diff --git a/docs/source/en/_toctree.yml b/docs/source/en/_toctree.yml index ba94de59219c..24c7d29248ed 100644 --- a/docs/source/en/_toctree.yml +++ b/docs/source/en/_toctree.yml @@ -304,6 +304,8 @@ title: Latent Consistency Models - local: api/pipelines/latent_diffusion title: Latent Diffusion + - local: api/pipelines/ledits_pp + title: LEDITS++ - local: api/pipelines/panorama title: MultiDiffusion - local: api/pipelines/musicldm diff --git a/docs/source/en/api/pipelines/ledits_pp.md b/docs/source/en/api/pipelines/ledits_pp.md new file mode 100644 index 000000000000..7b957b1a6337 --- /dev/null +++ b/docs/source/en/api/pipelines/ledits_pp.md @@ -0,0 +1,54 @@ + + +# LEDITS++ + +LEDITS++ was proposed in [LEDITS++: Limitless Image Editing using Text-to-Image Models](https://huggingface.co/papers/2311.16711) by Manuel Brack, Felix Friedrich, Katharina Kornmeier, Linoy Tsaban, Patrick Schramowski, Kristian Kersting, Apolinário Passos. + +The abstract from the paper is: + +*Text-to-image diffusion models have recently received increasing interest for their astonishing ability to produce high-fidelity images from solely text inputs. Subsequent research efforts aim to exploit and apply their capabilities to real image editing. However, existing image-to-image methods are often inefficient, imprecise, and of limited versatility. They either require time-consuming fine-tuning, deviate unnecessarily strongly from the input image, and/or lack support for multiple, simultaneous edits. To address these issues, we introduce LEDITS++, an efficient yet versatile and precise textual image manipulation technique. LEDITS++'s novel inversion approach requires no tuning nor optimization and produces high-fidelity results with a few diffusion steps. Second, our methodology supports multiple simultaneous edits and is architecture-agnostic. Third, we use a novel implicit masking technique that limits changes to relevant image regions. We propose the novel TEdBench++ benchmark as part of our exhaustive evaluation. Our results demonstrate the capabilities of LEDITS++ and its improvements over previous methods. The project page is available at https://leditsplusplus-project.static.hf.space .* + + + +You can find additional information about LEDITS++ on the [project page](https://leditsplusplus-project.static.hf.space/index.html) and try it out in a [demo](https://huggingface.co/spaces/editing-images/leditsplusplus). + + + + +Due to some backward compatability issues with the current diffusers implementation of [`~schedulers.DPMSolverMultistepScheduler`] this implementation of LEdits++ can no longer guarantee perfect inversion. +This issue is unlikely to have any noticeable effects on applied use-cases. However, we provide an alternative implementation that guarantees perfect inversion in a dedicated [GitHub repo](https://github.com/ml-research/ledits_pp). + + +We provide two distinct pipelines based on different pre-trained models. + +## LEditsPPPipelineStableDiffusion +[[autodoc]] pipelines.ledits_pp.LEditsPPPipelineStableDiffusion + - all + - __call__ + - invert + +## LEditsPPPipelineStableDiffusionXL +[[autodoc]] pipelines.ledits_pp.LEditsPPPipelineStableDiffusionXL + - all + - __call__ + - invert + + + +## LEditsPPDiffusionPipelineOutput +[[autodoc]] pipelines.ledits_pp.pipeline_output.LEditsPPDiffusionPipelineOutput + - all + +## LEditsPPInversionPipelineOutput +[[autodoc]] pipelines.ledits_pp.pipeline_output.LEditsPPInversionPipelineOutput + - all diff --git a/docs/source/en/api/pipelines/overview.md b/docs/source/en/api/pipelines/overview.md index 584915228b82..cd1232a90d6e 100644 --- a/docs/source/en/api/pipelines/overview.md +++ b/docs/source/en/api/pipelines/overview.md @@ -57,6 +57,7 @@ The table below lists all the pipelines currently available in 🤗 Diffusers an | [Latent Consistency Models](latent_consistency_models) | text2image | | [Latent Diffusion](latent_diffusion) | text2image, super-resolution | | [LDM3D](stable_diffusion/ldm3d_diffusion) | text2image, text-to-3D, text-to-pano, upscaling | +| [LEDITS++](ledits_pp) | image editing | | [MultiDiffusion](panorama) | text2image | | [MusicLDM](musicldm) | text2audio | | [Paint by Example](paint_by_example) | inpainting | diff --git a/docs/source/en/api/pipelines/semantic_stable_diffusion.md b/docs/source/en/api/pipelines/semantic_stable_diffusion.md index 7634363a67a7..19a0a8116989 100644 --- a/docs/source/en/api/pipelines/semantic_stable_diffusion.md +++ b/docs/source/en/api/pipelines/semantic_stable_diffusion.md @@ -30,6 +30,6 @@ Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) - all - __call__ -## StableDiffusionSafePipelineOutput +## SemanticStableDiffusionPipelineOutput [[autodoc]] pipelines.semantic_stable_diffusion.pipeline_output.SemanticStableDiffusionPipelineOutput - all diff --git a/src/diffusers/__init__.py b/src/diffusers/__init__.py index 10136dbd5e2f..86d78f174398 100644 --- a/src/diffusers/__init__.py +++ b/src/diffusers/__init__.py @@ -253,6 +253,8 @@ "LatentConsistencyModelImg2ImgPipeline", "LatentConsistencyModelPipeline", "LDMTextToImagePipeline", + "LEditsPPPipelineStableDiffusion", + "LEditsPPPipelineStableDiffusionXL", "MusicLDMPipeline", "PaintByExamplePipeline", "PIAPipeline", @@ -623,6 +625,8 @@ LatentConsistencyModelImg2ImgPipeline, LatentConsistencyModelPipeline, LDMTextToImagePipeline, + LEditsPPPipelineStableDiffusion, + LEditsPPPipelineStableDiffusionXL, MusicLDMPipeline, PaintByExamplePipeline, PIAPipeline, diff --git a/src/diffusers/pipelines/__init__.py b/src/diffusers/pipelines/__init__.py index 94e8d227f704..2b2277809b38 100644 --- a/src/diffusers/pipelines/__init__.py +++ b/src/diffusers/pipelines/__init__.py @@ -23,6 +23,7 @@ "controlnet_xs": [], "deprecated": [], "latent_diffusion": [], + "ledits_pp": [], "stable_diffusion": [], "stable_diffusion_xl": [], } @@ -171,6 +172,12 @@ "LatentConsistencyModelPipeline", ] _import_structure["latent_diffusion"].extend(["LDMTextToImagePipeline"]) + _import_structure["ledits_pp"].extend( + [ + "LEditsPPPipelineStableDiffusion", + "LEditsPPPipelineStableDiffusionXL", + ] + ) _import_structure["musicldm"] = ["MusicLDMPipeline"] _import_structure["paint_by_example"] = ["PaintByExamplePipeline"] _import_structure["pia"] = ["PIAPipeline"] @@ -424,6 +431,12 @@ LatentConsistencyModelPipeline, ) from .latent_diffusion import LDMTextToImagePipeline + from .ledits_pp import ( + LEditsPPDiffusionPipelineOutput, + LEditsPPInversionPipelineOutput, + LEditsPPPipelineStableDiffusion, + LEditsPPPipelineStableDiffusionXL, + ) from .musicldm import MusicLDMPipeline from .paint_by_example import PaintByExamplePipeline from .pia import PIAPipeline diff --git a/src/diffusers/pipelines/ledits_pp/__init__.py b/src/diffusers/pipelines/ledits_pp/__init__.py new file mode 100644 index 000000000000..aae3b1cb18ce --- /dev/null +++ b/src/diffusers/pipelines/ledits_pp/__init__.py @@ -0,0 +1,55 @@ +from typing import TYPE_CHECKING + +from ...utils import ( + DIFFUSERS_SLOW_IMPORT, + OptionalDependencyNotAvailable, + _LazyModule, + get_objects_from_module, + is_torch_available, + is_transformers_available, +) + + +_dummy_objects = {} +_import_structure = {} + +try: + if not (is_transformers_available() and is_torch_available()): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + from ...utils import dummy_torch_and_transformers_objects # noqa F403 + + _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects)) +else: + _import_structure["pipeline_leditspp_stable_diffusion"] = ["LEditsPPPipelineStableDiffusion"] + _import_structure["pipeline_leditspp_stable_diffusion_xl"] = ["LEditsPPPipelineStableDiffusionXL"] + + _import_structure["pipeline_output"] = ["LEditsPPDiffusionPipelineOutput", "LEditsPPDiffusionPipelineOutput"] + +if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT: + try: + if not (is_transformers_available() and is_torch_available()): + raise OptionalDependencyNotAvailable() + + except OptionalDependencyNotAvailable: + from ...utils.dummy_torch_and_transformers_objects import * + else: + from .pipeline_leditspp_stable_diffusion import ( + LEditsPPDiffusionPipelineOutput, + LEditsPPInversionPipelineOutput, + LEditsPPPipelineStableDiffusion, + ) + from .pipeline_leditspp_stable_diffusion_xl import LEditsPPPipelineStableDiffusionXL + +else: + import sys + + sys.modules[__name__] = _LazyModule( + __name__, + globals()["__file__"], + _import_structure, + module_spec=__spec__, + ) + + for name, value in _dummy_objects.items(): + setattr(sys.modules[__name__], name, value) diff --git a/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py b/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py new file mode 100644 index 000000000000..a6357c4cd3a1 --- /dev/null +++ b/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion.py @@ -0,0 +1,1505 @@ +import inspect +import math +from itertools import repeat +from typing import Any, Callable, Dict, List, Optional, Tuple, Union + +import torch +import torch.nn.functional as F +from packaging import version +from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer + +from ...configuration_utils import FrozenDict +from ...image_processor import PipelineImageInput, VaeImageProcessor +from ...loaders import FromSingleFileMixin, IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin +from ...models import AutoencoderKL, UNet2DConditionModel +from ...models.attention_processor import Attention, AttnProcessor +from ...models.lora import adjust_lora_scale_text_encoder +from ...pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker +from ...schedulers import DDIMScheduler, DPMSolverMultistepScheduler +from ...utils import ( + USE_PEFT_BACKEND, + deprecate, + logging, + replace_example_docstring, + scale_lora_layers, + unscale_lora_layers, +) +from ...utils.torch_utils import randn_tensor +from ..pipeline_utils import DiffusionPipeline +from .pipeline_output import LEditsPPDiffusionPipelineOutput, LEditsPPInversionPipelineOutput + + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + +EXAMPLE_DOC_STRING = """ + Examples: + ```py + >>> import PIL + >>> import requests + >>> import torch + >>> from io import BytesIO + + >>> from diffusers import LEditsPPPipelineStableDiffusion + + >>> pipe = LEditsPPPipelineStableDiffusion.from_pretrained( + ... "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16 + ... ) + >>> pipe = pipe.to("cuda") + + >>> def download_image(url): + ... response = requests.get(url) + ... return PIL.Image.open(BytesIO(response.content)).convert("RGB") + + >>> img_url = "https://www.aiml.informatik.tu-darmstadt.de/people/mbrack/cherry_blossom.png" + >>> image = download_image(img_url) + + >>> _ = pipe.invert( + ... image = image, + ... num_inversion_steps=50, + ... skip=0.1 + ... ) + + >>> edited_image = pipe( + ... editing_prompt=["cherry blossom"], + ... edit_guidance_scale=10.0, + ... edit_threshold=0.75, + ).images[0] + ``` +""" + + +# Modified from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionAttendAndExcitePipeline.AttentionStore +class LeditsAttentionStore: + @staticmethod + def get_empty_store(): + return {"down_cross": [], "mid_cross": [], "up_cross": [], "down_self": [], "mid_self": [], "up_self": []} + + def __call__(self, attn, is_cross: bool, place_in_unet: str, editing_prompts, PnP=False): + # attn.shape = batch_size * head_size, seq_len query, seq_len_key + if attn.shape[1] <= self.max_size: + bs = 1 + int(PnP) + editing_prompts + skip = 2 if PnP else 1 # skip PnP & unconditional + attn = torch.stack(attn.split(self.batch_size)).permute(1, 0, 2, 3) + source_batch_size = int(attn.shape[1] // bs) + self.forward(attn[:, skip * source_batch_size :], is_cross, place_in_unet) + + def forward(self, attn, is_cross: bool, place_in_unet: str): + key = f"{place_in_unet}_{'cross' if is_cross else 'self'}" + + self.step_store[key].append(attn) + + def between_steps(self, store_step=True): + if store_step: + if self.average: + if len(self.attention_store) == 0: + self.attention_store = self.step_store + else: + for key in self.attention_store: + for i in range(len(self.attention_store[key])): + self.attention_store[key][i] += self.step_store[key][i] + else: + if len(self.attention_store) == 0: + self.attention_store = [self.step_store] + else: + self.attention_store.append(self.step_store) + + self.cur_step += 1 + self.step_store = self.get_empty_store() + + def get_attention(self, step: int): + if self.average: + attention = { + key: [item / self.cur_step for item in self.attention_store[key]] for key in self.attention_store + } + else: + assert step is not None + attention = self.attention_store[step] + return attention + + def aggregate_attention( + self, attention_maps, prompts, res: Union[int, Tuple[int]], from_where: List[str], is_cross: bool, select: int + ): + out = [[] for x in range(self.batch_size)] + if isinstance(res, int): + num_pixels = res**2 + resolution = (res, res) + else: + num_pixels = res[0] * res[1] + resolution = res[:2] + + for location in from_where: + for bs_item in attention_maps[f"{location}_{'cross' if is_cross else 'self'}"]: + for batch, item in enumerate(bs_item): + if item.shape[1] == num_pixels: + cross_maps = item.reshape(len(prompts), -1, *resolution, item.shape[-1])[select] + out[batch].append(cross_maps) + + out = torch.stack([torch.cat(x, dim=0) for x in out]) + # average over heads + out = out.sum(1) / out.shape[1] + return out + + def __init__(self, average: bool, batch_size=1, max_resolution=16, max_size: int = None): + self.step_store = self.get_empty_store() + self.attention_store = [] + self.cur_step = 0 + self.average = average + self.batch_size = batch_size + if max_size is None: + self.max_size = max_resolution**2 + elif max_size is not None and max_resolution is None: + self.max_size = max_size + else: + raise ValueError("Only allowed to set one of max_resolution or max_size") + + +# Modified from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionAttendAndExcitePipeline.GaussianSmoothing +class LeditsGaussianSmoothing: + def __init__(self, device): + kernel_size = [3, 3] + sigma = [0.5, 0.5] + + # The gaussian kernel is the product of the gaussian function of each dimension. + kernel = 1 + meshgrids = torch.meshgrid([torch.arange(size, dtype=torch.float32) for size in kernel_size]) + for size, std, mgrid in zip(kernel_size, sigma, meshgrids): + mean = (size - 1) / 2 + kernel *= 1 / (std * math.sqrt(2 * math.pi)) * torch.exp(-(((mgrid - mean) / (2 * std)) ** 2)) + + # Make sure sum of values in gaussian kernel equals 1. + kernel = kernel / torch.sum(kernel) + + # Reshape to depthwise convolutional weight + kernel = kernel.view(1, 1, *kernel.size()) + kernel = kernel.repeat(1, *[1] * (kernel.dim() - 1)) + + self.weight = kernel.to(device) + + def __call__(self, input): + """ + Arguments: + Apply gaussian filter to input. + input (torch.Tensor): Input to apply gaussian filter on. + Returns: + filtered (torch.Tensor): Filtered output. + """ + return F.conv2d(input, weight=self.weight.to(input.dtype)) + + +class LEDITSCrossAttnProcessor: + def __init__(self, attention_store, place_in_unet, pnp, editing_prompts): + self.attnstore = attention_store + self.place_in_unet = place_in_unet + self.editing_prompts = editing_prompts + self.pnp = pnp + + def __call__( + self, + attn: Attention, + hidden_states, + encoder_hidden_states, + attention_mask=None, + temb=None, + ): + batch_size, sequence_length, _ = ( + hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape + ) + attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) + + query = attn.to_q(hidden_states) + + if encoder_hidden_states is None: + encoder_hidden_states = hidden_states + elif attn.norm_cross: + encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) + + key = attn.to_k(encoder_hidden_states) + value = attn.to_v(encoder_hidden_states) + + query = attn.head_to_batch_dim(query) + key = attn.head_to_batch_dim(key) + value = attn.head_to_batch_dim(value) + + attention_probs = attn.get_attention_scores(query, key, attention_mask) + self.attnstore( + attention_probs, + is_cross=True, + place_in_unet=self.place_in_unet, + editing_prompts=self.editing_prompts, + PnP=self.pnp, + ) + + hidden_states = torch.bmm(attention_probs, value) + hidden_states = attn.batch_to_head_dim(hidden_states) + + # linear proj + hidden_states = attn.to_out[0](hidden_states) + # dropout + hidden_states = attn.to_out[1](hidden_states) + + hidden_states = hidden_states / attn.rescale_output_factor + return hidden_states + + +# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg +def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0): + """ + Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and + Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4 + """ + std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True) + std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True) + # rescale the results from guidance (fixes overexposure) + noise_pred_rescaled = noise_cfg * (std_text / std_cfg) + # mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images + noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg + return noise_cfg + + +class LEditsPPPipelineStableDiffusion( + DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, IPAdapterMixin, FromSingleFileMixin +): + """ + Pipeline for textual image editing using LEDits++ with Stable Diffusion. + + This model inherits from [`DiffusionPipeline`] and builds on the [`StableDiffusionPipeline`]. Check the superclass + documentation for the generic methods implemented for all pipelines (downloading, saving, running on a particular + device, etc.). + + Args: + vae ([`AutoencoderKL`]): + Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. + text_encoder ([`~transformers.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 ([`~transformers.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 ([`DPMSolverMultistepScheduler`] or [`DDIMScheduler`]): + A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of + [`DPMSolverMultistepScheduler`] or [`DDIMScheduler`]. If any other scheduler is passed it will automatically + be set to [`DPMSolverMultistepScheduler`]. + safety_checker ([`StableDiffusionSafetyChecker`]): + Classification module that estimates whether generated images could be considered offensive or harmful. + Please, refer to the [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) for details. + feature_extractor ([`~transformers.CLIPImageProcessor`]): + Model that extracts features from generated images to be used as inputs for the `safety_checker`. + """ + + model_cpu_offload_seq = "text_encoder->unet->vae" + _exclude_from_cpu_offload = ["safety_checker"] + _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"] + _optional_components = ["safety_checker", "feature_extractor", "image_encoder"] + + def __init__( + self, + vae: AutoencoderKL, + text_encoder: CLIPTextModel, + tokenizer: CLIPTokenizer, + unet: UNet2DConditionModel, + scheduler: Union[DDIMScheduler, DPMSolverMultistepScheduler], + safety_checker: StableDiffusionSafetyChecker, + feature_extractor: CLIPImageProcessor, + requires_safety_checker: bool = True, + ): + super().