feat: add repaint (#974)

* feat: add repaint * fix: fix quality check with `make fix-copies` * fix: remove old unnecessary arg * chore: change default to DDPM (looks better in experiments) * ".to(device)" changed to "device=" Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com> * make generator device-specific Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com> * make generator device-specific and change shape Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com> * fix: add preprocessing for image and mask Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com> * fix: update test Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com> * Update src/diffusers/pipelines/repaint/pipeline_repaint.py Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * Add docs and examples * Fix toctree Co-authored-by: fja <fja@zurich.ibm.com> Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com> Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> Co-authored-by: Anton Lozhkov <anton@huggingface.co>
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diff --git a/docs/source/_toctree.yml b/docs/source/_toctree.yml
@@ -96,5 +96,7 @@
       title: "Stochastic Karras VE"
     - local: api/pipelines/dance_diffusion
       title: "Dance Diffusion"
+    - local: api/pipelines/repaint
+      title: "RePaint"
     title: "Pipelines"
   title: "API"
diff --git a/docs/source/api/pipelines/overview.mdx b/docs/source/api/pipelines/overview.mdx
@@ -41,19 +41,21 @@ If you are looking for *official* training examples, please have a look at [exam
 The following table summarizes all officially supported pipelines, their corresponding paper, and if 
 available a colab notebook to directly try them out.
 
-| Pipeline | Paper | Tasks | Colab
-|---|---|:---:|:---:|
-| [ddpm](./ddpm) | [**Denoising Diffusion Probabilistic Models**](https://arxiv.org/abs/2006.11239) | Unconditional Image Generation |
-| [ddim](./ddim) | [**Denoising Diffusion Implicit Models**](https://arxiv.org/abs/2010.02502) | Unconditional Image Generation | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb)
-| [latent_diffusion](./latent_diffusion) | [**High-Resolution Image Synthesis with Latent Diffusion Models**](https://arxiv.org/abs/2112.10752)| Text-to-Image Generation | 
-| [latent_diffusion_uncond](./latent_diffusion_uncond) | [**High-Resolution Image Synthesis with Latent Diffusion Models**](https://arxiv.org/abs/2112.10752) | Unconditional Image Generation | 
-| [pndm](./pndm) | [**Pseudo Numerical Methods for Diffusion Models on Manifolds**](https://arxiv.org/abs/2202.09778) | Unconditional Image Generation | 
-| [score_sde_ve](./score_sde_ve) | [**Score-Based Generative Modeling through Stochastic Differential Equations**](https://openreview.net/forum?id=PxTIG12RRHS) | Unconditional Image Generation | 
-| [score_sde_vp](./score_sde_vp) | [**Score-Based Generative Modeling through Stochastic Differential Equations**](https://openreview.net/forum?id=PxTIG12RRHS) | Unconditional Image Generation | 
-| [stable_diffusion](./stable_diffusion) | [**Stable Diffusion**](https://stability.ai/blog/stable-diffusion-public-release) | Text-to-Image Generation | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb)
-| [stable_diffusion](./stable_diffusion) | [**Stable Diffusion**](https://stability.ai/blog/stable-diffusion-public-release) | Image-to-Image Text-Guided Generation | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/image_2_image_using_diffusers.ipynb)
-| [stable_diffusion](./stable_diffusion) | [**Stable Diffusion**](https://stability.ai/blog/stable-diffusion-public-release) | Text-Guided Image Inpainting | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/in_painting_with_stable_diffusion_using_diffusers.ipynb)
-| [stochastic_karras_ve](./stochastic_karras_ve) | [**Elucidating the Design Space of Diffusion-Based Generative Models**](https://arxiv.org/abs/2206.00364) | Unconditional Image Generation | 
+| Pipeline                                             | Paper                                                                                                                        |                 Tasks                 | Colab
+|------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------:|:---:|
+| [ddpm](./ddpm)                                       | [**Denoising Diffusion Probabilistic Models**](https://arxiv.org/abs/2006.11239)                                             |    Unconditional Image Generation     |
+| [ddim](./ddim)                                       | [**Denoising Diffusion Implicit Models**](https://arxiv.org/abs/2010.02502)                                                  |    Unconditional Image Generation     | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb)
+| [latent_diffusion](./latent_diffusion)               | [**High-Resolution Image Synthesis with Latent Diffusion Models**](https://arxiv.org/abs/2112.10752)                         |       Text-to-Image Generation        | 
+| [latent_diffusion_uncond](./latent_diffusion_uncond) | [**High-Resolution Image Synthesis with Latent Diffusion Models**](https://arxiv.org/abs/2112.10752)                         |    Unconditional Image Generation     | 
+| [pndm](./pndm)                                       | [**Pseudo Numerical Methods for Diffusion Models on Manifolds**](https://arxiv.org/abs/2202.09778)                           |    Unconditional Image Generation     | 
+| [score_sde_ve](./score_sde_ve)                       | [**Score-Based Generative Modeling through Stochastic Differential Equations**](https://openreview.net/forum?id=PxTIG12RRHS) |    Unconditional Image Generation     | 
+| [score_sde_vp](./score_sde_vp)                       | [**Score-Based Generative Modeling through Stochastic Differential Equations**](https://openreview.net/forum?id=PxTIG12RRHS) |    Unconditional Image Generation     | 
+| [stable_diffusion](./stable_diffusion)               | [**Stable Diffusion**](https://stability.ai/blog/stable-diffusion-public-release)                                            |       Text-to-Image Generation        | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb)
+| [stable_diffusion](./stable_diffusion)               | [**Stable Diffusion**](https://stability.ai/blog/stable-diffusion-public-release)                                            | Image-to-Image Text-Guided Generation | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/image_2_image_using_diffusers.ipynb)
+| [stable_diffusion](./stable_diffusion)               | [**Stable Diffusion**](https://stability.ai/blog/stable-diffusion-public-release)                                            |     Text-Guided Image Inpainting      | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/in_painting_with_stable_diffusion_using_diffusers.ipynb)
+| [stochastic_karras_ve](./stochastic_karras_ve)       | [**Elucidating the Design Space of Diffusion-Based Generative Models**](https://arxiv.org/abs/2206.00364)                    |    Unconditional Image Generation     | 
+| [repaint](./repaint)                                 | [**RePaint: Inpainting using Denoising Diffusion Probabilistic Models**](https://arxiv.org/abs/2201.09865)                   |           Image Inpainting            | 
+
 
