Würstchen model

docs/source/en/_toctree.yml

@@ -310,6 +310,8 @@
       title: Versatile Diffusion
     - local: api/pipelines/vq_diffusion
       title: VQ Diffusion
+    - local: api/pipelines/wuerstchen
+      title: Wuerstchen
     title: Pipelines
   - sections:
     - local: api/schedulers/overview

docs/source/en/api/pipelines/wuerstchen.md

@@ -0,0 +1,110 @@
+# Würstchen
+
+[Würstchen: Efficient Pretraining of Text-to-Image Models](https://huggingface.co/papers/2306.00637) is by Pablo Pernias, Dominic Rampas, and Marc Aubreville.
+
+The abstract from the paper is:
+
+*We introduce Würstchen, a novel technique for text-to-image synthesis that unites competitive performance with unprecedented cost-effectiveness and ease of training on constrained hardware. Building on recent advancements in machine learning, our approach, which utilizes latent diffusion strategies at strong latent image compression rates, significantly reduces the computational burden, typically associated with state-of-the-art models, while preserving, if not enhancing, the quality of generated images. Wuerstchen achieves notable speed improvements at inference time, thereby rendering real-time applications more viable. One of the key advantages of our method lies in its modest training requirements of only 9,200 GPU hours, slashing the usual costs significantly without compromising the end performance. In a comparison against the state-of-the-art, we found the approach to yield strong competitiveness. This paper opens the door to a new line of research that prioritizes both performance and computational accessibility, hence democratizing the use of sophisticated AI technologies. Through Wuerstchen, we demonstrate a compelling stride forward in the realm of text-to-image synthesis, offering an innovative path to explore in future research.*
+
+## Würstchen v2 comes to Diffusers
+
+After the initial paper release, we have improved numerous things in the architecture, training and sampling, making Würstchen competetive to current state-of-the-art models in many ways. We are excited to release this new version together with Diffusers. Here is a list of the improvements.
+
+- Higher resolution (1024x1024 up to 2048x2048)
+- Faster inference
+- Multi Aspect Resolution Sampling
+- Better quality
+
+## Text-to-Image Generation
+
+For the sake of usability Würstchen can be used with a single pipeline. This pipeline is called `WuerstchenPipeline` and can be used as follows:
+
+```python
+import torch
+from diffusers import WuerstchenPipeline
+
+device = "cuda"
+dtype = torch.float16
+num_images_per_prompt = 2
+
+pipeline = WuerstchenPipeline.from_pretrained(
+    "warp-diffusion/WuerstchenPipeline", torch_dtype=dtype
+).to(device)
+
+caption = "A captivating artwork of a mysterious stone golem"
+negative_prompt = "bad anatomy, blurry, fuzzy, extra arms, extra fingers, poorly drawn hands, disfigured, tiling, deformed, mutated"
+
+output = pipeline(
+    prompt=caption,
+    height=1024,
+    width=1024,
+    negative_prompt=negative_prompt,
+    guidance_scale=8.0,
+    num_images_per_prompt=num_images_per_prompt,
+    output_type="pil",
+).images
+```
+
+For explanation purposes, we can also initialize the two main pipelines of Würstchen individually. Würstchen consists of 3 stages: Stage C, Stage B, Stage A. They all have different jobs and work only together. When generating text-conditional images, Stage C will first generate the latents in a very compressed latent space. This is what happens in the `prior_pipeline`. Afterwards, the generated latents will be passed to Stage B, which decompresses the latents into a bigger latent space of a VQGAN. These latents can then be decoded by Stage A, which is a VQGAN, into the pixel-space. Stage B & Stage A are both encapsulated in the `decoder_pipeline`. For more details, take a look the [paper](https://huggingface.co/papers/2306.00637).
+
+```python
+import torch
+from diffusers import WuerstchenDecoderPipeline, WuerstchenPriorPipeline
+
+device = "cuda"
+dtype = torch.float16
+num_images_per_prompt = 2
+
+prior_pipeline = WuerstchenPriorPipeline.from_pretrained(
+    "warp-diffusion/WuerstchenPriorPipeline", torch_dtype=dtype
+).to(device)
+decoder_pipeline = WuerstchenDecoderPipeline.from_pretrained(
+    "warp-diffusion/WuerstchenDecoderPipeline", torch_dtype=dtype
+).to(device)
+
+caption = "A captivating artwork of a mysterious stone golem"
+negative_prompt = "bad anatomy, blurry, fuzzy, extra arms, extra fingers, poorly drawn hands, disfigured, tiling, deformed, mutated"
+
+prior_output = prior_pipeline(
+    prompt=caption,
+    height=1024,
+    width=1024,
+    negative_prompt=negative_prompt,
+    guidance_scale=8.0,
+    num_images_per_prompt=num_images_per_prompt,
+)
+decoder_output = decoder_pipeline(
+    predicted_image_embeddings=prior_output.image_embeds,
+    prompt=caption,
+    negative_prompt=negative_prompt,
+    num_images_per_prompt=num_images_per_prompt,
+    guidance_scale=0.0,
+    output_type="pil",
+).images
+
+```
+
+The original codebase, as well as experimental ideas, can be found at [dome272/Wuerstchen](https://github.com/dome272/Wuerstchen).
+
+## WuerschenPipeline
+
+[[autodoc]] WuerstchenPipeline
+	- all
+	- __call__
+
+## WuerstchenPriorPipeline
+
+[[autodoc]] WuerstchenDecoderPipeline
+
+	- all
+	- __call__
+
+## WuerstchenPriorPipelineOutput
+
+[[autodoc]] pipelines.wuerstchen.pipeline_wuerstchen_prior.WuerstchenPriorPipelineOutput
+
+## WuerstchenDecoderPipeline
+
+[[autodoc]] WuerstchenDecoderPipeline
+	- all
+	- __call__

