-1;return n+"://"+(o?"["+this.opts.hostname+"]":this.opts.hostname)+r+this.opts.path+(i.length?"?"+i:"")}check(){return!!Cb}}const b6e={websocket:y6e,polling:m6e},S6e=/^(?:(?![^:@]+:[^:@\/]*@)(http|https|ws|wss):\/\/)?((?:(([^:@]*)(?::([^:@]*))?)?@)?((?:[a-f0-9]{0,4}:){2,7}[a-f0-9]{0,4}|[^:\/?#]*)(?::(\d*))?)(((\/(?:[^?#](?![^?#\/]*\.[^?#\/.]+(?:[?#]|$)))*\/?)?([^?#\/]*))(?:\?([^#]*))?(?:#(.*))?)/,x6e=["source","protocol","authority","userInfo","user","password","host","port","relative","path","directory","file","query","anchor"];function l8(e){const t=e,n=e.indexOf("["),r=e.indexOf("]");n!=-1&&r!=-1&&(e=e.substring(0,n)+e.substring(n,r).replace(/:/g,";")+e.substring(r,e.length));let i=S6e.exec(e||""),o={},a=14;for(;a--;)o[x6e[a]]=i[a]||"";return n!=-1&&r!=-1&&(o.source=t,o.host=o.host.substring(1,o.host.length-1).replace(/;/g,":"),o.authority=o.authority.replace("[","").replace("]","").replace(/;/g,":"),o.ipv6uri=!0),o.pathNames=w6e(o,o.path),o.queryKey=C6e(o,o.query),o}function w6e(e,t){const n=/\/{2,9}/g,r=t.replace(n,"/").split("/");return(t.slice(0,1)=="/"||t.length===0)&&r.splice(0,1),t.slice(-1)=="/"&&r.splice(r.length-1,1),r}function C6e(e,t){const n={};return t.replace(/(?:^|&)([^&=]*)=?([^&]*)/g,function(r,i,o){i&&(n[i]=o)}),n}class kd extends ai{constructor(t,n={}){super(),t&&typeof t=="object"&&(n=t,t=null),t?(t=l8(t),n.hostname=t.host,n.secure=t.protocol==="https"||t.protocol==="wss",n.port=t.port,t.query&&(n.query=t.query)):n.host&&(n.hostname=l8(n.host).host),xx(this,n),this.secure=n.secure!=null?n.secure:typeof location<"u"&&location.protocol==="https:",n.hostname&&!n.port&&(n.port=this.secure?"443":"80"),this.hostname=n.hostname||(typeof location<"u"?location.hostname:"localhost"),this.port=n.port||(typeof location<"u"&&location.port?location.port:this.secure?"443":"80"),this.transports=n.transports||["polling","websocket"],this.readyState="",this.writeBuffer=[],this.prevBufferLen=0,this.opts=Object.assign({path:"/engine.io",agent:!1,withCredentials:!1,upgrade:!0,timestampParam:"t",rememberUpgrade:!1,rejectUnauthorized:!0,perMessageDeflate:{threshold:1024},transportOptions:{},closeOnBeforeunload:!0},n),this.opts.path=this.opts.path.replace(/\/$/,"")+"/",typeof this.opts.query=="string"&&(this.opts.query=f6e(this.opts.query)),this.id=null,this.upgrades=null,this.pingInterval=null,this.pingTimeout=null,this.pingTimeoutTimer=null,typeof addEventListener=="function"&&(this.opts.closeOnBeforeunload&&(this.beforeunloadEventListener=()=>{this.transport&&(this.transport.removeAllListeners(),this.transport.close())},addEventListener("beforeunload",this.beforeunloadEventListener,!1)),this.hostname!=="localhost"&&(this.offlineEventListener=()=>{this.onClose("transport close",{description:"network connection lost"})},addEventListener("offline",this.offlineEventListener,!1))),this.open()}createTransport(t){const n=Object.assign({},this.opts.query);n.EIO=EW,n.transport=t,this.id&&(n.sid=this.id);const r=Object.assign({},this.opts.transportOptions[t],this.opts,{query:n,socket:this,hostname:this.hostname,secure:this.secure,port:this.port});return new b6e[t](r)}open(){let t;if(this.opts.rememberUpgrade&&kd.priorWebsocketSuccess&&this.transports.indexOf("websocket")!==-1)t="websocket";else if(this.transports.length===0){this.setTimeoutFn(()=>{this.emitReserved("error","No transports available")},0);return}else t=this.transports[0];this.readyState="opening";try{t=this.createTransport(t)}catch(n){this.transports.shift(),this.open();return}t.open(),this.setTransport(t)}setTransport(t){this.transport&&this.transport.removeAllListeners(),this.transport=t,t.on("drain",this.onDrain.bind(this)).on("packet",this.onPacket.bind(this)).on("error",this.onError.bind(this)).on("close",n=>this.onClose("transport close",n))}probe(t){let n=this.createTransport(t),r=!1;kd.priorWebsocketSuccess=!1;const i=()=>{r||(n.send([{type:"ping",data:"probe"}]),n.once("packet",p=>{if(!r)if(p.type==="pong"&&p.data==="probe"){if(this.upgrading=!0,this.emitReserved("upgrading",n),!n)return;kd.priorWebsocketSuccess=n.name==="websocket",this.transport.pause(()=>{r||this.readyState!=="closed"&&(f(),this.setTransport(n),n.send([{type:"upgrade"}]),this.emitReserved("upgrade",n),n=null,this.upgrading=!1,this.flush())})}else{const m=new Error("probe error");m.transport=n.name,this.emitReserved("upgradeError",m)}}))};function o(){r||(r=!0,f(),n.close(),n=null)}const a=p=>{const m=new Error("probe error: "+p);m.transport=n.name,o(),this.emitReserved("upgradeError",m)};function s(){a("transport closed")}function l(){a("socket closed")}function u(p){n&&p.name!==n.name&&o()}const f=()=>{n.removeListener("open",i),n.removeListener("error",a),n.removeListener("close",s),this.off("close",l),this.off("upgrading",u)};n.once("open",i),n.once("error",a),n.once("close",s),this.once("close",l),this.once("upgrading",u),n.open()}onOpen(){if(this.readyState="open",kd.priorWebsocketSuccess=this.transport.name==="websocket",this.emitReserved("open"),this.flush(),this.readyState==="open"&&this.opts.upgrade&&this.transport.pause){let t=0;const n=this.upgrades.length;for(;t<n;t++)this.probe(this.upgrades[t])}}onPacket(t){if(this.readyState==="opening"||this.readyState==="open"||this.readyState==="closing")switch(this.emitReserved("packet",t),this.emitReserved("heartbeat"),t.type){case"open":this.onHandshake(JSON.parse(t.data));break;case"ping":this.resetPingTimeout(),this.sendPacket("pong"),this.emitReserved("ping"),this.emitReserved("pong");break;case"error":const n=new Error("server error");n.code=t.data,this.onError(n);break;case"message":this.emitReserved("data",t.data),this.emitReserved("message",t.data);break}}onHandshake(t){this.emitReserved("handshake",t),this.id=t.sid,this.transport.query.sid=t.sid,this.upgrades=this.filterUpgrades(t.upgrades),this.pingInterval=t.pingInterval,this.pingTimeout=t.pingTimeout,this.maxPayload=t.maxPayload,this.onOpen(),this.readyState!=="closed"&&this.resetPingTimeout()}resetPingTimeout(){this.clearTimeoutFn(this.pingTimeoutTimer),this.pingTimeoutTimer=this.setTimeoutFn(()=>{this.onClose("ping timeout")},this.pingInterval+this.pingTimeout),this.opts.autoUnref&&this.pingTimeoutTimer.unref()}onDrain(){this.writeBuffer.splice(0,this.prevBufferLen),this.prevBufferLen=0,this.writeBuffer.length===0?this.emitReserved("drain"):this.flush()}flush(){if(this.readyState!=="closed"&&this.transport.writable&&!this.upgrading&&this.writeBuffer.length){const t=this.getWritablePackets();this.transport.send(t),this.prevBufferLen=t.length,this.emitReserved("flush")}}getWritablePackets(){if(!(this.maxPayload&&this.transport.name==="polling"&&this.writeBuffer.length>1))return this.writeBuffer;let n=1;for(let r=0;r<this.writeBuffer.length;r++){const i=this.writeBuffer[r].data;if(i&&(n+=l6e(i)),r>0&&n>this.maxPayload)return this.writeBuffer.slice(0,r);n+=2}return this.writeBuffer}write(t,n,r){return this.sendPacket("message",t,n,r),this}send(t,n,r){return this.sendPacket("message",t,n,r),this}sendPacket(t,n,r,i){if(typeof n=="function"&&(i=n,n=void 0),typeof r=="function"&&(i=r,r=null),this.readyState==="closing"||this.readyState==="closed")return;r=r||{},r.compress=r.compress!==!1;const o={type:t,data:n,options:r};this.emitReserved("packetCreate",o),this.writeBuffer.push(o),i&&this.once("flush",i),this.flush()}close(){const t=()=>{this.onClose("forced close"),this.transport.close()},n=()=>{this.off("upgrade",n),this.off("upgradeError",n),t()},r=()=>{this.once("upgrade",n),this.once("upgradeError",n)};return(this.readyState==="opening"||this.readyState==="open")&&(this.readyState="closing",this.writeBuffer.length?this.once("drain",()=>{this.upgrading?r():t()}):this.upgrading?r():t()),this}onError(t){kd.priorWebsocketSuccess=!1,this.emitReserved("error",t),this.onClose("transport error",t)}onClose(t,n){(this.readyState==="opening"||this.readyState==="open"||this.readyState==="closing")&&(this.clearTimeoutFn(this.pingTimeoutTimer),this.transport.removeAllListeners("close"),this.transport.close(),this.transport.removeAllListeners(),typeof removeEventListener=="function"&&(removeEventListener("beforeunload",this.beforeunloadEventListener,!1),removeEventListener("offline",this.offlineEventListener,!1)),this.readyState="closed",this.id=null,this.emitReserved("close",t,n),this.writeBuffer=[],this.prevBufferLen=0)}filterUpgrades(t){const n=[];let r=0;const i=t.length;for(;r<i;r++)~this.transports.indexOf(t[r])&&n.push(t[r]);return n}}kd.protocol=EW;function _6e(e,t="",n){let r=e;n=n||typeof location<"u"&&location,e==null&&(e=n.protocol+"//"+n.host),typeof e=="string"&&(e.charAt(0)==="/"&&(e.charAt(1)==="/"?e=n.protocol+e:e=n.host+e),/^(https?|wss?):\/\//.test(e)||(typeof n<"u"?e=n.protocol+"//"+e:e="https://"+e),r=l8(e)),r.port||(/^(http|ws)$/.test(r.protocol)?r.port="80":/^(http|ws)s$/.test(r.protocol)&&(r.port="443")),r.path=r.path||"/";const o=r.host.indexOf(":")!==-1?"["+r.host+"]":r.host;return r.id=r.protocol+"://"+o+":"+r.port+t,r.href=r.protocol+"://"+o+(n&&n.port===r.port?"":":"+r.port),r}const k6e=typeof ArrayBuffer=="function",E6e=e=>typeof ArrayBuffer.isView=="function"?ArrayBuffer.isView(e):e.buffer instanceof ArrayBuffer,DW=Object.prototype.toString,P6e=typeof Blob=="function"||typeof Blob<"u"&&DW.call(Blob)==="[object BlobConstructor]",T6e=typeof File=="function"||typeof File<"u"&&DW.call(File)==="[object FileConstructor]";function ZE(e){return k6e&&(e instanceof ArrayBuffer||E6e(e))||P6e&&e instanceof Blob||T6e&&e instanceof File}function E4(e,t){if(!e||typeof e!="object")return!1;if(Array.isArray(e)){for(let n=0,r=e.length;n<r;n++)if(E4(e[n]))return!0;return!1}if(ZE(e))return!0;if(e.toJSON&&typeof e.toJSON=="function"&&arguments.length===1)return E4(e.toJSON(),!0);for(const n in e)if(Object.prototype.hasOwnProperty.call(e,n)&&E4(e[n]))return!0;return!1}function L6e(e){const t=[],n=e.data,r=e;return r.data=u8(n,t),r.attachments=t.length,{packet:r,buffers:t}}function u8(e,t){if(!e)return e;if(ZE(e)){const n={_placeholder:!0,num:t.length};return t.push(e),n}else if(Array.isArray(e)){const n=new Array(e.length);for(let r=0;r<e.length;r++)n[r]=u8(e[r],t);return n}else if(typeof e=="object"&&!(e instanceof Date)){const n={};for(const r in e)Object.prototype.hasOwnProperty.call(e,r)&&(n[r]=u8(e[r],t));return n}return e}function A6e(e,t){return e.data=c8(e.data,t),e.attachments=void 0,e}function c8(e,t){if(!e)return e;if(e&&e._placeholder===!0){if(typeof e.num=="number"&&e.num>=0&&e.num<t.length)return t[e.num];throw new Error("illegal attachments")}else if(Array.isArray(e))for(let n=0;n<e.length;n++)e[n]=c8(e[n],t);else if(typeof e=="object")for(const n in e)Object.prototype.hasOwnProperty.call(e,n)&&(e[n]=c8(e[n],t));return e}const O6e=5;var dn;(function(e){e[e.CONNECT=0]="CONNECT",e[e.DISCONNECT=1]="DISCONNECT",e[e.EVENT=2]="EVENT",e[e.ACK=3]="ACK",e[e.CONNECT_ERROR=4]="CONNECT_ERROR",e[e.BINARY_EVENT=5]="BINARY_EVENT",e[e.BINARY_ACK=6]="BINARY_ACK"})(dn||(dn={}));class M6e{constructor(t){this.replacer=t}encode(t){return(t.type===dn.EVENT||t.type===dn.ACK)&&E4(t)?(t.type=t.type===dn.EVENT?dn.BINARY_EVENT:dn.BINARY_ACK,this.encodeAsBinary(t)):[this.encodeAsString(t)]}encodeAsString(t){let n=""+t.type;return(t.type===dn.BINARY_EVENT||t.type===dn.BINARY_ACK)&&(n+=t.attachments+"-"),t.nsp&&t.nsp!=="/"&&(n+=t.nsp+","),t.id!=null&&(n+=t.id),t.data!=null&&(n+=JSON.stringify(t.data,this.replacer)),n}encodeAsBinary(t){const n=L6e(t),r=this.encodeAsString(n.packet),i=n.buffers;return i.unshift(r),i}}class QE extends ai{constructor(t){super(),this.reviver=t}add(t){let n;if(typeof t=="string"){if(this.reconstructor)throw new Error("got plaintext data when reconstructing a packet");n=this.decodeString(t),n.type===dn.BINARY_EVENT||n.type===dn.BINARY_ACK?(this.reconstructor=new R6e(n),n.attachments===0&&super.emitReserved("decoded",n)):super.emitReserved("decoded",n)}else if(ZE(t)||t.base64)if(this.reconstructor)n=this.reconstructor.takeBinaryData(t),n&&(this.reconstructor=null,super.emitReserved("decoded",n));else throw new Error("got binary data when not reconstructing a packet");else throw new Error("Unknown type: "+t)}decodeString(t){let n=0;const r={type:Number(t.charAt(0))};if(dn[r.type]===void 0)throw new Error("unknown packet type "+r.type);if(r.type===dn.BINARY_EVENT||r.type===dn.BINARY_ACK){const o=n+1;for(;t.charAt(++n)!=="-"&&n!=t.length;);const a=t.substring(o,n);if(a!=Number(a)||t.charAt(n)!=="-")throw new Error("Illegal attachments");r.attachments=Number(a)}if(t.charAt(n+1)==="/"){const o=n+1;for(;++n&&!(t.charAt(n)===","||n===t.length););r.nsp=t.substring(o,n)}else r.nsp="/";const i=t.charAt(n+1);if(i!==""&&Number(i)==i){const o=n+1;for(;++n;){const a=t.charAt(n);if(a==null||Number(a)!=a){--n;break}if(n===t.length)break}r.id=Number(t.substring(o,n+1))}if(t.charAt(++n)){const o=this.tryParse(t.substr(n));if(QE.isPayloadValid(r.type,o))r.data=o;else throw new Error("invalid payload")}return r}tryParse(t){try{return JSON.parse(t,this.reviver)}catch(n){return!1}}static isPayloadValid(t,n){switch(t){case dn.CONNECT:return typeof n=="object";case dn.DISCONNECT:return n===void 0;case dn.CONNECT_ERROR:return typeof n=="string"||typeof n=="object";case dn.EVENT:case dn.BINARY_EVENT:return Array.isArray(n)&&n.length>0;case dn.ACK:case dn.BINARY_ACK:return Array.isArray(n)}}destroy(){this.reconstructor&&this.reconstructor.finishedReconstruction()}}class R6e{constructor(t){this.packet=t,this.buffers=[],this.reconPack=t}takeBinaryData(t){if(this.buffers.push(t),this.buffers.length===this.reconPack.attachments){const n=A6e(this.reconPack,this.buffers);return this.finishedReconstruction(),n}return null}finishedReconstruction(){this.reconPack=null,this.buffers=[]}}const I6e=Object.freeze(Object.defineProperty({__proto__:null,protocol:O6e,get PacketType(){return dn},Encoder:M6e,Decoder:QE},Symbol.toStringTag,{value:"Module"}));function Vs(e,t,n){return e.on(t,n),function(){e.off(t,n)}}const D6e=Object.freeze({connect:1,connect_error:1,disconnect:1,disconnecting:1,newListener:1,removeListener:1});class NW extends ai{constructor(t,n,r){super(),this.connected=!1,this.receiveBuffer=[],this.sendBuffer=[],this.ids=0,this.acks={},this.flags={},this.io=t,this.nsp=n,r&&r.auth&&(this.auth=r.auth),this.io._autoConnect&&this.open()}get disconnected(){return!this.connected}subEvents(){if(this.subs)return;const t=this.io;this.subs=[Vs(t,"open",this.onopen.bind(this)),Vs(t,"packet",this.onpacket.bind(this)),Vs(t,"error",this.onerror.bind(this)),Vs(t,"close",this.onclose.bind(this))]}get active(){return!!this.subs}connect(){return this.connected?this:(this.subEvents(),this.io._reconnecting||this.io.open(),this.io._readyState==="open"&&this.onopen(),this)}open(){return this.connect()}send(...t){return t.unshift("message"),this.emit.apply(this,t),this}emit(t,...n){if(D6e.hasOwnProperty(t))throw new Error('"'+t.toString()+'" is a reserved event name');n.unshift(t);const r={type:dn.EVENT,data:n};if(r.options={},r.options.compress=this.flags.compress!==!1,typeof n[n.length-1]=="function"){const a=this.ids++,s=n.pop();this._registerAckCallback(a,s),r.id=a}const i=this.io.engine&&this.io.engine.transport&&this.io.engine.transport.writable;return this.flags.volatile&&(!i||!this.connected)||(this.connected?(this.notifyOutgoingListeners(r),this.packet(r)):this.sendBuffer.push(r)),this.flags={},this}_registerAckCallback(t,n){const r=this.flags.timeout;if(r===void 0){this.acks[t]=n;return}const i=this.io.setTimeoutFn(()=>{delete this.acks[t];for(let o=0;o<this.sendBuffer.length;o++)this.sendBuffer[o].id===t&&this.sendBuffer.splice(o,1);n.call(this,new Error("operation has timed out"))},r);this.acks[t]=(...o)=>{this.io.clearTimeoutFn(i),n.apply(this,[null,...o])}}packet(t){t.nsp=this.nsp,this.io._packet(t)}onopen(){typeof this.auth=="function"?this.auth(t=>{this.packet({type:dn.CONNECT,data:t})}):this.packet({type:dn.CONNECT,data:this.auth})}onerror(t){this.connected||this.emitReserved("connect_error",t)}onclose(t,n){this.connected=!1,delete this.id,this.emitReserved("disconnect",t,n)}onpacket(t){if(t.nsp===this.nsp)switch(t.type){case dn.CONNECT:if(t.data&&t.data.sid){const i=t.data.sid;this.onconnect(i)}else this.emitReserved("connect_error",new Error("It seems you are trying to reach a Socket.IO server in v2.x with a v3.x client, but they are not compatible (more information here: https://socket.io/docs/v3/migrating-from-2-x-to-3-0/)"));break;case dn.EVENT:case dn.BINARY_EVENT:this.onevent(t);break;case dn.ACK:case dn.BINARY_ACK:this.onack(t);break;case dn.DISCONNECT:this.ondisconnect();break;case dn.CONNECT_ERROR:this.destroy();const r=new Error(t.data.message);r.data=t.data.data,this.emitReserved("connect_error",r);break}}onevent(t){const n=t.data||[];t.id!=null&&n.push(this.ack(t.id)),this.connected?this.emitEvent(n):this.receiveBuffer.push(Object.freeze(n))}emitEvent(t){if(this._anyListeners&&this._anyListeners.length){const n=this._anyListeners.slice();for(const r of n)r.apply(this,t)}super.emit.apply(this,t)}ack(t){const n=this;let r=!1;return function(...i){r||(r=!0,n.packet({type:dn.ACK,id:t,data:i}))}}onack(t){const n=this.acks[t.id];typeof n=="function"&&(n.apply(this,t.data),delete this.acks[t.id])}onconnect(t){this.id=t,this.connected=!0,this.emitBuffered(),this.emitReserved("connect")}emitBuffered(){this.receiveBuffer.forEach(t=>this.emitEvent(t)),this.receiveBuffer=[],this.sendBuffer.forEach(t=>{this.notifyOutgoingListeners(t),this.packet(t)}),this.sendBuffer=[]}ondisconnect(){this.destroy(),this.onclose("io server disconnect")}destroy(){this.subs&&(this.subs.forEach(t=>t()),this.subs=void 0),this.io._destroy(this)}disconnect(){return this.connected&&this.packet({type:dn.DISCONNECT}),this.destroy(),this.connected&&this.onclose("io client disconnect"),this}close(){return this.disconnect()}compress(t){return this.flags.compress=t,this}get volatile(){return this.flags.volatile=!0,this}timeout(t){return this.flags.timeout=t,this}onAny(t){return this._anyListeners=this._anyListeners||[],this._anyListeners.push(t),this}prependAny(t){return this._anyListeners=this._anyListeners||[],this._anyListeners.unshift(t),this}offAny(t){if(!this._anyListeners)return this;if(t){const n=this._anyListeners;for(let r=0;r<n.length;r++)if(t===n[r])return n.splice(r,1),this}else this._anyListeners=[];return this}listenersAny(){return this._anyListeners||[]}onAnyOutgoing(t){return this._anyOutgoingListeners=this._anyOutgoingListeners||[],this._anyOutgoingListeners.push(t),this}prependAnyOutgoing(t){return this._anyOutgoingListeners=this._anyOutgoingListeners||[],this._anyOutgoingListeners.unshift(t),this}offAnyOutgoing(t){if(!this._anyOutgoingListeners)return this;if(t){const n=this._anyOutgoingListeners;for(let r=0;r<n.length;r++)if(t===n[r])return n.splice(r,1),this}else this._anyOutgoingListeners=[];return this}listenersAnyOutgoing(){return this._anyOutgoingListeners||[]}notifyOutgoingListeners(t){if(this._anyOutgoingListeners&&this._anyOutgoingListeners.length){const n=this._anyOutgoingListeners.slice();for(const r of n)r.apply(this,t.data)}}}function _1(e){e=e||{},this.ms=e.min||100,this.max=e.max||1e4,this.factor=e.factor||2,this.jitter=e.jitter>0&&e.jitter<=1?e.jitter:0,this.attempts=0}_1.prototype.duration=function(){var e=this.ms*Math.pow(this.factor,this.attempts++);if(this.jitter){var t=Math.random(),n=Math.floor(t*this.jitter*e);e=(Math.floor(t*10)&1)==0?e-n:e+n}return Math.min(e,this.max)|0};_1.prototype.reset=function(){this.attempts=0};_1.prototype.setMin=function(e){this.ms=e};_1.prototype.setMax=function(e){this.max=e};_1.prototype.setJitter=function(e){this.jitter=e};class d8 extends ai{constructor(t,n){var r;super(),this.nsps={},this.subs=[],t&&typeof t=="object"&&(n=t,t=void 0),n=n||{},n.path=n.path||"/socket.io",this.opts=n,xx(this,n),this.reconnection(n.reconnection!==!1),this.reconnectionAttempts(n.reconnectionAttempts||1/0),this.reconnectionDelay(n.reconnectionDelay||1e3),this.reconnectionDelayMax(n.reconnectionDelayMax||5e3),this.randomizationFactor((r=n.randomizationFactor)!==null&&r!==void 0?r:.5),this.backoff=new _1({min:this.reconnectionDelay(),max:this.reconnectionDelayMax(),jitter:this.randomizationFactor()}),this.timeout(n.timeout==null?2e4:n.timeout),this._readyState="closed",this.uri=t;const i=n.parser||I6e;this.encoder=new i.Encoder,this.decoder=new i.Decoder,this._autoConnect=n.autoConnect!==!1,this._autoConnect&&this.open()}reconnection(t){return arguments.length?(this._reconnection=!!t,this):this._reconnection}reconnectionAttempts(t){return t===void 0?this._reconnectionAttempts:(this._reconnectionAttempts=t,this)}reconnectionDelay(t){var n;return t===void 0?this._reconnectionDelay:(this._reconnectionDelay=t,(n=this.backoff)===null||n===void 0||n.setMin(t),this)}randomizationFactor(t){var n;return t===void 0?this._randomizationFactor:(this._randomizationFactor=t,(n=this.backoff)===null||n===void 0||n.setJitter(t),this)}reconnectionDelayMax(t){var n;return t===void 0?this._reconnectionDelayMax:(this._reconnectionDelayMax=t,(n=this.backoff)===null||n===void 0||n.setMax(t),this)}timeout(t){return arguments.length?(this._timeout=t,this):this._timeout}maybeReconnectOnOpen(){!this._reconnecting&&this._reconnection&&this.backoff.attempts===0&&this.reconnect()}open(t){if(~this._readyState.indexOf("open"))return this;this.engine=new kd(this.uri,this.opts);const n=this.engine,r=this;this._readyState="opening",this.skipReconnect=!1;const i=Vs(n,"open",function(){r.onopen(),t&&t()}),o=Vs(n,"error",a=>{r.cleanup(),r._readyState="closed",this.emitReserved("error",a),t?t(a):r.maybeReconnectOnOpen()});if(this._timeout!==!1){const a=this._timeout;a===0&&i();const s=this.setTimeoutFn(()=>{i(),n.close(),n.emit("error",new Error("timeout"))},a);this.opts.autoUnref&&s.unref(),this.subs.push(function(){clearTimeout(s)})}return this.subs.push(i),this.subs.push(o),this}connect(t){return this.open(t)}onopen(){this.cleanup(),this._readyState="open",this.emitReserved("open");const t=this.engine;this.subs.push(Vs(t,"ping",this.onping.bind(this)),Vs(t,"data",this.ondata.bind(this)),Vs(t,"error",this.onerror.bind(this)),Vs(t,"close",this.onclose.bind(this)),Vs(this.decoder,"decoded",this.ondecoded.bind(this)))}onping(){this.emitReserved("ping")}ondata(t){try{this.decoder.add(t)}catch(n){this.onclose("parse error",n)}}ondecoded(t){IW(()=>{this.emitReserved("packet",t)},this.setTimeoutFn)}onerror(t){this.emitReserved("error",t)}socket(t,n){let r=this.nsps[t];return r||(r=new NW(this,t,n),this.nsps[t]=r),r}_destroy(t){const n=Object.keys(this.nsps);for(const r of n)if(this.nsps[r].active)return;this._close()}_packet(t){const n=this.encoder.encode(t);for(let r=0;r<n.length;r++)this.engine.write(n[r],t.options)}cleanup(){this.subs.forEach(t=>t()),this.subs.length=0,this.decoder.destroy()}_close(){this.skipReconnect=!0,this._reconnecting=!1,this.onclose("forced close"),this.engine&&this.engine.close()}disconnect(){return this._close()}onclose(t,n){this.cleanup(),this.backoff.reset(),this._readyState="closed",this.emitReserved("close",t,n),this._reconnection&&!this.skipReconnect&&this.reconnect()}reconnect(){if(this._reconnecting||this.skipReconnect)return this;const t=this;if(this.backoff.attempts>=this._reconnectionAttempts)this.backoff.reset(),this.emitReserved("reconnect_failed"),this._reconnecting=!1;else{const n=this.backoff.duration();this._reconnecting=!0;const r=this.setTimeoutFn(()=>{t.skipReconnect||(this.emitReserved("reconnect_attempt",t.backoff.attempts),!t.skipReconnect&&t.open(i=>{i?(t._reconnecting=!1,t.reconnect(),this.emitReserved("reconnect_error",i)):t.onreconnect()}))},n);this.opts.autoUnref&&r.unref(),this.subs.push(function(){clearTimeout(r)})}}onreconnect(){const t=this.backoff.attempts;this._reconnecting=!1,this.backoff.reset(),this.emitReserved("reconnect",t)}}const ov={};function P4(e,t){typeof e=="object"&&(t=e,e=void 0),t=t||{};const n=_6e(e,t.path||"/socket.io"),r=n.source,i=n.id,o=n.path,a=ov[i]&&o in ov[i].nsps,s=t.forceNew||t["force new connection"]||t.multiplex===!1||a;let l;return s?l=new d8(r,t):(ov[i]||(ov[i]=new d8(r,t)),l=ov[i]),n.query&&!t.query&&(t.query=n.queryKey),l.socket(n.path,t)}Object.assign(P4,{Manager:d8,Socket:NW,io:P4,connect:P4});let _b;const N6e=new Uint8Array(16);function B6e(){if(!_b&&(_b=typeof crypto<"u"&&crypto.getRandomValues&&crypto.getRandomValues.bind(crypto),!_b))throw new Error("crypto.getRandomValues() not supported. 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n=t._getLinearGradient();n&&(this.setAttr("fillStyle",n),t._fillFunc(this))}_fillRadialGradient(t){var n=t._getRadialGradient();n&&(this.setAttr("fillStyle",n),t._fillFunc(this))}_fill(t){var n=t.fill(),r=t.getFillPriority();if(n&&r==="color"){this._fillColor(t);return}var i=t.getFillPatternImage();if(i&&r==="pattern"){this._fillPattern(t);return}var o=t.getFillLinearGradientColorStops();if(o&&r==="linear-gradient"){this._fillLinearGradient(t);return}var a=t.getFillRadialGradientColorStops();if(a&&r==="radial-gradient"){this._fillRadialGradient(t);return}n?this._fillColor(t):i?this._fillPattern(t):o?this._fillLinearGradient(t):a&&this._fillRadialGradient(t)}_strokeLinearGradient(t){var n=t.getStrokeLinearGradientStartPoint(),r=t.getStrokeLinearGradientEndPoint(),i=t.getStrokeLinearGradientColorStops(),o=this.createLinearGradient(n.x,n.y,r.x,r.y);if(i){for(var a=0;a<i.length;a+=2)o.addColorStop(i[a],i[a+1]);this.setAttr("strokeStyle",o)}}_stroke(t){var n=t.dash(),r=t.getStrokeScaleEnabled();if(t.hasStroke()){if(!r){this.save();var i=this.getCanvas().getPixelRatio();this.setTransform(i,0,0,i,0,0)}this._applyLineCap(t),n&&t.dashEnabled()&&(this.setLineDash(n),this.setAttr("lineDashOffset",t.dashOffset())),this.setAttr("lineWidth",t.strokeWidth()),t.getShadowForStrokeEnabled()||this.setAttr("shadowColor","rgba(0,0,0,0)");var o=t.getStrokeLinearGradientColorStops();o?this._strokeLinearGradient(t):this.setAttr("strokeStyle",t.stroke()),t._strokeFunc(this),r||this.restore()}}_applyShadow(t){var n,r,i,o=(n=t.getShadowRGBA())!==null&&n!==void 0?n:"black",a=(r=t.getShadowBlur())!==null&&r!==void 0?r:5,s=(i=t.getShadowOffset())!==null&&i!==void 0?i:{x:0,y:0},l=t.getAbsoluteScale(),u=this.canvas.getPixelRatio(),f=l.x*u,p=l.y*u;this.setAttr("shadowColor",o),this.setAttr("shadowBlur",a*Math.min(Math.abs(f),Math.abs(p))),this.setAttr("shadowOffsetX",s.x*f),this.setAttr("shadowOffsetY",s.y*p)}}class ICe extends Cx{constructor(t){super(t),this._context=t._canvas.getContext("2d",{willReadFrequently:!0})}_fill(t){this.save(),this.setAttr("fillStyle",t.colorKey),t._fillFuncHit(this),this.restore()}strokeShape(t){t.hasHitStroke()&&this._stroke(t)}_stroke(t){if(t.hasHitStroke()){var n=t.getStrokeScaleEnabled();if(!n){this.save();var r=this.getCanvas().getPixelRatio();this.setTransform(r,0,0,r,0,0)}this._applyLineCap(t);var i=t.hitStrokeWidth(),o=i==="auto"?t.strokeWidth():i;this.setAttr("lineWidth",o),this.setAttr("strokeStyle",t.colorKey),t._strokeFuncHit(this),n||this.restore()}}}var Eb;function DCe(){if(Eb)return Eb;var e=he.createCanvasElement(),t=e.getContext("2d");return Eb=function(){var n=gt._global.devicePixelRatio||1,r=t.webkitBackingStorePixelRatio||t.mozBackingStorePixelRatio||t.msBackingStorePixelRatio||t.oBackingStorePixelRatio||t.backingStorePixelRatio||1;return n/r}(),he.releaseCanvas(e),Eb}class _x{constructor(t){this.pixelRatio=1,this.width=0,this.height=0,this.isCache=!1;var n=t||{},r=n.pixelRatio||gt.pixelRatio||DCe();this.pixelRatio=r,this._canvas=he.createCanvasElement(),this._canvas.style.padding="0",this._canvas.style.margin="0",this._canvas.style.border="0",this._canvas.style.background="transparent",this._canvas.style.position="absolute",this._canvas.style.top="0",this._canvas.style.left="0"}getContext(){return this.context}getPixelRatio(){return this.pixelRatio}setPixelRatio(t){var n=this.pixelRatio;this.pixelRatio=t,this.setSize(this.getWidth()/n,this.getHeight()/n)}setWidth(t){this.width=this._canvas.width=t*this.pixelRatio,this._canvas.style.width=t+"px";var n=this.pixelRatio,r=this.getContext()._context;r.scale(n,n)}setHeight(t){this.height=this._canvas.height=t*this.pixelRatio,this._canvas.style.height=t+"px";var n=this.pixelRatio,r=this.getContext()._context;r.scale(n,n)}getWidth(){return this.width}getHeight(){return this.height}setSize(t,n){this.setWidth(t||0),this.setHeight(n||0)}toDataURL(t,n){try{return this._canvas.toDataURL(t,n)}catch(r){try{return this._canvas.toDataURL()}catch(i){return he.error("Unable to get data URL. "+i.message+" For more info read https://konvajs.org/docs/posts/Tainted_Canvas.html."),""}}}}ee.addGetterSetter(_x,"pixelRatio",void 0,Ge());class Fg extends _x{constructor(t={width:0,height:0}){super(t),this.context=new RCe(this),this.setSize(t.width,t.height)}}class nP extends _x{constructor(t={width:0,height:0}){super(t),this.hitCanvas=!0,this.context=new ICe(this),this.setSize(t.width,t.height)}}const yn={get isDragging(){var e=!1;return yn._dragElements.forEach(t=>{t.dragStatus==="dragging"&&(e=!0)}),e},justDragged:!1,get node(){var e;return yn._dragElements.forEach(t=>{e=t.node}),e},_dragElements:new Map,_drag(e){const t=[];yn._dragElements.forEach((n,r)=>{const{node:i}=n,o=i.getStage();o.setPointersPositions(e),n.pointerId===void 0&&(n.pointerId=he._getFirstPointerId(e));const a=o._changedPointerPositions.find(u=>u.id===n.pointerId);if(!!a){if(n.dragStatus!=="dragging"){var s=i.dragDistance(),l=Math.max(Math.abs(a.x-n.startPointerPos.x),Math.abs(a.y-n.startPointerPos.y));if(l<s||(i.startDrag({evt:e}),!i.isDragging()))return}i._setDragPosition(e,n),t.push(i)}}),t.forEach(n=>{n.fire("dragmove",{type:"dragmove",target:n,evt:e},!0)})},_endDragBefore(e){const t=[];yn._dragElements.forEach(n=>{const{node:r}=n,i=r.getStage();if(e&&i.setPointersPositions(e),!i._changedPointerPositions.find(s=>s.id===n.pointerId))return;(n.dragStatus==="dragging"||n.dragStatus==="stopped")&&(yn.justDragged=!0,gt._mouseListenClick=!1,gt._touchListenClick=!1,gt._pointerListenClick=!1,n.dragStatus="stopped");const a=n.node.getLayer()||n.node instanceof gt.Stage&&n.node;a&&t.indexOf(a)===-1&&t.push(a)}),t.forEach(n=>{n.draw()})},_endDragAfter(e){yn._dragElements.forEach((t,n)=>{t.dragStatus==="stopped"&&t.node.fire("dragend",{type:"dragend",target:t.node,evt:e},!0),t.dragStatus!=="dragging"&&yn._dragElements.delete(n)})}};gt.isBrowser&&(window.addEventListener("mouseup",yn._endDragBefore,!0),window.addEventListener("touchend",yn._endDragBefore,!0),window.addEventListener("mousemove",yn._drag),window.addEventListener("touchmove",yn._drag),window.addEventListener("mouseup",yn._endDragAfter,!1),window.addEventListener("touchend",yn._endDragAfter,!1));var T4="absoluteOpacity",Pb="allEventListeners",Uu="absoluteTransform",AI="absoluteScale",dh="canvas",NCe="Change",BCe="children",$Ce="konva",p8="listening",OI="mouseenter",MI="mouseleave",RI="set",II="Shape",L4=" ",DI="stage",md="transform",FCe="Stage",g8="visible",zCe=["xChange.konva","yChange.konva","scaleXChange.konva","scaleYChange.konva","skewXChange.konva","skewYChange.konva","rotationChange.konva","offsetXChange.konva","offsetYChange.konva","transformsEnabledChange.konva"].join(L4);let jCe=1;class qe{constructor(t){this._id=jCe++,this.eventListeners={},this.attrs={},this.index=0,this._allEventListeners=null,this.parent=null,this._cache=new Map,this._attachedDepsListeners=new Map,this._lastPos=null,this._batchingTransformChange=!1,this._needClearTransformCache=!1,this._filterUpToDate=!1,this._isUnderCache=!1,this._dragEventId=null,this._shouldFireChangeEvents=!1,this.setAttrs(t),this._shouldFireChangeEvents=!0}hasChildren(){return!1}_clearCache(t){(t===md||t===Uu)&&this._cache.get(t)?this._cache.get(t).dirty=!0:t?this._cache.delete(t):this._cache.clear()}_getCache(t,n){var r=this._cache.get(t),i=t===md||t===Uu,o=r===void 0||i&&r.dirty===!0;return o&&(r=n.call(this),this._cache.set(t,r)),r}_calculate(t,n,r){if(!this._attachedDepsListeners.get(t)){const i=n.map(o=>o+"Change.konva").join(L4);this.on(i,()=>{this._clearCache(t)}),this._attachedDepsListeners.set(t,!0)}return this._getCache(t,r)}_getCanvasCache(){return this._cache.get(dh)}_clearSelfAndDescendantCache(t){this._clearCache(t),t===Uu&&this.fire("absoluteTransformChange")}clearCache(){if(this._cache.has(dh)){const{scene:t,filter:n,hit:r}=this._cache.get(dh);he.releaseCanvas(t,n,r),this._cache.delete(dh)}return this._clearSelfAndDescendantCache(),this._requestDraw(),this}cache(t){var n=t||{},r={};(n.x===void 0||n.y===void 0||n.width===void 0||n.height===void 0)&&(r=this.getClientRect({skipTransform:!0,relativeTo:this.getParent()}));var i=Math.ceil(n.width||r.width),o=Math.ceil(n.height||r.height),a=n.pixelRatio,s=n.x===void 0?Math.floor(r.x):n.x,l=n.y===void 0?Math.floor(r.y):n.y,u=n.offset||0,f=n.drawBorder||!1,p=n.hitCanvasPixelRatio||1;if(!i||!o){he.error("Can not cache the node. Width or height of the node equals 0. Caching is skipped.");return}i+=u*2+1,o+=u*2+1,s-=u,l-=u;var m=new Fg({pixelRatio:a,width:i,height:o}),v=new Fg({pixelRatio:a,width:0,height:0}),b=new nP({pixelRatio:p,width:i,height:o}),x=m.getContext(),E=b.getContext();return b.isCache=!0,m.isCache=!0,this._cache.delete(dh),this._filterUpToDate=!1,n.imageSmoothingEnabled===!1&&(m.getContext()._context.imageSmoothingEnabled=!1,v.getContext()._context.imageSmoothingEnabled=!1),x.save(),E.save(),x.translate(-s,-l),E.translate(-s,-l),this._isUnderCache=!0,this._clearSelfAndDescendantCache(T4),this._clearSelfAndDescendantCache(AI),this.drawScene(m,this),this.drawHit(b,this),this._isUnderCache=!1,x.restore(),E.restore(),f&&(x.save(),x.beginPath(),x.rect(0,0,i,o),x.closePath(),x.setAttr("strokeStyle","red"),x.setAttr("lineWidth",5),x.stroke(),x.restore()),this._cache.set(dh,{scene:m,filter:v,hit:b,x:s,y:l}),this._requestDraw(),this}isCached(){return this._cache.has(dh)}getClientRect(t){throw new Error('abstract "getClientRect" method call')}_transformedRect(t,n){var r=[{x:t.x,y:t.y},{x:t.x+t.width,y:t.y},{x:t.x+t.width,y:t.y+t.height},{x:t.x,y:t.y+t.height}],i,o,a,s,l=this.getAbsoluteTransform(n);return r.forEach(function(u){var f=l.point(u);i===void 0&&(i=a=f.x,o=s=f.y),i=Math.min(i,f.x),o=Math.min(o,f.y),a=Math.max(a,f.x),s=Math.max(s,f.y)}),{x:i,y:o,width:a-i,height:s-o}}_drawCachedSceneCanvas(t){t.save(),t._applyOpacity(this),t._applyGlobalCompositeOperation(this);const n=this._getCanvasCache();t.translate(n.x,n.y);var r=this._getCachedSceneCanvas(),i=r.pixelRatio;t.drawImage(r._canvas,0,0,r.width/i,r.height/i),t.restore()}_drawCachedHitCanvas(t){var n=this._getCanvasCache(),r=n.hit;t.save(),t.translate(n.x,n.y),t.drawImage(r._canvas,0,0,r.width/r.pixelRatio,r.height/r.pixelRatio),t.restore()}_getCachedSceneCanvas(){var t=this.filters(),n=this._getCanvasCache(),r=n.scene,i=n.filter,o=i.getContext(),a,s,l,u;if(t){if(!this._filterUpToDate){var f=r.pixelRatio;i.setSize(r.width/r.pixelRatio,r.height/r.pixelRatio);try{for(a=t.length,o.clear(),o.drawImage(r._canvas,0,0,r.getWidth()/f,r.getHeight()/f),s=o.getImageData(0,0,i.getWidth(),i.getHeight()),l=0;l<a;l++){if(u=t[l],typeof u!="function"){he.error("Filter should be type of function, but got "+typeof u+" instead. Please check correct filters");continue}u.call(this,s),o.putImageData(s,0,0)}}catch(p){he.error("Unable to apply filter. "+p.message+" This post my help you https://konvajs.org/docs/posts/Tainted_Canvas.html.")}this._filterUpToDate=!0}return i}return r}on(t,n){if(this._cache&&this._cache.delete(Pb),arguments.length===3)return this._delegate.apply(this,arguments);var r=t.split(L4),i=r.length,o,a,s,l,u;for(o=0;o<i;o++)a=r[o],s=a.split("."),l=s[0],u=s[1]||"",this.eventListeners[l]||(this.eventListeners[l]=[]),this.eventListeners[l].push({name:u,handler:n});return this}off(t,n){var r=(t||"").split(L4),i=r.length,o,a,s,l,u,f;if(this._cache&&this._cache.delete(Pb),!t)for(a in this.eventListeners)this._off(a);for(o=0;o<i;o++)if(s=r[o],l=s.split("."),u=l[0],f=l[1],u)this.eventListeners[u]&&this._off(u,f,n);else for(a in this.eventListeners)this._off(a,f,n);return this}dispatchEvent(t){var n={target:this,type:t.type,evt:t};return this.fire(t.type,n),this}addEventListener(t,n){return this.on(t,function(r){n.call(this,r.evt)}),this}removeEventListener(t){return this.off(t),this}_delegate(t,n,r){var i=this;this.on(t,function(o){for(var a=o.target.findAncestors(n,!0,i),s=0;s<a.length;s++)o=he.cloneObject(o),o.currentTarget=a[s],r.call(a[s],o)})}remove(){return this.isDragging()&&this.stopDrag(),yn._dragElements.delete(this._id),this._remove(),this}_clearCaches(){this._clearSelfAndDescendantCache(Uu),this._clearSelfAndDescendantCache(T4),this._clearSelfAndDescendantCache(AI),this._clearSelfAndDescendantCache(DI),this._clearSelfAndDescendantCache(g8),this._clearSelfAndDescendantCache(p8)}_remove(){this._clearCaches();var t=this.getParent();t&&t.children&&(t.children.splice(this.index,1),t._setChildrenIndices(),this.parent=null)}destroy(){return this.remove(),this.clearCache(),this}getAttr(t){var n="get"+he._capitalize(t);return he._isFunction(this[n])?this[n]():this.attrs[t]}getAncestors(){for(var t=this.getParent(),n=[];t;)n.push(t),t=t.getParent();return n}getAttrs(){return this.attrs||{}}setAttrs(t){return this._batchTransformChanges(()=>{var n,r;if(!t)return this;for(n in t)n!==BCe&&(r=RI+he._capitalize(n),he._isFunction(this[r])?this[r](t[n]):this._setAttr(n,t[n]))}),this}isListening(){return this._getCache(p8,this._isListening)}_isListening(t){if(!this.listening())return!1;const r=this.getParent();return r&&r!==t&&this!==t?r._isListening(t):!0}isVisible(){return this._getCache(g8,this._isVisible)}_isVisible(t){if(!this.visible())return!1;const r=this.getParent();return r&&r!==t&&this!==t?r._isVisible(t):!0}shouldDrawHit(t,n=!1){if(t)return this._isVisible(t)&&this._isListening(t);var r=this.getLayer(),i=!1;yn._dragElements.forEach(a=>{a.dragStatus==="dragging"&&(a.node.nodeType==="Stage"||a.node.getLayer()===r)&&(i=!0)});var o=!n&&!gt.hitOnDragEnabled&&i;return this.isListening()&&this.isVisible()&&!o}show(){return this.visible(!0),this}hide(){return this.visible(!1),this}getZIndex(){return this.index||0}getAbsoluteZIndex(){var t=this.getDepth(),n=this,r=0,i,o,a,s;function l(u){for(i=[],o=u.length,a=0;a<o;a++)s=u[a],r++,s.nodeType!==II&&(i=i.concat(s.getChildren().slice())),s._id===n._id&&(a=o);i.length>0&&i[0].getDepth()<=t&&l(i)}return n.nodeType!==FCe&&l(n.getStage().getChildren()),r}getDepth(){for(var t=0,n=this.parent;n;)t++,n=n.parent;return t}_batchTransformChanges(t){this._batchingTransformChange=!0,t(),this._batchingTransformChange=!1,this._needClearTransformCache&&(this._clearCache(md),this._clearSelfAndDescendantCache(Uu)),this._needClearTransformCache=!1}setPosition(t){return this._batchTransformChanges(()=>{this.x(t.x),this.y(t.y)}),this}getPosition(){return{x:this.x(),y:this.y()}}getRelativePointerPosition(){if(!this.getStage())return null;var t=this.getStage().getPointerPosition();if(!t)return null;var n=this.getAbsoluteTransform().copy();return n.invert(),n.point(t)}getAbsolutePosition(t){let n=!1,r=this.parent;for(;r;){if(r.isCached()){n=!0;break}r=r.parent}n&&!t&&(t=!0);var i=this.getAbsoluteTransform(t).getMatrix(),o=new Pa,a=this.offset();return o.m=i.slice(),o.translate(a.x,a.y),o.getTranslation()}setAbsolutePosition(t){var n=this._clearTransform();this.attrs.x=n.x,this.attrs.y=n.y,delete n.x,delete n.y,this._clearCache(md);var r=this._getAbsoluteTransform().copy();return r.invert(),r.translate(t.x,t.y),t={x:this.attrs.x+r.getTranslation().x,y:this.attrs.y+r.getTranslation().y},this._setTransform(n),this.setPosition({x:t.x,y:t.y}),this._clearCache(md),this._clearSelfAndDescendantCache(Uu),this}_setTransform(t){var n;for(n in t)this.attrs[n]=t[n]}_clearTransform(){var t={x:this.x(),y:this.y(),rotation:this.rotation(),scaleX:this.scaleX(),scaleY:this.scaleY(),offsetX:this.offsetX(),offsetY:this.offsetY(),skewX:this.skewX(),skewY:this.skewY()};return this.attrs.x=0,this.attrs.y=0,this.attrs.rotation=0,this.attrs.scaleX=1,this.attrs.scaleY=1,this.attrs.offsetX=0,this.attrs.offsetY=0,this.attrs.skewX=0,this.attrs.skewY=0,t}move(t){var n=t.x,r=t.y,i=this.x(),o=this.y();return n!==void 0&&(i+=n),r!==void 0&&(o+=r),this.setPosition({x:i,y:o}),this}_eachAncestorReverse(t,n){var r=[],i=this.getParent(),o,a;if(!(n&&n._id===this._id)){for(r.unshift(this);i&&(!n||i._id!==n._id);)r.unshift(i),i=i.parent;for(o=r.length,a=0;a<o;a++)t(r[a])}}rotate(t){return this.rotation(this.rotation()+t),this}moveToTop(){if(!this.parent)return he.warn("Node has no parent. moveToTop function is ignored."),!1;var t=this.index,n=this.parent.getChildren().length;return t<n-1?(this.parent.children.splice(t,1),this.parent.children.push(this),this.parent._setChildrenIndices(),!0):!1}moveUp(){if(!this.parent)return he.warn("Node has no parent. moveUp function is ignored."),!1;var t=this.index,n=this.parent.getChildren().length;return t<n-1?(this.parent.children.splice(t,1),this.parent.children.splice(t+1,0,this),this.parent._setChildrenIndices(),!0):!1}moveDown(){if(!this.parent)return he.warn("Node has no parent. moveDown function is ignored."),!1;var t=this.index;return t>0?(this.parent.children.splice(t,1),this.parent.children.splice(t-1,0,this),this.parent._setChildrenIndices(),!0):!1}moveToBottom(){if(!this.parent)return he.warn("Node has no parent. moveToBottom function is ignored."),!1;var t=this.index;return t>0?(this.parent.children.splice(t,1),this.parent.children.unshift(this),this.parent._setChildrenIndices(),!0):!1}setZIndex(t){if(!this.parent)return he.warn("Node has no parent. zIndex parameter is ignored."),this;(t<0||t>=this.parent.children.length)&&he.warn("Unexpected value "+t+" for zIndex property. zIndex is just index of a node in children of its parent. Expected value is from 0 to "+(this.parent.children.length-1)+".");var n=this.index;return this.parent.children.splice(n,1),this.parent.children.splice(t,0,this),this.parent._setChildrenIndices(),this}getAbsoluteOpacity(){return this._getCache(T4,this._getAbsoluteOpacity)}_getAbsoluteOpacity(){var t=this.opacity(),n=this.getParent();return n&&!n._isUnderCache&&(t*=n.getAbsoluteOpacity()),t}moveTo(t){return this.getParent()!==t&&(this._remove(),t.add(this)),this}toObject(){var t={},n=this.getAttrs(),r,i,o,a,s;t.attrs={};for(r in n)i=n[r],s=he.isObject(i)&&!he._isPlainObject(i)&&!he._isArray(i),!s&&(o=typeof this[r]=="function"&&this[r],delete n[r],a=o?o.call(this):null,n[r]=i,a!==i&&(t.attrs[r]=i));return t.className=this.getClassName(),he._prepareToStringify(t)}toJSON(){return JSON.stringify(this.toObject())}getParent(){return this.parent}findAncestors(t,n,r){var i=[];n&&this._isMatch(t)&&i.push(this);for(var o=this.parent;o;){if(o===r)return i;o._isMatch(t)&&i.push(o),o=o.parent}return i}isAncestorOf(t){return!1}findAncestor(t,n,r){return this.findAncestors(t,n,r)[0]}_isMatch(t){if(!t)return!1;if(typeof t=="function")return t(this);var n=t.replace(/ /g,"").split(","),r=n.length,i,o;for(i=0;i<r;i++)if(o=n[i],he.isValidSelector(o)||(he.warn('Selector "'+o+'" is invalid. Allowed selectors examples are "#foo", ".bar" or "Group".'),he.warn('If you have a custom shape with such className, please change it to start with upper letter like "Triangle".'),he.warn("Konva is awesome, right?")),o.charAt(0)==="#"){if(this.id()===o.slice(1))return!0}else if(o.charAt(0)==="."){if(this.hasName(o.slice(1)))return!0}else if(this.className===o||this.nodeType===o)return!0;return!1}getLayer(){var t=this.getParent();return t?t.getLayer():null}getStage(){return this._getCache(DI,this._getStage)}_getStage(){var t=this.getParent();if(t)return t.getStage()}fire(t,n={},r){return n.target=n.target||this,r?this._fireAndBubble(t,n):this._fire(t,n),this}getAbsoluteTransform(t){return t?this._getAbsoluteTransform(t):this._getCache(Uu,this._getAbsoluteTransform)}_getAbsoluteTransform(t){var n;if(t)return n=new Pa,this._eachAncestorReverse(function(i){var o=i.transformsEnabled();o==="all"?n.multiply(i.getTransform()):o==="position"&&n.translate(i.x()-i.offsetX(),i.y()-i.offsetY())},t),n;n=this._cache.get(Uu)||new Pa,this.parent?this.parent.getAbsoluteTransform().copyInto(n):n.reset();var r=this.transformsEnabled();if(r==="all")n.multiply(this.getTransform());else if(r==="position"){const i=this.attrs.x||0,o=this.attrs.y||0,a=this.attrs.offsetX||0,s=this.attrs.offsetY||0;n.translate(i-a,o-s)}return n.dirty=!1,n}getAbsoluteScale(t){for(var n=this;n;)n._isUnderCache&&(t=n),n=n.getParent();const i=this.getAbsoluteTransform(t).decompose();return{x:i.scaleX,y:i.scaleY}}getAbsoluteRotation(){return this.getAbsoluteTransform().decompose().rotation}getTransform(){return this._getCache(md,this._getTransform)}_getTransform(){var t,n,r=this._cache.get(md)||new Pa;r.reset();var i=this.x(),o=this.y(),a=gt.getAngle(this.rotation()),s=(t=this.attrs.scaleX)!==null&&t!==void 0?t:1,l=(n=this.attrs.scaleY)!==null&&n!==void 0?n:1,u=this.attrs.skewX||0,f=this.attrs.skewY||0,p=this.attrs.offsetX||0,m=this.attrs.offsetY||0;return(i!==0||o!==0)&&r.translate(i,o),a!==0&&r.rotate(a),(u!==0||f!==0)&&r.skew(u,f),(s!==1||l!==1)&&r.scale(s,l),(p!==0||m!==0)&&r.translate(-1*p,-1*m),r.dirty=!1,r}clone(t){var n=he.cloneObject(this.attrs),r,i,o,a,s;for(r in t)n[r]=t[r];var l=new this.constructor(n);for(r in this.eventListeners)for(i=this.eventListeners[r],o=i.length,a=0;a<o;a++)s=i[a],s.name.indexOf($Ce)<0&&(l.eventListeners[r]||(l.eventListeners[r]=[]),l.eventListeners[r].push(s));return l}_toKonvaCanvas(t){t=t||{};var n=this.getClientRect(),r=this.getStage(),i=t.x!==void 0?t.x:Math.floor(n.x),o=t.y!==void 0?t.y:Math.floor(n.y),a=t.pixelRatio||1,s=new Fg({width:t.width||Math.ceil(n.width)||(r?r.width():0),height:t.height||Math.ceil(n.height)||(r?r.height():0),pixelRatio:a}),l=s.getContext();return t.imageSmoothingEnabled===!1&&(l._context.imageSmoothingEnabled=!1),l.save(),(i||o)&&l.translate(-1*i,-1*o),this.drawScene(s),l.restore(),s}toCanvas(t){return this._toKonvaCanvas(t)._canvas}toDataURL(t){t=t||{};var n=t.mimeType||null,r=t.quality||null,i=this._toKonvaCanvas(t).toDataURL(n,r);return t.callback&&t.callback(i),i}toImage(t){return new Promise((n,r)=>{try{const i=t==null?void 0:t.callback;i&&delete t.callback,he._urlToImage(this.toDataURL(t),function(o){n(o),i==null||i(o)})}catch(i){r(i)}})}toBlob(t){return new Promise((n,r)=>{try{const i=t==null?void 0:t.callback;i&&delete t.callback,this.toCanvas(t).toBlob(o=>{n(o),i==null||i(o)})}catch(i){r(i)}})}setSize(t){return this.width(t.width),this.height(t.height),this}getSize(){return{width:this.width(),height:this.height()}}getClassName(){return this.className||this.nodeType}getType(){return this.nodeType}getDragDistance(){return this.attrs.dragDistance!==void 0?this.attrs.dragDistance:this.parent?this.parent.getDragDistance():gt.dragDistance}_off(t,n,r){var i=this.eventListeners[t],o,a,s;for(o=0;o<i.length;o++)if(a=i[o].name,s=i[o].handler,(a!=="konva"||n==="konva")&&(!n||a===n)&&(!r||r===s)){if(i.splice(o,1),i.length===0){delete this.eventListeners[t];break}o--}}_fireChangeEvent(t,n,r){this._fire(t+NCe,{oldVal:n,newVal:r})}addName(t){if(!this.hasName(t)){var n=this.name(),r=n?n+" "+t:t;this.name(r)}return this}hasName(t){if(!t)return!1;const n=this.name();if(!n)return!1;var r=(n||"").split(/\s/g);return r.indexOf(t)!==-1}removeName(t){var n=(this.name()||"").split(/\s/g),r=n.indexOf(t);return r!==-1&&(n.splice(r,1),this.name(n.join(" "))),this}setAttr(t,n){var r=this[RI+he._capitalize(t)];return he._isFunction(r)?r.call(this,n):this._setAttr(t,n),this}_requestDraw(){if(gt.autoDrawEnabled){const t=this.getLayer()||this.getStage();t==null||t.batchDraw()}}_setAttr(t,n){var r=this.attrs[t];r===n&&!he.isObject(n)||(n==null?delete this.attrs[t]:this.attrs[t]=n,this._shouldFireChangeEvents&&this._fireChangeEvent(t,r,n),this._requestDraw())}_setComponentAttr(t,n,r){var i;r!==void 0&&(i=this.attrs[t],i||(this.attrs[t]=this.getAttr(t)),this.attrs[t][n]=r,this._fireChangeEvent(t,i,r))}_fireAndBubble(t,n,r){n&&this.nodeType===II&&(n.target=this);var i=(t===OI||t===MI)&&(r&&(this===r||this.isAncestorOf&&this.isAncestorOf(r))||this.nodeType==="Stage"&&!r);if(!i){this._fire(t,n);var o=(t===OI||t===MI)&&r&&r.isAncestorOf&&r.isAncestorOf(this)&&!r.isAncestorOf(this.parent);(n&&!n.cancelBubble||!n)&&this.parent&&this.parent.isListening()&&!o&&(r&&r.parent?this._fireAndBubble.call(this.parent,t,n,r):this._fireAndBubble.call(this.parent,t,n))}}_getProtoListeners(t){let n=this._cache.get(Pb);if(!n){n={};let i=Object.getPrototypeOf(this);for(;i;){if(!i.eventListeners){i=Object.getPrototypeOf(i);continue}for(var r in i.eventListeners){const o=i.eventListeners[r],a=n[r]||[];n[r]=o.concat(a)}i=Object.getPrototypeOf(i)}this._cache.set(Pb,n)}return n[t]}_fire(t,n){n=n||{},n.currentTarget=this,n.type=t;const r=this._getProtoListeners(t);if(r)for(var i=0;i<r.length;i++)r[i].handler.call(this,n);const o=this.eventListeners[t];if(o)for(var i=0;i<o.length;i++)o[i].handler.call(this,n)}draw(){return this.drawScene(),this.drawHit(),this}_createDragElement(t){var n=t?t.pointerId:void 0,r=this.getStage(),i=this.getAbsolutePosition(),o=r._getPointerById(n)||r._changedPointerPositions[0]||i;yn._dragElements.set(this._id,{node:this,startPointerPos:o,offset:{x:o.x-i.x,y:o.y-i.y},dragStatus:"ready",pointerId:n})}startDrag(t,n=!0){yn._dragElements.has(this._id)||this._createDragElement(t);const r=yn._dragElements.get(this._id);r.dragStatus="dragging",this.fire("dragstart",{type:"dragstart",target:this,evt:t&&t.evt},n)}_setDragPosition(t,n){const r=this.getStage()._getPointerById(n.pointerId);if(!!r){var i={x:r.x-n.offset.x,y:r.y-n.offset.y},o=this.dragBoundFunc();if(o!==void 0){const a=o.call(this,i,t);a?i=a:he.warn("dragBoundFunc did not return any value. That is unexpected behavior. You must return new absolute position from dragBoundFunc.")}(!this._lastPos||this._lastPos.x!==i.x||this._lastPos.y!==i.y)&&(this.setAbsolutePosition(i),this._requestDraw()),this._lastPos=i}}stopDrag(t){const n=yn._dragElements.get(this._id);n&&(n.dragStatus="stopped"),yn._endDragBefore(t),yn._endDragAfter(t)}setDraggable(t){this._setAttr("draggable",t),this._dragChange()}isDragging(){const t=yn._dragElements.get(this._id);return t?t.dragStatus==="dragging":!1}_listenDrag(){this._dragCleanup(),this.on("mousedown.konva touchstart.konva",function(t){var n=t.evt.button!==void 0,r=!n||gt.dragButtons.indexOf(t.evt.button)>=0;if(!!r&&!this.isDragging()){var i=!1;yn._dragElements.forEach(o=>{this.isAncestorOf(o.node)&&(i=!0)}),i||this._createDragElement(t)}})}_dragChange(){if(this.attrs.draggable)this._listenDrag();else{this._dragCleanup();var t=this.getStage();if(!t)return;const n=yn._dragElements.get(this._id),r=n&&n.dragStatus==="dragging",i=n&&n.dragStatus==="ready";r?this.stopDrag():i&&yn._dragElements.delete(this._id)}}_dragCleanup(){this.off("mousedown.konva"),this.off("touchstart.konva")}isClientRectOnScreen(t={x:0,y:0}){const n=this.getStage();if(!n)return!1;const r={x:-t.x,y:-t.y,width:n.width()+2*t.x,height:n.height()+2*t.y};return he.haveIntersection(r,this.getClientRect())}static create(t,n){return he._isString(t)&&(t=JSON.parse(t)),this._createNode(t,n)}static _createNode(t,n){var r=qe.prototype.getClassName.call(t),i=t.children,o,a,s;n&&(t.attrs.container=n),gt[r]||(he.warn('Can not find a node with class name "'+r+'". Fallback to "Shape".'),r="Shape");const l=gt[r];if(o=new l(t.attrs),i)for(a=i.length,s=0;s<a;s++)o.add(qe._createNode(i[s]));return o}}qe.prototype.nodeType="Node";qe.prototype._attrsAffectingSize=[];qe.prototype.eventListeners={};qe.prototype.on.call(qe.prototype,zCe,function(){if(this._batchingTransformChange){this._needClearTransformCache=!0;return}this._clearCache(md),this._clearSelfAndDescendantCache(Uu)});qe.prototype.on.call(qe.prototype,"visibleChange.konva",function(){this._clearSelfAndDescendantCache(g8)});qe.prototype.on.call(qe.prototype,"listeningChange.konva",function(){this._clearSelfAndDescendantCache(p8)});qe.prototype.on.call(qe.prototype,"opacityChange.konva",function(){this._clearSelfAndDescendantCache(T4)});const Yn=ee.addGetterSetter;Yn(qe,"zIndex");Yn(qe,"absolutePosition");Yn(qe,"position");Yn(qe,"x",0,Ge());Yn(qe,"y",0,Ge());Yn(qe,"globalCompositeOperation","source-over",k1());Yn(qe,"opacity",1,Ge());Yn(qe,"name","",k1());Yn(qe,"id","",k1());Yn(qe,"rotation",0,Ge());ee.addComponentsGetterSetter(qe,"scale",["x","y"]);Yn(qe,"scaleX",1,Ge());Yn(qe,"scaleY",1,Ge());ee.addComponentsGetterSetter(qe,"skew",["x","y"]);Yn(qe,"skewX",0,Ge());Yn(qe,"skewY",0,Ge());ee.addComponentsGetterSetter(qe,"offset",["x","y"]);Yn(qe,"offsetX",0,Ge());Yn(qe,"offsetY",0,Ge());Yn(qe,"dragDistance",null,Ge());Yn(qe,"width",0,Ge());Yn(qe,"height",0,Ge());Yn(qe,"listening",!0,ol());Yn(qe,"preventDefault",!0,ol());Yn(qe,"filters",null,function(e){return this._filterUpToDate=!1,e});Yn(qe,"visible",!0,ol());Yn(qe,"transformsEnabled","all",k1());Yn(qe,"size");Yn(qe,"dragBoundFunc");Yn(qe,"draggable",!1,ol());ee.backCompat(qe,{rotateDeg:"rotate",setRotationDeg:"setRotation",getRotationDeg:"getRotation"});class Ma extends qe{constructor(){super(...arguments),this.children=[]}getChildren(t){if(!t)return this.children||[];const n=this.children||[];var r=[];return n.forEach(function(i){t(i)&&r.push(i)}),r}hasChildren(){return this.getChildren().length>0}removeChildren(){return this.getChildren().forEach(t=>{t.parent=null,t.index=0,t.remove()}),this.children=[],this._requestDraw(),this}destroyChildren(){return this.getChildren().forEach(t=>{t.parent=null,t.index=0,t.destroy()}),this.children=[],this._requestDraw(),this}add(...t){if(arguments.length>1){for(var n=0;n<arguments.length;n++)this.add(arguments[n]);return this}var r=t[0];return r.getParent()?(r.moveTo(this),this):(this._validateAdd(r),r.index=this.getChildren().length,r.parent=this,r._clearCaches(),this.getChildren().push(r),this._fire("add",{child:r}),this._requestDraw(),this)}destroy(){return this.hasChildren()&&this.destroyChildren(),super.destroy(),this}find(t){return this._generalFind(t,!1)}findOne(t){var n=this._generalFind(t,!0);return n.length>0?n[0]:void 0}_generalFind(t,n){var r=[];return this._descendants(i=>{const o=i._isMatch(t);return o&&r.push(i),!!(o&&n)}),r}_descendants(t){let n=!1;const r=this.getChildren();for(const i of r){if(n=t(i),n)return!0;if(!!i.hasChildren()&&(n=i._descendants(t),n))return!0}return!1}toObject(){var t=qe.prototype.toObject.call(this);return t.children=[],this.getChildren().forEach(n=>{t.children.push(n.toObject())}),t}isAncestorOf(t){for(var n=t.getParent();n;){if(n._id===this._id)return!0;n=n.getParent()}return!1}clone(t){var n=qe.prototype.clone.call(this,t);return this.getChildren().forEach(function(r){n.add(r.clone())}),n}getAllIntersections(t){var n=[];return this.find("Shape").forEach(function(r){r.isVisible()&&r.intersects(t)&&n.push(r)}),n}_clearSelfAndDescendantCache(t){var n;super._clearSelfAndDescendantCache(t),!this.isCached()&&((n=this.children)===null||n===void 0||n.forEach(function(r){r._clearSelfAndDescendantCache(t)}))}_setChildrenIndices(){var t;(t=this.children)===null||t===void 0||t.forEach(function(n,r){n.index=r}),this._requestDraw()}drawScene(t,n){var r=this.getLayer(),i=t||r&&r.getCanvas(),o=i&&i.getContext(),a=this._getCanvasCache(),s=a&&a.scene,l=i&&i.isCache;if(!this.isVisible()&&!l)return this;if(s){o.save();var u=this.getAbsoluteTransform(n).getMatrix();o.transform(u[0],u[1],u[2],u[3],u[4],u[5]),this._drawCachedSceneCanvas(o),o.restore()}else this._drawChildren("drawScene",i,n);return this}drawHit(t,n){if(!this.shouldDrawHit(n))return this;var r=this.getLayer(),i=t||r&&r.hitCanvas,o=i&&i.getContext(),a=this._getCanvasCache(),s=a&&a.hit;if(s){o.save();var l=this.getAbsoluteTransform(n).getMatrix();o.transform(l[0],l[1],l[2],l[3],l[4],l[5]),this._drawCachedHitCanvas(o),o.restore()}else this._drawChildren("drawHit",i,n);return this}_drawChildren(t,n,r){var i,o=n&&n.getContext(),a=this.clipWidth(),s=this.clipHeight(),l=this.clipFunc(),u=a&&s||l;const f=r===this;if(u){o.save();var p=this.getAbsoluteTransform(r),m=p.getMatrix();if(o.transform(m[0],m[1],m[2],m[3],m[4],m[5]),o.beginPath(),l)l.call(this,o,this);else{var v=this.clipX(),b=this.clipY();o.rect(v,b,a,s)}o.clip(),m=p.copy().invert().getMatrix(),o.transform(m[0],m[1],m[2],m[3],m[4],m[5])}var x=!f&&this.globalCompositeOperation()!=="source-over"&&t==="drawScene";x&&(o.save(),o._applyGlobalCompositeOperation(this)),(i=this.children)===null||i===void 0||i.forEach(function(E){E[t](n,r)}),x&&o.restore(),u&&o.restore()}getClientRect(t){var n;t=t||{};var r=t.skipTransform,i=t.relativeTo,o,a,s,l,u={x:1/0,y:1/0,width:0,height:0},f=this;(n=this.children)===null||n===void 0||n.forEach(function(x){if(!!x.visible()){var E=x.getClientRect({relativeTo:f,skipShadow:t.skipShadow,skipStroke:t.skipStroke});E.width===0&&E.height===0||(o===void 0?(o=E.x,a=E.y,s=E.x+E.width,l=E.y+E.height):(o=Math.min(o,E.x),a=Math.min(a,E.y),s=Math.max(s,E.x+E.width),l=Math.max(l,E.y+E.height)))}});for(var p=this.find("Shape"),m=!1,v=0;v<p.length;v++){var b=p[v];if(b._isVisible(this)){m=!0;break}}return m&&o!==void 0?u={x:o,y:a,width:s-o,height:l-a}:u={x:0,y:0,width:0,height:0},r?u:this._transformedRect(u,i)}}ee.addComponentsGetterSetter(Ma,"clip",["x","y","width","height"]);ee.addGetterSetter(Ma,"clipX",void 0,Ge());ee.addGetterSetter(Ma,"clipY",void 0,Ge());ee.addGetterSetter(Ma,"clipWidth",void 0,Ge());ee.addGetterSetter(Ma,"clipHeight",void 0,Ge());ee.addGetterSetter(Ma,"clipFunc");const Q2=new Map,HW=gt._global.PointerEvent!==void 0;function bC(e){return Q2.get(e)}function rP(e){return{evt:e,pointerId:e.pointerId}}function VW(e,t){return Q2.get(e)===t}function UW(e,t){n2(e),t.getStage()&&(Q2.set(e,t),HW&&t._fire("gotpointercapture",rP(new PointerEvent("gotpointercapture"))))}function n2(e,t){const n=Q2.get(e);if(!n)return;const r=n.getStage();r&&r.content,Q2.delete(e),HW&&n._fire("lostpointercapture",rP(new PointerEvent("lostpointercapture")))}var HCe="Stage",VCe="string",NI="px",UCe="mouseout",WW="mouseleave",GW="mouseover",qW="mouseenter",YW="mousemove",KW="mousedown",XW="mouseup",Ev="pointermove",Pv="pointerdown",ug="pointerup",Tv="pointercancel",WCe="lostpointercapture",Tb="pointerout",Lb="pointerleave",Ab="pointerover",Ob="pointerenter",m8="contextmenu",ZW="touchstart",QW="touchend",JW="touchmove",eG="touchcancel",v8="wheel",GCe=5,qCe=[[qW,"_pointerenter"],[KW,"_pointerdown"],[YW,"_pointermove"],[XW,"_pointerup"],[WW,"_pointerleave"],[ZW,"_pointerdown"],[JW,"_pointermove"],[QW,"_pointerup"],[eG,"_pointercancel"],[GW,"_pointerover"],[v8,"_wheel"],[m8,"_contextmenu"],[Pv,"_pointerdown"],[Ev,"_pointermove"],[ug,"_pointerup"],[Tv,"_pointercancel"],[WCe,"_lostpointercapture"]];const SC={mouse:{[Tb]:UCe,[Lb]:WW,[Ab]:GW,[Ob]:qW,[Ev]:YW,[Pv]:KW,[ug]:XW,[Tv]:"mousecancel",pointerclick:"click",pointerdblclick:"dblclick"},touch:{[Tb]:"touchout",[Lb]:"touchleave",[Ab]:"touchover",[Ob]:"touchenter",[Ev]:JW,[Pv]:ZW,[ug]:QW,[Tv]:eG,pointerclick:"tap",pointerdblclick:"dbltap"},pointer:{[Tb]:Tb,[Lb]:Lb,[Ab]:Ab,[Ob]:Ob,[Ev]:Ev,[Pv]:Pv,[ug]:ug,[Tv]:Tv,pointerclick:"pointerclick",pointerdblclick:"pointerdblclick"}},Lv=e=>e.indexOf("pointer")>=0?"pointer":e.indexOf("touch")>=0?"touch":"mouse",I0=e=>{const t=Lv(e);if(t==="pointer")return gt.pointerEventsEnabled&&SC.pointer;if(t==="touch")return SC.touch;if(t==="mouse")return SC.mouse};function BI(e={}){return(e.clipFunc||e.clipWidth||e.clipHeight)&&he.warn("Stage does not support clipping. Please use clip for Layers or Groups."),e}const YCe="Pointer position is missing and not registered by the stage. Looks like it is outside of the stage container. You can set it manually from event: stage.setPointersPositions(event);",A4=[];class kx extends Ma{constructor(t){super(BI(t)),this._pointerPositions=[],this._changedPointerPositions=[],this._buildDOM(),this._bindContentEvents(),A4.push(this),this.on("widthChange.konva heightChange.konva",this._resizeDOM),this.on("visibleChange.konva",this._checkVisibility),this.on("clipWidthChange.konva clipHeightChange.konva clipFuncChange.konva",()=>{BI(this.attrs)}),this._checkVisibility()}_validateAdd(t){const n=t.getType()==="Layer",r=t.getType()==="FastLayer";n||r||he.throw("You may only add layers to the stage.")}_checkVisibility(){if(!this.content)return;const t=this.visible()?"":"none";this.content.style.display=t}setContainer(t){if(typeof t===VCe){if(t.charAt(0)==="."){var n=t.slice(1);t=document.getElementsByClassName(n)[0]}else{var r;t.charAt(0)!=="#"?r=t:r=t.slice(1),t=document.getElementById(r)}if(!t)throw"Can not find container in document with id "+r}return this._setAttr("container",t),this.content&&(this.content.parentElement&&this.content.parentElement.removeChild(this.content),t.appendChild(this.content)),this}shouldDrawHit(){return!0}clear(){var t=this.children,n=t.length,r;for(r=0;r<n;r++)t[r].clear();return this}clone(t){return t||(t={}),t.container=typeof document<"u"&&document.createElement("div"),Ma.prototype.clone.call(this,t)}destroy(){super.destroy();var t=this.content;t&&he._isInDocument(t)&&this.container().removeChild(t);var n=A4.indexOf(this);return n>-1&&A4.splice(n,1),he.releaseCanvas(this.bufferCanvas._canvas,this.bufferHitCanvas._canvas),this}getPointerPosition(){const t=this._pointerPositions[0]||this._changedPointerPositions[0];return t?{x:t.x,y:t.y}:(he.warn(YCe),null)}_getPointerById(t){return this._pointerPositions.find(n=>n.id===t)}getPointersPositions(){return this._pointerPositions}getStage(){return this}getContent(){return this.content}_toKonvaCanvas(t){t=t||{},t.x=t.x||0,t.y=t.y||0,t.width=t.width||this.width(),t.height=t.height||this.height();var n=new Fg({width:t.width,height:t.height,pixelRatio:t.pixelRatio||1}),r=n.getContext()._context,i=this.children;return(t.x||t.y)&&r.translate(-1*t.x,-1*t.y),i.forEach(function(o){if(!!o.isVisible()){var a=o._toKonvaCanvas(t);r.drawImage(a._canvas,t.x,t.y,a.getWidth()/a.getPixelRatio(),a.getHeight()/a.getPixelRatio())}}),n}getIntersection(t){if(!t)return null;var n=this.children,r=n.length,i=r-1,o;for(o=i;o>=0;o--){const a=n[o].getIntersection(t);if(a)return a}return null}_resizeDOM(){var t=this.width(),n=this.height();this.content&&(this.content.style.width=t+NI,this.content.style.height=n+NI),this.bufferCanvas.setSize(t,n),this.bufferHitCanvas.setSize(t,n),this.children.forEach(r=>{r.setSize({width:t,height:n}),r.draw()})}add(t,...n){if(arguments.length>1){for(var r=0;r<arguments.length;r++)this.add(arguments[r]);return this}super.add(t);var i=this.children.length;return i>GCe&&he.warn("The stage has "+i+" layers. Recommended maximum number of layers is 3-5. Adding more layers into the stage may drop the performance. Rethink your tree structure, you can use Konva.Group."),t.setSize({width:this.width(),height:this.height()}),t.draw(),gt.isBrowser&&this.content.appendChild(t.canvas._canvas),this}getParent(){return null}getLayer(){return null}hasPointerCapture(t){return VW(t,this)}setPointerCapture(t){UW(t,this)}releaseCapture(t){n2(t)}getLayers(){return this.children}_bindContentEvents(){!gt.isBrowser||qCe.forEach(([t,n])=>{this.content.addEventListener(t,r=>{this[n](r)},{passive:!1})})}_pointerenter(t){this.setPointersPositions(t);const n=I0(t.type);this._fire(n.pointerenter,{evt:t,target:this,currentTarget:this})}_pointerover(t){this.setPointersPositions(t);const n=I0(t.type);this._fire(n.pointerover,{evt:t,target:this,currentTarget:this})}_getTargetShape(t){let n=this[t+"targetShape"];return n&&!n.getStage()&&(n=null),n}_pointerleave(t){const n=I0(t.type),r=Lv(t.type);if(!!n){this.setPointersPositions(t);var i=this._getTargetShape(r),o=!yn.isDragging||gt.hitOnDragEnabled;i&&o?(i._fireAndBubble(n.pointerout,{evt:t}),i._fireAndBubble(n.pointerleave,{evt:t}),this._fire(n.pointerleave,{evt:t,target:this,currentTarget:this}),this[r+"targetShape"]=null):o&&(this._fire(n.pointerleave,{evt:t,target:this,currentTarget:this}),this._fire(n.pointerout,{evt:t,target:this,currentTarget:this})),this.pointerPos=void 0,this._pointerPositions=[]}}_pointerdown(t){const n=I0(t.type),r=Lv(t.type);if(!!n){this.setPointersPositions(t);var i=!1;this._changedPointerPositions.forEach(o=>{var a=this.getIntersection(o);if(yn.justDragged=!1,gt["_"+r+"ListenClick"]=!0,!(a&&a.isListening()))return;gt.capturePointerEventsEnabled&&a.setPointerCapture(o.id),this[r+"ClickStartShape"]=a,a._fireAndBubble(n.pointerdown,{evt:t,pointerId:o.id}),i=!0;const l=t.type.indexOf("touch")>=0;a.preventDefault()&&t.cancelable&&l&&t.preventDefault()}),i||this._fire(n.pointerdown,{evt:t,target:this,currentTarget:this,pointerId:this._pointerPositions[0].id})}}_pointermove(t){const n=I0(t.type),r=Lv(t.type);if(!n)return;yn.isDragging&&yn.node.preventDefault()&&t.cancelable&&t.preventDefault(),this.setPointersPositions(t);var i=!yn.isDragging||gt.hitOnDragEnabled;if(!i)return;var o={};let a=!1;var s=this._getTargetShape(r);this._changedPointerPositions.forEach(l=>{const u=bC(l.id)||this.getIntersection(l),f=l.id,p={evt:t,pointerId:f};var m=s!==u;if(m&&s&&(s._fireAndBubble(n.pointerout,Object.assign({},p),u),s._fireAndBubble(n.pointerleave,Object.assign({},p),u)),u){if(o[u._id])return;o[u._id]=!0}u&&u.isListening()?(a=!0,m&&(u._fireAndBubble(n.pointerover,Object.assign({},p),s),u._fireAndBubble(n.pointerenter,Object.assign({},p),s),this[r+"targetShape"]=u),u._fireAndBubble(n.pointermove,Object.assign({},p))):s&&(this._fire(n.pointerover,{evt:t,target:this,currentTarget:this,pointerId:f}),this[r+"targetShape"]=null)}),a||this._fire(n.pointermove,{evt:t,target:this,currentTarget:this,pointerId:this._changedPointerPositions[0].id})}_pointerup(t){const n=I0(t.type),r=Lv(t.type);if(!n)return;this.setPointersPositions(t);const i=this[r+"ClickStartShape"],o=this[r+"ClickEndShape"];var a={};let s=!1;this._changedPointerPositions.forEach(l=>{const u=bC(l.id)||this.getIntersection(l);if(u){if(u.releaseCapture(l.id),a[u._id])return;a[u._id]=!0}const f=l.id,p={evt:t,pointerId:f};let m=!1;gt["_"+r+"InDblClickWindow"]?(m=!0,clearTimeout(this[r+"DblTimeout"])):yn.justDragged||(gt["_"+r+"InDblClickWindow"]=!0,clearTimeout(this[r+"DblTimeout"])),this[r+"DblTimeout"]=setTimeout(function(){gt["_"+r+"InDblClickWindow"]=!1},gt.dblClickWindow),u&&u.isListening()?(s=!0,this[r+"ClickEndShape"]=u,u._fireAndBubble(n.pointerup,Object.assign({},p)),gt["_"+r+"ListenClick"]&&i&&i===u&&(u._fireAndBubble(n.pointerclick,Object.assign({},p)),m&&o&&o===u&&u._fireAndBubble(n.pointerdblclick,Object.assign({},p)))):(this[r+"ClickEndShape"]=null,gt["_"+r+"ListenClick"]&&this._fire(n.pointerclick,{evt:t,target:this,currentTarget:this,pointerId:f}),m&&this._fire(n.pointerdblclick,{evt:t,target:this,currentTarget:this,pointerId:f}))}),s||this._fire(n.pointerup,{evt:t,target:this,currentTarget:this,pointerId:this._changedPointerPositions[0].id}),gt["_"+r+"ListenClick"]=!1,t.cancelable&&r!=="touch"&&t.preventDefault()}_contextmenu(t){this.setPointersPositions(t);var n=this.getIntersection(this.getPointerPosition());n&&n.isListening()?n._fireAndBubble(m8,{evt:t}):this._fire(m8,{evt:t,target:this,currentTarget:this})}_wheel(t){this.setPointersPositions(t);var n=this.getIntersection(this.getPointerPosition());n&&n.isListening()?n._fireAndBubble(v8,{evt:t}):this._fire(v8,{evt:t,target:this,currentTarget:this})}_pointercancel(t){this.setPointersPositions(t);const n=bC(t.pointerId)||this.getIntersection(this.getPointerPosition());n&&n._fireAndBubble(ug,rP(t)),n2(t.pointerId)}_lostpointercapture(t){n2(t.pointerId)}setPointersPositions(t){var n=this._getContentPosition(),r=null,i=null;t=t||window.event,t.touches!==void 0?(this._pointerPositions=[],this._changedPointerPositions=[],Array.prototype.forEach.call(t.touches,o=>{this._pointerPositions.push({id:o.identifier,x:(o.clientX-n.left)/n.scaleX,y:(o.clientY-n.top)/n.scaleY})}),Array.prototype.forEach.call(t.changedTouches||t.touches,o=>{this._changedPointerPositions.push({id:o.identifier,x:(o.clientX-n.left)/n.scaleX,y:(o.clientY-n.top)/n.scaleY})})):(r=(t.clientX-n.left)/n.scaleX,i=(t.clientY-n.top)/n.scaleY,this.pointerPos={x:r,y:i},this._pointerPositions=[{x:r,y:i,id:he._getFirstPointerId(t)}],this._changedPointerPositions=[{x:r,y:i,id:he._getFirstPointerId(t)}])}_setPointerPosition(t){he.warn('Method _setPointerPosition is deprecated. Use "stage.setPointersPositions(event)" instead.'),this.setPointersPositions(t)}_getContentPosition(){if(!this.content||!this.content.getBoundingClientRect)return{top:0,left:0,scaleX:1,scaleY:1};var t=this.content.getBoundingClientRect();return{top:t.top,left:t.left,scaleX:t.width/this.content.clientWidth||1,scaleY:t.height/this.content.clientHeight||1}}_buildDOM(){if(this.bufferCanvas=new Fg({width:this.width(),height:this.height()}),this.bufferHitCanvas=new nP({pixelRatio:1,width:this.width(),height:this.height()}),!!gt.isBrowser){var t=this.container();if(!t)throw"Stage has no container. A container is required.";t.innerHTML="",this.content=document.createElement("div"),this.content.style.position="relative",this.content.style.userSelect="none",this.content.className="konvajs-content",this.content.setAttribute("role","presentation"),t.appendChild(this.content),this._resizeDOM()}}cache(){return he.warn("Cache function is not allowed for stage. You may use cache only for layers, groups and shapes."),this}clearCache(){return this}batchDraw(){return this.getChildren().forEach(function(t){t.batchDraw()}),this}}kx.prototype.nodeType=HCe;Er(kx);ee.addGetterSetter(kx,"container");var tG="hasShadow",nG="shadowRGBA",rG="patternImage",iG="linearGradient",oG="radialGradient";let Mb;function xC(){return Mb||(Mb=he.createCanvasElement().getContext("2d"),Mb)}const r2={};function KCe(e){e.fill()}function XCe(e){e.stroke()}function ZCe(e){e.fill()}function QCe(e){e.stroke()}function JCe(){this._clearCache(tG)}function e7e(){this._clearCache(nG)}function t7e(){this._clearCache(rG)}function n7e(){this._clearCache(iG)}function r7e(){this._clearCache(oG)}class $e extends qe{constructor(t){super(t);let n;for(;n=he.getRandomColor(),!(n&&!(n in r2)););this.colorKey=n,r2[n]=this}getContext(){return he.warn("shape.getContext() method is deprecated. Please do not use it."),this.getLayer().getContext()}getCanvas(){return he.warn("shape.getCanvas() method is deprecated. Please do not use it."),this.getLayer().getCanvas()}getSceneFunc(){return this.attrs.sceneFunc||this._sceneFunc}getHitFunc(){return this.attrs.hitFunc||this._hitFunc}hasShadow(){return this._getCache(tG,this._hasShadow)}_hasShadow(){return this.shadowEnabled()&&this.shadowOpacity()!==0&&!!(this.shadowColor()||this.shadowBlur()||this.shadowOffsetX()||this.shadowOffsetY())}_getFillPattern(){return this._getCache(rG,this.__getFillPattern)}__getFillPattern(){if(this.fillPatternImage()){var t=xC();const n=t.createPattern(this.fillPatternImage(),this.fillPatternRepeat()||"repeat");if(n&&n.setTransform){const r=new Pa;r.translate(this.fillPatternX(),this.fillPatternY()),r.rotate(gt.getAngle(this.fillPatternRotation())),r.scale(this.fillPatternScaleX(),this.fillPatternScaleY()),r.translate(-1*this.fillPatternOffsetX(),-1*this.fillPatternOffsetY());const i=r.getMatrix(),o=typeof DOMMatrix>"u"?{a:i[0],b:i[1],c:i[2],d:i[3],e:i[4],f:i[5]}:new DOMMatrix(i);n.setTransform(o)}return n}}_getLinearGradient(){return this._getCache(iG,this.__getLinearGradient)}__getLinearGradient(){var t=this.fillLinearGradientColorStops();if(t){for(var n=xC(),r=this.fillLinearGradientStartPoint(),i=this.fillLinearGradientEndPoint(),o=n.createLinearGradient(r.x,r.y,i.x,i.y),a=0;a<t.length;a+=2)o.addColorStop(t[a],t[a+1]);return o}}_getRadialGradient(){return this._getCache(oG,this.__getRadialGradient)}__getRadialGradient(){var t=this.fillRadialGradientColorStops();if(t){for(var n=xC(),r=this.fillRadialGradientStartPoint(),i=this.fillRadialGradientEndPoint(),o=n.createRadialGradient(r.x,r.y,this.fillRadialGradientStartRadius(),i.x,i.y,this.fillRadialGradientEndRadius()),a=0;a<t.length;a+=2)o.addColorStop(t[a],t[a+1]);return o}}getShadowRGBA(){return this._getCache(nG,this._getShadowRGBA)}_getShadowRGBA(){if(!!this.hasShadow()){var t=he.colorToRGBA(this.shadowColor());if(t)return"rgba("+t.r+","+t.g+","+t.b+","+t.a*(this.shadowOpacity()||1)+")"}}hasFill(){return this._calculate("hasFill",["fillEnabled","fill","fillPatternImage","fillLinearGradientColorStops","fillRadialGradientColorStops"],()=>this.fillEnabled()&&!!(this.fill()||this.fillPatternImage()||this.fillLinearGradientColorStops()||this.fillRadialGradientColorStops()))}hasStroke(){return this._calculate("hasStroke",["strokeEnabled","strokeWidth","stroke","strokeLinearGradientColorStops"],()=>this.strokeEnabled()&&this.strokeWidth()&&!!(this.stroke()||this.strokeLinearGradientColorStops()))}hasHitStroke(){const t=this.hitStrokeWidth();return t==="auto"?this.hasStroke():this.strokeEnabled()&&!!t}intersects(t){var n=this.getStage(),r=n.bufferHitCanvas,i;return r.getContext().clear(),this.drawHit(r,null,!0),i=r.context.getImageData(Math.round(t.x),Math.round(t.y),1,1).data,i[3]>0}destroy(){return qe.prototype.destroy.call(this),delete r2[this.colorKey],delete this.colorKey,this}_useBufferCanvas(t){var n;if(!this.getStage()||!((n=this.attrs.perfectDrawEnabled)!==null&&n!==void 0?n:!0))return!1;const i=t||this.hasFill(),o=this.hasStroke(),a=this.getAbsoluteOpacity()!==1;if(i&&o&&a)return!0;const s=this.hasShadow(),l=this.shadowForStrokeEnabled();return!!(i&&o&&s&&l)}setStrokeHitEnabled(t){he.warn("strokeHitEnabled property is deprecated. Please use hitStrokeWidth instead."),t?this.hitStrokeWidth("auto"):this.hitStrokeWidth(0)}getStrokeHitEnabled(){return this.hitStrokeWidth()!==0}getSelfRect(){var t=this.size();return{x:this._centroid?-t.width/2:0,y:this._centroid?-t.height/2:0,width:t.width,height:t.height}}getClientRect(t={}){const n=t.skipTransform,r=t.relativeTo,i=this.getSelfRect(),a=!t.skipStroke&&this.hasStroke()&&this.strokeWidth()||0,s=i.width+a,l=i.height+a,u=!t.skipShadow&&this.hasShadow(),f=u?this.shadowOffsetX():0,p=u?this.shadowOffsetY():0,m=s+Math.abs(f),v=l+Math.abs(p),b=u&&this.shadowBlur()||0,x=m+b*2,E=v+b*2,k={width:x,height:E,x:-(a/2+b)+Math.min(f,0)+i.x,y:-(a/2+b)+Math.min(p,0)+i.y};return n?k:this._transformedRect(k,r)}drawScene(t,n){var r=this.getLayer(),i=t||r.getCanvas(),o=i.getContext(),a=this._getCanvasCache(),s=this.getSceneFunc(),l=this.hasShadow(),u,f,p,m=i.isCache,v=n===this;if(!this.isVisible()&&!v)return this;if(a){o.save();var b=this.getAbsoluteTransform(n).getMatrix();return o.transform(b[0],b[1],b[2],b[3],b[4],b[5]),this._drawCachedSceneCanvas(o),o.restore(),this}if(!s)return this;if(o.save(),this._useBufferCanvas()&&!m){u=this.getStage(),f=u.bufferCanvas,p=f.getContext(),p.clear(),p.save(),p._applyLineJoin(this);var x=this.getAbsoluteTransform(n).getMatrix();p.transform(x[0],x[1],x[2],x[3],x[4],x[5]),s.call(this,p,this),p.restore();var E=f.pixelRatio;l&&o._applyShadow(this),o._applyOpacity(this),o._applyGlobalCompositeOperation(this),o.drawImage(f._canvas,0,0,f.width/E,f.height/E)}else{if(o._applyLineJoin(this),!v){var x=this.getAbsoluteTransform(n).getMatrix();o.transform(x[0],x[1],x[2],x[3],x[4],x[5]),o._applyOpacity(this),o._applyGlobalCompositeOperation(this)}l&&o._applyShadow(this),s.call(this,o,this)}return o.restore(),this}drawHit(t,n,r=!1){if(!this.shouldDrawHit(n,r))return this;var i=this.getLayer(),o=t||i.hitCanvas,a=o&&o.getContext(),s=this.hitFunc()||this.sceneFunc(),l=this._getCanvasCache(),u=l&&l.hit;if(this.colorKey||he.warn("Looks like your canvas has a destroyed shape in it. Do not reuse shape after you destroyed it. If you want to reuse shape you should call remove() instead of destroy()"),u){a.save();var f=this.getAbsoluteTransform(n).getMatrix();return a.transform(f[0],f[1],f[2],f[3],f[4],f[5]),this._drawCachedHitCanvas(a),a.restore(),this}if(!s)return this;if(a.save(),a._applyLineJoin(this),!