 **Note**: Pipelines are simple examples of how to play around with the diffusion systems as described in the corresponding papers. 
 

diff --git a/docs/source/api/pipelines/repaint.mdx b/docs/source/api/pipelines/repaint.mdx
@@ -0,0 +1,77 @@
+<!--Copyright 2022 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.
+-->
+
+# RePaint
+
+## Overview
+
+[RePaint: Inpainting using Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2201.09865) (PNDM) by Andreas Lugmayr, Martin Danelljan, Andres Romero, Fisher Yu, Radu Timofte, Luc Van Gool.
+
+The abstract of the paper is the following:
+
+Free-form inpainting is the task of adding new content to an image in the regions specified by an arbitrary binary mask. Most existing approaches train for a certain distribution of masks, which limits their generalization capabilities to unseen mask types. Furthermore, training with pixel-wise and perceptual losses often leads to simple textural extensions towards the missing areas instead of semantically meaningful generation. In this work, we propose RePaint: A Denoising Diffusion Probabilistic Model (DDPM) based inpainting approach that is applicable to even extreme masks. We employ a pretrained unconditional DDPM as the generative prior. To condition the generation process, we only alter the reverse diffusion iterations by sampling the unmasked regions using the given image information. Since this technique does not modify or condition the original DDPM network itself, the model produces high-quality and diverse output images for any inpainting form. We validate our method for both faces and general-purpose image inpainting using standard and extreme masks.
+RePaint outperforms state-of-the-art Autoregressive, and GAN approaches for at least five out of six mask distributions.
+
+The original codebase can be found [here](https://github.com/andreas128/RePaint).
+
+## Available Pipelines:
+
+| Pipeline                                                                                                                      | Tasks              | Colab
+|-------------------------------------------------------------------------------------------------------------------------------|--------------------|:---:|
+| [pipeline_repaint.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/repaint/pipeline_repaint.py) | *Image Inpainting* | - |
+
+## Usage example
+
+```python
+from io import BytesIO
+
+import torch
+
+import PIL
+import requests
+from diffusers import RePaintPipeline, RePaintScheduler
+
+
+def download_image(url):
+    response = requests.get(url)
+    return PIL.Image.open(BytesIO(response.content)).convert("RGB")
+
+
+img_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/celeba_hq_256.png"
+mask_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png"
+
+# Load the original image and the mask as PIL images
+original_image = download_image(img_url).resize((256, 256))
+mask_image = download_image(mask_url).resize((256, 256))
+
+# Load the RePaint scheduler and pipeline based on a pretrained DDPM model
+scheduler = RePaintScheduler.from_config("google/ddpm-ema-celebahq-256")
+pipe = RePaintPipeline.from_pretrained("google/ddpm-ema-celebahq-256", scheduler=scheduler)
+pipe = pipe.to("cuda")
+
+generator = torch.Generator(device="cuda").manual_seed(0)
+output = pipe(
+    original_image=original_image,
+    mask_image=mask_image,
+    num_inference_steps=250,
+    eta=0.0,
+    jump_length=10,
+    jump_n_sample=10,
+    generator=generator,
+)
+inpainted_image = output.images[0]
+```
+
+## RePaintPipeline
+[[autodoc]] pipelines.repaint.pipeline_repaint.RePaintPipeline
+    - __call__
+
diff --git a/docs/source/api/schedulers.mdx b/docs/source/api/schedulers.mdx
@@ -127,4 +127,14 @@ Fast scheduler which often times generates good outputs with 20-30 steps.
 Ancestral sampling with Euler method steps. Based on the original (k-diffusion)[https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L72] implementation by Katherine Crowson.
 Fast scheduler which often times generates good outputs with 20-30 steps.
 
