Merge branch 'main' of https://github.com/huggingface/diffusers into diffedit-inpainting-pipeline

Author: clarencechen

docs/source/en/_toctree.yml

@@ -191,6 +191,8 @@
         title: MultiDiffusion Panorama
       - local: api/pipelines/stable_diffusion/controlnet
         title: Text-to-Image Generation with ControlNet Conditioning
+      - local: api/pipelines/stable_diffusion/model_editing
+        title: Text-to-Image Model Editing
       - local: api/pipelines/stable_diffusion/diffedit
         title: DiffEdit
       title: Stable Diffusion

docs/source/en/api/pipelines/stable_diffusion/model_editing.mdx

@@ -0,0 +1,61 @@
+<!--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.
+-->
+
+# Editing Implicit Assumptions in Text-to-Image Diffusion Models
+
+## Overview
+
+[Editing Implicit Assumptions in Text-to-Image Diffusion Models](https://arxiv.org/abs/2303.08084) by Hadas Orgad, Bahjat Kawar, and Yonatan Belinkov.
+
+The abstract of the paper is the following:
+
+*Text-to-image diffusion models often make implicit assumptions about the world when generating images. While some assumptions are useful (e.g., the sky is blue), they can also be outdated, incorrect, or reflective of social biases present in the training data. Thus, there is a need to control these assumptions without requiring explicit user input or costly re-training. In this work, we aim to edit a given implicit assumption in a pre-trained diffusion model. Our Text-to-Image Model Editing method, TIME for short, receives a pair of inputs: a "source" under-specified prompt for which the model makes an implicit assumption (e.g., "a pack of roses"), and a "destination" prompt that describes the same setting, but with a specified desired attribute (e.g., "a pack of blue roses"). TIME then updates the model's cross-attention layers, as these layers assign visual meaning to textual tokens. We edit the projection matrices in these layers such that the source prompt is projected close to the destination prompt. Our method is highly efficient, as it modifies a mere 2.2% of the model's parameters in under one second. To evaluate model editing approaches, we introduce TIMED (TIME Dataset), containing 147 source and destination prompt pairs from various domains. Our experiments (using Stable Diffusion) show that TIME is successful in model editing, generalizes well for related prompts unseen during editing, and imposes minimal effect on unrelated generations.*
+
+Resources:
+
+* [Project Page](https://time-diffusion.github.io/).
+* [Paper](https://arxiv.org/abs/2303.08084).
+* [Original Code](https://github.com/bahjat-kawar/time-diffusion).
+* [Demo](https://huggingface.co/spaces/bahjat-kawar/time-diffusion).
+
+## Available Pipelines:
+
+| Pipeline | Tasks | Demo
+|---|---|:---:|
+| [StableDiffusionModelEditingPipeline](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_model_editing.py) | *Text-to-Image Model Editing* | [🤗 Space](https://huggingface.co/spaces/bahjat-kawar/time-diffusion)) |
+
+This pipeline enables editing the diffusion model weights, such that its assumptions on a given concept are changed. The resulting change is expected to take effect in all prompt generations pertaining to the edited concept.
+
+## Usage example
+
+```python
+import torch
+from diffusers import StableDiffusionModelEditingPipeline
+
+model_ckpt = "CompVis/stable-diffusion-v1-4"
+pipe = StableDiffusionModelEditingPipeline.from_pretrained(model_ckpt)
+
+pipe = pipe.to("cuda")
+
+source_prompt = "A pack of roses"
+destination_prompt = "A pack of blue roses"
+pipe.edit_model(source_prompt, destination_prompt)
+
+prompt = "A field of roses"
+image = pipe(prompt).images[0]
+image.save("field_of_roses.png")
+```
+
+## StableDiffusionModelEditingPipeline
+[[autodoc]] StableDiffusionModelEditingPipeline
+	- __call__
+	- all