(this===n)){var m=this.getAbsoluteTransform(n).getMatrix();a.transform(m[0],m[1],m[2],m[3],m[4],m[5])}return s.call(this,a,this),a.restore(),this}drawHitFromCache(t=0){var n=this._getCanvasCache(),r=this._getCachedSceneCanvas(),i=n.hit,o=i.getContext(),a=i.getWidth(),s=i.getHeight(),l,u,f,p,m,v;o.clear(),o.drawImage(r._canvas,0,0,a,s);try{for(l=o.getImageData(0,0,a,s),u=l.data,f=u.length,p=he._hexToRgb(this.colorKey),m=0;m<f;m+=4)v=u[m+3],v>t?(u[m]=p.r,u[m+1]=p.g,u[m+2]=p.b,u[m+3]=255):u[m+3]=0;o.putImageData(l,0,0)}catch(b){he.error("Unable to draw hit graph from cached scene canvas. "+b.message)}return this}hasPointerCapture(t){return VW(t,this)}setPointerCapture(t){UW(t,this)}releaseCapture(t){n2(t)}}$e.prototype._fillFunc=KCe;$e.prototype._strokeFunc=XCe;$e.prototype._fillFuncHit=ZCe;$e.prototype._strokeFuncHit=QCe;$e.prototype._centroid=!1;$e.prototype.nodeType="Shape";Er($e);$e.prototype.eventListeners={};$e.prototype.on.call($e.prototype,"shadowColorChange.konva shadowBlurChange.konva shadowOffsetChange.konva shadowOpacityChange.konva shadowEnabledChange.konva",JCe);$e.prototype.on.call($e.prototype,"shadowColorChange.konva shadowOpacityChange.konva shadowEnabledChange.konva",e7e);$e.prototype.on.call($e.prototype,"fillPriorityChange.konva fillPatternImageChange.konva fillPatternRepeatChange.konva fillPatternScaleXChange.konva fillPatternScaleYChange.konva fillPatternOffsetXChange.konva fillPatternOffsetYChange.konva fillPatternXChange.konva fillPatternYChange.konva fillPatternRotationChange.konva",t7e);$e.prototype.on.call($e.prototype,"fillPriorityChange.konva fillLinearGradientColorStopsChange.konva fillLinearGradientStartPointXChange.konva fillLinearGradientStartPointYChange.konva fillLinearGradientEndPointXChange.konva fillLinearGradientEndPointYChange.konva",n7e);$e.prototype.on.call($e.prototype,"fillPriorityChange.konva fillRadialGradientColorStopsChange.konva fillRadialGradientStartPointXChange.konva fillRadialGradientStartPointYChange.konva fillRadialGradientEndPointXChange.konva fillRadialGradientEndPointYChange.konva fillRadialGradientStartRadiusChange.konva fillRadialGradientEndRadiusChange.konva",r7e);ee.addGetterSetter($e,"stroke",void 0,jW());ee.addGetterSetter($e,"strokeWidth",2,Ge());ee.addGetterSetter($e,"fillAfterStrokeEnabled",!1);ee.addGetterSetter($e,"hitStrokeWidth","auto",tP());ee.addGetterSetter($e,"strokeHitEnabled",!0,ol());ee.addGetterSetter($e,"perfectDrawEnabled",!0,ol());ee.addGetterSetter($e,"shadowForStrokeEnabled",!0,ol());ee.addGetterSetter($e,"lineJoin");ee.addGetterSetter($e,"lineCap");ee.addGetterSetter($e,"sceneFunc");ee.addGetterSetter($e,"hitFunc");ee.addGetterSetter($e,"dash");ee.addGetterSetter($e,"dashOffset",0,Ge());ee.addGetterSetter($e,"shadowColor",void 0,k1());ee.addGetterSetter($e,"shadowBlur",0,Ge());ee.addGetterSetter($e,"shadowOpacity",1,Ge());ee.addComponentsGetterSetter($e,"shadowOffset",["x","y"]);ee.addGetterSetter($e,"shadowOffsetX",0,Ge());ee.addGetterSetter($e,"shadowOffsetY",0,Ge());ee.addGetterSetter($e,"fillPatternImage");ee.addGetterSetter($e,"fill",void 0,jW());ee.addGetterSetter($e,"fillPatternX",0,Ge());ee.addGetterSetter($e,"fillPatternY",0,Ge());ee.addGetterSetter($e,"fillLinearGradientColorStops");ee.addGetterSetter($e,"strokeLinearGradientColorStops");ee.addGetterSetter($e,"fillRadialGradientStartRadius",0);ee.addGetterSetter($e,"fillRadialGradientEndRadius",0);ee.addGetterSetter($e,"fillRadialGradientColorStops");ee.addGetterSetter($e,"fillPatternRepeat","repeat");ee.addGetterSetter($e,"fillEnabled",!0);ee.addGetterSetter($e,"strokeEnabled",!0);ee.addGetterSetter($e,"shadowEnabled",!0);ee.addGetterSetter($e,"dashEnabled",!0);ee.addGetterSetter($e,"strokeScaleEnabled",!0);ee.addGetterSetter($e,"fillPriority","color");ee.addComponentsGetterSetter($e,"fillPatternOffset",["x","y"]);ee.addGetterSetter($e,"fillPatternOffsetX",0,Ge());ee.addGetterSetter($e,"fillPatternOffsetY",0,Ge());ee.addComponentsGetterSetter($e,"fillPatternScale",["x","y"]);ee.addGetterSetter($e,"fillPatternScaleX",1,Ge());ee.addGetterSetter($e,"fillPatternScaleY",1,Ge());ee.addComponentsGetterSetter($e,"fillLinearGradientStartPoint",["x","y"]);ee.addComponentsGetterSetter($e,"strokeLinearGradientStartPoint",["x","y"]);ee.addGetterSetter($e,"fillLinearGradientStartPointX",0);ee.addGetterSetter($e,"strokeLinearGradientStartPointX",0);ee.addGetterSetter($e,"fillLinearGradientStartPointY",0);ee.addGetterSetter($e,"strokeLinearGradientStartPointY",0);ee.addComponentsGetterSetter($e,"fillLinearGradientEndPoint",["x","y"]);ee.addComponentsGetterSetter($e,"strokeLinearGradientEndPoint",["x","y"]);ee.addGetterSetter($e,"fillLinearGradientEndPointX",0);ee.addGetterSetter($e,"strokeLinearGradientEndPointX",0);ee.addGetterSetter($e,"fillLinearGradientEndPointY",0);ee.addGetterSetter($e,"strokeLinearGradientEndPointY",0);ee.addComponentsGetterSetter($e,"fillRadialGradientStartPoint",["x","y"]);ee.addGetterSetter($e,"fillRadialGradientStartPointX",0);ee.addGetterSetter($e,"fillRadialGradientStartPointY",0);ee.addComponentsGetterSetter($e,"fillRadialGradientEndPoint",["x","y"]);ee.addGetterSetter($e,"fillRadialGradientEndPointX",0);ee.addGetterSetter($e,"fillRadialGradientEndPointY",0);ee.addGetterSetter($e,"fillPatternRotation",0);ee.backCompat($e,{dashArray:"dash",getDashArray:"getDash",setDashArray:"getDash",drawFunc:"sceneFunc",getDrawFunc:"getSceneFunc",setDrawFunc:"setSceneFunc",drawHitFunc:"hitFunc",getDrawHitFunc:"getHitFunc",setDrawHitFunc:"setHitFunc"});var i7e="#",o7e="beforeDraw",a7e="draw",aG=[{x:0,y:0},{x:-1,y:-1},{x:1,y:-1},{x:1,y:1},{x:-1,y:1}],s7e=aG.length;class hp extends Ma{constructor(t){super(t),this.canvas=new Fg,this.hitCanvas=new nP({pixelRatio:1}),this._waitingForDraw=!1,this.on("visibleChange.konva",this._checkVisibility),this._checkVisibility(),this.on("imageSmoothingEnabledChange.konva",this._setSmoothEnabled),this._setSmoothEnabled()}createPNGStream(){return this.canvas._canvas.createPNGStream()}getCanvas(){return this.canvas}getNativeCanvasElement(){return this.canvas._canvas}getHitCanvas(){return this.hitCanvas}getContext(){return this.getCanvas().getContext()}clear(t){return this.getContext().clear(t),this.getHitCanvas().getContext().clear(t),this}setZIndex(t){super.setZIndex(t);var n=this.getStage();return n&&n.content&&(n.content.removeChild(this.getNativeCanvasElement()),t<n.children.length-1?n.content.insertBefore(this.getNativeCanvasElement(),n.children[t+1].getCanvas()._canvas):n.content.appendChild(this.getNativeCanvasElement())),this}moveToTop(){qe.prototype.moveToTop.call(this);var t=this.getStage();return t&&t.content&&(t.content.removeChild(this.getNativeCanvasElement()),t.content.appendChild(this.getNativeCanvasElement())),!0}moveUp(){var t=qe.prototype.moveUp.call(this);if(!t)return!1;var n=this.getStage();return!n||!n.content?!1:(n.content.removeChild(this.getNativeCanvasElement()),this.index<n.children.length-1?n.content.insertBefore(this.getNativeCanvasElement(),n.children[this.index+1].getCanvas()._canvas):n.content.appendChild(this.getNativeCanvasElement()),!0)}moveDown(){if(qe.prototype.moveDown.call(this)){var t=this.getStage();if(t){var n=t.children;t.content&&(t.content.removeChild(this.getNativeCanvasElement()),t.content.insertBefore(this.getNativeCanvasElement(),n[this.index+1].getCanvas()._canvas))}return!0}return!1}moveToBottom(){if(qe.prototype.moveToBottom.call(this)){var t=this.getStage();if(t){var n=t.children;t.content&&(t.content.removeChild(this.getNativeCanvasElement()),t.content.insertBefore(this.getNativeCanvasElement(),n[1].getCanvas()._canvas))}return!0}return!1}getLayer(){return this}remove(){var t=this.getNativeCanvasElement();return qe.prototype.remove.call(this),t&&t.parentNode&&he._isInDocument(t)&&t.parentNode.removeChild(t),this}getStage(){return this.parent}setSize({width:t,height:n}){return this.canvas.setSize(t,n),this.hitCanvas.setSize(t,n),this._setSmoothEnabled(),this}_validateAdd(t){var n=t.getType();n!=="Group"&&n!=="Shape"&&he.throw("You may only add groups and shapes to a layer.")}_toKonvaCanvas(t){return t=t||{},t.width=t.width||this.getWidth(),t.height=t.height||this.getHeight(),t.x=t.x!==void 0?t.x:this.x(),t.y=t.y!==void 0?t.y:this.y(),qe.prototype._toKonvaCanvas.call(this,t)}_checkVisibility(){this.visible()?this.canvas._canvas.style.display="block":this.canvas._canvas.style.display="none"}_setSmoothEnabled(){this.getContext()._context.imageSmoothingEnabled=this.imageSmoothingEnabled()}getWidth(){if(this.parent)return this.parent.width()}setWidth(){he.warn('Can not change width of layer. Use "stage.width(value)" function instead.')}getHeight(){if(this.parent)return this.parent.height()}setHeight(){he.warn('Can not change height of layer. Use "stage.height(value)" function instead.')}batchDraw(){return this._waitingForDraw||(this._waitingForDraw=!0,he.requestAnimFrame(()=>{this.draw(),this._waitingForDraw=!1})),this}getIntersection(t){if(!this.isListening()||!this.isVisible())return null;for(var n=1,r=!1;;){for(let i=0;i<s7e;i++){const o=aG[i],a=this._getIntersection({x:t.x+o.x*n,y:t.y+o.y*n}),s=a.shape;if(s)return s;if(r=!!a.antialiased,!a.antialiased)break}if(r)n+=1;else return null}}_getIntersection(t){const n=this.hitCanvas.pixelRatio,r=this.hitCanvas.context.getImageData(Math.round(t.x*n),Math.round(t.y*n),1,1).data,i=r[3];if(i===255){const o=he._rgbToHex(r[0],r[1],r[2]),a=r2[i7e+o];return a?{shape:a}:{antialiased:!0}}else if(i>0)return{antialiased:!0};return{}}drawScene(t,n){var r=this.getLayer(),i=t||r&&r.getCanvas();return this._fire(o7e,{node:this}),this.clearBeforeDraw()&&i.getContext().clear(),Ma.prototype.drawScene.call(this,i,n),this._fire(a7e,{node:this}),this}drawHit(t,n){var r=this.getLayer(),i=t||r&&r.hitCanvas;return r&&r.clearBeforeDraw()&&r.getHitCanvas().getContext().clear(),Ma.prototype.drawHit.call(this,i,n),this}enableHitGraph(){return this.hitGraphEnabled(!0),this}disableHitGraph(){return this.hitGraphEnabled(!1),this}setHitGraphEnabled(t){he.warn("hitGraphEnabled method is deprecated. Please use layer.listening() instead."),this.listening(t)}getHitGraphEnabled(t){return he.warn("hitGraphEnabled method is deprecated. Please use layer.listening() instead."),this.listening()}toggleHitCanvas(){if(!(!this.parent||!this.parent.content)){var t=this.parent,n=!!this.hitCanvas._canvas.parentNode;n?t.content.removeChild(this.hitCanvas._canvas):t.content.appendChild(this.hitCanvas._canvas)}}destroy(){return he.releaseCanvas(this.getNativeCanvasElement(),this.getHitCanvas()._canvas),super.destroy()}}hp.prototype.nodeType="Layer";Er(hp);ee.addGetterSetter(hp,"imageSmoothingEnabled",!0);ee.addGetterSetter(hp,"clearBeforeDraw",!0);ee.addGetterSetter(hp,"hitGraphEnabled",!0,ol());class iP extends hp{constructor(t){super(t),this.listening(!1),he.warn('Konva.Fast layer is deprecated. Please use "new Konva.Layer({ listening: false })" instead.')}}iP.prototype.nodeType="FastLayer";Er(iP);class n1 extends Ma{_validateAdd(t){var n=t.getType();n!=="Group"&&n!=="Shape"&&he.throw("You may only add groups and shapes to groups.")}}n1.prototype.nodeType="Group";Er(n1);var wC=function(){return $g.performance&&$g.performance.now?function(){return $g.performance.now()}:function(){return new Date().getTime()}}();class is{constructor(t,n){this.id=is.animIdCounter++,this.frame={time:0,timeDiff:0,lastTime:wC(),frameRate:0},this.func=t,this.setLayers(n)}setLayers(t){var n=[];return t?t.length>0?n=t:n=[t]:n=[],this.layers=n,this}getLayers(){return this.layers}addLayer(t){var n=this.layers,r=n.length,i;for(i=0;i<r;i++)if(n[i]._id===t._id)return!1;return this.layers.push(t),!0}isRunning(){var t=is,n=t.animations,r=n.length,i;for(i=0;i<r;i++)if(n[i].id===this.id)return!0;return!1}start(){return this.stop(),this.frame.timeDiff=0,this.frame.lastTime=wC(),is._addAnimation(this),this}stop(){return is._removeAnimation(this),this}_updateFrameObject(t){this.frame.timeDiff=t-this.frame.lastTime,this.frame.lastTime=t,this.frame.time+=this.frame.timeDiff,this.frame.frameRate=1e3/this.frame.timeDiff}static _addAnimation(t){this.animations.push(t),this._handleAnimation()}static _removeAnimation(t){var n=t.id,r=this.animations,i=r.length,o;for(o=0;o<i;o++)if(r[o].id===n){this.animations.splice(o,1);break}}static _runFrames(){var t={},n=this.animations,r,i,o,a,s,l,u,f,p;for(a=0;a<n.length;a++)if(r=n[a],i=r.layers,o=r.func,r._updateFrameObject(wC()),l=i.length,o?p=o.call(r,r.frame)!==!1:p=!0,!!p)for(s=0;s<l;s++)u=i[s],u._id!==void 0&&(t[u._id]=u);for(f in t)!t.hasOwnProperty(f)||t[f].batchDraw()}static _animationLoop(){var t=is;t.animations.length?(t._runFrames(),he.requestAnimFrame(t._animationLoop)):t.animRunning=!1}static _handleAnimation(){this.animRunning||(this.animRunning=!0,he.requestAnimFrame(this._animationLoop))}}is.animations=[];is.animIdCounter=0;is.animRunning=!1;var l7e={node:1,duration:1,easing:1,onFinish:1,yoyo:1},u7e=1,$I=2,FI=3,c7e=0,zI=["fill","stroke","shadowColor"];class d7e{constructor(t,n,r,i,o,a,s){this.prop=t,this.propFunc=n,this.begin=i,this._pos=i,this.duration=a,this._change=0,this.prevPos=0,this.yoyo=s,this._time=0,this._position=0,this._startTime=0,this._finish=0,this.func=r,this._change=o-this.begin,this.pause()}fire(t){var n=this[t];n&&n()}setTime(t){t>this.duration?this.yoyo?(this._time=this.duration,this.reverse()):this.finish():t<0?this.yoyo?(this._time=0,this.play()):this.reset():(this._time=t,this.update())}getTime(){return this._time}setPosition(t){this.prevPos=this._pos,this.propFunc(t),this._pos=t}getPosition(t){return t===void 0&&(t=this._time),this.func(t,this.begin,this._change,this.duration)}play(){this.state=$I,this._startTime=this.getTimer()-this._time,this.onEnterFrame(),this.fire("onPlay")}reverse(){this.state=FI,this._time=this.duration-this._time,this._startTime=this.getTimer()-this._time,this.onEnterFrame(),this.fire("onReverse")}seek(t){this.pause(),this._time=t,this.update(),this.fire("onSeek")}reset(){this.pause(),this._time=0,this.update(),this.fire("onReset")}finish(){this.pause(),this._time=this.duration,this.update(),this.fire("onFinish")}update(){this.setPosition(this.getPosition(this._time)),this.fire("onUpdate")}onEnterFrame(){var t=this.getTimer()-this._startTime;this.state===$I?this.setTime(t):this.state===FI&&this.setTime(this.duration-t)}pause(){this.state=u7e,this.fire("onPause")}getTimer(){return new Date().getTime()}}class Yr{constructor(t){var n=this,r=t.node,i=r._id,o,a=t.easing||i2.Linear,s=!!t.yoyo,l;typeof t.duration>"u"?o=.3:t.duration===0?o=.001:o=t.duration,this.node=r,this._id=c7e++;var u=r.getLayer()||(r instanceof gt.Stage?r.getLayers():null);u||he.error("Tween constructor have `node` that is not in a layer. Please add node into layer first."),this.anim=new is(function(){n.tween.onEnterFrame()},u),this.tween=new d7e(l,function(f){n._tweenFunc(f)},a,0,1,o*1e3,s),this._addListeners(),Yr.attrs[i]||(Yr.attrs[i]={}),Yr.attrs[i][this._id]||(Yr.attrs[i][this._id]={}),Yr.tweens[i]||(Yr.tweens[i]={});for(l in t)l7e[l]===void 0&&this._addAttr(l,t[l]);this.reset(),this.onFinish=t.onFinish,this.onReset=t.onReset,this.onUpdate=t.onUpdate}_addAttr(t,n){var r=this.node,i=r._id,o,a,s,l,u,f,p,m;if(s=Yr.tweens[i][t],s&&delete Yr.attrs[i][s][t],o=r.getAttr(t),he._isArray(n))if(a=[],u=Math.max(n.length,o.length),t==="points"&&n.length!==o.length&&(n.length>o.length?(p=o,o=he._prepareArrayForTween(o,n,r.closed())):(f=n,n=he._prepareArrayForTween(n,o,r.closed()))),t.indexOf("fill")===0)for(l=0;l<u;l++)if(l%2===0)a.push(n[l]-o[l]);else{var v=he.colorToRGBA(o[l]);m=he.colorToRGBA(n[l]),o[l]=v,a.push({r:m.r-v.r,g:m.g-v.g,b:m.b-v.b,a:m.a-v.a})}else for(l=0;l<u;l++)a.push(n[l]-o[l]);else zI.indexOf(t)!==-1?(o=he.colorToRGBA(o),m=he.colorToRGBA(n),a={r:m.r-o.r,g:m.g-o.g,b:m.b-o.b,a:m.a-o.a}):a=n-o;Yr.attrs[i][this._id][t]={start:o,diff:a,end:n,trueEnd:f,trueStart:p},Yr.tweens[i][t]=this._id}_tweenFunc(t){var n=this.node,r=Yr.attrs[n._id][this._id],i,o,a,s,l,u,f,p;for(i in r){if(o=r[i],a=o.start,s=o.diff,p=o.end,he._isArray(a))if(l=[],f=Math.max(a.length,p.length),i.indexOf("fill")===0)for(u=0;u<f;u++)u%2===0?l.push((a[u]||0)+s[u]*t):l.push("rgba("+Math.round(a[u].r+s[u].r*t)+","+Math.round(a[u].g+s[u].g*t)+","+Math.round(a[u].b+s[u].b*t)+","+(a[u].a+s[u].a*t)+")");else for(u=0;u<f;u++)l.push((a[u]||0)+s[u]*t);else zI.indexOf(i)!==-1?l="rgba("+Math.round(a.r+s.r*t)+","+Math.round(a.g+s.g*t)+","+Math.round(a.b+s.b*t)+","+(a.a+s.a*t)+")":l=a+s*t;n.setAttr(i,l)}}_addListeners(){this.tween.onPlay=()=>{this.anim.start()},this.tween.onReverse=()=>{this.anim.start()},this.tween.onPause=()=>{this.anim.stop()},this.tween.onFinish=()=>{var t=this.node,n=Yr.attrs[t._id][this._id];n.points&&n.points.trueEnd&&t.setAttr("points",n.points.trueEnd),this.onFinish&&this.onFinish.call(this)},this.tween.onReset=()=>{var t=this.node,n=Yr.attrs[t._id][this._id];n.points&&n.points.trueStart&&t.points(n.points.trueStart),this.onReset&&this.onReset()},this.tween.onUpdate=()=>{this.onUpdate&&this.onUpdate.call(this)}}play(){return this.tween.play(),this}reverse(){return this.tween.reverse(),this}reset(){return this.tween.reset(),this}seek(t){return this.tween.seek(t*1e3),this}pause(){return this.tween.pause(),this}finish(){return this.tween.finish(),this}destroy(){var t=this.node._id,n=this._id,r=Yr.tweens[t],i;this.pause();for(i in r)delete Yr.tweens[t][i];delete Yr.attrs[t][n]}}Yr.attrs={};Yr.tweens={};qe.prototype.to=function(e){var t=e.onFinish;e.node=this,e.onFinish=function(){this.destroy(),t&&t()};var n=new Yr(e);n.play()};const i2={BackEaseIn(e,t,n,r){var i=1.70158;return n*(e/=r)*e*((i+1)*e-i)+t},BackEaseOut(e,t,n,r){var i=1.70158;return n*((e=e/r-1)*e*((i+1)*e+i)+1)+t},BackEaseInOut(e,t,n,r){var i=1.70158;return(e/=r/2)<1?n/2*(e*e*(((i*=1.525)+1)*e-i))+t:n/2*((e-=2)*e*(((i*=1.525)+1)*e+i)+2)+t},ElasticEaseIn(e,t,n,r,i,o){var a=0;return e===0?t:(e/=r)===1?t+n:(o||(o=r*.3),!i||i<Math.abs(n)?(i=n,a=o/4):a=o/(2*Math.PI)*Math.asin(n/i),-(i*Math.pow(2,10*(e-=1))*Math.sin((e*r-a)*(2*Math.PI)/o))+t)},ElasticEaseOut(e,t,n,r,i,o){var a=0;return e===0?t:(e/=r)===1?t+n:(o||(o=r*.3),!i||i<Math.abs(n)?(i=n,a=o/4):a=o/(2*Math.PI)*Math.asin(n/i),i*Math.pow(2,-10*e)*Math.sin((e*r-a)*(2*Math.PI)/o)+n+t)},ElasticEaseInOut(e,t,n,r,i,o){var a=0;return e===0?t:(e/=r/2)===2?t+n:(o||(o=r*(.3*1.5)),!i||i<Math.abs(n)?(i=n,a=o/4):a=o/(2*Math.PI)*Math.asin(n/i),e<1?-.5*(i*Math.pow(2,10*(e-=1))*Math.sin((e*r-a)*(2*Math.PI)/o))+t:i*Math.pow(2,-10*(e-=1))*Math.sin((e*r-a)*(2*Math.PI)/o)*.5+n+t)},BounceEaseOut(e,t,n,r){return(e/=r)<1/2.75?n*(7.5625*e*e)+t:e<2/2.75?n*(7.5625*(e-=1.5/2.75)*e+.75)+t:e<2.5/2.75?n*(7.5625*(e-=2.25/2.75)*e+.9375)+t:n*(7.5625*(e-=2.625/2.75)*e+.984375)+t},BounceEaseIn(e,t,n,r){return n-i2.BounceEaseOut(r-e,0,n,r)+t},BounceEaseInOut(e,t,n,r){return e<r/2?i2.BounceEaseIn(e*2,0,n,r)*.5+t:i2.BounceEaseOut(e*2-r,0,n,r)*.5+n*.5+t},EaseIn(e,t,n,r){return n*(e/=r)*e+t},EaseOut(e,t,n,r){return-n*(e/=r)*(e-2)+t},EaseInOut(e,t,n,r){return(e/=r/2)<1?n/2*e*e+t:-n/2*(--e*(e-2)-1)+t},StrongEaseIn(e,t,n,r){return n*(e/=r)*e*e*e*e+t},StrongEaseOut(e,t,n,r){return n*((e=e/r-1)*e*e*e*e+1)+t},StrongEaseInOut(e,t,n,r){return(e/=r/2)<1?n/2*e*e*e*e*e+t:n/2*((e-=2)*e*e*e*e+2)+t},Linear(e,t,n,r){return n*e/r+t}},rp=he._assign(gt,{Util:he,Transform:Pa,Node:qe,Container:Ma,Stage:kx,stages:A4,Layer:hp,FastLayer:iP,Group:n1,DD:yn,Shape:$e,shapes:r2,Animation:is,Tween:Yr,Easings:i2,Context:Cx,Canvas:_x});class gc extends $e{_sceneFunc(t){var n=gt.getAngle(this.angle()),r=this.clockwise();t.beginPath(),t.arc(0,0,this.outerRadius(),0,n,r),t.arc(0,0,this.innerRadius(),n,0,!r),t.closePath(),t.fillStrokeShape(this)}getWidth(){return this.outerRadius()*2}getHeight(){return this.outerRadius()*2}setWidth(t){this.outerRadius(t/2)}setHeight(t){this.outerRadius(t/2)}getSelfRect(){const t=this.innerRadius(),n=this.outerRadius(),r=this.clockwise(),i=gt.getAngle(r?360-this.angle():this.angle()),o=Math.cos(Math.min(i,Math.PI)),a=1,s=Math.sin(Math.min(Math.max(Math.PI,i),3*Math.PI/2)),l=Math.sin(Math.min(i,Math.PI/2)),u=o*(o>0?t:n),f=a*n,p=s*(s>0?t:n),m=l*(l>0?n:t);return{x:u,y:r?-1*m:p,width:f-u,height:m-p}}}gc.prototype._centroid=!0;gc.prototype.className="Arc";gc.prototype._attrsAffectingSize=["innerRadius","outerRadius"];Er(gc);ee.addGetterSetter(gc,"innerRadius",0,Ge());ee.addGetterSetter(gc,"outerRadius",0,Ge());ee.addGetterSetter(gc,"angle",0,Ge());ee.addGetterSetter(gc,"clockwise",!1,ol());function y8(e,t,n,r,i,o,a){var s=Math.sqrt(Math.pow(n-e,2)+Math.pow(r-t,2)),l=Math.sqrt(Math.pow(i-n,2)+Math.pow(o-r,2)),u=a*s/(s+l),f=a*l/(s+l),p=n-u*(i-e),m=r-u*(o-t),v=n+f*(i-e),b=r+f*(o-t);return[p,m,v,b]}function jI(e,t){var n=e.length,r=[],i,o;for(i=2;i<n-2;i+=2)o=y8(e[i-2],e[i-1],e[i],e[i+1],e[i+2],e[i+3],t),!isNaN(o[0])&&(r.push(o[0]),r.push(o[1]),r.push(e[i]),r.push(e[i+1]),r.push(o[2]),r.push(o[3]));return r}class mc extends $e{constructor(t){super(t),this.on("pointsChange.konva tensionChange.konva closedChange.konva bezierChange.konva",function(){this._clearCache("tensionPoints")})}_sceneFunc(t){var n=this.points(),r=n.length,i=this.tension(),o=this.closed(),a=this.bezier(),s,l,u;if(!!r){if(t.beginPath(),t.moveTo(n[0],n[1]),i!==0&&r>4){for(s=this.getTensionPoints(),l=s.length,u=o?0:4,o||t.quadraticCurveTo(s[0],s[1],s[2],s[3]);u<l-2;)t.bezierCurveTo(s[u++],s[u++],s[u++],s[u++],s[u++],s[u++]);o||t.quadraticCurveTo(s[l-2],s[l-1],n[r-2],n[r-1])}else if(a)for(u=2;u<r;)t.bezierCurveTo(n[u++],n[u++],n[u++],n[u++],n[u++],n[u++]);else for(u=2;u<r;u+=2)t.lineTo(n[u],n[u+1]);o?(t.closePath(),t.fillStrokeShape(this)):t.strokeShape(this)}}getTensionPoints(){return this._getCache("tensionPoints",this._getTensionPoints)}_getTensionPoints(){return this.closed()?this._getTensionPointsClosed():jI(this.points(),this.tension())}_getTensionPointsClosed(){var t=this.points(),n=t.length,r=this.tension(),i=y8(t[n-2],t[n-1],t[0],t[1],t[2],t[3],r),o=y8(t[n-4],t[n-3],t[n-2],t[n-1],t[0],t[1],r),a=jI(t,r),s=[i[2],i[3]].concat(a).concat([o[0],o[1],t[n-2],t[n-1],o[2],o[3],i[0],i[1],t[0],t[1]]);return s}getWidth(){return this.getSelfRect().width}getHeight(){return this.getSelfRect().height}getSelfRect(){var t=this.points();if(t.length<4)return{x:t[0]||0,y:t[1]||0,width:0,height:0};this.tension()!==0?t=[t[0],t[1],...this._getTensionPoints(),t[t.length-2],t[t.length-1]]:t=this.points();for(var n=t[0],r=t[0],i=t[1],o=t[1],a,s,l=0;l<t.length/2;l++)a=t[l*2],s=t[l*2+1],n=Math.min(n,a),r=Math.max(r,a),i=Math.min(i,s),o=Math.max(o,s);return{x:n,y:i,width:r-n,height:o-i}}}mc.prototype.className="Line";mc.prototype._attrsAffectingSize=["points","bezier","tension"];Er(mc);ee.addGetterSetter(mc,"closed",!1);ee.addGetterSetter(mc,"bezier",!1);ee.addGetterSetter(mc,"tension",0,Ge());ee.addGetterSetter(mc,"points",[],xCe());class zn extends $e{constructor(t){super(t),this.dataArray=[],this.pathLength=0,this.dataArray=zn.parsePathData(this.data()),this.pathLength=0;for(var n=0;n<this.dataArray.length;++n)this.pathLength+=this.dataArray[n].pathLength;this.on("dataChange.konva",function(){this.dataArray=zn.parsePathData(this.data()),this.pathLength=0;for(var r=0;r<this.dataArray.length;++r)this.pathLength+=this.dataArray[r].pathLength})}_sceneFunc(t){var n=this.dataArray;t.beginPath();for(var r=!1,i=0;i<n.length;i++){var o=n[i].command,a=n[i].points;switch(o){case"L":t.lineTo(a[0],a[1]);break;case"M":t.moveTo(a[0],a[1]);break;case"C":t.bezierCurveTo(a[0],a[1],a[2],a[3],a[4],a[5]);break;case"Q":t.quadraticCurveTo(a[0],a[1],a[2],a[3]);break;case"A":var s=a[0],l=a[1],u=a[2],f=a[3],p=a[4],m=a[5],v=a[6],b=a[7],x=u>f?u:f,E=u>f?1:u/f,k=u>f?f/u:1;t.translate(s,l),t.rotate(v),t.scale(E,k),t.arc(0,0,x,p,p+m,1-b),t.scale(1/E,1/k),t.rotate(-v),t.translate(-s,-l);break;case"z":r=!0,t.closePath();break}}!r&&!this.hasFill()?t.strokeShape(this):t.fillStrokeShape(this)}getSelfRect(){var t=[];this.dataArray.forEach(function(u){if(u.command==="A"){var f=u.points[4],p=u.points[5],m=u.points[4]+p,v=Math.PI/180;if(Math.abs(f-m)<v&&(v=Math.abs(f-m)),p<0)for(let b=f-v;b>m;b-=v){const x=zn.getPointOnEllipticalArc(u.points[0],u.points[1],u.points[2],u.points[3],b,0);t.push(x.x,x.y)}else for(let b=f+v;b<m;b+=v){const x=zn.getPointOnEllipticalArc(u.points[0],u.points[1],u.points[2],u.points[3],b,0);t.push(x.x,x.y)}}else if(u.command==="C")for(let b=0;b<=1;b+=.01){const x=zn.getPointOnCubicBezier(b,u.start.x,u.start.y,u.points[0],u.points[1],u.points[2],u.points[3],u.points[4],u.points[5]);t.push(x.x,x.y)}else t=t.concat(u.points)});for(var n=t[0],r=t[0],i=t[1],o=t[1],a,s,l=0;l<t.length/2;l++)a=t[l*2],s=t[l*2+1],isNaN(a)||(n=Math.min(n,a),r=Math.max(r,a)),isNaN(s)||(i=Math.min(i,s),o=Math.max(o,s));return{x:n,y:i,width:r-n,height:o-i}}getLength(){return this.pathLength}getPointAtLength(t){var n,r=0,i=this.dataArray.length;if(!i)return null;for(;r<i&&t>this.dataArray[r].pathLength;)t-=this.dataArray[r].pathLength,++r;if(r===i)return n=this.dataArray[r-1].points.slice(-2),{x:n[0],y:n[1]};if(t<.01)return n=this.dataArray[r].points.slice(0,2),{x:n[0],y:n[1]};var o=this.dataArray[r],a=o.points;switch(o.command){case"L":return zn.getPointOnLine(t,o.start.x,o.start.y,a[0],a[1]);case"C":return zn.getPointOnCubicBezier(t/o.pathLength,o.start.x,o.start.y,a[0],a[1],a[2],a[3],a[4],a[5]);case"Q":return zn.getPointOnQuadraticBezier(t/o.pathLength,o.start.x,o.start.y,a[0],a[1],a[2],a[3]);case"A":var s=a[0],l=a[1],u=a[2],f=a[3],p=a[4],m=a[5],v=a[6];return p+=m*t/o.pathLength,zn.getPointOnEllipticalArc(s,l,u,f,p,v)}return null}static getLineLength(t,n,r,i){return Math.sqrt((r-t)*(r-t)+(i-n)*(i-n))}static getPointOnLine(t,n,r,i,o,a,s){a===void 0&&(a=n),s===void 0&&(s=r);var l=(o-r)/(i-n+1e-8),u=Math.sqrt(t*t/(1+l*l));i<n&&(u*=-1);var f=l*u,p;if(i===n)p={x:a,y:s+f};else if((s-r)/(a-n+1e-8)===l)p={x:a+u,y:s+f};else{var m,v,b=this.getLineLength(n,r,i,o),x=(a-n)*(i-n)+(s-r)*(o-r);x=x/(b*b),m=n+x*(i-n),v=r+x*(o-r);var E=this.getLineLength(a,s,m,v),k=Math.sqrt(t*t-E*E);u=Math.sqrt(k*k/(1+l*l)),i<n&&(u*=-1),f=l*u,p={x:m+u,y:v+f}}return p}static getPointOnCubicBezier(t,n,r,i,o,a,s,l,u){function f(E){return E*E*E}function p(E){return 3*E*E*(1-E)}function m(E){return 3*E*(1-E)*(1-E)}function v(E){return(1-E)*(1-E)*(1-E)}var b=l*f(t)+a*p(t)+i*m(t)+n*v(t),x=u*f(t)+s*p(t)+o*m(t)+r*v(t);return{x:b,y:x}}static getPointOnQuadraticBezier(t,n,r,i,o,a,s){function l(v){return v*v}function u(v){return 2*v*(1-v)}function f(v){return(1-v)*(1-v)}var p=a*l(t)+i*u(t)+n*f(t),m=s*l(t)+o*u(t)+r*f(t);return{x:p,y:m}}static getPointOnEllipticalArc(t,n,r,i,o,a){var s=Math.cos(a),l=Math.sin(a),u={x:r*Math.cos(o),y:i*Math.sin(o)};return{x:t+(u.x*s-u.y*l),y:n+(u.x*l+u.y*s)}}static parsePathData(t){if(!t)return[];var n=t,r=["m","M","l","L","v","V","h","H","z","Z","c","C","q","Q","t","T","s","S","a","A"];n=n.replace(new RegExp(" ","g"),",");for(var i=0;i<r.length;i++)n=n.replace(new RegExp(r[i],"g"),"|"+r[i]);var o=n.split("|"),a=[],s=[],l=0,u=0,f=/([-+]?((\d+\.\d+)|((\d+)|(\.\d+)))(?:e[-+]?\d+)?)/gi,p;for(i=1;i<o.length;i++){var m=o[i],v=m.charAt(0);for(m=m.slice(1),s.length=0;p=f.exec(m);)s.push(p[0]);for(var b=[],x=0,E=s.length;x<E;x++){if(s[x]==="00"){b.push(0,0);continue}var k=parseFloat(s[x]);isNaN(k)?b.push(0):b.push(k)}for(;b.length>0&&!isNaN(b[0]);){var _=null,T=[],A=l,R=u,I,D,B,z,j,K,te,G,$,V;switch(v){case"l":l+=b.shift(),u+=b.shift(),_="L",T.push(l,u);break;case"L":l=b.shift(),u=b.shift(),T.push(l,u);break;case"m":var J=b.shift(),Z=b.shift();if(l+=J,u+=Z,_="M",a.length>2&&a[a.length-1].command==="z"){for(var U=a.length-2;U>=0;U--)if(a[U].command==="M"){l=a[U].points[0]+J,u=a[U].points[1]+Z;break}}T.push(l,u),v="l";break;case"M":l=b.shift(),u=b.shift(),_="M",T.push(l,u),v="L";break;case"h":l+=b.shift(),_="L",T.push(l,u);break;case"H":l=b.shift(),_="L",T.push(l,u);break;case"v":u+=b.shift(),_="L",T.push(l,u);break;case"V":u=b.shift(),_="L",T.push(l,u);break;case"C":T.push(b.shift(),b.shift(),b.shift(),b.shift()),l=b.shift(),u=b.shift(),T.push(l,u);break;case"c":T.push(l+b.shift(),u+b.shift(),l+b.shift(),u+b.shift()),l+=b.shift(),u+=b.shift(),_="C",T.push(l,u);break;case"S":D=l,B=u,I=a[a.length-1],I.command==="C"&&(D=l+(l-I.points[2]),B=u+(u-I.points[3])),T.push(D,B,b.shift(),b.shift()),l=b.shift(),u=b.shift(),_="C",T.push(l,u);break;case"s":D=l,B=u,I=a[a.length-1],I.command==="C"&&(D=l+(l-I.points[2]),B=u+(u-I.points[3])),T.push(D,B,l+b.shift(),u+b.shift()),l+=b.shift(),u+=b.shift(),_="C",T.push(l,u);break;case"Q":T.push(b.shift(),b.shift()),l=b.shift(),u=b.shift(),T.push(l,u);break;case"q":T.push(l+b.shift(),u+b.shift()),l+=b.shift(),u+=b.shift(),_="Q",T.push(l,u);break;case"T":D=l,B=u,I=a[a.length-1],I.command==="Q"&&(D=l+(l-I.points[0]),B=u+(u-I.points[1])),l=b.shift(),u=b.shift(),_="Q",T.push(D,B,l,u);break;case"t":D=l,B=u,I=a[a.length-1],I.command==="Q"&&(D=l+(l-I.points[0]),B=u+(u-I.points[1])),l+=b.shift(),u+=b.shift(),_="Q",T.push(D,B,l,u);break;case"A":z=b.shift(),j=b.shift(),K=b.shift(),te=b.shift(),G=b.shift(),$=l,V=u,l=b.shift(),u=b.shift(),_="A",T=this.convertEndpointToCenterParameterization($,V,l,u,te,G,z,j,K);break;case"a":z=b.shift(),j=b.shift(),K=b.shift(),te=b.shift(),G=b.shift(),$=l,V=u,l+=b.shift(),u+=b.shift(),_="A",T=this.convertEndpointToCenterParameterization($,V,l,u,te,G,z,j,K);break}a.push({command:_||v,points:T,start:{x:A,y:R},pathLength:this.calcLength(A,R,_||v,T)})}(v==="z"||v==="Z")&&a.push({command:"z",points:[],start:void 0,pathLength:0})}return a}static calcLength(t,n,r,i){var o,a,s,l,u=zn;switch(r){case"L":return u.getLineLength(t,n,i[0],i[1]);case"C":for(o=0,a=u.getPointOnCubicBezier(0,t,n,i[0],i[1],i[2],i[3],i[4],i[5]),l=.01;l<=1;l+=.01)s=u.getPointOnCubicBezier(l,t,n,i[0],i[1],i[2],i[3],i[4],i[5]),o+=u.getLineLength(a.x,a.y,s.x,s.y),a=s;return o;case"Q":for(o=0,a=u.getPointOnQuadraticBezier(0,t,n,i[0],i[1],i[2],i[3]),l=.01;l<=1;l+=.01)s=u.getPointOnQuadraticBezier(l,t,n,i[0],i[1],i[2],i[3]),o+=u.getLineLength(a.x,a.y,s.x,s.y),a=s;return o;case"A":o=0;var f=i[4],p=i[5],m=i[4]+p,v=Math.PI/180;if(Math.abs(f-m)<v&&(v=Math.abs(f-m)),a=u.getPointOnEllipticalArc(i[0],i[1],i[2],i[3],f,0),p<0)for(l=f-v;l>m;l-=v)s=u.getPointOnEllipticalArc(i[0],i[1],i[2],i[3],l,0),o+=u.getLineLength(a.x,a.y,s.x,s.y),a=s;else for(l=f+v;l<m;l+=v)s=u.getPointOnEllipticalArc(i[0],i[1],i[2],i[3],l,0),o+=u.getLineLength(a.x,a.y,s.x,s.y),a=s;return s=u.getPointOnEllipticalArc(i[0],i[1],i[2],i[3],m,0),o+=u.getLineLength(a.x,a.y,s.x,s.y),o}return 0}static convertEndpointToCenterParameterization(t,n,r,i,o,a,s,l,u){var f=u*(Math.PI/180),p=Math.cos(f)*(t-r)/2+Math.sin(f)*(n-i)/2,m=-1*Math.sin(f)*(t-r)/2+Math.cos(f)*(n-i)/2,v=p*p/(s*s)+m*m/(l*l);v>1&&(s*=Math.sqrt(v),l*=Math.sqrt(v));var b=Math.sqrt((s*s*(l*l)-s*s*(m*m)-l*l*(p*p))/(s*s*(m*m)+l*l*(p*p)));o===a&&(b*=-1),isNaN(b)&&(b=0);var x=b*s*m/l,E=b*-l*p/s,k=(t+r)/2+Math.cos(f)*x-Math.sin(f)*E,_=(n+i)/2+Math.sin(f)*x+Math.cos(f)*E,T=function(j){return Math.sqrt(j[0]*j[0]+j[1]*j[1])},A=function(j,K){return(j[0]*K[0]+j[1]*K[1])/(T(j)*T(K))},R=function(j,K){return(j[0]*K[1]<j[1]*K[0]?-1:1)*Math.acos(A(j,K))},I=R([1,0],[(p-x)/s,(m-E)/l]),D=[(p-x)/s,(m-E)/l],B=[(-1*p-x)/s,(-1*m-E)/l],z=R(D,B);return A(D,B)<=-1&&(z=Math.PI),A(D,B)>=1&&(z=0),a===0&&z>0&&(z=z-2*Math.PI),a===1&&z<0&&(z=z+2*Math.PI),[k,_,s,l,I,z,f,a]}}zn.prototype.className="Path";zn.prototype._attrsAffectingSize=["data"];Er(zn);ee.addGetterSetter(zn,"data");class pp extends mc{_sceneFunc(t){super._sceneFunc(t);var n=Math.PI*2,r=this.points(),i=r,o=this.tension()!==0&&r.length>4;o&&(i=this.getTensionPoints());var a=this.pointerLength(),s=r.length,l,u;if(o){const m=[i[i.length-4],i[i.length-3],i[i.length-2],i[i.length-1],r[s-2],r[s-1]],v=zn.calcLength(i[i.length-4],i[i.length-3],"C",m),b=zn.getPointOnQuadraticBezier(Math.min(1,1-a/v),m[0],m[1],m[2],m[3],m[4],m[5]);l=r[s-2]-b.x,u=r[s-1]-b.y}else l=r[s-2]-r[s-4],u=r[s-1]-r[s-3];var f=(Math.atan2(u,l)+n)%n,p=this.pointerWidth();this.pointerAtEnding()&&(t.save(),t.beginPath(),t.translate(r[s-2],r[s-1]),t.rotate(f),t.moveTo(0,0),t.lineTo(-a,p/2),t.lineTo(-a,-p/2),t.closePath(),t.restore(),this.__fillStroke(t)),this.pointerAtBeginning()&&(t.save(),t.beginPath(),t.translate(r[0],r[1]),o?(l=(i[0]+i[2])/2-r[0],u=(i[1]+i[3])/2-r[1]):(l=r[2]-r[0],u=r[3]-r[1]),t.rotate((Math.atan2(-u,-l)+n)%n),t.moveTo(0,0),t.lineTo(-a,p/2),t.lineTo(-a,-p/2),t.closePath(),t.restore(),this.__fillStroke(t))}__fillStroke(t){var n=this.dashEnabled();n&&(this.attrs.dashEnabled=!1,t.setLineDash([])),t.fillStrokeShape(this),n&&(this.attrs.dashEnabled=!0)}getSelfRect(){const t=super.getSelfRect(),n=this.pointerWidth()/2;return{x:t.x-n,y:t.y-n,width:t.width+n*2,height:t.height+n*2}}}pp.prototype.className="Arrow";Er(pp);ee.addGetterSetter(pp,"pointerLength",10,Ge());ee.addGetterSetter(pp,"pointerWidth",10,Ge());ee.addGetterSetter(pp,"pointerAtBeginning",!1);ee.addGetterSetter(pp,"pointerAtEnding",!0);class E1 extends $e{_sceneFunc(t){t.beginPath(),t.arc(0,0,this.attrs.radius||0,0,Math.PI*2,!1),t.closePath(),t.fillStrokeShape(this)}getWidth(){return this.radius()*2}getHeight(){return this.radius()*2}setWidth(t){this.radius()!==t/2&&this.radius(t/2)}setHeight(t){this.radius()!==t/2&&this.radius(t/2)}}E1.prototype._centroid=!0;E1.prototype.className="Circle";E1.prototype._attrsAffectingSize=["radius"];Er(E1);ee.addGetterSetter(E1,"radius",0,Ge());class mf extends $e{_sceneFunc(t){var n=this.radiusX(),r=this.radiusY();t.beginPath(),t.save(),n!==r&&t.scale(1,r/n),t.arc(0,0,n,0,Math.PI*2,!1),t.restore(),t.closePath(),t.fillStrokeShape(this)}getWidth(){return this.radiusX()*2}getHeight(){return this.radiusY()*2}setWidth(t){this.radiusX(t/2)}setHeight(t){this.radiusY(t/2)}}mf.prototype.className="Ellipse";mf.prototype._centroid=!0;mf.prototype._attrsAffectingSize=["radiusX","radiusY"];Er(mf);ee.addComponentsGetterSetter(mf,"radius",["x","y"]);ee.addGetterSetter(mf,"radiusX",0,Ge());ee.addGetterSetter(mf,"radiusY",0,Ge());class al extends $e{constructor(t){super(t),this.on("imageChange.konva",()=>{this._setImageLoad()}),this._setImageLoad()}_setImageLoad(){const t=this.image();t&&t.complete||t&&t.readyState===4||t&&t.addEventListener&&t.addEventListener("load",()=>{this._requestDraw()})}_useBufferCanvas(){return super._useBufferCanvas(!0)}_sceneFunc(t){const n=this.getWidth(),r=this.getHeight(),i=this.attrs.image;let o;if(i){const a=this.attrs.cropWidth,s=this.attrs.cropHeight;a&&s?o=[i,this.cropX(),this.cropY(),a,s,0,0,n,r]:o=[i,0,0,n,r]}(this.hasFill()||this.hasStroke())&&(t.beginPath(),t.rect(0,0,n,r),t.closePath(),t.fillStrokeShape(this)),i&&t.drawImage.apply(t,o)}_hitFunc(t){var n=this.width(),r=this.height();t.beginPath(),t.rect(0,0,n,r),t.closePath(),t.fillStrokeShape(this)}getWidth(){var t,n;return(t=this.attrs.width)!==null&&t!==void 0?t:(n=this.image())===null||n===void 0?void 0:n.width}getHeight(){var t,n;return(t=this.attrs.height)!==null&&t!==void 0?t:(n=this.image())===null||n===void 0?void 0:n.height}static fromURL(t,n,r=null){var i=he.createImageElement();i.onload=function(){var o=new al({image:i});n(o)},i.onerror=r,i.crossOrigin="Anonymous",i.src=t}}al.prototype.className="Image";Er(al);ee.addGetterSetter(al,"image");ee.addComponentsGetterSetter(al,"crop",["x","y","width","height"]);ee.addGetterSetter(al,"cropX",0,Ge());ee.addGetterSetter(al,"cropY",0,Ge());ee.addGetterSetter(al,"cropWidth",0,Ge());ee.addGetterSetter(al,"cropHeight",0,Ge());var sG=["fontFamily","fontSize","fontStyle","padding","lineHeight","text","width","height","pointerDirection","pointerWidth","pointerHeight"],f7e="Change.konva",h7e="none",b8="up",S8="right",x8="down",w8="left",p7e=sG.length;class oP extends n1{constructor(t){super(t),this.on("add.konva",function(n){this._addListeners(n.child),this._sync()})}getText(){return this.find("Text")[0]}getTag(){return this.find("Tag")[0]}_addListeners(t){var n=this,r,i=function(){n._sync()};for(r=0;r<p7e;r++)t.on(sG[r]+f7e,i)}getWidth(){return this.getText().width()}getHeight(){return this.getText().height()}_sync(){var t=this.getText(),n=this.getTag(),r,i,o,a,s,l,u;if(t&&n){switch(r=t.width(),i=t.height(),o=n.pointerDirection(),a=n.pointerWidth(),u=n.pointerHeight(),s=0,l=0,o){case b8:s=r/2,l=-1*u;break;case S8:s=r+a,l=i/2;break;case x8:s=r/2,l=i+u;break;case w8:s=-1*a,l=i/2;break}n.setAttrs({x:-1*s,y:-1*l,width:r,height:i}),t.setAttrs({x:-1*s,y:-1*l})}}}oP.prototype.className="Label";Er(oP);class gp extends $e{_sceneFunc(t){var n=this.width(),r=this.height(),i=this.pointerDirection(),o=this.pointerWidth(),a=this.pointerHeight(),s=this.cornerRadius();let l=0,u=0,f=0,p=0;typeof s=="number"?l=u=f=p=Math.min(s,n/2,r/2):(l=Math.min(s[0]||0,n/2,r/2),u=Math.min(s[1]||0,n/2,r/2),p=Math.min(s[2]||0,n/2,r/2),f=Math.min(s[3]||0,n/2,r/2)),t.beginPath(),t.moveTo(l,0),i===b8&&(t.lineTo((n-o)/2,0),t.lineTo(n/2,-1*a),t.lineTo((n+o)/2,0)),t.lineTo(n-u,0),t.arc(n-u,u,u,Math.PI*3/2,0,!1),i===S8&&(t.lineTo(n,(r-a)/2),t.lineTo(n+o,r/2),t.lineTo(n,(r+a)/2)),t.lineTo(n,r-p),t.arc(n-p,r-p,p,0,Math.PI/2,!1),i===x8&&(t.lineTo((n+o)/2,r),t.lineTo(n/2,r+a),t.lineTo((n-o)/2,r)),t.lineTo(f,r),t.arc(f,r-f,f,Math.PI/2,Math.PI,!1),i===w8&&(t.lineTo(0,(r+a)/2),t.lineTo(-1*o,r/2),t.lineTo(0,(r-a)/2)),t.lineTo(0,l),t.arc(l,l,l,Math.PI,Math.PI*3/2,!1),t.closePath(),t.fillStrokeShape(this)}getSelfRect(){var t=0,n=0,r=this.pointerWidth(),i=this.pointerHeight(),o=this.pointerDirection(),a=this.width(),s=this.height();return o===b8?(n-=i,s+=i):o===x8?s+=i:o===w8?(t-=r*1.5,a+=r):o===S8&&(a+=r*1.5),{x:t,y:n,width:a,height:s}}}gp.prototype.className="Tag";Er(gp);ee.addGetterSetter(gp,"pointerDirection",h7e);ee.addGetterSetter(gp,"pointerWidth",0,Ge());ee.addGetterSetter(gp,"pointerHeight",0,Ge());ee.addGetterSetter(gp,"cornerRadius",0,zW(4));class My extends $e{_sceneFunc(t){var n=this.cornerRadius(),r=this.width(),i=this.height();if(t.beginPath(),!n)t.rect(0,0,r,i);else{let o=0,a=0,s=0,l=0;typeof n=="number"?o=a=s=l=Math.min(n,r/2,i/2):(o=Math.min(n[0]||0,r/2,i/2),a=Math.min(n[1]||0,r/2,i/2),l=Math.min(n[2]||0,r/2,i/2),s=Math.min(n[3]||0,r/2,i/2)),t.moveTo(o,0),t.lineTo(r-a,0),t.arc(r-a,a,a,Math.PI*3/2,0,!1),t.lineTo(r,i-l),t.arc(r-l,i-l,l,0,Math.PI/2,!1),t.lineTo(s,i),t.arc(s,i-s,s,Math.PI/2,Math.PI,!1),t.lineTo(0,o),t.arc(o,o,o,Math.PI,Math.PI*3/2,!1)}t.closePath(),t.fillStrokeShape(this)}}My.prototype.className="Rect";Er(My);ee.addGetterSetter(My,"cornerRadius",0,zW(4));class mp extends $e{_sceneFunc(t){const n=this._getPoints();t.beginPath(),t.moveTo(n[0].x,n[0].y);for(var r=1;r<n.length;r++)t.lineTo(n[r].x,n[r].y);t.closePath(),t.fillStrokeShape(this)}_getPoints(){const t=this.attrs.sides,n=this.attrs.radius||0,r=[];for(var i=0;i<t;i++)r.push({x:n*Math.sin(i*2*Math.PI/t),y:-1*n*Math.cos(i*2*Math.PI/t)});return r}getSelfRect(){const t=this._getPoints();var n=t[0].x,r=t[0].y,i=t[0].x,o=t[0].y;return t.forEach(a=>{n=Math.min(n,a.x),r=Math.max(r,a.x),i=Math.min(i,a.y),o=Math.max(o,a.y)}),{x:n,y:i,width:r-n,height:o-i}}getWidth(){return this.radius()*2}getHeight(){return this.radius()*2}setWidth(t){this.radius(t/2)}setHeight(t){this.radius(t/2)}}mp.prototype.className="RegularPolygon";mp.prototype._centroid=!0;mp.prototype._attrsAffectingSize=["radius"];Er(mp);ee.addGetterSetter(mp,"radius",0,Ge());ee.addGetterSetter(mp,"sides",0,Ge());var HI=Math.PI*2;class vp extends $e{_sceneFunc(t){t.beginPath(),t.arc(0,0,this.innerRadius(),0,HI,!1),t.moveTo(this.outerRadius(),0),t.arc(0,0,this.outerRadius(),HI,0,!0),t.closePath(),t.fillStrokeShape(this)}getWidth(){return this.outerRadius()*2}getHeight(){return this.outerRadius()*2}setWidth(t){this.outerRadius(t/2)}setHeight(t){this.outerRadius(t/2)}}vp.prototype.className="Ring";vp.prototype._centroid=!0;vp.prototype._attrsAffectingSize=["innerRadius","outerRadius"];Er(vp);ee.addGetterSetter(vp,"innerRadius",0,Ge());ee.addGetterSetter(vp,"outerRadius",0,Ge());class bu extends $e{constructor(t){super(t),this._updated=!0,this.anim=new is(()=>{var n=this._updated;return this._updated=!1,n}),this.on("animationChange.konva",function(){this.frameIndex(0)}),this.on("frameIndexChange.konva",function(){this._updated=!0}),this.on("frameRateChange.konva",function(){!this.anim.isRunning()||(clearInterval(this.interval),this._setInterval())})}_sceneFunc(t){var n=this.animation(),r=this.frameIndex(),i=r*4,o=this.animations()[n],a=this.frameOffsets(),s=o[i+0],l=o[i+1],u=o[i+2],f=o[i+3],p=this.image();if((this.hasFill()||this.hasStroke())&&(t.beginPath(),t.rect(0,0,u,f),t.closePath(),t.fillStrokeShape(this)),p)if(a){var m=a[n],v=r*2;t.drawImage(p,s,l,u,f,m[v+0],m[v+1],u,f)}else t.drawImage(p,s,l,u,f,0,0,u,f)}_hitFunc(t){var n=this.animation(),r=this.frameIndex(),i=r*4,o=this.animations()[n],a=this.frameOffsets(),s=o[i+2],l=o[i+3];if(t.beginPath(),a){var u=a[n],f=r*2;t.rect(u[f+0],u[f+1],s,l)}else t.rect(0,0,s,l);t.closePath(),t.fillShape(this)}_useBufferCanvas(){return super._useBufferCanvas(!0)}_setInterval(){var t=this;this.interval=setInterval(function(){t._updateIndex()},1e3/this.frameRate())}start(){if(!this.isRunning()){var t=this.getLayer();this.anim.setLayers(t),this._setInterval(),this.anim.start()}}stop(){this.anim.stop(),clearInterval(this.interval)}isRunning(){return this.anim.isRunning()}_updateIndex(){var t=this.frameIndex(),n=this.animation(),r=this.animations(),i=r[n],o=i.length/4;t<o-1?this.frameIndex(t+1):this.frameIndex(0)}}bu.prototype.className="Sprite";Er(bu);ee.addGetterSetter(bu,"animation");ee.addGetterSetter(bu,"animations");ee.addGetterSetter(bu,"frameOffsets");ee.addGetterSetter(bu,"image");ee.addGetterSetter(bu,"frameIndex",0,Ge());ee.addGetterSetter(bu,"frameRate",17,Ge());ee.backCompat(bu,{index:"frameIndex",getIndex:"getFrameIndex",setIndex:"setFrameIndex"});class vf extends $e{_sceneFunc(t){var n=this.innerRadius(),r=this.outerRadius(),i=this.numPoints();t.beginPath(),t.moveTo(0,0-r);for(var o=1;o<i*2;o++){var a=o%2===0?r:n,s=a*Math.sin(o*Math.PI/i),l=-1*a*Math.cos(o*Math.PI/i);t.lineTo(s,l)}t.closePath(),t.fillStrokeShape(this)}getWidth(){return this.outerRadius()*2}getHeight(){return this.outerRadius()*2}setWidth(t){this.outerRadius(t/2)}setHeight(t){this.outerRadius(t/2)}}vf.prototype.className="Star";vf.prototype._centroid=!0;vf.prototype._attrsAffectingSize=["innerRadius","outerRadius"];Er(vf);ee.addGetterSetter(vf,"numPoints",5,Ge());ee.addGetterSetter(vf,"innerRadius",0,Ge());ee.addGetterSetter(vf,"outerRadius",0,Ge());function lG(e){return Array.from(e)}var D0="auto",g7e="center",lv="justify",m7e="Change.konva",v7e="2d",VI="-",uG="left",y7e="text",b7e="Text",S7e="top",x7e="bottom",UI="middle",cG="normal",w7e="px ",Rb=" ",C7e="right",_7e="word",k7e="char",WI="none",CC="\u2026",dG=["fontFamily","fontSize","fontStyle","fontVariant","padding","align","verticalAlign","lineHeight","text","width","height","wrap","ellipsis","letterSpacing"],E7e=dG.length;function P7e(e){return e.split(",").map(t=>{t=t.trim();const n=t.indexOf(" ")>=0,r=t.indexOf('"')>=0||t.indexOf("'")>=0;return n&&!r&&(t=`"${t}"`),t}).join(", ")}var Ib;function _C(){return Ib||(Ib=he.createCanvasElement().getContext(v7e),Ib)}function T7e(e){e.fillText(this._partialText,this._partialTextX,this._partialTextY)}function L7e(e){e.strokeText(this._partialText,this._partialTextX,this._partialTextY)}function A7e(e){return e=e||{},!e.fillLinearGradientColorStops&&!e.fillRadialGradientColorStops&&!e.fillPatternImage&&(e.fill=e.fill||"black"),e}class kr extends $e{constructor(t){super(A7e(t)),this._partialTextX=0,this._partialTextY=0;for(var n=0;n<E7e;n++)this.on(dG[n]+m7e,this._setTextData);this._setTextData()}_sceneFunc(t){var n=this.textArr,r=n.length;if(!!this.text()){var i=this.padding(),o=this.fontSize(),a=this.lineHeight()*o,s=this.verticalAlign(),l=0,u=this.align(),f=this.getWidth(),p=this.letterSpacing(),m=this.fill(),v=this.textDecoration(),b=v.indexOf("underline")!==-1,x=v.indexOf("line-through")!==-1,E,k=0,k=a/2,_=0,T=0;for(t.setAttr("font",this._getContextFont()),t.setAttr("textBaseline",UI),t.setAttr("textAlign",uG),s===UI?l=(this.getHeight()-r*a-i*2)/2:s===x7e&&(l=this.getHeight()-r*a-i*2),t.translate(i,l+i),E=0;E<r;E++){var _=0,T=0,A=n[E],R=A.text,I=A.width,D=A.lastInParagraph,B,z,j;if(t.save(),u===C7e?_+=f-I-i*2:u===g7e&&(_+=(f-I-i*2)/2),b&&(t.save(),t.beginPath(),t.moveTo(_,k+T+Math.round(o/2)),B=R.split(" ").length-1,z=B===0,j=u===lv&&D&&!z?f-i*2:I,t.lineTo(_+Math.round(j),k+T+Math.round(o/2)),t.lineWidth=o/15,t.strokeStyle=m,t.stroke(),t.restore()),x&&(t.save(),t.beginPath(),t.moveTo(_,k+T),B=R.split(" ").length-1,z=B===0,j=u===lv&&D&&!z?f-i*2:I,t.lineTo(_+Math.round(j),k+T),t.lineWidth=o/15,t.strokeStyle=m,t.stroke(),t.restore()),p!==0||u===lv){B=R.split(" ").length-1;for(var K=lG(R),te=0;te<K.length;te++){var G=K[te];G===" "&&!D&&u===lv&&(_+=(f-i*2-I)/B),this._partialTextX=_,this._partialTextY=k+T,this._partialText=G,t.fillStrokeShape(this),_+=this.measureSize(G).width+p}}else this._partialTextX=_,this._partialTextY=k+T,this._partialText=R,t.fillStrokeShape(this);t.restore(),r>1&&(k+=a)}}}_hitFunc(t){var n=this.getWidth(),r=this.getHeight();t.beginPath(),t.rect(0,0,n,r),t.closePath(),t.fillStrokeShape(this)}setText(t){var n=he._isString(t)?t:t==null?"":t+"";return this._setAttr(y7e,n),this}getWidth(){var t=this.attrs.width===D0||this.attrs.width===void 0;return t?this.getTextWidth()+this.padding()*2:this.attrs.width}getHeight(){var t=this.attrs.height===D0||this.attrs.height===void 0;return t?this.fontSize()*this.textArr.length*this.lineHeight()+this.padding()*2:this.attrs.height}getTextWidth(){return this.textWidth}getTextHeight(){return he.warn("text.getTextHeight() method is deprecated. Use text.height() - for full height and text.fontSize() - for one line height."),this.textHeight}measureSize(t){var n=_C(),r=this.fontSize(),i;return n.save(),n.font=this._getContextFont(),i=n.measureText(t),n.restore(),{width:i.width,height:r}}_getContextFont(){return this.fontStyle()+Rb+this.fontVariant()+Rb+(this.fontSize()+w7e)+P7e(this.fontFamily())}_addTextLine(t){this.align()===lv&&(t=t.trim());var n=this._getTextWidth(t);return this.textArr.push({text:t,width:n,lastInParagraph:!1})}_getTextWidth(t){var n=this.letterSpacing(),r=t.length;return _C().measureText(t).width+(r?n*(r-1):0)}_setTextData(){var t=this.text().split(`
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Use text.height() - for full height and text.fontSize() - for one line height."),this.textHeight}setText(t){return kr.prototype.setText.call(this,t)}_getContextFont(){return kr.prototype._getContextFont.call(this)}_getTextSize(t){var n=this.dummyCanvas,r=n.getContext("2d");r.save(),r.font=this._getContextFont();var i=r.measureText(t);return r.restore(),{width:i.width,height:parseInt(this.attrs.fontSize,10)}}_setTextData(){var t=this,n=this._getTextSize(this.attrs.text),r=this.letterSpacing(),i=this.align(),o=this.kerningFunc();this.textWidth=n.width,this.textHeight=n.height;var a=Math.max(this.textWidth+((this.attrs.text||"").length-1)*r,0);this.glyphInfo=[];for(var s=0,l=0;l<t.dataArray.length;l++)t.dataArray[l].pathLength>0&&(s+=t.dataArray[l].pathLength);var u=0;i==="center"&&(u=Math.max(0,s/2-a/2)),i==="right"&&(u=Math.max(0,s-a));for(var f=lG(this.text()),p=this.text().split(" ").length-1,m,v,b,x=-1,E=0,k=function(){E=0;for(var te=t.dataArray,G=x+1;G<te.length;G++){if(te[G].pathLength>0)return x=G,te[G];te[G].command==="M"&&(m={x:te[G].points[0],y:te[G].points[1]})}return{}},_=function(te){var G=t._getTextSize(te).width+r;te===" "&&i==="justify"&&(G+=(s-a)/p);var $=0,V=0;for(v=void 0;Math.abs(G-$)/G>.01&&V<20;){V++;for(var J=$;b===void 0;)b=k(),b&&J+b.pathLength<G&&(J+=b.pathLength,b=void 0);if(Object.keys(b).length===0||m===void 0)return;var Z=!1;switch(b.command){case"L":zn.getLineLength(m.x,m.y,b.points[0],b.points[1])>G?v=zn.getPointOnLine(G,m.x,m.y,b.points[0],b.points[1],m.x,m.y):b=void 0;break;case"A":var U=b.points[4],X=b.points[5],le=b.points[4]+X;E===0?E=U+1e-8:G>$?E+=Math.PI/180*X/Math.abs(X):E-=Math.PI/360*X/Math.abs(X),(X<0&&E<le||X>=0&&E>le)&&(E=le,Z=!0),v=zn.getPointOnEllipticalArc(b.points[0],b.points[1],b.points[2],b.points[3],E,b.points[6]);break;case"C":E===0?G>b.pathLength?E=1e-8:E=G/b.pathLength:G>$?E+=(G-$)/b.pathLength/2:E=Math.max(E-($-G)/b.pathLength/2,0),E>1&&(E=1,Z=!0),v=zn.getPointOnCubicBezier(E,b.start.x,b.start.y,b.points[0],b.points[1],b.points[2],b.points[3],b.points[4],b.points[5]);break;case"Q":E===0?E=G/b.pathLength:G>$?E+=(G-$)/b.pathLength:E-=($-G)/b.pathLength,E>1&&(E=1,Z=!0),v=zn.getPointOnQuadraticBezier(E,b.start.x,b.start.y,b.points[0],b.points[1],b.points[2],b.points[3]);break}v!==void 0&&($=zn.getLineLength(m.x,m.y,v.x,v.y)),Z&&(Z=!1,b=void 0)}},T="C",A=t._getTextSize(T).width+r,R=u/A-1,I=0;I<R&&(_(T),!(m===void 0||v===void 0));I++)m=v;for(var D=0;D<f.length&&(_(f[D]),!(m===void 0||v===void 0));D++){var B=zn.getLineLength(m.x,m.y,v.x,v.y),z=0;if(o)try{z=o(f[D-1],f[D])*this.fontSize()}catch(te){z=0}m.x+=z,v.x+=z,this.textWidth+=z;var j=zn.getPointOnLine(z+B/2,m.x,m.y,v.x,v.y),K=Math.atan2(v.y-m.y,v.x-m.x);this.glyphInfo.push({transposeX:j.x,transposeY:j.y,text:f[D],rotation:K,p0:m,p1:v}),m=v}}getSelfRect(){if(!this.glyphInfo.length)return{x:0,y:0,width:0,height:0};var t=[];this.glyphInfo.forEach(function(f){t.push(f.p0.x),t.push(f.p0.y),t.push(f.p1.x),t.push(f.p1.y)});for(var n=t[0]||0,r=t[0]||0,i=t[1]||0,o=t[1]||0,a,s,l=0;l<t.length/2;l++)a=t[l*2],s=t[l*2+1],n=Math.min(n,a),r=Math.max(r,a),i=Math.min(i,s),o=Math.max(o,s);var u=this.fontSize();return{x:n-u/2,y:i-u/2,width:r-n+u,height:o-i+u}}destroy(){return he.releaseCanvas(this.dummyCanvas),super.destroy()}}bi.prototype._fillFunc=hG;bi.prototype._strokeFunc=pG;bi.prototype._fillFuncHit=hG;bi.prototype._strokeFuncHit=pG;bi.prototype.className="TextPath";bi.prototype._attrsAffectingSize=["text","fontSize","data"];Er(bi);ee.addGetterSetter(bi,"data");ee.addGetterSetter(bi,"fontFamily","Arial");ee.addGetterSetter(bi,"fontSize",12,Ge());ee.addGetterSetter(bi,"fontStyle",fG);ee.addGetterSetter(bi,"align","left");ee.addGetterSetter(bi,"letterSpacing",0,Ge());ee.addGetterSetter(bi,"textBaseline","middle");ee.addGetterSetter(bi,"fontVariant",fG);ee.addGetterSetter(bi,"text",O7e);ee.addGetterSetter(bi,"textDecoration",null);ee.addGetterSetter(bi,"kerningFunc",null);var gG="tr-konva",M7e=["resizeEnabledChange","rotateAnchorOffsetChange","rotateEnabledChange","enabledAnchorsChange","anchorSizeChange","borderEnabledChange","borderStrokeChange","borderStrokeWidthChange","borderDashChange","anchorStrokeChange","anchorStrokeWidthChange","anchorFillChange","anchorCornerRadiusChange","ignoreStrokeChange"].map(e=>e+`.${gG}`).join(" "),GI="nodesRect",R7e=["widthChange","heightChange","scaleXChange","scaleYChange","skewXChange","skewYChange","rotationChange","offsetXChange","offsetYChange","transformsEnabledChange","strokeWidthChange"],I7e={"top-left":-45,"top-center":0,"top-right":45,"middle-right":-90,"middle-left":90,"bottom-left":-135,"bottom-center":180,"bottom-right":135};const D7e="ontouchstart"in gt._global;function N7e(e,t){if(e==="rotater")return"crosshair";t+=he.degToRad(I7e[e]||0);var n=(he.radToDeg(t)%360+360)%360;return he._inRange(n,315+22.5,360)||he._inRange(n,0,22.5)?"ns-resize":he._inRange(n,45-22.5,45+22.5)?"nesw-resize":he._inRange(n,90-22.5,90+22.5)?"ew-resize":he._inRange(n,135-22.5,135+22.5)?"nwse-resize":he._inRange(n,180-22.5,180+22.5)?"ns-resize":he._inRange(n,225-22.5,225+22.5)?"nesw-resize":he._inRange(n,270-22.5,270+22.5)?"ew-resize":he._inRange(n,315-22.5,315+22.5)?"nwse-resize":(he.error("Transformer has unknown angle for cursor detection: "+n),"pointer")}var G5=["top-left","top-center","top-right","middle-right","middle-left","bottom-left","bottom-center","bottom-right"],qI=1e8;function B7e(e){return{x:e.x+e.width/2*Math.cos(e.rotation)+e.height/2*Math.sin(-e.rotation),y:e.y+e.height/2*Math.cos(e.rotation)+e.width/2*Math.sin(e.rotation)}}function mG(e,t,n){const r=n.x+(e.x-n.x)*Math.cos(t)-(e.y-n.y)*Math.sin(t),i=n.y+(e.x-n.x)*Math.sin(t)+(e.y-n.y)*Math.cos(t);return Object.assign(Object.assign({},e),{rotation:e.rotation+t,x:r,y:i})}function $7e(e,t){const n=B7e(e);return mG(e,t,n)}function F7e(e,t,n){let r=t;for(let i=0;i<e.length;i++){const o=gt.getAngle(e[i]),a=Math.abs(o-t)%(Math.PI*2);Math.min(a,Math.PI*2-a)<n&&(r=o)}return r}class On extends n1{constructor(t){super(t),this._transforming=!1,this._createElements(),this._handleMouseMove=this._handleMouseMove.bind(this),this._handleMouseUp=this._handleMouseUp.bind(this),this.update=this.update.bind(this),this.on(M7e,this.update),this.getNode()&&this.update()}attachTo(t){return this.setNode(t),this}setNode(t){return he.warn("tr.setNode(shape), tr.node(shape) and tr.attachTo(shape) methods are deprecated. Please use tr.nodes(nodesArray) instead."),this.setNodes([t])}getNode(){return this._nodes&&this._nodes[0]}_getEventNamespace(){return gG+this._id}setNodes(t=[]){this._nodes&&this._nodes.length&&this.detach(),this._nodes=t,t.length===1&&this.useSingleNodeRotation()?this.rotation(t[0].getAbsoluteRotation()):this.rotation(0),this._nodes.forEach(r=>{const i=()=>{this.nodes().length===1&&this.useSingleNodeRotation()&&this.rotation(this.nodes()[0].getAbsoluteRotation()),this._resetTransformCache(),!this._transforming&&!this.isDragging()&&this.update()},o=r._attrsAffectingSize.map(a=>a+"Change."+this._getEventNamespace()).join(" ");r.on(o,i),r.on(R7e.map(a=>a+`.${this._getEventNamespace()}`).join(" "),i),r.on(`absoluteTransformChange.${this._getEventNamespace()}`,i),this._proxyDrag(r)}),this._resetTransformCache();var n=!!this.findOne(".top-left");return n&&this.update(),this}_proxyDrag(t){let n;t.on(`dragstart.${this._getEventNamespace()}`,r=>{n=t.getAbsolutePosition(),!this.isDragging()&&t!==this.findOne(".back")&&this.startDrag(r,!1)}),t.on(`dragmove.${this._getEventNamespace()}`,r=>{if(!n)return;const i=t.getAbsolutePosition(),o=i.x-n.x,a=i.y-n.y;this.nodes().forEach(s=>{if(s===t||s.isDragging())return;const l=s.getAbsolutePosition();s.setAbsolutePosition({x:l.x+o,y:l.y+a}),s.startDrag(r)}),n=null})}getNodes(){return this._nodes||[]}getActiveAnchor(){return this._movingAnchorName}detach(){this._nodes&&this._nodes.forEach(t=>{t.off("."+this._getEventNamespace())}),this._nodes=[],this._resetTransformCache()}_resetTransformCache(){this._clearCache(GI),this._clearCache("transform"),this._clearSelfAndDescendantCache("absoluteTransform")}_getNodeRect(){return this._getCache(GI,this.__getNodeRect)}__getNodeShape(t,n=this.rotation(),r){var i=t.getClientRect({skipTransform:!0,skipShadow:!0,skipStroke:this.ignoreStroke()}),o=t.getAbsoluteScale(r),a=t.getAbsolutePosition(r),s=i.x*o.x-t.offsetX()*o.x,l=i.y*o.y-t.offsetY()*o.y;const u=(gt.getAngle(t.getAbsoluteRotation())+Math.PI*2)%(Math.PI*2),f={x:a.x+s*Math.cos(u)+l*Math.sin(-u),y:a.y+l*Math.cos(u)+s*Math.sin(u),width:i.width*o.x,height:i.height*o.y,rotation:u};return mG(f,-gt.getAngle(n),{x:0,y:0})}__getNodeRect(){var t=this.getNode();if(!t)return{x:-qI,y:-qI,width:0,height:0,rotation:0};const n=[];this.nodes().map(u=>{const f=u.getClientRect({skipTransform:!0,skipShadow:!0,skipStroke:this.ignoreStroke()});var p=[{x:f.x,y:f.y},{x:f.x+f.width,y:f.y},{x:f.x+f.width,y:f.y+f.height},{x:f.x,y:f.y+f.height}],m=u.getAbsoluteTransform();p.forEach(function(v){var b=m.point(v);n.push(b)})});const r=new Pa;r.rotate(-gt.getAngle(this.rotation()));var i,o,a,s;n.forEach(function(u){var f=r.point(u);i===void 0&&(i=a=f.x,o=s=f.y),i=Math.min(i,f.x),o=Math.min(o,f.y),a=Math.max(a,f.x),s=Math.max(s,f.y)}),r.invert();const l=r.point({x:i,y:o});return{x:l.x,y:l.y,width:a-i,height:s-o,rotation:gt.getAngle(this.rotation())}}getX(){return this._getNodeRect().x}getY(){return this._getNodeRect().y}getWidth(){return this._getNodeRect().width}getHeight(){return this._getNodeRect().height}_createElements(){this._createBack(),G5.forEach(function(t){this._createAnchor(t)}.bind(this)),this._createAnchor("rotater")}_createAnchor(t){var n=new My({stroke:"rgb(0, 161, 255)",fill:"white",strokeWidth:1,name:t+" _anchor",dragDistance:0,draggable:!0,hitStrokeWidth:D7e?10:"auto"}),r=this;n.on("mousedown touchstart",function(i){r._handleMouseDown(i)}),n.on("dragstart",i=>{n.stopDrag(),i.cancelBubble=!0}),n.on("dragend",i=>{i.cancelBubble=!0}),n.on("mouseenter",()=>{var i=gt.getAngle(this.rotation()),o=N7e(t,i);n.getStage().content&&(n.getStage().content.style.cursor=o),this._cursorChange=!0}),n.on("mouseout",()=>{n.getStage().content&&(n.getStage().content.style.cursor=""),this._cursorChange=!1}),this.add(n)}_createBack(){var t=new $e({name:"back",width:0,height:0,draggable:!0,sceneFunc(n){var r=this.getParent(),i=r.padding();n.beginPath(),n.rect(-i,-i,this.width()+i*2,this.height()+i*2),n.moveTo(this.width()/2,-i),r.rotateEnabled()&&n.lineTo(this.width()/2,-r.rotateAnchorOffset()*he._sign(this.height())-i),n.fillStrokeShape(this)},hitFunc:(n,r)=>{if(!!this.shouldOverdrawWholeArea()){var i=this.padding();n.beginPath(),n.rect(-i,-i,r.width()+i*2,r.height()+i*2),n.fillStrokeShape(r)}}});this.add(t),this._proxyDrag(t),t.on("dragstart",n=>{n.cancelBubble=!0}),t.on("dragmove",n=>{n.cancelBubble=!0}),t.on("dragend",n=>{n.cancelBubble=!0}),this.on("dragmove",n=>{this.update()})}_handleMouseDown(t){this._movingAnchorName=t.target.name().split(" ")[0];var n=this._getNodeRect(),r=n.width,i=n.height,o=Math.sqrt(Math.pow(r,2)+Math.pow(i,2));this.sin=Math.abs(i/o),this.cos=Math.abs(r/o),typeof window<"u"&&(window.addEventListener("mousemove",this._handleMouseMove),window.addEventListener("touchmove",this._handleMouseMove),window.addEventListener("mouseup",this._handleMouseUp,!0),window.addEventListener("touchend",this._handleMouseUp,!0)),this._transforming=!0;var a=t.target.getAbsolutePosition(),s=t.target.getStage().getPointerPosition();this._anchorDragOffset={x:s.x-a.x,y:s.y-a.y},this._fire("transformstart",{evt:t.evt,target:this.getNode()}),this._nodes.forEach(l=>{l._fire("transformstart",{evt:t.evt,target:l})})}_handleMouseMove(t){var n,r,i,o=this.findOne("."+this._movingAnchorName),a=o.getStage();a.setPointersPositions(t);const s=a.getPointerPosition();let l={x:s.x-this._anchorDragOffset.x,y:s.y-this._anchorDragOffset.y};const u=o.getAbsolutePosition();this.anchorDragBoundFunc()&&(l=this.anchorDragBoundFunc()(u,l,t)),o.setAbsolutePosition(l);const f=o.getAbsolutePosition();if(!(u.x===f.x&&u.y===f.y)){if(this._movingAnchorName==="rotater"){var p=this._getNodeRect();n=o.x()-p.width/2,r=-o.y()+p.height/2;let te=Math.atan2(-r,n)+Math.PI/2;p.height<0&&(te-=Math.PI);var m=gt.getAngle(this.rotation());const G=m+te,$=gt.getAngle(this.rotationSnapTolerance()),J=F7e(this.rotationSnaps(),G,$)-p.rotation,Z=$7e(p,J);this._fitNodesInto(Z,t);return}var v=this.keepRatio()||t.shiftKey,_=this.centeredScaling()||t.altKey;if(this._movingAnchorName==="top-left"){if(v){var b=_?{x:this.width()/2,y:this.height()/2}:{x:this.findOne(".bottom-right").x(),y:this.findOne(".bottom-right").y()};i=Math.sqrt(Math.pow(b.x-o.x(),2)+Math.pow(b.y-o.y(),2));var x=this.findOne(".top-left").x()>b.x?-1:1,E=this.findOne(".top-left").y()>b.y?-1:1;n=i*this.cos*x,r=i*this.sin*E,this.findOne(".top-left").x(b.x-n),this.findOne(".top-left").y(b.y-r)}}else if(this._movingAnchorName==="top-center")this.findOne(".top-left").y(o.y());else if(this._movingAnchorName==="top-right"){if(v){var b=_?{x:this.width()/2,y:this.height()/2}:{x:this.findOne(".bottom-left").x(),y:this.findOne(".bottom-left").y()};i=Math.sqrt(Math.pow(o.x()-b.x,2)+Math.pow(b.y-o.y(),2));var x=this.findOne(".top-right").x()<b.x?-1:1,E=this.findOne(".top-right").y()>b.y?-1:1;n=i*this.cos*x,r=i*this.sin*E,this.findOne(".top-right").x(b.x+n),this.findOne(".top-right").y(b.y-r)}var k=o.position();this.findOne(".top-left").y(k.y),this.findOne(".bottom-right").x(k.x)}else if(this._movingAnchorName==="middle-left")this.findOne(".top-left").x(o.x());else if(this._movingAnchorName==="middle-right")this.findOne(".bottom-right").x(o.x());else if(this._movingAnchorName==="bottom-left"){if(v){var b=_?{x:this.width()/2,y:this.height()/2}:{x:this.findOne(".top-right").x(),y:this.findOne(".top-right").y()};i=Math.sqrt(Math.pow(b.x-o.x(),2)+Math.pow(o.y()-b.y,2));var x=b.x<o.x()?-1:1,E=o.y()<b.y?-1:1;n=i*this.cos*x,r=i*this.sin*E,o.x(b.x-n),o.y(b.y+r)}k=o.position(),this.findOne(".top-left").x(k.x),this.findOne(".bottom-right").y(k.y)}else if(this._movingAnchorName==="bottom-center")this.findOne(".bottom-right").y(o.y());else if(this._movingAnchorName==="bottom-right"){if(v){var b=_?{x:this.width()/2,y:this.height()/2}:{x:this.findOne(".top-left").x(),y:this.findOne(".top-left").y()};i=Math.sqrt(Math.pow(o.x()-b.x,2)+Math.pow(o.y()-b.y,2));var x=this.findOne(".bottom-right").x()<b.x?-1:1,E=this.findOne(".bottom-right").y()<b.y?-1:1;n=i*this.cos*x,r=i*this.sin*E,this.findOne(".bottom-right").x(b.x+n),this.findOne(".bottom-right").y(b.y+r)}}else console.error(new Error("Wrong position argument of selection resizer: "+this._movingAnchorName));var _=this.centeredScaling()||t.altKey;if(_){var T=this.findOne(".top-left"),A=this.findOne(".bottom-right"),R=T.x(),I=T.y(),D=this.getWidth()-A.x(),B=this.getHeight()-A.y();A.move({x:-R,y:-I}),T.move({x:D,y:B})}var z=this.findOne(".top-left").getAbsolutePosition();n=z.x,r=z.y;var j=this.findOne(".bottom-right").x()-this.findOne(".top-left").x(),K=this.findOne(".bottom-right").y()-this.findOne(".top-left").y();this._fitNodesInto({x:n,y:r,width:j,height:K,rotation:gt.getAngle(this.rotation())},t)}}_handleMouseUp(t){this._removeEvents(t)}getAbsoluteTransform(){return this.getTransform()}_removeEvents(t){if(this._transforming){this._transforming=!1,typeof window<"u"&&(window.removeEventListener("mousemove",this._handleMouseMove),window.removeEventListener("touchmove",this._handleMouseMove),window.removeEventListener("mouseup",this._handleMouseUp,!0),window.removeEventListener("touchend",this._handleMouseUp,!0));var n=this.getNode();this._fire("transformend",{evt:t,target:n}),n&&this._nodes.forEach(r=>{r._fire("transformend",{evt:t,target:r})}),this._movingAnchorName=null}}_fitNodesInto(t,n){var r=this._getNodeRect();const i=1;if(he._inRange(t.width,-this.padding()*2-i,i)){this.update();return}if(he._inRange(t.height,-this.padding()*2-i,i)){this.update();return}const o=this.flipEnabled();var a=new Pa;if(a.rotate(gt.getAngle(this.rotation())),this._movingAnchorName&&t.width<0&&this._movingAnchorName.indexOf("left")>=0){const p=a.point({x:-this.padding()*2,y:0});if(t.x+=p.x,t.y+=p.y,t.width+=this.padding()*2,this._movingAnchorName=this._movingAnchorName.replace("left","right"),this._anchorDragOffset.x-=p.x,this._anchorDragOffset.y-=p.y,!o){this.update();return}}else if(this._movingAnchorName&&t.width<0&&this._movingAnchorName.indexOf("right")>=0){const p=a.point({x:this.padding()*2,y:0});if(this._movingAnchorName=this._movingAnchorName.replace("right","left"),this._anchorDragOffset.x-=p.x,this._anchorDragOffset.y-=p.y,t.width+=this.padding()*2,!o){this.update();return}}if(this._movingAnchorName&&t.height<0&&this._movingAnchorName.indexOf("top")>=0){const p=a.point({x:0,y:-this.padding()*2});if(t.x+=p.x,t.y+=p.y,this._movingAnchorName=this._movingAnchorName.replace("top","bottom"),this._anchorDragOffset.x-=p.x,this._anchorDragOffset.y-=p.y,t.height+=this.padding()*2,!o){this.update();return}}else if(this._movingAnchorName&&t.height<0&&this._movingAnchorName.indexOf("bottom")>=0){const p=a.point({x:0,y:this.padding()*2});if(this._movingAnchorName=this._movingAnchorName.replace("bottom","top"),this._anchorDragOffset.x-=p.x,this._anchorDragOffset.y-=p.y,t.height+=this.padding()*2,!o){this.update();return}}if(this.boundBoxFunc()){const p=this.boundBoxFunc()(r,t);p?t=p:he.warn("boundBoxFunc returned falsy. You should return new bound rect from it!")}const s=1e7,l=new Pa;l.translate(r.x,r.y),l.rotate(r.rotation),l.scale(r.width/s,r.height/s);const u=new Pa;u.translate(t.x,t.y),u.rotate(t.rotation),u.scale(t.width/s,t.height/s);const f=u.multiply(l.invert());this._nodes.forEach(p=>{var m;const v=p.getParent().getAbsoluteTransform(),b=p.getTransform().copy();b.translate(p.offsetX(),p.offsetY());const x=new Pa;x.multiply(v.copy().invert()).multiply(f).multiply(v).multiply(b);const E=x.decompose();p.setAttrs(E),this._fire("transform",{evt:n,target:p}),p._fire("transform",{evt:n,target:p}),(m=p.getLayer())===null||m===void 0||m.batchDraw()}),this.rotation(he._getRotation(t.rotation)),this._resetTransformCache(),this.update(),this.getLayer().batchDraw()}forceUpdate(){this._resetTransformCache(),this.update()}_batchChangeChild(t,n){this.findOne(t).setAttrs(n)}update(){var t,n=this._getNodeRect();this.rotation(he._getRotation(n.rotation));var r=n.width,i=n.height,o=this.enabledAnchors(),a=this.resizeEnabled(),s=this.padding(),l=this.anchorSize();this.find("._anchor").forEach(u=>{u.setAttrs({width:l,height:l,offsetX:l/2,offsetY:l/2,stroke:this.anchorStroke(),strokeWidth:this.anchorStrokeWidth(),fill:this.anchorFill(),cornerRadius:this.anchorCornerRadius()})}),this._batchChangeChild(".top-left",{x:0,y:0,offsetX:l/2+s,offsetY:l/2+s,visible:a&&o.indexOf("top-left")>=0}),this._batchChangeChild(".top-center",{x:r/2,y:0,offsetY:l/2+s,visible:a&&o.indexOf("top-center")>=0}),this._batchChangeChild(".top-right",{x:r,y:0,offsetX:l/2-s,offsetY:l/2+s,visible:a&&o.indexOf("top-right")>=0}),this._batchChangeChild(".middle-left",{x:0,y:i/2,offsetX:l/2+s,visible:a&&o.indexOf("middle-left")>=0}),this._batchChangeChild(".middle-right",{x:r,y:i/2,offsetX:l/2-s,visible:a&&o.indexOf("middle-right")>=0}),this._batchChangeChild(".bottom-left",{x:0,y:i,offsetX:l/2+s,offsetY:l/2-s,visible:a&&o.indexOf("bottom-left")>=0}),this._batchChangeChild(".bottom-center",{x:r/2,y:i,offsetY:l/2-s,visible:a&&o.indexOf("bottom-center")>=0}),this._batchChangeChild(".bottom-right",{x:r,y:i,offsetX:l/2-s,offsetY:l/2-s,visible:a&&o.indexOf("bottom-right")>=0}),this._batchChangeChild(".rotater",{x:r/2,y:-this.rotateAnchorOffset()*he._sign(i)-s,visible:this.rotateEnabled()}),this._batchChangeChild(".back",{width:r,height:i,visible:this.borderEnabled(),stroke:this.borderStroke(),strokeWidth:this.borderStrokeWidth(),dash:this.borderDash(),x:0,y:0}),(t=this.getLayer())===null||t===void 0||t.batchDraw()}isTransforming(){return this._transforming}stopTransform(){if(this._transforming){this._removeEvents();var t=this.findOne("."+this._movingAnchorName);t&&t.stopDrag()}}destroy(){return this.getStage()&&this._cursorChange&&this.getStage().content&&(this.getStage().content.style.cursor=""),n1.prototype.destroy.call(this),this.detach(),this._removeEvents(),this}toObject(){return qe.prototype.toObject.call(this)}getClientRect(){return this.nodes().length>0?super.getClientRect():{x:0,y:0,width:0,height:0}}}function z7e(e){return e instanceof Array||he.warn("enabledAnchors value should be an array"),e instanceof Array&&e.forEach(function(t){G5.indexOf(t)===-1&&he.warn("Unknown anchor name: "+t+". Available names are: "+G5.join(", "))}),e||[]}On.prototype.className="Transformer";Er(On);ee.addGetterSetter(On,"enabledAnchors",G5,z7e);ee.addGetterSetter(On,"flipEnabled",!0,ol());ee.addGetterSetter(On,"resizeEnabled",!0);ee.addGetterSetter(On,"anchorSize",10,Ge());ee.addGetterSetter(On,"rotateEnabled",!0);ee.addGetterSetter(On,"rotationSnaps",[]);ee.addGetterSetter(On,"rotateAnchorOffset",50,Ge());ee.addGetterSetter(On,"rotationSnapTolerance",5,Ge());ee.addGetterSetter(On,"borderEnabled",!0);ee.addGetterSetter(On,"anchorStroke","rgb(0, 161, 255)");ee.addGetterSetter(On,"anchorStrokeWidth",1,Ge());ee.addGetterSetter(On,"anchorFill","white");ee.addGetterSetter(On,"anchorCornerRadius",0,Ge());ee.addGetterSetter(On,"borderStroke","rgb(0, 161, 255)");ee.addGetterSetter(On,"borderStrokeWidth",1,Ge());ee.addGetterSetter(On,"borderDash");ee.addGetterSetter(On,"keepRatio",!0);ee.addGetterSetter(On,"centeredScaling",!1);ee.addGetterSetter(On,"ignoreStroke",!1);ee.addGetterSetter(On,"padding",0,Ge());ee.addGetterSetter(On,"node");ee.addGetterSetter(On,"nodes");ee.addGetterSetter(On,"boundBoxFunc");ee.addGetterSetter(On,"anchorDragBoundFunc");ee.addGetterSetter(On,"shouldOverdrawWholeArea",!1);ee.addGetterSetter(On,"useSingleNodeRotation",!0);ee.backCompat(On,{lineEnabled:"borderEnabled",rotateHandlerOffset:"rotateAnchorOffset",enabledHandlers:"enabledAnchors"});class vc extends $e{_sceneFunc(t){t.beginPath(),t.arc(0,0,this.radius(),0,gt.getAngle(this.angle()),this.clockwise()),t.lineTo(0,0),t.closePath(),t.fillStrokeShape(this)}getWidth(){return this.radius()*2}getHeight(){return this.radius()*2}setWidth(t){this.radius(t/2)}setHeight(t){this.radius(t/2)}}vc.prototype.className="Wedge";vc.prototype._centroid=!0;vc.prototype._attrsAffectingSize=["radius"];Er(vc);ee.addGetterSetter(vc,"radius",0,Ge());ee.addGetterSetter(vc,"angle",0,Ge());ee.addGetterSetter(vc,"clockwise",!1);ee.backCompat(vc,{angleDeg:"angle",getAngleDeg:"getAngle",setAngleDeg:"setAngle"});function YI(){this.r=0,this.g=0,this.b=0,this.a=0,this.next=null}var 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U7e=function(t){var n=Math.round(this.blurRadius());n>0&&V7e(t,n)};ee.addGetterSetter(qe,"blurRadius",0,Ge(),ee.afterSetFilter);const W7e=function(e){var t=this.brightness()*255,n=e.data,r=n.length,i;for(i=0;i<r;i+=4)n[i]+=t,n[i+1]+=t,n[i+2]+=t};ee.addGetterSetter(qe,"brightness",0,Ge(),ee.afterSetFilter);const G7e=function(e){var t=Math.pow((this.contrast()+100)/100,2),n=e.data,r=n.length,i=150,o=150,a=150,s;for(s=0;s<r;s+=4)i=n[s],o=n[s+1],a=n[s+2],i/=255,i-=.5,i*=t,i+=.5,i*=255,o/=255,o-=.5,o*=t,o+=.5,o*=255,a/=255,a-=.5,a*=t,a+=.5,a*=255,i=i<0?0:i>255?255:i,o=o<0?0:o>255?255:o,a=a<0?0:a>255?255:a,n[s]=i,n[s+1]=o,n[s+2]=a};ee.addGetterSetter(qe,"contrast",0,Ge(),ee.afterSetFilter);const q7e=function(e){var 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yLe=globalThis&&globalThis.__extends||function(){var e=function(t,n){return e=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(r,i){r.__proto__=i}||function(r,i){for(var o in i)Object.prototype.hasOwnProperty.call(i,o)&&(r[o]=i[o])},e(t,n)};return function(t,n){e(t,n);function r(){this.constructor=t}t.prototype=n===null?Object.create(n):(r.prototype=n.prototype,new r)}}(),Jr=globalThis&&globalThis.__assign||function(){return Jr=Object.assign||function(e){for(var t,n=1,r=arguments.length;n<r;n++){t=arguments[n];for(var i in t)Object.prototype.hasOwnProperty.call(t,i)&&(e[i]=t[i])}return e},Jr.apply(this,arguments)},ND={width:"100%",height:"10px",top:"0px",left:"0px",cursor:"row-resize"},BD={width:"10px",height:"100%",top:"0px",left:"0px",cursor:"col-resize"},Bb={width:"20px",height:"20px",position:"absolute"},bLe={top:Jr(Jr({},ND),{top:"-5px"}),right:Jr(Jr({},BD),{left:void 0,right:"-5px"}),bottom:Jr(Jr({},ND),{top:void 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n=this.window.getComputedStyle(this.resizable);this.setState({width:this.state.width||this.size.width,height:this.state.height||this.size.height,flexBasis:n.flexBasis!=="auto"?n.flexBasis:void 0})}},t.prototype.componentWillUnmount=function(){this.window&&this.unbindEvents()},t.prototype.createSizeForCssProperty=function(n,r){var i=this.propsSize&&this.propsSize[r];return this.state[r]==="auto"&&this.state.original[r]===n&&(typeof i>"u"||i==="auto")?"auto":n},t.prototype.calculateNewMaxFromBoundary=function(n,r){var i=this.props.boundsByDirection,o=this.state.direction,a=i&&N0("left",o),s=i&&N0("top",o),l,u;if(this.props.bounds==="parent"){var f=this.parentNode;f&&(l=a?this.resizableRight-this.parentLeft:f.offsetWidth+(this.parentLeft-this.resizableLeft),u=s?this.resizableBottom-this.parentTop:f.offsetHeight+(this.parentTop-this.resizableTop))}else 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N0("right",s)&&(v=l.width+(n-l.x)*o/i,f&&(b=(v-E)/this.ratio+x)),N0("left",s)&&(v=l.width-(n-l.x)*o/i,f&&(b=(v-E)/this.ratio+x)),N0("bottom",s)&&(b=l.height+(r-l.y)*o/i,f&&(v=(b-x)*this.ratio+E)),N0("top",s)&&(b=l.height-(r-l.y)*o/i,f&&(v=(b-x)*this.ratio+E)),{newWidth:v,newHeight:b}},t.prototype.calculateNewSizeFromAspectRatio=function(n,r,i,o){var a=this.props,s=a.lockAspectRatio,l=a.lockAspectRatioExtraHeight,u=a.lockAspectRatioExtraWidth,f=typeof o.width>"u"?10:o.width,p=typeof i.width>"u"||i.width<0?n:i.width,m=typeof o.height>"u"?10:o.height,v=typeof i.height>"u"||i.height<0?r:i.height,b=l||0,x=u||0;if(s){var E=(m-b)*this.ratio+x,k=(v-b)*this.ratio+x,_=(f-x)/this.ratio+b,T=(p-x)/this.ratio+b,A=Math.max(f,E),R=Math.min(p,k),I=Math.max(m,_),D=Math.min(v,T);n=$b(n,A,R),r=$b(r,I,D)}else n=$b(n,f,p),r=$b(r,m,v);return{newWidth:n,newHeight:r}},t.prototype.setBoundingClientRect=function(){if(this.props.bounds==="parent"){var n=this.parentNode;if(n){var r=n.getBoundingClientRect();this.parentLeft=r.left,this.parentTop=r.top}}if(this.props.bounds&&typeof this.props.bounds!="string"){var i=this.props.bounds.getBoundingClientRect();this.targetLeft=i.left,this.targetTop=i.top}if(this.resizable){var o=this.resizable.getBoundingClientRect(),a=o.left,s=o.top,l=o.right,u=o.bottom;this.resizableLeft=a,this.resizableRight=l,this.resizableTop=s,this.resizableBottom=u}},t.prototype.onResizeStart=function(n,r){if(!(!this.resizable||!this.window)){var i=0,o=0;if(n.nativeEvent&&CLe(n.nativeEvent)?(i=n.nativeEvent.clientX,o=n.nativeEvent.clientY):n.nativeEvent&&Fb(n.nativeEvent)&&(i=n.nativeEvent.touches[0].clientX,o=n.nativeEvent.touches[0].clientY),this.props.onResizeStart&&this.resizable){var a=this.props.onResizeStart(n,r,this.resizable);if(a===!1)return}this.props.size&&(typeof this.props.size.height<"u"&&this.props.size.height!==this.state.height&&this.setState({height:this.props.size.height}),typeof 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i=this.props,o=i.maxWidth,a=i.maxHeight,s=i.minWidth,l=i.minHeight,u=Fb(n)?n.touches[0].clientX:n.clientX,f=Fb(n)?n.touches[0].clientY:n.clientY,p=this.state,m=p.direction,v=p.original,b=p.width,x=p.height,E=this.getParentSize(),k=_Le(E,this.window.innerWidth,this.window.innerHeight,o,a,s,l);o=k.maxWidth,a=k.maxHeight,s=k.minWidth,l=k.minHeight;var _=this.calculateNewSizeFromDirection(u,f),T=_.newHeight,A=_.newWidth,R=this.calculateNewMaxFromBoundary(o,a);this.props.snap&&this.props.snap.x&&(A=FD(A,this.props.snap.x,this.props.snapGap)),this.props.snap&&this.props.snap.y&&(T=FD(T,this.props.snap.y,this.props.snapGap));var I=this.calculateNewSizeFromAspectRatio(A,T,{width:R.maxWidth,height:R.maxHeight},{width:s,height:l});if(A=I.newWidth,T=I.newHeight,this.props.grid){var D=$D(A,this.props.grid[0]),B=$D(T,this.props.grid[1]),z=this.props.snapGap||0;A=z===0||Math.abs(D-A)<=z?D:A,T=z===0||Math.abs(B-T)<=z?B:T}var j={width:A-v.width,height:T-v.height};if(b&&typeof 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this.resizable?this.resizable.parentNode:null},enumerable:!1,configurable:!0}),Object.defineProperty(t.prototype,"window",{get:function(){return!this.resizable||!this.resizable.ownerDocument?null:this.resizable.ownerDocument.defaultView},enumerable:!1,configurable:!0}),Object.defineProperty(t.prototype,"propsSize",{get:function(){return this.props.size||this.props.defaultSize||_Le},enumerable:!1,configurable:!0}),Object.defineProperty(t.prototype,"size",{get:function(){var n=0,r=0;if(this.resizable&&this.window){var i=this.resizable.offsetWidth,o=this.resizable.offsetHeight,a=this.resizable.style.position;a!=="relative"&&(this.resizable.style.position="relative"),n=this.resizable.style.width!=="auto"?this.resizable.offsetWidth:i,r=this.resizable.style.height!