scripts/convert_wuerstchen.py

@@ -0,0 +1,115 @@
+# Run inside root directory of official source code
+import os
+
+import torch
+from transformers import AutoTokenizer, CLIPTextModel
+from vqgan import VQModel
+
+from diffusers import (
+    DDPMWuerstchenScheduler,
+    WuerstchenDecoderPipeline,
+    WuerstchenPipeline,
+    WuerstchenPriorPipeline,
+)
+from diffusers.pipelines.wuerstchen import PaellaVQModel, WuerstchenDiffNeXt, WuerstchenPrior
+
+
+model_path = "models/"
+device = "cpu"
+
+paella_vqmodel = VQModel()
+state_dict = torch.load(os.path.join(model_path, "vqgan_f4_v1_500k.pt"), map_location=device)["state_dict"]
+paella_vqmodel.load_state_dict(state_dict)
+
+state_dict["vquantizer.embedding.weight"] = state_dict["vquantizer.codebook.weight"]
+state_dict.pop("vquantizer.codebook.weight")
+vqmodel = PaellaVQModel(num_vq_embeddings=paella_vqmodel.codebook_size, latent_channels=paella_vqmodel.c_latent)
+vqmodel.load_state_dict(state_dict)
+
+# Clip Text encoder and tokenizer
+text_encoder = CLIPTextModel.from_pretrained("laion/CLIP-ViT-bigG-14-laion2B-39B-b160k")
+tokenizer = AutoTokenizer.from_pretrained("laion/CLIP-ViT-bigG-14-laion2B-39B-b160k")
+
+# Generator
+gen_text_encoder = CLIPTextModel.from_pretrained("laion/CLIP-ViT-H-14-laion2B-s32B-b79K").to("cpu")
+gen_tokenizer = AutoTokenizer.from_pretrained("laion/CLIP-ViT-H-14-laion2B-s32B-b79K")
+
+orig_state_dict = torch.load(os.path.join(model_path, "model_v2_stage_b.pt"), map_location=device)["state_dict"]
+state_dict = {}
+for key in orig_state_dict.keys():
+    if key.endswith("in_proj_weight"):
+        weights = orig_state_dict[key].chunk(3, 0)
+        state_dict[key.replace("attn.in_proj_weight", "to_q.weight")] = weights[0]
+        state_dict[key.replace("attn.in_proj_weight", "to_k.weight")] = weights[1]
+        state_dict[key.replace("attn.in_proj_weight", "to_v.weight")] = weights[2]
+    elif key.endswith("in_proj_bias"):
+        weights = orig_state_dict[key].chunk(3, 0)
+        state_dict[key.replace("attn.in_proj_bias", "to_q.bias")] = weights[0]
+        state_dict[key.replace("attn.in_proj_bias", "to_k.bias")] = weights[1]
+        state_dict[key.replace("attn.in_proj_bias", "to_v.bias")] = weights[2]
+    elif key.endswith("out_proj.weight"):
+        weights = orig_state_dict[key]
+        state_dict[key.replace("attn.out_proj.weight", "to_out.0.weight")] = weights
+    elif key.endswith("out_proj.bias"):
+        weights = orig_state_dict[key]
+        state_dict[key.replace("attn.out_proj.bias", "to_out.0.bias")] = weights