__init__() + + if not isinstance(scheduler, DDIMScheduler) and not isinstance(scheduler, DPMSolverMultistepScheduler): + scheduler = DPMSolverMultistepScheduler.from_config( + scheduler.config, algorithm_type="sde-dpmsolver++", solver_order=2 + ) + logger.warning( + "This pipeline only supports DDIMScheduler and DPMSolverMultistepScheduler. " + "The scheduler has been changed to DPMSolverMultistepScheduler." + ) + + if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1: + deprecation_message = ( + f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`" + f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure " + "to update the config accordingly as leaving `steps_offset` might led to incorrect results" + " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub," + " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`" + " file" + ) + deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False) + new_config = dict(scheduler.config) + new_config["steps_offset"] = 1 + scheduler._internal_dict = FrozenDict(new_config) + + if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True: + deprecation_message = ( + f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`." + " `clip_sample` should be set to False in the configuration file. Please make sure to update the" + " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in" + " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very" + " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file" + ) + deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False) + new_config = dict(scheduler.config) + new_config["clip_sample"] = False + scheduler._internal_dict = FrozenDict(new_config) + + if safety_checker is None and requires_safety_checker: + logger.warning( + f"You have disabled the safety checker for {self.__class__} 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 ." + ) + + if safety_checker is not None and feature_extractor is None: + raise ValueError( + "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety" + " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead." + ) + + is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse( + version.parse(unet.config._diffusers_version).base_version + ) < version.parse("0.9.0.dev0") + is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64 + if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64: + deprecation_message = ( + "The configuration file of the unet has set the default `sample_size` to smaller than" + " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the" + " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" + " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" + " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" + " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" + " in the config might lead to incorrect results in future versions. If you have downloaded this" + " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" + " the `unet/config.json` file" + ) + deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False) + new_config = dict(unet.config) + new_config["sample_size"] = 64 + unet._internal_dict = FrozenDict(new_config) + + self.register_modules( + vae=vae, + text_encoder=text_encoder, + tokenizer=tokenizer, + unet=unet, + scheduler=scheduler, + safety_checker=safety_checker, + feature_extractor=feature_extractor, + ) + self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) + self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) + self.register_to_config(requires_safety_checker=requires_safety_checker) + + self.inversion_steps = None + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker + def run_safety_checker(self, image, device, dtype): + if self.safety_checker is None: + has_nsfw_concept = None + else: + if torch.is_tensor(image): + feature_extractor_input = self.image_processor.postprocess(image, output_type="pil") + else: + feature_extractor_input = self.image_processor.numpy_to_pil(image) + safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device) + image, has_nsfw_concept = self.safety_checker( + images=image, clip_input=safety_checker_input.pixel_values.to(dtype) + ) + return image, has_nsfw_concept + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents + def decode_latents(self, latents): + deprecation_message = "The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead" + deprecate("decode_latents", "1.0.0", deprecation_message, standard_warn=False) + + latents = 1 / self.vae.config.scaling_factor * latents + image = self.vae.decode(latents, return_dict=False)[0] + image = (image / 2 + 0.5).clamp(0, 1) + # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 + image = image.cpu().permute(0, 2, 3, 1).float().numpy() + return image + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs + def prepare_extra_step_kwargs(self, eta, generator=None): + # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature + # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. + # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 + # and should be between [0, 1] + + accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) + extra_step_kwargs = {} + if accepts_eta: + extra_step_kwargs["eta"] = eta + + # check if the scheduler accepts generator + accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) + if accepts_generator: + extra_step_kwargs["generator"] = generator + return extra_step_kwargs + + # Modified from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.check_inputs + def check_inputs( + self, + negative_prompt=None, + editing_prompt_embeddings=None, + negative_prompt_embeds=None, + callback_on_step_end_tensor_inputs=None, + ): + if callback_on_step_end_tensor_inputs is not None and not all( + k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs + ): + raise ValueError( + f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}" + ) + if negative_prompt is not None and negative_prompt_embeds is not None: + raise ValueError( + f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" + f" {negative_prompt_embeds}. Please make sure to only forward one of the two." + ) + + if editing_prompt_embeddings is not None and negative_prompt_embeds is not None: + if editing_prompt_embeddings.shape != negative_prompt_embeds.shape: + raise ValueError( + "`editing_prompt_embeddings` and `negative_prompt_embeds` must have the same shape when passed directly, but" + f" got: `editing_prompt_embeddings` {editing_prompt_embeddings.shape} != `negative_prompt_embeds`" + f" {negative_prompt_embeds.shape}." + ) + + # Modified from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents + def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, latents): + # shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor) + + # if latents.shape != shape: + # raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}") + + latents = latents.to(device) + + # scale the initial noise by the standard deviation required by the scheduler + latents = latents * self.scheduler.init_noise_sigma + return latents + + def prepare_unet(self, attention_store, PnP: bool = False): + attn_procs = {} + for name in self.unet.attn_processors.keys(): + if name.startswith("mid_block"): + place_in_unet = "mid" + elif name.startswith("up_blocks"): + place_in_unet = "up" + elif name.startswith("down_blocks"): + place_in_unet = "down" + else: + continue + + if "attn2" in name and place_in_unet != "mid": + attn_procs[name] = LEDITSCrossAttnProcessor( + attention_store=attention_store, + place_in_unet=place_in_unet, + pnp=PnP, + editing_prompts=self.enabled_editing_prompts, + ) + else: + attn_procs[name] = AttnProcessor() + + self.unet.set_attn_processor(attn_procs) + + def encode_prompt( + self, + device, + num_images_per_prompt, + enable_edit_guidance, + negative_prompt=None, + editing_prompt=None, + negative_prompt_embeds: Optional[torch.FloatTensor] = None, + editing_prompt_embeds: Optional[torch.FloatTensor] = None, + lora_scale: Optional[float] = None, + clip_skip: Optional[int] = None, + ): + r""" + Encodes the prompt into text encoder hidden states. + + Args: + device: (`torch.device`): + torch device + num_images_per_prompt (`int`): + number of images that should be generated per prompt + enable_edit_guidance (`bool`): + whether to perform any editing or reconstruct the input image instead + negative_prompt (`str` or `List[str]`, *optional*): + The prompt or prompts not to guide the image generation. If not defined, one has to pass + `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is + less than `1`). + editing_prompt (`str` or `List[str]`, *optional*): + Editing prompt(s) to be encoded. If not defined, one has to pass + `editing_prompt_embeds` instead. + editing_prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not + provided, text embeddings will be generated from `prompt` input argument. + negative_prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt + weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input + argument. + lora_scale (`float`, *optional*): + A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded. + clip_skip (`int`, *optional*): + Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that + the output of the pre-final layer will be used for computing the prompt embeddings. + """ + # set lora scale so that monkey patched LoRA + # function of text encoder can correctly access it + if lora_scale is not None and isinstance(self, LoraLoaderMixin): + self._lora_scale = lora_scale + + # dynamically adjust the LoRA scale + if not USE_PEFT_BACKEND: + adjust_lora_scale_text_encoder(self.text_encoder, lora_scale) + else: + scale_lora_layers(self.text_encoder, lora_scale) + + batch_size = self.batch_size + num_edit_tokens = None + + if negative_prompt_embeds is None: + uncond_tokens: List[str] + if negative_prompt is None: + uncond_tokens = [""] * batch_size + elif isinstance(negative_prompt, str): + uncond_tokens = [negative_prompt] + elif batch_size != len(negative_prompt): + raise ValueError( + f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but exoected" + f"{batch_size} based on the input images. Please make sure that passed `negative_prompt` matches" + " the batch size of `prompt`." + ) + else: + uncond_tokens = negative_prompt + + # textual inversion: procecss multi-vector tokens if necessary + if isinstance(self, TextualInversionLoaderMixin): + uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer) + + uncond_input = self.tokenizer( + uncond_tokens, + padding="max_length", + max_length=self.tokenizer.model_max_length, + truncation=True, + return_tensors="pt", + ) + + if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: + attention_mask = uncond_input.attention_mask.to(device) + else: + attention_mask = None + + negative_prompt_embeds = self.text_encoder( + uncond_input.input_ids.to(device), + attention_mask=attention_mask, + ) + negative_prompt_embeds = negative_prompt_embeds[0] + + if self.text_encoder is not None: + prompt_embeds_dtype = self.text_encoder.dtype + elif self.unet is not None: + prompt_embeds_dtype = self.unet.dtype + else: + prompt_embeds_dtype = negative_prompt_embeds.dtype + + negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device) + + if enable_edit_guidance: + if editing_prompt_embeds is None: + # textual inversion: procecss multi-vector tokens if necessary + # if isinstance(self, TextualInversionLoaderMixin): + # prompt = self.maybe_convert_prompt(prompt, self.tokenizer) + if isinstance(editing_prompt, str): + editing_prompt = [editing_prompt] + + max_length = negative_prompt_embeds.shape[1] + text_inputs = self.tokenizer( + [x for item in editing_prompt for x in repeat(item, batch_size)], + padding="max_length", + max_length=max_length, + truncation=True, + return_tensors="pt", + return_length=True, + ) + + num_edit_tokens = text_inputs.length - 2 # not counting startoftext and endoftext + text_input_ids = text_inputs.input_ids + untruncated_ids = self.tokenizer( + [x for item in editing_prompt for x in repeat(item, batch_size)], + padding="longest", + return_tensors="pt", + ).input_ids + + if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( + text_input_ids, untruncated_ids + ): + removed_text = self.tokenizer.batch_decode( + untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] + ) + logger.warning( + "The following part of your input was truncated because CLIP can only handle sequences up to" + f" {self.tokenizer.model_max_length} tokens: {removed_text}" + ) + + if ( + hasattr(self.text_encoder.config, "use_attention_mask") + and self.text_encoder.config.use_attention_mask + ): + attention_mask = text_inputs.attention_mask.to(device) + else: + attention_mask = None + + if clip_skip is None: + editing_prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask) + editing_prompt_embeds = editing_prompt_embeds[0] + else: + editing_prompt_embeds = self.text_encoder( + text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True + ) + # Access the `hidden_states` first, that contains a tuple of + # all the hidden states from the encoder layers. Then index into + # the tuple to access the hidden states from the desired layer. + editing_prompt_embeds = editing_prompt_embeds[-1][-(clip_skip + 1)] + # We also need to apply the final LayerNorm here to not mess with the + # representations. The `last_hidden_states` that we typically use for + # obtaining the final prompt representations passes through the LayerNorm + # layer. + editing_prompt_embeds = self.text_encoder.text_model.final_layer_norm(editing_prompt_embeds) + + editing_prompt_embeds = editing_prompt_embeds.to(dtype=negative_prompt_embeds.dtype, device=device) + + bs_embed_edit, seq_len, _ = editing_prompt_embeds.shape + editing_prompt_embeds = editing_prompt_embeds.to(dtype=negative_prompt_embeds.dtype, device=device) + editing_prompt_embeds = editing_prompt_embeds.repeat(1, num_images_per_prompt, 1) + editing_prompt_embeds = editing_prompt_embeds.view(bs_embed_edit * num_images_per_prompt, seq_len, -1) + + # get unconditional embeddings for classifier free guidance + + # duplicate unconditional embeddings for each generation per prompt, using mps friendly method + seq_len = negative_prompt_embeds.shape[1] + + negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device) + + negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) + negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) + + if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND: + # Retrieve the original scale by scaling back the LoRA layers + unscale_lora_layers(self.text_encoder, lora_scale) + + return editing_prompt_embeds, negative_prompt_embeds, num_edit_tokens + + @property + def guidance_rescale(self): + return self._guidance_rescale + + @property + def clip_skip(self): + return self._clip_skip + + @property + def cross_attention_kwargs(self): + return self._cross_attention_kwargs + + @torch.no_grad() + @replace_example_docstring(EXAMPLE_DOC_STRING) + def __call__( + self, + negative_prompt: Optional[Union[str, List[str]]] = None, + generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, + output_type: Optional[str] = "pil", + return_dict: bool = True, + editing_prompt: Optional[Union[str, List[str]]] = None, + editing_prompt_embeds: Optional[torch.Tensor] = None, + negative_prompt_embeds: Optional[torch.FloatTensor] = None, + reverse_editing_direction: Optional[Union[bool, List[bool]]] = False, + edit_guidance_scale: Optional[Union[float, List[float]]] = 5, + edit_warmup_steps: Optional[Union[int, List[int]]] = 0, + edit_cooldown_steps: Optional[Union[int, List[int]]] = None, + edit_threshold: Optional[Union[float, List[float]]] = 0.9, + user_mask: Optional[torch.FloatTensor] = None, + sem_guidance: Optional[List[torch.Tensor]] = None, + use_cross_attn_mask: bool = False, + use_intersect_mask: bool = True, + attn_store_steps: Optional[List[int]] = [], + store_averaged_over_steps: bool = True, + cross_attention_kwargs: Optional[Dict[str, Any]] = None, + guidance_rescale: float = 0.0, + clip_skip: Optional[int] = None, + callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None, + callback_on_step_end_tensor_inputs: List[str] = ["latents"], + **kwargs, + ): + r""" + The call function to the pipeline for editing. The [`~pipelines.ledits_pp.LEditsPPPipelineStableDiffusion.invert`] + method has to be called beforehand. Edits will always be performed for the last inverted image(s). + + Args: + negative_prompt (`str` or `List[str]`, *optional*): + The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored + if `guidance_scale` is less than `1`). + generator (`torch.Generator`, *optional*): + One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) + to make generation deterministic. + output_type (`str`, *optional*, defaults to `"pil"`): + The output format of the generate image. Choose between + [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`~pipelines.ledits_pp.LEditsPPDiffusionPipelineOutput`] instead of a + plain tuple. + editing_prompt (`str` or `List[str]`, *optional*): + The prompt or prompts to guide the image generation. The image is reconstructed by setting + `editing_prompt = None`. Guidance direction of prompt should be specified via `reverse_editing_direction`. + editing_prompt_embeds (`torch.Tensor>`, *optional*): + Pre-computed embeddings to use for guiding the image generation. Guidance direction of embedding should be + specified via `reverse_editing_direction`. + negative_prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If + not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument. + reverse_editing_direction (`bool` or `List[bool]`, *optional*, defaults to `False`): + Whether the corresponding prompt in `editing_prompt` should be increased or decreased. + edit_guidance_scale (`float` or `List[float]`, *optional*, defaults to 5): + Guidance scale for guiding the image generation. If provided as list values should correspond to `editing_prompt`. + `edit_guidance_scale` is defined as `s_e` of equation 12 of + [LEDITS++ Paper](https://arxiv.org/abs/2301.12247). + edit_warmup_steps (`float` or `List[float]`, *optional*, defaults to 10): + Number of diffusion steps (for each prompt) for which guidance will not be applied. + edit_cooldown_steps (`float` or `List[float]`, *optional*, defaults to `None`): + Number of diffusion steps (for each prompt) after which guidance will no longer be applied. + edit_threshold (`float` or `List[float]`, *optional*, defaults to 0.9): + Masking threshold of guidance. Threshold should be proportional to the image region that is modified. + 'edit_threshold' is defined as 'λ' of equation 12 of [LEDITS++ Paper](https://arxiv.org/abs/2301.12247). + user_mask (`torch.FloatTensor`, *optional*): + User-provided mask for even better control over the editing process. This is helpful when LEDITS++'s implicit + masks do not meet user preferences. + sem_guidance (`List[torch.Tensor]`, *optional*): + List of pre-generated guidance vectors to be applied at generation. Length of the list has to + correspond to `num_inference_steps`. + use_cross_attn_mask (`bool`, defaults to `False`): + Whether cross-attention masks are used. Cross-attention masks are always used when use_intersect_mask + is set to true. Cross-attention masks are defined as 'M^1' of equation 12 of + [LEDITS++ paper](https://arxiv.org/pdf/2311.16711.pdf). + use_intersect_mask (`bool`, defaults to `True`): + Whether the masking term is calculated as intersection of cross-attention masks and masks derived + from the noise estimate. Cross-attention mask are defined as 'M^1' and masks derived from the noise + estimate are defined as 'M^2' of equation 12 of [LEDITS++ paper](https://arxiv.org/pdf/2311.16711.pdf). + attn_store_steps (`List[int]`, *optional*): + Steps for which the attention maps are stored in the AttentionStore. Just for visualization purposes. + store_averaged_over_steps (`bool`, defaults to `True`): + Whether the attention maps for the 'attn_store_steps' are stored averaged over the diffusion steps. + If False, attention maps for each step are stores separately. Just for visualization purposes. + cross_attention_kwargs (`dict`, *optional*): + A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in + [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). + guidance_rescale (`float`, *optional*, defaults to 0.0): + Guidance rescale factor from [Common Diffusion Noise Schedules and Sample Steps are + Flawed](https://arxiv.org/pdf/2305.08891.pdf). Guidance rescale factor should fix overexposure when + using zero terminal SNR. + clip_skip (`int`, *optional*): + Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that + the output of the pre-final layer will be used for computing the prompt embeddings. + callback_on_step_end (`Callable`, *optional*): + A function that calls at the end of each denoising steps during the inference. The function is called + with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, + callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by + `callback_on_step_end_tensor_inputs`. + callback_on_step_end_tensor_inputs (`List`, *optional*): + The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list + will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the + `._callback_tensor_inputs` attribute of your pipeline class. + + Examples: + + Returns: + [`~pipelines.ledits_pp.LEditsPPDiffusionPipelineOutput`] or `tuple`: + [`~pipelines.ledits_pp.LEditsPPDiffusionPipelineOutput`] 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`. + """ + + if self.inversion_steps is None: + raise ValueError( + "You need to invert an input image first before calling the pipeline. The `invert` method has to be called beforehand. Edits will always be performed for the last inverted image(s)." + ) + + eta = self.eta + num_images_per_prompt = 1 + latents = self.init_latents + + zs = self.zs + self.scheduler.set_timesteps(len(self.scheduler.timesteps)) + + if use_intersect_mask: + use_cross_attn_mask = True + + if use_cross_attn_mask: + self.smoothing = LeditsGaussianSmoothing(self.device) + + if user_mask is not None: + user_mask = user_mask.to(self.device) + + org_prompt = "" + + # 1. Check inputs. Raise error if not correct + self.check_inputs( + negative_prompt, + editing_prompt_embeds, + negative_prompt_embeds, + callback_on_step_end_tensor_inputs, + ) + + self._guidance_rescale = guidance_rescale + self._clip_skip = clip_skip + self._cross_attention_kwargs = cross_attention_kwargs + + # 2. Define call parameters + batch_size = self.batch_size + + if editing_prompt: + enable_edit_guidance = True + if isinstance(editing_prompt, str): + editing_prompt = [editing_prompt] + self.enabled_editing_prompts = len(editing_prompt) + elif editing_prompt_embeds is not None: + enable_edit_guidance = True + self.enabled_editing_prompts = editing_prompt_embeds.shape[0] + else: + self.enabled_editing_prompts = 0 + enable_edit_guidance = False + + # 3. Encode input prompt + lora_scale = ( + self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None + ) + + edit_concepts, uncond_embeddings, num_edit_tokens = self.encode_prompt( + editing_prompt=editing_prompt, + device=self.device, + num_images_per_prompt=num_images_per_prompt, + enable_edit_guidance=enable_edit_guidance, + negative_prompt=negative_prompt, + editing_prompt_embeds=editing_prompt_embeds, + negative_prompt_embeds=negative_prompt_embeds, + lora_scale=lora_scale, + clip_skip=self.clip_skip, + ) + + # 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 + if enable_edit_guidance: + text_embeddings = torch.cat([uncond_embeddings, edit_concepts]) + self.text_cross_attention_maps = [editing_prompt] if isinstance(editing_prompt, str) else editing_prompt + else: + text_embeddings = torch.cat([uncond_embeddings]) + + # 4. Prepare timesteps + # self.scheduler.set_timesteps(num_inference_steps, device=self.device) + timesteps = self.inversion_steps + t_to_idx = {int(v): k for k, v in enumerate(timesteps[-zs.shape[0] :])} + + if use_cross_attn_mask: + self.attention_store = LeditsAttentionStore( + average=store_averaged_over_steps, + batch_size=batch_size, + max_size=(latents.shape[-2] / 4.0) * (latents.shape[-1] / 4.0), + max_resolution=None, + ) + self.prepare_unet(self.attention_store, PnP=False) + resolution = latents.shape[-2:] + att_res = (int(resolution[0] / 4), int(resolution[1] / 4)) + + # 5. Prepare latent variables + num_channels_latents = self.unet.config.in_channels + latents = self.prepare_latents( + batch_size * num_images_per_prompt, + num_channels_latents, + None, + None, + text_embeddings.dtype, + self.device, + latents, + ) + + # 6. Prepare extra step kwargs. + extra_step_kwargs = self.prepare_extra_step_kwargs(eta) + + self.sem_guidance = None + self.activation_mask = None + + # 7. Denoising loop + num_warmup_steps = 0 + with self.progress_bar(total=len(timesteps)) as progress_bar: + for i, t in enumerate(timesteps): + # expand the latents if we are doing classifier free guidance + + if enable_edit_guidance: + latent_model_input = torch.cat([latents] * (1 + self.enabled_editing_prompts)) + else: + latent_model_input = latents + + latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) + + text_embed_input = text_embeddings + + # predict the noise residual + noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embed_input).sample + + noise_pred_out = noise_pred.chunk(1 + self.enabled_editing_prompts) # [b,4, 64, 64] + noise_pred_uncond = noise_pred_out[0] + noise_pred_edit_concepts = noise_pred_out[1:] + + noise_guidance_edit = torch.zeros( + noise_pred_uncond.shape, + device=self.device, + dtype=noise_pred_uncond.dtype, + ) + + if sem_guidance is not None and len(sem_guidance) > i: + noise_guidance_edit += sem_guidance[i].to(self.device) + + elif enable_edit_guidance: + if self.activation_mask is None: + self.activation_mask = torch.zeros( + (len(timesteps), len(noise_pred_edit_concepts), *noise_pred_edit_concepts[0].shape) + ) + + if self.sem_guidance is None: + self.sem_guidance = torch.zeros((len(timesteps), *noise_pred_uncond.shape)) + + for c, noise_pred_edit_concept in enumerate(noise_pred_edit_concepts): + if isinstance(edit_warmup_steps, list): + edit_warmup_steps_c = edit_warmup_steps[c] + else: + edit_warmup_steps_c = edit_warmup_steps + if i < edit_warmup_steps_c: + continue + + if isinstance(edit_guidance_scale, list): + edit_guidance_scale_c = edit_guidance_scale[c] + else: + edit_guidance_scale_c = edit_guidance_scale + + if isinstance(edit_threshold, list): + edit_threshold_c = edit_threshold[c] + else: + edit_threshold_c = edit_threshold + if isinstance(reverse_editing_direction, list): + reverse_editing_direction_c = reverse_editing_direction[c] + else: + reverse_editing_direction_c = reverse_editing_direction + + if isinstance(edit_cooldown_steps, list): + edit_cooldown_steps_c = edit_cooldown_steps[c] + elif edit_cooldown_steps is None: + edit_cooldown_steps_c = i + 1 + else: + edit_cooldown_steps_c = edit_cooldown_steps + + if i >= edit_cooldown_steps_c: + continue + + noise_guidance_edit_tmp = noise_pred_edit_concept - noise_pred_uncond + + if reverse_editing_direction_c: + noise_guidance_edit_tmp = noise_guidance_edit_tmp * -1 + + noise_guidance_edit_tmp = noise_guidance_edit_tmp * edit_guidance_scale_c + + if user_mask is not None: + noise_guidance_edit_tmp = noise_guidance_edit_tmp * user_mask + + if use_cross_attn_mask: + out = self.attention_store.aggregate_attention( + attention_maps=self.attention_store.step_store, + prompts=self.text_cross_attention_maps, + res=att_res, + from_where=["up", "down"], + is_cross=True, + select=self.text_cross_attention_maps.index(editing_prompt[c]), + ) + attn_map = out[:, :, :, 1 : 1 + num_edit_tokens[c]] # 0 -> startoftext + + # average over all tokens + if attn_map.shape[3] != num_edit_tokens[c]: + raise ValueError( + f"Incorrect shape of attention_map. Expected size {num_edit_tokens[c]}, but found {attn_map.shape[3]}!" + ) + + attn_map = torch.sum(attn_map, dim=3) + + # gaussian_smoothing + attn_map = F.pad(attn_map.unsqueeze(1), (1, 1, 1, 1), mode="reflect") + attn_map = self.smoothing(attn_map).squeeze(1) + + # torch.quantile function expects float32 + if attn_map.dtype == torch.float32: + tmp = torch.quantile(attn_map.flatten(start_dim=1), edit_threshold_c, dim=1) + else: + tmp = torch.quantile( + attn_map.flatten(start_dim=1).to(torch.float32), edit_threshold_c, dim=1 + ).to(attn_map.dtype) + attn_mask = torch.where( + attn_map >= tmp.unsqueeze(1).unsqueeze(1).repeat(1, *att_res), 1.0, 0.0 + ) + + # resolution must match latent space dimension + attn_mask = F.interpolate( + attn_mask.unsqueeze(1), + noise_guidance_edit_tmp.shape[-2:], # 64,64 + ).repeat(1, 4, 1, 1) + self.activation_mask[i, c] = attn_mask.detach().cpu() + if not use_intersect_mask: + noise_guidance_edit_tmp = noise_guidance_edit_tmp * attn_mask + + if use_intersect_mask: + if t <= 800: + noise_guidance_edit_tmp_quantile = torch.abs(noise_guidance_edit_tmp) + noise_guidance_edit_tmp_quantile = torch.sum( + noise_guidance_edit_tmp_quantile, dim=1, keepdim=True + ) + noise_guidance_edit_tmp_quantile = noise_guidance_edit_tmp_quantile.repeat( + 1, self.unet.config.in_channels, 1, 1 + ) + + # torch.quantile function expects float32 + if noise_guidance_edit_tmp_quantile.dtype == torch.float32: + tmp = torch.quantile( + noise_guidance_edit_tmp_quantile.flatten(start_dim=2), + edit_threshold_c, + dim=2, + keepdim=False, + ) + else: + tmp = torch.quantile( + noise_guidance_edit_tmp_quantile.flatten(start_dim=2).to(torch.float32), + edit_threshold_c, + dim=2, + keepdim=False, + ).to(noise_guidance_edit_tmp_quantile.dtype) + + intersect_mask = ( + torch.where( + noise_guidance_edit_tmp_quantile >= tmp[:, :, None, None], + torch.ones_like(noise_guidance_edit_tmp), + torch.zeros_like(noise_guidance_edit_tmp), + ) + * attn_mask + ) + + self.activation_mask[i, c] = intersect_mask.detach().cpu() + + noise_guidance_edit_tmp = noise_guidance_edit_tmp * intersect_mask + + else: + # print(f"only attention mask for step {i}") + noise_guidance_edit_tmp = noise_guidance_edit_tmp * attn_mask + + elif not use_cross_attn_mask: + # calculate quantile + noise_guidance_edit_tmp_quantile = torch.abs(noise_guidance_edit_tmp) + noise_guidance_edit_tmp_quantile = torch.sum( + noise_guidance_edit_tmp_quantile, dim=1, keepdim=True + ) + noise_guidance_edit_tmp_quantile = noise_guidance_edit_tmp_quantile.repeat(1, 4, 1, 1) + + # torch.quantile function expects float32 + if noise_guidance_edit_tmp_quantile.dtype == torch.float32: + tmp = torch.quantile( + noise_guidance_edit_tmp_quantile.flatten(start_dim=2), + edit_threshold_c, + dim=2, + keepdim=False, + ) + else: + tmp = torch.quantile( + noise_guidance_edit_tmp_quantile.flatten(start_dim=2).to(torch.float32), + edit_threshold_c, + dim=2, + keepdim=False, + ).to(noise_guidance_edit_tmp_quantile.dtype) + + self.activation_mask[i, c] = ( + torch.where( + noise_guidance_edit_tmp_quantile >= tmp[:, :, None, None], + torch.ones_like(noise_guidance_edit_tmp), + torch.zeros_like(noise_guidance_edit_tmp), + ) + .detach() + .cpu() + ) + + noise_guidance_edit_tmp = torch.where( + noise_guidance_edit_tmp_quantile >= tmp[:, :, None, None], + noise_guidance_edit_tmp, + torch.zeros_like(noise_guidance_edit_tmp), + ) + + noise_guidance_edit += noise_guidance_edit_tmp + + self.sem_guidance[i] = noise_guidance_edit.detach().cpu() + + noise_pred = noise_pred_uncond + noise_guidance_edit + + if enable_edit_guidance and self.guidance_rescale > 0.0: + # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf + noise_pred = rescale_noise_cfg( + noise_pred, + noise_pred_edit_concepts.mean(dim=0, keepdim=False), + guidance_rescale=self.guidance_rescale, + ) + + idx = t_to_idx[int(t)] + latents = self.scheduler.step( + noise_pred, t, latents, variance_noise=zs[idx], **extra_step_kwargs + ).prev_sample + + # step callback + if use_cross_attn_mask: + store_step = i in attn_store_steps + self.attention_store.between_steps(store_step) + + if callback_on_step_end is not None: + callback_kwargs = {} + for k in callback_on_step_end_tensor_inputs: + callback_kwargs[k] = locals()[k] + callback_outputs = callback_on_step_end(self, i, t, callback_kwargs) + + latents = callback_outputs.pop("latents", latents) + # prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds) + negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds) + + # call the callback, if provided + if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): + progress_bar.update() + + # 8. Post-processing + if not output_type == "latent": + image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False, generator=generator)[ + 0 + ] + image, has_nsfw_concept = self.run_safety_checker(image, self.device, text_embeddings.dtype) + else: + image = latents + has_nsfw_concept = None + + if has_nsfw_concept is None: + do_denormalize = [True] * image.shape[0] + else: + do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept] + + image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize) + + # Offload all models + self.maybe_free_model_hooks() + + if not return_dict: + return (image, has_nsfw_concept) + + return LEditsPPDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept) + + @torch.no_grad() + def invert( + self, + image: PipelineImageInput, + source_prompt: str = "", + source_guidance_scale: float = 3.5, + num_inversion_steps: int = 30, + skip: float = 0.15, + generator: Optional[torch.Generator] = None, + cross_attention_kwargs: Optional[Dict[str, Any]] = None, + clip_skip: Optional[int] = None, + height: Optional[int] = None, + width: Optional[int] = None, + resize_mode: Optional[str] = "default", + crops_coords: Optional[Tuple[int, int, int, int]] = None, + ): + r""" + The function to the pipeline for image inversion as described by the [LEDITS++ Paper](https://arxiv.org/abs/2301.12247). + If the scheduler is set to [`~schedulers.DDIMScheduler`] the inversion proposed by [edit-friendly DPDM](https://arxiv.org/abs/2304.06140) + will be performed instead. + + Args: + image (`PipelineImageInput`): + Input for the image(s) that are to be edited. Multiple input images have to default to the same aspect + ratio. + source_prompt (`str`, defaults to `""`): + Prompt describing the input image that will be used for guidance during inversion. Guidance is disabled + if the `source_prompt` is `""`. + source_guidance_scale (`float`, defaults to `3.5`): + Strength of guidance during inversion. + num_inversion_steps (`int`, defaults to `30`): + Number of total performed inversion steps after discarding the initial `skip` steps. + skip (`float`, defaults to `0.15`): + Portion of initial steps that will be ignored for inversion and subsequent generation. Lower values + will lead to stronger changes to the input image. `skip` has to be between `0` and `1`. + generator (`torch.Generator`, *optional*): + A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make + inversion deterministic. + cross_attention_kwargs (`dict`, *optional*): + A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in + [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). + clip_skip (`int`, *optional*): + Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that + the output of the pre-final layer will be used for computing the prompt embeddings. + height (`int`, *optional*, defaults to `None`): + The height in preprocessed image. If `None`, will use the `get_default_height_width()` to get default height. + width (`int`, *optional*`, defaults to `None`): + The width in preprocessed. If `None`, will use get_default_height_width()` to get the default width. + resize_mode (`str`, *optional*, defaults to `default`): + The resize mode, can be one of `default` or `fill`. If `default`, will resize the image to fit + within the specified width and height, and it may not maintaining the original aspect ratio. + If `fill`, will resize the image to fit within the specified width and height, maintaining the aspect ratio, and then center the image + within the dimensions, filling empty with data from image. + If `crop`, will resize the image to fit within the specified width and height, maintaining the aspect ratio, and then center the image + within the dimensions, cropping the excess. + Note that resize_mode `fill` and `crop` are only supported for PIL image input. + crops_coords (`List[Tuple[int, int, int, int]]`, *optional*, defaults to `None`): + The crop coordinates for each image in the batch. If `None`, will not crop the image. + + Returns: + [`~pipelines.ledits_pp.LEditsPPInversionPipelineOutput`]: + Output will contain the resized input image(s) and respective VAE reconstruction(s). + """ + # Reset attn processor, we do not want to store attn maps during inversion + self.unet.set_attn_processor(AttnProcessor()) + + self.eta = 1.0 + + self.scheduler.config.timestep_spacing = "leading" + self.scheduler.set_timesteps(int(num_inversion_steps * (1 + skip))) + self.inversion_steps = self.scheduler.timesteps[-num_inversion_steps:] + timesteps = self.inversion_steps + + # 1. encode image + x0, resized = self.encode_image( + image, + dtype=self.text_encoder.dtype, + height=height, + width=width, + resize_mode=resize_mode, + crops_coords=crops_coords, + ) + self.batch_size = x0.shape[0] + + # autoencoder reconstruction + image_rec = self.vae.decode(x0 / self.vae.config.scaling_factor, return_dict=False, generator=generator)[0] + image_rec = self.image_processor.postprocess(image_rec, output_type="pil") + + # 2. get embeddings + do_classifier_free_guidance = source_guidance_scale > 1.0 + + lora_scale = cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None + + uncond_embedding, text_embeddings, _ = self.encode_prompt( + num_images_per_prompt=1, + device=self.device, + negative_prompt=None, + enable_edit_guidance=do_classifier_free_guidance, + editing_prompt=source_prompt, + lora_scale=lora_scale, + clip_skip=clip_skip, + ) + + # 3. find zs and xts + variance_noise_shape = (num_inversion_steps, *x0.shape) + + # intermediate latents + t_to_idx = {int(v): k for k, v in enumerate(timesteps)} + xts = torch.zeros(size=variance_noise_shape, device=self.device, dtype=uncond_embedding.dtype) + + for t in reversed(timesteps): + idx = num_inversion_steps - t_to_idx[int(t)] - 1 + noise = randn_tensor(shape=x0.shape, generator=generator, device=self.device, dtype=x0.dtype) + xts[idx] = self.scheduler.add_noise(x0, noise, torch.Tensor([t])) + xts = torch.cat([x0.unsqueeze(0), xts], dim=0) + + self.scheduler.set_timesteps(len(self.scheduler.timesteps)) + # noise maps + zs = torch.zeros(size=variance_noise_shape, device=self.device, dtype=uncond_embedding.dtype) + + with self.progress_bar(total=len(timesteps)) as progress_bar: + for t in timesteps: + idx = num_inversion_steps - t_to_idx[int(t)] - 1 + # 1. predict noise residual + xt = xts[idx + 1] + + noise_pred = self.unet(xt, timestep=t, encoder_hidden_states=uncond_embedding).sample + + if not source_prompt == "": + noise_pred_cond = self.unet(xt, timestep=t, encoder_hidden_states=text_embeddings).sample + noise_pred = noise_pred + source_guidance_scale * (noise_pred_cond - noise_pred) + + xtm1 = xts[idx] + z, xtm1_corrected = compute_noise(self.scheduler, xtm1, xt, t, noise_pred, self.eta) + zs[idx] = z + + # correction to avoid error accumulation + xts[idx] = xtm1_corrected + + progress_bar.update() + + self.init_latents = xts[-1].expand(self.batch_size, -1, -1, -1) + zs = zs.flip(0) + self.zs = zs + + return LEditsPPInversionPipelineOutput(images=resized, vae_reconstruction_images=image_rec) + + @torch.no_grad() + def encode_image(self, image, dtype=None, height=None, width=None, resize_mode="default", crops_coords=None): + image = self.image_processor.preprocess( + image=image, height=height, width=width, resize_mode=resize_mode, crops_coords=crops_coords + ) + resized = self.image_processor.postprocess(image=image, output_type="pil") + + if max(image.shape[-2:]) > self.vae.config["sample_size"] * 1.5: + logger.warning( + "Your input images far exceed the default resolution of the underlying diffusion model. " + "The output images may contain severe artifacts! " + "Consider down-sampling the input using the `height` and `width` parameters" + ) + image = image.to(dtype) + + x0 = self.vae.encode(image.to(self.device)).latent_dist.mode() + x0 = x0.to(dtype) + x0 = self.vae.config.scaling_factor * x0 + return x0, resized + + +def compute_noise_ddim(scheduler, prev_latents, latents, timestep, noise_pred, eta): + # 1. get previous step value (=t-1) + prev_timestep = timestep - scheduler.config.num_train_timesteps // scheduler.num_inference_steps + + # 2. compute alphas, betas + alpha_prod_t = scheduler.alphas_cumprod[timestep] + alpha_prod_t_prev = ( + scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else scheduler.final_alpha_cumprod + ) + + beta_prod_t = 1 - alpha_prod_t + + # 3. compute predicted original sample from predicted noise also called + # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf + pred_original_sample = (latents - beta_prod_t ** (0.5) * noise_pred) / alpha_prod_t ** (0.5) + + # 4. Clip "predicted x_0" + if scheduler.config.clip_sample: + pred_original_sample = torch.clamp(pred_original_sample, -1, 1) + + # 5. compute variance: "sigma_t(η)" -> see formula (16) + # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1) + variance = scheduler._get_variance(timestep, prev_timestep) + std_dev_t = eta * variance ** (0.5) + + # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf + pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * noise_pred + + # modifed so that updated xtm1 is returned as well (to avoid error accumulation) + mu_xt = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction + if variance > 0.0: + noise = (prev_latents - mu_xt) / (variance ** (0.5) * eta) + else: + noise = torch.tensor([0.0]).to(latents.device) + + return noise, mu_xt + (eta * variance**0.5) * noise + + +def compute_noise_sde_dpm_pp_2nd(scheduler, prev_latents, latents, timestep, noise_pred, eta): + def first_order_update(model_output, sample): # timestep, prev_timestep, sample): + sigma_t, sigma_s = scheduler.sigmas[scheduler.step_index + 1], scheduler.sigmas[scheduler.step_index] + alpha_t, sigma_t = scheduler._sigma_to_alpha_sigma_t(sigma_t) + alpha_s, sigma_s = scheduler._sigma_to_alpha_sigma_t(sigma_s) + lambda_t = torch.log(alpha_t) - torch.log(sigma_t) + lambda_s = torch.log(alpha_s) - torch.log(sigma_s) + + h = lambda_t - lambda_s + + mu_xt = (sigma_t / sigma_s * torch.exp(-h)) * sample + (alpha_t * (1 - torch.exp(-2.0 * h))) * model_output + + mu_xt = scheduler.dpm_solver_first_order_update( + model_output=model_output, sample=sample, noise=torch.zeros_like(sample) + ) + + sigma = sigma_t * torch.sqrt(1.0 - torch.exp(-2 * h)) + if sigma > 0.0: + noise = (prev_latents - mu_xt) / sigma + else: + noise = torch.tensor([0.0]).to(sample.device) + + prev_sample = mu_xt + sigma * noise + return noise, prev_sample + + def second_order_update(model_output_list, sample): # timestep_list, prev_timestep, sample): + sigma_t, sigma_s0, sigma_s1 = ( + scheduler.sigmas[scheduler.step_index + 1], + scheduler.sigmas[scheduler.step_index], + scheduler.sigmas[scheduler.step_index - 1], + ) + + alpha_t, sigma_t = scheduler._sigma_to_alpha_sigma_t(sigma_t) + alpha_s0, sigma_s0 = scheduler._sigma_to_alpha_sigma_t(sigma_s0) + alpha_s1, sigma_s1 = scheduler._sigma_to_alpha_sigma_t(sigma_s1) + + lambda_t = torch.log(alpha_t) - torch.log(sigma_t) + lambda_s0 = torch.log(alpha_s0) - torch.log(sigma_s0) + lambda_s1 = torch.log(alpha_s1) - torch.log(sigma_s1) + + m0, m1 = model_output_list[-1], model_output_list[-2] + + h, h_0 = lambda_t - lambda_s0, lambda_s0 - lambda_s1 + r0 = h_0 / h + D0, D1 = m0, (1.0 / r0) * (m0 - m1) + + mu_xt = ( + (sigma_t / sigma_s0 * torch.exp(-h)) * sample + + (alpha_t * (1 - torch.exp(-2.0 * h))) * D0 + + 0.5 * (alpha_t * (1 - torch.exp(-2.0 * h))) * D1 + ) + + sigma = sigma_t * torch.sqrt(1.0 - torch.exp(-2 * h)) + if sigma > 0.0: + noise = (prev_latents - mu_xt) / sigma + else: + noise = torch.tensor([0.0]).to(sample.device) + + prev_sample = mu_xt + sigma * noise + + return noise, prev_sample + + if scheduler.step_index is None: + scheduler._init_step_index(timestep) + + model_output = scheduler.convert_model_output(model_output=noise_pred, sample=latents) + for i in range(scheduler.config.solver_order - 1): + scheduler.model_outputs[i] = scheduler.model_outputs[i + 1] + scheduler.model_outputs[-1] = model_output + + if scheduler.lower_order_nums < 1: + noise, prev_sample = first_order_update(model_output, latents) + else: + noise, prev_sample = second_order_update(scheduler.model_outputs, latents) + + if scheduler.lower_order_nums < scheduler.config.solver_order: + scheduler.lower_order_nums += 1 + + # upon completion increase step index by one + scheduler._step_index += 1 + + return noise, prev_sample + + +def compute_noise(scheduler, *args): + if isinstance(scheduler, DDIMScheduler): + return compute_noise_ddim(scheduler, *args) + elif ( + isinstance(scheduler, DPMSolverMultistepScheduler) + and scheduler.config.algorithm_type == "sde-dpmsolver++" + and scheduler.config.solver_order == 2 + ): + return compute_noise_sde_dpm_pp_2nd(scheduler, *args) + else: + raise NotImplementedError diff --git a/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py b/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py new file mode 100644 index 000000000000..874a10a7ccd5 --- /dev/null +++ b/src/diffusers/pipelines/ledits_pp/pipeline_leditspp_stable_diffusion_xl.py @@ -0,0 +1,1797 @@ +# Copyright 2023 The HuggingFace Team. All rights reserved. +# +# 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. + +import inspect +import math +from typing import Any, Callable, Dict, List, Optional, Tuple, Union + +import torch +import torch.nn.functional as F +from transformers import ( + CLIPImageProcessor, + CLIPTextModel, + CLIPTextModelWithProjection, + CLIPTokenizer, + CLIPVisionModelWithProjection, +) + +from ...image_processor import PipelineImageInput, VaeImageProcessor +from ...loaders import ( + FromSingleFileMixin, + IPAdapterMixin, + StableDiffusionXLLoraLoaderMixin, + TextualInversionLoaderMixin, +) +from ...models import AutoencoderKL, UNet2DConditionModel +from ...models.attention_processor import ( + Attention, + AttnProcessor, + AttnProcessor2_0, + LoRAAttnProcessor2_0, + LoRAXFormersAttnProcessor, + XFormersAttnProcessor, +) +from ...models.lora import adjust_lora_scale_text_encoder +from ...schedulers import DDIMScheduler, DPMSolverMultistepScheduler +from ...utils import ( + USE_PEFT_BACKEND, + is_invisible_watermark_available, + is_torch_xla_available, + logging, + replace_example_docstring, + scale_lora_layers, + unscale_lora_layers, +) +from ...utils.torch_utils import randn_tensor +from ..pipeline_utils import DiffusionPipeline +from .pipeline_output import LEditsPPDiffusionPipelineOutput, LEditsPPInversionPipelineOutput + + +if is_invisible_watermark_available(): + from ..stable_diffusion_xl.watermark import StableDiffusionXLWatermarker + +if is_torch_xla_available(): + import torch_xla.core.xla_model as xm + + XLA_AVAILABLE = True +else: + XLA_AVAILABLE = False + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + +EXAMPLE_DOC_STRING = """ + Examples: + ```py + >>> import torch + >>> import PIL + >>> import requests + >>> from io import BytesIO + + >>> from diffusers import LEditsPPPipelineStableDiffusionXL + + >>> pipe = LEditsPPPipelineStableDiffusionXL.from_pretrained( + ... "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16 + ... ) + >>> pipe = pipe.to("cuda") + + >>> def download_image(url): + ... response = requests.get(url) + ... return PIL.Image.open(BytesIO(response.content)).convert("RGB") + + >>> img_url = "https://www.aiml.informatik.tu-darmstadt.de/people/mbrack/tennis.jpg" + >>> image = download_image(img_url) + + >>> _ = pipe.invert( + ... image = image, + ... num_inversion_steps=50, + ... skip=0.2 + ... ) + + >>> edited_image = pipe( + ... editing_prompt=["tennis ball","tomato"], + ... reverse_editing_direction=[True,False], + ... edit_guidance_scale=[5.0,10.0], + ... edit_threshold=[0.9,0.85], + ).images[0] + ``` +""" + + +# Copied from diffusers.pipelines.ledits_pp.pipeline_leditspp_stable_diffusion.LeditsAttentionStore +class LeditsAttentionStore: + @staticmethod + def get_empty_store(): + return {"down_cross": [], "mid_cross": [], "up_cross": [], "down_self": [], "mid_self": [], "up_self": []} + + def __call__(self, attn, is_cross: bool, place_in_unet: str, editing_prompts, PnP=False): + # attn.shape = batch_size * head_size, seq_len query, seq_len_key + if attn.shape[1] <= self.max_size: + bs = 1 + int(PnP) + editing_prompts + skip = 2 if PnP else 1 # skip PnP & unconditional + attn = torch.stack(attn.split(self.batch_size)).permute(1, 0, 2, 3) + source_batch_size = int(attn.shape[1] // bs) + self.forward(attn[:, skip * source_batch_size :], is_cross, place_in_unet) + + def forward(self, attn, is_cross: bool, place_in_unet: str): + key = f"{place_in_unet}_{'cross' if is_cross else 'self'}" + + self.step_store[key].append(attn) + + def between_steps(self, store_step=True): + if store_step: + if self.average: + if len(self.attention_store) == 0: + self.attention_store = self.step_store + else: + for key in self.attention_store: + for i in range(len(self.attention_store[key])): + self.attention_store[key][i] += self.step_store[key][i] + else: + if len(self.attention_store) == 0: + self.attention_store = [self.step_store] + else: + self.attention_store.append(self.step_store) + + self.cur_step += 1 + self.step_store = self.get_empty_store() + + def get_attention(self, step: int): + if self.average: + attention = { + key: [item / self.cur_step for item in self.attention_store[key]] for key in self.attention_store + } + else: + assert step is not None + attention = self.attention_store[step] + return attention + + def aggregate_attention( + self, attention_maps, prompts, res: Union[int, Tuple[int]], from_where: List[str], is_cross: bool, select: int + ): + out = [[] for x in range(self.batch_size)] + if isinstance(res, int): + num_pixels = res**2 + resolution = (res, res) + else: + num_pixels = res[0] * res[1] + resolution = res[:2] + + for location in from_where: + for bs_item in attention_maps[f"{location}_{'cross' if is_cross else 'self'}"]: + for batch, item in enumerate(bs_item): + if item.shape[1] == num_pixels: + cross_maps = item.reshape(len(prompts), -1, *resolution, item.shape[-1])[select] + out[batch].append(cross_maps) + + out = torch.stack([torch.cat(x, dim=0) for x in out]) + # average over heads + out = out.sum(1) / out.shape[1] + return out + + def __init__(self, average: bool, batch_size=1, max_resolution=16, max_size: int = None): + self.step_store = self.get_empty_store() + self.attention_store = [] + self.cur_step = 0 + self.average = average + self.batch_size = batch_size + if max_size is None: + self.max_size = max_resolution**2 + elif max_size is not None and max_resolution is None: + self.max_size = max_size + else: + raise ValueError("Only allowed to set one of max_resolution or max_size") + + +# Copied from diffusers.pipelines.ledits_pp.pipeline_leditspp_stable_diffusion.LeditsGaussianSmoothing +class LeditsGaussianSmoothing: + def __init__(self, device): + kernel_size = [3, 3] + sigma = [0.5, 0.5] + + # The gaussian kernel is the product of the gaussian function of each dimension. + kernel = 1 + meshgrids = torch.meshgrid([torch.arange(size, dtype=torch.float32) for size in kernel_size]) + for size, std, mgrid in zip(kernel_size, sigma, meshgrids): + mean = (size - 1) / 2 + kernel *= 1 / (std * math.sqrt(2 * math.pi)) * torch.exp(-(((mgrid - mean) / (2 * std)) ** 2)) + + # Make sure sum of values in gaussian kernel equals 1. + kernel = kernel / torch.sum(kernel) + + # Reshape to depthwise convolutional weight + kernel = kernel.view(1, 1, *kernel.size()) + kernel = kernel.repeat(1, *[1] * (kernel.dim() - 1)) + + self.weight = kernel.to(device) + + def __call__(self, input): + """ + Arguments: + Apply gaussian filter to input. + input (torch.Tensor): Input to apply gaussian filter on. + Returns: + filtered (torch.Tensor): Filtered output. + """ + return F.conv2d(input, weight=self.weight.to(input.dtype)) + + +# Copied from diffusers.pipelines.ledits_pp.pipeline_leditspp_stable_diffusion.LEDITSCrossAttnProcessor +class LEDITSCrossAttnProcessor: + def __init__(self, attention_store, place_in_unet, pnp, editing_prompts): + self.attnstore = attention_store + self.place_in_unet = place_in_unet + self.editing_prompts = editing_prompts + self.pnp = pnp + + def __call__( + self, + attn: Attention, + hidden_states, + encoder_hidden_states, + attention_mask=None, + temb=None, + ): + batch_size, sequence_length, _ = ( + hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape + ) + attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size) + + query = attn.to_q(hidden_states) + + if encoder_hidden_states is None: + encoder_hidden_states = hidden_states + elif attn.norm_cross: + encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states) + + key = attn.to_k(encoder_hidden_states) + value = attn.to_v(encoder_hidden_states) + + query = attn.head_to_batch_dim(query) + key = attn.head_to_batch_dim(key) + value = attn.head_to_batch_dim(value) + + attention_probs = attn.get_attention_scores(query, key, attention_mask) + self.attnstore( + attention_probs, + is_cross=True, + place_in_unet=self.place_in_unet, + editing_prompts=self.editing_prompts, + PnP=self.pnp, + ) + + hidden_states = torch.bmm(attention_probs, value) + hidden_states = attn.batch_to_head_dim(hidden_states) + + # linear proj + hidden_states = attn.to_out[0](hidden_states) + # dropout + hidden_states = attn.to_out[1](hidden_states) + + hidden_states = hidden_states / attn.rescale_output_factor + return hidden_states + + +class LEditsPPPipelineStableDiffusionXL( + DiffusionPipeline, + FromSingleFileMixin, + StableDiffusionXLLoraLoaderMixin, + TextualInversionLoaderMixin, + IPAdapterMixin, +): + """ + Pipeline for textual image editing using LEDits++ with Stable Diffusion XL. + + This model inherits from [`DiffusionPipeline`] and builds on the [`StableDiffusionXLPipeline`]. Check the superclass + documentation for the generic methods implemented for all pipelines (downloading, saving, running on a particular + device, etc.). + + In addition the pipeline inherits the following loading methods: + - *LoRA*: [`LEditsPPPipelineStableDiffusionXL.load_lora_weights`] + - *Ckpt*: [`loaders.FromSingleFileMixin.from_single_file`] + + as well as the following saving methods: + - *LoRA*: [`loaders.StableDiffusionXLPipeline.save_lora_weights`] + + Args: + vae ([`AutoencoderKL`]): + Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. + text_encoder ([`~transformers.CLIPTextModel`]): + Frozen text-encoder. Stable Diffusion XL 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. + text_encoder_2 ([`~transformers.CLIPTextModelWithProjection`]): + Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of + [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection), + specifically the + [laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k) + variant. + tokenizer ([`~transformers.CLIPTokenizer`]): + Tokenizer of class + [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). + tokenizer_2 ([`~transformers.CLIPTokenizer`]): + Second 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 ([`DPMSolverMultistepScheduler`] or [`DDIMScheduler`]): + A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of + [`DPMSolverMultistepScheduler`] or [`DDIMScheduler`]. If any other scheduler is passed it will automatically + be set to [`DPMSolverMultistepScheduler`]. + force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `"True"`): + Whether the negative prompt embeddings shall be forced to always be set to 0. Also see the config of + `stabilityai/stable-diffusion-xl-base-1-0`. + add_watermarker (`bool`, *optional*): + Whether to use the [invisible_watermark library](https://github.com/ShieldMnt/invisible-watermark/) to + watermark output images. If not defined, it will default to True if the package is installed, otherwise no + watermarker will be used. + """ + + model_cpu_offload_seq = "text_encoder->text_encoder_2->unet->vae" + _optional_components = [ + "tokenizer", + "tokenizer_2", + "text_encoder", + "text_encoder_2", + "image_encoder", + "feature_extractor", + ] + _callback_tensor_inputs = [ + "latents", + "prompt_embeds", + "negative_prompt_embeds", + "add_text_embeds", + "add_time_ids", + "negative_pooled_prompt_embeds", + "negative_add_time_ids", + ] + + def __init__( + self, + vae: AutoencoderKL, + text_encoder: CLIPTextModel, + text_encoder_2: CLIPTextModelWithProjection, + tokenizer: CLIPTokenizer, + tokenizer_2: CLIPTokenizer, + unet: UNet2DConditionModel, + scheduler: Union[DPMSolverMultistepScheduler, DDIMScheduler], + image_encoder: CLIPVisionModelWithProjection = None, + feature_extractor: CLIPImageProcessor = None, + force_zeros_for_empty_prompt: bool = True, + add_watermarker: Optional[bool] = None, + ): + super().