=="auto"?this.resizable.offsetHeight:o,this.resizable.style.position=a}return{width:n,height:r}},enumerable:!1,configurable:!0}),Object.defineProperty(t.prototype,"sizeStyle",{get:function(){var n=this,r=this.props.size,i=function(s){if(typeof n.state[s]>"u"||n.state[s]==="auto")return"auto";if(n.propsSize&&n.propsSize[s]&&n.propsSize[s].toString().endsWith("%")){if(n.state[s].toString().endsWith("%"))return n.state[s].toString();var l=n.getParentSize(),u=Number(n.state[s].toString().replace("px","")),f=u/l[s]*100;return f+"%"}return RC(n.state[s])},o=r&&typeof r.width<"u"&&!this.state.isResizing?RC(r.width):i("width"),a=r&&typeof r.height<"u"&&!this.state.isResizing?RC(r.height):i("height");return{width:o,height:a}},enumerable:!1,configurable:!0}),t.prototype.getParentSize=function(){if(!this.parentNode)return this.window?{width:this.window.innerWidth,height:this.window.innerHeight}:{width:0,height:0};var n=this.appendBase();if(!n)return{width:0,height:0};var r=!1,i=this.parentNode.style.flexWrap;i!=="wrap"&&(r=!0,this.parentNode.style.flexWrap="wrap"),n.style.position="relative",n.style.minWidth="100%",n.style.minHeight="100%";var o={width:n.offsetWidth,height:n.offsetHeight};return r&&(this.parentNode.style.flexWrap=i),this.removeBase(n),o},t.prototype.bindEvents=function(){this.window&&(this.window.addEventListener("mouseup",this.onMouseUp),this.window.addEventListener("mousemove",this.onMouseMove),this.window.addEventListener("mouseleave",this.onMouseUp),this.window.addEventListener("touchmove",this.onMouseMove,{capture:!0,passive:!1}),this.window.addEventListener("touchend",this.onMouseUp))},t.prototype.unbindEvents=function(){this.window&&(this.window.removeEventListener("mouseup",this.onMouseUp),this.window.removeEventListener("mousemove",this.onMouseMove),this.window.removeEventListener("mouseleave",this.onMouseUp),this.window.removeEventListener("touchmove",this.onMouseMove,!0),this.window.removeEventListener("touchend",this.onMouseUp))},t.prototype.componentDidMount=function(){if(!(!this.resizable||!this.window)){var n=this.window.getComputedStyle(this.resizable);this.setState({width:this.state.width||this.size.width,height:this.state.height||this.size.height,flexBasis:n.flexBasis!=="auto"?n.flexBasis:void 0})}},t.prototype.componentWillUnmount=function(){this.window&&this.unbindEvents()},t.prototype.createSizeForCssProperty=function(n,r){var i=this.propsSize&&this.propsSize[r];return this.state[r]==="auto"&&this.state.original[r]===n&&(typeof i>"u"||i==="auto")?"auto":n},t.prototype.calculateNewMaxFromBoundary=function(n,r){var i=this.props.boundsByDirection,o=this.state.direction,a=i&&N0("left",o),s=i&&N0("top",o),l,u;if(this.props.bounds==="parent"){var f=this.parentNode;f&&(l=a?this.resizableRight-this.parentLeft:f.offsetWidth+(this.parentLeft-this.resizableLeft),u=s?this.resizableBottom-this.parentTop:f.offsetHeight+(this.parentTop-this.resizableTop))}else this.props.bounds==="window"?this.window&&(l=a?this.resizableRight:this.window.innerWidth-this.resizableLeft,u=s?this.resizableBottom:this.window.innerHeight-this.resizableTop):this.props.bounds&&(l=a?this.resizableRight-this.targetLeft:this.props.bounds.offsetWidth+(this.targetLeft-this.resizableLeft),u=s?this.resizableBottom-this.targetTop:this.props.bounds.offsetHeight+(this.targetTop-this.resizableTop));return l&&Number.isFinite(l)&&(n=n&&n<l?n:l),u&&Number.isFinite(u)&&(r=r&&r<u?r:u),{maxWidth:n,maxHeight:r}},t.prototype.calculateNewSizeFromDirection=function(n,r){var i=this.props.scale||1,o=this.props.resizeRatio||1,a=this.state,s=a.direction,l=a.original,u=this.props,f=u.lockAspectRatio,p=u.lockAspectRatioExtraHeight,m=u.lockAspectRatioExtraWidth,v=l.width,b=l.height,x=p||0,E=m||0;return N0("right",s)&&(v=l.width+(n-l.x)*o/i,f&&(b=(v-E)/this.ratio+x)),N0("left",s)&&(v=l.width-(n-l.x)*o/i,f&&(b=(v-E)/this.ratio+x)),N0("bottom",s)&&(b=l.height+(r-l.y)*o/i,f&&(v=(b-x)*this.ratio+E)),N0("top",s)&&(b=l.height-(r-l.y)*o/i,f&&(v=(b-x)*this.ratio+E)),{newWidth:v,newHeight:b}},t.prototype.calculateNewSizeFromAspectRatio=function(n,r,i,o){var a=this.props,s=a.lockAspectRatio,l=a.lockAspectRatioExtraHeight,u=a.lockAspectRatioExtraWidth,f=typeof o.width>"u"?10:o.width,p=typeof i.width>"u"||i.width<0?n:i.width,m=typeof o.height>"u"?10:o.height,v=typeof i.height>"u"||i.height<0?r:i.height,b=l||0,x=u||0;if(s){var E=(m-b)*this.ratio+x,k=(v-b)*this.ratio+x,_=(f-x)/this.ratio+b,T=(p-x)/this.ratio+b,A=Math.max(f,E),R=Math.min(p,k),I=Math.max(m,_),D=Math.min(v,T);n=$b(n,A,R),r=$b(r,I,D)}else n=$b(n,f,p),r=$b(r,m,v);return{newWidth:n,newHeight:r}},t.prototype.setBoundingClientRect=function(){if(this.props.bounds==="parent"){var n=this.parentNode;if(n){var r=n.getBoundingClientRect();this.parentLeft=r.left,this.parentTop=r.top}}if(this.props.bounds&&typeof this.props.bounds!="string"){var i=this.props.bounds.getBoundingClientRect();this.targetLeft=i.left,this.targetTop=i.top}if(this.resizable){var o=this.resizable.getBoundingClientRect(),a=o.left,s=o.top,l=o.right,u=o.bottom;this.resizableLeft=a,this.resizableRight=l,this.resizableTop=s,this.resizableBottom=u}},t.prototype.onResizeStart=function(n,r){if(!(!this.resizable||!this.window)){var i=0,o=0;if(n.nativeEvent&&kLe(n.nativeEvent)?(i=n.nativeEvent.clientX,o=n.nativeEvent.clientY):n.nativeEvent&&Fb(n.nativeEvent)&&(i=n.nativeEvent.touches[0].clientX,o=n.nativeEvent.touches[0].clientY),this.props.onResizeStart&&this.resizable){var a=this.props.onResizeStart(n,r,this.resizable);if(a===!1)return}this.props.size&&(typeof this.props.size.height<"u"&&this.props.size.height!==this.state.height&&this.setState({height:this.props.size.height}),typeof this.props.size.width<"u"&&this.props.size.width!==this.state.width&&this.setState({width:this.props.size.width})),this.ratio=typeof this.props.lockAspectRatio=="number"?this.props.lockAspectRatio:this.size.width/this.size.height;var s,l=this.window.getComputedStyle(this.resizable);if(l.flexBasis!=="auto"){var u=this.parentNode;if(u){var f=this.window.getComputedStyle(u).flexDirection;this.flexDir=f.startsWith("row")?"row":"column",s=l.flexBasis}}this.setBoundingClientRect(),this.bindEvents();var p={original:{x:i,y:o,width:this.size.width,height:this.size.height},isResizing:!0,backgroundStyle:Fl(Fl({},this.state.backgroundStyle),{cursor:this.window.getComputedStyle(n.target).cursor||"auto"}),direction:r,flexBasis:s};this.setState(p)}},t.prototype.onMouseMove=function(n){var r=this;if(!(!this.state.isResizing||!this.resizable||!this.window)){if(this.window.TouchEvent&&Fb(n))try{n.preventDefault(),n.stopPropagation()}catch(V){}var i=this.props,o=i.maxWidth,a=i.maxHeight,s=i.minWidth,l=i.minHeight,u=Fb(n)?n.touches[0].clientX:n.clientX,f=Fb(n)?n.touches[0].clientY:n.clientY,p=this.state,m=p.direction,v=p.original,b=p.width,x=p.height,E=this.getParentSize(),k=ELe(E,this.window.innerWidth,this.window.innerHeight,o,a,s,l);o=k.maxWidth,a=k.maxHeight,s=k.minWidth,l=k.minHeight;var _=this.calculateNewSizeFromDirection(u,f),T=_.newHeight,A=_.newWidth,R=this.calculateNewMaxFromBoundary(o,a);this.props.snap&&this.props.snap.x&&(A=FD(A,this.props.snap.x,this.props.snapGap)),this.props.snap&&this.props.snap.y&&(T=FD(T,this.props.snap.y,this.props.snapGap));var I=this.calculateNewSizeFromAspectRatio(A,T,{width:R.maxWidth,height:R.maxHeight},{width:s,height:l});if(A=I.newWidth,T=I.newHeight,this.props.grid){var D=$D(A,this.props.grid[0]),B=$D(T,this.props.grid[1]),z=this.props.snapGap||0;A=z===0||Math.abs(D-A)<=z?D:A,T=z===0||Math.abs(B-T)<=z?B:T}var j={width:A-v.width,height:T-v.height};if(b&&typeof b=="string"){if(b.endsWith("%")){var K=A/E.width*100;A=K+"%"}else if(b.endsWith("vw")){var te=A/this.window.innerWidth*100;A=te+"vw"}else if(b.endsWith("vh")){var G=A/this.window.innerHeight*100;A=G+"vh"}}if(x&&typeof x=="string"){if(x.endsWith("%")){var K=T/E.height*100;T=K+"%"}else if(x.endsWith("vw")){var te=T/this.window.innerWidth*100;T=te+"vw"}else if(x.endsWith("vh")){var G=T/this.window.innerHeight*100;T=G+"vh"}}var $={width:this.createSizeForCssProperty(A,"width"),height:this.createSizeForCssProperty(T,"height")};this.flexDir==="row"?$.flexBasis=$.width:this.flexDir==="column"&&($.flexBasis=$.height),pu.exports.flushSync(function(){r.setState($)}),this.props.onResize&&this.props.onResize(n,m,this.resizable,j)}},t.prototype.onMouseUp=function(n){var r=this.state,i=r.isResizing,o=r.direction,a=r.original;if(!(!i||!this.resizable)){var s={width:this.size.width-a.width,height:this.size.height-a.height};this.props.onResizeStop&&this.props.onResizeStop(n,o,this.resizable,s),this.props.size&&this.setState(this.props.size),this.unbindEvents(),this.setState({isResizing:!1,backgroundStyle:Fl(Fl({},this.state.backgroundStyle),{cursor:"auto"})})}},t.prototype.updateSize=function(n){this.setState({width:n.width,height:n.height})},t.prototype.renderResizer=function(){var n=this,r=this.props,i=r.enable,o=r.handleStyles,a=r.handleClasses,s=r.handleWrapperStyle,l=r.handleWrapperClass,u=r.handleComponent;if(!i)return null;var f=Object.keys(i).map(function(p){return i[p]!==!1?y(wLe,{direction:p,onResizeStart:n.onResizeStart,replaceStyles:o&&o[p],className:a&&a[p],children:u&&u[p]?u[p]:null},p):null});return y("div",{className:l,style:s,children:f})},t.prototype.render=function(){var n=this,r=Object.keys(this.props).reduce(function(a,s){return PLe.indexOf(s)!==-1||(a[s]=n.props[s]),a},{}),i=Fl(Fl(Fl({position:"relative",userSelect:this.state.isResizing?"none":"auto"},this.props.style),this.sizeStyle),{maxWidth:this.props.maxWidth,maxHeight:this.props.maxHeight,minWidth:this.props.minWidth,minHeight:this.props.minHeight,boxSizing:"border-box",flexShrink:0});this.state.flexBasis&&(i.flexBasis=this.state.flexBasis);var o=this.props.as||"div";return q(o,{...Fl({ref:this.ref,style:i,className:this.props.className},r),children:[this.state.isResizing&&y("div",{style:this.state.backgroundStyle}),this.props.children,this.renderResizer()]})},t.defaultProps={as:"div",onResizeStart:function(){},onResize:function(){},onResizeStop:function(){},enable:{top:!0,right:!0,bottom:!0,left:!0,topRight:!0,bottomRight:!0,bottomLeft:!0,topLeft:!0},style:{},grid:[1,1],lockAspectRatio:!1,lockAspectRatioExtraWidth:0,lockAspectRatioExtraHeight:0,scale:1,resizeRatio:1,snapGap:0},t}(C.exports.PureComponent),TLe=Object.create,Eq=Object.defineProperty,LLe=Object.getOwnPropertyDescriptor,ALe=Object.getOwnPropertyNames,OLe=Object.getPrototypeOf,MLe=Object.prototype.hasOwnProperty,Ue=(e,t)=>()=>(t||e((t={exports:{}}).exports,t),t.exports),RLe=(e,t,n,r)=>{if(t&&typeof t=="object"||typeof t=="function")for(let i of ALe(t))!MLe.call(e,i)&&i!==n&&Eq(e,i,{get:()=>t[i],enumerable:!(r=LLe(t,i))||r.enumerable});return 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n.pressedKeys[i]})):!0},n.setComponents=function(r,i){n.wrapperComponent=r,n.contentComponent=i,s1(n,n.transformState.scale),n.handleInitializeWrapperEvents(r),n.handleInitialize(),n.handleRef(),n.isInitialized=!0,Pi(ni(n),void 0,n.props.onInit)},n.setTransformState=function(r,i,o){!isNaN(r)&&!isNaN(i)&&!isNaN(o)?(r!==n.transformState.scale&&(n.transformState.previousScale=n.transformState.scale,n.transformState.scale=r),n.transformState.positionX=i,n.transformState.positionY=o,n.applyTransformation()):console.error("Detected NaN set state values")},n.setCenter=function(){if(n.wrapperComponent&&n.contentComponent){var r=jY(n.transformState.scale,n.wrapperComponent,n.contentComponent);n.setTransformState(r.scale,r.positionX,r.positionY)}},n.applyTransformation=function(){if(!(!n.mounted||!n.contentComponent)){var r=n.transformState,i=r.scale,o=r.positionX,a=r.positionY,s=FDe(o,a,i);n.contentComponent.style.transform=s,n.handleRef()}},n.handleRef=function(){n.props.setRef(ni(n))},n}return t.prototype.componentDidMount=function(){var n=jC();window.addEventListener("mousedown",this.onPanningStart,n),window.addEventListener("mousemove",this.onPanning,n),window.addEventListener("mouseup",this.onPanningStop,n),document.addEventListener("mouseleave",this.clearPanning,n),window.addEventListener("keyup",this.setKeyUnPressed,n),window.addEventListener("keydown",this.setKeyPressed,n),this.handleRef()},t.prototype.componentWillUnmount=function(){var n=jC();window.removeEventListener("mousedown",this.onPanningStart,n),window.removeEventListener("mousemove",this.onPanning,n),window.removeEventListener("mouseup",this.onPanningStop,n),window.removeEventListener("keyup",this.setKeyUnPressed,n),window.removeEventListener("keydown",this.setKeyPressed,n),Gl(this)},t.prototype.componentDidUpdate=function(n){n!==this.props&&(s1(this,this.transformState.scale),this.setup=gN(this.props))},t.prototype.render=function(){var n=ni(this),r=this.props.children,i=typeof r=="function"?r(n):r;return y(UY.Provider,{value:su(su({},this.transformState),{setComponents:this.setComponents,contextInstance:this}),children:i})},t}(C.exports.Component),oNe=oe.forwardRef(function(e,t){var n=C.exports.useState(null),r=n[0],i=n[1];return C.exports.useImperativeHandle(t,function(){return r},[r]),y(iNe,{...su({},e,{setRef:i})})});function aNe(e,t){t===void 0&&(t={});var n=t.insertAt;if(!(!e||typeof document>"u")){var 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@@ -506,7 +506,7 @@ In order to be iterable, non-array objects must have a [Symbol.iterator]() metho
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lNe(){const e=Be(),t=ve(m=>m.options.isLightBoxOpen),{viewerImageToDisplay:n,shouldShowImageDetails:r,isOnFirstImage:i,isOnLastImage:o}=ve(Jq),[a,s]=C.exports.useState(!1),l=()=>{s(!0)},u=()=>{s(!1)},f=()=>{e(VE())},p=()=>{e(HE())};return Ze("Esc",()=>{t&&e(Qu(!1))},[t]),K("div",{className:"lightbox-container",children:[y(Qe,{icon:y(rLe,{}),"aria-label":"Exit Viewer",className:"lightbox-close-btn",onClick:()=>{e(Qu(!1))},fontSize:20}),K("div",{className:"lightbox-display-container",children:[K("div",{className:"lightbox-preview-wrapper",children:[y(Zq,{}),!r&&K("div",{className:"current-image-next-prev-buttons",children:[y("div",{className:"next-prev-button-trigger-area prev-button-trigger-area",onMouseOver:l,onMouseOut:u,children:a&&!i&&y(fs,{"aria-label":"Previous image",icon:y(DG,{className:"next-prev-button"}),variant:"unstyled",onClick:f})}),y("div",{className:"next-prev-button-trigger-area 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K(at,{direction:"column",gap:"1rem",children:[y(Zo,{sliderMarkRightOffset:-6,label:o("options:seamSize"),min:1,max:256,sliderNumberInputProps:{max:512},value:t,onChange:a=>{e(yR(a))},handleReset:()=>e(yR(96)),withInput:!0,withSliderMarks:!0,withReset:!0}),y(Zo,{sliderMarkRightOffset:-4,label:o("options:seamBlur"),min:0,max:64,sliderNumberInputProps:{max:512},value:n,onChange:a=>{e(vR(a))},handleReset:()=>{e(vR(16))},withInput:!0,withSliderMarks:!0,withReset:!0}),y(Zo,{sliderMarkRightOffset:-7,label:o("options:seamStrength"),min:.01,max:.99,step:.01,value:r,onChange:a=>{e(SR(a))},handleReset:()=>{e(SR(.7))},withInput:!0,withSliderMarks:!0,withReset:!0}),y(Zo,{sliderMarkRightOffset:-4,label:o("options:seamSteps"),min:1,max:32,sliderNumberInputProps:{max:100},value:i,onChange:a=>{e(bR(a))},handleReset:()=>{e(bR(10))},withInput:!0,withSliderMarks:!0,withReset:!0})]})},dNe=lt(rn,e=>{const{boundingBoxDimensions:t,boundingBoxScaleMethod:n}=e;return{boundingBoxDimensions:t,boundingBoxScale:n}},{memoizeOptions:{resultEqualityCheck:He.isEqual}}),fNe=()=>{const e=Be(),{boundingBoxDimensions:t}=ve(dNe),{t:n}=Ke(),r=s=>{e(_v({...t,width:Math.floor(s)}))},i=s=>{e(_v({...t,height:Math.floor(s)}))},o=()=>{e(_v({...t,width:Math.floor(512)}))},a=()=>{e(_v({...t,height:Math.floor(512)}))};return K(at,{direction:"column",gap:"1rem",children:[y(Zo,{label:n("options:width"),min:64,max:1024,step:64,value:t.width,onChange:r,handleReset:o,sliderNumberInputProps:{max:4096},withSliderMarks:!0,withInput:!0,withReset:!0}),y(Zo,{label:n("options:height"),min:64,max:1024,step:64,value:t.height,onChange:i,handleReset:a,sliderNumberInputProps:{max:4096},withSliderMarks:!0,withInput:!0,withReset:!0})]})},hNe=lt(rn,e=>{const{inpaintReplace:t,shouldUseInpaintReplace:n}=e;return{inpaintReplace:t,shouldUseInpaintReplace:n}},{memoizeOptions:{resultEqualityCheck:He.isEqual}});function pNe(){const{inpaintReplace:e,shouldUseInpaintReplace:t}=ve(hNe),n=Be(),{t:r}=Ke();return K(at,{alignItems:"center",columnGap:"1rem",children:[y(Zo,{label:r("options:inpaintReplace"),value:e,onChange:i=>{n(dI(i))},min:0,max:1,step:.05,isInteger:!1,isSliderDisabled:!t,withSliderMarks:!0,sliderMarkRightOffset:-2,withReset:!0,handleReset:()=>n(dI(1)),isResetDisabled:!t}),y(os,{isChecked:t,onChange:i=>n(Wwe(i.target.checked)),marginTop:"1.25rem"})]})}const gNe=lt([dP,al,rn],(e,t,n)=>{const{tileSize:r,infillMethod:i}=e,{infill_methods:o}=t,{boundingBoxScaleMethod:a,scaledBoundingBoxDimensions:s}=n;return{boundingBoxScale:a,scaledBoundingBoxDimensions:s,tileSize:r,infillMethod:i,availableInfillMethods:o,isManual:a==="manual"}},{memoizeOptions:{resultEqualityCheck:He.isEqual}}),mNe=()=>{const 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K(at,{direction:"column",gap:"1rem",children:[y(nl,{label:s("options:scaleBeforeProcessing"),validValues:xwe,value:i,onChange:m}),y(Zo,{isInputDisabled:!o,isResetDisabled:!o,isSliderDisabled:!o,label:s("options:scaledWidth"),min:64,max:1024,step:64,value:a.width,onChange:l,handleReset:f,sliderNumberInputProps:{max:4096},withSliderMarks:!0,withInput:!0,withReset:!0}),y(Zo,{isInputDisabled:!o,isResetDisabled:!o,isSliderDisabled:!o,label:s("options:scaledHeight"),min:64,max:1024,step:64,value:a.height,onChange:u,handleReset:p,sliderNumberInputProps:{max:4096},withSliderMarks:!0,withInput:!0,withReset:!0}),y(pNe,{}),y(nl,{label:s("options:infillMethod"),value:n,validValues:r,onChange:v=>e(kU(v.target.value))}),y(Zo,{isInputDisabled:n!=="tile",isResetDisabled:n!=="tile",isSliderDisabled:n!=="tile",sliderMarkRightOffset:-4,label:s("options:tileSize"),min:16,max:64,sliderNumberInputProps:{max:256},value:t,onChange:v=>{e(xR(v))},handleReset:()=>{e(xR(32))},withInput:!0,withSliderMarks:!0,withReset:!0})]})};function vNe(){const{t:e}=Ke(),t={boundingBox:{header:`${e("options:boundingBoxHeader")}`,feature:oo.BOUNDING_BOX,content:y(fNe,{})},seamCorrection:{header:`${e("options:seamCorrectionHeader")}`,feature:oo.SEAM_CORRECTION,content:y(cNe,{})},infillAndScaling:{header:`${e("options:infillScalingHeader")}`,feature:oo.INFILL_AND_SCALING,content:y(mNe,{})},seed:{header:`${e("options:seed")}`,feature:oo.SEED,content:y(TP,{})},variations:{header:`${e("options:variations")}`,feature:oo.VARIATIONS,content:y(OP,{}),additionalHeaderComponents:y(AP,{})}};return K(FP,{children:[y(BP,{}),y(NP,{}),y(MP,{}),y(jq,{label:e("options:img2imgStrength"),styleClass:"main-option-block image-to-image-strength-main-option"}),y(RP,{accordionInfo:t})]})}const yNe=lt(rn,Uq,_r,(e,t,n)=>{const{doesCanvasNeedScaling:r,isCanvasInitialized:i}=e;return{doesCanvasNeedScaling:r,activeTabName:n,initialCanvasImage:t,isCanvasInitialized:i}}),WY=()=>{const e=Be(),{doesCanvasNeedScaling:t,activeTabName:n,initialCanvasImage:r,isCanvasInitialized:i}=ve(yNe),o=C.exports.useRef(null);return C.exports.useLayoutEffect(()=>{window.setTimeout(()=>{if(!o.current)return;const{clientWidth:a,clientHeight:s}=o.current;e(Fwe({width:a,height:s})),e(i?Bwe():bx()),e(vi(!1))},0)},[e,r,t,n,i]),y("div",{ref:o,className:"inpainting-canvas-area",children:y(by,{thickness:"2px",speed:"1s",size:"xl"})})};var GY={exports:{}};/**
+`,vN={wrapper:"transform-component-module_wrapper__1_Fgj",content:"transform-component-module_content__2jYgh"};aNe(sNe);var lNe=function(e){var t=e.children,n=e.wrapperClass,r=n===void 0?"":n,i=e.contentClass,o=i===void 0?"":i,a=e.wrapperStyle,s=e.contentStyle,l=C.exports.useContext(UY).setComponents,u=C.exports.useRef(null),f=C.exports.useRef(null);return C.exports.useEffect(function(){var p=u.current,m=f.current;p!==null&&m!==null&&l&&l(p,m)},[]),y("div",{ref:u,className:"react-transform-wrapper "+vN.wrapper+" "+r,style:a,children:y("div",{ref:f,className:"react-transform-component "+vN.content+" "+o,style:s,children:t})})};function uNe({image:e,alt:t,ref:n,styleClass:r}){const[i,o]=C.exports.useState(0),[a,s]=C.exports.useState(!1),l=()=>{o(i===-3?0:i-1)},u=()=>{o(i===3?0:i+1)},f=()=>{s(!a)};return y(oNe,{centerOnInit:!0,minScale:.1,initialPositionX:50,initialPositionY:50,children:({zoomIn:p,zoomOut:m,resetTransform:v,centerView:b})=>q(fn,{children:[q("div",{className:"lightbox-image-options",children:[y(Qe,{icon:y(aLe,{}),"aria-label":"Zoom In",tooltip:"Zoom In",onClick:()=>p(),fontSize:20}),y(Qe,{icon:y(sLe,{}),"aria-label":"Zoom Out",tooltip:"Zoom Out",onClick:()=>m(),fontSize:20}),y(Qe,{icon:y(iLe,{}),"aria-label":"Rotate Left",tooltip:"Rotate Left",onClick:l,fontSize:20}),y(Qe,{icon:y(oLe,{}),"aria-label":"Rotate Right",tooltip:"Rotate Right",onClick:u,fontSize:20}),y(Qe,{icon:y(eLe,{}),"aria-label":"Flip Image",tooltip:"Flip Image",onClick:f,fontSize:20}),y(Qe,{icon:y(Ux,{}),"aria-label":"Reset",tooltip:"Reset",onClick:()=>{v(),o(0),s(!1)},fontSize:20})]}),y(lNe,{wrapperStyle:{width:"100%",height:"100%"},children:y("img",{style:{transform:`rotate(${i*90}deg) scaleX(${a?-1:1})`,width:"100%"},src:e,alt:t,ref:n,className:r||"",onLoad:()=>b(1,0,"easeOut")})})]})})}function cNe(){const e=Be(),t=ge(m=>m.options.isLightBoxOpen),{viewerImageToDisplay:n,shouldShowImageDetails:r,isOnFirstImage:i,isOnLastImage:o}=ge(Jq),[a,s]=C.exports.useState(!1),l=()=>{s(!0)},u=()=>{s(!1)},f=()=>{e(GE())},p=()=>{e(WE())};return Ze("Esc",()=>{t&&e(Qu(!1))},[t]),q("div",{className:"lightbox-container",children:[y(Qe,{icon:y(rLe,{}),"aria-label":"Exit Viewer",className:"lightbox-close-btn",onClick:()=>{e(Qu(!1))},fontSize:20}),q("div",{className:"lightbox-display-container",children:[q("div",{className:"lightbox-preview-wrapper",children:[y(Zq,{}),!r&&q("div",{className:"current-image-next-prev-buttons",children:[y("div",{className:"next-prev-button-trigger-area prev-button-trigger-area",onMouseOver:l,onMouseOut:u,children:a&&!i&&y(hs,{"aria-label":"Previous image",icon:y(DG,{className:"next-prev-button"}),variant:"unstyled",onClick:f})}),y("div",{className:"next-prev-button-trigger-area next-button-trigger-area",onMouseOver:l,onMouseOut:u,children:a&&!o&&y(hs,{"aria-label":"Next image",icon:y(NG,{className:"next-prev-button"}),variant:"unstyled",onClick:p})})]}),n&&q(fn,{children:[y(uNe,{image:n.url,styleClass:"lightbox-image"}),r&&y(Qq,{image:n})]})]}),y(OY,{})]})]})}const dNe=st([hP],e=>{const{seamSize:t,seamBlur:n,seamStrength:r,seamSteps:i}=e;return{seamSize:t,seamBlur:n,seamStrength:r,seamSteps:i}},{memoizeOptions:{resultEqualityCheck:Fe.isEqual}}),fNe=()=>{const e=Be(),{seamSize:t,seamBlur:n,seamStrength:r,seamSteps:i}=ge(dNe),{t:o}=Ye();return q(tt,{direction:"column",gap:"1rem",children:[y(Qo,{sliderMarkRightOffset:-6,label:o("options:seamSize"),min:1,max:256,sliderNumberInputProps:{max:512},value:t,onChange:a=>{e(bR(a))},handleReset:()=>e(bR(96)),withInput:!0,withSliderMarks:!0,withReset:!0}),y(Qo,{sliderMarkRightOffset:-4,label:o("options:seamBlur"),min:0,max:64,sliderNumberInputProps:{max:512},value:n,onChange:a=>{e(yR(a))},handleReset:()=>{e(yR(16))},withInput:!0,withSliderMarks:!0,withReset:!0}),y(Qo,{sliderMarkRightOffset:-7,label:o("options:seamStrength"),min:.01,max:.99,step:.01,value:r,onChange:a=>{e(xR(a))},handleReset:()=>{e(xR(.7))},withInput:!0,withSliderMarks:!0,withReset:!0}),y(Qo,{sliderMarkRightOffset:-4,label:o("options:seamSteps"),min:1,max:32,sliderNumberInputProps:{max:100},value:i,onChange:a=>{e(SR(a))},handleReset:()=>{e(SR(10))},withInput:!0,withSliderMarks:!0,withReset:!0})]})},hNe=st(rn,e=>{const{boundingBoxDimensions:t,boundingBoxScaleMethod:n}=e;return{boundingBoxDimensions:t,boundingBoxScale:n}},{memoizeOptions:{resultEqualityCheck:Fe.isEqual}}),pNe=()=>{const e=Be(),{boundingBoxDimensions:t}=ge(hNe),{t:n}=Ye(),r=s=>{e(_v({...t,width:Math.floor(s)}))},i=s=>{e(_v({...t,height:Math.floor(s)}))},o=()=>{e(_v({...t,width:Math.floor(512)}))},a=()=>{e(_v({...t,height:Math.floor(512)}))};return q(tt,{direction:"column",gap:"1rem",children:[y(Qo,{label:n("options:width"),min:64,max:1024,step:64,value:t.width,onChange:r,handleReset:o,sliderNumberInputProps:{max:4096},withSliderMarks:!0,withInput:!0,withReset:!0}),y(Qo,{label:n("options:height"),min:64,max:1024,step:64,value:t.height,onChange:i,handleReset:a,sliderNumberInputProps:{max:4096},withSliderMarks:!0,withInput:!0,withReset:!0})]})},gNe=st(rn,e=>{const{inpaintReplace:t,shouldUseInpaintReplace:n}=e;return{inpaintReplace:t,shouldUseInpaintReplace:n}},{memoizeOptions:{resultEqualityCheck:Fe.isEqual}});function mNe(){const{inpaintReplace:e,shouldUseInpaintReplace:t}=ge(gNe),n=Be(),{t:r}=Ye();return q(tt,{alignItems:"center",columnGap:"1rem",children:[y(Qo,{label:r("options:inpaintReplace"),value:e,onChange:i=>{n(fI(i))},min:0,max:1,step:.05,isInteger:!1,isSliderDisabled:!t,withSliderMarks:!0,sliderMarkRightOffset:-2,withReset:!0,handleReset:()=>n(fI(1)),isResetDisabled:!t}),y(as,{isChecked:t,onChange:i=>n(Wwe(i.target.checked)),marginTop:"1.25rem"})]})}const vNe=st([hP,bs,rn],(e,t,n)=>{const{tileSize:r,infillMethod:i}=e,{infill_methods:o}=t,{boundingBoxScaleMethod:a,scaledBoundingBoxDimensions:s}=n;return{boundingBoxScale:a,scaledBoundingBoxDimensions:s,tileSize:r,infillMethod:i,availableInfillMethods:o,isManual:a==="manual"}},{memoizeOptions:{resultEqualityCheck:Fe.isEqual}}),yNe=()=>{const e=Be(),{tileSize:t,infillMethod:n,availableInfillMethods:r,boundingBoxScale:i,isManual:o,scaledBoundingBoxDimensions:a}=ge(vNe),{t:s}=Ye(),l=v=>{e(xb({...a,width:Math.floor(v)}))},u=v=>{e(xb({...a,height:Math.floor(v)}))},f=()=>{e(xb({...a,width:Math.floor(512)}))},p=()=>{e(xb({...a,height:Math.floor(512)}))},m=v=>{e($we(v.target.value))};return q(tt,{direction:"column",gap:"1rem",children:[y(il,{label:s("options:scaleBeforeProcessing"),validValues:xwe,value:i,onChange:m}),y(Qo,{isInputDisabled:!o,isResetDisabled:!o,isSliderDisabled:!o,label:s("options:scaledWidth"),min:64,max:1024,step:64,value:a.width,onChange:l,handleReset:f,sliderNumberInputProps:{max:4096},withSliderMarks:!0,withInput:!0,withReset:!0}),y(Qo,{isInputDisabled:!o,isResetDisabled:!o,isSliderDisabled:!o,label:s("options:scaledHeight"),min:64,max:1024,step:64,value:a.height,onChange:u,handleReset:p,sliderNumberInputProps:{max:4096},withSliderMarks:!0,withInput:!0,withReset:!0}),y(mNe,{}),y(il,{label:s("options:infillMethod"),value:n,validValues:r,onChange:v=>e(kU(v.target.value))}),y(Qo,{isInputDisabled:n!=="tile",isResetDisabled:n!=="tile",isSliderDisabled:n!=="tile",sliderMarkRightOffset:-4,label:s("options:tileSize"),min:16,max:64,sliderNumberInputProps:{max:256},value:t,onChange:v=>{e(wR(v))},handleReset:()=>{e(wR(32))},withInput:!0,withSliderMarks:!0,withReset:!0})]})};function bNe(){const{t:e}=Ye(),t={boundingBox:{header:`${e("options:boundingBoxHeader")}`,feature:lo.BOUNDING_BOX,content:y(pNe,{})},seamCorrection:{header:`${e("options:seamCorrectionHeader")}`,feature:lo.SEAM_CORRECTION,content:y(fNe,{})},infillAndScaling:{header:`${e("options:infillScalingHeader")}`,feature:lo.INFILL_AND_SCALING,content:y(yNe,{})},seed:{header:`${e("options:seed")}`,feature:lo.SEED,content:y(LP,{})},variations:{header:`${e("options:variations")}`,feature:lo.VARIATIONS,content:y(MP,{}),additionalHeaderComponents:y(OP,{})}};return q(zP,{children:[y($P,{}),y(BP,{}),y(RP,{}),y(jq,{label:e("options:img2imgStrength"),styleClass:"main-option-block image-to-image-strength-main-option"}),y(IP,{accordionInfo:t})]})}const SNe=st(rn,Uq,Pr,(e,t,n)=>{const{doesCanvasNeedScaling:r,isCanvasInitialized:i}=e;return{doesCanvasNeedScaling:r,activeTabName:n,initialCanvasImage:t,isCanvasInitialized:i}}),WY=()=>{const e=Be(),{doesCanvasNeedScaling:t,activeTabName:n,initialCanvasImage:r,isCanvasInitialized:i}=ge(SNe),o=C.exports.useRef(null);return C.exports.useLayoutEffect(()=>{window.setTimeout(()=>{if(!o.current)return;const{clientWidth:a,clientHeight:s}=o.current;e(Fwe({width:a,height:s})),e(i?Bwe():bx()),e(vi(!1))},0)},[e,r,t,n,i]),y("div",{ref:o,className:"inpainting-canvas-area",children:y(by,{thickness:"2px",speed:"1s",size:"xl"})})};var GY={exports:{}};/**
  * @license React
  * react-reconciler.production.min.js
  *
@@ -514,17 +514,17 @@ In order to be iterable, non-array objects must have a [Symbol.iterator]() metho
  *
  * This source code is licensed under the MIT license found in the
  * LICENSE file in the root directory of this source tree.
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d=M!==null?M.baseLanes|S:S,h.lanes=h.childLanes=1073741824,h.memoizedState={baseLanes:d,cachePool:null,transitions:null},h.updateQueue=null,kn(xl,fo),fo|=d,null;h.memoizedState={baseLanes:0,cachePool:null,transitions:null},P=M!==null?M.baseLanes:S,kn(xl,fo),fo|=P}else M!==null?(P=M.baseLanes|S,h.memoizedState=null):P=S,kn(xl,fo),fo|=P;return Di(d,h,L,S),h.child}function s3(d,h){var S=h.ref;(d===null&&S!==null||d!==null&&d.ref!==S)&&(h.flags|=512,h.flags|=2097152)}function dm(d,h,S,P,L){var M=si(S)?la:Br.current;return M=dl(h,M),Ic(h,L),S=nm(d,h,S,P,M,L),P=Fc(),d!==null&&!Xi?(h.updateQueue=d.updateQueue,h.flags&=-2053,d.lanes&=~L,ks(d,h,L)):(Hn&&P&&Mi(h),h.flags|=1,Di(d,h,S,L),h.child)}function l3(d,h,S,P,L){if(si(S)){var M=!0;xs(h)}else M=!1;if(Ic(h,L),h.stateNode===null)Wa(d,h),qy(h,S,P),Q1(h,S,P,L),P=!0;else if(d===null){var W=h.stateNode,se=h.memoizedProps;W.props=se;var pe=W.context,je=S.contextType;typeof je=="object"&&je!==null?je=uo(je):(je=si(S)?la:Br.current,je=dl(h,je));var ht=S.getDerivedStateFromProps,$t=typeof ht=="function"||typeof W.getSnapshotBeforeUpdate=="function";$t||typeof W.UNSAFE_componentWillReceiveProps!="function"&&typeof W.componentWillReceiveProps!="function"||(se!==P||pe!==je)&&Yy(h,W,P,je),ws=!1;var rt=h.memoizedState;W.state=rt,Fp(h,P,W,L),pe=h.memoizedState,se!==P||rt!==pe||ai.current||ws?(typeof ht=="function"&&(jp(h,S,ht,P),pe=h.memoizedState),(se=ws||$f(h,S,se,P,rt,pe,je))?($t||typeof W.UNSAFE_componentWillMount!="function"&&typeof W.componentWillMount!="function"||(typeof W.componentWillMount=="function"&&W.componentWillMount(),typeof W.UNSAFE_componentWillMount=="function"&&W.UNSAFE_componentWillMount()),typeof W.componentDidMount=="function"&&(h.flags|=4194308)):(typeof W.componentDidMount=="function"&&(h.flags|=4194308),h.memoizedProps=P,h.memoizedState=pe),W.props=P,W.state=pe,W.context=je,P=se):(typeof W.componentDidMount=="function"&&(h.flags|=4194308),P=!1)}else{W=h.stateNode,Wy(d,h),se=h.memoizedProps,je=h.type===h.elementType?se:ca(h.type,se),W.props=je,$t=h.pendingProps,rt=W.context,pe=S.contextType,typeof pe=="object"&&pe!==null?pe=uo(pe):(pe=si(S)?la:Br.current,pe=dl(h,pe));var Un=S.getDerivedStateFromProps;(ht=typeof Un=="function"||typeof W.getSnapshotBeforeUpdate=="function")||typeof W.UNSAFE_componentWillReceiveProps!="function"&&typeof W.componentWillReceiveProps!="function"||(se!==$t||rt!==pe)&&Yy(h,W,P,pe),ws=!1,rt=h.memoizedState,W.state=rt,Fp(h,P,W,L);var Rn=h.memoizedState;se!==$t||rt!==Rn||ai.current||ws?(typeof Un=="function"&&(jp(h,S,Un,P),Rn=h.memoizedState),(je=ws||$f(h,S,je,P,rt,Rn,pe)||!1)?(ht||typeof W.UNSAFE_componentWillUpdate!="function"&&typeof W.componentWillUpdate!="function"||(typeof W.componentWillUpdate=="function"&&W.componentWillUpdate(P,Rn,pe),typeof W.UNSAFE_componentWillUpdate=="function"&&W.UNSAFE_componentWillUpdate(P,Rn,pe)),typeof W.componentDidUpdate=="function"&&(h.flags|=4),typeof W.getSnapshotBeforeUpdate=="function"&&(h.flags|=1024)):(typeof W.componentDidUpdate!="function"||se===d.memoizedProps&&rt===d.memoizedState||(h.flags|=4),typeof W.getSnapshotBeforeUpdate!="function"||se===d.memoizedProps&&rt===d.memoizedState||(h.flags|=1024),h.memoizedProps=P,h.memoizedState=Rn),W.props=P,W.state=Rn,W.context=pe,P=je):(typeof W.componentDidUpdate!="function"||se===d.memoizedProps&&rt===d.memoizedState||(h.flags|=4),typeof W.getSnapshotBeforeUpdate!="function"||se===d.memoizedProps&&rt===d.memoizedState||(h.flags|=1024),P=!1)}return ha(d,h,S,P,M,L)}function ha(d,h,S,P,L,M){s3(d,h);var W=(h.flags&128)!==0;if(!P&&!W)return L&&kf(h,S,!1),ks(d,h,M);P=h.stateNode,Jt.current=h;var se=W&&typeof S.getDerivedStateFromError!="function"?null:P.render();return h.flags|=1,d!==null&&W?(h.child=Nc(h,d.child,null,M),h.child=Nc(h,null,se,M)):Di(d,h,se,M),h.memoizedState=P.state,L&&kf(h,S,!0),h.child}function u3(d){var h=d.stateNode;h.pendingContext?_f(d,h.pendingContext,h.pendingContext!==h.context):h.context&&_f(d,h.context,!1),ye(d,h.containerInfo)}function c3(d,h,S,P,L){return Mc(),X1(L),h.flags|=256,Di(d,h,S,P),h.child}var fm={dehydrated:null,treeContext:null,retryLane:0};function hm(d){return{baseLanes:d,cachePool:null,transitions:null}}function d3(d,h,S){var P=h.pendingProps,L=zt.current,M=!1,W=(h.flags&128)!==0,se;if((se=W)||(se=d!==null&&d.memoizedState===null?!1:(L&2)!==0),se?(M=!0,h.flags&=-129):(d===null||d.memoizedState!==null)&&(L|=1),kn(zt,L&1),d===null)return Of(h),d=h.memoizedState,d!==null&&(d=d.dehydrated,d!==null)?((h.mode&1)===0?h.lanes=1:Cf(d)?h.lanes=8:h.lanes=1073741824,null):(W=P.children,d=P.fallback,M?(P=h.mode,M=h.child,W={mode:"hidden",children:W},(P&1)===0&&M!==null?(M.childLanes=0,M.pendingProps=W):M=m0(W,P,0,null),d=Tl(d,P,S,null),M.return=h,d.return=h,M.sibling=d,h.child=M,h.child.memoizedState=hm(S),h.memoizedState=fm,d):pa(h,W));if(L=d.memoizedState,L!==null&&(se=L.dehydrated,se!==null))return cw(d,h,W,P,se,L,S);if(M){M=P.fallback,W=h.mode,L=d.child,se=L.sibling;var pe={mode:"hidden",children:P.children};return(W&1)===0&&h.child!==L?(P=h.child,P.childLanes=0,P.pendingProps=pe,h.deletions=null):(P=po(L,pe),P.subtreeFlags=L.subtreeFlags&14680064),se!==null?M=po(se,M):(M=Tl(M,W,S,null),M.flags|=2),M.return=h,P.return=h,P.sibling=M,h.child=P,P=M,M=h.child,W=d.child.memoizedState,W=W===null?hm(S):{baseLanes:W.baseLanes|S,cachePool:null,transitions:W.transitions},M.memoizedState=W,M.childLanes=d.childLanes&~S,h.memoizedState=fm,P}return M=d.child,d=M.sibling,P=po(M,{mode:"visible",children:P.children}),(h.mode&1)===0&&(P.lanes=S),P.return=h,P.sibling=null,d!==null&&(S=h.deletions,S===null?(h.deletions=[d],h.flags|=16):S.push(d)),h.child=P,h.memoizedState=null,P}function pa(d,h){return h=m0({mode:"visible",children:h},d.mode,0,null),h.return=d,d.child=h}function Wp(d,h,S,P){return P!==null&&X1(P),Nc(h,d.child,null,S),d=pa(h,h.pendingProps.children),d.flags|=2,h.memoizedState=null,d}function cw(d,h,S,P,L,M,W){if(S)return h.flags&256?(h.flags&=-257,P=um(Error(a(422))),Wp(d,h,W,P)):h.memoizedState!==null?(h.child=d.child,h.flags|=128,null):(M=P.fallback,L=h.mode,P=m0({mode:"visible",children:P.children},L,0,null),M=Tl(M,L,W,null),M.flags|=2,P.return=h,M.return=h,P.sibling=M,h.child=P,(h.mode&1)!==0&&Nc(h,d.child,null,W),h.child.memoizedState=hm(W),h.memoizedState=fm,M);if((h.mode&1)===0)return Wp(d,h,W,null);if(Cf(L))return P=I1(L).digest,M=Error(a(419)),P=um(M,P,void 0),Wp(d,h,W,P);if(S=(W&d.childLanes)!==0,Xi||S){if(P=Zr,P!==null){switch(W&-W){case 4:L=2;break;case 16:L=8;break;case 64:case 128:case 256:case 512:case 1024:case 2048:case 4096:case 8192:case 16384:case 32768:case 65536:case 131072:case 262144:case 524288:case 1048576:case 2097152:case 4194304:case 8388608:case 16777216:case 33554432:case 67108864:L=32;break;case 536870912:L=268435456;break;default:L=0}L=(L&(P.suspendedLanes|W))!==0?0:L,L!==0&&L!==M.retryLane&&(M.retryLane=L,da(d,L),Bo(P,d,L,-1))}return th(),P=um(Error(a(421))),Wp(d,h,W,P)}return Cc(L)?(h.flags|=128,h.child=d.child,h=mw.bind(null,d),sl(L,h),null):(d=M.treeContext,xt&&(Mo=B1(L),Kn=h,Hn=!0,Yi=null,Ro=!1,d!==null&&(Sn[li++]=lo,Sn[li++]=ja,Sn[li++]=Oo,lo=d.id,ja=d.overflow,Oo=h)),h=pa(h,P.children),h.flags|=4096,h)}function f3(d,h,S){d.lanes|=h;var P=d.alternate;P!==null&&(P.lanes|=h),Nf(d.return,h,S)}function pm(d,h,S,P,L){var M=d.memoizedState;M===null?d.memoizedState={isBackwards:h,rendering:null,renderingStartTime:0,last:P,tail:S,tailMode:L}:(M.isBackwards=h,M.rendering=null,M.renderingStartTime=0,M.last=P,M.tail=S,M.tailMode=L)}function h3(d,h,S){var P=h.pendingProps,L=P.revealOrder,M=P.tail;if(Di(d,h,P.children,S),P=zt.current,(P&2)!==0)P=P&1|2,h.flags|=128;else{if(d!==null&&(d.flags&128)!==0)e:for(d=h.child;d!==null;){if(d.tag===13)d.memoizedState!==null&&f3(d,S,h);else if(d.tag===19)f3(d,S,h);else if(d.child!==null){d.child.return=d,d=d.child;continue}if(d===h)break e;for(;d.sibling===null;){if(d.return===null||d.return===h)break e;d=d.return}d.sibling.return=d.return,d=d.sibling}P&=1}if(kn(zt,P),(h.mode&1)===0)h.memoizedState=null;else switch(L){case"forwards":for(S=h.child,L=null;S!==null;)d=S.alternate,d!==null&&vn(d)===null&&(L=S),S=S.sibling;S=L,S===null?(L=h.child,h.child=null):(L=S.sibling,S.sibling=null),pm(h,!1,L,S,M);break;case"backwards":for(S=null,L=h.child,h.child=null;L!==null;){if(d=L.alternate,d!==null&&vn(d)===null){h.child=L;break}d=L.sibling,L.sibling=S,S=L,L=d}pm(h,!0,S,null,M);break;case"together":pm(h,!1,null,null,void 0);break;default:h.memoizedState=null}return h.child}function Wa(d,h){(h.mode&1)===0&&d!==null&&(d.alternate=null,h.alternate=null,h.flags|=2)}function ks(d,h,S){if(d!==null&&(h.dependencies=d.dependencies),wl|=h.lanes,(S&h.childLanes)===0)return null;if(d!==null&&h.child!==d.child)throw Error(a(153));if(h.child!==null){for(d=h.child,S=po(d,d.pendingProps),h.child=S,S.return=h;d.sibling!==null;)d=d.sibling,S=S.sibling=po(d,d.pendingProps),S.return=h;S.sibling=null}return h.child}function dw(d,h,S){switch(h.tag){case 3:u3(h),Mc();break;case 5:It(h);break;case 1:si(h.type)&&xs(h);break;case 4:ye(h,h.stateNode.containerInfo);break;case 10:Uy(h,h.type._context,h.memoizedProps.value);break;case 13:var P=h.memoizedState;if(P!==null)return P.dehydrated!==null?(kn(zt,zt.current&1),h.flags|=128,null):(S&h.child.childLanes)!==0?d3(d,h,S):(kn(zt,zt.current&1),d=ks(d,h,S),d!==null?d.sibling:null);kn(zt,zt.current&1);break;case 19:if(P=(S&h.childLanes)!==0,(d.flags&128)!==0){if(P)return h3(d,h,S);h.flags|=128}var L=h.memoizedState;if(L!==null&&(L.rendering=null,L.tail=null,L.lastEffect=null),kn(zt,zt.current),P)break;return null;case 22:case 23:return h.lanes=0,a3(d,h,S)}return ks(d,h,S)}function Lr(d){d.flags|=4}function p3(d,h){if(d!==null&&d.child===h.child)return!0;if((h.flags&16)!==0)return!1;for(d=h.child;d!==null;){if((d.flags&12854)!==0||(d.subtreeFlags&12854)!==0)return!1;d=d.sibling}return!0}var Kf,Xf,Gp,qp;if(dt)Kf=function(d,h){for(var S=h.child;S!==null;){if(S.tag===5||S.tag===6)Se(d,S.stateNode);else if(S.tag!==4&&S.child!==null){S.child.return=S,S=S.child;continue}if(S===h)break;for(;S.sibling===null;){if(S.return===null||S.return===h)return;S=S.return}S.sibling.return=S.return,S=S.sibling}},Xf=function(){},Gp=function(d,h,S,P,L){if(d=d.memoizedProps,d!==P){var M=h.stateNode,W=we(Io.current);S=be(M,S,d,P,L,W),(h.updateQueue=S)&&Lr(h)}},qp=function(d,h,S,P){S!==P&&Lr(h)};else if(vt){Kf=function(d,h,S,P){for(var L=h.child;L!==null;){if(L.tag===5){var M=L.stateNode;S&&P&&(M=Ep(M,L.type,L.memoizedProps,L)),Se(d,M)}else if(L.tag===6)M=L.stateNode,S&&P&&(M=Pp(M,L.memoizedProps,L)),Se(d,M);else if(L.tag!==4){if(L.tag===22&&L.memoizedState!==null)M=L.child,M!==null&&(M.return=L),Kf(d,L,!0,!0);else if(L.child!==null){L.child.return=L,L=L.child;continue}}if(L===h)break;for(;L.sibling===null;){if(L.return===null||L.return===h)return;L=L.return}L.sibling.return=L.return,L=L.sibling}};var gm=function(d,h,S,P){for(var L=h.child;L!==null;){if(L.tag===5){var M=L.stateNode;S&&P&&(M=Ep(M,L.type,L.memoizedProps,L)),kp(d,M)}else if(L.tag===6)M=L.stateNode,S&&P&&(M=Pp(M,L.memoizedProps,L)),kp(d,M);else if(L.tag!==4){if(L.tag===22&&L.memoizedState!==null)M=L.child,M!==null&&(M.return=L),gm(d,L,!0,!0);else if(L.child!==null){L.child.return=L,L=L.child;continue}}if(L===h)break;for(;L.sibling===null;){if(L.return===null||L.return===h)return;L=L.return}L.sibling.return=L.return,L=L.sibling}};Xf=function(d,h){var S=h.stateNode;if(!p3(d,h)){d=S.containerInfo;var P=Fa(d);gm(P,h,!1,!1),S.pendingChildren=P,Lr(h),M1(d,P)}},Gp=function(d,h,S,P,L){var M=d.stateNode,W=d.memoizedProps;if((d=p3(d,h))&&W===P)h.stateNode=M;else{var se=h.stateNode,pe=we(Io.current),je=null;W!==P&&(je=be(se,S,W,P,L,pe)),d&&je===null?h.stateNode=M:(M=O1(M,je,S,W,P,h,d,se),Ae(M,S,P,L,pe)&&Lr(h),h.stateNode=M,d?Lr(h):Kf(M,h,!1,!1))}},qp=function(d,h,S,P){S!==P?(d=we(ie.current),S=we(Io.current),h.stateNode=De(P,d,S,h),Lr(h)):h.stateNode=d.stateNode}}else Xf=function(){},Gp=function(){},qp=function(){};function Xc(d,h){if(!Hn)switch(d.tailMode){case"hidden":h=d.tail;for(var S=null;h!==null;)h.alternate!==null&&(S=h),h=h.sibling;S===null?d.tail=null:S.sibling=null;break;case"collapsed":S=d.tail;for(var P=null;S!==null;)S.alternate!==null&&(P=S),S=S.sibling;P===null?h||d.tail===null?d.tail=null:d.tail.sibling=null:P.sibling=null}}function xi(d){var h=d.alternate!==null&&d.alternate.child===d.child,S=0,P=0;if(h)for(var L=d.child;L!==null;)S|=L.lanes|L.childLanes,P|=L.subtreeFlags&14680064,P|=L.flags&14680064,L.return=d,L=L.sibling;else for(L=d.child;L!==null;)S|=L.lanes|L.childLanes,P|=L.subtreeFlags,P|=L.flags,L.return=d,L=L.sibling;return d.subtreeFlags|=P,d.childLanes=S,h}function fw(d,h,S){var P=h.pendingProps;switch(K1(h),h.tag){case 2:case 16:case 15:case 0:case 11:case 7:case 8:case 12:case 9:case 14:return xi(h),null;case 1:return si(h.type)&&Ss(),xi(h),null;case 3:return S=h.stateNode,nt(),Mn(ai),Mn(Br),em(),S.pendingContext&&(S.context=S.pendingContext,S.pendingContext=null),(d===null||d.child===null)&&(Oc(h)?Lr(h):d===null||d.memoizedState.isDehydrated&&(h.flags&256)===0||(h.flags|=1024,Yi!==null&&(f0(Yi),Yi=null))),Xf(d,h),xi(h),null;case 5:on(h),S=we(ie.current);var L=h.type;if(d!==null&&h.stateNode!=null)Gp(d,h,L,P,S),d.ref!==h.ref&&(h.flags|=512,h.flags|=2097152);else{if(!P){if(h.stateNode===null)throw Error(a(166));return xi(h),null}if(d=we(Io.current),Oc(h)){if(!xt)throw Error(a(175));d=$1(h.stateNode,h.type,h.memoizedProps,S,d,h,!Ro),h.updateQueue=d,d!==null&&Lr(h)}else{var M=fe(L,P,S,d,h);Kf(M,h,!1,!1),h.stateNode=M,Ae(M,L,P,S,d)&&Lr(h)}h.ref!==null&&(h.flags|=512,h.flags|=2097152)}return xi(h),null;case 6:if(d&&h.stateNode!=null)qp(d,h,d.memoizedProps,P);else{if(typeof P!="string"&&h.stateNode===null)throw Error(a(166));if(d=we(ie.current),S=we(Io.current),Oc(h)){if(!xt)throw Error(a(176));if(d=h.stateNode,S=h.memoizedProps,(P=ys(d,S,h,!Ro))&&(L=Kn,L!==null))switch(L.tag){case 3:W1(L.stateNode.containerInfo,d,S,(L.mode&1)!==0);break;case 5:bs(L.type,L.memoizedProps,L.stateNode,d,S,(L.mode&1)!==0)}P&&Lr(h)}else h.stateNode=De(P,d,S,h)}return xi(h),null;case 13:if(Mn(zt),P=h.memoizedState,d===null||d.memoizedState!==null&&d.memoizedState.dehydrated!==null){if(Hn&&Mo!==null&&(h.mode&1)!==0&&(h.flags&128)===0)Hy(),Mc(),h.flags|=98560,L=!1;else if(L=Oc(h),P!==null&&P.dehydrated!==null){if(d===null){if(!L)throw Error(a(318));if(!xt)throw Error(a(344));if(L=h.memoizedState,L=L!==null?L.dehydrated:null,!L)throw Error(a(317));F1(L,h)}else 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W.componentDidMount=="function"&&(h.flags|=4194308),P=!1)}else{W=h.stateNode,Wy(d,h),se=h.memoizedProps,He=h.type===h.elementType?se:da(h.type,se),W.props=He,$t=h.pendingProps,it=W.context,pe=S.contextType,typeof pe=="object"&&pe!==null?pe=ho(pe):(pe=li(S)?ua:zr.current,pe=dl(h,pe));var Un=S.getDerivedStateFromProps;(ht=typeof Un=="function"||typeof W.getSnapshotBeforeUpdate=="function")||typeof W.UNSAFE_componentWillReceiveProps!="function"&&typeof W.componentWillReceiveProps!="function"||(se!==$t||it!==pe)&&Yy(h,W,P,pe),_s=!1,it=h.memoizedState,W.state=it,Fp(h,P,W,L);var Rn=h.memoizedState;se!==$t||it!==Rn||si.current||_s?(typeof Un=="function"&&(jp(h,S,Un,P),Rn=h.memoizedState),(He=_s||$f(h,S,He,P,it,Rn,pe)||!1)?(ht||typeof W.UNSAFE_componentWillUpdate!="function"&&typeof W.componentWillUpdate!="function"||(typeof W.componentWillUpdate=="function"&&W.componentWillUpdate(P,Rn,pe),typeof W.UNSAFE_componentWillUpdate=="function"&&W.UNSAFE_componentWillUpdate(P,Rn,pe)),typeof W.componentDidUpdate=="function"&&(h.flags|=4),typeof W.getSnapshotBeforeUpdate=="function"&&(h.flags|=1024)):(typeof W.componentDidUpdate!="function"||se===d.memoizedProps&&it===d.memoizedState||(h.flags|=4),typeof W.getSnapshotBeforeUpdate!="function"||se===d.memoizedProps&&it===d.memoizedState||(h.flags|=1024),h.memoizedProps=P,h.memoizedState=Rn),W.props=P,W.state=Rn,W.context=pe,P=He):(typeof W.componentDidUpdate!="function"||se===d.memoizedProps&&it===d.memoizedState||(h.flags|=4),typeof W.getSnapshotBeforeUpdate!="function"||se===d.memoizedProps&&it===d.memoizedState||(h.flags|=1024),P=!1)}return pa(d,h,S,P,M,L)}function pa(d,h,S,P,L,M){s3(d,h);var W=(h.flags&128)!==0;if(!P&&!W)return L&&kf(h,S,!1),Ps(d,h,M);P=h.stateNode,Jt.current=h;var se=W&&typeof S.getDerivedStateFromError!="function"?null:P.render();return h.flags|=1,d!==null&&W?(h.child=Nc(h,d.child,null,M),h.child=Nc(h,null,se,M)):Di(d,h,se,M),h.memoizedState=P.state,L&&kf(h,S,!0),h.child}function u3(d){var h=d.stateNode;h.pendingContext?_f(d,h.pendingContext,h.pendingContext!==h.context):h.context&&_f(d,h.context,!1),ye(d,h.containerInfo)}function c3(d,h,S,P,L){return Mc(),X1(L),h.flags|=256,Di(d,h,S,P),h.child}var fm={dehydrated:null,treeContext:null,retryLane:0};function hm(d){return{baseLanes:d,cachePool:null,transitions:null}}function d3(d,h,S){var P=h.pendingProps,L=zt.current,M=!1,W=(h.flags&128)!==0,se;if((se=W)||(se=d!==null&&d.memoizedState===null?!1:(L&2)!==0),se?(M=!0,h.flags&=-129):(d===null||d.memoizedState!==null)&&(L|=1),kn(zt,L&1),d===null)return Of(h),d=h.memoizedState,d!==null&&(d=d.dehydrated,d!==null)?((h.mode&1)===0?h.lanes=1:Cf(d)?h.lanes=8:h.lanes=1073741824,null):(W=P.children,d=P.fallback,M?(P=h.mode,M=h.child,W={mode:"hidden",children:W},(P&1)===0&&M!==null?(M.childLanes=0,M.pendingProps=W):M=m0(W,P,0,null),d=Tl(d,P,S,null),M.return=h,d.return=h,M.sibling=d,h.child=M,h.child.memoizedState=hm(S),h.memoizedState=fm,d):ga(h,W));if(L=d.memoizedState,L!==null&&(se=L.dehydrated,se!==null))return fw(d,h,W,P,se,L,S);if(M){M=P.fallback,W=h.mode,L=d.child,se=L.sibling;var pe={mode:"hidden",children:P.children};return(W&1)===0&&h.child!==L?(P=h.child,P.childLanes=0,P.pendingProps=pe,h.deletions=null):(P=vo(L,pe),P.subtreeFlags=L.subtreeFlags&14680064),se!==null?M=vo(se,M):(M=Tl(M,W,S,null),M.flags|=2),M.return=h,P.return=h,P.sibling=M,h.child=P,P=M,M=h.child,W=d.child.memoizedState,W=W===null?hm(S):{baseLanes:W.baseLanes|S,cachePool:null,transitions:W.transitions},M.memoizedState=W,M.childLanes=d.childLanes&~S,h.memoizedState=fm,P}return M=d.child,d=M.sibling,P=vo(M,{mode:"visible",children:P.children}),(h.mode&1)===0&&(P.lanes=S),P.return=h,P.sibling=null,d!==null&&(S=h.deletions,S===null?(h.deletions=[d],h.flags|=16):S.push(d)),h.child=P,h.memoizedState=null,P}function ga(d,h){return h=m0({mode:"visible",children:h},d.mode,0,null),h.return=d,d.child=h}function Wp(d,h,S,P){return P!==null&&X1(P),Nc(h,d.child,null,S),d=ga(h,h.pendingProps.children),d.flags|=2,h.memoizedState=null,d}function fw(d,h,S,P,L,M,W){if(S)return h.flags&256?(h.flags&=-257,P=um(Error(a(422))),Wp(d,h,W,P)):h.memoizedState!==null?(h.child=d.child,h.flags|=128,null):(M=P.fallback,L=h.mode,P=m0({mode:"visible",children:P.children},L,0,null),M=Tl(M,L,W,null),M.flags|=2,P.return=h,M.return=h,P.sibling=M,h.child=P,(h.mode&1)!==0&&Nc(h,d.child,null,W),h.child.memoizedState=hm(W),h.memoizedState=fm,M);if((h.mode&1)===0)return Wp(d,h,W,null);if(Cf(L))return P=I1(L).digest,M=Error(a(419)),P=um(M,P,void 0),Wp(d,h,W,P);if(S=(W&d.childLanes)!==0,Ji||S){if(P=Jr,P!==null){switch(W&-W){case 4:L=2;break;case 16:L=8;break;case 64:case 128:case 256:case 512:case 1024:case 2048:case 4096:case 8192:case 16384:case 32768:case 65536:case 131072:case 262144:case 524288:case 1048576:case 2097152:case 4194304:case 8388608:case 16777216:case 33554432:case 67108864:L=32;break;case 536870912:L=268435456;break;default:L=0}L=(L&(P.suspendedLanes|W))!==0?0:L,L!==0&&L!==M.retryLane&&(M.retryLane=L,fa(d,L),jo(P,d,L,-1))}return th(),P=um(Error(a(421))),Wp(d,h,W,P)}return Cc(L)?(h.flags|=128,h.child=d.child,h=yw.bind(null,d),sl(L,h),null):(d=M.treeContext,xt&&(No=B1(L),Xn=h,Hn=!0,Zi=null,Bo=!1,d!==null&&(Sn[ui++]=fo,Sn[ui++]=Va,Sn[ui++]=Do,fo=d.id,Va=d.overflow,Do=h)),h=ga(h,P.children),h.flags|=4096,h)}function f3(d,h,S){d.lanes|=h;var P=d.alternate;P!==null&&(P.lanes|=h),Nf(d.return,h,S)}function pm(d,h,S,P,L){var M=d.memoizedState;M===null?d.memoizedState={isBackwards:h,rendering:null,renderingStartTime:0,last:P,tail:S,tailMode:L}:(M.isBackwards=h,M.rendering=null,M.renderingStartTime=0,M.last=P,M.tail=S,M.tailMode=L)}function h3(d,h,S){var P=h.pendingProps,L=P.revealOrder,M=P.tail;if(Di(d,h,P.children,S),P=zt.current,(P&2)!==0)P=P&1|2,h.flags|=128;else{if(d!==null&&(d.flags&128)!==0)e:for(d=h.child;d!==null;){if(d.tag===13)d.memoizedState!==null&&f3(d,S,h);else if(d.tag===19)f3(d,S,h);else if(d.child!==null){d.child.return=d,d=d.child;continue}if(d===h)break e;for(;d.sibling===null;){if(d.return===null||d.return===h)break e;d=d.return}d.sibling.return=d.return,d=d.sibling}P&=1}if(kn(zt,P),(h.mode&1)===0)h.memoizedState=null;else switch(L){case"forwards":for(S=h.child,L=null;S!==null;)d=S.alternate,d!==null&&vn(d)===null&&(L=S),S=S.sibling;S=L,S===null?(L=h.child,h.child=null):(L=S.sibling,S.sibling=null),pm(h,!1,L,S,M);break;case"backwards":for(S=null,L=h.child,h.child=null;L!==null;){if(d=L.alternate,d!==null&&vn(d)===null){h.child=L;break}d=L.sibling,L.sibling=S,S=L,L=d}pm(h,!0,S,null,M);break;case"together":pm(h,!1,null,null,void 0);break;default:h.memoizedState=null}return h.child}function qa(d,h){(h.mode&1)===0&&d!==null&&(d.alternate=null,h.alternate=null,h.flags|=2)}function Ps(d,h,S){if(d!==null&&(h.dependencies=d.dependencies),wl|=h.lanes,(S&h.childLanes)===0)return null;if(d!==null&&h.child!==d.child)throw Error(a(153));if(h.child!==null){for(d=h.child,S=vo(d,d.pendingProps),h.child=S,S.return=h;d.sibling!==null;)d=d.sibling,S=S.sibling=vo(d,d.pendingProps),S.return=h;S.sibling=null}return h.child}function hw(d,h,S){switch(h.tag){case 3:u3(h),Mc();break;case 5:It(h);break;case 1:li(h.type)&&Cs(h);break;case 4:ye(h,h.stateNode.containerInfo);break;case 10:Uy(h,h.type._context,h.memoizedProps.value);break;case 13:var P=h.memoizedState;if(P!==null)return P.dehydrated!==null?(kn(zt,zt.current&1),h.flags|=128,null):(S&h.child.childLanes)!==0?d3(d,h,S):(kn(zt,zt.current&1),d=Ps(d,h,S),d!==null?d.sibling:null);kn(zt,zt.current&1);break;case 19:if(P=(S&h.childLanes)!==0,(d.flags&128)!==0){if(P)return h3(d,h,S);h.flags|=128}var L=h.memoizedState;if(L!==null&&(L.rendering=null,L.tail=null,L.lastEffect=null),kn(zt,zt.current),P)break;return null;case 22:case 23:return h.lanes=0,a3(d,h,S)}return Ps(d,h,S)}function Mr(d){d.flags|=4}function p3(d,h){if(d!==null&&d.child===h.child)return!0;if((h.flags&16)!==0)return!1;for(d=h.child;d!==null;){if((d.flags&12854)!==0||(d.subtreeFlags&12854)!==0)return!1;d=d.sibling}return!0}var Kf,Xf,Gp,qp;if(dt)Kf=function(d,h){for(var S=h.child;S!==null;){if(S.tag===5||S.tag===6)Se(d,S.stateNode);else if(S.tag!==4&&S.child!==null){S.child.return=S,S=S.child;continue}if(S===h)break;for(;S.sibling===null;){if(S.return===null||S.return===h)return;S=S.return}S.sibling.return=S.return,S=S.sibling}},Xf=function(){},Gp=function(d,h,S,P,L){if(d=d.memoizedProps,d!==P){var M=h.stateNode,W=we($o.current);S=be(M,S,d,P,L,W),(h.updateQueue=S)&&Mr(h)}},qp=function(d,h,S,P){S!==P&&Mr(h)};else if(vt){Kf=function(d,h,S,P){for(var L=h.child;L!==null;){if(L.tag===5){var M=L.stateNode;S&&P&&(M=Ep(M,L.type,L.memoizedProps,L)),Se(d,M)}else if(L.tag===6)M=L.stateNode,S&&P&&(M=Pp(M,L.memoizedProps,L)),Se(d,M);else if(L.tag!==4){if(L.tag===22&&L.memoizedState!==null)M=L.child,M!==null&&(M.return=L),Kf(d,L,!0,!0);else if(L.child!==null){L.child.return=L,L=L.child;continue}}if(L===h)break;for(;L.sibling===null;){if(L.return===null||L.return===h)return;L=L.return}L.sibling.return=L.return,L=L.sibling}};var gm=function(d,h,S,P){for(var L=h.child;L!==null;){if(L.tag===5){var M=L.stateNode;S&&P&&(M=Ep(M,L.type,L.memoizedProps,L)),kp(d,M)}else if(L.tag===6)M=L.stateNode,S&&P&&(M=Pp(M,L.memoizedProps,L)),kp(d,M);else if(L.tag!==4){if(L.tag===22&&L.memoizedState!==null)M=L.child,M!==null&&(M.return=L),gm(d,L,!0,!0);else if(L.child!==null){L.child.return=L,L=L.child;continue}}if(L===h)break;for(;L.sibling===null;){if(L.return===null||L.return===h)return;L=L.return}L.sibling.return=L.return,L=L.sibling}};Xf=function(d,h){var S=h.stateNode;if(!p3(d,h)){d=S.containerInfo;var P=ja(d);gm(P,h,!1,!1),S.pendingChildren=P,Mr(h),M1(d,P)}},Gp=function(d,h,S,P,L){var M=d.stateNode,W=d.memoizedProps;if((d=p3(d,h))&&W===P)h.stateNode=M;else{var se=h.stateNode,pe=we($o.current),He=null;W!==P&&(He=be(se,S,W,P,L,pe)),d&&He===null?h.stateNode=M:(M=O1(M,He,S,W,P,h,d,se),Ae(M,S,P,L,pe)&&Mr(h),h.stateNode=M,d?Mr(h):Kf(M,h,!1,!1))}},qp=function(d,h,S,P){S!==P?(d=we(ie.current),S=we($o.current),h.stateNode=De(P,d,S,h),Mr(h)):h.stateNode=d.stateNode}}else Xf=function(){},Gp=function(){},qp=function(){};function Xc(d,h){if(!Hn)switch(d.tailMode){case"hidden":h=d.tail;for(var S=null;h!==null;)h.alternate!==null&&(S=h),h=h.sibling;S===null?d.tail=null:S.sibling=null;break;case"collapsed":S=d.tail;for(var P=null;S!==null;)S.alternate!==null&&(P=S),S=S.sibling;P===null?h||d.tail===null?d.tail=null:d.tail.sibling=null:P.sibling=null}}function xi(d){var h=d.alternate!==null&&d.alternate.child===d.child,S=0,P=0;if(h)for(var L=d.child;L!==null;)S|=L.lanes|L.childLanes,P|=L.subtreeFlags&14680064,P|=L.flags&14680064,L.return=d,L=L.sibling;else for(L=d.child;L!==null;)S|=L.lanes|L.childLanes,P|=L.subtreeFlags,P|=L.flags,L.return=d,L=L.sibling;return d.subtreeFlags|=P,d.childLanes=S,h}function pw(d,h,S){var P=h.pendingProps;switch(K1(h),h.tag){case 2:case 16:case 15:case 0:case 11:case 7:case 8:case 12:case 9:case 14:return xi(h),null;case 1:return li(h.type)&&ws(),xi(h),null;case 3:return S=h.stateNode,rt(),Mn(si),Mn(zr),em(),S.pendingContext&&(S.context=S.pendingContext,S.pendingContext=null),(d===null||d.child===null)&&(Oc(h)?Mr(h):d===null||d.memoizedState.isDehydrated&&(h.flags&256)===0||(h.flags|=1024,Zi!==null&&(f0(Zi),Zi=null))),Xf(d,h),xi(h),null;case 5:on(h),S=we(ie.current);var L=h.type;if(d!==null&&h.stateNode!=null)Gp(d,h,L,P,S),d.ref!==h.ref&&(h.flags|=512,h.flags|=2097152);else{if(!P){if(h.stateNode===null)throw Error(a(166));return xi(h),null}if(d=we($o.current),Oc(h)){if(!xt)throw Error(a(175));d=$1(h.stateNode,h.type,h.memoizedProps,S,d,h,!Bo),h.updateQueue=d,d!==null&&Mr(h)}else{var M=fe(L,P,S,d,h);Kf(M,h,!1,!1),h.stateNode=M,Ae(M,L,P,S,d)&&Mr(h)}h.ref!==null&&(h.flags|=512,h.flags|=2097152)}return xi(h),null;case 6:if(d&&h.stateNode!=null)qp(d,h,d.memoizedProps,P);else{if(typeof P!="string"&&h.stateNode===null)throw Error(a(166));if(d=we(ie.current),S=we($o.current),Oc(h)){if(!xt)throw Error(a(176));if(d=h.stateNode,S=h.memoizedProps,(P=Ss(d,S,h,!Bo))&&(L=Xn,L!==null))switch(L.tag){case 3:W1(L.stateNode.containerInfo,d,S,(L.mode&1)!==0);break;case 5:xs(L.type,L.memoizedProps,L.stateNode,d,S,(L.mode&1)!==0)}P&&Mr(h)}else h.stateNode=De(P,d,S,h)}return xi(h),null;case 13:if(Mn(zt),P=h.memoizedState,d===null||d.memoizedState!==null&&d.memoizedState.dehydrated!==null){if(Hn&&No!==null&&(h.mode&1)!==0&&(h.flags&128)===0)Hy(),Mc(),h.flags|=98560,L=!1;else if(L=Oc(h),P!==null&&P.dehydrated!==null){if(d===null){if(!L)throw Error(a(318));if(!xt)throw Error(a(344));if(L=h.memoizedState,L=L!==null?L.dehydrated:null,!L)throw Error(a(317));F1(L,h)}else Mc(),(h.flags&128)===0&&(h.memoizedState=null),h.flags|=4;xi(h),L=!1}else Zi!==null&&(f0(Zi),Zi=null),L=!0;if(!L)return h.flags&65536?h:null}return(h.flags&128)!==0?(h.lanes=S,h):(S=P!==null,S!==(d!==null&&d.memoizedState!==null)&&S&&(h.child.flags|=8192,(h.mode&1)!==0&&(d===null||(zt.current&1)!==0?Ir===0&&(Ir=3):th())),h.updateQueue!==null&&(h.flags|=4),xi(h),null);case 4:return rt(),Xf(d,h),d===null&&ut(h.stateNode.containerInfo),xi(h),null;case 10:return Df(h.type._context),xi(h),null;case 17:return li(h.type)&&ws(),xi(h),null;case 19:if(Mn(zt),L=h.memoizedState,L===null)return xi(h),null;if(P=(h.flags&128)!==0,M=L.rendering,M===null)if(P)Xc(L,!1);else{if(Ir!==0||d!==null&&(d.flags&128)!==0)for(d=h.child;d!==null;){if(M=vn(d),M!==null){for(h.flags|=128,Xc(L,!1),d=M.updateQueue,d!==null&&(h.updateQueue=d,h.flags|=4),h.subtreeFlags=0,d=S,S=h.child;S!==null;)P=S,L=d,P.flags&=14680066,M=P.alternate,M===null?(P.childLanes=0,P.lanes=L,P.child=null,P.subtreeFlags=0,P.memoizedProps=null,P.memoizedState=null,P.updateQueue=null,P.dependencies=null,P.stateNode=null):(P.childLanes=M.childLanes,P.lanes=M.lanes,P.child=M.child,P.subtreeFlags=0,P.deletions=null,P.memoizedProps=M.memoizedProps,P.memoizedState=M.memoizedState,P.updateQueue=M.updateQueue,P.type=M.type,L=M.dependencies,P.dependencies=L===null?null:{lanes:L.lanes,firstContext:L.firstContext}),S=S.sibling;return kn(zt,zt.current&1|2),h.child}d=d.sibling}L.tail!==null&&Kn()>s0&&(h.flags|=128,P=!0,Xc(L,!1),h.lanes=4194304)}else{if(!P)if(d=vn(M),d!==null){if(h.flags|=128,P=!0,d=d.updateQueue,d!==null&&(h.updateQueue=d,h.flags|=4),Xc(L,!0),L.tail===null&&L.tailMode==="hidden"&&!M.alternate&&!Hn)return xi(h),null}else 2*Kn()-L.renderingStartTime>s0&&S!==1073741824&&(h.flags|=128,P=!0,Xc(L,!1),h.lanes=4194304);L.isBackwards?(M.sibling=h.child,h.child=M):(d=L.last,d!==null?d.sibling=M:h.child=M,L.last=M)}return L.tail!==null?(h=L.tail,L.rendering=h,L.tail=h.sibling,L.renderingStartTime=Kn(),h.sibling=null,d=zt.current,kn(zt,P?d&1|2:d&1),h):(xi(h),null);case 22:case 23:return od(),S=h.memoizedState!==null,d!==null&&d.memoizedState!==null!==S&&(h.flags|=8192),S&&(h.mode&1)!==0?(go&1073741824)!==0&&(xi(h),dt&&h.subtreeFlags&6&&(h.flags|=8192)):xi(h),null;case 24:return null;case 25:return null}throw Error(a(156,h.tag))}function mm(d,h){switch(K1(h),h.tag){case 1:return 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5:Pl(d,Ni,As);break;default:throw Error(a(329))}}}return fi(d,Kn()),d.callbackNode===S?c0.bind(null,d):null}function d0(d,h){var S=rd;return d.current.memoizedState.isDehydrated&&(kl(d,h).flags|=256),d=ad(d,h),d!==2&&(h=Ni,Ni=S,h!==null&&f0(h)),d}function f0(d){Ni===null?Ni=d:Ni.push.apply(Ni,d)}function mo(d){for(var h=d;;){if(h.flags&16384){var S=h.updateQueue;if(S!==null&&(S=S.stores,S!==null))for(var P=0;P<S.length;P++){var L=S[P],M=L.getSnapshot;L=L.value;try{if(!Y(M(),L))return!1}catch(W){return!1}}}if(S=h.child,h.subtreeFlags&16384&&S!==null)S.return=h,h=S;else{if(h===d)break;for(;h.sibling===null;){if(h.return===null||h.return===d)return!0;h=h.return}h.sibling.return=h.return,h=h.sibling}}return!0}function Bi(d,h){for(h&=~Cm,h&=~Ru,d.suspendedLanes|=h,d.pingedLanes&=~h,d=d.expirationTimes;0<h;){var S=31-Oi(h),P=1<<S;d[S]=-1,h&=~P}}function h0(d){if((qt&6)!==0)throw Error(a(327));Du();var h=ku(d,0);if((h&1)===0)return fi(d,Kn()),null;var S=ad(d,h);if(d.tag!==0&&S===2){var P=Pc(d);P!==0&&(h=P,S=d0(d,P))}if(S===1)throw S=nd,kl(d,0),Bi(d,h),fi(d,Kn()),S;if(S===6)throw Error(a(345));return d.finishedWork=d.current.alternate,d.finishedLanes=h,Pl(d,Ni,As),fi(d,Kn()),null}function km(d){Mt!==null&&Mt.tag===0&&(qt&6)===0&&Du();var h=qt;qt|=1;var S=gr.transition,P=Xt;try{if(gr.transition=null,Xt=1,d)return d()}finally{Xt=P,gr.transition=S,qt=h,(qt&6)===0&&Pt()}}function od(){go=xl.current,Mn(xl)}function kl(d,h){d.finishedWork=null,d.finishedLanes=0;var S=d.timeoutHandle;if(S!==mt&&(d.timeoutHandle=mt,Ke(S)),mr!==null)for(S=mr.return;S!==null;){var P=S;switch(K1(P),P.tag){case 1:P=P.type.childContextTypes,P!=null&&ws();break;case 3:rt(),Mn(si),Mn(zr),em();break;case 5:on(P);break;case 4:rt();break;case 13:Mn(zt);break;case 19:Mn(zt);break;case 10:Df(P.type._context);break;case 22:case 23:od()}S=S.return}if(Jr=d,mr=d=vo(d.current,null),Ur=go=h,Ir=0,nd=null,Cm=Ru=wl=0,Ni=rd=null,jr!==null){for(h=0;h<jr.length;h++)if(S=jr[h],P=S.interleaved,P!==null){S.interleaved=null;var L=P.next,M=S.pending;if(M!==null){var W=M.next;M.next=L,P.next=W}S.pending=P}jr=null}return d}function p0(d,h){do{var S=mr;try{if(Bp(),Ri.current=Es,cn){for(var P=En.memoizedState;P!==null;){var L=P.queue;L!==null&&(L.pending=null),P=P.next}cn=!1}if(Lu=0,pr=ur=En=null,jf=!1,$c=0,a0.current=null,S===null||S.return===null){Ir=1,nd=h,mr=null;break}e:{var M=d,W=S.return,se=S,pe=h;if(h=Ur,se.flags|=32768,pe!==null&&typeof pe=="object"&&typeof pe.then=="function"){var He=pe,ht=se,$t=ht.tag;if((ht.mode&1)===0&&($t===0||$t===11||$t===15)){var it=ht.alternate;it?(ht.updateQueue=it.updateQueue,ht.memoizedState=it.memoizedState,ht.lanes=it.lanes):(ht.updateQueue=null,ht.memoizedState=null)}var Un=Yf(W);if(Un!==null){Un.flags&=-257,po(Un,W,se,M,h),Un.mode&1&&Ou(M,He,h),h=Un,pe=He;var Rn=h.updateQueue;if(Rn===null){var cr=new Set;cr.add(pe),h.updateQueue=cr}else Rn.add(pe);break e}else{if((h&1)===0){Ou(M,He,h),th();break e}pe=Error(a(426))}}else if(Hn&&se.mode&1){var Sa=Yf(W);if(Sa!==null){(Sa.flags&65536)===0&&(Sa.flags|=256),po(Sa,W,se,M,h),X1(Yc(pe,se));break e}}M=pe=Yc(pe,se),Ir!==4&&(Ir=2),rd===null?rd=[M]:rd.push(M),M=W;do{switch(M.tag){case 3:M.flags|=65536,h&=-h,M.lanes|=h;var ve=cm(M,pe,h);Tu(M,ve);break e;case 1:se=pe;var ce=M.type,xe=M.stateNode;if((M.flags&128)===0&&(typeof ce.getDerivedStateFromError=="function"||xe!==null&&typeof xe.componentDidCatch=="function"&&(_l===null||!_l.has(xe)))){M.flags|=65536,h&=-h,M.lanes|=h;var et=F(M,se,h);Tu(M,et);break e}}M=M.return}while(M!==null)}Pm(S)}catch(Me){h=Me,mr===S&&S!==null&&(mr=S=S.return);continue}break}while(1)}function Em(){var d=td.current;return td.current=Es,d===null?Es:d}function th(){(Ir===0||Ir===3||Ir===2)&&(Ir=4),Jr===null||(wl&268435455)===0&&(Ru&268435455)===0||Bi(Jr,Ur)}function ad(d,h){var S=qt;qt|=2;var P=Em();(Jr!==d||Ur!==h)&&(As=null,kl(d,h));do try{El();break}catch(L){p0(d,L)}while(1);if(Bp(),qt=S,td.current=P,mr!==null)throw Error(a(261));return Jr=null,Ur=0,Ir}function El(){for(;mr!==null;)Os(mr)}function vw(){for(;mr!==null&&!Mp();)Os(mr)}function Os(d){var h=Am(d.alternate,d,go);d.memoizedProps=d.pendingProps,h===null?Pm(d):mr=h,a0.current=null}function Pm(d){var h=d;do{var S=h.alternate;if(d=h.return,(h.flags&32768)===0){if(S=pw(S,h,go),S!==null){mr=S;return}}else{if(S=mm(S,h),S!==null){S.flags&=32767,mr=S;return}if(d!==null)d.flags|=32768,d.subtreeFlags=0,d.deletions=null;else{Ir=6,mr=null;return}}if(h=h.sibling,h!==null){mr=h;return}mr=h=d}while(h!==null);Ir===0&&(Ir=5)}function Pl(d,h,S){var P=Xt,L=gr.transition;try{gr.transition=null,Xt=1,Tm(d,h,S,P)}finally{gr.transition=L,Xt=P}return null}function Tm(d,h,S,P){do Du();while(Mt!==null);if((qt&6)!==0)throw Error(a(327));S=d.finishedWork;var L=d.finishedLanes;if(S===null)return null;if(d.finishedWork=null,d.finishedLanes=0,S===d.current)throw Error(a(177));d.callbackNode=null,d.callbackPriority=0;var M=S.lanes|S.childLanes;if(Tf(d,M),d===Jr&&(mr=Jr=null,Ur=0),(S.subtreeFlags&2064)===0&&(S.flags&2064)===0||id||(id=!0,$i(Af,function(){return Du(),null})),M=(S.flags&15990)!==0,(S.subtreeFlags&15990)!==0||M){M=gr.transition,gr.transition=null;var W=Xt;Xt=1;var se=qt;qt|=4,a0.current=null,Mu(d,S),ba(S,d),le(d.containerInfo),d.current=S,Jp(S),ca(),qt=se,Xt=W,gr.transition=M}else d.current=S;if(id&&(id=!1,Mt=d,l0=L),M=d.pendingLanes,M===0&&(_l=null),Dp(S.stateNode),fi(d,Kn()),h!==null)for(P=d.onRecoverableError,S=0;S<h.length;S++)L=h[S],P(L.value,{componentStack:L.stack,digest:L.digest});if(Zf)throw Zf=!1,d=Qf,Qf=null,d;return(l0&1)!==0&&d.tag!==0&&Du(),M=d.pendingLanes,(M&1)!==0?d===Iu?Jf++:(Jf=0,Iu=d):Jf=0,Pt(),null}function Du(){if(Mt!==null){var d=Op(l0),h=gr.transition,S=Xt;try{if(gr.transition=null,Xt=16>d?16:d,Mt===null)var P=!1;else{if(d=Mt,Mt=null,l0=0,(qt&6)!==0)throw Error(a(331));var L=qt;for(qt|=4,lt=d.current;lt!==null;){var M=lt,W=M.child;if((lt.flags&16)!==0){var se=M.deletions;if(se!==null){for(var pe=0;pe<se.length;pe++){var He=se[pe];for(lt=He;lt!==null;){var ht=lt;switch(ht.tag){case 0:case 11:case 15:wi(8,ht,M)}var $t=ht.child;if($t!==null)$t.return=ht,lt=$t;else for(;lt!==null;){ht=lt;var it=ht.sibling,Un=ht.return;if(vm(ht),ht===He){lt=null;break}if(it!==null){it.return=Un,lt=it;break}lt=Un}}}var Rn=M.alternate;if(Rn!==null){var cr=Rn.child;if(cr!==null){Rn.child=null;do{var Sa=cr.sibling;cr.sibling=null,cr=Sa}while(cr!==null)}}lt=M}}if((M.subtreeFlags&2064)!==0&&W!==null)W.return=M,lt=W;else e:for(;lt!==null;){if(M=lt,(M.flags&2048)!==0)switch(M.tag){case 0:case 11:case 15:wi(9,M,M.return)}var ve=M.sibling;if(ve!==null){ve.return=M.return,lt=ve;break e}lt=M.return}}var ce=d.current;for(lt=ce;lt!==null;){W=lt;var xe=W.child;if((W.subtreeFlags&2064)!==0&&xe!==null)xe.return=W,lt=xe;else e:for(W=ce;lt!==null;){if(se=lt,(se.flags&2048)!==0)try{switch(se.tag){case 0:case 11:case 15:Kp(9,se)}}catch(Me){Zn(se,se.return,Me)}if(se===W){lt=null;break e}var et=se.sibling;if(et!==null){et.return=se.return,lt=et;break e}lt=se.return}}if(qt=L,Pt(),Zt&&typeof Zt.onPostCommitFiberRoot=="function")try{Zt.onPostCommitFiberRoot(mn,d)}catch(Me){}P=!0}return P}finally{Xt=S,gr.transition=h}}return!1}function g3(d,h,S){h=Yc(S,h),h=cm(d,h,1),d=ml(d,h,1),h=_i(),d!==null&&(Ha(d,1,h),fi(d,h))}function Zn(d,h,S){if(d.tag===3)g3(d,d,S);else for(;h!==null;){if(h.tag===3){g3(h,d,S);break}else if(h.tag===1){var P=h.stateNode;if(typeof h.type.getDerivedStateFromError=="function"||typeof P.componentDidCatch=="function"&&(_l===null||!_l.has(P))){d=Yc(S,d),d=F(h,d,1),h=ml(h,d,1),d=_i(),h!==null&&(Ha(h,1,d),fi(h,d));break}}h=h.return}}function m3(d,h,S){var P=d.pingCache;P!==null&&P.delete(h),h=_i(),d.pingedLanes|=d.suspendedLanes&S,Jr===d&&(Ur&S)===S&&(Ir===4||Ir===3&&(Ur&130023424)===Ur&&500>Kn()-_m?kl(d,0):Cm|=S),fi(d,h)}function Lm(d,h){h===0&&((d.mode&1)===0?h=1:(h=Io,Io<<=1,(Io&130023424)===0&&(Io=4194304)));var S=_i();d=fa(d,h),d!==null&&(Ha(d,h,S),fi(d,S))}function yw(d){var h=d.memoizedState,S=0;h!==null&&(S=h.retryLane),Lm(d,S)}function v3(d,h){var S=0;switch(d.tag){case 13:var P=d.stateNode,L=d.memoizedState;L!==null&&(S=L.retryLane);break;case 19:P=d.stateNode;break;default:throw Error(a(314))}P!==null&&P.delete(h),Lm(d,S)}var Am;Am=function(d,h,S){if(d!==null)if(d.memoizedProps!==h.pendingProps||si.current)Ji=!0;else{if((d.lanes&S)===0&&(h.flags&128)===0)return Ji=!1,hw(d,h,S);Ji=(d.flags&131072)!==0}else Ji=!1,Hn&&(h.flags&1048576)!==0&&Y1(h,Ar,h.index);switch(h.lanes=0,h.tag){case 2:var P=h.type;qa(d,h),d=h.pendingProps;var L=dl(h,zr.current);Ic(h,S),L=nm(null,h,P,d,L,S);var M=Fc();return h.flags|=1,typeof L=="object"&&L!==null&&typeof L.render=="function"&&L.$$typeof===void 0?(h.tag=1,h.memoizedState=null,h.updateQueue=null,li(P)?(M=!0,Cs(h)):M=!1,h.memoizedState=L.state!==null&&L.state!==void 0?L.state:null,Z1(h),L.updater=ha,h.stateNode=L,L._reactInternals=h,Q1(h,P,d,S),h=pa(null,h,P,!0,M,S)):(h.tag=0,Hn&&M&&Mi(h),Di(null,h,L,S),h=h.child),h;case 16:P=h.elementType;e:{switch(qa(d,h),d=h.pendingProps,L=P._init,P=L(P._payload),h.type=P,L=h.tag=g0(P),d=da(P,d),L){case 0:h=dm(null,h,P,d,S);break e;case 1:h=l3(null,h,P,d,S);break e;case 11:h=i3(null,h,P,d,S);break e;case 14:h=yl(null,h,P,da(P.type,d),S);break e}throw Error(a(306,P,""))}return h;case 0:return P=h.type,L=h.pendingProps,L=h.elementType===P?L:da(P,L),dm(d,h,P,L,S);case 1:return P=h.type,L=h.pendingProps,L=h.elementType===P?L:da(P,L),l3(d,h,P,L,S);case 3:e:{if(u3(h),d===null)throw Error(a(387));P=h.pendingProps,M=h.memoizedState,L=M.element,Wy(d,h),Fp(h,P,null,S);var W=h.memoizedState;if(P=W.element,xt&&M.isDehydrated)if(M={element:P,isDehydrated:!1,cache:W.cache,pendingSuspenseBoundaries:W.pendingSuspenseBoundaries,transitions:W.transitions},h.updateQueue.baseState=M,h.memoizedState=M,h.flags&256){L=Yc(Error(a(423)),h),h=c3(d,h,P,S,L);break e}else if(P!==L){L=Yc(Error(a(424)),h),h=c3(d,h,P,S,L);break e}else for(xt&&(No=N1(h.stateNode.containerInfo),Xn=h,Hn=!0,Zi=null,Bo=!1),S=Xy(h,null,P,S),h.child=S;S;)S.flags=S.flags&-3|4096,S=S.sibling;else{if(Mc(),P===L){h=Ps(d,h,S);break e}Di(d,h,P,S)}h=h.child}return h;case 5:return It(h),d===null&&Of(h),P=h.type,L=h.pendingProps,M=d!==null?d.memoizedProps:null,W=L.children,ke(P,L)?W=null:M!==null&&ke(P,M)&&(h.flags|=32),s3(d,h),Di(d,h,W,S),h.child;case 6:return d===null&&Of(h),null;case 13:return d3(d,h,S);case 4:return ye(h,h.stateNode.containerInfo),P=h.pendingProps,d===null?h.child=Nc(h,null,P,S):Di(d,h,P,S),h.child;case 11:return P=h.type,L=h.pendingProps,L=h.elementType===P?L:da(P,L),i3(d,h,P,L,S);case 7:return Di(d,h,h.pendingProps,S),h.child;case 8:return Di(d,h,h.pendingProps.children,S),h.child;case 12:return Di(d,h,h.pendingProps.children,S),h.child;case 10:e:{if(P=h.type._context,L=h.pendingProps,M=h.memoizedProps,W=L.value,Uy(h,P,W),M!==null)if(Y(M.value,W)){if(M.children===L.children&&!si.current){h=Ps(d,h,S);break e}}else for(M=h.child,M!==null&&(M.return=h);M!==null;){var se=M.dependencies;if(se!==null){W=M.child;for(var pe=se.firstContext;pe!==null;){if(pe.context===P){if(M.tag===1){pe=ks(-1,S&-S),pe.tag=2;var He=M.updateQueue;if(He!==null){He=He.shared;var ht=He.pending;ht===null?pe.next=pe:(pe.next=ht.next,ht.next=pe),He.pending=pe}}M.lanes|=S,pe=M.alternate,pe!==null&&(pe.lanes|=S),Nf(M.return,S,h),se.lanes|=S;break}pe=pe.next}}else if(M.tag===10)W=M.type===h.type?null:M.child;else if(M.tag===18){if(W=M.return,W===null)throw Error(a(341));W.lanes|=S,se=W.alternate,se!==null&&(se.lanes|=S),Nf(W,S,h),W=M.sibling}else W=M.child;if(W!==null)W.return=M;else 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@@ -532,14 +532,14 @@ No matching component was found for:
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  * LICENSE file in the root directory of this source tree.
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@@ -617,9 +617,9 @@ For more info see: https://github.com/konvajs/react-konva/issues/194
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yr(yr({},e),{},{isDragActive:t.isDragActive,isDragAccept:t.isDragAccept,isDragReject:t.isDragReject});case"setFiles":return yr(yr({},e),{},{acceptedFiles:t.acceptedFiles,fileRejections:t.fileRejections});case"reset":return yr({},e_);default:return e}}function FN(){}const mze=e=>{const{isDragAccept:t,isDragReject:n,overlaySecondaryText:r,setIsHandlingUpload:i}=e;return Ze("esc",()=>{i(!1)}),q("div",{className:"dropzone-container",children:[t&&y("div",{className:"dropzone-overlay is-drag-accept",children:q(Hh,{size:"lg",children:["Upload Image",r]})}),n&&q("div",{className:"dropzone-overlay is-drag-reject",children:[y(Hh,{size:"lg",children:"Invalid Upload"}),y(Hh,{size:"md",children:"Must be single JPEG or PNG image"})]})]})},zN=e=>async(t,n)=>{const{imageFile:r}=e,i=n(),o=Pr(i),a=new FormData;a.append("file",r,r.name),a.append("data",JSON.stringify({kind:"init"}));const l=await(await fetch(window.location.origin+"/upload",{method:"POST",body:a})).json();console.log(l);const 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+`+I.message,"");i({title:o("toast:uploadFailed"),description:A,status:"error",isClosable:!0})},[o,i]),f=C.exports.useCallback(async T=>{n(zN({imageFile:T}))},[n]),p=C.exports.useCallback((T,A)=>{A.forEach(R=>{u(R)}),T.forEach(R=>{f(R)})},[f,u]),{getRootProps:m,getInputProps:v,isDragAccept:b,isDragReject:x,isDragActive:E,open:k}=bK({accept:{"image/png":[".png"],"image/jpeg":[".jpg",".jpeg",".png"]},noClick:!0,onDrop:p,onDragOver:()=>s(!0),maxFiles:1});l(k),C.exports.useEffect(()=>{const T=A=>{var B;const R=(B=A.clipboardData)==null?void 0:B.items;if(!R)return;const I=[];for(const z of R)z.kind==="file"&&["image/png","image/jpg"].includes(z.type)&&I.push(z);if(!I.length)return;if(A.stopImmediatePropagation(),I.length>1){i({description:o("toast:uploadFailedMultipleImagesDesc"),status:"error",isClosable:!0});return}const D=I[0].getAsFile();if(!D){i({description:o("toast:uploadFailedUnableToLoadDesc"),status:"error",isClosable:!0});return}n(zN({imageFile:D}))};return document.addEventListener("paste",T),()=>{document.removeEventListener("paste",T)}},[o,n,i,r]);const _=["img2img","unifiedCanvas"].includes(r)?` to ${rs[r].tooltip}`:"";return y(jP.Provider,{value:k,children:q("div",{...m({style:{}}),onKeyDown:T=>{T.key},children:[y("input",{...v()}),t,E&&a&&y(mze,{isDragAccept:b,isDragReject:x,overlaySecondaryText:_,setIsHandlingUpload:s})]})})},yze=()=>{const e=Be(),t=ge(K8e),n=Ty();C.exports.useEffect(()=>{t.forEach(r=>{n(r)}),t.length>0&&e(pwe())},[e,n,t])},SK=st([e=>e.options,e=>e.gallery,Pr],(e,t,n)=>{const{shouldPinOptionsPanel:r,shouldShowOptionsPanel:i,shouldHoldOptionsPanelOpen:o,shouldUseCanvasBetaLayout:a}=e,{shouldShowGallery:s,shouldPinGallery:l,shouldHoldGalleryOpen:u}=t,f=a&&n==="unifiedCanvas",p=!f&&!(i||o&&!r)&&["txt2img","img2img","unifiedCanvas"].includes(n),m=!(s||u&&!l)&&["txt2img","img2img","unifiedCanvas"].includes(n);return{shouldPinOptionsPanel:r,shouldShowProcessButtons:!f&&(!r||!i),shouldShowOptionsPanelButton:p,shouldShowOptionsPanel:i,shouldShowGallery:s,shouldPinGallery:l,shouldShowGalleryButton:m}},{memoizeOptions:{resultEqualityCheck:Fe.isEqual}}),bze=()=>{const e=Be(),{shouldShowOptionsPanel:t,shouldShowOptionsPanelButton:n,shouldShowProcessButtons:r,shouldPinOptionsPanel:i,shouldShowGallery:o,shouldPinGallery:a}=ge(SK),s=()=>{e(Ju(!0)),i&&setTimeout(()=>e(vi(!0)),400)};return Ze("f",()=>{o||t?(e(Ju(!1)),e(Vd(!1))):(e(Ju(!0)),e(Vd(!0))),(a||i)&&setTimeout(()=>e(vi(!0)),400)},[o,t]),n?q("div",{className:"show-hide-button-options",children:[y(Qe,{tooltip:"Show Options Panel (O)",tooltipProps:{placement:"top"},"aria-label":"Show Options Panel",onClick:s,children:y(dP,{})}),r&&q(fn,{children:[y(DP,{iconButton:!0}),y(NP,{})]})]}):null},Sze=()=>{const e=Be(),{shouldShowGallery:t,shouldShowGalleryButton:n,shouldPinGallery:r,shouldShowOptionsPanel:i,shouldPinOptionsPanel:o}=ge(SK),a=()=>{e(Vd(!0)),r&&e(vi(!0))};return Ze("f",()=>{t||i?(e(Ju(!1)),e(Vd(!1))):(e(Ju(!0)),e(Vd(!0))),(r||o)&&setTimeout(()=>e(vi(!0)),400)},[t,i]),n?y(Qe,{tooltip:"Show Gallery (G)",tooltipProps:{placement:"top"},"aria-label":"Show Gallery",styleClass:"floating-show-hide-button right show-hide-button-gallery",onClick:a,children:y(wq,{})}):null};dOe();const xze=()=>(yze(),q("div",{className:"App",children:[q(vze,{children:[y(m8e,{}),q("div",{className:"app-content",children:[y(bLe,{}),y(wFe,{})]}),y("div",{className:"app-console",children:y(cOe,{})})]}),y(bze,{}),y(Sze,{})]}));const wze=SF({key:"invokeai-style-cache",prepend:!0});WC.createRoot(document.getElementById("root")).render(y(oe.StrictMode,{children:y(s8e,{store:xG,children:y(OG,{loading:y(ZI,{}),persistor:MG,children:y(Lie,{value:wze,children:y(_4e,{children:y(oe.Suspense,{fallback:y(ZI,{}),children:y(xze,{})})})})})})}));export{_ze as __vite_legacy_guard};
diff --git a/frontend/dist/index.html b/frontend/dist/index.html
index d948b5c776b..36b26d64767 100644
--- a/frontend/dist/index.html
+++ b/frontend/dist/index.html
@@ -7,7 +7,7 @@
     <meta name="viewport" content="width=device-width, initial-scale=1.0" />
     <title>InvokeAI - A Stable Diffusion Toolkit</title>
     <link rel="shortcut icon" type="icon" href="./assets/favicon.0d253ced.ico" />
-    <script type="module" crossorigin src="./assets/index.ec2d89c6.js"></script>
+    <script type="module" crossorigin src="./assets/index.1b59e83a.js"></script>
     <link rel="stylesheet" href="./assets/index.0dadf5d0.css">
     <script type="module">try{import.meta.url;import("_").catch(()=>1);}catch(e){}window.__vite_is_modern_browser=true;</script>
     <script type="module">!function(){if(window.__vite_is_modern_browser)return;console.warn("vite: loading legacy build because dynamic import or import.meta.url is unsupported, syntax error above should be ignored");var e=document.getElementById("vite-legacy-polyfill"),n=document.createElement("script");n.src=e.src,n.onload=function(){System.import(document.getElementById('vite-legacy-entry').getAttribute('data-src'))},document.body.appendChild(n)}();</script>
@@ -18,6 +18,6 @@
     