docs/source/en/api/pipelines/stable_diffusion/overview.mdx

@@ -35,6 +35,7 @@ For more details about how Stable Diffusion works and how it differs from the ba
 | [StableDiffusionInstructPix2PixPipeline](./pix2pix) | **Experimental** – *Text-Based Image Editing * | | [InstructPix2Pix: Learning to Follow Image Editing Instructions](https://huggingface.co/spaces/timbrooks/instruct-pix2pix)
 | [StableDiffusionAttendAndExcitePipeline](./attend_and_excite) | **Experimental** – *Text-to-Image Generation * | | [Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models](https://huggingface.co/spaces/AttendAndExcite/Attend-and-Excite)
 | [StableDiffusionPix2PixZeroPipeline](./pix2pix_zero) | **Experimental** – *Text-Based Image Editing * | | [Zero-shot Image-to-Image Translation](https://arxiv.org/abs/2302.03027)
+| [StableDiffusionModelEditingPipeline](./model_editing) | **Experimental** – *Text-to-Image Model Editing * | | [Editing Implicit Assumptions in Text-to-Image Diffusion Models](https://arxiv.org/abs/2303.08084)
 
 
 

docs/source/en/api/pipelines/stable_unclip.mdx

@@ -16,6 +16,10 @@ Stable unCLIP checkpoints are finetuned from [stable diffusion 2.1](./stable_dif
 Stable unCLIP also still conditions on text embeddings. Given the two separate conditionings, stable unCLIP can be used
 for text guided image variation. When combined with an unCLIP prior, it can also be used for full text to image generation.
 
+To know more about the unCLIP process, check out the following paper:
+
+[Hierarchical Text-Conditional Image Generation with CLIP Latents](https://arxiv.org/abs/2204.06125) by Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, Mark Chen.
+
 ## Tips
 
 Stable unCLIP takes a `noise_level` as input during inference. `noise_level` determines how much noise is added 
@@ -24,23 +28,15 @@ we do not add any additional noise to the image embeddings i.e. `noise_level = 0
 
 ### Available checkpoints:
 
-TODO
+* Image variation
+	* [stabilityai/stable-diffusion-2-1-unclip](https://hf.co/stabilityai/stable-diffusion-2-1-unclip)
+	* [stabilityai/stable-diffusion-2-1-unclip-small](https://hf.co/stabilityai/stable-diffusion-2-1-unclip-small)
+* Text-to-image 
+	* Coming soon!
 
 ### Text-to-Image Generation
 
-```python
-import torch
-from diffusers import StableUnCLIPPipeline
-
-pipe = StableUnCLIPPipeline.from_pretrained(
-    "fusing/stable-unclip-2-1-l", torch_dtype=torch.float16
-)  # TODO update model path
-pipe = pipe.to("cuda")
-
-prompt = "a photo of an astronaut riding a horse on mars"
-images = pipe(prompt).images
-images[0].save("astronaut_horse.png")
-```
+Coming soon!
 
 
 ### Text guided Image-to-Image Variation
@@ -54,19 +50,25 @@ from io import BytesIO
 from diffusers import StableUnCLIPImg2ImgPipeline
 
 pipe = StableUnCLIPImg2ImgPipeline.from_pretrained(
-    "fusing/stable-unclip-2-1-l-img2img", torch_dtype=torch.float16
-)  # TODO update model path
+    "stabilityai/stable-diffusion-2-1-unclip", torch_dtype=torch.float16, variation="fp16"
+)
 pipe = pipe.to("cuda")
 
-url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
+url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/tarsila_do_amaral.png"
 
 response = requests.get(url)
 init_image = Image.open(BytesIO(response.content)).convert("RGB")
-init_image = init_image.resize((768, 512))
 
+images = pipe(init_image).images
+images[0].save("fantasy_landscape.png")
+```
+
+Optionally, you can also pass a prompt to `pipe` such as:
+
+```python 
 prompt = "A fantasy landscape, trending on artstation"
 