__init__() + + self.register_modules( + vae=vae, + text_encoder=text_encoder, + text_encoder_2=text_encoder_2, + tokenizer=tokenizer, + tokenizer_2=tokenizer_2, + unet=unet, + scheduler=scheduler, + image_encoder=image_encoder, + feature_extractor=feature_extractor, + ) + self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt) + self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) + self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) + + if not isinstance(scheduler, DDIMScheduler) and not isinstance(scheduler, DPMSolverMultistepScheduler): + self.scheduler = DPMSolverMultistepScheduler.from_config( + scheduler.config, algorithm_type="sde-dpmsolver++", solver_order=2 + ) + logger.warning( + "This pipeline only supports DDIMScheduler and DPMSolverMultistepScheduler. " + "The scheduler has been changed to DPMSolverMultistepScheduler." + ) + + self.default_sample_size = self.unet.config.sample_size + + add_watermarker = add_watermarker if add_watermarker is not None else is_invisible_watermark_available() + + if add_watermarker: + self.watermark = StableDiffusionXLWatermarker() + else: + self.watermark = None + self.inversion_steps = None + + def encode_prompt( + self, + device: Optional[torch.device] = None, + num_images_per_prompt: int = 1, + negative_prompt: Optional[str] = None, + negative_prompt_2: Optional[str] = None, + negative_prompt_embeds: Optional[torch.FloatTensor] = None, + negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None, + lora_scale: Optional[float] = None, + clip_skip: Optional[int] = None, + enable_edit_guidance: bool = True, + editing_prompt: Optional[str] = None, + editing_prompt_embeds: Optional[torch.FloatTensor] = None, + editing_pooled_prompt_embeds: Optional[torch.FloatTensor] = None, + ) -> object: + r""" + Encodes the prompt into text encoder hidden states. + + Args: + device: (`torch.device`): + torch device + num_images_per_prompt (`int`): + number of images that should be generated per prompt + negative_prompt (`str` or `List[str]`, *optional*): + The prompt or prompts not to guide the image generation. If not defined, one has to pass + `negative_prompt_embeds` instead. + negative_prompt_2 (`str` or `List[str]`, *optional*): + The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and + `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders + negative_prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt + weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input + argument. + negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt + weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt` + input argument. + lora_scale (`float`, *optional*): + A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded. + clip_skip (`int`, *optional*): + Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that + the output of the pre-final layer will be used for computing the prompt embeddings. + enable_edit_guidance (`bool`): + Whether to guide towards an editing prompt or not. + editing_prompt (`str` or `List[str]`, *optional*): + Editing prompt(s) to be encoded. If not defined and 'enable_edit_guidance' is True, one has to pass + `editing_prompt_embeds` instead. + editing_prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated edit text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt + weighting. If not provided and 'enable_edit_guidance' is True, editing_prompt_embeds will be generated from `editing_prompt` input + argument. + editing_pooled_prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated edit pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt + weighting. If not provided, pooled editing_pooled_prompt_embeds will be generated from `editing_prompt` + input argument. + """ + device = device or self._execution_device + + # set lora scale so that monkey patched LoRA + # function of text encoder can correctly access it + if lora_scale is not None and isinstance(self, StableDiffusionXLLoraLoaderMixin): + self._lora_scale = lora_scale + + # dynamically adjust the LoRA scale + if self.text_encoder is not None: + if not USE_PEFT_BACKEND: + adjust_lora_scale_text_encoder(self.text_encoder, lora_scale) + else: + scale_lora_layers(self.text_encoder, lora_scale) + + if self.text_encoder_2 is not None: + if not USE_PEFT_BACKEND: + adjust_lora_scale_text_encoder(self.text_encoder_2, lora_scale) + else: + scale_lora_layers(self.text_encoder_2, lora_scale) + + batch_size = self.batch_size + + # Define tokenizers and text encoders + tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2] + text_encoders = ( + [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2] + ) + num_edit_tokens = 0 + + # get unconditional embeddings for classifier free guidance + zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt + + if negative_prompt_embeds is None: + negative_prompt = negative_prompt or "" + negative_prompt_2 = negative_prompt_2 or negative_prompt + + # normalize str to list + negative_prompt = batch_size * [negative_prompt] if isinstance(negative_prompt, str) else negative_prompt + negative_prompt_2 = ( + batch_size * [negative_prompt_2] if isinstance(negative_prompt_2, str) else negative_prompt_2 + ) + + uncond_tokens: List[str] + + if batch_size != len(negative_prompt): + raise ValueError( + f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but image inversion " + f" has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" + " the batch size of the input images." + ) + else: + uncond_tokens = [negative_prompt, negative_prompt_2] + + negative_prompt_embeds_list = [] + for negative_prompt, tokenizer, text_encoder in zip(uncond_tokens, tokenizers, text_encoders): + if isinstance(self, TextualInversionLoaderMixin): + negative_prompt = self.maybe_convert_prompt(negative_prompt, tokenizer) + + uncond_input = tokenizer( + negative_prompt, + padding="max_length", + max_length=tokenizer.model_max_length, + truncation=True, + return_tensors="pt", + ) + + negative_prompt_embeds = text_encoder( + uncond_input.input_ids.to(device), + output_hidden_states=True, + ) + # We are only ALWAYS interested in the pooled output of the final text encoder + negative_pooled_prompt_embeds = negative_prompt_embeds[0] + negative_prompt_embeds = negative_prompt_embeds.hidden_states[-2] + + negative_prompt_embeds_list.append(negative_prompt_embeds) + + negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, dim=-1) + + if zero_out_negative_prompt: + negative_prompt_embeds = torch.zeros_like(negative_prompt_embeds) + negative_pooled_prompt_embeds = torch.zeros_like(negative_pooled_prompt_embeds) + + if enable_edit_guidance and editing_prompt_embeds is None: + editing_prompt_2 = editing_prompt + + editing_prompts = [editing_prompt, editing_prompt_2] + edit_prompt_embeds_list = [] + + for editing_prompt, tokenizer, text_encoder in zip(editing_prompts, tokenizers, text_encoders): + if isinstance(self, TextualInversionLoaderMixin): + editing_prompt = self.maybe_convert_prompt(editing_prompt, tokenizer) + + max_length = negative_prompt_embeds.shape[1] + edit_concepts_input = tokenizer( + # [x for item in editing_prompt for x in repeat(item, batch_size)], + editing_prompt, + padding="max_length", + max_length=max_length, + truncation=True, + return_tensors="pt", + return_length=True, + ) + num_edit_tokens = edit_concepts_input.length - 2 + + edit_concepts_embeds = text_encoder( + edit_concepts_input.input_ids.to(device), + output_hidden_states=True, + ) + # We are only ALWAYS interested in the pooled output of the final text encoder + editing_pooled_prompt_embeds = edit_concepts_embeds[0] + if clip_skip is None: + edit_concepts_embeds = edit_concepts_embeds.hidden_states[-2] + else: + # "2" because SDXL always indexes from the penultimate layer. + edit_concepts_embeds = edit_concepts_embeds.hidden_states[-(clip_skip + 2)] + + edit_prompt_embeds_list.append(edit_concepts_embeds) + + edit_concepts_embeds = torch.concat(edit_prompt_embeds_list, dim=-1) + elif not enable_edit_guidance: + edit_concepts_embeds = None + editing_pooled_prompt_embeds = None + + negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device) + bs_embed, seq_len, _ = negative_prompt_embeds.shape + # duplicate unconditional embeddings for each generation per prompt, using mps friendly method + seq_len = negative_prompt_embeds.shape[1] + negative_prompt_embeds = negative_prompt_embeds.to(dtype=self.text_encoder_2.dtype, device=device) + negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) + negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) + + if enable_edit_guidance: + bs_embed_edit, seq_len, _ = edit_concepts_embeds.shape + edit_concepts_embeds = edit_concepts_embeds.to(dtype=self.text_encoder_2.dtype, device=device) + edit_concepts_embeds = edit_concepts_embeds.repeat(1, num_images_per_prompt, 1) + edit_concepts_embeds = edit_concepts_embeds.view(bs_embed_edit * num_images_per_prompt, seq_len, -1) + + negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view( + bs_embed * num_images_per_prompt, -1 + ) + + if enable_edit_guidance: + editing_pooled_prompt_embeds = editing_pooled_prompt_embeds.repeat(1, num_images_per_prompt).view( + bs_embed_edit * num_images_per_prompt, -1 + ) + + if self.text_encoder is not None: + if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND: + # Retrieve the original scale by scaling back the LoRA layers + unscale_lora_layers(self.text_encoder, lora_scale) + + if self.text_encoder_2 is not None: + if isinstance(self, StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND: + # Retrieve the original scale by scaling back the LoRA layers + unscale_lora_layers(self.text_encoder_2, lora_scale) + + return ( + negative_prompt_embeds, + edit_concepts_embeds, + negative_pooled_prompt_embeds, + editing_pooled_prompt_embeds, + num_edit_tokens, + ) + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs + def prepare_extra_step_kwargs(self, eta, generator=None): + # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature + # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. + # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 + # and should be between [0, 1] + + accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) + extra_step_kwargs = {} + if accepts_eta: + extra_step_kwargs["eta"] = eta + + # check if the scheduler accepts generator + accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) + if accepts_generator: + extra_step_kwargs["generator"] = generator + return extra_step_kwargs + + def check_inputs( + self, + negative_prompt=None, + negative_prompt_2=None, + negative_prompt_embeds=None, + negative_pooled_prompt_embeds=None, + ): + if negative_prompt is not None and negative_prompt_embeds is not None: + raise ValueError( + f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" + f" {negative_prompt_embeds}. Please make sure to only forward one of the two." + ) + elif negative_prompt_2 is not None and negative_prompt_embeds is not None: + raise ValueError( + f"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:" + f" {negative_prompt_embeds}. Please make sure to only forward one of the two." + ) + + if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None: + raise ValueError( + "If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used to generate `negative_prompt_embeds`." + ) + + # Modified from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents + def prepare_latents(self, device, latents): + latents = latents.to(device) + + # scale the initial noise by the standard deviation required by the scheduler + latents = latents * self.scheduler.init_noise_sigma + return latents + + def _get_add_time_ids( + self, original_size, crops_coords_top_left, target_size, dtype, text_encoder_projection_dim=None + ): + add_time_ids = list(original_size + crops_coords_top_left + target_size) + + passed_add_embed_dim = ( + self.unet.config.addition_time_embed_dim * len(add_time_ids) + text_encoder_projection_dim + ) + expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features + + if expected_add_embed_dim != passed_add_embed_dim: + raise ValueError( + f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`." + ) + + add_time_ids = torch.tensor([add_time_ids], dtype=dtype) + return add_time_ids + + # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae + def upcast_vae(self): + dtype = self.vae.dtype + self.vae.to(dtype=torch.float32) + use_torch_2_0_or_xformers = isinstance( + self.vae.decoder.mid_block.attentions[0].processor, + ( + AttnProcessor2_0, + XFormersAttnProcessor, + LoRAXFormersAttnProcessor, + LoRAAttnProcessor2_0, + ), + ) + # if xformers or torch_2_0 is used attention block does not need + # to be in float32 which can save lots of memory + if use_torch_2_0_or_xformers: + self.vae.post_quant_conv.to(dtype) + self.vae.decoder.conv_in.to(dtype) + self.vae.decoder.mid_block.to(dtype) + + # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding + def get_guidance_scale_embedding(self, w, embedding_dim=512, dtype=torch.float32): + """ + See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298 + + Args: + timesteps (`torch.Tensor`): + generate embedding vectors at these timesteps + embedding_dim (`int`, *optional*, defaults to 512): + dimension of the embeddings to generate + dtype: + data type of the generated embeddings + + Returns: + `torch.FloatTensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)` + """ + assert len(w.shape) == 1 + w = w * 1000.0 + + half_dim = embedding_dim // 2 + emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1) + emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb) + emb = w.to(dtype)[:, None] * emb[None, :] + emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1) + if embedding_dim % 2 == 1: # zero pad + emb = torch.nn.functional.pad(emb, (0, 1)) + assert emb.shape == (w.shape[0], embedding_dim) + return emb + + @property + def guidance_scale(self): + return self._guidance_scale + + @property + def guidance_rescale(self): + return self._guidance_rescale + + @property + def clip_skip(self): + return self._clip_skip + + # 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. + @property + def do_classifier_free_guidance(self): + return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None + + @property + def cross_attention_kwargs(self): + return self._cross_attention_kwargs + + @property + def denoising_end(self): + return self._denoising_end + + @property + def num_timesteps(self): + return self._num_timesteps + + # Copied from diffusers.pipelines.ledits_pp.pipeline_leditspp_stable_diffusion.LEditsPPPipelineStableDiffusion.prepare_unet + def prepare_unet(self, attention_store, PnP: bool = False): + attn_procs = {} + for name in self.unet.attn_processors.keys(): + if name.startswith("mid_block"): + place_in_unet = "mid" + elif name.startswith("up_blocks"): + place_in_unet = "up" + elif name.startswith("down_blocks"): + place_in_unet = "down" + else: + continue + + if "attn2" in name and place_in_unet != "mid": + attn_procs[name] = LEDITSCrossAttnProcessor( + attention_store=attention_store, + place_in_unet=place_in_unet, + pnp=PnP, + editing_prompts=self.enabled_editing_prompts, + ) + else: + attn_procs[name] = AttnProcessor() + + self.unet.set_attn_processor(attn_procs) + + @torch.no_grad() + @replace_example_docstring(EXAMPLE_DOC_STRING) + def __call__( + self, + denoising_end: Optional[float] = None, + negative_prompt: Optional[Union[str, List[str]]] = None, + negative_prompt_2: Optional[Union[str, List[str]]] = None, + negative_prompt_embeds: Optional[torch.FloatTensor] = None, + negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None, + ip_adapter_image: Optional[PipelineImageInput] = None, + output_type: Optional[str] = "pil", + return_dict: bool = True, + cross_attention_kwargs: Optional[Dict[str, Any]] = None, + guidance_rescale: float = 0.0, + crops_coords_top_left: Tuple[int, int] = (0, 0), + target_size: Optional[Tuple[int, int]] = None, + editing_prompt: Optional[Union[str, List[str]]] = None, + editing_prompt_embeddings: Optional[torch.Tensor] = None, + editing_pooled_prompt_embeds: Optional[torch.Tensor] = None, + reverse_editing_direction: Optional[Union[bool, List[bool]]] = False, + edit_guidance_scale: Optional[Union[float, List[float]]] = 5, + edit_warmup_steps: Optional[Union[int, List[int]]] = 0, + edit_cooldown_steps: Optional[Union[int, List[int]]] = None, + edit_threshold: Optional[Union[float, List[float]]] = 0.9, + sem_guidance: Optional[List[torch.Tensor]] = None, + use_cross_attn_mask: bool = False, + use_intersect_mask: bool = False, + user_mask: Optional[torch.FloatTensor] = None, + attn_store_steps: Optional[List[int]] = [], + store_averaged_over_steps: bool = True, + clip_skip: Optional[int] = None, + callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None, + callback_on_step_end_tensor_inputs: List[str] = ["latents"], + **kwargs, + ): + r""" + The call function to the pipeline for editing. The [`~pipelines.ledits_pp.LEditsPPPipelineStableDiffusionXL.invert`] + method has to be called beforehand. Edits will always be performed for the last inverted image(s). + + Args: + denoising_end (`float`, *optional*): + When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be + completed before it is intentionally prematurely terminated. As a result, the returned sample will + still retain a substantial amount of noise as determined by the discrete timesteps selected by the + scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a + "Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refining the Image + negative_prompt (`str` or `List[str]`, *optional*): + The prompt or prompts not to guide the image generation. If not defined, one has to pass + `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is + less than `1`). + negative_prompt_2 (`str` or `List[str]`, *optional*): + The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and + `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders + negative_prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt + weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input + argument. + negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*): + Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt + weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt` + input argument. + ip_adapter_image: (`PipelineImageInput`, *optional*): + Optional image input to work with IP Adapters. + output_type (`str`, *optional*, defaults to `"pil"`): + The output format of the generate image. Choose between + [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead + of a plain tuple. + callback (`Callable`, *optional*): + A function that will be called every `callback_steps` steps during inference. The function will be + called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. + callback_steps (`int`, *optional*, defaults to 1): + The frequency at which the `callback` function will be called. If not specified, the callback will be + called at every step. + cross_attention_kwargs (`dict`, *optional*): + A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under + `self.processor` in + [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). + guidance_rescale (`float`, *optional*, defaults to 0.7): + Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are + Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of + [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). + Guidance rescale factor should fix overexposure when using zero terminal SNR. + crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)): + `crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position + `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting + `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of + [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). + target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)): + For most cases, `target_size` should be set to the desired height and width of the generated image. If + not specified it will default to `(width, height)`. Part of SDXL's micro-conditioning as explained in + section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). + editing_prompt (`str` or `List[str]`, *optional*): + The prompt or prompts to guide the image generation. The image is reconstructed by setting + `editing_prompt = None`. Guidance direction of prompt should be specified via `reverse_editing_direction`. + editing_prompt_embeddings (`torch.Tensor`, *optional*): + Pre-generated edit text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt + weighting. If not provided, editing_prompt_embeddings will be generated from `editing_prompt` input + argument. + editing_pooled_prompt_embeddings (`torch.Tensor`, *optional*): + Pre-generated pooled edit text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt + weighting. If not provided, editing_prompt_embeddings will be generated from `editing_prompt` input + argument. + reverse_editing_direction (`bool` or `List[bool]`, *optional*, defaults to `False`): + Whether the corresponding prompt in `editing_prompt` should be increased or decreased. + edit_guidance_scale (`float` or `List[float]`, *optional*, defaults to 5): + Guidance scale for guiding the image generation. If provided as list values should correspond to `editing_prompt`. + `edit_guidance_scale` is defined as `s_e` of equation 12 of + [LEDITS++ Paper](https://arxiv.org/abs/2301.12247). + edit_warmup_steps (`float` or `List[float]`, *optional*, defaults to 10): + Number of diffusion steps (for each prompt) for which guidance is not applied. + edit_cooldown_steps (`float` or `List[float]`, *optional*, defaults to `None`): + Number of diffusion steps (for each prompt) after which guidance is no longer applied. + edit_threshold (`float` or `List[float]`, *optional*, defaults to 0.9): + Masking threshold of guidance. Threshold should be proportional to the image region that is modified. + 'edit_threshold' is defined as 'λ' of equation 12 of [LEDITS++ Paper](https://arxiv.org/abs/2301.12247). + sem_guidance (`List[torch.Tensor]`, *optional*): + List of pre-generated guidance vectors to be applied at generation. Length of the list has to + correspond to `num_inference_steps`. + use_cross_attn_mask: + Whether cross-attention masks are used. Cross-attention masks are always used when use_intersect_mask + is set to true. Cross-attention masks are defined as 'M^1' of equation 12 of + [LEDITS++ paper](https://arxiv.org/pdf/2311.16711.pdf). + use_intersect_mask: + Whether the masking term is calculated as intersection of cross-attention masks and masks derived + from the noise estimate. Cross-attention mask are defined as 'M^1' and masks derived from the noise + estimate are defined as 'M^2' of equation 12 of [LEDITS++ paper](https://arxiv.org/pdf/2311.16711.pdf). + user_mask: + User-provided mask for even better control over the editing process. This is helpful when LEDITS++'s implicit + masks do not meet user preferences. + attn_store_steps: + Steps for which the attention maps are stored in the AttentionStore. Just for visualization purposes. + store_averaged_over_steps: + Whether the attention maps for the 'attn_store_steps' are stored averaged over the diffusion steps. + If False, attention maps for each step are stores separately. Just for visualization purposes. + clip_skip (`int`, *optional*): + Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that + the output of the pre-final layer will be used for computing the prompt embeddings. + callback_on_step_end (`Callable`, *optional*): + A function that calls at the end of each denoising steps during the inference. The function is called + with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, + callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by + `callback_on_step_end_tensor_inputs`. + callback_on_step_end_tensor_inputs (`List`, *optional*): + The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list + will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the + `._callback_tensor_inputs` attribute of your pipeline class. + + Examples: + + Returns: + [`~pipelines.ledits_pp.LEditsPPDiffusionPipelineOutput`] or `tuple`: + [`~pipelines.ledits_pp.LEditsPPDiffusionPipelineOutput`] if `return_dict` is True, + otherwise a `tuple. When returning a tuple, the first element is a list with the generated images. + """ + if self.inversion_steps is None: + raise ValueError( + "You need to invert an input image first before calling the pipeline. The `invert` method has to be called beforehand. Edits will always be performed for the last inverted image(s)." + ) + + eta = self.eta + num_images_per_prompt = 1 + latents = self.init_latents + + zs = self.zs + self.scheduler.set_timesteps(len(self.scheduler.timesteps)) + + if use_intersect_mask: + use_cross_attn_mask = True + + if use_cross_attn_mask: + self.smoothing = LeditsGaussianSmoothing(self.device) + + if user_mask is not None: + user_mask = user_mask.to(self.device) + + # TODO: Check inputs + # 1. Check inputs. Raise error if not correct + # self.check_inputs( + # callback_steps, + # negative_prompt, + # negative_prompt_2, + # prompt_embeds, + # negative_prompt_embeds, + # pooled_prompt_embeds, + # negative_pooled_prompt_embeds, + # ) + self._guidance_rescale = guidance_rescale + self._clip_skip = clip_skip + self._cross_attention_kwargs = cross_attention_kwargs + self._denoising_end = denoising_end + + # 2. Define call parameters + batch_size = self.batch_size + + device = self._execution_device + + if editing_prompt: + enable_edit_guidance = True + if isinstance(editing_prompt, str): + editing_prompt = [editing_prompt] + self.enabled_editing_prompts = len(editing_prompt) + elif editing_prompt_embeddings is not None: + enable_edit_guidance = True + self.enabled_editing_prompts = editing_prompt_embeddings.shape[0] + else: + self.enabled_editing_prompts = 0 + enable_edit_guidance = False + + # 3. Encode input prompt + text_encoder_lora_scale = ( + cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None + ) + ( + prompt_embeds, + edit_prompt_embeds, + negative_pooled_prompt_embeds, + pooled_edit_embeds, + num_edit_tokens, + ) = self.encode_prompt( + device=device, + num_images_per_prompt=num_images_per_prompt, + negative_prompt=negative_prompt, + negative_prompt_2=negative_prompt_2, + negative_prompt_embeds=negative_prompt_embeds, + negative_pooled_prompt_embeds=negative_pooled_prompt_embeds, + lora_scale=text_encoder_lora_scale, + clip_skip=self.clip_skip, + enable_edit_guidance=enable_edit_guidance, + editing_prompt=editing_prompt, + editing_prompt_embeds=editing_prompt_embeddings, + editing_pooled_prompt_embeds=editing_pooled_prompt_embeds, + ) + + # 4. Prepare timesteps + # self.scheduler.set_timesteps(num_inference_steps, device=device) + + timesteps = self.inversion_steps + t_to_idx = {int(v): k for k, v in enumerate(timesteps)} + + if use_cross_attn_mask: + self.attention_store = LeditsAttentionStore( + average=store_averaged_over_steps, + batch_size=batch_size, + max_size=(latents.shape[-2] / 4.0) * (latents.shape[-1] / 4.0), + max_resolution=None, + ) + self.prepare_unet(self.attention_store) + resolution = latents.shape[-2:] + att_res = (int(resolution[0] / 4), int(resolution[1] / 4)) + + # 5. Prepare latent variables + latents = self.prepare_latents(device=device, latents=latents) + + # 6. Prepare extra step kwargs. + extra_step_kwargs = self.prepare_extra_step_kwargs(eta) + + if self.text_encoder_2 is None: + text_encoder_projection_dim = int(negative_pooled_prompt_embeds.shape[-1]) + else: + text_encoder_projection_dim = self.text_encoder_2.config.projection_dim + + # 7. Prepare added time ids & embeddings + add_text_embeds = negative_pooled_prompt_embeds + add_time_ids = self._get_add_time_ids( + self.size, + crops_coords_top_left, + self.size, + dtype=negative_pooled_prompt_embeds.dtype, + text_encoder_projection_dim=text_encoder_projection_dim, + ) + + if enable_edit_guidance: + prompt_embeds = torch.cat([prompt_embeds, edit_prompt_embeds], dim=0) + add_text_embeds = torch.cat([add_text_embeds, pooled_edit_embeds], dim=0) + edit_concepts_time_ids = add_time_ids.repeat(edit_prompt_embeds.shape[0], 1) + add_time_ids = torch.cat([add_time_ids, edit_concepts_time_ids], dim=0) + self.text_cross_attention_maps = [editing_prompt] if isinstance(editing_prompt, str) else editing_prompt + + prompt_embeds = prompt_embeds.to(device) + add_text_embeds = add_text_embeds.to(device) + add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1) + + if ip_adapter_image is not None: + # TODO: fix image encoding + image_embeds, negative_image_embeds = self.encode_image(ip_adapter_image, device, num_images_per_prompt) + if self.do_classifier_free_guidance: + image_embeds = torch.cat([negative_image_embeds, image_embeds]) + image_embeds = image_embeds.to(device) + + # 8. Denoising loop + self.sem_guidance = None + self.activation_mask = None + + if ( + self.denoising_end is not None + and isinstance(self.denoising_end, float) + and self.denoising_end > 0 + and self.denoising_end < 1 + ): + discrete_timestep_cutoff = int( + round( + self.scheduler.config.num_train_timesteps + - (self.denoising_end * self.scheduler.config.num_train_timesteps) + ) + ) + num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps))) + timesteps = timesteps[:num_inference_steps] + + # 9. Optionally get Guidance Scale Embedding + timestep_cond = None + if self.unet.config.time_cond_proj_dim is not None: + guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt) + timestep_cond = self.get_guidance_scale_embedding( + guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim + ).to(device=device, dtype=latents.dtype) + + self._num_timesteps = len(timesteps) + with self.progress_bar(total=self._num_timesteps) as progress_bar: + for i, t in enumerate(timesteps): + # expand the latents if we are doing classifier free guidance + latent_model_input = torch.cat([latents] * (1 + self.enabled_editing_prompts)) + latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) + # predict the noise residual + added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids} + if ip_adapter_image is not None: + added_cond_kwargs["image_embeds"] = image_embeds + noise_pred = self.unet( + latent_model_input, + t, + encoder_hidden_states=prompt_embeds, + cross_attention_kwargs=cross_attention_kwargs, + added_cond_kwargs=added_cond_kwargs, + return_dict=False, + )[0] + + noise_pred_out = noise_pred.chunk(1 + self.enabled_editing_prompts) # [b,4, 64, 64] + noise_pred_uncond = noise_pred_out[0] + noise_pred_edit_concepts = noise_pred_out[1:] + + noise_guidance_edit = torch.zeros( + noise_pred_uncond.shape, + device=self.device, + dtype=noise_pred_uncond.dtype, + ) + + if sem_guidance is not None and len(sem_guidance) > i: + noise_guidance_edit += sem_guidance[i].to(self.device) + + elif enable_edit_guidance: + if self.activation_mask is None: + self.activation_mask = torch.zeros( + (len(timesteps), self.enabled_editing_prompts, *noise_pred_edit_concepts[0].shape) + ) + if self.sem_guidance is None: + self.sem_guidance = torch.zeros((len(timesteps), *noise_pred_uncond.shape)) + + # noise_guidance_edit = torch.zeros_like(noise_guidance) + for c, noise_pred_edit_concept in enumerate(noise_pred_edit_concepts): + if isinstance(edit_warmup_steps, list): + edit_warmup_steps_c = edit_warmup_steps[c] + else: + edit_warmup_steps_c = edit_warmup_steps + if i < edit_warmup_steps_c: + continue + + if isinstance(edit_guidance_scale, list): + edit_guidance_scale_c = edit_guidance_scale[c] + else: + edit_guidance_scale_c = edit_guidance_scale + + if isinstance(edit_threshold, list): + edit_threshold_c = edit_threshold[c] + else: + edit_threshold_c = edit_threshold + if isinstance(reverse_editing_direction, list): + reverse_editing_direction_c = reverse_editing_direction[c] + else: + reverse_editing_direction_c = reverse_editing_direction + + if isinstance(edit_cooldown_steps, list): + edit_cooldown_steps_c = edit_cooldown_steps[c] + elif edit_cooldown_steps is None: + edit_cooldown_steps_c = i + 1 + else: + edit_cooldown_steps_c = edit_cooldown_steps + + if i >= edit_cooldown_steps_c: + continue + + noise_guidance_edit_tmp = noise_pred_edit_concept - noise_pred_uncond + + if reverse_editing_direction_c: + noise_guidance_edit_tmp = noise_guidance_edit_tmp * -1 + + noise_guidance_edit_tmp = noise_guidance_edit_tmp * edit_guidance_scale_c + + if user_mask is not None: + noise_guidance_edit_tmp = noise_guidance_edit_tmp * user_mask + + if use_cross_attn_mask: + out = self.attention_store.aggregate_attention( + attention_maps=self.attention_store.step_store, + prompts=self.text_cross_attention_maps, + res=att_res, + from_where=["up", "down"], + is_cross=True, + select=self.text_cross_attention_maps.index(editing_prompt[c]), + ) + attn_map = out[:, :, :, 1 : 1 + num_edit_tokens[c]] # 0 -> startoftext + + # average over all tokens + if attn_map.shape[3] != num_edit_tokens[c]: + raise ValueError( + f"Incorrect shape of attention_map. Expected size {num_edit_tokens[c]}, but found {attn_map.shape[3]}!" + ) + attn_map = torch.sum(attn_map, dim=3) + + # gaussian_smoothing + attn_map = F.pad(attn_map.unsqueeze(1), (1, 1, 1, 1), mode="reflect") + attn_map = self.smoothing(attn_map).squeeze(1) + + # torch.quantile function expects float32 + if attn_map.dtype == torch.float32: + tmp = torch.quantile(attn_map.flatten(start_dim=1), edit_threshold_c, dim=1) + else: + tmp = torch.quantile( + attn_map.flatten(start_dim=1).to(torch.float32), edit_threshold_c, dim=1 + ).to(attn_map.dtype) + attn_mask = torch.where( + attn_map >= tmp.unsqueeze(1).unsqueeze(1).repeat(1, *att_res), 1.0, 0.0 + ) + + # resolution must match latent space dimension + attn_mask = F.interpolate( + attn_mask.unsqueeze(1), + noise_guidance_edit_tmp.shape[-2:], # 64,64 + ).repeat(1, 4, 1, 1) + self.activation_mask[i, c] = attn_mask.detach().cpu() + if not use_intersect_mask: + noise_guidance_edit_tmp = noise_guidance_edit_tmp * attn_mask + + if use_intersect_mask: + noise_guidance_edit_tmp_quantile = torch.abs(noise_guidance_edit_tmp) + noise_guidance_edit_tmp_quantile = torch.sum( + noise_guidance_edit_tmp_quantile, dim=1, keepdim=True + ) + noise_guidance_edit_tmp_quantile = noise_guidance_edit_tmp_quantile.repeat( + 1, self.unet.config.in_channels, 1, 1 + ) + + # torch.quantile function expects float32 + if noise_guidance_edit_tmp_quantile.dtype == torch.float32: + tmp = torch.quantile( + noise_guidance_edit_tmp_quantile.flatten(start_dim=2), + edit_threshold_c, + dim=2, + keepdim=False, + ) + else: + tmp = torch.quantile( + noise_guidance_edit_tmp_quantile.flatten(start_dim=2).to(torch.float32), + edit_threshold_c, + dim=2, + keepdim=False, + ).to(noise_guidance_edit_tmp_quantile.dtype) + + intersect_mask = ( + torch.where( + noise_guidance_edit_tmp_quantile >= tmp[:, :, None, None], + torch.ones_like(noise_guidance_edit_tmp), + torch.zeros_like(noise_guidance_edit_tmp), + ) + * attn_mask + ) + + self.activation_mask[i, c] = intersect_mask.detach().cpu() + + noise_guidance_edit_tmp = noise_guidance_edit_tmp * intersect_mask + + elif not use_cross_attn_mask: + # calculate quantile + noise_guidance_edit_tmp_quantile = torch.abs(noise_guidance_edit_tmp) + noise_guidance_edit_tmp_quantile = torch.sum( + noise_guidance_edit_tmp_quantile, dim=1, keepdim=True + ) + noise_guidance_edit_tmp_quantile = noise_guidance_edit_tmp_quantile.repeat(1, 4, 1, 1) + + # torch.quantile function expects float32 + if noise_guidance_edit_tmp_quantile.dtype == torch.float32: + tmp = torch.quantile( + noise_guidance_edit_tmp_quantile.flatten(start_dim=2), + edit_threshold_c, + dim=2, + keepdim=False, + ) + else: + tmp = torch.quantile( + noise_guidance_edit_tmp_quantile.flatten(start_dim=2).to(torch.float32), + edit_threshold_c, + dim=2, + keepdim=False, + ).to(noise_guidance_edit_tmp_quantile.dtype) + + self.activation_mask[i, c] = ( + torch.where( + noise_guidance_edit_tmp_quantile >= tmp[:, :, None, None], + torch.ones_like(noise_guidance_edit_tmp), + torch.zeros_like(noise_guidance_edit_tmp), + ) + .detach() + .cpu() + ) + + noise_guidance_edit_tmp = torch.where( + noise_guidance_edit_tmp_quantile >= tmp[:, :, None, None], + noise_guidance_edit_tmp, + torch.zeros_like(noise_guidance_edit_tmp), + ) + + noise_guidance_edit += noise_guidance_edit_tmp + + self.sem_guidance[i] = noise_guidance_edit.detach().cpu() + + noise_pred = noise_pred_uncond + noise_guidance_edit + + # compute the previous noisy sample x_t -> x_t-1 + if enable_edit_guidance and self.guidance_rescale > 0.0: + # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf + noise_pred = rescale_noise_cfg( + noise_pred, + noise_pred_edit_concepts.mean(dim=0, keepdim=False), + guidance_rescale=self.guidance_rescale, + ) + + idx = t_to_idx[int(t)] + latents = self.scheduler.step( + noise_pred, t, latents, variance_noise=zs[idx], **extra_step_kwargs, return_dict=False + )[0] + + # step callback + if use_cross_attn_mask: + store_step = i in attn_store_steps + self.attention_store.between_steps(store_step) + + if callback_on_step_end is not None: + callback_kwargs = {} + for k in callback_on_step_end_tensor_inputs: + callback_kwargs[k] = locals()[k] + callback_outputs = callback_on_step_end(self, i, t, callback_kwargs) + + latents = callback_outputs.pop("latents", latents) + prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds) + negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds) + add_text_embeds = callback_outputs.pop("add_text_embeds", add_text_embeds) + negative_pooled_prompt_embeds = callback_outputs.pop( + "negative_pooled_prompt_embeds", negative_pooled_prompt_embeds + ) + add_time_ids = callback_outputs.pop("add_time_ids", add_time_ids) + # negative_add_time_ids = callback_outputs.pop("negative_add_time_ids", negative_add_time_ids) + + # call the callback, if provided + if i == len(timesteps) - 1 or ((i + 1) > 0 and (i + 1) % self.scheduler.order == 0): + progress_bar.update() + + if XLA_AVAILABLE: + xm.mark_step() + + if not output_type == "latent": + # make sure the VAE is in float32 mode, as it overflows in float16 + needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast + + if needs_upcasting: + self.upcast_vae() + latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype) + + image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0] + + # cast back to fp16 if needed + if needs_upcasting: + self.vae.to(dtype=torch.float16) + else: + image = latents + + if not output_type == "latent": + # apply watermark if available + if self.watermark is not None: + image = self.watermark.apply_watermark(image) + + image = self.image_processor.postprocess(image, output_type=output_type) + + # Offload all models + self.maybe_free_model_hooks() + + if not return_dict: + return (image,) + + return LEditsPPDiffusionPipelineOutput(images=image, nsfw_content_detected=None) + + @torch.no_grad() + # Modified from diffusers.pipelines.ledits_pp.pipeline_leditspp_stable_diffusion.LEditsPPPipelineStableDiffusion.encode_image + def encode_image(self, image, dtype=None, height=None, width=None, resize_mode="default", crops_coords=None): + image = self.image_processor.preprocess( + image=image, height=height, width=width, resize_mode=resize_mode, crops_coords=crops_coords + ) + resized = self.image_processor.postprocess(image=image, output_type="pil") + + if max(image.shape[-2:]) > self.vae.config["sample_size"] * 1.5: + logger.warning( + "Your input images far exceed the default resolution of the underlying diffusion model. " + "The output images may contain severe artifacts! " + "Consider down-sampling the input using the `height` and `width` parameters" + ) + image = image.to(self.device, dtype=dtype) + needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast + + if needs_upcasting: + image = image.float() + self.upcast_vae() + image = image.to(next(iter(self.vae.post_quant_conv.parameters())).dtype) + + x0 = self.vae.encode(image).latent_dist.mode() + x0 = x0.to(dtype) + # cast back to fp16 if needed + if needs_upcasting: + self.vae.to(dtype=torch.float16) + + x0 = self.vae.config.scaling_factor * x0 + return x0, resized + + @torch.no_grad() + def invert( + self, + image: PipelineImageInput, + source_prompt: str = "", + source_guidance_scale=3.5, + negative_prompt: str = None, + negative_prompt_2: str = None, + num_inversion_steps: int = 50, + skip: float = 0.15, + generator: Optional[torch.Generator] = None, + crops_coords_top_left: Tuple[int, int] = (0, 0), + num_zero_noise_steps: int = 3, + cross_attention_kwargs: Optional[Dict[str, Any]] = None, + ): + r""" + The function to the pipeline for image inversion as described by the [LEDITS++ Paper](https://arxiv.org/abs/2301.12247). + If the scheduler is set to [`~schedulers.DDIMScheduler`] the inversion proposed by [edit-friendly DPDM](https://arxiv.org/abs/2304.06140) + will be performed instead. + + Args: + image (`PipelineImageInput`): + Input for the image(s) that are to be edited. Multiple input images have to default to the same aspect + ratio. + source_prompt (`str`, defaults to `""`): + Prompt describing the input image that will be used for guidance during inversion. Guidance is disabled + if the `source_prompt` is `""`. + source_guidance_scale (`float`, defaults to `3.5`): + Strength of guidance during inversion. + negative_prompt (`str` or `List[str]`, *optional*): + The prompt or prompts not to guide the image generation. If not defined, one has to pass + `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is + less than `1`). + negative_prompt_2 (`str` or `List[str]`, *optional*): + The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and + `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders + num_inversion_steps (`int`, defaults to `50`): + Number of total performed inversion steps after discarding the initial `skip` steps. + skip (`float`, defaults to `0.15`): + Portion of initial steps that will be ignored for inversion and subsequent generation. Lower values + will lead to stronger changes to the input image. `skip` has to be between `0` and `1`. + generator (`torch.Generator`, *optional*): + A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make + inversion deterministic. + crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)): + `crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position + `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting + `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of + [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). + num_zero_noise_steps (`int`, defaults to `3`): + Number of final diffusion steps that will not renoise the current image. If no steps are set to zero + SD-XL in combination with [`DPMSolverMultistepScheduler`] will produce noise artifacts. + cross_attention_kwargs (`dict`, *optional*): + A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under + `self.processor` in + [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). + + Returns: + [`~pipelines.ledits_pp.LEditsPPInversionPipelineOutput`]: + Output will contain the resized input image(s) and respective VAE reconstruction(s). + """ + + # Reset attn processor, we do not want to store attn maps during inversion + self.unet.set_attn_processor(AttnProcessor()) + + self.eta = 1.0 + + self.scheduler.config.timestep_spacing = "leading" + self.scheduler.set_timesteps(int(num_inversion_steps * (1 + skip))) + self.inversion_steps = self.scheduler.timesteps[-num_inversion_steps:] + timesteps = self.inversion_steps + + num_images_per_prompt = 1 + + device = self._execution_device + + # 0. Ensure that only uncond embedding is used if prompt = "" + if source_prompt == "": + # noise pred should only be noise_pred_uncond + source_guidance_scale = 0.0 + do_classifier_free_guidance = False + else: + do_classifier_free_guidance = source_guidance_scale > 1.0 + + # 1. prepare image + x0, resized = self.encode_image(image, dtype=self.text_encoder_2.dtype) + width = x0.shape[2] * self.vae_scale_factor + height = x0.shape[3] * self.vae_scale_factor + self.size = (height, width) + + self.batch_size = x0.shape[0] + + # 2. get embeddings + text_encoder_lora_scale = ( + cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None + ) + + if isinstance(source_prompt, str): + source_prompt = [source_prompt] * self.batch_size + + ( + negative_prompt_embeds, + prompt_embeds, + negative_pooled_prompt_embeds, + edit_pooled_prompt_embeds, + _, + ) = self.encode_prompt( + device=device, + num_images_per_prompt=num_images_per_prompt, + negative_prompt=negative_prompt, + negative_prompt_2=negative_prompt_2, + editing_prompt=source_prompt, + lora_scale=text_encoder_lora_scale, + enable_edit_guidance=do_classifier_free_guidance, + ) + if self.text_encoder_2 is None: + text_encoder_projection_dim = int(negative_pooled_prompt_embeds.shape[-1]) + else: + text_encoder_projection_dim = self.text_encoder_2.config.projection_dim + + # 3. Prepare added time ids & embeddings + add_text_embeds = negative_pooled_prompt_embeds + add_time_ids = self._get_add_time_ids( + self.size, + crops_coords_top_left, + self.size, + dtype=negative_prompt_embeds.dtype, + text_encoder_projection_dim=text_encoder_projection_dim, + ) + + if do_classifier_free_guidance: + negative_prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0) + add_text_embeds = torch.cat([add_text_embeds, edit_pooled_prompt_embeds], dim=0) + add_time_ids = torch.cat([add_time_ids, add_time_ids], dim=0) + + negative_prompt_embeds = negative_prompt_embeds.to(device) + + add_text_embeds = add_text_embeds.to(device) + add_time_ids = add_time_ids.to(device).repeat(self.batch_size * num_images_per_prompt, 1) + + # autoencoder reconstruction + if self.vae.dtype == torch.float16 and self.vae.config.force_upcast: + self.upcast_vae() + x0_tmp = x0.to(next(iter(self.vae.post_quant_conv.parameters())).dtype) + image_rec = self.vae.decode( + x0_tmp / self.vae.config.scaling_factor, return_dict=False, generator=generator + )[0] + elif self.vae.config.force_upcast: + x0_tmp = x0.to(next(iter(self.vae.post_quant_conv.parameters())).dtype) + image_rec = self.vae.decode( + x0_tmp / self.vae.config.scaling_factor, return_dict=False, generator=generator + )[0] + else: + image_rec = self.vae.decode(x0 / self.vae.config.scaling_factor, return_dict=False, generator=generator)[0] + + image_rec = self.image_processor.postprocess(image_rec, output_type="pil") + + # 5. find zs and xts + variance_noise_shape = (num_inversion_steps, *x0.shape) + + # intermediate latents + t_to_idx = {int(v): k for k, v in enumerate(timesteps)} + xts = torch.zeros(size=variance_noise_shape, device=self.device, dtype=negative_prompt_embeds.dtype) + + for t in reversed(timesteps): + idx = num_inversion_steps - t_to_idx[int(t)] - 1 + noise = randn_tensor(shape=x0.shape, generator=generator, device=self.device, dtype=x0.dtype) + xts[idx] = self.scheduler.add_noise(x0, noise, t.unsqueeze(0)) + xts = torch.cat([x0.unsqueeze(0), xts], dim=0) + + # noise maps + zs = torch.zeros(size=variance_noise_shape, device=self.device, dtype=negative_prompt_embeds.dtype) + + self.scheduler.set_timesteps(len(self.scheduler.timesteps)) + + for t in self.progress_bar(timesteps): + idx = num_inversion_steps - t_to_idx[int(t)] - 1 + # 1. predict noise residual + xt = xts[idx + 1] + + latent_model_input = torch.cat([xt] * 2) if do_classifier_free_guidance else xt + latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) + + added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids} + + noise_pred = self.unet( + latent_model_input, + t, + encoder_hidden_states=negative_prompt_embeds, + cross_attention_kwargs=cross_attention_kwargs, + added_cond_kwargs=added_cond_kwargs, + return_dict=False, + )[0] + + # 2. perform guidance + if do_classifier_free_guidance: + noise_pred_out = noise_pred.chunk(2) + noise_pred_uncond, noise_pred_text = noise_pred_out[0], noise_pred_out[1] + noise_pred = noise_pred_uncond + source_guidance_scale * (noise_pred_text - noise_pred_uncond) + + xtm1 = xts[idx] + z, xtm1_corrected = compute_noise(self.scheduler, xtm1, xt, t, noise_pred, self.eta) + zs[idx] = z + + # correction to avoid error accumulation + xts[idx] = xtm1_corrected + + self.init_latents = xts[-1] + zs = zs.flip(0) + + if num_zero_noise_steps > 0: + zs[-num_zero_noise_steps:] = torch.zeros_like(zs[-num_zero_noise_steps:]) + self.zs = zs + return LEditsPPInversionPipelineOutput(images=resized, vae_reconstruction_images=image_rec) + + +# Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.rescale_noise_cfg +def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0): + """ + Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and + Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4 + """ + std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), keepdim=True) + std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True) + # rescale the results from guidance (fixes overexposure) + noise_pred_rescaled = noise_cfg * (std_text / std_cfg) + # mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images + noise_cfg = guidance_rescale * noise_pred_rescaled + (1 - guidance_rescale) * noise_cfg + return noise_cfg + + +# Copied from diffusers.pipelines.ledits_pp.pipeline_leditspp_stable_diffusion.compute_noise_ddim +def compute_noise_ddim(scheduler, prev_latents, latents, timestep, noise_pred, eta): + # 1. get previous step value (=t-1) + prev_timestep = timestep - scheduler.config.num_train_timesteps // scheduler.num_inference_steps + + # 2. compute alphas, betas + alpha_prod_t = scheduler.alphas_cumprod[timestep] + alpha_prod_t_prev = ( + scheduler.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else scheduler.final_alpha_cumprod + ) + + beta_prod_t = 1 - alpha_prod_t + + # 3. compute predicted original sample from predicted noise also called + # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf + pred_original_sample = (latents - beta_prod_t ** (0.5) * noise_pred) / alpha_prod_t ** (0.5) + + # 4. Clip "predicted x_0" + if scheduler.config.clip_sample: + pred_original_sample = torch.clamp(pred_original_sample, -1, 1) + + # 5. compute variance: "sigma_t(η)" -> see formula (16) + # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1) + variance = scheduler._get_variance(timestep, prev_timestep) + std_dev_t = eta * variance ** (0.5) + + # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf + pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * noise_pred + + # modifed so that updated xtm1 is returned as well (to avoid error accumulation) + mu_xt = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction + if variance > 0.0: + noise = (prev_latents - mu_xt) / (variance ** (0.5) * eta) + else: + noise = torch.tensor([0.0]).to(latents.device) + + return noise, mu_xt + (eta * variance**0.5) * noise + + +# Copied from diffusers.pipelines.ledits_pp.pipeline_leditspp_stable_diffusion.compute_noise_sde_dpm_pp_2nd +def compute_noise_sde_dpm_pp_2nd(scheduler, prev_latents, latents, timestep, noise_pred, eta): + def first_order_update(model_output, sample): # timestep, prev_timestep, sample): + sigma_t, sigma_s = scheduler.sigmas[scheduler.step_index + 1], scheduler.sigmas[scheduler.step_index] + alpha_t, sigma_t = scheduler._sigma_to_alpha_sigma_t(sigma_t) + alpha_s, sigma_s = scheduler._sigma_to_alpha_sigma_t(sigma_s) + lambda_t = torch.log(alpha_t) - torch.log(sigma_t) + lambda_s = torch.log(alpha_s) - torch.log(sigma_s) + + h = lambda_t - lambda_s + + mu_xt = (sigma_t / sigma_s * torch.exp(-h)) * sample + (alpha_t * (1 - torch.exp(-2.0 * h))) * model_output + + mu_xt = scheduler.dpm_solver_first_order_update( + model_output=model_output, sample=sample, noise=torch.zeros_like(sample) + ) + + sigma = sigma_t * torch.sqrt(1.0 - torch.exp(-2 * h)) + if sigma > 0.0: + noise = (prev_latents - mu_xt) / sigma + else: + noise = torch.tensor([0.0]).to(sample.device) + + prev_sample = mu_xt + sigma * noise + return noise, prev_sample + + def second_order_update(model_output_list, sample): # timestep_list, prev_timestep, sample): + sigma_t, sigma_s0, sigma_s1 = ( + scheduler.sigmas[scheduler.step_index + 1], + scheduler.sigmas[scheduler.step_index], + scheduler.sigmas[scheduler.step_index - 1], + ) + + alpha_t, sigma_t = scheduler._sigma_to_alpha_sigma_t(sigma_t) + alpha_s0, sigma_s0 = scheduler._sigma_to_alpha_sigma_t(sigma_s0) + alpha_s1, sigma_s1 = scheduler._sigma_to_alpha_sigma_t(sigma_s1) + + lambda_t = torch.log(alpha_t) - torch.log(sigma_t) + lambda_s0 = torch.log(alpha_s0) - torch.log(sigma_s0) + lambda_s1 = torch.log(alpha_s1) - torch.log(sigma_s1) + + m0, m1 = model_output_list[-1], model_output_list[-2] + + h, h_0 = lambda_t - lambda_s0, lambda_s0 - lambda_s1 + r0 = h_0 / h + D0, D1 = m0, (1.0 / r0) * (m0 - m1) + + mu_xt = ( + (sigma_t / sigma_s0 * torch.exp(-h)) * sample + + (alpha_t * (1 - torch.exp(-2.0 * h))) * D0 + + 0.5 * (alpha_t * (1 - torch.exp(-2.0 * h))) * D1 + ) + + sigma = sigma_t * torch.sqrt(1.0 - torch.exp(-2 * h)) + if sigma > 0.0: + noise = (prev_latents - mu_xt) / sigma + else: + noise = torch.tensor([0.0]).to(sample.device) + + prev_sample = mu_xt + sigma * noise + + return noise, prev_sample + + if scheduler.step_index is None: + scheduler._init_step_index(timestep) + + model_output = scheduler.convert_model_output(model_output=noise_pred, sample=latents) + for i in range(scheduler.config.solver_order - 1): + scheduler.model_outputs[i] = scheduler.model_outputs[i + 1] + scheduler.model_outputs[-1] = model_output + + if scheduler.lower_order_nums < 1: + noise, prev_sample = first_order_update(model_output, latents) + else: + noise, prev_sample = second_order_update(scheduler.model_outputs, latents) + + if scheduler.lower_order_nums < scheduler.config.solver_order: + scheduler.lower_order_nums += 1 + + # upon completion increase step index by one + scheduler._step_index += 1 + + return noise, prev_sample + + +# Copied from diffusers.pipelines.ledits_pp.pipeline_leditspp_stable_diffusion.compute_noise +def compute_noise(scheduler, *args): + if isinstance(scheduler, DDIMScheduler): + return compute_noise_ddim(scheduler, *args) + elif ( + isinstance(scheduler, DPMSolverMultistepScheduler) + and scheduler.config.algorithm_type == "sde-dpmsolver++" + and scheduler.config.solver_order == 2 + ): + return compute_noise_sde_dpm_pp_2nd(scheduler, *args) + else: + raise NotImplementedError diff --git a/src/diffusers/pipelines/ledits_pp/pipeline_output.py b/src/diffusers/pipelines/ledits_pp/pipeline_output.py new file mode 100644 index 000000000000..b90005c97c4a --- /dev/null +++ b/src/diffusers/pipelines/ledits_pp/pipeline_output.py @@ -0,0 +1,43 @@ +from dataclasses import dataclass +from typing import List, Optional, Union + +import numpy as np +import PIL.Image + +from ...utils import BaseOutput + + +@dataclass +class LEditsPPDiffusionPipelineOutput(BaseOutput): + """ + Output class for LEdits++ Diffusion pipelines. + + Args: + images (`List[PIL.Image.Image]` or `np.ndarray`) + List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width, + num_channels)`. + nsfw_content_detected (`List[bool]`) + List indicating whether the corresponding generated image contains “not-safe-for-work” (nsfw) content or + `None` if safety checking could not be performed. + """ + + images: Union[List[PIL.Image.Image], np.ndarray] + nsfw_content_detected: Optional[List[bool]] + + +@dataclass +class LEditsPPInversionPipelineOutput(BaseOutput): + """ + Output class for LEdits++ Diffusion pipelines. + + Args: + input_images (`List[PIL.Image.Image]` or `np.ndarray`) + List of the cropped and resized input images as PIL images of length `batch_size` or NumPy array of shape ` + (batch_size, height, width, num_channels)`. + vae_reconstruction_images (`List[PIL.Image.Image]` or `np.ndarray`) + List of VAE reconstruction of all input images as PIL images of length `batch_size` or NumPy array of shape ` + (batch_size, height, width, num_channels)`. + """ + + images: Union[List[PIL.Image.Image], np.ndarray] + vae_reconstruction_images: Union[List[PIL.Image.Image], np.ndarray] diff --git a/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py b/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py index 5c6d03fa229c..bca370f5f501 100644 --- a/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py +++ b/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py @@ -899,6 +899,7 @@ def step( timestep: int, sample: torch.FloatTensor, generator=None, + variance_noise: Optional[torch.FloatTensor] = None, return_dict: bool = True, ) -> Union[SchedulerOutput, Tuple]: """ @@ -914,6 +915,9 @@ def step( A current instance of a sample created by the diffusion process. generator (`torch.Generator`, *optional*): A random number generator. + variance_noise (`torch.FloatTensor`): + Alternative to generating noise with `generator` by directly providing the noise for the variance + itself. Useful for methods such as [`LEdits++`]. return_dict (`bool`): Whether or not to return a [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`. @@ -948,11 +952,12 @@ def step( # Upcast to avoid precision issues when computing prev_sample sample = sample.to(torch.float32) - - if self.config.algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]: + if self.config.algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"] and variance_noise is None: noise = randn_tensor( model_output.shape, generator=generator, device=model_output.device, dtype=torch.float32 ) + elif self.config.algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]: + noise = variance_noise.to(device=model_output.device, dtype=torch.float32) else: noise = None diff --git a/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py b/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py index f775c35c4f10..63ef971a4d80 100644 --- a/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py +++ b/src/diffusers/schedulers/scheduling_dpmsolver_multistep_inverse.py @@ -792,6 +792,7 @@ def step( timestep: int, sample: torch.FloatTensor, generator=None, + variance_noise: Optional[torch.FloatTensor] = None, return_dict: bool = True, ) -> Union[SchedulerOutput, Tuple]: """ @@ -807,6 +808,9 @@ def step( A current instance of a sample created by the diffusion process. generator (`torch.Generator`, *optional*): A random number generator. + variance_noise (`torch.FloatTensor`): + Alternative to generating noise with `generator` by directly providing the noise for the variance + itself. Useful for methods such as [`CycleDiffusion`]. return_dict (`bool`): Whether or not to return a [`~schedulers.scheduling_utils.SchedulerOutput`] or `tuple`. @@ -837,10 +841,12 @@ def step( self.model_outputs[i] = self.model_outputs[i + 1] self.model_outputs[-1] = model_output - if self.config.algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]: + if self.config.algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"] and variance_noise is None: noise = randn_tensor( model_output.shape, generator=generator, device=model_output.device, dtype=model_output.dtype ) + elif self.config.algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]: + noise = variance_noise else: noise = None diff --git a/src/diffusers/utils/dummy_torch_and_transformers_objects.py b/src/diffusers/utils/dummy_torch_and_transformers_objects.py index 3d5439878648..f64c15702087 100644 --- a/src/diffusers/utils/dummy_torch_and_transformers_objects.py +++ b/src/diffusers/utils/dummy_torch_and_transformers_objects.py @@ -647,6 +647,36 @@ def from_pretrained(cls, *args, **kwargs): requires_backends(cls, ["torch", "transformers"]) +class LEditsPPPipelineStableDiffusion(metaclass=DummyObject): + _backends = ["torch", "transformers"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch", "transformers"]) + + @classmethod + def from_config(cls, *args, **kwargs): + requires_backends(cls, ["torch", "transformers"]) + + @classmethod + def from_pretrained(cls, *args, **kwargs): + requires_backends(cls, ["torch", "transformers"]) + + +class LEditsPPPipelineStableDiffusionXL(metaclass=DummyObject): + _backends = ["torch", "transformers"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch", "transformers"]) + + @classmethod + def from_config(cls, *args, **kwargs): + requires_backends(cls, ["torch", "transformers"]) + + @classmethod + def from_pretrained(cls, *args, **kwargs): + requires_backends(cls, ["torch", "transformers"]) + + class MusicLDMPipeline(metaclass=DummyObject): _backends = ["torch", "transformers"] diff --git a/tests/pipelines/ledits_pp/__init__.py b/tests/pipelines/ledits_pp/__init__.py new file mode 100644 index 000000000000..e69de29bb2d1 diff --git a/tests/pipelines/ledits_pp/test_ledits_pp_stable_diffusion.py b/tests/pipelines/ledits_pp/test_ledits_pp_stable_diffusion.py new file mode 100644 index 000000000000..9ff75a918084 --- /dev/null +++ b/tests/pipelines/ledits_pp/test_ledits_pp_stable_diffusion.py @@ -0,0 +1,244 @@ +# coding=utf-8 +# Copyright 2023 HuggingFace Inc. +# +# 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. + +import gc +import random +import unittest + +import numpy as np +import torch +from PIL import Image +from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer + +from diffusers import ( + AutoencoderKL, + DPMSolverMultistepScheduler, + LEditsPPPipelineStableDiffusion, + UNet2DConditionModel, +) +from diffusers.utils.testing_utils import ( + enable_full_determinism, + floats_tensor, + load_image, + require_torch_gpu, + skip_mps, + slow, + torch_device, +) + + +enable_full_determinism() + + +@skip_mps +class LEditsPPPipelineStableDiffusionFastTests(unittest.TestCase): + pipeline_class = LEditsPPPipelineStableDiffusion + + def get_dummy_components(self): + torch.manual_seed(0) + unet = UNet2DConditionModel( + block_out_channels=(32, 64, 64), + layers_per_block=2, + sample_size=32, + in_channels=4, + out_channels=4, + down_block_types=("DownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D"), + up_block_types=("CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "UpBlock2D"), + cross_attention_dim=32, + ) + scheduler = DPMSolverMultistepScheduler(algorithm_type="sde-dpmsolver++", solver_order=2) + torch.manual_seed(0) + vae = AutoencoderKL( + block_out_channels=[32, 64], + in_channels=3, + out_channels=3, + down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"], + up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"], + latent_channels=4, + ) + torch.manual_seed(0) + text_encoder_config = CLIPTextConfig( + bos_token_id=0, + eos_token_id=2, + hidden_size=32, + intermediate_size=37, + layer_norm_eps=1e-05, + num_attention_heads=4, + num_hidden_layers=5, + pad_token_id=1, + vocab_size=1000, + ) + text_encoder = CLIPTextModel(text_encoder_config) + tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") + + components = { + "unet": unet, + "scheduler": scheduler, + "vae": vae, + "text_encoder": text_encoder, + "tokenizer": tokenizer, + "safety_checker": None, + "feature_extractor": None, + } + return components + + def get_dummy_inputs(self, device, seed=0): + if str(device).startswith("mps"): + generator = torch.manual_seed(seed) + else: + generator = torch.Generator(device=device).manual_seed(seed) + inputs = { + "generator": generator, + "editing_prompt": ["wearing glasses", "sunshine"], + "reverse_editing_direction": [False, True], + "edit_guidance_scale": [10.0, 5.0], + } + return inputs + + def get_dummy_inversion_inputs(self, device, seed=0): + images = floats_tensor((2, 3, 32, 32), rng=random.Random(0)).cpu().permute(0, 2, 3, 1) + images = 255 * images + image_1 = Image.fromarray(np.uint8(images[0])).convert("RGB") + image_2 = Image.fromarray(np.uint8(images[1])).convert("RGB") + + if str(device).startswith("mps"): + generator = torch.manual_seed(seed) + else: + generator = torch.Generator(device=device).manual_seed(seed) + + inputs = { + "image": [image_1, image_2], + "source_prompt": "", + "source_guidance_scale": 3.5, + "num_inversion_steps": 20, + "skip": 0.15, + "generator": generator, + } + return inputs + + def test_ledits_pp_inversion(self): + device = "cpu" # ensure determinism for the device-dependent torch.Generator + components = self.get_dummy_components() + sd_pipe = LEditsPPPipelineStableDiffusion(**components) + sd_pipe = sd_pipe.to(torch_device) + sd_pipe.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inversion_inputs(device) + inputs["image"] = inputs["image"][0] + sd_pipe.invert(**inputs) + assert sd_pipe.init_latents.shape == ( + 1, + 4, + int(32 / sd_pipe.vae_scale_factor), + int(32 / sd_pipe.vae_scale_factor), + ) + + latent_slice = sd_pipe.init_latents[0, -1, -3:, -3:].to(device) + print(latent_slice.flatten()) + expected_slice = np.array([-0.9084, -0.0367, 0.2940, 0.0839, 0.6890, 0.2651, -0.7104, 2.1090, -0.7822]) + assert np.abs(latent_slice.flatten() - expected_slice).max() < 1e-3 + + def test_ledits_pp_inversion_batch(self): + device = "cpu" # ensure determinism for the device-dependent torch.Generator + components = self.get_dummy_components() + sd_pipe = LEditsPPPipelineStableDiffusion(**components) + sd_pipe = sd_pipe.to(torch_device) + sd_pipe.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inversion_inputs(device) + sd_pipe.invert(**inputs) + assert sd_pipe.init_latents.shape == ( + 2, + 4, + int(32 / sd_pipe.vae_scale_factor), + int(32 / sd_pipe.vae_scale_factor), + ) + + latent_slice = sd_pipe.init_latents[0, -1, -3:, -3:].to(device) + print(latent_slice.flatten()) + expected_slice = np.array([0.2528, 0.1458, -0.2166, 0.4565, -0.5657, -1.0286, -0.9961, 0.5933, 1.1173]) + assert np.abs(latent_slice.flatten() - expected_slice).max() < 1e-3 + + latent_slice = sd_pipe.init_latents[1, -1, -3:, -3:].to(device) + print(latent_slice.flatten()) + expected_slice = np.array([-0.0796, 2.0583, 0.5501, 0.5358, 0.0282, -0.2803, -1.0470, 0.7023, -0.0072]) + assert np.abs(latent_slice.flatten() - expected_slice).max() < 1e-3 + + def test_ledits_pp_warmup_steps(self): + device = "cpu" # ensure determinism for the device-dependent torch.Generator + components = self.get_dummy_components() + pipe = LEditsPPPipelineStableDiffusion(**components) + pipe = pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + inversion_inputs = self.get_dummy_inversion_inputs(device) + pipe.invert(**inversion_inputs) + + inputs = self.get_dummy_inputs(device) + + inputs["edit_warmup_steps"] = [0, 5] + pipe(**inputs).images + + inputs["edit_warmup_steps"] = [5, 0] + pipe(**inputs).images + + inputs["edit_warmup_steps"] = [5, 10] + pipe(**inputs).images + + inputs["edit_warmup_steps"] = [10, 5] + pipe(**inputs).images + + +@slow +@require_torch_gpu +class LEditsPPPipelineStableDiffusionSlowTests(unittest.TestCase): + def tearDown(self): + super().tearDown() + gc.collect() + torch.cuda.empty_cache() + + @classmethod + def setUpClass(cls): + raw_image = load_image( + "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/pix2pix/cat_6.png" + ) + raw_image = raw_image.convert("RGB").resize((512, 512)) + cls.raw_image = raw_image + + def test_ledits_pp_editing(self): + pipe = LEditsPPPipelineStableDiffusion.from_pretrained( + "runwayml/stable-diffusion-v1-5", safety_checker=None, torch_dtype=torch.float16 + ) + pipe = pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + generator = torch.manual_seed(0) + _ = pipe.invert(image=self.raw_image, generator=generator) + generator = torch.manual_seed(0) + inputs = { + "generator": generator, + "editing_prompt": ["cat", "dog"], + "reverse_editing_direction": [True, False], + "edit_guidance_scale": [5.0, 5.0], + "edit_threshold": [0.8, 0.8], + } + reconstruction = pipe(**inputs, output_type="np").images[0] + + output_slice = reconstruction[150:153, 140:143, -1] + output_slice = output_slice.flatten() + expected_slice = np.array( + [0.9453125, 0.93310547, 0.84521484, 0.94628906, 0.9111328, 0.80859375, 0.93847656, 0.9042969, 0.8144531] + ) + assert np.abs(output_slice - expected_slice).max() < 1e-2 diff --git a/tests/pipelines/ledits_pp/test_ledits_pp_stable_diffusion_xl.py b/tests/pipelines/ledits_pp/test_ledits_pp_stable_diffusion_xl.py new file mode 100644 index 000000000000..fcfd0aa51b9f --- /dev/null +++ b/tests/pipelines/ledits_pp/test_ledits_pp_stable_diffusion_xl.py @@ -0,0 +1,289 @@ +# coding=utf-8 +# Copyright 2023 HuggingFace Inc. +# +# 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. + +import random +import unittest + +import numpy as np +import torch +from PIL import Image +from transformers import ( + CLIPImageProcessor, + CLIPTextConfig, + CLIPTextModel, + CLIPTextModelWithProjection, + CLIPTokenizer, + CLIPVisionConfig, + CLIPVisionModelWithProjection, +) + +from diffusers import ( + AutoencoderKL, + DPMSolverMultistepScheduler, + LEditsPPPipelineStableDiffusionXL, + UNet2DConditionModel, +) + +# from diffusers.image_processor import VaeImageProcessor +from diffusers.utils.testing_utils import ( + enable_full_determinism, + floats_tensor, + load_image, + require_torch_gpu, + skip_mps, + slow, + torch_device, +) + + +enable_full_determinism() + + +@skip_mps +class LEditsPPPipelineStableDiffusionXLFastTests(unittest.TestCase): + pipeline_class = LEditsPPPipelineStableDiffusionXL + + def get_dummy_components(self, skip_first_text_encoder=False, time_cond_proj_dim=None): + torch.manual_seed(0) + unet = UNet2DConditionModel( + block_out_channels=(32, 64), + layers_per_block=2, + sample_size=32, + in_channels=4, + out_channels=4, + time_cond_proj_dim=time_cond_proj_dim, + down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"), + up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"), + # SD2-specific config below + attention_head_dim=(2, 4), + use_linear_projection=True, + addition_embed_type="text_time", + addition_time_embed_dim=8, + transformer_layers_per_block=(1, 2), + projection_class_embeddings_input_dim=80, # 6 * 8 + 32 + cross_attention_dim=64 if not skip_first_text_encoder else 32, + ) + scheduler = DPMSolverMultistepScheduler(algorithm_type="sde-dpmsolver++", solver_order=2) + torch.manual_seed(0) + vae = AutoencoderKL( + block_out_channels=[32, 64], + in_channels=3, + out_channels=3, + down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"], + up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"], + latent_channels=4, + sample_size=128, + ) + torch.manual_seed(0) + image_encoder_config = CLIPVisionConfig( + hidden_size=32, + image_size=224, + projection_dim=32, + intermediate_size=37, + num_attention_heads=4, + num_channels=3, + num_hidden_layers=5, + patch_size=14, + ) + image_encoder = CLIPVisionModelWithProjection(image_encoder_config) + + feature_extractor = CLIPImageProcessor( + crop_size=224, + do_center_crop=True, + do_normalize=True, + do_resize=True, + image_mean=[0.48145466, 0.4578275, 0.40821073], + image_std=[0.26862954, 0.26130258, 0.27577711], + resample=3, + size=224, + ) + + torch.manual_seed(0) + text_encoder_config = CLIPTextConfig( + bos_token_id=0, + eos_token_id=2, + hidden_size=32, + intermediate_size=37, + layer_norm_eps=1e-05, + num_attention_heads=4, + num_hidden_layers=5, + pad_token_id=1, + vocab_size=1000, + # SD2-specific config below + hidden_act="gelu", + projection_dim=32, + ) + text_encoder = CLIPTextModel(text_encoder_config) + tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") + + text_encoder_2 = CLIPTextModelWithProjection(text_encoder_config) + tokenizer_2 = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") + + components = { + "unet": unet, + "scheduler": scheduler, + "vae": vae, + "text_encoder": text_encoder if not skip_first_text_encoder else None, + "tokenizer": tokenizer if not skip_first_text_encoder else None, + "text_encoder_2": text_encoder_2, + "tokenizer_2": tokenizer_2, + "image_encoder": image_encoder, + "feature_extractor": feature_extractor, + } + return components + + def get_dummy_inputs(self, device, seed=0): + if str(device).startswith("mps"): + generator = torch.manual_seed(seed) + else: + generator = torch.Generator(device=device).manual_seed(seed) + inputs = { + "generator": generator, + "editing_prompt": ["wearing glasses", "sunshine"], + "reverse_editing_direction": [False, True], + "edit_guidance_scale": [10.0, 5.0], + } + return inputs + + def get_dummy_inversion_inputs(self, device, seed=0): + images = floats_tensor((2, 3, 32, 32), rng=random.Random(0)).cpu().permute(0, 2, 3, 1) + images = 255 * images + image_1 = Image.fromarray(np.uint8(images[0])).convert("RGB") + image_2 = Image.fromarray(np.uint8(images[1])).convert("RGB") + + if str(device).startswith("mps"): + generator = torch.manual_seed(seed) + else: + generator = torch.Generator(device=device).manual_seed(seed) + + inputs = { + "image": [image_1, image_2], + "source_prompt": "", + "source_guidance_scale": 3.5, + "num_inversion_steps": 20, + "skip": 0.15, + "generator": generator, + } + return inputs + + def test_ledits_pp_inversion(self): + device = "cpu" # ensure determinism for the device-dependent torch.Generator + components = self.get_dummy_components() + sd_pipe = LEditsPPPipelineStableDiffusionXL(**components) + sd_pipe = sd_pipe.to(torch_device) + sd_pipe.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inversion_inputs(device) + inputs["image"] = inputs["image"][0] + sd_pipe.invert(**inputs) + assert sd_pipe.init_latents.shape == ( + 1, + 4, + int(32 / sd_pipe.vae_scale_factor), + int(32 / sd_pipe.vae_scale_factor), + ) + + latent_slice = sd_pipe.init_latents[0, -1, -3:, -3:].to(device) + expected_slice = np.array([-0.9084, -0.0367, 0.2940, 0.0839, 0.6890, 0.2651, -0.7103, 2.1090, -0.7821]) + assert np.abs(latent_slice.flatten() - expected_slice).max() < 1e-3 + + def test_ledits_pp_inversion_batch(self): + device = "cpu" # ensure determinism for the device-dependent torch.Generator + components = self.get_dummy_components() + sd_pipe = LEditsPPPipelineStableDiffusionXL(**components) + sd_pipe = sd_pipe.to(torch_device) + sd_pipe.set_progress_bar_config(disable=None) + + inputs = self.get_dummy_inversion_inputs(device) + sd_pipe.invert(**inputs) + assert sd_pipe.init_latents.shape == ( + 2, + 4, + int(32 / sd_pipe.vae_scale_factor), + int(32 / sd_pipe.vae_scale_factor), + ) + + latent_slice = sd_pipe.init_latents[0, -1, -3:, -3:].to(device) + print(latent_slice.flatten()) + expected_slice = np.array([0.2528, 0.1458, -0.2166, 0.4565, -0.5656, -1.0286, -0.9961, 0.5933, 1.1172]) + assert np.abs(latent_slice.flatten() - expected_slice).max() < 1e-3 + + latent_slice = sd_pipe.init_latents[1, -1, -3:, -3:].to(device) + print(latent_slice.flatten()) + expected_slice = np.array([-0.0796, 2.0583, 0.5500, 0.5358, 0.0282, -0.2803, -1.0470, 0.7024, -0.0072]) + print(latent_slice.flatten()) + assert np.abs(latent_slice.flatten() - expected_slice).max() < 1e-3 + + def test_ledits_pp_warmup_steps(self): + device = "cpu" # ensure determinism for the device-dependent torch.Generator + components = self.get_dummy_components() + pipe = LEditsPPPipelineStableDiffusionXL(**components) + pipe = pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + inversion_inputs = self.get_dummy_inversion_inputs(device) + inversion_inputs["image"] = inversion_inputs["image"][0] + pipe.invert(**inversion_inputs) + + inputs = self.get_dummy_inputs(device) + + inputs["edit_warmup_steps"] = [0, 5] + pipe(**inputs).images + + inputs["edit_warmup_steps"] = [5, 0] + pipe(**inputs).images + + inputs["edit_warmup_steps"] = [5, 10] + pipe(**inputs).images + + inputs["edit_warmup_steps"] = [10, 5] + pipe(**inputs).images + + +@slow +@require_torch_gpu +class LEditsPPPipelineStableDiffusionXLSlowTests(unittest.TestCase): + @classmethod + def setUpClass(cls): + raw_image = load_image( + "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/pix2pix/cat_6.png" + ) + raw_image = raw_image.convert("RGB").resize((512, 512)) + cls.raw_image = raw_image + + def test_ledits_pp_edit(self): + pipe = LEditsPPPipelineStableDiffusionXL.from_pretrained( + "stabilityai/stable-diffusion-xl-base-1.0", safety_checker=None, add_watermarker=None + ) + pipe = pipe.to(torch_device) + pipe.set_progress_bar_config(disable=None) + + generator = torch.manual_seed(0) + _ = pipe.invert(image=self.raw_image, generator=generator, num_zero_noise_steps=0) + inputs = { + "generator": generator, + "editing_prompt": ["cat", "dog"], + "reverse_editing_direction": [True, False], + "edit_guidance_scale": [2.0, 4.0], + "edit_threshold": [0.8, 0.8], + } + reconstruction = pipe(**inputs, output_type="np").images[0] + + output_slice = reconstruction[150:153, 140:143, -1] + output_slice = output_slice.flatten() + expected_slice = np.array( + [0.56419, 0.44121838, 0.2765603, 0.5708484, 0.42763475, 0.30945742, 0.5387106, 0.4735807, 0.3547244] + ) + assert np.abs(output_slice - expected_slice).max() < 1e-3