     <script nomodule>!function(){var e=document,t=e.createElement("script");if(!("noModule"in t)&&"onbeforeload"in t){var n=!1;e.addEventListener("beforeload",(function(e){if(e.target===t)n=!0;else if(!e.target.hasAttribute("nomodule")||!n)return;e.preventDefault()}),!0),t.type="module",t.src=".",e.head.appendChild(t),t.remove()}}();</script>
     <script nomodule crossorigin id="vite-legacy-polyfill" src="./assets/polyfills-legacy-dde3a68a.js"></script>
-    <script nomodule crossorigin id="vite-legacy-entry" data-src="./assets/index-legacy-5c5a479d.js">System.import(document.getElementById('vite-legacy-entry').getAttribute('data-src'))</script>
+    <script nomodule crossorigin id="vite-legacy-entry" data-src="./assets/index-legacy-474a75fe.js">System.import(document.getElementById('vite-legacy-entry').getAttribute('data-src'))</script>
   </body>
 </html>

From e735652a13ce1e637eaff1fcd9dabae7613c4726 Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Tue, 10 Jan 2023 03:25:00 +0000
Subject: [PATCH 159/199] fix dictconfig-not-serializable issue

---
 ldm/invoke/model_manager.py | 31 ++++++++++---------------------
 1 file changed, 10 insertions(+), 21 deletions(-)