-images = pipe(prompt, init_image).images
+images = pipe(init_image, prompt=prompt).images
 images[0].save("fantasy_landscape.png")
 ```
 

docs/source/en/index.mdx

@@ -76,6 +76,7 @@ The library has three main components:
 | [stable_diffusion_self_attention_guidance](./api/pipelines/stable_diffusion/self_attention_guidance) | [Improving Sample Quality of Diffusion Models Using Self-Attention Guidance](https://arxiv.org/abs/2210.00939) | Text-to-Image Generation |
 | [stable_diffusion_image_variation](./stable_diffusion/image_variation) | [Stable Diffusion Image Variations](https://github.com/LambdaLabsML/lambda-diffusers#stable-diffusion-image-variations) | Image-to-Image Generation |
 | [stable_diffusion_latent_upscale](./stable_diffusion/latent_upscale) | [Stable Diffusion Latent Upscaler](https://twitter.com/StabilityAI/status/1590531958815064065) | Text-Guided Super Resolution Image-to-Image |
+| [stable_diffusion_model_editing](./api/pipelines/stable_diffusion/model_editing) | [Editing Implicit Assumptions in Text-to-Image Diffusion Models](https://time-diffusion.github.io/) | Text-to-Image Model Editing |
 | [stable_diffusion_2](./api/pipelines/stable_diffusion_2) | [Stable Diffusion 2](https://stability.ai/blog/stable-diffusion-v2-release) | Text-to-Image Generation |
 | [stable_diffusion_2](./api/pipelines/stable_diffusion_2) | [Stable Diffusion 2](https://stability.ai/blog/stable-diffusion-v2-release) | Text-Guided Image Inpainting |
 | [stable_diffusion_2](./api/pipelines/stable_diffusion_2) | [Depth-Conditional Stable Diffusion](https://github.com/Stability-AI/stablediffusion#depth-conditional-stable-diffusion) | Depth-to-Image Generation |
@@ -89,4 +90,4 @@ The library has three main components:
 | [versatile_diffusion](./api/pipelines/versatile_diffusion) | [Versatile Diffusion: Text, Images and Variations All in One Diffusion Model](https://arxiv.org/abs/2211.08332) | Text-to-Image Generation |
 | [versatile_diffusion](./api/pipelines/versatile_diffusion) | [Versatile Diffusion: Text, Images and Variations All in One Diffusion Model](https://arxiv.org/abs/2211.08332) | Image Variations Generation |
 | [versatile_diffusion](./api/pipelines/versatile_diffusion) | [Versatile Diffusion: Text, Images and Variations All in One Diffusion Model](https://arxiv.org/abs/2211.08332) | Dual Image and Text Guided Generation |
-| [vq_diffusion](./api/pipelines/vq_diffusion) | [Vector Quantized Diffusion Model for Text-to-Image Synthesis](https://arxiv.org/abs/2111.14822) | Text-to-Image Generation |
+| [vq_diffusion](./api/pipelines/vq_diffusion) | [Vector Quantized Diffusion Model for Text-to-Image Synthesis](https://arxiv.org/abs/2111.14822) | Text-to-Image Generation |

examples/README.md

@@ -42,6 +42,8 @@ Training examples show how to pretrain or fine-tune diffusion models for a varie
 | [**Text-to-Image fine-tuning**](./text_to_image) | ✅ | ✅ | 
 | [**Textual Inversion**](./textual_inversion) | ✅ | - | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb)
 | [**Dreambooth**](./dreambooth) | ✅ | - | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb)
+| [**ControlNet**](./controlnet) | ✅ | ✅ | -
+| [**InstructPix2Pix**](./instruct_pix2pix) | ✅ | ✅ | -
 | [**Reinforcement Learning for Control**](https://github.com/huggingface/diffusers/blob/main/examples/rl/run_diffusers_locomotion.py)                    | - | - | coming soon.
 
 ## Community

examples/community/checkpoint_merger.py

@@ -199,24 +199,20 @@ def merge(self, pretrained_model_name_or_path_list: List[Union[str, os.PathLike]
             if not attr.startswith("_"):
                 checkpoint_path_1 = os.path.join(cached_folders[1], attr)
                 if os.path.exists(checkpoint_path_1):
-                    files = list(
-                        (
-                            *glob.glob(os.path.join(checkpoint_path_1, "*.safetensors")),