+    else:
+        state_dict[key] = orig_state_dict[key]
+generator = WuerstchenDiffNeXt()
+generator.load_state_dict(state_dict)
+
+# Prior
+orig_state_dict = torch.load(os.path.join(model_path, "model_v3_stage_c.pt"), map_location=device)["ema_state_dict"]
+state_dict = {}
+for key in orig_state_dict.keys():
+    if key.endswith("in_proj_weight"):
+        weights = orig_state_dict[key].chunk(3, 0)
+        state_dict[key.replace("attn.in_proj_weight", "to_q.weight")] = weights[0]
+        state_dict[key.replace("attn.in_proj_weight", "to_k.weight")] = weights[1]
+        state_dict[key.replace("attn.in_proj_weight", "to_v.weight")] = weights[2]
+    elif key.endswith("in_proj_bias"):
+        weights = orig_state_dict[key].chunk(3, 0)
+        state_dict[key.replace("attn.in_proj_bias", "to_q.bias")] = weights[0]
+        state_dict[key.replace("attn.in_proj_bias", "to_k.bias")] = weights[1]
+        state_dict[key.replace("attn.in_proj_bias", "to_v.bias")] = weights[2]
+    elif key.endswith("out_proj.weight"):
+        weights = orig_state_dict[key]
+        state_dict[key.replace("attn.out_proj.weight", "to_out.0.weight")] = weights
+    elif key.endswith("out_proj.bias"):
+        weights = orig_state_dict[key]
+        state_dict[key.replace("attn.out_proj.bias", "to_out.0.bias")] = weights
+    else:
+        state_dict[key] = orig_state_dict[key]
+prior_model = WuerstchenPrior(c_in=16, c=1536, c_cond=1280, c_r=64, depth=32, nhead=24).to(device)
+prior_model.load_state_dict(state_dict)
+
+# scheduler
+scheduler = DDPMWuerstchenScheduler()
+
+# Prior pipeline
+prior_pipeline = WuerstchenPriorPipeline(
+    prior=prior_model, text_encoder=text_encoder, tokenizer=tokenizer, scheduler=scheduler
+)
+
+prior_pipeline.save_pretrained("warp-diffusion/WuerstchenPriorPipeline")
+
+decoder_pipeline = WuerstchenDecoderPipeline(
+    text_encoder=gen_text_encoder, tokenizer=gen_tokenizer, vqgan=vqmodel, generator=generator, scheduler=scheduler
+)
+decoder_pipeline.save_pretrained("warp-diffusion/WuerstchenDecoderPipeline")
+
+# Wuerstchen pipeline
+wuerstchen_pipeline = WuerstchenPipeline(
+    # Decoder
+    text_encoder=gen_text_encoder,
+    tokenizer=gen_tokenizer,
+    generator=generator,
+    scheduler=scheduler,
+    vqgan=vqmodel,
+    # Prior
+    prior_tokenizer=tokenizer,
+    prior_text_encoder=text_encoder,
+    prior_prior=prior_model,
+    prior_scheduler=scheduler,
+)
+wuerstchen_pipeline.save_pretrained("warp-diffusion/WuerstchenPipeline")