diff --git a/ldm/invoke/model_manager.py b/ldm/invoke/model_manager.py
index 212d71df39d..38e985b78a1 100644
--- a/ldm/invoke/model_manager.py
+++ b/ldm/invoke/model_manager.py
@@ -163,7 +163,6 @@ def list_models(self) -> dict:
         for name in self.config:
             stanza = self.config[name]
             models[name] = dict()
-            
             format = stanza.get('format','ckpt') # Determine Format
 
             # Common Attribs
@@ -177,36 +176,26 @@ def list_models(self) -> dict:
             
             # Checkpoint Config Parse
             if format == 'ckpt':
-                config = stanza.get('config', None)
-                weights = stanza.get('weights', None)
-                vae = stanza.get('vae', None)
-                width = stanza.get('width', 512)
-                height = stanza.get('height', 512)
                 models[name].update(
                     description = description,
                     format = format,
-                    config = config,
-                    weights = weights,
-                    vae = vae,
-                    width = width,
-                    height = height,
-                    status = status
+                    config = str(stanza.get('config', None)),
+                    weights = str(stanza.get('weights', None)),
+                    vae = str(stanza.get('vae', None)),
+                    width = str(stanza.get('width', 512)),
+                    height = str(stanza.get('height', 512)),
+                    status = status,
                 )
                 
             # Diffusers Config Parse
             if format == 'diffusers':
-                repo_id = stanza.get('repo_id', None)
-                vae = None
-                if 'vae' in stanza:
-                    vae = stanza['vae'].get('repo_id',None)
                 models[name].update(
                     description = description,
-                    format = format,
-                    repo_id = repo_id,
-                    vae = vae,
-                    status = status
+                    repo_id = str(stanza.get('repo_id', None)),
+                    path = str(stanza.get('path',None)),
+                    status = status,
                 )
-
+        
         return models
 
     def print_models(self) -> None:

From e1364388b9dc2328f0c32a444d3c85bf31c9fd82 Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Tue, 10 Jan 2023 03:32:37 +0000
Subject: [PATCH 160/199] fix NoneType' object is not subscriptable crash in
 model_manager

---
 ldm/invoke/model_manager.py | 33 +++++++++++++++------------------
 1 file changed, 15 insertions(+), 18 deletions(-)

diff --git a/ldm/invoke/model_manager.py b/ldm/invoke/model_manager.py
index 34854099532..8ac8748c2ce 100644
--- a/ldm/invoke/model_manager.py
+++ b/ldm/invoke/model_manager.py
@@ -163,7 +163,6 @@ def list_models(self) -> dict:
         for name in self.config:
             stanza = self.config[name]
             models[name] = dict()
-            
             format = stanza.get('format','ckpt') # Determine Format
 
             # Common Attribs
@@ -177,34 +176,32 @@ def list_models(self) -> dict:
             
             # Checkpoint Config Parse
             if format == 'ckpt':
-                config = stanza.get('config', None)
-                weights = stanza.get('weights', None)
-                vae = stanza.get('vae', None)
-                width = stanza.get('width', 512)
-                height = stanza.get('height', 512)
                 models[name].update(
                     description = description,
                     format = format,
-                    config = config,
-                    weights = weights,
-                    vae = vae,
-                    width = width,
-                    height = height,
-                    status = status
+                    config = str(stanza.get('config', None)),
+                    weights = str(stanza.get('weights', None)),
+                    vae = str(stanza.get('vae', None)),
+                    width = str(stanza.get('width', 512)),
+                    height = str(stanza.get('height', 512)),
+                    status = status,
                 )
                 
             # Diffusers Config Parse
+            if (vae := stanza.get('vae',None)):
+                vae = dict(
+                    repo_id = str(vae.get('repo_id',None)),
+                    path = str(vae.get('path',None))
+                )
             if format == 'diffusers':
-                repo_id = stanza.get('repo_id', None)
-                vae = stanza.get('vae', None)['repo_id']
                 models[name].update(
                     description = description,
-                    format = format,
-                    repo_id = repo_id,
                     vae = vae,
-                    status = status
+                    repo_id = str(stanza.get('repo_id', None)),
+                    path = str(stanza.get('path',None)),
+                    status = status,
                 )
-
+        
         return models
 
     def print_models(self) -> None:

From f264cc6d07bbf1338ba3c6303633d5ac4b26752e Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Tue, 10 Jan 2023 03:35:25 +0000
Subject: [PATCH 161/199] fix "str has no attribute get" error in model_manager
 list_models()

---
 ldm/invoke/model_manager.py | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/ldm/invoke/model_manager.py b/ldm/invoke/model_manager.py
index 8ac8748c2ce..64f537e6711 100644
--- a/ldm/invoke/model_manager.py
+++ b/ldm/invoke/model_manager.py
@@ -189,10 +189,11 @@ def list_models(self) -> dict:
                 
             # Diffusers Config Parse
             if (vae := stanza.get('vae',None)):
-                vae = dict(
-                    repo_id = str(vae.get('repo_id',None)),
-                    path = str(vae.get('path',None))
-                )
+                if isinstance(vae,DictConfig):
+                    vae = dict(
+                        repo_id = str(vae.get('repo_id',None)),
+                        path = str(vae.get('path',None)),
+                    )
             if format == 'diffusers':
                 models[name].update(
                     description = description,

From 499c4e2ac1d29263a90112b89d68ce10f3767a65 Mon Sep 17 00:00:00 2001
From: blessedcoolant <54517381+blessedcoolant@users.noreply.github.com>
Date: Tue, 10 Jan 2023 21:51:15 +1300
Subject: [PATCH 162/199] Add path and repo_id support for Diffusers Model
 Manager

Also fixes bugs
---
 frontend/public/locales/modelmanager/en.json  |  11 +-
 frontend/src/app/invokeai.d.ts                |  16 +-
 .../ModelManager/AddCheckpointModel.tsx       |   2 +-
 .../ModelManager/AddDiffusersModel.tsx        | 325 ++++++++++++------
 .../ModelManager/DiffusersModelEdit.tsx       |  75 +++-
 ldm/invoke/model_manager.py                   |   2 +
 6 files changed, 309 insertions(+), 122 deletions(-)

diff --git a/frontend/public/locales/modelmanager/en.json b/frontend/public/locales/modelmanager/en.json
index f57d237d11a..cbc2930ed6c 100644
--- a/frontend/public/locales/modelmanager/en.json
+++ b/frontend/public/locales/modelmanager/en.json
@@ -16,10 +16,13 @@
   "config": "Config",
   "configValidationMsg": "Path to the config file of your model.",
   "modelLocation": "Model Location",
-  "repo_id": "Repo ID",
   "modelLocationValidationMsg": "Path to where your model is located.",
+  "repo_id": "Repo ID",
+  "repoIDValidationMsg": "Online repository of your model",
   "vaeLocation": "VAE Location",
   "vaeLocationValidationMsg": "Path to where your VAE is located.",
+  "vaeRepoID": "VAE Repo ID",
+  "vaeRepoIDValidationMsg": "Online repository of your VAE",
   "width": "Width",
   "widthValidationMsg": "Default width of your model.",
   "height": "Height",
@@ -50,5 +53,9 @@
   "deleteModel": "Delete Model",
   "deleteConfig": "Delete Config",
   "deleteMsg1": "Are you sure you want to delete this model entry from InvokeAI?",
-  "deleteMsg2": "This will not delete the model checkpoint file from your disk. You can readd them if you wish to."
+  "deleteMsg2": "This will not delete the model checkpoint file from your disk. You can readd them if you wish to.",
+  "formMessageDiffusersModelLocation": "Diffusers Model Location",
+  "formMessageDiffusersModelLocationDesc": "Please enter at least one.",
+  "formMessageDiffusersVAELocation": "VAE Location",
+  "formMessageDiffusersVAELocationDesc": "If not provided, InvokeAI will look for the VAE file inside the model location given above."
 }
diff --git a/frontend/src/app/invokeai.d.ts b/frontend/src/app/invokeai.d.ts
index d3b027b58c4..b30ec1f353b 100644
--- a/frontend/src/app/invokeai.d.ts
+++ b/frontend/src/app/invokeai.d.ts
@@ -171,10 +171,22 @@ export declare type Model = {
   height?: number;
   default?: boolean;
   format?: string;
+};
+
+export declare type DiffusersModel = {
+  status: ModelStatus;
+  description: string;
   repo_id?: string;
+  path?: string;
+  vae?: {
+    repo_id?: string;
+    path?: string;
+  };
+  format?: string;
+  default?: boolean;
 };
 