-                            *glob.glob(os.path.join(checkpoint_path_1, "*.bin")),
-                        )
-                    )
+                    files = [
+                        *glob.glob(os.path.join(checkpoint_path_1, "*.safetensors")),
+                        *glob.glob(os.path.join(checkpoint_path_1, "*.bin")),
+                    ]
                     checkpoint_path_1 = files[0] if len(files) > 0 else None
                 if len(cached_folders) < 3:
                     checkpoint_path_2 = None
                 else:
                     checkpoint_path_2 = os.path.join(cached_folders[2], attr)
                     if os.path.exists(checkpoint_path_2):
-                        files = list(
-                            (
-                                *glob.glob(os.path.join(checkpoint_path_2, "*.safetensors")),
-                                *glob.glob(os.path.join(checkpoint_path_2, "*.bin")),
-                            )
-                        )
+                        files = [
+                            *glob.glob(os.path.join(checkpoint_path_2, "*.safetensors")),
+                            *glob.glob(os.path.join(checkpoint_path_2, "*.bin")),
+                        ]
                         checkpoint_path_2 = files[0] if len(files) > 0 else None
                 # For an attr if both checkpoint_path_1 and 2 are None, ignore.
                 # If atleast one is present, deal with it according to interp method, of course only if the state_dict keys match.

examples/community/imagic_stable_diffusion.py

@@ -48,7 +48,7 @@
 
 def preprocess(image):
     w, h = image.size
-    w, h = map(lambda x: x - x % 32, (w, h))  # resize to integer multiple of 32
+    w, h = (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
     image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"])
     image = np.array(image).astype(np.float32) / 255.0
     image = image[None].transpose(0, 3, 1, 2)

examples/community/lpw_stable_diffusion.py

@@ -376,7 +376,7 @@ def get_weighted_text_embeddings(
 
 def preprocess_image(image):
     w, h = image.size
-    w, h = map(lambda x: x - x % 32, (w, h))  # resize to integer multiple of 32
+    w, h = (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
     image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"])
     image = np.array(image).astype(np.float32) / 255.0
     image = image[None].transpose(0, 3, 1, 2)
@@ -387,7 +387,7 @@ def preprocess_image(image):
 def preprocess_mask(mask, scale_factor=8):
     mask = mask.convert("L")
     w, h = mask.size
-    w, h = map(lambda x: x - x % 32, (w, h))  # resize to integer multiple of 32
+    w, h = (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
     mask = mask.resize((w // scale_factor, h // scale_factor), resample=PIL_INTERPOLATION["nearest"])
     mask = np.array(mask).astype(np.float32) / 255.0
     mask = np.tile(mask, (4, 1, 1))

examples/community/lpw_stable_diffusion_onnx.py

@@ -403,7 +403,7 @@ def get_weighted_text_embeddings(
 
 def preprocess_image(image):
     w, h = image.size
-    w, h = map(lambda x: x - x % 32, (w, h))  # resize to integer multiple of 32
+    w, h = (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
     image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"])
     image = np.array(image).astype(np.float32) / 255.0
     image = image[None].transpose(0, 3, 1, 2)
@@ -413,7 +413,7 @@ def preprocess_image(image):
 def preprocess_mask(mask, scale_factor=8):
     mask = mask.convert("L")
     w, h = mask.size
-    w, h = map(lambda x: x - x % 32, (w, h))  # resize to integer multiple of 32
+    w, h = (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
     mask = mask.resize((w // scale_factor, h // scale_factor), resample=PIL_INTERPOLATION["nearest"])
     mask = np.array(mask).astype(np.float32) / 255.0
     mask = np.tile(mask, (4, 1, 1))