src/diffusers/__init__.py

@@ -88,6 +88,7 @@
         DDIMScheduler,
         DDPMParallelScheduler,
         DDPMScheduler,
+        DDPMWuerstchenScheduler,
         DEISMultistepScheduler,
         DPMSolverMultistepInverseScheduler,
         DPMSolverMultistepScheduler,
@@ -216,6 +217,9 @@
         VersatileDiffusionTextToImagePipeline,
         VideoToVideoSDPipeline,
         VQDiffusionPipeline,
+        WuerstchenDecoderPipeline,
+        WuerstchenPipeline,
+        WuerstchenPriorPipeline,
     )
 
 try:

src/diffusers/models/attention_processor.py

@@ -188,7 +188,7 @@ def set_use_memory_efficient_attention_xformers(
         if use_memory_efficient_attention_xformers:
             if is_added_kv_processor and (is_lora or is_custom_diffusion):
                 raise NotImplementedError(
-                    f"Memory efficient attention is currently not supported for LoRA or custom diffuson for attention processor type {self.processor}"
+                    f"Memory efficient attention is currently not supported for LoRA or custom diffusion for attention processor type {self.processor}"
                 )
             if not is_xformers_available():
                 raise ModuleNotFoundError(

src/diffusers/models/vae.py

@@ -52,7 +52,7 @@ def __init__(
         super().__init__()
         self.layers_per_block = layers_per_block
 
-        self.conv_in = torch.nn.Conv2d(
+        self.conv_in = nn.Conv2d(
             in_channels,
             block_out_channels[0],
             kernel_size=3,

src/diffusers/models/vq_model.py

@@ -132,7 +132,7 @@ def decode(
     ) -> Union[DecoderOutput, torch.FloatTensor]:
         # also go through quantization layer
         if not force_not_quantize:
-            quant, emb_loss, info = self.quantize(h)
+            quant, _, _ = self.quantize(h)
         else:
             quant = h
         quant2 = self.post_quant_conv(quant)

src/diffusers/pipelines/__init__.py

@@ -132,6 +132,7 @@
         VersatileDiffusionTextToImagePipeline,
     )
     from .vq_diffusion import VQDiffusionPipeline
+    from .wuerstchen import WuerstchenDecoderPipeline, WuerstchenPipeline, WuerstchenPriorPipeline
 
 
 try:

src/diffusers/pipelines/auto_pipeline.py

@@ -52,6 +52,7 @@
     StableDiffusionXLInpaintPipeline,
     StableDiffusionXLPipeline,
 )
+from .wuerstchen import WuerstchenDecoderPipeline, WuerstchenPipeline
 
 
 AUTO_TEXT2IMAGE_PIPELINES_MAPPING = OrderedDict(
@@ -63,6 +64,7 @@
         ("kandinsky22", KandinskyV22CombinedPipeline),
         ("stable-diffusion-controlnet", StableDiffusionControlNetPipeline),
         ("stable-diffusion-xl-controlnet", StableDiffusionXLControlNetPipeline),
+        ("wuerstchen", WuerstchenPipeline),
     ]
 )
 
@@ -93,6 +95,7 @@
     [
         ("kandinsky", KandinskyPipeline),
         ("kandinsky22", KandinskyV22Pipeline),
+        ("wuerstchen", WuerstchenDecoderPipeline),
     ]
 )
 _AUTO_IMAGE2IMAGE_DECODER_PIPELINES_MAPPING = OrderedDict(

src/diffusers/pipelines/wuerstchen/__init__.py

@@ -0,0 +1,10 @@
+from ...utils import is_torch_available, is_transformers_available
+
+
+if is_transformers_available() and is_torch_available():
+    from .modeling_paella_vq_model import PaellaVQModel
+    from .modeling_wuerstchen_diffnext import WuerstchenDiffNeXt
+    from .modeling_wuerstchen_prior import WuerstchenPrior
+    from .pipeline_wuerstchen import WuerstchenDecoderPipeline
+    from .pipeline_wuerstchen_combined import WuerstchenPipeline
+    from .pipeline_wuerstchen_prior import WuerstchenPriorPipeline