-export declare type ModelList = Record<string, Model>;
+export declare type ModelList = Record<string, Model & DiffusersModel>;
 
 export declare type FoundModel = {
   name: string;
@@ -197,10 +209,12 @@ export declare type InvokeDiffusersModelConfigProps = {
   name: string | undefined;
   description: string | undefined;
   repo_id: string | undefined;
+  path: string | undefined;
   default: boolean | undefined;
   format: string | undefined;
   vae: {
     repo_id: string | undefined;
+    path: string | undefined;
   };
 };
 
diff --git a/frontend/src/features/system/components/ModelManager/AddCheckpointModel.tsx b/frontend/src/features/system/components/ModelManager/AddCheckpointModel.tsx
index 7b996900690..efa44c1d770 100644
--- a/frontend/src/features/system/components/ModelManager/AddCheckpointModel.tsx
+++ b/frontend/src/features/system/components/ModelManager/AddCheckpointModel.tsx
@@ -42,7 +42,7 @@ export default function AddCheckpointModel() {
   );
 
   function hasWhiteSpace(s: string) {
-    return /\\s/g.test(s);
+    return /\s/.test(s);
   }
 
   function baseValidation(value: string) {
diff --git a/frontend/src/features/system/components/ModelManager/AddDiffusersModel.tsx b/frontend/src/features/system/components/ModelManager/AddDiffusersModel.tsx
index ee7940e5df3..57033597865 100644
--- a/frontend/src/features/system/components/ModelManager/AddDiffusersModel.tsx
+++ b/frontend/src/features/system/components/ModelManager/AddDiffusersModel.tsx
@@ -4,6 +4,7 @@ import {
   FormErrorMessage,
   FormHelperText,
   FormLabel,
+  Text,
   VStack,
 } from '@chakra-ui/react';
 import { useAppDispatch, useAppSelector } from 'app/storeHooks';
@@ -15,11 +16,31 @@ import { Field, Formik } from 'formik';
 import React from 'react';
 import { useTranslation } from 'react-i18next';
 import { BiArrowBack } from 'react-icons/bi';
-
-import type { RootState } from 'app/store';
 import { InvokeDiffusersModelConfigProps } from 'app/invokeai';
 import { addNewModel } from 'app/socketio/actions';
 
+import type { RootState } from 'app/store';
+import type { ReactElement } from 'react';
+
+function FormItemWrapper({
+  children,
+}: {
+  children: ReactElement | ReactElement[];
+}) {
+  return (
+    <Flex
+      flexDirection="column"
+      backgroundColor="var(--background-color)"
+      padding="1rem 1rem"
+      borderRadius="0.5rem"
+      rowGap="1rem"
+      width="100%"
+    >
+      {children}
+    </Flex>
+  );
+}
+
 export default function AddDiffusersModel() {
   const dispatch = useAppDispatch();
   const { t } = useTranslation();
@@ -29,7 +50,7 @@ export default function AddDiffusersModel() {
   );
 
   function hasWhiteSpace(s: string) {
-    return /\\s/g.test(s);
+    return /\s/.test(s);
   }
 
   function baseValidation(value: string) {
@@ -42,10 +63,12 @@ export default function AddDiffusersModel() {
     name: '',
     description: '',
     repo_id: '',
+    path: '',
     format: 'diffusers',
     default: false,
     vae: {
       repo_id: '',
+      path: '',
     },
   };
 
@@ -54,10 +77,17 @@ export default function AddDiffusersModel() {
   ) => {
     let diffusersModelToAdd = values;
 
-    if (values.vae.repo_id == '') {
+    if (values.repo_id !== '' && values.vae.repo_id === '') {
       diffusersModelToAdd = {
         ...diffusersModelToAdd,
-        vae: { repo_id: values.repo_id + '/vae' },
+        vae: { repo_id: values.repo_id + '/vae', path: '' },
+      };
+    }
+
+    if (values.path !== '' && values.vae.path === '') {
+      diffusersModelToAdd = {
+        ...diffusersModelToAdd,
+        vae: { repo_id: '', path: values.path + '/vae' },
       };
     }
 
@@ -86,108 +116,189 @@ export default function AddDiffusersModel() {
         {({ handleSubmit, errors, touched }) => (
           <form onSubmit={handleSubmit}>
             <VStack rowGap={'0.5rem'}>
-              {/* Name */}
-              <FormControl isInvalid={!!errors.name && touched.name} isRequired>
-                <FormLabel htmlFor="name" fontSize="sm">
-                  {t('modelmanager:name')}
-                </FormLabel>
-                <VStack alignItems={'start'}>
-                  <Field
-                    as={IAIInput}
-                    id="name"
-                    name="name"
-                    type="text"
-                    validate={baseValidation}
-                    width="2xl"
-                  />
-                  {!!errors.name && touched.name ? (
-                    <FormErrorMessage>{errors.name}</FormErrorMessage>
-                  ) : (
-                    <FormHelperText margin={0}>
-                      {t('modelmanager:nameValidationMsg')}
-                    </FormHelperText>
-                  )}
-                </VStack>
-              </FormControl>
-
-              {/* Description */}
-              <FormControl
-                isInvalid={!!errors.description && touched.description}
-                isRequired
-              >
-                <FormLabel htmlFor="description" fontSize="sm">
-                  {t('modelmanager:description')}
-                </FormLabel>
-                <VStack alignItems={'start'}>
-                  <Field
-                    as={IAIInput}
-                    id="description"
-                    name="description"
-                    type="text"
-                    width="2xl"
-                  />
-                  {!!errors.description && touched.description ? (
-                    <FormErrorMessage>{errors.description}</FormErrorMessage>
-                  ) : (
-                    <FormHelperText margin={0}>
-                      {t('modelmanager:descriptionValidationMsg')}
-                    </FormHelperText>
-                  )}
-                </VStack>
-              </FormControl>
-
-              {/* Repo ID */}
-              <FormControl
-                isInvalid={!!errors.repo_id && touched.repo_id}
-                isRequired
-              >
-                <FormLabel htmlFor="repo_id" fontSize="sm">
-                  {t('modelmanager:modelLocation')} /{' '}
-                  {t('modelmanager:repo_id')}
-                </FormLabel>
-                <VStack alignItems={'start'}>
-                  <Field
-                    as={IAIInput}
-                    id="repo_id"
-                    name="repo_id"
-                    type="text"
-                    width="2xl"
-                  />
-                  {!!errors.repo_id && touched.repo_id ? (
-                    <FormErrorMessage>{errors.repo_id}</FormErrorMessage>
-                  ) : (
-                    <FormHelperText margin={0}>
-                      {t('modelmanager:modelLocationValidationMsg')}
-                    </FormHelperText>
-                  )}
-                </VStack>
-              </FormControl>
-
-              {/* VAE */}
-              <FormControl
-                isInvalid={!!errors.vae?.repo_id && touched.vae?.repo_id}
-                isRequired
-              >
-                <FormLabel htmlFor="vae.repo_id" fontSize="sm">
-                  {t('modelmanager:vaeLocation')}
-                </FormLabel>
-                <VStack alignItems={'start'}>
-                  <Field
-                    as={IAIInput}
-                    id="vae.repo_id"
-                    name="vae.repo_id"
-                    type="text"
-                    width="2xl"
-                  />
-                  {!!errors.vae?.repo_id && touched.vae?.repo_id ? (
-                    <FormErrorMessage>{errors.vae?.repo_id}</FormErrorMessage>
-                  ) : (
-                    <FormHelperText margin={0}>
-                      {t('modelmanager:vaeLocationValidationMsg')}
-                    </FormHelperText>
-                  )}
-                </VStack>
-              </FormControl>
+              <FormItemWrapper>
+                {/* Name */}
+                <FormControl
+                  isInvalid={!!errors.name && touched.name}
+                  isRequired
+                >
+                  <FormLabel htmlFor="name" fontSize="sm">
+                    {t('modelmanager:name')}
+                  </FormLabel>
+                  <VStack alignItems={'start'}>
+                    <Field
+                      as={IAIInput}
+                      id="name"
+                      name="name"
+                      type="text"
+                      validate={baseValidation}
+                      width="2xl"
+                      isRequired
+                    />
+                    {!!errors.name && touched.name ? (
+                      <FormErrorMessage>{errors.name}</FormErrorMessage>
+                    ) : (
+                      <FormHelperText margin={0}>
+                        {t('modelmanager:nameValidationMsg')}
+                      </FormHelperText>
+                    )}
+                  </VStack>
+                </FormControl>
+              </FormItemWrapper>
+
+              <FormItemWrapper>
+                {/* Description */}
+                <FormControl
+                  isInvalid={!!errors.description && touched.description}
+                  isRequired
+                >
+                  <FormLabel htmlFor="description" fontSize="sm">
+                    {t('modelmanager:description')}
+                  </FormLabel>
+                  <VStack alignItems={'start'}>
+                    <Field
+                      as={IAIInput}
+                      id="description"
+                      name="description"
+                      type="text"
+                      width="2xl"
+                      isRequired
+                    />
+                    {!!errors.description && touched.description ? (
+                      <FormErrorMessage>{errors.description}</FormErrorMessage>
+                    ) : (
+                      <FormHelperText margin={0}>
+                        {t('modelmanager:descriptionValidationMsg')}
+                      </FormHelperText>
+                    )}
+                  </VStack>
+                </FormControl>
+              </FormItemWrapper>
+
+              <FormItemWrapper>
+                <Text fontWeight="bold" fontSize="sm">
+                  {t('modelmanager:formMessageDiffusersModelLocation')}
+                </Text>
+                <Text
+                  fontSize="sm"
+                  fontStyle="italic"
+                  color="var(--text-color-secondary)"
+                >
+                  {t('modelmanager:formMessageDiffusersModelLocationDesc')}
+                </Text>
+
+                {/* Path */}
+                <FormControl
+                  isInvalid={!!errors.path && touched.path}
+                  isRequired
+                >
+                  <FormLabel htmlFor="path" fontSize="sm">
+                    {t('modelmanager:modelLocation')}
+                  </FormLabel>
+                  <VStack alignItems={'start'}>
+                    <Field
+                      as={IAIInput}
+                      id="path"
+                      name="path"
+                      type="text"
+                      width="2xl"
+                      isRequired
+                    />
+                    {!!errors.path && touched.path ? (
+                      <FormErrorMessage>{errors.path}</FormErrorMessage>
+                    ) : (
+                      <FormHelperText margin={0}>
+                        {t('modelmanager:modelLocationValidationMsg')}
+                      </FormHelperText>
+                    )}
+                  </VStack>
+                </FormControl>
+
+                {/* Repo ID */}
+                <FormControl isInvalid={!!errors.repo_id && touched.repo_id}>
+                  <FormLabel htmlFor="repo_id" fontSize="sm">
+                    {t('modelmanager:repo_id')}
+                  </FormLabel>
+                  <VStack alignItems={'start'}>
+                    <Field
+                      as={IAIInput}
+                      id="repo_id"
+                      name="repo_id"
+                      type="text"
+                      width="2xl"
+                    />
+                    {!!errors.repo_id && touched.repo_id ? (
+                      <FormErrorMessage>{errors.repo_id}</FormErrorMessage>
+                    ) : (
+                      <FormHelperText margin={0}>
+                        {t('modelmanager:repoIDValidationMsg')}
+                      </FormHelperText>
+                    )}
+                  </VStack>
+                </FormControl>
+              </FormItemWrapper>
+
+              <FormItemWrapper>
+                {/* VAE Path */}
+                <Text fontWeight="bold">
+                  {t('modelmanager:formMessageDiffusersVAELocation')}
+                </Text>
+                <Text
+                  fontSize="sm"
+                  fontStyle="italic"
+                  color="var(--text-color-secondary)"
+                >
+                  {t('modelmanager:formMessageDiffusersVAELocationDesc')}
+                </Text>
+                <FormControl
+                  isInvalid={!!errors.vae?.path && touched.vae?.path}
+                >
+                  <FormLabel htmlFor="vae.path" fontSize="sm">
+                    {t('modelmanager:vaeLocation')}
+                  </FormLabel>
+                  <VStack alignItems={'start'}>
+                    <Field
+                      as={IAIInput}
+                      id="vae.path"
+                      name="vae.path"
+                      type="text"
+                      width="2xl"
+                    />
+                    {!!errors.vae?.path && touched.vae?.path ? (
+                      <FormErrorMessage>{errors.vae?.path}</FormErrorMessage>
+                    ) : (
+                      <FormHelperText margin={0}>
+                        {t('modelmanager:vaeLocationValidationMsg')}
+                      </FormHelperText>
+                    )}
+                  </VStack>
+                </FormControl>
+
+                {/* VAE Repo ID */}
+                <FormControl
+                  isInvalid={!!errors.vae?.repo_id && touched.vae?.repo_id}
+                >
+                  <FormLabel htmlFor="vae.repo_id" fontSize="sm">
+                    {t('modelmanager:vaeRepoID')}
+                  </FormLabel>
+                  <VStack alignItems={'start'}>
+                    <Field
+                      as={IAIInput}
+                      id="vae.repo_id"
+                      name="vae.repo_id"
+                      type="text"
+                      width="2xl"
+                    />
+                    {!!errors.vae?.repo_id && touched.vae?.repo_id ? (
+                      <FormErrorMessage>{errors.vae?.repo_id}</FormErrorMessage>
+                    ) : (
+                      <FormHelperText margin={0}>
+                        {t('modelmanager:vaeRepoIDValidationMsg')}
+                      </FormHelperText>
+                    )}
+                  </VStack>
+                </FormControl>
+              </FormItemWrapper>
 
               <IAIButton
                 type="submit"
diff --git a/frontend/src/features/system/components/ModelManager/DiffusersModelEdit.tsx b/frontend/src/features/system/components/ModelManager/DiffusersModelEdit.tsx
index 6dc0d7fbbcb..3989815691c 100644
--- a/frontend/src/features/system/components/ModelManager/DiffusersModelEdit.tsx
+++ b/frontend/src/features/system/components/ModelManager/DiffusersModelEdit.tsx
@@ -57,7 +57,8 @@ export default function DiffusersModelEdit() {
       name: '',
       description: '',
       repo_id: '',
-      vae: { repo_id: '' },
+      path: '',
+      vae: { repo_id: '', path: '' },
       default: false,
       format: 'diffusers',
     });
@@ -71,10 +72,14 @@ export default function DiffusersModelEdit() {
       setEditModelFormValues({
         name: openModel,
         description: retrievedModel[openModel]?.description,
+        path: retrievedModel[openModel]?.path,
         repo_id: retrievedModel[openModel]?.repo_id,
         vae: {
-          repo_id: retrievedModel[openModel]?.vae
-            ? retrievedModel[openModel]?.vae
+          repo_id: retrievedModel[openModel]?.vae?.repo_id
+            ? retrievedModel[openModel]?.vae?.repo_id
+            : '',
+          path: retrievedModel[openModel]?.vae?.path
+            ? retrievedModel[openModel]?.vae?.path
             : '',
         },
         default: retrievedModel[openModel]?.default,
@@ -136,14 +141,37 @@ export default function DiffusersModelEdit() {
                   </VStack>
                 </FormControl>
 
-                {/* Repo ID */}
+                {/* Path */}
                 <FormControl
-                  isInvalid={!!errors.repo_id && touched.repo_id}
+                  isInvalid={!!errors.path && touched.path}
                   isRequired
                 >
-                  <FormLabel htmlFor="config" fontSize="sm">
+                  <FormLabel htmlFor="path" fontSize="sm">
                     {t('modelmanager:modelLocation')}
                   </FormLabel>
+                  <VStack alignItems={'start'}>
+                    <Field
+                      as={IAIInput}
+                      id="path"
+                      name="path"
+                      type="text"
+                      width="lg"
+                    />
+                    {!!errors.path && touched.path ? (
+                      <FormErrorMessage>{errors.path}</FormErrorMessage>
+                    ) : (
+                      <FormHelperText margin={0}>
+                        {t('modelmanager:modelLocationValidationMsg')}
+                      </FormHelperText>
+                    )}
+                  </VStack>
+                </FormControl>
+
+                {/* Repo ID */}
+                <FormControl isInvalid={!!errors.repo_id && touched.repo_id}>
+                  <FormLabel htmlFor="repo_id" fontSize="sm">
+                    {t('modelmanager:repo_id')}
+                  </FormLabel>
                   <VStack alignItems={'start'}>
                     <Field
                       as={IAIInput}
@@ -156,19 +184,44 @@ export default function DiffusersModelEdit() {
                       <FormErrorMessage>{errors.repo_id}</FormErrorMessage>
                     ) : (
                       <FormHelperText margin={0}>
-                        {t('modelmanager:modelLocationValidationMsg')}
+                        {t('modelmanager:repoIDValidationMsg')}
                       </FormHelperText>
                     )}
                   </VStack>
                 </FormControl>
 
-                {/* VAE */}
+                {/* VAE Path */}
                 <FormControl
-                  isInvalid={!!errors.vae?.repo_id && touched.vae?.repo_id}
+                  isInvalid={!!errors.vae?.path && touched.vae?.path}
                 >
-                  <FormLabel htmlFor="vae" fontSize="sm">
+                  <FormLabel htmlFor="vae.path" fontSize="sm">
                     {t('modelmanager:vaeLocation')}
                   </FormLabel>
+                  <VStack alignItems={'start'}>
+                    <Field
+                      as={IAIInput}
+                      id="vae.path"
+                      name="vae.path"
+                      type="text"
+                      width="lg"
+                    />
+                    {!!errors.vae?.path && touched.vae?.path ? (
+                      <FormErrorMessage>{errors.vae?.path}</FormErrorMessage>
+                    ) : (
+                      <FormHelperText margin={0}>
+                        {t('modelmanager:vaeLocationValidationMsg')}
+                      </FormHelperText>
+                    )}
+                  </VStack>
+                </FormControl>
+
+                {/* VAE Repo ID */}
+                <FormControl
+                  isInvalid={!!errors.vae?.repo_id && touched.vae?.repo_id}
+                >
+                  <FormLabel htmlFor="vae.repo_id" fontSize="sm">
+                    {t('modelmanager:vaeRepoID')}
+                  </FormLabel>
                   <VStack alignItems={'start'}>
                     <Field
                       as={IAIInput}
@@ -181,7 +234,7 @@ export default function DiffusersModelEdit() {
                       <FormErrorMessage>{errors.vae?.repo_id}</FormErrorMessage>
                     ) : (
                       <FormHelperText margin={0}>
-                        {t('modelmanager:vaeLocationValidationMsg')}
+                        {t('modelmanager:vaeRepoIDValidationMsg')}
                       </FormHelperText>
                     )}
                   </VStack>
diff --git a/ldm/invoke/model_manager.py b/ldm/invoke/model_manager.py
index 64f537e6711..2e96621f6a6 100644
--- a/ldm/invoke/model_manager.py
+++ b/ldm/invoke/model_manager.py
@@ -194,9 +194,11 @@ def list_models(self) -> dict:
                         repo_id = str(vae.get('repo_id',None)),
                         path = str(vae.get('path',None)),
                     )
+                    
             if format == 'diffusers':
                 models[name].update(
                     description = description,
+                    format = format,
                     vae = vae,
                     repo_id = str(stanza.get('repo_id', None)),
                     path = str(stanza.get('path',None)),

From 11c8e606d5d81ac137ec8204d91f43feab1ccffa Mon Sep 17 00:00:00 2001
From: blessedcoolant <54517381+blessedcoolant@users.noreply.github.com>
Date: Tue, 3 Jan 2023 02:08:03 +1300
Subject: [PATCH 163/199] Fix tooltip IT localization not working

---
 frontend/public/locales/tooltip/it.json  | 16 +++++++++++++++-
 frontend/public/locales/tooltips/it.json | 15 ---------------
 2 files changed, 15 insertions(+), 16 deletions(-)
 delete mode 100644 frontend/public/locales/tooltips/it.json

diff --git a/frontend/public/locales/tooltip/it.json b/frontend/public/locales/tooltip/it.json
index 0967ef424bc..cf1993c8ccf 100644
--- a/frontend/public/locales/tooltip/it.json
+++ b/frontend/public/locales/tooltip/it.json
@@ -1 +1,15 @@
-{}
+{
+  "feature": {
+    "prompt": "Questo è il campo del prompt. Il prompt include oggetti di generazione e termini stilistici. Puoi anche aggiungere il peso (importanza del token) nel prompt, ma i comandi e i parametri dell'interfaccia a linea di comando non funzioneranno.",
+    "gallery": "Galleria visualizza le generazioni dalla cartella degli output man mano che vengono create. Le impostazioni sono memorizzate all'interno di file e accessibili dal menu contestuale.",
+    "other": "Queste opzioni abiliteranno modalità di elaborazione alternative per Invoke. 'Piastrella senza cuciture' creerà modelli ripetuti nell'output. 'Ottimizzzazione Alta risoluzione' è la generazione in due passaggi con 'Immagine a Immagine': usa questa impostazione quando vuoi un'immagine più grande e più coerente senza artefatti. Ci vorrà più tempo del solito 'Testo a Immagine'.",
+    "seed": "Il valore del Seme influenza il rumore iniziale da cui è formata l'immagine. Puoi usare i semi già esistenti dalle immagini precedenti. 'Soglia del rumore' viene utilizzato per mitigare gli artefatti a valori CFG elevati (provare l'intervallo 0-10) e Perlin per aggiungere il rumore Perlin durante la generazione: entrambi servono per aggiungere variazioni ai risultati.",
+    "variations": "Prova una variazione con un valore compreso tra 0.1 e 1.0 per modificare il risultato per un dato seme. Variazioni interessanti del seme sono comprese tra 0.1 e 0.3.",
+    "upscale": "Utilizza ESRGAN per ingrandire l'immagine subito dopo la generazione.",
+    "faceCorrection": "Correzione del volto con GFPGAN o Codeformer: l'algoritmo rileva i volti nell'immagine e corregge eventuali difetti. Un valore alto cambierà maggiormente l'immagine, dando luogo a volti più attraenti. Codeformer con una maggiore fedeltà preserva l'immagine originale a scapito di una correzione facciale più forte.",
+    "imageToImage": "Da Immagine a Immagine carica qualsiasi immagine come iniziale, che viene quindi utilizzata per generarne una nuova in base al prompt. Più alto è il valore, più cambierà l'immagine risultante. Sono possibili valori da 0.0 a 1.0, l'intervallo consigliato è 0.25-0.75",
+    "boundingBox": "Il riquadro di selezione è lo stesso delle impostazioni Larghezza e Altezza per da Testo a Immagine o da Immagine a Immagine. Verrà elaborata solo l'area nella casella.",
+    "seamCorrection": "Controlla la gestione delle giunzioni visibili che si verificano tra le immagini generate sulla tela.",
+    "infillAndScaling": "Gestisce i metodi di riempimento (utilizzati su aree mascherate o cancellate dell'area di disegno) e il ridimensionamento (utile per i riquadri di selezione di piccole dimensioni)."
+  }
+}
\ No newline at end of file
diff --git a/frontend/public/locales/tooltips/it.json b/frontend/public/locales/tooltips/it.json
deleted file mode 100644
index 182fa9e62af..00000000000
--- a/frontend/public/locales/tooltips/it.json
+++ /dev/null
@@ -1,15 +0,0 @@
-{
-  "feature": {
-    "prompt": "Questo è il campo del prompt. Il prompt include oggetti di generazione e termini stilistici. Puoi anche aggiungere il peso (importanza del token) nel prompt, ma i comandi e i parametri dell'interfaccia a linea di comando non funzioneranno.",
-    "gallery": "Galleria visualizza le generazioni dalla cartella degli output man mano che vengono create. Le impostazioni sono memorizzate all'interno di file e accessibili dal menu contestuale.",
-    "other": "Queste opzioni abiliteranno modalità di elaborazione alternative per Invoke. 'Piastrella senza cuciture' creerà modelli ripetuti nell'output. 'Ottimizzzazione Alta risoluzione' è la generazione in due passaggi con 'Immagine a Immagine': usa questa impostazione quando vuoi un'immagine più grande e più coerente senza artefatti. Ci vorrà più tempo del solito 'Testo a Immagine'.",
-    "seed": "Il valore del Seme influenza il rumore iniziale da cui è formata l'immagine. Puoi usare i semi già esistenti dalle immagini precedenti. 'Soglia del rumore' viene utilizzato per mitigare gli artefatti a valori CFG elevati (provare l'intervallo 0-10) e Perlin per aggiungere il rumore Perlin durante la generazione: entrambi servono per aggiungere variazioni ai risultati.",
-    "variations": "Prova una variazione con un valore compreso tra 0.1 e 1.0 per modificare il risultato per un dato seme. Variazioni interessanti del seme sono comprese tra 0.1 e 0.3.",
-    "upscale": "Utilizza ESRGAN per ingrandire l'immagine subito dopo la generazione.",
-    "faceCorrection": "Correzione del volto con GFPGAN o Codeformer: l'algoritmo rileva i volti nell'immagine e corregge eventuali difetti. Un valore alto cambierà maggiormente l'immagine, dando luogo a volti più attraenti. Codeformer con una maggiore fedeltà preserva l'immagine originale a scapito di una correzione facciale più forte.",
-    "imageToImage": "Da Immagine a Immagine carica qualsiasi immagine come iniziale, che viene quindi utilizzata per generarne una nuova in base al prompt. Più alto è il valore, più cambierà l'immagine risultante. Sono possibili valori da 0.0 a 1.0, l'intervallo consigliato è 0.25-0.75",
-    "boundingBox": "Il riquadro di selezione è lo stesso delle impostazioni Larghezza e Altezza per dat Testo a Immagine o da Immagine a Immagine. Verrà elaborata solo l'area nella casella.",
-    "seamCorrection": "Controlla la gestione delle giunzioni visibili che si verificano tra le immagini generate sulla tela.",
-    "infillAndScaling": "Gestisce i metodi di riempimento (utilizzati su aree mascherate o cancellate dell'area di disegno) e il ridimensionamento (utile per i riquadri di selezione di piccole dimensioni)."
-  }
-}

From 6a5539325a8b7fb7974d0bbc8bb8b53b6d6062a0 Mon Sep 17 00:00:00 2001
From: blessedcoolant <54517381+blessedcoolant@users.noreply.github.com>
Date: Tue, 3 Jan 2023 02:10:12 +1300
Subject: [PATCH 164/199] Add Version Number To WebUI

---
 .../system/components/SiteHeader.scss         |  4 ----
 .../features/system/components/SiteHeader.tsx | 22 +++++++++++++++----
 2 files changed, 18 insertions(+), 8 deletions(-)

diff --git a/frontend/src/features/system/components/SiteHeader.scss b/frontend/src/features/system/components/SiteHeader.scss
index 00bafb1382d..59edc9a4036 100644
--- a/frontend/src/features/system/components/SiteHeader.scss
+++ b/frontend/src/features/system/components/SiteHeader.scss
@@ -13,10 +13,6 @@
     width: 32px;
     height: 32px;
   }
-
-  h1 {
-    font-size: 1.4rem;
-  }
 }
 
 .site-header-right-side {
diff --git a/frontend/src/features/system/components/SiteHeader.tsx b/frontend/src/features/system/components/SiteHeader.tsx
index fe76321d6f2..f74adcabc97 100644
--- a/frontend/src/features/system/components/SiteHeader.tsx
+++ b/frontend/src/features/system/components/SiteHeader.tsx
@@ -1,4 +1,4 @@
-import { Link } from '@chakra-ui/react';
+import { Flex, Link, Text } from '@chakra-ui/react';
 
 import { FaGithub, FaDiscord, FaBug, FaKeyboard, FaCube } from 'react-icons/fa';
 
@@ -17,20 +17,34 @@ import LanguagePicker from './LanguagePicker';
 
 import { useTranslation } from 'react-i18next';
 import { MdSettings } from 'react-icons/md';
+import { useAppSelector } from 'app/storeHooks';
+import type { RootState } from 'app/store';
 
 /**
  * Header, includes color mode toggle, settings button, status message.
  */
 const SiteHeader = () => {
   const { t } = useTranslation();
+  const appVersion = useAppSelector(
+    (state: RootState) => state.system.app_version
+  );
 
   return (
     <div className="site-header">
       <div className="site-header-left-side">
         <img src={InvokeAILogo} alt="invoke-ai-logo" />
-        <h1>
-          invoke <strong>ai</strong>
-        </h1>
+        <Flex alignItems="center" columnGap="0.6rem">
+          <Text fontSize="1.4rem">
+            invoke <strong>ai</strong>
+          </Text>
+          <Text
+            fontWeight="bold"
+            color="var(--text-color-secondary)"
+            marginTop="0.2rem"
+          >
+            {appVersion}
+          </Text>
+        </Flex>
       </div>
 
       <div className="site-header-right-side">

From 9cb7fe02a08811552d4bd32fb0c8ed86aa87771f Mon Sep 17 00:00:00 2001
From: blessedcoolant <54517381+blessedcoolant@users.noreply.github.com>
Date: Tue, 3 Jan 2023 03:50:32 +1300
Subject: [PATCH 165/199] Optimize Model Search

---
 .../components/ModelManager/ModelList.tsx     | 25 ++++++++++++-------
 1 file changed, 16 insertions(+), 9 deletions(-)

diff --git a/frontend/src/features/system/components/ModelManager/ModelList.tsx b/frontend/src/features/system/components/ModelManager/ModelList.tsx
index f335e4b23ce..fd3521eed07 100644
--- a/frontend/src/features/system/components/ModelManager/ModelList.tsx
+++ b/frontend/src/features/system/components/ModelManager/ModelList.tsx
@@ -1,4 +1,4 @@
-import { useState } from 'react';
+import React, { useState, useTransition, useMemo } from 'react';
 import { Box, Flex, Text } from '@chakra-ui/react';
 import { createSelector } from '@reduxjs/toolkit';
 import IAIInput from 'common/components/IAIInput';
@@ -21,8 +21,12 @@ const modelListSelector = createSelector(
     const models = _.map(system.model_list, (model, key) => {
       return { name: key, ...model };
     });
-
     return models;
+  },
+  {
+    memoizeOptions: {
+      resultEqualityCheck: _.isEqual,
+    },
   }
 );
 
@@ -30,20 +34,23 @@ const ModelList = () => {
   const models = useAppSelector(modelListSelector);
 
   const [searchText, setSearchText] = useState<string>('');
+  const [_, startTransition] = useTransition();
 
   const { t } = useTranslation();
 
-  const handleSearchFilter = _.debounce((e: ChangeEvent<HTMLInputElement>) => {
-    setSearchText(e.target.value);
-  }, 400);
+  const handleSearchFilter = (e: ChangeEvent<HTMLInputElement>) => {
+    startTransition(() => {
+      setSearchText(e.target.value);
+    });
+  };
 
-  const renderModelListItems = () => {
+  const renderModelListItems = useMemo(() => {
     const ckptModelListItemsToRender: ReactNode[] = [];
     const diffusersModelListItemsToRender: ReactNode[] = [];
     const filteredModelListItemsToRender: ReactNode[] = [];
 
     models.forEach((model, i) => {
-      if (model.name.toLowerCase().startsWith(searchText.toLowerCase())) {
+      if (model.name.toLowerCase().includes(searchText.toLowerCase())) {
         filteredModelListItemsToRender.push(
           <ModelListItem
             key={i}
@@ -106,7 +113,7 @@ const ModelList = () => {
         </Box>
       </Flex>
     );
-  };
+  }, [models, searchText]);
 
   return (
     <Flex flexDirection={'column'} rowGap="2rem" width="50%" minWidth="50%">
@@ -129,7 +136,7 @@ const ModelList = () => {
         overflow={'scroll'}
         paddingRight="1rem"
       >
-        {renderModelListItems()}
+        {renderModelListItems}
       </Flex>
     </Flex>
   );

From bdf5dc675b9c113139ef1bdc74c3c06698bb927b Mon Sep 17 00:00:00 2001
From: blessedcoolant <54517381+blessedcoolant@users.noreply.github.com>
Date: Tue, 3 Jan 2023 08:02:03 +1300
Subject: [PATCH 166/199] Fix incorrect font on the Model Manager UI

---
 frontend/src/features/system/components/Modal.scss            | 1 +
 .../system/components/ModelManager/ModelManagerModal.tsx      | 4 +++-
 2 files changed, 4 insertions(+), 1 deletion(-)

diff --git a/frontend/src/features/system/components/Modal.scss b/frontend/src/features/system/components/Modal.scss
index 2a1500d07f7..7c97221c6b4 100644
--- a/frontend/src/features/system/components/Modal.scss
+++ b/frontend/src/features/system/components/Modal.scss
@@ -3,6 +3,7 @@
 .modal {
   background-color: var(--background-color-secondary);
   color: var(--text-color);
+  font-family: Inter;
 }
 
 .modal-close-btn {
diff --git a/frontend/src/features/system/components/ModelManager/ModelManagerModal.tsx b/frontend/src/features/system/components/ModelManager/ModelManagerModal.tsx
index 7e8c9724f38..eedd72f88f2 100644
--- a/frontend/src/features/system/components/ModelManager/ModelManagerModal.tsx
+++ b/frontend/src/features/system/components/ModelManager/ModelManagerModal.tsx
@@ -55,7 +55,9 @@ export default function ModelManagerModal({
         <ModalOverlay />
         <ModalContent className="modal" fontFamily="Inter">
           <ModalCloseButton className="modal-close-btn" />
-          <ModalHeader>{t('modelmanager:modelManager')}</ModalHeader>
+          <ModalHeader fontWeight="bold">
+            {t('modelmanager:modelManager')}
+          </ModalHeader>
           <Flex
             padding={'0 1.5rem 1.5rem 1.5rem'}
             width="100%"

From 0c3cbf5e14b111234a46e3f95cf3435a68682f71 Mon Sep 17 00:00:00 2001
From: blessedcoolant <54517381+blessedcoolant@users.noreply.github.com>
Date: Wed, 4 Jan 2023 02:27:12 +1300
Subject: [PATCH 167/199] Fix image degradation on merge fixes - [Experimental]

This change should effectively fix a couple of things.

- Fix image degradation on subsequent merges of the canvas layers.
- Fix the slight transparent border that is left behind when filling the bounding box with a color.
- Fix the left over line of color when filling a bounding box with color.

So far there are no side effects for this. If any, please report.
---
 .../features/canvas/util/layerToDataURL.ts    | 24 +++++++++----------
 1 file changed, 12 insertions(+), 12 deletions(-)

diff --git a/frontend/src/features/canvas/util/layerToDataURL.ts b/frontend/src/features/canvas/util/layerToDataURL.ts
index 5e92eba5bc1..14d402c08c3 100644
--- a/frontend/src/features/canvas/util/layerToDataURL.ts
+++ b/frontend/src/features/canvas/util/layerToDataURL.ts
@@ -22,16 +22,16 @@ const layerToDataURL = (
   const { x, y, width, height } = layer.getClientRect();
   const dataURLBoundingBox = boundingBox
     ? {
-        x: Math.round(boundingBox.x + stageCoordinates.x),
-        y: Math.round(boundingBox.y + stageCoordinates.y),
-        width: Math.round(boundingBox.width),
-        height: Math.round(boundingBox.height),
+        x: boundingBox.x + stageCoordinates.x,
+        y: boundingBox.y + stageCoordinates.y,
+        width: boundingBox.width,
+        height: boundingBox.height,
       }
     : {
-        x: Math.round(x),
-        y: Math.round(y),
-        width: Math.round(width),
-        height: Math.round(height),
+        x: x,
+        y: y,
+        width: width,
+        height: height,
       };
 
   const dataURL = layer.toDataURL(dataURLBoundingBox);
@@ -42,10 +42,10 @@ const layerToDataURL = (
   return {
     dataURL,
     boundingBox: {
-      x: Math.round(relativeClientRect.x),
-      y: Math.round(relativeClientRect.y),
-      width: Math.round(width),
-      height: Math.round(height),
+      x: relativeClientRect.x,
+      y: relativeClientRect.y,
+      width: width,
+      height: height,
     },
   };
 };

From 8e99addf9180f9d3e9898cae99ead76873f32328 Mon Sep 17 00:00:00 2001
From: blessedcoolant <54517381+blessedcoolant@users.noreply.github.com>
Date: Tue, 10 Jan 2023 22:34:42 +1300
Subject: [PATCH 168/199] Add local model filtering for Diffusers / Checkpoints

---
 frontend/public/locales/modelmanager/en.json  |   3 +
 .../components/ModelManager/ModelList.tsx     | 130 ++++++++++++++----
 2 files changed, 105 insertions(+), 28 deletions(-)

diff --git a/frontend/public/locales/modelmanager/en.json b/frontend/public/locales/modelmanager/en.json
index cbc2930ed6c..05cdb93f760 100644
--- a/frontend/public/locales/modelmanager/en.json
+++ b/frontend/public/locales/modelmanager/en.json
@@ -1,6 +1,9 @@
 {
   "modelManager": "Model Manager",
   "model": "Model",
+  "allModels": "All Models",
+  "checkpointModels": "Checkpoint Models",
+  "diffusersModels": "Diffusers Models",
   "modelAdded": "Model Added",
   "modelUpdated": "Model Updated",
   "modelEntryDeleted": "Model Entry Deleted",
diff --git a/frontend/src/features/system/components/ModelManager/ModelList.tsx b/frontend/src/features/system/components/ModelManager/ModelList.tsx
index fd3521eed07..d4b461064cd 100644
--- a/frontend/src/features/system/components/ModelManager/ModelList.tsx
+++ b/frontend/src/features/system/components/ModelManager/ModelList.tsx
@@ -14,6 +14,7 @@ import _ from 'lodash';
 import type { ChangeEvent, ReactNode } from 'react';
 import type { RootState } from 'app/store';
 import type { SystemState } from 'features/system/store/systemSlice';
+import IAIButton from 'common/components/IAIButton';
 
 const modelListSelector = createSelector(
   (state: RootState) => state.system,
@@ -30,10 +31,37 @@ const modelListSelector = createSelector(
   }
 );
 
+function ModelFilterButton({
+  label,
+  isActive,
+  onClick,
+}: {
+  label: string;
+  isActive: boolean;
+  onClick: () => void;
+}) {
+  return (
+    <IAIButton
+      onClick={onClick}
+      isActive={isActive}
+      _active={{
+        backgroundColor: 'var(--accent-color)',
+        _hover: { backgroundColor: 'var(--accent-color)' },
+      }}
+      size="sm"
+    >
+      {label}
+    </IAIButton>
+  );
+}
+
 const ModelList = () => {
   const models = useAppSelector(modelListSelector);
 
   const [searchText, setSearchText] = useState<string>('');
+  const [isSelectedFilter, setIsSelectedFilter] = useState<
+    'all' | 'ckpt' | 'diffusers'
+  >('all');
   const [_, startTransition] = useTransition();
 
   const { t } = useTranslation();
@@ -48,6 +76,7 @@ const ModelList = () => {
     const ckptModelListItemsToRender: ReactNode[] = [];
     const diffusersModelListItemsToRender: ReactNode[] = [];
     const filteredModelListItemsToRender: ReactNode[] = [];
+    const localFilteredModelListItemsToRender: ReactNode[] = [];
 
     models.forEach((model, i) => {
       if (model.name.toLowerCase().includes(searchText.toLowerCase())) {
@@ -59,6 +88,16 @@ const ModelList = () => {
             description={model.description}
           />
         );
+        if (model.format === isSelectedFilter) {
+          localFilteredModelListItemsToRender.push(
+            <ModelListItem
+              key={i}
+              name={model.name}
+              status={model.status}
+              description={model.description}
+            />
+          );
+        }
       }
       if (model.format !== 'diffusers') {
         ckptModelListItemsToRender.push(
@@ -82,38 +121,56 @@ const ModelList = () => {
     });
 
     return searchText !== '' ? (
-      filteredModelListItemsToRender
+      isSelectedFilter === 'all' ? (
+        filteredModelListItemsToRender
+      ) : (
+        localFilteredModelListItemsToRender
+      )
     ) : (
       <Flex flexDirection="column" rowGap="1.5rem">
-        <Box>
-          <Text
-            fontWeight="bold"
-            backgroundColor="var(--background-color)"
-            padding="0.5rem 1rem"
-            borderRadius="0.5rem"
-            marginBottom="0.5rem"
-            width="max-content"
-          >
-            Checkpoint Models
-          </Text>
-          {ckptModelListItemsToRender}
-        </Box>
-        <Box>
-          <Text
-            fontWeight="bold"
-            backgroundColor="var(--background-color)"
-            padding="0.5rem 1rem"
-            borderRadius="0.5rem"
-            marginBottom="0.5rem"
-            width="max-content"
-          >
-            Diffusers Models
-          </Text>
-          {diffusersModelListItemsToRender}
-        </Box>
+        {isSelectedFilter === 'all' && (
+          <>
+            <Box>
+              <Text
+                fontWeight="bold"
+                backgroundColor="var(--background-color)"
+                padding="0.5rem 1rem"
+                borderRadius="0.5rem"
+                margin="1rem 0"
+                width="max-content"
+                fontSize="14"
+              >
+                {t('modelmanager:checkpointModels')}
+              </Text>
+              {ckptModelListItemsToRender}
+            </Box>
+            <Box>
+              <Text
+                fontWeight="bold"
+                backgroundColor="var(--background-color)"
+                padding="0.5rem 1rem"
+                borderRadius="0.5rem"
+                marginBottom="0.5rem"
+                width="max-content"
+                fontSize="14"
+              >
+                {t('modelmanager:diffusersModels')}
+              </Text>
+              {diffusersModelListItemsToRender}
+            </Box>
+          </>
+        )}
+
+        {isSelectedFilter === 'ckpt' && (
+          <Flex flexDirection="column">{ckptModelListItemsToRender}</Flex>
+        )}
+
+        {isSelectedFilter === 'diffusers' && (
+          <Flex flexDirection="column">{diffusersModelListItemsToRender}</Flex>
+        )}
       </Flex>
     );
-  }, [models, searchText]);
+  }, [models, searchText, t, isSelectedFilter]);
 
   return (
     <Flex flexDirection={'column'} rowGap="2rem" width="50%" minWidth="50%">
@@ -136,6 +193,23 @@ const ModelList = () => {
         overflow={'scroll'}
         paddingRight="1rem"
       >
+        <Flex columnGap="0.5rem">
+          <ModelFilterButton
+            label={t('modelmanager:allModels')}
+            onClick={() => setIsSelectedFilter('all')}
+            isActive={isSelectedFilter === 'all'}
+          />
+          <ModelFilterButton
+            label={t('modelmanager:checkpointModels')}
+            onClick={() => setIsSelectedFilter('ckpt')}
+            isActive={isSelectedFilter === 'ckpt'}
+          />
+          <ModelFilterButton
+            label={t('modelmanager:diffusersModels')}
+            onClick={() => setIsSelectedFilter('diffusers')}
+            isActive={isSelectedFilter === 'diffusers'}
+          />
+        </Flex>
         {renderModelListItems}
       </Flex>
     </Flex>

From 5e04b3d9ddee484bfbcd54c623f2b1b91a5a2970 Mon Sep 17 00:00:00 2001
From: blessedcoolant <54517381+blessedcoolant@users.noreply.github.com>
Date: Tue, 10 Jan 2023 22:38:15 +1300
Subject: [PATCH 169/199] Go to home on modal close for the Add Modal UI

---
 .../src/features/system/components/ModelManager/AddModel.tsx     | 1 +
 1 file changed, 1 insertion(+)

diff --git a/frontend/src/features/system/components/ModelManager/AddModel.tsx b/frontend/src/features/system/components/ModelManager/AddModel.tsx
index 3447f3a1a0e..766b62a02fe 100644
--- a/frontend/src/features/system/components/ModelManager/AddModel.tsx
+++ b/frontend/src/features/system/components/ModelManager/AddModel.tsx
@@ -63,6 +63,7 @@ export default function AddModel() {
 
   const addModelModalClose = () => {
     onClose();
+    dispatch(setAddNewModelUIOption(null));
   };
 
   return (

From 7268836d8691a6aa20ec0c65cfa779b549ca81d8 Mon Sep 17 00:00:00 2001
From: blessedcoolant <54517381+blessedcoolant@users.noreply.github.com>
Date: Tue, 10 Jan 2023 22:44:25 +1300
Subject: [PATCH 170/199] Styling Fixes

---
 .../system/components/ModelManager/ModelList.tsx     | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/frontend/src/features/system/components/ModelManager/ModelList.tsx b/frontend/src/features/system/components/ModelManager/ModelList.tsx
index d4b461064cd..0166a611f38 100644
--- a/frontend/src/features/system/components/ModelManager/ModelList.tsx
+++ b/frontend/src/features/system/components/ModelManager/ModelList.tsx
@@ -122,9 +122,9 @@ const ModelList = () => {
 
     return searchText !== '' ? (
       isSelectedFilter === 'all' ? (
-        filteredModelListItemsToRender
+        <Box marginTop="1rem">{filteredModelListItemsToRender}</Box>
       ) : (
-        localFilteredModelListItemsToRender
+        <Box marginTop="1rem">{localFilteredModelListItemsToRender}</Box>
       )
     ) : (
       <Flex flexDirection="column" rowGap="1.5rem">
@@ -162,11 +162,15 @@ const ModelList = () => {
         )}
 
         {isSelectedFilter === 'ckpt' && (
-          <Flex flexDirection="column">{ckptModelListItemsToRender}</Flex>
+          <Flex flexDirection="column" marginTop="1rem">
+            {ckptModelListItemsToRender}
+          </Flex>
         )}
 
         {isSelectedFilter === 'diffusers' && (
-          <Flex flexDirection="column">{diffusersModelListItemsToRender}</Flex>
+          <Flex flexDirection="column" marginTop="1rem">
+            {diffusersModelListItemsToRender}
+          </Flex>
         )}
       </Flex>
     );

From b8b927aa6866d2622bb840865bbd199d64830d37 Mon Sep 17 00:00:00 2001
From: blessedcoolant <54517381+blessedcoolant@users.noreply.github.com>
Date: Tue, 10 Jan 2023 23:00:13 +1300
Subject: [PATCH 171/199] Model Manager Diffusers Localization Update

---
 frontend/public/locales/common/en-US.json       |  3 +++
 frontend/public/locales/modelmanager/en-US.json | 17 +++++++++++++++--
 frontend/public/locales/modelmanager/en.json    |  4 ++--
 3 files changed, 20 insertions(+), 4 deletions(-)

diff --git a/frontend/public/locales/common/en-US.json b/frontend/public/locales/common/en-US.json
index 81d1ee1ecf0..b67799e7895 100644
--- a/frontend/public/locales/common/en-US.json
+++ b/frontend/public/locales/common/en-US.json
@@ -17,6 +17,8 @@
   "langPortuguese": "Portuguese",
   "langFrench": "French",
   "langPolish": "Polish",
+  "langSimplifiedChinese": "Simplified Chinese",
+  "langSpanish": "Spanish",
   "text2img": "Text To Image",
   "img2img": "Image To Image",
   "unifiedCanvas": "Unified Canvas",
@@ -32,6 +34,7 @@
   "upload": "Upload",
   "close": "Close",
   "load": "Load",
+  "back": "Back",
   "statusConnected": "Connected",
   "statusDisconnected": "Disconnected",
   "statusError": "Error",
diff --git a/frontend/public/locales/modelmanager/en-US.json b/frontend/public/locales/modelmanager/en-US.json
index 30bb6004490..936b50bf7e1 100644
--- a/frontend/public/locales/modelmanager/en-US.json
+++ b/frontend/public/locales/modelmanager/en-US.json
@@ -1,6 +1,9 @@
 {
   "modelManager": "Model Manager",
   "model": "Model",
+  "allModels": "All Models",
+  "checkpointModels": "Checkpoints",
+  "diffusersModels": "Diffusers",
   "modelAdded": "Model Added",
   "modelUpdated": "Model Updated",
   "modelEntryDeleted": "Model Entry Deleted",
@@ -16,10 +19,13 @@
   "config": "Config",
   "configValidationMsg": "Path to the config file of your model.",
   "modelLocation": "Model Location",
-  "repo_id": "Repo ID",
   "modelLocationValidationMsg": "Path to where your model is located.",
+  "repo_id": "Repo ID",
+  "repoIDValidationMsg": "Online repository of your model",
   "vaeLocation": "VAE Location",
   "vaeLocationValidationMsg": "Path to where your VAE is located.",
+  "vaeRepoID": "VAE Repo ID",
+  "vaeRepoIDValidationMsg": "Online repository of your VAE",
   "width": "Width",
   "widthValidationMsg": "Default width of your model.",
   "height": "Height",
@@ -35,6 +41,7 @@
   "checkpointFolder": "Checkpoint Folder",
   "clearCheckpointFolder": "Clear Checkpoint Folder",
   "findModels": "Find Models",
+  "scanAgain": "Scan Again",
   "modelsFound": "Models Found",
   "selectFolder": "Select Folder",
   "selected": "Selected",
@@ -43,9 +50,15 @@
   "showExisting": "Show Existing",
   "addSelected": "Add Selected",
   "modelExists": "Model Exists",
+  "selectAndAdd": "Select and Add Models Listed Below",
+  "noModelsFound": "No Models Found",
   "delete": "Delete",
   "deleteModel": "Delete Model",
   "deleteConfig": "Delete Config",
   "deleteMsg1": "Are you sure you want to delete this model entry from InvokeAI?",
-  "deleteMsg2": "This will not delete the model checkpoint file from your disk. You can readd them if you wish to."
+  "deleteMsg2": "This will not delete the model checkpoint file from your disk. You can readd them if you wish to.",
+  "formMessageDiffusersModelLocation": "Diffusers Model Location",
+  "formMessageDiffusersModelLocationDesc": "Please enter at least one.",
+  "formMessageDiffusersVAELocation": "VAE Location",
+  "formMessageDiffusersVAELocationDesc": "If not provided, InvokeAI will look for the VAE file inside the model location given above."
 }
diff --git a/frontend/public/locales/modelmanager/en.json b/frontend/public/locales/modelmanager/en.json
index 05cdb93f760..936b50bf7e1 100644
--- a/frontend/public/locales/modelmanager/en.json
+++ b/frontend/public/locales/modelmanager/en.json
@@ -2,8 +2,8 @@
   "modelManager": "Model Manager",
   "model": "Model",
   "allModels": "All Models",
-  "checkpointModels": "Checkpoint Models",
-  "diffusersModels": "Diffusers Models",
+  "checkpointModels": "Checkpoints",
+  "diffusersModels": "Diffusers",
   "modelAdded": "Model Added",
   "modelUpdated": "Model Updated",
   "modelEntryDeleted": "Model Entry Deleted",

From 450299434e420b9fa7864550561e084b9e2535b0 Mon Sep 17 00:00:00 2001
From: blessedcoolant <54517381+blessedcoolant@users.noreply.github.com>
Date: Tue, 10 Jan 2023 23:34:30 +1300
Subject: [PATCH 172/199] Add Safe Tensor scanning to Model Manager

---
 frontend/public/locales/modelmanager/en-US.json          | 3 +++
 frontend/public/locales/modelmanager/en.json             | 3 +++
 .../features/system/components/ModelManager/AddModel.tsx | 4 ++--
 ldm/invoke/model_manager.py                              | 9 ++++++---
 4 files changed, 14 insertions(+), 5 deletions(-)

diff --git a/frontend/public/locales/modelmanager/en-US.json b/frontend/public/locales/modelmanager/en-US.json
index 936b50bf7e1..c5c5eda0543 100644
--- a/frontend/public/locales/modelmanager/en-US.json
+++ b/frontend/public/locales/modelmanager/en-US.json
@@ -4,12 +4,15 @@
   "allModels": "All Models",
   "checkpointModels": "Checkpoints",
   "diffusersModels": "Diffusers",
+  "safetensorModels": "SafeTensors",
   "modelAdded": "Model Added",
   "modelUpdated": "Model Updated",
   "modelEntryDeleted": "Model Entry Deleted",
   "cannotUseSpaces": "Cannot Use Spaces",
   "addNew": "Add New",
   "addNewModel": "Add New Model",
+  "addCheckpointModel": "Add Checkpoint / Safetensor Model",
+  "addDiffuserModel": "Add Diffusers",
   "addManually": "Add Manually",
   "manual": "Manual",
   "name": "Name",
diff --git a/frontend/public/locales/modelmanager/en.json b/frontend/public/locales/modelmanager/en.json
index 936b50bf7e1..c5c5eda0543 100644
--- a/frontend/public/locales/modelmanager/en.json
+++ b/frontend/public/locales/modelmanager/en.json
@@ -4,12 +4,15 @@
   "allModels": "All Models",
   "checkpointModels": "Checkpoints",
   "diffusersModels": "Diffusers",
+  "safetensorModels": "SafeTensors",
   "modelAdded": "Model Added",
   "modelUpdated": "Model Updated",
   "modelEntryDeleted": "Model Entry Deleted",
   "cannotUseSpaces": "Cannot Use Spaces",
   "addNew": "Add New",
   "addNewModel": "Add New Model",
+  "addCheckpointModel": "Add Checkpoint / Safetensor Model",
+  "addDiffuserModel": "Add Diffusers",
   "addManually": "Add Manually",
   "manual": "Manual",
   "name": "Name",
diff --git a/frontend/src/features/system/components/ModelManager/AddModel.tsx b/frontend/src/features/system/components/ModelManager/AddModel.tsx
index 766b62a02fe..8255833a142 100644
--- a/frontend/src/features/system/components/ModelManager/AddModel.tsx
+++ b/frontend/src/features/system/components/ModelManager/AddModel.tsx
@@ -95,11 +95,11 @@ export default function AddModel() {
             {addNewModelUIOption == null && (
               <Flex columnGap="1rem">
                 <AddModelBox
-                  text="Add Checkpoint Model"
+                  text={t('modelmanager:addCheckpointModel')}
                   onClick={() => dispatch(setAddNewModelUIOption('ckpt'))}
                 />
                 <AddModelBox
-                  text="Add Diffusers Model"
+                  text={t('modelmanager:addDiffuserModel')}
                   onClick={() => dispatch(setAddNewModelUIOption('diffusers'))}
                 />
               </Flex>
diff --git a/ldm/invoke/model_manager.py b/ldm/invoke/model_manager.py
index 2e96621f6a6..b0191b8af2c 100644
--- a/ldm/invoke/model_manager.py
+++ b/ldm/invoke/model_manager.py
@@ -668,11 +668,14 @@ def del_config(self, model_name:str, gen, opt, completer):
         completer.del_model(model_name)
 
     def search_models(self, search_folder):
-
         print(f'>> Finding Models In: {search_folder}')
-        models_folder = Path(search_folder).glob('**/*.ckpt')
+        models_folder_ckpt = Path(search_folder).glob('**/*.ckpt')
+        models_folder_safetensors = Path(search_folder).glob('**/*.safetensors')
+
+        ckpt_files = [x for x in models_folder_ckpt if x.is_file()]
+        safetensor_files = [x for x in models_folder_safetensors if x.is_file]
 
-        files = [x for x in models_folder if x.is_file()]
+        files = ckpt_files + safetensor_files
 
         found_models = []
         for file in files:

From 666d6aea6810c4f424f7965302723fa953129466 Mon Sep 17 00:00:00 2001
From: blessedcoolant <54517381+blessedcoolant@users.noreply.github.com>
Date: Wed, 11 Jan 2023 00:18:50 +1300
Subject: [PATCH 173/199] Fix model edit form dispatching string values instead
 of numbers.

---
 .../components/ModelManager/CheckpointModelEdit.tsx       | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/frontend/src/features/system/components/ModelManager/CheckpointModelEdit.tsx b/frontend/src/features/system/components/ModelManager/CheckpointModelEdit.tsx
index 8999f71281b..6747963d981 100644
--- a/frontend/src/features/system/components/ModelManager/CheckpointModelEdit.tsx
+++ b/frontend/src/features/system/components/ModelManager/CheckpointModelEdit.tsx
@@ -91,7 +91,13 @@ export default function CheckpointModelEdit() {
   }, [model_list, openModel]);
 
   const editModelFormSubmitHandler = (values: InvokeModelConfigProps) => {
-    dispatch(addNewModel(values));
+    dispatch(
+      addNewModel({
+        ...values,
+        width: Number(values.width),
+        height: Number(values.height),
+      })
+    );
   };
 
   return openModel ? (

From 6c992735ddfba76741fe5444e2df74c4fa5fda51 Mon Sep 17 00:00:00 2001
From: blessedcoolant <54517381+blessedcoolant@users.noreply.github.com>
Date: Wed, 11 Jan 2023 04:37:01 +1300
Subject: [PATCH 174/199] Resolve VAE handling / edge cases for supplied repos

---
 backend/invoke_ai_web_server.py               |  2 ++
 .../ModelManager/AddDiffusersModel.tsx        | 30 ++++++++-----------
 2 files changed, 14 insertions(+), 18 deletions(-)

diff --git a/backend/invoke_ai_web_server.py b/backend/invoke_ai_web_server.py
index f41385bad64..1720c44f28d 100644
--- a/backend/invoke_ai_web_server.py
+++ b/backend/invoke_ai_web_server.py
@@ -334,6 +334,8 @@ def handle_add_model(new_model_config: dict):
                 model_name = new_model_config['name']
                 del new_model_config['name']
                 model_attributes = new_model_config
+                if len(model_attributes['vae']) == 0:
+                    del model_attributes['vae']
                 update = False
                 current_model_list = self.generate.model_manager.list_models()
                 if model_name in current_model_list:
diff --git a/frontend/src/features/system/components/ModelManager/AddDiffusersModel.tsx b/frontend/src/features/system/components/ModelManager/AddDiffusersModel.tsx
index 57033597865..91ebaf87152 100644
--- a/frontend/src/features/system/components/ModelManager/AddDiffusersModel.tsx
+++ b/frontend/src/features/system/components/ModelManager/AddDiffusersModel.tsx
@@ -75,21 +75,19 @@ export default function AddDiffusersModel() {
   const addModelFormSubmitHandler = (
     values: InvokeDiffusersModelConfigProps
   ) => {
-    let diffusersModelToAdd = values;
+    const diffusersModelToAdd = values;
 
-    if (values.repo_id !== '' && values.vae.repo_id === '') {
-      diffusersModelToAdd = {
-        ...diffusersModelToAdd,
-        vae: { repo_id: values.repo_id + '/vae', path: '' },
-      };
-    }
-
-    if (values.path !== '' && values.vae.path === '') {
-      diffusersModelToAdd = {
-        ...diffusersModelToAdd,
-        vae: { repo_id: '', path: values.path + '/vae' },
-      };
+    if (values.path === '') diffusersModelToAdd['path'] = undefined;
+    if (values.repo_id === '') diffusersModelToAdd['repo_id'] = undefined;
+    if (values.vae.path === '') {
+      if (values.path === undefined) {
+        diffusersModelToAdd['vae']['path'] = undefined;
+      } else {
+        diffusersModelToAdd['vae']['path'] = values.path + '/vae';
+      }
     }
+    if (values.vae.repo_id === '')
+      diffusersModelToAdd['vae']['repo_id'] = undefined;
 
     dispatch(addNewModel(diffusersModelToAdd));
     dispatch(setAddNewModelUIOption(null));
@@ -188,10 +186,7 @@ export default function AddDiffusersModel() {
                 </Text>
 
                 {/* Path */}
-                <FormControl
-                  isInvalid={!!errors.path && touched.path}
-                  isRequired
-                >
+                <FormControl isInvalid={!!errors.path && touched.path}>
                   <FormLabel htmlFor="path" fontSize="sm">
                     {t('modelmanager:modelLocation')}
                   </FormLabel>
@@ -202,7 +197,6 @@ export default function AddDiffusersModel() {
                       name="path"
                       type="text"
                       width="2xl"
-                      isRequired
                     />
                     {!!errors.path && touched.path ? (
                       <FormErrorMessage>{errors.path}</FormErrorMessage>

From 488c7228913429f72327d094a9b87ecc5294e122 Mon Sep 17 00:00:00 2001
From: Damian Stewart <d@damianstewart.com>
Date: Tue, 10 Jan 2023 18:58:10 +0100
Subject: [PATCH 175/199] defer injecting tokens for textual inversions until
 they're used for the first time

---
 ldm/generate.py                               |  8 +-
 ldm/invoke/conditioning.py                    |  5 ++
 ldm/modules/encoders/modules.py               |  3 +-
 ldm/modules/prompt_to_embeddings_converter.py |  2 +-
 ldm/modules/textual_inversion_manager.py      | 74 +++++++++++-----
 tests/test_textual_inversion.py               | 85 ++++++++++++++-----
 6 files changed, 124 insertions(+), 53 deletions(-)

diff --git a/ldm/generate.py b/ldm/generate.py
index a5511fcc206..8d1d7d1d66f 100644
--- a/ldm/generate.py
+++ b/ldm/generate.py
@@ -861,14 +861,10 @@ def set_model(self,model_name):
         seed_everything(random.randrange(0, np.iinfo(np.uint32).max))
         if self.embedding_path is not None:
             for root, _, files in os.walk(self.embedding_path):
-                # loading textual inversions is slow
-                # see note in TextualInversionManager._get_or_create_token_id_and_assign_embedding()
-                verbose = len(files)>4
                 for name in files:
-                    if verbose:
-                        print(f'>> Loading textual inversion from {name}')
                     ti_path = os.path.join(root, name)
-                    self.model.textual_inversion_manager.load_textual_inversion(ti_path)
+                    self.model.textual_inversion_manager.load_textual_inversion(ti_path,
+                                                                                defer_injecting_tokens=True)
             print(f'>> Textual inversions available: {", ".join(self.model.textual_inversion_manager.get_all_trigger_strings())}')
 
         self.model_name = model_name
diff --git a/ldm/invoke/conditioning.py b/ldm/invoke/conditioning.py
index 4930d3c25bf..fec3c7e7b1f 100644
--- a/ldm/invoke/conditioning.py
+++ b/ldm/invoke/conditioning.py
@@ -20,6 +20,11 @@
 
 
 def get_uc_and_c_and_ec(prompt_string, model, log_tokens=False, skip_normalize_legacy_blend=False):
+
+    # lazy-load any deferred textual inversions.
+    # this might take a couple of seconds the first time a textual inversion is used.
+    model.textual_inversion_manager.create_deferred_token_ids_for_any_trigger_terms(prompt_string)
+
     prompt, negative_prompt = get_prompt_structure(prompt_string,
                                                    skip_normalize_legacy_blend=skip_normalize_legacy_blend)
     conditioning = _get_conditioning_for_prompt(prompt, negative_prompt, model, log_tokens)
diff --git a/ldm/modules/encoders/modules.py b/ldm/modules/encoders/modules.py
index e119bdef5ad..d691e157d84 100644
--- a/ldm/modules/encoders/modules.py
+++ b/ldm/modules/encoders/modules.py
@@ -589,6 +589,7 @@ def get_token_ids(self, fragments: list[str], include_start_and_end_markers: boo
         :param include_start_and_end_markers:
         :return:
         """
+
         # for args documentation see ENCODE_KWARGS_DOCSTRING in tokenization_utils_base.py (in `transformers` lib)
         token_ids_list = self.tokenizer(
             fragments,
@@ -604,7 +605,7 @@ def get_token_ids(self, fragments: list[str], include_start_and_end_markers: boo
             # trim eos/bos
             token_ids = token_ids[1:-1]
             # pad for textual inversions with vector length >1
-            token_ids = self.textual_inversion_manager.expand_textual_inversion_token_ids(token_ids)
+            token_ids = self.textual_inversion_manager.expand_textual_inversion_token_ids_if_necessary(token_ids)
             # restrict length to max_length-2 (leaving room for bos/eos)
             token_ids = token_ids[0:self.max_length - 2]
             # add back eos/bos if requested
diff --git a/ldm/modules/prompt_to_embeddings_converter.py b/ldm/modules/prompt_to_embeddings_converter.py
index a024a1bc006..31017d8084c 100644
--- a/ldm/modules/prompt_to_embeddings_converter.py
+++ b/ldm/modules/prompt_to_embeddings_converter.py
@@ -135,7 +135,7 @@ def get_token_ids(self, fragments: list[str], include_start_and_end_markers: boo
             # trim eos/bos
             token_ids = token_ids[1:-1]
             # pad for textual inversions with vector length >1
-            token_ids = self.textual_inversion_manager.expand_textual_inversion_token_ids(token_ids)
+            token_ids = self.textual_inversion_manager.expand_textual_inversion_token_ids_if_necessary(token_ids)
             # restrict length to max_length-2 (leaving room for bos/eos)
             token_ids = token_ids[0:self.max_length - 2]
             # add back eos/bos if requested
diff --git a/ldm/modules/textual_inversion_manager.py b/ldm/modules/textual_inversion_manager.py
index f5eecaa949e..02513dab49f 100644
--- a/ldm/modules/textual_inversion_manager.py
+++ b/ldm/modules/textual_inversion_manager.py
@@ -1,9 +1,9 @@
 import os
 import traceback
-from typing import Union
+from typing import Optional
 
 import torch
-from attr import dataclass
+from dataclasses import dataclass
 from picklescan.scanner import scan_file_path
 from transformers import CLIPTokenizer, CLIPTextModel
 
@@ -13,9 +13,9 @@
 @dataclass
 class TextualInversion:
     trigger_string: str
-    trigger_token_id: int
-    pad_token_ids: list[int]
     embedding: torch.Tensor
+    trigger_token_id: Optional[int] = None
+    pad_token_ids: Optional[list[int]] = None
 
     @property
     def embedding_vector_length(self) -> int:
@@ -40,13 +40,13 @@ def load_huggingface_concepts(self, concepts: list[str]):
             bin_file = self.hf_concepts_library.get_concept_model_path(concept_name)
             if not bin_file:
                 continue
-            self.load_textual_inversion(bin_file)
+            self.load_textual_inversion(bin_file, defer_injecting_tokens=False)
             self.hf_concepts_library.concepts_loaded[concept_name]=True
 
     def get_all_trigger_strings(self) -> list[str]:
         return [ti.trigger_string for ti in self.textual_inversions]
 
-    def load_textual_inversion(self, ckpt_path):
+    def load_textual_inversion(self, ckpt_path, defer_injecting_tokens: bool=False):
         try:
             scan_result = scan_file_path(ckpt_path)
             if scan_result.infected_files == 1:
@@ -59,11 +59,13 @@ def load_textual_inversion(self, ckpt_path):
 
         embedding_info = self._parse_embedding(ckpt_path)
         if embedding_info:
-            self._add_textual_inversion(embedding_info['name'], embedding_info['embedding'])
+            self._add_textual_inversion(embedding_info['name'],
+                                        embedding_info['embedding'],
+                                        defer_injecting_tokens=defer_injecting_tokens)
         else:
             print(f'>> Failed to load embedding located at {ckpt_path}. Unsupported file.')
 
-    def _add_textual_inversion(self, trigger_str, embedding) -> int:
+    def _add_textual_inversion(self, trigger_str, embedding, defer_injecting_tokens=False) -> TextualInversion:
         """
         Add a textual inversion to be recognised.
         :param trigger_str: The trigger text in the prompt that activates this textual inversion. If unknown to the embedder's tokenizer, will be added.
@@ -80,21 +82,15 @@ def _add_textual_inversion(self, trigger_str, embedding) -> int:
         elif len(embedding.shape) > 2:
             raise ValueError(f"TextualInversionManager cannot add {trigger_str} because the embedding shape {embedding.shape} is incorrect. The embedding must have shape [token_dim] or [V, token_dim] where V is vector length and token_dim is 768 for SD1 or 1280 for SD2.")
 
-        # for embeddings with vector length > 1
-        pad_token_strings = [trigger_str + "-!pad-" + str(pad_index) for pad_index in range(1, embedding.shape[0])]
-
         try:
-            trigger_token_id = self._get_or_create_token_id_and_assign_embedding(trigger_str, embedding[0])
-            # todo: batched UI for faster loading when vector length >2
-            pad_token_ids = [self._get_or_create_token_id_and_assign_embedding(pad_token_str, embedding[1 + i]) \
-                             for (i, pad_token_str) in enumerate(pad_token_strings)]
-            self.textual_inversions.append(TextualInversion(
+            ti = TextualInversion(
                 trigger_string=trigger_str,
-                trigger_token_id=trigger_token_id,
-                pad_token_ids=pad_token_ids,
                 embedding=embedding
-            ))
-            return trigger_token_id
+            )
+            if not defer_injecting_tokens:
+                self._inject_tokens_and_assign_embeddings(ti)
+            self.textual_inversions.append(ti)
+            return ti
 
         except ValueError as e:
             if str(e).startswith('Warning'):
@@ -104,6 +100,26 @@ def _add_textual_inversion(self, trigger_str, embedding) -> int:
                 print(f">> TextualInversionManager was unable to add a textual inversion with trigger string {trigger_str}.")
                 raise
 
+    def _inject_tokens_and_assign_embeddings(self, ti: TextualInversion) -> int:
+
+        if ti.trigger_token_id is not None:
+            raise ValueError(f"Tokens already injected for textual inversion with trigger '{ti.trigger_string}'")
+
+        trigger_token_id = self._get_or_create_token_id_and_assign_embedding(ti.trigger_string, ti.embedding[0])
+
+        if ti.embedding_vector_length > 1:
+            # for embeddings with vector length > 1
+            pad_token_strings = [ti.trigger_string + "-!pad-" + str(pad_index) for pad_index in range(1, ti.embedding_vector_length)]
+            # todo: batched UI for faster loading when vector length >2
+            pad_token_ids = [self._get_or_create_token_id_and_assign_embedding(pad_token_str, ti.embedding[1 + i]) \
+                                 for (i, pad_token_str) in enumerate(pad_token_strings)]
+        else:
+            pad_token_ids = []
+
+        ti.trigger_token_id = trigger_token_id
+        ti.pad_token_ids = pad_token_ids
+        return ti.trigger_token_id
+
 
     def has_textual_inversion_for_trigger_string(self, trigger_string: str) -> bool:
         try:
@@ -112,6 +128,7 @@ def has_textual_inversion_for_trigger_string(self, trigger_string: str) -> bool:
         except StopIteration:
             return False
 
+
     def get_textual_inversion_for_trigger_string(self, trigger_string: str) -> TextualInversion:
         return next(ti for ti in self.textual_inversions if ti.trigger_string == trigger_string)
 
@@ -119,7 +136,19 @@ def get_textual_inversion_for_trigger_string(self, trigger_string: str) -> Textu
     def get_textual_inversion_for_token_id(self, token_id: int) -> TextualInversion:
         return next(ti for ti in self.textual_inversions if ti.trigger_token_id == token_id)
 
-    def expand_textual_inversion_token_ids(self, prompt_token_ids: list[int]) -> list[int]:
+    def create_deferred_token_ids_for_any_trigger_terms(self, prompt_string: str) -> list[int]:
+        injected_token_ids = []
+        for ti in self.textual_inversions:
+            if ti.trigger_token_id is None and ti.trigger_string in prompt_string:
+                if ti.embedding_vector_length > 1:
+                    print(f">> Preparing tokens for textual inversion {ti.trigger_string}...")
+                self._inject_tokens_and_assign_embeddings(ti)
+                injected_token_ids.append(ti.trigger_token_id)
+                injected_token_ids.extend(ti.pad_token_ids)
+        return injected_token_ids
+
+
+    def expand_textual_inversion_token_ids_if_necessary(self, prompt_token_ids: list[int]) -> list[int]:
         """
         Insert padding tokens as necessary into the passed-in list of token ids to match any textual inversions it includes.
 
@@ -144,7 +173,8 @@ def expand_textual_inversion_token_ids(self, prompt_token_ids: list[int]) -> lis
 
         return prompt_token_ids
 
-    def _get_or_create_token_id_and_assign_embedding(self, token_str: str, embedding: torch.Tensor):
+
+    def _get_or_create_token_id_and_assign_embedding(self, token_str: str, embedding: torch.Tensor) -> int:
         if len(embedding.shape) != 1:
             raise ValueError("Embedding has incorrect shape - must be [token_dim] where token_dim is 768 for SD1 or 1280 for SD2")
         existing_token_id = self.tokenizer.convert_tokens_to_ids(token_str)
diff --git a/tests/test_textual_inversion.py b/tests/test_textual_inversion.py
index 6e150f1d21d..54b396489e3 100644
--- a/tests/test_textual_inversion.py
+++ b/tests/test_textual_inversion.py
@@ -20,9 +20,14 @@ def __getattr__(self, name):
         elif name == 'data':
             return self
 
+def make_dummy_embedding():
+    return torch.randn([768])
+
 class DummyTransformer:
+
+
     def __init__(self):
-        self.embeddings = DummyEmbeddingsList([0] * len(KNOWN_WORDS))
+        self.embeddings = DummyEmbeddingsList([make_dummy_embedding() for _ in range(len(KNOWN_WORDS))])
 
     def resize_token_embeddings(self, new_size=None):
         if new_size is None:
@@ -31,7 +36,7 @@ def resize_token_embeddings(self, new_size=None):
             while len(self.embeddings) > new_size:
                 self.embeddings.pop(-1)
             while len(self.embeddings) < new_size:
-                self.embeddings.append(0)
+                self.embeddings.append(make_dummy_embedding())
 
     def get_input_embeddings(self):
         return self.embeddings
@@ -94,8 +99,8 @@ def test_add_embedding_for_known_token(self):
 
         pre_embeddings_count = len(tim.text_encoder.resize_token_embeddings(None))
 
-        token_id = tim._add_textual_inversion(test_embedding_name, test_embedding)
-        self.assertEqual(token_id, 0)
+        ti = tim._add_textual_inversion(test_embedding_name, test_embedding)
+        self.assertEqual(ti.trigger_token_id, 0)
 
 
         # check adding 'test' did not create a new word
@@ -108,7 +113,7 @@ def test_add_embedding_for_known_token(self):
         self.assertIsNotNone(textual_inversion)
         self.assertTrue(torch.equal(textual_inversion.embedding, test_embedding))
         self.assertEqual(textual_inversion.trigger_string, test_embedding_name)
-        self.assertEqual(textual_inversion.trigger_token_id, token_id)
+        self.assertEqual(textual_inversion.trigger_token_id, ti.trigger_token_id)
 
     def test_add_embedding_for_unknown_token(self):
         tim = make_dummy_textual_inversion_manager()
@@ -117,7 +122,7 @@ def test_add_embedding_for_unknown_token(self):
 
         pre_embeddings_count = len(tim.text_encoder.resize_token_embeddings(None))
 
-        added_token_id_1 = tim._add_textual_inversion(test_embedding_name_1, test_embedding_1)
+        added_token_id_1 = tim._add_textual_inversion(test_embedding_name_1, test_embedding_1).trigger_token_id
         # new token id should get added on the end
         self.assertEqual(added_token_id_1, len(KNOWN_WORDS))
 
@@ -139,7 +144,7 @@ def test_add_embedding_for_unknown_token(self):
 
         pre_embeddings_count = len(tim.text_encoder.resize_token_embeddings(None))
 
-        added_token_id_2 = tim._add_textual_inversion(test_embedding_name_2, test_embedding_2)
+        added_token_id_2 = tim._add_textual_inversion(test_embedding_name_2, test_embedding_2).trigger_token_id
         self.assertEqual(added_token_id_2, len(KNOWN_WORDS)+1)
 
         # check adding did create a new word
@@ -169,63 +174,63 @@ def test_pad_raises_on_eos_bos(self):
                                          [KNOWN_WORDS_TOKEN_IDS] + \
                                          [tim.tokenizer.eos_token_id]
         with self.assertRaises(ValueError):
-            tim.expand_textual_inversion_token_ids(prompt_token_ids=prompt_token_ids_with_eos_bos)
+            tim.expand_textual_inversion_token_ids_if_necessary(prompt_token_ids=prompt_token_ids_with_eos_bos)
 
     def test_pad_tokens_list_vector_length_1(self):
         tim = make_dummy_textual_inversion_manager()
         prompt_token_ids = KNOWN_WORDS_TOKEN_IDS.copy()
 
-        expanded_prompt_token_ids = tim.expand_textual_inversion_token_ids(prompt_token_ids=prompt_token_ids)
+        expanded_prompt_token_ids = tim.expand_textual_inversion_token_ids_if_necessary(prompt_token_ids=prompt_token_ids)
         self.assertEqual(prompt_token_ids, expanded_prompt_token_ids)
 
         test_embedding_1v = torch.randn([1, 768])
         test_embedding_1v_token = "<inversion-trigger-vector-length-1>"
-        test_embedding_1v_token_id = tim._add_textual_inversion(test_embedding_1v_token, test_embedding_1v)
+        test_embedding_1v_token_id = tim._add_textual_inversion(test_embedding_1v_token, test_embedding_1v).trigger_token_id
         self.assertEqual(test_embedding_1v_token_id, len(KNOWN_WORDS))
 
         # at the end
         prompt_token_ids_1v_append = prompt_token_ids + [test_embedding_1v_token_id]
-        expanded_prompt_token_ids = tim.expand_textual_inversion_token_ids(prompt_token_ids=prompt_token_ids_1v_append)
+        expanded_prompt_token_ids = tim.expand_textual_inversion_token_ids_if_necessary(prompt_token_ids=prompt_token_ids_1v_append)
         self.assertEqual(prompt_token_ids_1v_append, expanded_prompt_token_ids)
 
         # at the start
         prompt_token_ids_1v_prepend = [test_embedding_1v_token_id] + prompt_token_ids
-        expanded_prompt_token_ids = tim.expand_textual_inversion_token_ids(prompt_token_ids=prompt_token_ids_1v_prepend)
+        expanded_prompt_token_ids = tim.expand_textual_inversion_token_ids_if_necessary(prompt_token_ids=prompt_token_ids_1v_prepend)
         self.assertEqual(prompt_token_ids_1v_prepend, expanded_prompt_token_ids)
 
         # in the middle
         prompt_token_ids_1v_insert = prompt_token_ids[0:2] + [test_embedding_1v_token_id] + prompt_token_ids[2:3]
-        expanded_prompt_token_ids = tim.expand_textual_inversion_token_ids(prompt_token_ids=prompt_token_ids_1v_insert)
+        expanded_prompt_token_ids = tim.expand_textual_inversion_token_ids_if_necessary(prompt_token_ids=prompt_token_ids_1v_insert)
         self.assertEqual(prompt_token_ids_1v_insert, expanded_prompt_token_ids)
 
     def test_pad_tokens_list_vector_length_2(self):
         tim = make_dummy_textual_inversion_manager()
         prompt_token_ids = KNOWN_WORDS_TOKEN_IDS.copy()
 
-        expanded_prompt_token_ids = tim.expand_textual_inversion_token_ids(prompt_token_ids=prompt_token_ids)
+        expanded_prompt_token_ids = tim.expand_textual_inversion_token_ids_if_necessary(prompt_token_ids=prompt_token_ids)
         self.assertEqual(prompt_token_ids, expanded_prompt_token_ids)
 
         test_embedding_2v = torch.randn([2, 768])
         test_embedding_2v_token = "<inversion-trigger-vector-length-2>"
-        test_embedding_2v_token_id = tim._add_textual_inversion(test_embedding_2v_token, test_embedding_2v)
+        test_embedding_2v_token_id = tim._add_textual_inversion(test_embedding_2v_token, test_embedding_2v).trigger_token_id
         test_embedding_2v_pad_token_ids = tim.get_textual_inversion_for_token_id(test_embedding_2v_token_id).pad_token_ids
         self.assertEqual(test_embedding_2v_token_id, len(KNOWN_WORDS))
 
         # at the end
         prompt_token_ids_2v_append = prompt_token_ids + [test_embedding_2v_token_id]
-        expanded_prompt_token_ids = tim.expand_textual_inversion_token_ids(prompt_token_ids=prompt_token_ids_2v_append)
+        expanded_prompt_token_ids = tim.expand_textual_inversion_token_ids_if_necessary(prompt_token_ids=prompt_token_ids_2v_append)
         self.assertNotEqual(prompt_token_ids_2v_append, expanded_prompt_token_ids)
         self.assertEqual(prompt_token_ids + [test_embedding_2v_token_id] + test_embedding_2v_pad_token_ids, expanded_prompt_token_ids)
 
         # at the start
         prompt_token_ids_2v_prepend = [test_embedding_2v_token_id] + prompt_token_ids
-        expanded_prompt_token_ids = tim.expand_textual_inversion_token_ids(prompt_token_ids=prompt_token_ids_2v_prepend)
+        expanded_prompt_token_ids = tim.expand_textual_inversion_token_ids_if_necessary(prompt_token_ids=prompt_token_ids_2v_prepend)
         self.assertNotEqual(prompt_token_ids_2v_prepend, expanded_prompt_token_ids)
         self.assertEqual([test_embedding_2v_token_id] + test_embedding_2v_pad_token_ids + prompt_token_ids, expanded_prompt_token_ids)
 
         # in the middle
         prompt_token_ids_2v_insert = prompt_token_ids[0:2] + [test_embedding_2v_token_id] + prompt_token_ids[2:3]
-        expanded_prompt_token_ids = tim.expand_textual_inversion_token_ids(prompt_token_ids=prompt_token_ids_2v_insert)
+        expanded_prompt_token_ids = tim.expand_textual_inversion_token_ids_if_necessary(prompt_token_ids=prompt_token_ids_2v_insert)
         self.assertNotEqual(prompt_token_ids_2v_insert, expanded_prompt_token_ids)
         self.assertEqual(prompt_token_ids[0:2] + [test_embedding_2v_token_id] + test_embedding_2v_pad_token_ids + prompt_token_ids[2:3], expanded_prompt_token_ids)
 
@@ -233,30 +238,64 @@ def test_pad_tokens_list_vector_length_8(self):
         tim = make_dummy_textual_inversion_manager()
         prompt_token_ids = KNOWN_WORDS_TOKEN_IDS.copy()
 
-        expanded_prompt_token_ids = tim.expand_textual_inversion_token_ids(prompt_token_ids=prompt_token_ids)
+        expanded_prompt_token_ids = tim.expand_textual_inversion_token_ids_if_necessary(prompt_token_ids=prompt_token_ids)
         self.assertEqual(prompt_token_ids, expanded_prompt_token_ids)
 
         test_embedding_8v = torch.randn([8, 768])
         test_embedding_8v_token = "<inversion-trigger-vector-length-8>"
-        test_embedding_8v_token_id = tim._add_textual_inversion(test_embedding_8v_token, test_embedding_8v)
+        test_embedding_8v_token_id = tim._add_textual_inversion(test_embedding_8v_token, test_embedding_8v).trigger_token_id
         test_embedding_8v_pad_token_ids = tim.get_textual_inversion_for_token_id(test_embedding_8v_token_id).pad_token_ids
         self.assertEqual(test_embedding_8v_token_id, len(KNOWN_WORDS))
 
         # at the end
         prompt_token_ids_8v_append = prompt_token_ids + [test_embedding_8v_token_id]
-        expanded_prompt_token_ids = tim.expand_textual_inversion_token_ids(prompt_token_ids=prompt_token_ids_8v_append)
+        expanded_prompt_token_ids = tim.expand_textual_inversion_token_ids_if_necessary(prompt_token_ids=prompt_token_ids_8v_append)
         self.assertNotEqual(prompt_token_ids_8v_append, expanded_prompt_token_ids)
         self.assertEqual(prompt_token_ids + [test_embedding_8v_token_id] + test_embedding_8v_pad_token_ids, expanded_prompt_token_ids)
 
         # at the start
         prompt_token_ids_8v_prepend = [test_embedding_8v_token_id] + prompt_token_ids
-        expanded_prompt_token_ids = tim.expand_textual_inversion_token_ids(prompt_token_ids=prompt_token_ids_8v_prepend)
+        expanded_prompt_token_ids = tim.expand_textual_inversion_token_ids_if_necessary(prompt_token_ids=prompt_token_ids_8v_prepend)
         self.assertNotEqual(prompt_token_ids_8v_prepend, expanded_prompt_token_ids)
         self.assertEqual([test_embedding_8v_token_id] + test_embedding_8v_pad_token_ids + prompt_token_ids, expanded_prompt_token_ids)
 
         # in the middle
         prompt_token_ids_8v_insert = prompt_token_ids[0:2] + [test_embedding_8v_token_id] + prompt_token_ids[2:3]
-        expanded_prompt_token_ids = tim.expand_textual_inversion_token_ids(prompt_token_ids=prompt_token_ids_8v_insert)
+        expanded_prompt_token_ids = tim.expand_textual_inversion_token_ids_if_necessary(prompt_token_ids=prompt_token_ids_8v_insert)
         self.assertNotEqual(prompt_token_ids_8v_insert, expanded_prompt_token_ids)
         self.assertEqual(prompt_token_ids[0:2] + [test_embedding_8v_token_id] + test_embedding_8v_pad_token_ids + prompt_token_ids[2:3], expanded_prompt_token_ids)
 
+
+    def test_deferred_loading(self):
+        tim = make_dummy_textual_inversion_manager()
+        test_embedding = torch.randn([1, 768])
+        test_embedding_name = UNKNOWN_WORDS[0]
+        self.assertFalse(tim.has_textual_inversion_for_trigger_string(test_embedding_name))
+
+        pre_embeddings_count = len(tim.text_encoder.resize_token_embeddings(None))
+
+        ti = tim._add_textual_inversion(test_embedding_name, test_embedding, defer_injecting_tokens=True)
+        self.assertIsNone(ti.trigger_token_id)
+
+        # check that a new word is not yet created
+        embeddings_count = len(tim.text_encoder.resize_token_embeddings(None))
+        self.assertEqual(pre_embeddings_count, embeddings_count)
+
+        # check it was added
+        self.assertTrue(tim.has_textual_inversion_for_trigger_string(test_embedding_name))
+        textual_inversion = tim.get_textual_inversion_for_trigger_string(test_embedding_name)
+        self.assertIsNotNone(textual_inversion)
+        self.assertTrue(torch.equal(textual_inversion.embedding, test_embedding))
+        self.assertEqual(textual_inversion.trigger_string, test_embedding_name)
+        self.assertIsNone(textual_inversion.trigger_token_id, ti.trigger_token_id)
+
+        # check it lazy-loads
+        prompt = " ".join([KNOWN_WORDS[0], UNKNOWN_WORDS[0], KNOWN_WORDS[1]])
+        tim.create_deferred_token_ids_for_any_trigger_terms(prompt)
+
+        embeddings_count = len(tim.text_encoder.resize_token_embeddings(None))
+        self.assertEqual(pre_embeddings_count+1, embeddings_count)
+
+        textual_inversion = tim.get_textual_inversion_for_trigger_string(test_embedding_name)
+        self.assertEqual(textual_inversion.trigger_string, test_embedding_name)
+        self.assertEqual(textual_inversion.trigger_token_id, len(KNOWN_WORDS))

From ee1b9654489494b380ef5da5e9dc990ea1e6e540 Mon Sep 17 00:00:00 2001
From: Damian Stewart <d@damianstewart.com>
Date: Tue, 10 Jan 2023 19:07:48 +0100
Subject: [PATCH 176/199] squash a console warning

---
 ldm/invoke/concepts_lib.py               | 12 ++++++------
 ldm/modules/textual_inversion_manager.py |  5 ++++-
 2 files changed, 10 insertions(+), 7 deletions(-)

diff --git a/ldm/invoke/concepts_lib.py b/ldm/invoke/concepts_lib.py
index a2a9072c4c2..246dea362a3 100644
--- a/ldm/invoke/concepts_lib.py
+++ b/ldm/invoke/concepts_lib.py
@@ -29,7 +29,7 @@ def __init__(self, root=None):
 
     def list_concepts(self)->list:
         '''
-        Return a list of all the concepts by name, without the 'sd-concepts-library' part. 
+        Return a list of all the concepts by name, without the 'sd-concepts-library' part.
         Also adds local concepts in invokeai/embeddings folder.
         '''
         local_concepts_now = self.get_local_concepts(os.path.join(self.root, 'embeddings'))
@@ -71,11 +71,11 @@ def concept_to_trigger(self, concept_name:str)->str:
         if concept_name in self.triggers:
             return self.triggers[concept_name]
         elif self.concept_is_local(concept_name):
-            trigger = f'<{concept_name}>'   
+            trigger = f'<{concept_name}>'
             self.triggers[concept_name] = trigger
             self.concept_names[trigger] = concept_name
             return trigger
-        
+
         file = self.get_concept_file(concept_name, 'token_identifier.txt', local_only=True)
         if not file:
             return None
@@ -135,7 +135,7 @@ def do_replace(match)->str:
     def get_concept_file(self, concept_name:str, file_name:str='learned_embeds.bin' , local_only:bool=False)->str:
         if not (self.concept_is_downloaded(concept_name) or self.concept_is_local(concept_name) or local_only):
             self.download_concept(concept_name)
-        
+
         # get local path in invokeai/embeddings if local concept
         if self.concept_is_local(concept_name):
             concept_path = self._concept_local_path(concept_name)
@@ -144,7 +144,7 @@ def get_concept_file(self, concept_name:str, file_name:str='learned_embeds.bin'
             concept_path = self._concept_path(concept_name)
             path = os.path.join(concept_path, file_name)
         return path if os.path.exists(path) else None
-    
+
     def concept_is_local(self, concept_name)->bool:
         return concept_name in self.local_concepts
 
@@ -197,7 +197,7 @@ def _concept_path(self, concept_name:str)->str:
         return os.path.join(self.root,'models','sd-concepts-library',concept_name)
 
     def _concept_local_path(self, concept_name:str)->str:
-        filename = self.local_concepts[concept_name] 
+        filename = self.local_concepts[concept_name]
         return os.path.join(self.root,'embeddings',filename)
 
     def get_local_concepts(self, loc_dir:str):
diff --git a/ldm/modules/textual_inversion_manager.py b/ldm/modules/textual_inversion_manager.py
index 02513dab49f..9015edbeb1b 100644
--- a/ldm/modules/textual_inversion_manager.py
+++ b/ldm/modules/textual_inversion_manager.py
@@ -37,10 +37,13 @@ def load_huggingface_concepts(self, concepts: list[str]):
         for concept_name in concepts:
             if concept_name in self.hf_concepts_library.concepts_loaded:
                 continue
+            trigger = self.hf_concepts_library.concept_to_trigger(concept_name)
+            if self.has_textual_inversion_for_trigger_string(trigger):
+                continue
             bin_file = self.hf_concepts_library.get_concept_model_path(concept_name)
             if not bin_file:
                 continue
-            self.load_textual_inversion(bin_file, defer_injecting_tokens=False)
+            self.load_textual_inversion(bin_file)
             self.hf_concepts_library.concepts_loaded[concept_name]=True
 
     def get_all_trigger_strings(self) -> list[str]:

From 2c4e3516fd5603eb8152fe34f6f8901c110444b6 Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Wed, 11 Jan 2023 06:03:15 +0000
Subject: [PATCH 177/199] implement model migration check

---
 ldm/generate.py                 |  1 +
 ldm/invoke/CLI.py               |  6 +++-
 ldm/invoke/generator/omnibus.py |  2 ++
 ldm/invoke/model_manager.py     | 64 ++++++++++++++++++++++++++++++---
 4 files changed, 67 insertions(+), 6 deletions(-)

diff --git a/ldm/generate.py b/ldm/generate.py
index 8081ffc89dd..d9c0b128cf5 100644
--- a/ldm/generate.py
+++ b/ldm/generate.py
@@ -712,6 +712,7 @@ def select_generator(
             force_outpaint:bool=False,
     ):
         inpainting_model_in_use = self.sampler.uses_inpainting_model()
+        print(f'DEBUG: in select_generator(), inpainting_model_in_use = {inpainting_model_in_use}')
 
         if hires_fix:
             return self._make_txt2img2img()
diff --git a/ldm/invoke/CLI.py b/ldm/invoke/CLI.py
index bddc691ee00..0191b467cbc 100644
--- a/ldm/invoke/CLI.py
+++ b/ldm/invoke/CLI.py
@@ -12,6 +12,7 @@
 from ldm.invoke.pngwriter import PngWriter, retrieve_metadata, write_metadata
 from ldm.invoke.image_util import make_grid
 from ldm.invoke.log import write_log
+from ldm.invoke.model_manager import ModelManager
 from pathlib import Path
 from argparse import Namespace
 import pyparsing
@@ -53,7 +54,7 @@ def main():
             print('** This script will now exit.')
             sys.exit(-1)
 
-    print(f'>> {ldm.invoke.__app_name__} {ldm.invoke.__version__}')
+    print(f'>> {ldm.invoke.__app_name__}, version {ldm.invoke.__version__}')
     print(f'>> InvokeAI runtime directory is "{Globals.root}"')
 
     # loading here to avoid long delays on startup
@@ -79,6 +80,9 @@ def main():
     else:
         embedding_path = None
 
+    # migrate legacy models
+    ModelManager.migrate_models()
+
     # load the infile as a list of lines
     if opt.infile:
         try:
diff --git a/ldm/invoke/generator/omnibus.py b/ldm/invoke/generator/omnibus.py
index a6fae3e567b..4d5d0d13fc3 100644
--- a/ldm/invoke/generator/omnibus.py
+++ b/ldm/invoke/generator/omnibus.py
@@ -40,6 +40,8 @@ def get_make_image(
         self.perlin = perlin
         num_samples = 1
 
+        print('DEBUG: IN OMNIBUS')
+
         sampler.make_schedule(
             ddim_num_steps=steps, ddim_eta=ddim_eta, verbose=False
         )
diff --git a/ldm/invoke/model_manager.py b/ldm/invoke/model_manager.py
index 42a322e430f..aeefe588024 100644
--- a/ldm/invoke/model_manager.py
+++ b/ldm/invoke/model_manager.py
@@ -162,6 +162,11 @@ def list_models(self) -> dict:
         models = {}
         for name in self.config:
             stanza = self.config[name]
+
+            # don't include VAEs in listing (legacy style)
+            if 'config' in stanza and '/VAE/' in stanza['config']:
+                continue
+            
             models[name] = dict()
             format = stanza.get('format','ckpt') # Determine Format
 
@@ -173,18 +178,20 @@ def list_models(self) -> dict:
                 status = 'cached'
             else:
                 status = 'not loaded'
+            models[name].update(
+                description = description,
+                format = format,
+                status = status,
+            )
             
             # Checkpoint Config Parse
             if format == 'ckpt':
                 models[name].update(
-                    description = description,
-                    format = format,
                     config = str(stanza.get('config', None)),
                     weights = str(stanza.get('weights', None)),
                     vae = str(stanza.get('vae', None)),
                     width = str(stanza.get('width', 512)),
                     height = str(stanza.get('height', 512)),
-                    status = status,
                 )
                 
             # Diffusers Config Parse
@@ -193,14 +200,13 @@ def list_models(self) -> dict:
                     vae = dict(
                         repo_id = str(vae.get('repo_id',None)),
                         path = str(vae.get('path',None)),
+                        subfolder = str(vae.get('subfolder',None))
                     )
             if format == 'diffusers':
                 models[name].update(
-                    description = description,
                     vae = vae,
                     repo_id = str(stanza.get('repo_id', None)),
                     path = str(stanza.get('path',None)),
-                    status = status,
                 )
         
         return models
@@ -722,6 +728,54 @@ def preamble(self) -> str:
             # was trained on.
         ''')
 
+    @classmethod
+    def migrate_models(cls):
+        '''
+        Migrate the ~/invokeai/models directory from the legacy format used through 2.2.5
+        to the 2.3.0 "diffusers" version. This should be a one-time operation, called at
+        script startup time.
+        '''
+        # Three transformer models to check: bert, clip and safety checker
+        legacy_locations = [
+            Path('CompVis/stable-diffusion-safety-checker/models--CompVis--stable-diffusion-safety-checker'),
+            Path('bert-base-uncased/models--bert-base-uncased'),
+            Path('openai/clip-vit-large-patch14')
+        ]
+        models_dir = Path(Globals.root,'models')
+        legacy_layout = False
+        for model in legacy_locations:
+            legacy_layout = legacy_layout or Path(models_dir,model).exists()
+        if not legacy_layout:
+            return
+
+        print('** Legacy version <= 2.2.5 model directory layout detected. Reorganizing.')
+        print('** This is a quick one-time operation.')
+        from shutil import move
+        
+        # transformer files get moved into the hub directory
+        hub = models_dir / 'hub'
+        os.makedirs(hub, exist_ok=True)
+        for model in legacy_locations:
+            move(models_dir / model, hub)
+
+        # anything else gets moved into the diffusers directory
+        diffusers = models_dir / 'diffusers'
+        os.makedirs(diffusers, exist_ok=True)
+        for root, dirs, _ in os.walk(models_dir, topdown=False):
+            for dir in dirs:
+                full_path = Path(root,dir)
+                if full_path.is_relative_to(hub) or full_path.is_relative_to(diffusers):
+                    continue
+                if Path(dir).match('models--*--*'):
+                    move(full_path,diffusers)
+
+        # now clean up by removing any empty directories
+        empty = [root for root, dirs, files, in os.walk(models_dir) if not len(dirs) and not len(files)]
+        for d in empty:
+            os.rmdir(d)
+    print('** Migration is done. Continuing...')
+
+
     def _resolve_path(self, source:Union[str,Path], dest_directory:str)->Path:
         resolved_path = None
         if source.startswith(('http:','https:','ftp:')):

From 025c60be1c1da589eeefccc46a9893e89704a48a Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Wed, 11 Jan 2023 07:02:43 +0000
Subject: [PATCH 178/199] add_model() overwrites previous config rather than
 merges

---
 ldm/invoke/CLI.py           | 49 +++++++++++++++++++++----------------
 ldm/invoke/model_manager.py | 20 ++++++++-------
 2 files changed, 39 insertions(+), 30 deletions(-)

diff --git a/ldm/invoke/CLI.py b/ldm/invoke/CLI.py
index 0191b467cbc..a80f1560ab0 100644
--- a/ldm/invoke/CLI.py
+++ b/ldm/invoke/CLI.py
@@ -502,7 +502,7 @@ def do_command(command:str, gen, opt:Args, completer) -> tuple:
         if len(path) < 2:
             print('** please provide the name of a model')
         else:
-            edit_config(path[1], gen, opt, completer)
+            edit_model(path[1], gen, opt, completer)
         completer.add_history(command)
         operation = None
 
@@ -600,7 +600,7 @@ def import_diffuser_model(path_or_repo:str, gen, opt, completer)->str:
     manager = gen.model_manager
     default_name = Path(path_or_repo).stem
     default_description = f'Imported model {default_name}'
-    model_name, model_description, default = _get_model_name_and_desc(
+    model_name, model_description = _get_model_name_and_desc(
         manager,
         completer,
         model_name=default_name,
@@ -613,7 +613,7 @@ def import_diffuser_model(path_or_repo:str, gen, opt, completer)->str:
             description = model_description):
         print('** model failed to import')
         return None
-    if default:
+    if input('Make this the default model? [n] ').startswith(('y','Y')):
         manager.set_default_model(model_name)
     return model_name
 
@@ -621,7 +621,7 @@ def import_ckpt_model(path_or_url:str, gen, opt, completer)->str:
     manager = gen.model_manager
     default_name = Path(path_or_url).stem
     default_description = f'Imported model {default_name}'
-    model_name, model_description, default = _get_model_name_and_desc(
+    model_name, model_description = _get_model_name_and_desc(
         manager,
         completer,
         model_name=default_name,
@@ -630,7 +630,7 @@ def import_ckpt_model(path_or_url:str, gen, opt, completer)->str:
     config_file = None
 
     completer.complete_extensions(('.yaml','.yml'))
-    completer.linebuffer = 'configs/stable-diffusion/v1-inference.yaml'
+    completer.set_line('configs/stable-diffusion/v1-inference.yaml')
     done = False
     while not done:
         config_file = input('Configuration file for this model: ').strip()
@@ -641,12 +641,13 @@ def import_ckpt_model(path_or_url:str, gen, opt, completer)->str:
             path_or_url,
             config = config_file,
             model_name = model_name,
-            model_description = model_description
+            model_description = model_description,
+            commit_to_conf = opt.conf,
     ):
         print('** model failed to import')
         return None
 
-    if default:
+    if input('Make this the default model? [n] ').startswith(('y','Y')):
         manager.set_model_default(model_name)
     return model_name
 
@@ -665,10 +666,9 @@ def _verify_load(model_name:str, gen)->bool:
 
 def _get_model_name_and_desc(model_manager,completer,model_name:str='',model_description:str=''):
     model_name = _get_model_name(model_manager.list_models(),completer,model_name)
-    completer.linebuffer = model_description
+    completer.set_line(model_description)
     model_description = input(f'Description for this model [{model_description}]: ').strip() or model_description
-    default = input(f'Make this model the default? [n] ').startswith(('y','Y'))
-    return model_name, model_description, default
+    return model_name, model_description
 
 def optimize_model(model_name_or_path:str, gen, opt, completer):
     manager = gen.model_manager
@@ -684,7 +684,7 @@ def optimize_model(model_name_or_path:str, gen, opt, completer):
             return
     elif os.path.exists(model_name_or_path):
         ckpt_path = Path(model_name_or_path)
-        model_name,model_description, default = _get_model_name_and_desc(
+        model_name,model_description = _get_model_name_and_desc(
             manager,
             completer,
             ckpt_path.stem,
@@ -731,7 +731,7 @@ def del_config(model_name:str, gen, opt, completer):
     print(f'** {model_name} deleted')
     completer.update_models(gen.model_manager.list_models())
 
-def edit_config(model_name:str, gen, opt, completer):
+def edit_model(model_name:str, gen, opt, completer):
     current_model = gen.model_name
 #    if model_name == current_model:
 #        print("** Can't edit the active model. !switch to another model first. **")
@@ -743,16 +743,23 @@ def edit_config(model_name:str, gen, opt, completer):
         return
 
     print(f'\n>> Editing model {model_name} from configuration file {opt.conf}')
-    new_name,new_description,default = _get_model_name_and_desc(gen.model_manager,
-                                                                completer,
-                                                                model_name=model_name,
-                                                                model_description=info['description']
-    )
-    info['description'] = new_description
+    new_name = _get_model_name(manager.list_models(),completer,model_name)
+
+    for attribute in info.keys():
+        if type(info[attribute]) != str:
+            continue
+        if attribute == 'format':
+            continue
+        completer.set_line(info[attribute])
+        info[attribute] = input(f'{attribute}: ') or info[attribute]
+        
     if new_name != model_name:
-        manager.add_model(new_name,info)
         manager.del_model(model_name)
-    if default:
+
+    # this does the update
+    manager.add_model(new_name, info, True)
+
+    if input('Make this the default model? [n] ').startswith(('y','Y')):
         manager.set_default_model(new_name)
     manager.commit(opt.conf)
     completer.update_models(manager.list_models())
@@ -760,7 +767,7 @@ def edit_config(model_name:str, gen, opt, completer):
 
 def _get_model_name(existing_names,completer,default_name:str='')->str:
     done = False
-    completer.linebuffer = default_name
+    completer.set_line(default_name)
     while not done:
         model_name = input(f'Short name for this model [{default_name}]: ').strip()
         if len(model_name)==0:
diff --git a/ldm/invoke/model_manager.py b/ldm/invoke/model_manager.py
index aeefe588024..3dca8e77660 100644
--- a/ldm/invoke/model_manager.py
+++ b/ldm/invoke/model_manager.py
@@ -252,13 +252,12 @@ def add_model(self, model_name:str, model_attributes:dict, clobber:bool=False) -
 
         assert (clobber or model_name not in omega), f'attempt to overwrite existing model definition "{model_name}"'
 
-        config = omega[model_name] if model_name in omega else {}
-        for field in model_attributes:
-            if field == 'weights':
-                field.replace('\\', '/')
-            config[field] = model_attributes[field]
+        if model_name not in omega:
+            omega[model_name] = dict()
+        omega[model_name].update(model_attributes,merge=False)
+        if 'weights' in omega[model_name]:
+            omega[model_name]['weights'].replace('\\','/')
 
-        omega[model_name] = config
         if clobber:
             self._invalidate_cached_model(model_name)
 
@@ -739,7 +738,7 @@ def migrate_models(cls):
         legacy_locations = [
             Path('CompVis/stable-diffusion-safety-checker/models--CompVis--stable-diffusion-safety-checker'),
             Path('bert-base-uncased/models--bert-base-uncased'),
-            Path('openai/clip-vit-large-patch14')
+            Path('openai/clip-vit-large-patch14/models--openai--clip-vit-large-patch14')
         ]
         models_dir = Path(Globals.root,'models')
         legacy_layout = False
@@ -756,7 +755,10 @@ def migrate_models(cls):
         hub = models_dir / 'hub'
         os.makedirs(hub, exist_ok=True)
         for model in legacy_locations:
-            move(models_dir / model, hub)
+            source = models_dir /model
+            if source.exists():
+                print(f'DEBUG: Moving {models_dir / model} into hub')
+                move(models_dir / model, hub)
 
         # anything else gets moved into the diffusers directory
         diffusers = models_dir / 'diffusers'
@@ -773,7 +775,7 @@ def migrate_models(cls):
         empty = [root for root, dirs, files, in os.walk(models_dir) if not len(dirs) and not len(files)]
         for d in empty:
             os.rmdir(d)
-    print('** Migration is done. Continuing...')
+        print('** Migration is done. Continuing...')
 
 
     def _resolve_path(self, source:Union[str,Path], dest_directory:str)->Path:

From 2510ff2f7c6fb81cca0cf118cb25bdf5fc4cf092 Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Wed, 11 Jan 2023 11:28:28 -0500
Subject: [PATCH 179/199] fix model config file attribute merging

---
 ldm/invoke/model_manager.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ldm/invoke/model_manager.py b/ldm/invoke/model_manager.py
index 478066ccb09..b1e13ab62bf 100644
--- a/ldm/invoke/model_manager.py
+++ b/ldm/invoke/model_manager.py
@@ -255,7 +255,7 @@ def add_model(self, model_name:str, model_attributes:dict, clobber:bool=False) -
 
         if model_name not in omega:
             omega[model_name] = dict()
-        omega[model_name].update(model_attributes,merge=False)
+        OmegaConf.update(omega,model_name,model_attributes,merge=False)
         if 'weights' in omega[model_name]:
             omega[model_name]['weights'].replace('\\','/')
 

From fee971de8cf911a82026a9aeabaa98fb16f12ee0 Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Wed, 11 Jan 2023 20:57:37 +0000
Subject: [PATCH 180/199] fix precision handling in textual inversion script

---
 ldm/invoke/textual_inversion_training.py |  8 ++++----
 scripts/textual_inversion_fe.py          | 23 ++++++++++++++++-------
 2 files changed, 20 insertions(+), 11 deletions(-)

diff --git a/ldm/invoke/textual_inversion_training.py b/ldm/invoke/textual_inversion_training.py
index 7615353ecb6..e0759aec3c3 100644
--- a/ldm/invoke/textual_inversion_training.py
+++ b/ldm/invoke/textual_inversion_training.py
@@ -32,7 +32,7 @@
 from huggingface_hub import HfFolder, Repository, whoami
 
 # invokeai stuff
-from ldm.invoke.globals import Globals
+from ldm.invoke.globals import Globals, global_cache_dir
 from omegaconf import OmegaConf
 
 # TODO: remove and import from diffusers.utils when the new version of diffusers is released
@@ -448,7 +448,7 @@ def do_textual_inversion_training(
         push_to_hub:bool=False,
         hub_token:str=None,
         logging_dir:Path=Path('logs'),
-        mixed_precision:str='no',
+        mixed_precision:str='fp16',
         allow_tf32:bool=False,
         report_to:str='tensorboard',
         local_rank:int=-1,
@@ -517,11 +517,11 @@ def do_textual_inversion_training(
     assert model_conf.get('format','diffusers')=='diffusers', "This script only works with models of type 'diffusers'"
     pretrained_model_name_or_path = model_conf.get('repo_id',None) or Path(model_conf.get('path'))
     assert pretrained_model_name_or_path, f"models.yaml error: neither 'repo_id' nor 'path' is defined for {model}"
-    pipeline_args = dict()
+    pipeline_args = dict(cache_dir=global_cache_dir('diffusers'))
     
     # Load tokenizer
     if tokenizer_name:
-        tokenizer = CLIPTokenizer.from_pretrained(tokenizer_name)
+        tokenizer = CLIPTokenizer.from_pretrained(tokenizer_name,cache_dir=global_cache_dir('transformers'))
     else:
         tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_name_or_path, subfolder="tokenizer", **pipeline_args)
 
diff --git a/scripts/textual_inversion_fe.py b/scripts/textual_inversion_fe.py
index 187ac089ba6..57af415f99d 100755
--- a/scripts/textual_inversion_fe.py
+++ b/scripts/textual_inversion_fe.py
@@ -5,7 +5,7 @@
 import sys
 import curses
 import re
-from ldm.invoke.globals import Globals, set_root
+from ldm.invoke.globals import Globals, global_set_root
 from omegaconf import OmegaConf
 from pathlib import Path
 from typing import List
@@ -17,6 +17,7 @@ class textualInversionForm(npyscreen.FormMultiPageAction):
                 "linear", "cosine", "cosine_with_restarts",
                 "polynomial","constant", "constant_with_warmup"
     ]
+    precisions = ['no','fp16','bf16']
 
     def afterEditing(self):
         self.parentApp.setNextForm(None)
@@ -35,7 +36,7 @@ def create(self):
         )
         self.placeholder_token = self.add_widget_intelligent(
             npyscreen.TitleText,
-            name="Placeholder token",
+            name='Trigger term ("placeholder token")',
             value='',
         )
         self.nextrely -= 1
@@ -70,7 +71,7 @@ def create(self):
             name='Output Destination Directory',
             select_dir=True,
             must_exist=False,
-            value=Path(Globals.root) / 'trained-checkpoints' / default_placeholder_token
+            value=Path(Globals.root) / 'text-inversion-model' / default_placeholder_token
         )
         self.resolution = self.add_widget_intelligent(
             npyscreen.TitleSelectOne,
@@ -91,7 +92,14 @@ def create(self):
             out_of=50,
             step=1,
             lowest=1,
-            value=16
+            value=8
+        )
+        self.mixed_precision = self.add_widget_intelligent(
+            npyscreen.TitleSelectOne,
+            name='Mixed Precision',
+            values=self.precisions,
+            value=1,
+            max_height=4,
         )
         self.gradient_accumulation_steps = self.add_widget_intelligent(
             npyscreen.TitleSlider,
@@ -145,7 +153,7 @@ def initializer_changed(self):
         placeholder = self.placeholder_token.value
         self.prompt_token.value = f'(Trigger by using <{placeholder}> in your prompts)'
         self.train_data_dir.value = Path(Globals.root) / 'training-data' / placeholder
-        self.output_dir.value = Path(Globals.root) / 'embeddings' / placeholder
+        self.output_dir.value = Path(Globals.root) / 'text-inversion-model' / placeholder
         
     def on_ok(self):
         if self.validate_field_values():
@@ -188,7 +196,8 @@ def marshall_arguments(self):
         args.update(
             model = self.model_names[self.model.value[0]],
             resolution = self.resolutions[self.resolution.value[0]],
-            lr_scheduler = self.lr_schedulers[self.lr_scheduler.value[0]]
+            lr_scheduler = self.lr_schedulers[self.lr_scheduler.value[0]],
+            mixed_precision = self.precisions[self.mixed_precision.value[0]],
         )
 
         # all the strings
@@ -223,7 +232,7 @@ def onStart(self):
         help='Path to the invokeai runtime directory',
     )
     args = parser.parse_args()
-    set_root(args.root_dir)
+    global_set_root(args.root_dir)
     
     myapplication = MyApplication()
     myapplication.run()

From 8ff37d6704635c6fad118abc976a795416468fba Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Wed, 11 Jan 2023 20:58:04 +0000
Subject: [PATCH 181/199] allow ckpt conversion script to work with safetensors
 .ckpts

Applied patch here:
https://github.com/huggingface/diffusers/commit/beb932c5d111872c5e45387e7b1b2b3dd0524a47
---
 ldm/invoke/ckpt_to_diffuser.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/ldm/invoke/ckpt_to_diffuser.py b/ldm/invoke/ckpt_to_diffuser.py
index 6e8e2194787..86281623a6a 100644
--- a/ldm/invoke/ckpt_to_diffuser.py
+++ b/ldm/invoke/ckpt_to_diffuser.py
@@ -445,8 +445,9 @@ def convert_ldm_unet_checkpoint(checkpoint, config, path=None, extract_ema=False
                 paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
             )
 
-            if ["conv.weight", "conv.bias"] in output_block_list.values():
-                index = list(output_block_list.values()).index(["conv.weight", "conv.bias"])
+            output_block_list = {k: sorted(v) for k, v in output_block_list.items()}
+            if ["conv.bias", "conv.weight"] in output_block_list.values():
+                index = list(output_block_list.values()).index(["conv.bias", "conv.weight"])
                 new_checkpoint[f"up_blocks.{block_id}.upsamplers.0.conv.weight"] = unet_state_dict[
                     f"output_blocks.{i}.{index}.conv.weight"
                 ]

From 30c282aa8862e517595ea38b316ecd9bc04d1721 Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Wed, 11 Jan 2023 22:02:32 +0000
Subject: [PATCH 182/199] fix name "args" is not defined crash in
 textual_inversion_training

---
 ldm/invoke/textual_inversion_training.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/ldm/invoke/textual_inversion_training.py b/ldm/invoke/textual_inversion_training.py
index e0759aec3c3..95ad4d9a6f3 100644
--- a/ldm/invoke/textual_inversion_training.py
+++ b/ldm/invoke/textual_inversion_training.py
@@ -68,10 +68,10 @@
 logger = get_logger(__name__)
 
 
-def save_progress(text_encoder, placeholder_token_id, accelerator, args, save_path):
+def save_progress(text_encoder, placeholder_token_id, accelerator, placeholder_token, save_path):
     logger.info("Saving embeddings")
     learned_embeds = accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[placeholder_token_id]
-    learned_embeds_dict = {args.placeholder_token: learned_embeds.detach().cpu()}
+    learned_embeds_dict = {placeholder_token: learned_embeds.detach().cpu()}
     torch.save(learned_embeds_dict, save_path)
 
 def parse_args():
@@ -756,7 +756,7 @@ def do_textual_inversion_training(
                 global_step += 1
                 if global_step % save_steps == 0:
                     save_path = os.path.join(output_dir, f"learned_embeds-steps-{global_step}.bin")
-                    save_progress(text_encoder, placeholder_token_id, accelerator, args, save_path)
+                    save_progress(text_encoder, placeholder_token_id, accelerator, placeholder_token, save_path)
 
                 if global_step % checkpointing_steps == 0:
                     if accelerator.is_main_process:

From 19663b9f1bc4b3d790fad2273cad685cc7819fb7 Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Thu, 12 Jan 2023 02:47:33 +0000
Subject: [PATCH 183/199] fix a second NameError: name 'args' is not defined
 crash

---
 ldm/invoke/textual_inversion_training.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ldm/invoke/textual_inversion_training.py b/ldm/invoke/textual_inversion_training.py
index 95ad4d9a6f3..ab53f0801d6 100644
--- a/ldm/invoke/textual_inversion_training.py
+++ b/ldm/invoke/textual_inversion_training.py
@@ -791,7 +791,7 @@ def do_textual_inversion_training(
             pipeline.save_pretrained(output_dir)
         # Save the newly trained embeddings
         save_path = os.path.join(output_dir, "learned_embeds.bin")
-        save_progress(text_encoder, placeholder_token_id, accelerator, args, save_path)
+        save_progress(text_encoder, placeholder_token_id, accelerator, placeholder_token, save_path)
 
         if push_to_hub:
             repo.push_to_hub(commit_message="End of training", blocking=False, auto_lfs_prune=True)

From d4e85a5d7a2a04ed6f9d6b4950f6c85f41592bd4 Mon Sep 17 00:00:00 2001
From: Eugene Brodsky <ebr@users.noreply.github.com>
Date: Thu, 12 Jan 2023 11:24:02 -0500
Subject: [PATCH 184/199] fix loading of the safety checker from the global
 cache dir

---
 ldm/generate.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/ldm/generate.py b/ldm/generate.py
index 4d0e10dedc4..87e3f1a49d4 100644
--- a/ldm/generate.py
+++ b/ldm/generate.py
@@ -29,7 +29,7 @@
 from ldm.invoke.conditioning import get_uc_and_c_and_ec
 from ldm.invoke.devices import choose_torch_device, choose_precision
 from ldm.invoke.generator.inpaint import infill_methods
-from ldm.invoke.globals import Globals
+from ldm.invoke.globals import global_cache_dir
 from ldm.invoke.image_util import InitImageResizer
 from ldm.invoke.model_manager import ModelManager
 from ldm.invoke.pngwriter import PngWriter
@@ -226,7 +226,7 @@ def __init__(
                 from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
                 from transformers import AutoFeatureExtractor
                 safety_model_id = "CompVis/stable-diffusion-safety-checker"
-                safety_model_path = os.path.join(Globals.root,'models',safety_model_id)
+                safety_model_path = global_cache_dir("hub")
                 self.safety_checker = StableDiffusionSafetyChecker.from_pretrained(safety_model_id,
                                                                                    local_files_only=True,
                                                                                    cache_dir=safety_model_path,

From 6a1c4c2074e85b707273f2c75501e6e62f92348a Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Thu, 12 Jan 2023 18:28:03 +0000
Subject: [PATCH 185/199] add installation step to textual inversion frontend

- After a successful training run, the script will copy learned_embeds.bin
  to a subfolder of the embeddings directory.
- User given the option to delete the logs and intermediate checkpoints
  (which together use 7-8G of space)
- If textual inversion training fails, reports the error gracefully.
---
 scripts/textual_inversion_fe.py | 25 +++++++++++++++++++++----
 1 file changed, 21 insertions(+), 4 deletions(-)

diff --git a/scripts/textual_inversion_fe.py b/scripts/textual_inversion_fe.py
index 57af415f99d..bbd1f0d6df9 100755
--- a/scripts/textual_inversion_fe.py
+++ b/scripts/textual_inversion_fe.py
@@ -5,6 +5,7 @@
 import sys
 import curses
 import re
+import shutil
 from ldm.invoke.globals import Globals, global_set_root
 from omegaconf import OmegaConf
 from pathlib import Path
@@ -71,7 +72,7 @@ def create(self):
             name='Output Destination Directory',
             select_dir=True,
             must_exist=False,
-            value=Path(Globals.root) / 'text-inversion-model' / default_placeholder_token
+            value=Path(Globals.root) / 'text-inversion-training' / default_placeholder_token
         )
         self.resolution = self.add_widget_intelligent(
             npyscreen.TitleSelectOne,
@@ -153,7 +154,7 @@ def initializer_changed(self):
         placeholder = self.placeholder_token.value
         self.prompt_token.value = f'(Trigger by using <{placeholder}> in your prompts)'
         self.train_data_dir.value = Path(Globals.root) / 'training-data' / placeholder
-        self.output_dir.value = Path(Globals.root) / 'text-inversion-model' / placeholder
+        self.output_dir.value = Path(Globals.root) / 'text-inversion-training' / placeholder
         
     def on_ok(self):
         if self.validate_field_values():
@@ -189,7 +190,7 @@ def get_model_names(self)->(List[str],int):
         defaults = [idx for idx in range(len(model_names)) if 'default' in conf[model_names[idx]]]
         return (model_names,defaults[0])
 
-    def marshall_arguments(self):
+    def marshall_arguments(self)->dict:
         args = dict()
 
         # the choices
@@ -223,6 +224,16 @@ def onStart(self):
         npyscreen.setTheme(npyscreen.Themes.DefaultTheme)
         self.main = self.addForm('MAIN', textualInversionForm, name='Textual Inversion Settings')
 
+def copy_to_embeddings_folder(args:dict):
+    source = Path(args['output_dir'],'learned_embeds.bin')
+    destination = Path(Globals.root,'embeddings',args['placeholder_token'])
+    print(f'>> Training completed. Copying learned_embeds.bin into {str(destination)}')
+    shutil.copy(source,destination)
+    if (input('Delete training logs and intermediate checkpoints (~8 Gb)? [y] ') or 'y').startswith(('y','Y')):
+        shutil.rmtree(Path(args['output_dir']))
+    else:
+        print(f'>> Keeping {args["output_dir"]}')
+
 if __name__ == '__main__':
     parser = argparse.ArgumentParser(description='InvokeAI textual inversion training')
     parser.add_argument(
@@ -240,5 +251,11 @@ def onStart(self):
     from ldm.invoke.textual_inversion_training import do_textual_inversion_training
     if args := myapplication.ti_arguments:
         os.makedirs(args['output_dir'],exist_ok=True)
-        do_textual_inversion_training(**args)
+        try:
+            do_textual_inversion_training(**args)
+            copy_to_embeddings_folder(args)
+        except Exception as e:
+            print(f'** An exception occurred during training. The exception was:')
+            print(str(e))
+        
     

From b9cd54d19ffbb04dc924f8a264bc24a806a1b17e Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Thu, 12 Jan 2023 19:07:50 +0000
Subject: [PATCH 186/199] don't crash out on incompatible embeddings

- put try: blocks around places where the system tries to load an embedding
  which is incompatible with the currently loaded model
---
 ldm/generate.py                          |  1 -
 ldm/modules/textual_inversion_manager.py | 16 ++++++++++++----
 2 files changed, 12 insertions(+), 5 deletions(-)

diff --git a/ldm/generate.py b/ldm/generate.py
index 87e3f1a49d4..7670448a93d 100644
--- a/ldm/generate.py
+++ b/ldm/generate.py
@@ -712,7 +712,6 @@ def select_generator(
             force_outpaint:bool=False,
     ):
         inpainting_model_in_use = self.sampler.uses_inpainting_model()
-        print(f'DEBUG: in select_generator(), inpainting_model_in_use = {inpainting_model_in_use}')
 
         if hires_fix:
             return self._make_txt2img2img()
diff --git a/ldm/modules/textual_inversion_manager.py b/ldm/modules/textual_inversion_manager.py
index 9015edbeb1b..471a8ee07bc 100644
--- a/ldm/modules/textual_inversion_manager.py
+++ b/ldm/modules/textual_inversion_manager.py
@@ -62,9 +62,12 @@ def load_textual_inversion(self, ckpt_path, defer_injecting_tokens: bool=False):
 
         embedding_info = self._parse_embedding(ckpt_path)
         if embedding_info:
-            self._add_textual_inversion(embedding_info['name'],
-                                        embedding_info['embedding'],
-                                        defer_injecting_tokens=defer_injecting_tokens)
+            try:
+                self._add_textual_inversion(embedding_info['name'],
+                                            embedding_info['embedding'],
+                                            defer_injecting_tokens=defer_injecting_tokens)
+            except ValueError:
+                print(f'   | ignoring incompatible embedding {embedding_info["name"]}')
         else:
             print(f'>> Failed to load embedding located at {ckpt_path}. Unsupported file.')
 
@@ -108,6 +111,7 @@ def _inject_tokens_and_assign_embeddings(self, ti: TextualInversion) -> int:
         if ti.trigger_token_id is not None:
             raise ValueError(f"Tokens already injected for textual inversion with trigger '{ti.trigger_string}'")
 
+        print(f'DEBUG: Injecting token {ti.trigger_string}')
         trigger_token_id = self._get_or_create_token_id_and_assign_embedding(ti.trigger_string, ti.embedding[0])
 
         if ti.embedding_vector_length > 1:
@@ -145,7 +149,11 @@ def create_deferred_token_ids_for_any_trigger_terms(self, prompt_string: str) ->
             if ti.trigger_token_id is None and ti.trigger_string in prompt_string:
                 if ti.embedding_vector_length > 1:
                     print(f">> Preparing tokens for textual inversion {ti.trigger_string}...")
-                self._inject_tokens_and_assign_embeddings(ti)
+                try:
+                    self._inject_tokens_and_assign_embeddings(ti)
+                except ValueError as e:
+                    print(f'   | ignoring incompatible embedding trigger {ti.trigger_string}')
+                    continue
                 injected_token_ids.append(ti.trigger_token_id)
                 injected_token_ids.extend(ti.pad_token_ids)
         return injected_token_ids

From 0518c373f39b2780512e8708a89f1e7e92fbe95b Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Thu, 12 Jan 2023 20:27:49 +0000
Subject: [PATCH 187/199] add support for checkpoint resuming

---
 scripts/textual_inversion_fe.py | 53 ++++++++++++++++++++++-----------
 1 file changed, 36 insertions(+), 17 deletions(-)

diff --git a/scripts/textual_inversion_fe.py b/scripts/textual_inversion_fe.py
index bbd1f0d6df9..a822d8dad01 100755
--- a/scripts/textual_inversion_fe.py
+++ b/scripts/textual_inversion_fe.py
@@ -6,6 +6,7 @@
 import curses
 import re
 import shutil
+import traceback
 from ldm.invoke.globals import Globals, global_set_root
 from omegaconf import OmegaConf
 from pathlib import Path
@@ -30,14 +31,14 @@ def create(self):
 
         self.model = self.add_widget_intelligent(
             npyscreen.TitleSelectOne,
-            name='Model Name',
+            name='Model Name:',
             values=self.model_names,
             value=default,
             max_height=len(self.model_names)+1
         )
         self.placeholder_token = self.add_widget_intelligent(
             npyscreen.TitleText,
-            name='Trigger term ("placeholder token")',
+            name='Trigger word:',
             value='',
         )
         self.nextrely -= 1
@@ -50,33 +51,38 @@ def create(self):
         self.nextrelx -= 30
         self.initializer_token = self.add_widget_intelligent(
             npyscreen.TitleText,
-            name="Initializer token",
+            name="Initializer token:",
             value=default_initializer_token,
         )
         self.learnable_property = self.add_widget_intelligent(
             npyscreen.TitleSelectOne,
-            name="Learnable property",
+            name="Learnable property:",
             values=['object','style'],
             value=0,
             max_height=3,
         )
         self.train_data_dir = self.add_widget_intelligent(
             npyscreen.TitleFilenameCombo,
-            name='Data Training Directory',
+            name='Data Training Directory:',
             select_dir=True,
             must_exist=True,
             value=Path(Globals.root) / 'training-data' / default_placeholder_token
         )
         self.output_dir = self.add_widget_intelligent(
             npyscreen.TitleFilenameCombo,
-            name='Output Destination Directory',
+            name='Output Destination Directory:',
             select_dir=True,
             must_exist=False,
             value=Path(Globals.root) / 'text-inversion-training' / default_placeholder_token
         )
+        self.resume_from_checkpoint = self.add_widget_intelligent(
+            npyscreen.Checkbox,
+            name="Resume from last saved checkpoint",
+            value=False,
+        )
         self.resolution = self.add_widget_intelligent(
             npyscreen.TitleSelectOne,
-            name='Image resolution (pixels)',
+            name='Image resolution (pixels):',
             values = self.resolutions,
             value=0,
             scroll_exit = True,
@@ -89,7 +95,7 @@ def create(self):
         )
         self.train_batch_size = self.add_widget_intelligent(
             npyscreen.TitleSlider,
-            name='Batch Size',
+            name='Batch Size:',
             out_of=50,
             step=1,
             lowest=1,
@@ -97,14 +103,14 @@ def create(self):
         )
         self.mixed_precision = self.add_widget_intelligent(
             npyscreen.TitleSelectOne,
-            name='Mixed Precision',
+            name='Mixed Precision:',
             values=self.precisions,
             value=1,
             max_height=4,
         )
         self.gradient_accumulation_steps = self.add_widget_intelligent(
             npyscreen.TitleSlider,
-            name='Gradient Accumulation Steps',
+            name='Gradient Accumulation Steps:',
             out_of=10,
             step=1,
             lowest=1,
@@ -112,7 +118,7 @@ def create(self):
         )
         self.max_train_steps = self.add_widget_intelligent(
             npyscreen.TitleSlider,
-            name='Max Training Steps',
+            name='Max Training Steps:',
             out_of=10000,
             step=500,
             lowest=1,
@@ -120,7 +126,7 @@ def create(self):
         )
         self.learning_rate = self.add_widget_intelligent(
             npyscreen.TitleText,
-            name="Learning Rate",
+            name="Learning Rate:",
             value='5.0e-04',
         )
         self.scale_lr = self.add_widget_intelligent(
@@ -135,14 +141,14 @@ def create(self):
         )
         self.lr_scheduler = self.add_widget_intelligent(
             npyscreen.TitleSelectOne,
-            name='Learning rate scheduler',
+            name='Learning rate scheduler:',
             values = self.lr_schedulers,
             max_height=7,
             scroll_exit = True,
             value=4)
         self.lr_warmup_steps = self.add_widget_intelligent(
             npyscreen.TitleSlider,
-            name='Warmup Steps',
+            name='Warmup Steps:',
             out_of=100,
             step=1,
             lowest=0,
@@ -204,6 +210,7 @@ def marshall_arguments(self)->dict:
         # all the strings
         for attr in ('initializer_token','placeholder_token','train_data_dir','output_dir','scale_lr'):
             args[attr] = getattr(self,attr).value
+            
         # all the integers
         for attr in ('train_batch_size','gradient_accumulation_steps',
                      'max_train_steps','lr_warmup_steps'):
@@ -213,6 +220,11 @@ def marshall_arguments(self)->dict:
         args.update(
             learning_rate = float(self.learning_rate.value)
         )
+
+        # the booleans
+        if self.resume_from_checkpoint.value and Path(self.output_dir.value).exists():
+            args['resume_from_checkpoint'] = 'latest'
+
         return args
 
 class MyApplication(npyscreen.NPSAppManaged):
@@ -229,7 +241,7 @@ def copy_to_embeddings_folder(args:dict):
     destination = Path(Globals.root,'embeddings',args['placeholder_token'])
     print(f'>> Training completed. Copying learned_embeds.bin into {str(destination)}')
     shutil.copy(source,destination)
-    if (input('Delete training logs and intermediate checkpoints (~8 Gb)? [y] ') or 'y').startswith(('y','Y')):
+    if (input('Delete training logs and intermediate checkpoints? [y] ') or 'y').startswith(('y','Y')):
         shutil.rmtree(Path(args['output_dir']))
     else:
         print(f'>> Keeping {args["output_dir"]}')
@@ -251,11 +263,18 @@ def copy_to_embeddings_folder(args:dict):
     from ldm.invoke.textual_inversion_training import do_textual_inversion_training
     if args := myapplication.ti_arguments:
         os.makedirs(args['output_dir'],exist_ok=True)
+        
+        # Automatically add angle brackets around the trigger
+        if not re.match('^<.+>$',args['placeholder_token']):
+            args['placeholder_token'] = f"<{args['placeholder_token']}>"
+
+        args['only_save_embeds'] = True
+
         try:
             do_textual_inversion_training(**args)
             copy_to_embeddings_folder(args)
         except Exception as e:
             print(f'** An exception occurred during training. The exception was:')
             print(str(e))
-        
-    
+            print('** DETAILS:')
+            print(traceback.format_exc())

From de5130a7150339c267cacf0e4e0fb4ba0586c35b Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Thu, 12 Jan 2023 22:12:05 +0000
Subject: [PATCH 188/199] textual inversion preferences are saved and restored
 between sessions

- Preferences are stored in a file named text-inversion-training/preferences.conf
- Currently the resume-from-checkpoint option is not working correctly. Possible
  bug in textual_inversion_training.py?
---
 scripts/textual_inversion_fe.py | 150 +++++++++++++++++++++-----------
 1 file changed, 101 insertions(+), 49 deletions(-)

diff --git a/scripts/textual_inversion_fe.py b/scripts/textual_inversion_fe.py
index a822d8dad01..6be995a3045 100755
--- a/scripts/textual_inversion_fe.py
+++ b/scripts/textual_inversion_fe.py
@@ -13,6 +13,10 @@
 from typing import List
 import argparse
 
+TRAINING_DATA = 'training-data'
+TRAINING_DIR = 'text-inversion-training'
+CONF_FILE = 'preferences.conf'
+
 class textualInversionForm(npyscreen.FormMultiPageAction):
     resolutions = [512, 768, 1024]
     lr_schedulers = [
@@ -20,6 +24,11 @@ class textualInversionForm(npyscreen.FormMultiPageAction):
                 "polynomial","constant", "constant_with_warmup"
     ]
     precisions = ['no','fp16','bf16']
+    learnable_properties = ['object','style']
+
+    def __init__(self, parentApp, name, saved_args=None):
+        self.saved_args = saved_args or {}
+        super().__init__(parentApp, name)
 
     def afterEditing(self):
         self.parentApp.setNextForm(None)
@@ -28,6 +37,12 @@ def create(self):
         self.model_names, default = self.get_model_names()
         default_initializer_token = '★'
         default_placeholder_token = ''
+        saved_args = self.saved_args
+
+        try:
+            default = self.model_names.index(saved_args['model'])
+        except:
+            pass
 
         self.model = self.add_widget_intelligent(
             npyscreen.TitleSelectOne,
@@ -38,9 +53,10 @@ def create(self):
         )
         self.placeholder_token = self.add_widget_intelligent(
             npyscreen.TitleText,
-            name='Trigger word:',
-            value='',
+            name='Trigger Term:',
+            value='', # saved_args.get('placeholder_token',''), # to restore previous term
         )
+        self.placeholder_token.when_value_edited = self.initializer_changed
         self.nextrely -= 1
         self.nextrelx += 30
         self.prompt_token = self.add_widget_intelligent(
@@ -51,93 +67,85 @@ def create(self):
         self.nextrelx -= 30
         self.initializer_token = self.add_widget_intelligent(
             npyscreen.TitleText,
-            name="Initializer token:",
-            value=default_initializer_token,
+            name='Initializer:',
+            value=saved_args.get('initializer_token',default_initializer_token),
+        )
+        self.resume_from_checkpoint = self.add_widget_intelligent(
+            npyscreen.Checkbox,
+            name="Resume from last saved checkpoint",
+            value=False,
         )
         self.learnable_property = self.add_widget_intelligent(
             npyscreen.TitleSelectOne,
             name="Learnable property:",
-            values=['object','style'],
-            value=0,
-            max_height=3,
+            values=self.learnable_properties,
+            value=self.learnable_properties.index(saved_args.get('learnable_property','object')),
+            max_height=4,
         )
         self.train_data_dir = self.add_widget_intelligent(
             npyscreen.TitleFilenameCombo,
             name='Data Training Directory:',
             select_dir=True,
             must_exist=True,
-            value=Path(Globals.root) / 'training-data' / default_placeholder_token
+            value=saved_args.get('train_data_dir',Path(Globals.root) / TRAINING_DATA / default_placeholder_token)
         )
         self.output_dir = self.add_widget_intelligent(
             npyscreen.TitleFilenameCombo,
             name='Output Destination Directory:',
             select_dir=True,
             must_exist=False,
-            value=Path(Globals.root) / 'text-inversion-training' / default_placeholder_token
-        )
-        self.resume_from_checkpoint = self.add_widget_intelligent(
-            npyscreen.Checkbox,
-            name="Resume from last saved checkpoint",
-            value=False,
+            value=saved_args.get('output_dir',Path(Globals.root) / TRAINING_DIR / default_placeholder_token)
         )
         self.resolution = self.add_widget_intelligent(
             npyscreen.TitleSelectOne,
             name='Image resolution (pixels):',
             values = self.resolutions,
-            value=0,
+            value=self.resolutions.index(saved_args.get('resolution',512)),
             scroll_exit = True,
             max_height=4,
         )
         self.center_crop = self.add_widget_intelligent(
             npyscreen.Checkbox,
             name="Center crop images before resizing to resolution",
-            value=False,
-        )
-        self.train_batch_size = self.add_widget_intelligent(
-            npyscreen.TitleSlider,
-            name='Batch Size:',
-            out_of=50,
-            step=1,
-            lowest=1,
-            value=8
+            value=saved_args.get('center_crop',False)
         )
         self.mixed_precision = self.add_widget_intelligent(
             npyscreen.TitleSelectOne,
             name='Mixed Precision:',
             values=self.precisions,
-            value=1,
+            value=self.precisions.index(saved_args.get('mixed_precision','fp16')),
             max_height=4,
         )
-        self.gradient_accumulation_steps = self.add_widget_intelligent(
-            npyscreen.TitleSlider,
-            name='Gradient Accumulation Steps:',
-            out_of=10,
-            step=1,
-            lowest=1,
-            value=4
-        )
         self.max_train_steps = self.add_widget_intelligent(
             npyscreen.TitleSlider,
             name='Max Training Steps:',
             out_of=10000,
             step=500,
             lowest=1,
-            value=3000
+            value=saved_args.get('max_train_steps',3000)
+        )
+        self.train_batch_size = self.add_widget_intelligent(
+            npyscreen.TitleSlider,
+            name='Batch Size (reduce if you run out of memory):',
+            out_of=50,
+            step=1,
+            lowest=1,
+            value=saved_args.get('train_batch_size',8),
         )
         self.learning_rate = self.add_widget_intelligent(
             npyscreen.TitleText,
             name="Learning Rate:",
-            value='5.0e-04',
+            value=str(saved_args.get('learning_rate','5.0e-04'),)
         )
         self.scale_lr = self.add_widget_intelligent(
             npyscreen.Checkbox,
             name="Scale learning rate by number GPUs, steps and batch size",
-            value=True
+            value=saved_args.get('scale_lr',True),
         )
         self.enable_xformers_memory_efficient_attention = self.add_widget_intelligent(
             npyscreen.Checkbox,
             name="Use xformers acceleration",
-            value=False,
+            value=saved_args.get('enable_xformers_memory_efficient_attention',False),
         )
         self.lr_scheduler = self.add_widget_intelligent(
             npyscreen.TitleSelectOne,
@@ -145,22 +153,31 @@ def create(self):
             values = self.lr_schedulers,
             max_height=7,
             scroll_exit = True,
-            value=4)
+            value=self.lr_schedulers.index(saved_args.get('lr_scheduler','constant')),
+        )
+        self.gradient_accumulation_steps = self.add_widget_intelligent(
+            npyscreen.TitleSlider,
+            name='Gradient Accumulation Steps:',
+            out_of=10,
+            step=1,
+            lowest=1,
+            value=saved_args.get('gradient_accumulation_steps',4)
+        )
         self.lr_warmup_steps = self.add_widget_intelligent(
             npyscreen.TitleSlider,
             name='Warmup Steps:',
             out_of=100,
             step=1,
             lowest=0,
-            value=0
+            value=saved_args.get('lr_warmup_steps',0),
         )
-        self.placeholder_token.when_value_edited = self.initializer_changed
 
     def initializer_changed(self):
         placeholder = self.placeholder_token.value
         self.prompt_token.value = f'(Trigger by using <{placeholder}> in your prompts)'
-        self.train_data_dir.value = Path(Globals.root) / 'training-data' / placeholder
-        self.output_dir.value = Path(Globals.root) / 'text-inversion-training' / placeholder
+        self.train_data_dir.value = Path(Globals.root) / TRAINING_DATA / placeholder
+        self.output_dir.value = Path(Globals.root) / TRAINING_DIR / placeholder
+        self.resume_from_checkpoint.value = Path(self.output_dir.value).exists()
         
     def on_ok(self):
         if self.validate_field_values():
@@ -173,10 +190,15 @@ def on_ok(self):
         else:
             self.editing = True
 
+    def ok_cancel(self):
+        sys.exit(0)
+
     def validate_field_values(self)->bool:
         bad_fields = []
         if self.model.value is None:
             bad_fields.append('Model Name must correspond to a known model in models.yaml')
+        if not re.match('^[a-zA-Z0-9.-]+$',self.placeholder_token.value):
+            bad_fields.append('Trigger term must only contain alphanumeric characters, the dot and hyphen')
         if self.train_data_dir.value is None:
             bad_fields.append('Data Training Directory cannot be empty')
         if self.output_dir.value is None:
@@ -205,10 +227,12 @@ def marshall_arguments(self)->dict:
             resolution = self.resolutions[self.resolution.value[0]],
             lr_scheduler = self.lr_schedulers[self.lr_scheduler.value[0]],
             mixed_precision = self.precisions[self.mixed_precision.value[0]],
+            learnable_property = self.learnable_properties[self.learnable_property.value[0]],
         )
 
-        # all the strings
-        for attr in ('initializer_token','placeholder_token','train_data_dir','output_dir','scale_lr'):
+        # all the strings and booleans
+        for attr in ('initializer_token','placeholder_token','train_data_dir',
+                     'output_dir','scale_lr','center_crop','enable_xformers_memory_efficient_attention'):
             args[attr] = getattr(self,attr).value
             
         # all the integers
@@ -221,22 +245,27 @@ def marshall_arguments(self)->dict:
             learning_rate = float(self.learning_rate.value)
         )
 
-        # the booleans
+        # a special case
         if self.resume_from_checkpoint.value and Path(self.output_dir.value).exists():
             args['resume_from_checkpoint'] = 'latest'
 
         return args
 
 class MyApplication(npyscreen.NPSAppManaged):
-    def __init__(self):
+    def __init__(self, saved_args=None):
         super().__init__()
         self.ti_arguments=None
+        self.saved_args=saved_args
 
     def onStart(self):
         npyscreen.setTheme(npyscreen.Themes.DefaultTheme)
-        self.main = self.addForm('MAIN', textualInversionForm, name='Textual Inversion Settings')
+        self.main = self.addForm('MAIN', textualInversionForm, name='Textual Inversion Settings', saved_args=self.saved_args)
 
 def copy_to_embeddings_folder(args:dict):
+    '''
+    Copy learned_embeds.bin into the embeddings folder, and offer to
+    delete the full model and checkpoints.
+    '''
     source = Path(args['output_dir'],'learned_embeds.bin')
     destination = Path(Globals.root,'embeddings',args['placeholder_token'])
     print(f'>> Training completed. Copying learned_embeds.bin into {str(destination)}')
@@ -246,6 +275,27 @@ def copy_to_embeddings_folder(args:dict):
     else:
         print(f'>> Keeping {args["output_dir"]}')
 
+def save_args(args:dict):
+    '''
+    Save the current argument values to an omegaconf file
+    '''
+    conf_file = Path(Globals.root) / TRAINING_DIR / CONF_FILE
+    conf = OmegaConf.create(args)
+    OmegaConf.save(config=conf, f=conf_file)
+
+def previous_args()->dict:
+    '''
+    Get the previous arguments used.
+    '''
+    conf_file = Path(Globals.root) / TRAINING_DIR / CONF_FILE
+    try:
+        conf = OmegaConf.load(conf_file)
+        conf['placeholder_token'] = conf['placeholder_token'].strip('<>')
+    except:
+        conf= None
+
+    return conf
+
 if __name__ == '__main__':
     parser = argparse.ArgumentParser(description='InvokeAI textual inversion training')
     parser.add_argument(
@@ -256,8 +306,9 @@ def copy_to_embeddings_folder(args:dict):
     )
     args = parser.parse_args()
     global_set_root(args.root_dir)
-    
-    myapplication = MyApplication()
+
+    saved_args = previous_args()
+    myapplication = MyApplication(saved_args=saved_args)
     myapplication.run()
     
     from ldm.invoke.textual_inversion_training import do_textual_inversion_training
@@ -269,6 +320,7 @@ def copy_to_embeddings_folder(args:dict):
             args['placeholder_token'] = f"<{args['placeholder_token']}>"
 
         args['only_save_embeds'] = True
+        save_args(args)
 
         try:
             do_textual_inversion_training(**args)

From 1e2f8710b7ee508d3120450acb38a8a68e87801f Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Fri, 13 Jan 2023 13:04:50 +0000
Subject: [PATCH 189/199] copy learned_embeddings.bin into right location

---
 scripts/textual_inversion_fe.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/scripts/textual_inversion_fe.py b/scripts/textual_inversion_fe.py
index 6be995a3045..941afcf6138 100755
--- a/scripts/textual_inversion_fe.py
+++ b/scripts/textual_inversion_fe.py
@@ -3,7 +3,6 @@
 import npyscreen
 import os
 import sys
-import curses
 import re
 import shutil
 import traceback
@@ -267,7 +266,9 @@ def copy_to_embeddings_folder(args:dict):
     delete the full model and checkpoints.
     '''
     source = Path(args['output_dir'],'learned_embeds.bin')
-    destination = Path(Globals.root,'embeddings',args['placeholder_token'])
+    dest_dir_name = args['placeholder_token'].strip('<>')
+    destination = Path(Globals.root,'embeddings',dest_dir_name)
+    os.makedirs(destination,exist_ok=True)
     print(f'>> Training completed. Copying learned_embeds.bin into {str(destination)}')
     shutil.copy(source,destination)
     if (input('Delete training logs and intermediate checkpoints? [y] ') or 'y').startswith(('y','Y')):
@@ -326,7 +327,7 @@ def previous_args()->dict:
             do_textual_inversion_training(**args)
             copy_to_embeddings_folder(args)
         except Exception as e:
-            print(f'** An exception occurred during training. The exception was:')
+            print('** An exception occurred during training. The exception was:')
             print(str(e))
             print('** DETAILS:')
             print(traceback.format_exc())

From e717c02c5a6d0b447d0af790f5c5975552c69fa8 Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Fri, 13 Jan 2023 21:46:26 +0000
Subject: [PATCH 190/199] add front end for diffusers model merging

- Front end doesn't do anything yet!!!!
- Made change to model name parsing in CLI to support ability to have merged models
  with the "+" character in their names.
---
 ldm/invoke/CLI.py   |   6 +-
 scripts/merge_fe.py | 156 ++++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 159 insertions(+), 3 deletions(-)
 create mode 100644 scripts/merge_fe.py

diff --git a/ldm/invoke/CLI.py b/ldm/invoke/CLI.py
index a80f1560ab0..cfe9a64ed59 100644
--- a/ldm/invoke/CLI.py
+++ b/ldm/invoke/CLI.py
@@ -574,7 +574,7 @@ def import_model(model_path:str, gen, opt, completer):
         model_name = import_ckpt_model(model_path, gen, opt, completer)
     elif os.path.exists(model_path) and model_path.endswith('.ckpt') and os.path.isfile(model_path):
         model_name = import_ckpt_model(model_path, gen, opt, completer)        
-    elif re.match('^[\w.-]+/[\w.-]+$',model_path):
+    elif re.match('^[\w.+-]+/[\w.+-]+$',model_path):
         model_name = import_diffuser_model(model_path, gen, opt, completer)
     elif os.path.isdir(model_path):
         model_name = import_diffuser_model(model_path, gen, opt, completer)
@@ -772,8 +772,8 @@ def _get_model_name(existing_names,completer,default_name:str='')->str:
         model_name = input(f'Short name for this model [{default_name}]: ').strip()
         if len(model_name)==0:
             model_name = default_name
-        if not re.match('^[\w._-]+$',model_name):
-            print('** model name must contain only words, digits and the characters [._-] **')
+        if not re.match('^[\w._+-]+$',model_name):
+            print('** model name must contain only words, digits and the characters "._+-" **')
         elif model_name != default_name and model_name in existing_names:
             print(f'** the name {model_name} is already in use. Pick another.')
         else:
diff --git a/scripts/merge_fe.py b/scripts/merge_fe.py
new file mode 100644
index 00000000000..df01235599a
--- /dev/null
+++ b/scripts/merge_fe.py
@@ -0,0 +1,156 @@
+#!/usr/bin/env python
+
+import npyscreen
+import os
+import sys
+import re
+import shutil
+import traceback
+import argparse
+from ldm.invoke.globals import Globals, global_set_root
+from omegaconf import OmegaConf
+from pathlib import Path
+from typing import List
+
+class FloatSlider(npyscreen.Slider):
+    # this is supposed to adjust display precision, but doesn't
+    def translate_value(self):
+        stri = "%3.2f / %3.2f" %(self.value, self.out_of)
+        l = (len(str(self.out_of)))*2+4
+        stri = stri.rjust(l)
+        return stri
+
+class FloatTitleSlider(npyscreen.TitleText):
+    _entry_type = FloatSlider
+
+class mergeModelsForm(npyscreen.FormMultiPageAction):
+
+    interpolations = ['weighted_sum',
+                      'sigmoid',
+                      'inv_sigmoid',
+                      'add_difference']
+
+    def afterEditing(self):
+        self.parentApp.setNextForm(None)
+
+    def create(self):
+        self.model_names = self.get_model_names()
+        
+        self.add_widget_intelligent(
+            npyscreen.FixedText,
+            name="Select up to three models to merge",
+            value=''
+        )
+        self.model1 = self.add_widget_intelligent(
+            npyscreen.TitleSelectOne,
+            name='First Model:',
+            values=self.model_names,
+            value=0,
+            max_height=len(self.model_names)+1
+        )
+        self.model2 = self.add_widget_intelligent(
+            npyscreen.TitleSelectOne,
+            name='Second Model:',
+            values=self.model_names,
+            value=1,
+            max_height=len(self.model_names)+1
+        )
+        models_plus_none = self.model_names.copy()
+        models_plus_none.insert(0,'None')
+        self.model3 = self.add_widget_intelligent(
+            npyscreen.TitleSelectOne,
+            name='Third Model:',
+            values=models_plus_none,
+            value=0,
+            max_height=len(self.model_names)+1,
+        )
+        
+        for m in [self.model1,self.model2,self.model3]:
+            m.when_value_edited = self.models_changed
+            
+        self.merge_method = self.add_widget_intelligent(
+            npyscreen.TitleSelectOne,
+            name='Merge Method:',
+            values=self.interpolations,
+            value=0,
+            max_height=len(self.interpolations),
+        )
+        self.alpha = self.add_widget_intelligent(
+            FloatTitleSlider,
+            name='Weight (alpha) to assign to second and third models:',
+            out_of=1,
+            step=0.05,
+            lowest=0,
+            value=0.5,
+        )
+        self.merged_model_name = self.add_widget_intelligent(
+            npyscreen.TitleText,
+            name='Name for merged model',
+            value='',
+        )
+
+    def models_changed(self):
+        models = self.model1.values
+        selected_model1 = self.model1.value[0]
+        selected_model2 = self.model2.value[0]
+        selected_model3 = self.model3.value[0]
+        merged_model_name = f'{models[selected_model1]}+{models[selected_model2]}'
+        self.merged_model_name.value = merged_model_name
+        
+        if selected_model3 > 0:
+            self.merge_method.values=['add_difference'],
+            self.merged_model_name.value += f'+{models[selected_model3]}'
+        else:
+            self.merge_method.values=self.interpolations
+        self.merge_method.value=0
+        
+    def on_ok(self):
+        if self.validate_field_values():
+            self.parentApp.setNextForm(None)
+            self.editing = False
+        else:
+            self.editing = True
+
+    def ok_cancel(self):
+        sys.exit(0)
+
+    def validate_field_values(self)->bool:
+        bad_fields = []
+        selected_models = set((self.model1.value[0],self.model2.value[0],self.model3.value[0]))
+        if len(selected_models) < 3:
+            bad_fields.append('Please select two or three DIFFERENT models to compare')
+        if len(bad_fields) > 0:
+            message = 'The following problems were detected and must be corrected:'
+            for problem in bad_fields:
+                message += f'\n* {problem}'
+            npyscreen.notify_confirm(message)
+            return False
+        else:
+            return True
+
+    def get_model_names(self)->List[str]:
+        conf = OmegaConf.load(os.path.join(Globals.root,'configs/models.yaml'))
+        model_names = [name for name in conf.keys() if conf[name].get('format',None)=='diffusers']
+        return sorted(model_names)
+
+class MyApplication(npyscreen.NPSAppManaged):
+    def __init__(self):
+        super().__init__()
+
+    def onStart(self):
+        npyscreen.setTheme(npyscreen.Themes.DefaultTheme)
+        self.main = self.addForm('MAIN', mergeModelsForm, name='Merge Models Settings')
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='InvokeAI textual inversion training')
+    parser.add_argument(
+        '--root_dir','--root-dir',
+        type=Path,
+        default=Globals.root,
+        help='Path to the invokeai runtime directory',
+    )
+    args = parser.parse_args()
+    global_set_root(args.root_dir)
+
+    myapplication = MyApplication()
+    myapplication.run()

From 2d3e5cf5392716be88d24d6c47e997a9182c34b1 Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Sat, 14 Jan 2023 08:46:37 -0500
Subject: [PATCH 191/199] improve inpainting experience

- recommend ckpt version of inpainting-1.5 to user
- fix get_noise() bug in ckpt version of omnibus.py
---
 configs/INITIAL_MODELS.yaml          | 7 ++++++-
 ldm/invoke/ckpt_generator/omnibus.py | 2 +-
 2 files changed, 7 insertions(+), 2 deletions(-)

diff --git a/configs/INITIAL_MODELS.yaml b/configs/INITIAL_MODELS.yaml
index f994db5c7b4..5df64d742d8 100644
--- a/configs/INITIAL_MODELS.yaml
+++ b/configs/INITIAL_MODELS.yaml
@@ -15,7 +15,12 @@ stable-diffusion-1.5:
 inpainting-1.5:
    description: RunwayML SD 1.5 model optimized for inpainting (4.27 GB)
    repo_id: runwayml/stable-diffusion-inpainting
-   format: diffusers
+   config: v1-inpainting-inference.yaml
+   file: sd-v1-5-inpainting.ckpt
+   format: ckpt
+   vae:
+      repo_id: stabilityai/sd-vae-ft-mse-original
+      file: vae-ft-mse-840000-ema-pruned.ckpt
    recommended: True
    width: 512
    height: 512
diff --git a/ldm/invoke/ckpt_generator/omnibus.py b/ldm/invoke/ckpt_generator/omnibus.py
index b728e6b4488..a479ac85ec9 100644
--- a/ldm/invoke/ckpt_generator/omnibus.py
+++ b/ldm/invoke/ckpt_generator/omnibus.py
@@ -159,7 +159,7 @@ def get_noise(self, width:int, height:int):
         if self.init_latent is not None:
             height = self.init_latent.shape[2]
             width = self.init_latent.shape[3]
-        return Txt2Img.get_noise(self,width,height)
+        return CkptTxt2Img.get_noise(self,width,height)
 
 
     def sample_to_image(self, samples)->Image.Image:

From b6e83d64718b00861c10d087697c0ee57dbf66d3 Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Sat, 14 Jan 2023 19:22:51 -0500
Subject: [PATCH 192/199] update environment*yml

---
 environments-and-requirements/environment-lin-aarch64.yml | 3 +++
 environments-and-requirements/environment-lin-amd.yml     | 4 +++-
 environments-and-requirements/environment-lin-cuda.yml    | 6 ++++--
 environments-and-requirements/environment-mac.yml         | 4 +++-
 environments-and-requirements/environment-win-cuda.yml    | 5 ++++-
 ldm/invoke/model_manager.py                               | 2 +-
 6 files changed, 18 insertions(+), 6 deletions(-)

diff --git a/environments-and-requirements/environment-lin-aarch64.yml b/environments-and-requirements/environment-lin-aarch64.yml
index b2944066576..9dc49c1255a 100644
--- a/environments-and-requirements/environment-lin-aarch64.yml
+++ b/environments-and-requirements/environment-lin-aarch64.yml
@@ -31,11 +31,14 @@ dependencies:
   - transformers~=4.25
   - pip:
       - accelerate
+      - diffusers[torch]~=0.11
       - getpass_asterisk
+      - huggingface-hub>=0.11.1
       - omegaconf==2.1.1
       - picklescan
       - pyreadline3
       - realesrgan
+      - requests==2.25.1
       - safetensors
       - taming-transformers-rom1504
       - test-tube>=0.7.5
diff --git a/environments-and-requirements/environment-lin-amd.yml b/environments-and-requirements/environment-lin-amd.yml
index 3c061a9a507..cbddbb5fc82 100644
--- a/environments-and-requirements/environment-lin-amd.yml
+++ b/environments-and-requirements/environment-lin-amd.yml
@@ -11,13 +11,14 @@ dependencies:
     - --extra-index-url  https://download.pytorch.org/whl/rocm5.2/
     - accelerate
     - albumentations==0.4.3
-    - diffusers~=0.11
+    - diffusers[torch]~=0.11
     - einops==0.3.0
     - eventlet
     - flask==2.1.3
     - flask_cors==3.0.10
     - flask_socketio==5.3.0
     - getpass_asterisk
+    - huggingface-hub>=0.11.1
     - imageio-ffmpeg==0.4.2
     - imageio==2.9.0
     - kornia==0.6.0
@@ -29,6 +30,7 @@ dependencies:
     - pyreadline3
     - pytorch-lightning==1.7.7
     - realesrgan
+    - requests==2.25.1
     - safetensors
     - send2trash==1.8.0
     - streamlit==1.12.0
diff --git a/environments-and-requirements/environment-lin-cuda.yml b/environments-and-requirements/environment-lin-cuda.yml
index 82b3a8a3703..20cc636624b 100644
--- a/environments-and-requirements/environment-lin-cuda.yml
+++ b/environments-and-requirements/environment-lin-cuda.yml
@@ -14,13 +14,14 @@ dependencies:
   - pip:
     - accelerate~=0.13
     - albumentations==0.4.3
-    - diffusers~=0.11
+    - diffusers[torch]~=0.11
     - einops==0.3.0
     - eventlet
     - flask==2.1.3
     - flask_cors==3.0.10
     - flask_socketio==5.3.0
     - getpass_asterisk
+    - huggingface-hub>=0.11.1
     - imageio-ffmpeg==0.4.2
     - imageio==2.9.0
     - kornia==0.6.0
@@ -32,7 +33,8 @@ dependencies:
     - pyreadline3
     - pytorch-lightning==1.7.7
     - realesrgan
-    - safetensors
+    - requests==2.25.1
+    - safetensors~=0.2
     - send2trash==1.8.0
     - streamlit==1.12.0
     - taming-transformers-rom1504
diff --git a/environments-and-requirements/environment-mac.yml b/environments-and-requirements/environment-mac.yml
index e46d214db5f..7ced428ebfb 100644
--- a/environments-and-requirements/environment-mac.yml
+++ b/environments-and-requirements/environment-mac.yml
@@ -23,7 +23,6 @@ dependencies:
   - accelerate
   - albumentations=1.2
   - coloredlogs=15.0
-  - diffusers~=0.11
   - einops=0.3
   - eventlet
   - grpcio=1.46
@@ -52,9 +51,12 @@ dependencies:
   - tensorboard=2.10
   - transformers~=4.25
   - pip:
+      - diffusers[torch]~=0.11
       - safetensors~=0.2
       - getpass_asterisk
+      - huggingface-hub
       - picklescan
+      - requests==2.25.1
       - taming-transformers-rom1504
       - test-tube==0.7.5
       - git+https://github.com/openai/CLIP.git@main#egg=clip
diff --git a/environments-and-requirements/environment-win-cuda.yml b/environments-and-requirements/environment-win-cuda.yml
index 9f91d66cb9b..c7ad5996419 100644
--- a/environments-and-requirements/environment-win-cuda.yml
+++ b/environments-and-requirements/environment-win-cuda.yml
@@ -14,13 +14,14 @@ dependencies:
   - pip:
     - accelerate
     - albumentations==0.4.3
-    - diffusers~=0.11
+    - diffusers[torch]~=0.11
     - einops==0.3.0
     - eventlet
     - flask==2.1.3
     - flask_cors==3.0.10
     - flask_socketio==5.3.0
     - getpass_asterisk
+    - huggingface-hub>=0.11.1
     - imageio-ffmpeg==0.4.2
     - imageio==2.9.0
     - kornia==0.6.0
@@ -32,6 +33,7 @@ dependencies:
     - pyreadline3
     - pytorch-lightning==1.7.7
     - realesrgan
+    - requests==2.25.1
     - safetensors
     - send2trash==1.8.0
     - streamlit==1.12.0
@@ -40,6 +42,7 @@ dependencies:
     - torch-fidelity==0.3.0
     - torchmetrics==0.7.0
     - transformers~=4.25
+    - windows-curses
     - git+https://github.com/openai/CLIP.git@main#egg=clip
     - git+https://github.com/Birch-san/k-diffusion.git@mps#egg=k_diffusion
     - git+https://github.com/invoke-ai/clipseg.git@relaxed-python-requirement#egg=clipseg
diff --git a/ldm/invoke/model_manager.py b/ldm/invoke/model_manager.py
index b1e13ab62bf..880d75476fc 100644
--- a/ldm/invoke/model_manager.py
+++ b/ldm/invoke/model_manager.py
@@ -160,7 +160,7 @@ def list_models(self) -> dict:
         object derived from models.yaml
         '''
         models = {}
-        for name in self.config:
+        for name in sorted(self.config):
             stanza = self.config[name]
 
             # don't include VAEs in listing (legacy style)

From 1a045c9dce07d6dc455e47ab16cc9f88924bf5e8 Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Sat, 14 Jan 2023 19:39:00 -0500
Subject: [PATCH 193/199] tweak instructions to install HuggingFace token

---
 scripts/configure_invokeai.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/scripts/configure_invokeai.py b/scripts/configure_invokeai.py
index 64ae1e356df..22ade19cdb5 100755
--- a/scripts/configure_invokeai.py
+++ b/scripts/configure_invokeai.py
@@ -283,7 +283,8 @@ def authenticate(yes_to_all=False):
 
 Visit https://huggingface.co/settings/tokens to generate a token. (Sign up for an account if needed).
 
-Paste the token below using Ctrl-Shift-V (macOS/Linux) or right-click (Windows), and/or 'Enter' to continue.
+Paste the token below using Ctrl-V on macOS/Linux, or Ctrl-Shift-V or right-click on Windows. 
+Alternatively press 'Enter' to skip this step and continue.
 You may re-run the configuration script again in the future if you do not wish to set the token right now.
         ''')
         again = True

From 75426d050421e278f329823b8e52e0f159fb5895 Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Sat, 14 Jan 2023 20:50:45 -0500
Subject: [PATCH 194/199] bump version number

---
 ldm/invoke/__init__.py | 2 +-
 setup.py               | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/ldm/invoke/__init__.py b/ldm/invoke/__init__.py
index 412e035dbce..b792ceca76a 100644
--- a/ldm/invoke/__init__.py
+++ b/ldm/invoke/__init__.py
@@ -1,3 +1,3 @@
 __app_id__= 'invoke-ai/InvokeAI'
 __app_name__= 'InvokeAI'
-__version__='2.2.5'
+__version__='2.3.0+a0'
diff --git a/setup.py b/setup.py
index 06e53057e3b..6117fea2b1c 100644
--- a/setup.py
+++ b/setup.py
@@ -9,7 +9,7 @@ def list_files(directory):
         listing.append(pair)
     return listing
 
-VERSION = '2.2.5'
+VERSION = '2.3.0+a0'
 DESCRIPTION = ('An implementation of Stable Diffusion which provides various new features'
                ' and options to aid the image generation process')
 LONG_DESCRIPTION = ('This version of Stable Diffusion features a slick WebGUI, an'

From fb28078ee956d86403898d87a72a5cf6c6a16e17 Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Sat, 14 Jan 2023 20:59:03 -0500
Subject: [PATCH 195/199] enhance update scripts

- update scripts will now fetch new INITIAL_MODELS.yaml so that
  configure_invokeai.py will know about the diffusers versions.
---
 installer/templates/update.bat.in | 3 +++
 installer/templates/update.sh.in  | 2 ++
 2 files changed, 5 insertions(+)

diff --git a/installer/templates/update.bat.in b/installer/templates/update.bat.in
index 307a26972a2..517b12686ea 100644
--- a/installer/templates/update.bat.in
+++ b/installer/templates/update.bat.in
@@ -23,6 +23,7 @@ if "%arg%" neq "" (
 
 set INVOKE_AI_SRC="https://github.com/invoke-ai/InvokeAI/archive/!INVOKE_AI_VERSION!.zip"
 set INVOKE_AI_DEP=https://raw.githubusercontent.com/invoke-ai/InvokeAI/!INVOKE_AI_VERSION!/environments-and-requirements/requirements-base.txt
+set INVOKE_AI_MODELS=https://raw.githubusercontent.com/invoke-ai/InvokeAI/$INVOKE_AI_VERSION/configs/INITIAL_MODELS.yaml
  
 call curl -I "%INVOKE_AI_DEP%" -fs >.tmp.out
 if %errorlevel% neq 0 (
@@ -38,6 +39,8 @@ echo If you do not want to do this, press control-C now!
 pause
 
 call curl -L "%INVOKE_AI_DEP%" > environments-and-requirements/requirements-base.txt
+call curl -L "%INVOKE_AI_MODELS%" > configs/INITIAL_MODELS.yaml
+
 
 call .venv\Scripts\activate.bat
 call .venv\Scripts\python -mpip install -r requirements.txt
diff --git a/installer/templates/update.sh.in b/installer/templates/update.sh.in
index 2f45926f2d4..876ffc6bb31 100644
--- a/installer/templates/update.sh.in
+++ b/installer/templates/update.sh.in
@@ -18,6 +18,7 @@ INVOKE_AI_VERSION=${1:-latest}
     
 INVOKE_AI_SRC="https://github.com/invoke-ai/InvokeAI/archive/$INVOKE_AI_VERSION.zip"
 INVOKE_AI_DEP=https://raw.githubusercontent.com/invoke-ai/InvokeAI/$INVOKE_AI_VERSION/environments-and-requirements/requirements-base.txt
+INVOKE_AI_MODELS=https://raw.githubusercontent.com/invoke-ai/InvokeAI/$INVOKE_AI_VERSION/configs/INITIAL_MODELS.yaml
 
 # ensure we're in the correct folder in case user's CWD is somewhere else
 scriptdir=$(dirname "$0")
@@ -44,6 +45,7 @@ echo If you do not want to do this, press control-C now!
 read -p "Press any key to continue, or CTRL-C to exit..."
 
 curl -L "$INVOKE_AI_DEP" > environments-and-requirements/requirements-base.txt
+curl -L "$INVOKE_AI_MODELS" > configs/INITIAL_MODELS.yaml
 
 . .venv/bin/activate
 

From 2a3fcfbcb55ba9eccb6ec19269a3fc1a16729254 Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Sat, 14 Jan 2023 21:03:26 -0500
Subject: [PATCH 196/199] enhance invoke.sh/invoke.bat launchers

- added configure_invokeai.py to menu
- menu defaults to browser-based invoke
---
 installer/templates/invoke.bat.in | 10 +++++++---
 installer/templates/invoke.sh.in  |  8 +++++---
 2 files changed, 12 insertions(+), 6 deletions(-)

diff --git a/installer/templates/invoke.bat.in b/installer/templates/invoke.bat.in
index 3a67cb2f577..114b8c5070c 100644
--- a/installer/templates/invoke.bat.in
+++ b/installer/templates/invoke.bat.in
@@ -9,10 +9,11 @@ set INVOKEAI_ROOT=.
 echo Do you want to generate images using the
 echo 1. command-line
 echo 2. browser-based UI
-echo 3. textual inversion training
+echo 3. run textual inversion training
 echo 4. open the developer console
-set /P restore="Please enter 1, 2, 3 or 4: [1] "
-if not defined restore set restore=1
+echo 5. re-run the configure script to download new models
+set /P restore="Please enter 1, 2, 3, 4 or 5: [5] "
+if not defined restore set restore=2
 IF /I "%restore%" == "1" (
     echo Starting the InvokeAI command-line..
     python .venv\Scripts\invoke.py %*
@@ -34,6 +35,9 @@ IF /I "%restore%" == "1" (
     echo *************************
     echo *** Type `exit` to quit this shell and deactivate the Python virtual environment ***
     call cmd /k
+) ELSE IF /I "%restore%" == "5" (
+    echo Running configure_invokeai.py...
+    python .venv\Scripts\configure_invokeai.py --web %*
 ) ELSE (
     echo Invalid selection
     pause
diff --git a/installer/templates/invoke.sh.in b/installer/templates/invoke.sh.in
index 143ce49beaf..44ee8c5b909 100644
--- a/installer/templates/invoke.sh.in
+++ b/installer/templates/invoke.sh.in
@@ -19,15 +19,17 @@ if [ "$0" != "bash" ]; then
     echo "Do you want to generate images using the"
     echo "1. command-line"
     echo "2. browser-based UI"
-    echo "3. textual inversion training"
+    echo "3. run textual inversion training"
     echo "4. open the developer console"
-    read -p "Please enter 1, 2, 3 or 4: [1] " yn
-    choice=${yn:='1'}
+    echo "5. re-run the configure script to download new models"
+    read -p "Please enter 1, 2, 3, 4 or 5: [1] " yn
+    choice=${yn:='2'}
     case $choice in
         1 ) printf "\nStarting the InvokeAI command-line..\n"; .venv/bin/python .venv/bin/invoke.py  $*;;
         2 ) printf "\nStarting the InvokeAI browser-based UI..\n"; .venv/bin/python .venv/bin/invoke.py --web $*;;
         3 ) printf "\nStarting Textual Inversion:\n"; .venv/bin/python .venv/bin/textual_inversion_fe.py $*;;
 	4 ) printf "\nDeveloper Console:\n"; file_name=$(basename "${BASH_SOURCE[0]}"); bash --init-file "$file_name";;
+	5 ) printf "\nRunning configure_invokeai.py:\n"; .venv/bin/python .venv/bin/configure_invokeai.py $*;;
         * ) echo "Invalid selection"; exit;;
     esac
 else # in developer console

From 84274e25cc5bf0da9d058add5bd442f60d4aeb3d Mon Sep 17 00:00:00 2001
From: Matthias Wild <40327258+mauwii@users.noreply.github.com>
Date: Sun, 15 Jan 2023 04:51:40 +0100
Subject: [PATCH 197/199] remove conda workflow (#2321)

---
 .github/workflows/test-invoke-conda.yml | 156 ------------------------
 1 file changed, 156 deletions(-)
 delete mode 100644 .github/workflows/test-invoke-conda.yml

diff --git a/.github/workflows/test-invoke-conda.yml b/.github/workflows/test-invoke-conda.yml
deleted file mode 100644
index 6372efd6f80..00000000000
--- a/.github/workflows/test-invoke-conda.yml
+++ /dev/null
@@ -1,156 +0,0 @@
-name: Test invoke.py
-on:
-  push:
-    branches:
-      - 'main'
-  pull_request:
-    types:
-      - 'ready_for_review'
-      - 'opened'
-      - 'synchronize'
-      - 'converted_to_draft'
-
-concurrency:
-   group: ${{ github.workflow }}-${{ github.head_ref || github.run_id }}
-   cancel-in-progress: true
-
-jobs:
-  # fail_if_pull_request_is_draft:
-  #   if: github.event.pull_request.draft == true
-  #   runs-on: ubuntu-22.04
-  #   steps:
-  #   - name: Fails in order to indicate that pull request needs to be marked as ready to review and unit tests workflow needs to pass.
-  #     run: exit 1
-
-  matrix:
-    if: github.event.pull_request.draft == false ||  github.head_ref == 'dev/diffusers'
-    strategy:
-      matrix:
-        stable-diffusion-model:
-          - diffusers-1.5
-        environment-yaml:
-          - environment-lin-amd.yml
-          - environment-lin-cuda.yml
-          - environment-mac.yml
-          - environment-win-cuda.yml
-        include:
-          - environment-yaml: environment-lin-amd.yml
-            os: ubuntu-22.04
-            github-env: $GITHUB_ENV
-            default-shell: bash -l {0}
-          - environment-yaml: environment-lin-cuda.yml
-            os: ubuntu-22.04
-            github-env: $GITHUB_ENV
-            default-shell: bash -l {0}
-          - environment-yaml: environment-mac.yml
-            os: macos-12
-            github-env: $GITHUB_ENV
-            default-shell: bash -l {0}
-          - environment-yaml: environment-win-cuda.yml
-            os: windows-2022
-            github-env: $env:GITHUB_ENV
-            default-shell: pwsh
-    name: ${{ matrix.environment-yaml }} on ${{ matrix.os }}
-    runs-on: ${{ matrix.os }}
-    env:
-      INVOKE_MODEL_RECONFIGURE: '--yes'
-      CONDA_ENV_NAME: invokeai
-      INVOKEAI_ROOT: '${{ github.workspace }}/invokeai'
-      PYTHONUNBUFFERED: 1
-      HAVE_SECRETS: ${{ secrets.HUGGINGFACE_TOKEN != '' }}
-    defaults:
-      run:
-        shell: ${{ matrix.default-shell }}
-    steps:
-      - name: Checkout sources
-        id: checkout-sources
-        uses: actions/checkout@v3
-
-      - name: create models.yaml from example
-        run: |
-          mkdir -p ${{ env.INVOKEAI_ROOT }}/configs
-          cp configs/models.yaml.example ${{ env.INVOKEAI_ROOT }}/configs/models.yaml
-
-      - name: create environment.yml
-        run: cp "environments-and-requirements/${{ matrix.environment-yaml }}" environment.yml
-
-      - name: Use Cached Stable Diffusion Model
-        id: cache-sd-model
-        uses: actions/cache@v3
-        env:
-          cache-name: huggingface-${{ matrix.stable-diffusion-model }}
-        with:
-          path: ~/.cache/huggingface
-          key: ${{ env.cache-name }}
-
-      - name: Use cached conda packages
-        id: use-cached-conda-packages
-        uses: actions/cache@v3
-        with:
-          path: ~/conda_pkgs_dir
-          key: conda-pkgs-${{ runner.os }}-${{ runner.arch }}-${{ hashFiles(matrix.environment-yaml) }}
-
-      - name: Activate Conda Env
-        id: activate-conda-env
-        uses: conda-incubator/setup-miniconda@v2
-        with:
-          activate-environment: ${{ env.CONDA_ENV_NAME }}
-          environment-file: environment.yml
-          miniconda-version: latest
-
-      - name: set test prompt to main branch validation
-        if: ${{ github.ref == 'refs/heads/main' }}
-        run: echo "TEST_PROMPTS=tests/preflight_prompts.txt" >> ${{ matrix.github-env }}
-
-      - name: set test prompt to development branch validation
-        if: ${{ github.ref == 'refs/heads/development' }}
-        run: echo "TEST_PROMPTS=tests/dev_prompts.txt" >> ${{ matrix.github-env }}
-
-      - name: set test prompt to Pull Request validation
-        if: ${{ github.ref != 'refs/heads/main' && github.ref != 'refs/heads/development' }}
-        run: echo "TEST_PROMPTS=tests/validate_pr_prompt.txt" >> ${{ matrix.github-env }}
-
-      - name: run configure_invokeai.py
-        id: run-preload-models
-        env:
-          HUGGING_FACE_HUB_TOKEN: ${{ secrets.HUGGINGFACE_TOKEN }}
-        run: >
-          python scripts/configure_invokeai.py
-          --skip-sd-weights --yes
-          --full-precision  # can't use fp16 weights without a GPU
-
-      - name: cat invokeai.init
-        id: cat-invokeai
-        run: cat ${{ env.INVOKEAI_ROOT }}/invokeai.init
-
-      - name: Run the tests
-        id: run-tests
-        if: matrix.os != 'windows-2022'
-        env:
-          # Set offline mode to make sure configure preloaded successfully.
-          HF_HUB_OFFLINE: 1
-          HF_DATASETS_OFFLINE: 1
-          TRANSFORMERS_OFFLINE: 1
-        run: >
-          python scripts/invoke.py
-          --no-patchmatch
-          --no-nsfw_checker
-          --model ${{ matrix.stable-diffusion-model }}
-          --from_file ${{ env.TEST_PROMPTS }}
-          --root="${{ env.INVOKEAI_ROOT }}"
-          --outdir="${{ env.INVOKEAI_ROOT }}/outputs"
-
-      - name: export conda env
-        id: export-conda-env
-        if: matrix.os != 'windows-2022'
-        run: |
-          mkdir -p outputs/img-samples
-          conda env export --name ${{ env.CONDA_ENV_NAME }} > ${{ env.INVOKEAI_ROOT }}/outputs/environment-${{ runner.os }}-${{ runner.arch }}.yml
-
-      - name: Archive results
-        if: matrix.os != 'windows-2022'
-        id: archive-results
-        uses: actions/upload-artifact@v3
-        with:
-          name: results_${{ matrix.requirements-file }}_${{ matrix.python-version }}
-          path: ${{ env.INVOKEAI_ROOT }}/outputs

From dfc24dc72c6e4217f2b951e1d4171dec06d0308c Mon Sep 17 00:00:00 2001
From: Damian Stewart <d@damianstewart.com>
Date: Sun, 15 Jan 2023 09:39:32 +0100
Subject: [PATCH 198/199] fix `token_ids has shape torch.Size([79]) - expected
 [77]`

---
 ldm/modules/prompt_to_embeddings_converter.py | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/ldm/modules/prompt_to_embeddings_converter.py b/ldm/modules/prompt_to_embeddings_converter.py
index 31017d8084c..b52577c83c9 100644
--- a/ldm/modules/prompt_to_embeddings_converter.py
+++ b/ldm/modules/prompt_to_embeddings_converter.py
@@ -188,14 +188,15 @@ def get_token_ids_and_expand_weights(self, fragments: list[str], weights: list[f
             per_token_weights += [float(weight)] * len(this_fragment_token_ids)
 
         # leave room for bos/eos
-        if len(all_token_ids) > self.max_length - 2:
-            excess_token_count = len(all_token_ids) - self.max_length - 2
+        max_token_count_without_bos_eos_markers = self.max_length - 2
+        if len(all_token_ids) > max_token_count_without_bos_eos_markers:
+            excess_token_count = len(all_token_ids) - max_token_count_without_bos_eos_markers
             # TODO build nice description string of how the truncation was applied
             # this should be done by calling self.tokenizer.convert_ids_to_tokens() then passing the result to
             # self.tokenizer.convert_tokens_to_string() for the token_ids on each side of the truncation limit.
             print(f">> Prompt is {excess_token_count} token(s) too long and has been truncated")
-            all_token_ids = all_token_ids[0:self.max_length]
-            per_token_weights = per_token_weights[0:self.max_length]
+            all_token_ids = all_token_ids[0:max_token_count_without_bos_eos_markers]
+            per_token_weights = per_token_weights[0:max_token_count_without_bos_eos_markers]
 
         # pad out to a self.max_length-entry array: [eos_token, <prompt tokens>, eos_token, ..., eos_token]
         # (typically self.max_length == 77)

From 8c450d443a7b6025262f3c7104ffd0db7e3fb047 Mon Sep 17 00:00:00 2001
From: Lincoln Stein <lincoln.stein@gmail.com>
Date: Sun, 15 Jan 2023 08:51:28 -0500
Subject: [PATCH 199/199] update CHANGELOG.md with 2.3.* info

- Add information on how formats have changed and the upgrade process.
- Add short bug list.
---
 README.md              |  9 +++--
 docs/CHANGELOG.md      | 91 ++++++++++++++++++++++++++++++++++++++++++
 ldm/invoke/_version.py |  2 +-
 3 files changed, 97 insertions(+), 5 deletions(-)

diff --git a/README.md b/README.md
index e4f841641c3..07501a42420 100644
--- a/README.md
+++ b/README.md
@@ -100,7 +100,6 @@ to render 512x512 images.
 
 - At least 12 GB of free disk space for the machine learning model, Python, and all its dependencies.
 
-
 ## Features
 
 Feature documentation can be reviewed by navigating to [the InvokeAI Documentation page](https://invoke-ai.github.io/InvokeAI/features/)
@@ -118,6 +117,8 @@ InvokeAI's advanced prompt syntax allows for token weighting, cross-attention co
 For users utilizing a terminal-based environment, or who want to take advantage of CLI features, InvokeAI offers an extensive and actively supported command-line interface that provides the full suite of generation functionality available in the tool.
 
 ### Other features
+- *Support for both ckpt and diffusers models*
+- *SD 2.0, 2.1 support*
 - *Noise Control & Tresholding*
 - *Popular Sampler Support*
 - *Upscaling & Face Restoration Tools*
@@ -125,14 +126,14 @@ For users utilizing a terminal-based environment, or who want to take advantage
 - *Model Manager & Support*
 
 ### Coming Soon
-- *2.0/2.1 Model Support*
-- *Depth2Img Support*
 - *Node-Based Architecture & UI*
 - And more...
 
 ### Latest Changes
 
-For our latest changes, view our [Release Notes](https://github.com/invoke-ai/InvokeAI/releases)
+For our latest changes, view our [Release
+Notes](https://github.com/invoke-ai/InvokeAI/releases) and the
+[CHANGELOG](docs/CHANGELOG.md).
 
 ## Troubleshooting
 
diff --git a/docs/CHANGELOG.md b/docs/CHANGELOG.md
index 639a9a94662..e9461264dbe 100644
--- a/docs/CHANGELOG.md
+++ b/docs/CHANGELOG.md
@@ -4,6 +4,97 @@ title: Changelog
 
 # :octicons-log-16: **Changelog**
 
+## v2.3.0 <small>(15 January 2023)</small>	
+
+**Transition to diffusers
+
+Version 2.3 provides support for both the traditional `.ckpt` weight
+checkpoint files as well as the HuggingFace `diffusers` format. This
+introduces several changes you should know about.
+
+1. The models.yaml format has been updated. There are now two
+   different type of configuration stanza. The traditional ckpt
+   one will look like this, with a `format` of `ckpt` and a
+   `weights` field that points to the absolute or ROOTDIR-relative
+   location of the ckpt file.
+
+   ```
+   inpainting-1.5:
+      description: RunwayML SD 1.5 model optimized for inpainting (4.27 GB)
+      repo_id: runwayml/stable-diffusion-inpainting
+      format: ckpt
+      width: 512
+      height: 512
+      weights: models/ldm/stable-diffusion-v1/sd-v1-5-inpainting.ckpt
+      config: configs/stable-diffusion/v1-inpainting-inference.yaml
+      vae: models/ldm/stable-diffusion-v1/vae-ft-mse-840000-ema-pruned.ckpt
+   ```
+
+  A configuration stanza for a diffusers model hosted at HuggingFace will look like this,
+  with a `format` of `diffusers` and a `repo_id` that points to the
+  repository ID of the model on HuggingFace:
+
+  ```
+  stable-diffusion-2.1:
+  description: Stable Diffusion version 2.1 diffusers model (5.21 GB)
+  repo_id: stabilityai/stable-diffusion-2-1
+  format: diffusers
+  ```
+
+  A configuration stanza for a diffuers model stored locally should
+  look like this, with a `format` of `diffusers`, but a `path` field
+  that points at the directory that contains `model_index.json`:
+  
+  ```
+  waifu-diffusion:
+  description: Latest waifu diffusion 1.4
+  format: diffusers
+  path: models/diffusers/hakurei-haifu-diffusion-1.4
+  ```
+
+2. The format of the models directory has changed to mimic the
+   HuggingFace cache directory. By default, diffusers models are
+   now automatically downloaded and retrieved from the directory
+   `ROOTDIR/models/diffusers`, while other models are stored in
+   the directory `ROOTDIR/models/hub`. This organization is the
+   same as that used by HuggingFace for its cache management.
+
+   This allows you to share diffusers and ckpt model files easily with
+   other machine learning applications that use the HuggingFace
+   libraries. To do this, set the environment variable HF_HOME
+   before starting up InvokeAI to tell it what directory to
+   cache models in. To tell InvokeAI to use the standard HuggingFace
+   cache directory, you would set HF_HOME like this (Linux/Mac):
+
+   `export HF_HOME=~/.cache/hugging_face`
+
+3. If you upgrade to InvokeAI 2.3.* from an earlier version, there
+   will be a one-time migration from the old models directory format
+   to the new one. You will see a message about this the first time
+   you start `invoke.py`.
+
+4. Both the front end back ends of the model manager have been
+   rewritten to accommodate diffusers. You can import models using
+   their local file path, using their URLs, or their HuggingFace
+   repo_ids. On the command line, all these syntaxes work:
+
+   ```
+   !import_model stabilityai/stable-diffusion-2-1-base
+   !import_model /opt/sd-models/sd-1.4.ckpt
+   !import_model https://huggingface.co/Fictiverse/Stable_Diffusion_PaperCut_Model/blob/main/PaperCut_v1.ckpt
+   ```
+   
+**KNOWN BUGS (15 January 2023)
+
+1. On CUDA systems, the 768 pixel stable-diffusion-2.0 and
+   stable-diffusion-2.1 models can only be run as `diffusers` models
+   when the `xformer` library is installed and configured. Without
+   `xformers`, InvokeAI returns black images.
+
+2. Inpainting and outpainting have regressed in quality.
+
+Both these issues are being actively worked on.
+
 ## v2.2.4 <small>(11 December 2022)</small>
 
 **the `invokeai` directory**
diff --git a/ldm/invoke/_version.py b/ldm/invoke/_version.py
index 312ecd88c55..cc77f736709 100644
--- a/ldm/invoke/_version.py
+++ b/ldm/invoke/_version.py
@@ -1 +1 @@
-__version__='2.2.6+a0'
+__version__='2.3.0+a0'