-[[autodoc]] EulerAncestralDiscreteScheduler
+[[autodoc]] EulerAncestralDiscreteScheduler
+
+
+#### RePaint scheduler
+
+DDPM-based inpainting scheduler for unsupervised inpainting with extreme masks. 
+Intended for use with [`RePaintPipeline`].
+Based on the paper [RePaint: Inpainting using Denoising Diffusion Probabilistic Models](https://arxiv.org/abs/2201.09865) 
+and the original implementation by Andreas Lugmayr et al.: https://github.com/andreas128/RePaint
+
+[[autodoc]] RePaintScheduler
diff --git a/src/diffusers/__init__.py b/src/diffusers/__init__.py
@@ -36,6 +36,7 @@
         KarrasVePipeline,
         LDMPipeline,
         PNDMPipeline,
+        RePaintPipeline,
         ScoreSdeVePipeline,
     )
     from .schedulers import (
@@ -46,6 +47,7 @@
         IPNDMScheduler,
         KarrasVeScheduler,
         PNDMScheduler,
+        RePaintScheduler,
         SchedulerMixin,
         ScoreSdeVeScheduler,
     )

diff --git a/src/diffusers/pipelines/__init__.py b/src/diffusers/pipelines/__init__.py
@@ -7,6 +7,7 @@
     from .ddpm import DDPMPipeline
     from .latent_diffusion_uncond import LDMPipeline
     from .pndm import PNDMPipeline
+    from .repaint import RePaintPipeline
     from .score_sde_ve import ScoreSdeVePipeline
     from .stochastic_karras_ve import KarrasVePipeline
 else:

diff --git a/src/diffusers/pipelines/repaint/__init__.py b/src/diffusers/pipelines/repaint/__init__.py
@@ -0,0 +1 @@
+from .pipeline_repaint import RePaintPipeline
diff --git a/src/diffusers/pipelines/repaint/pipeline_repaint.py b/src/diffusers/pipelines/repaint/pipeline_repaint.py
@@ -0,0 +1,140 @@
+# Copyright 2022 ETH Zurich Computer Vision Lab and 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.
+
+
+from typing import Optional, Tuple, Union
+
+import numpy as np
+import torch
+
+import PIL
+from tqdm.auto import tqdm
+
+from ...models import UNet2DModel
+from ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput
+from ...schedulers import RePaintScheduler
+
+
+def _preprocess_image(image: PIL.Image.Image):
+    image = np.array(image.convert("RGB"))
+    image = image[None].transpose(0, 3, 1, 2)
+    image = torch.from_numpy(image).to(dtype=torch.float32) / 127.5 - 1.0
+    return image
+
+
+def _preprocess_mask(mask: PIL.Image.Image):
+    mask = np.array(mask.convert("L"))
+    mask = mask.astype(np.float32) / 255.0
+    mask = mask[None, None]
+    mask[mask < 0.5] = 0
+    mask[mask >= 0.5] = 1
+    mask = torch.from_numpy(mask)
+    return mask
+
+
+class RePaintPipeline(DiffusionPipeline):
+    unet: UNet2DModel
+    scheduler: RePaintScheduler
+
+    def __init__(self, unet, scheduler):
+        super().__init__()
+        self.register_modules(unet=unet, scheduler=scheduler)
+
+    @torch.no_grad()
+    def __call__(
+        self,
+        original_image: Union[torch.FloatTensor, PIL.Image.Image],
+        mask_image: Union[torch.FloatTensor, PIL.Image.Image],
+        num_inference_steps: int = 250,
+        eta: float = 0.0,
+        jump_length: int = 10,
+        jump_n_sample: int = 10,
+        generator: Optional[torch.Generator] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+    ) -> Union[ImagePipelineOutput, Tuple]:
+        r"""
+        Args:
+            original_image (`torch.FloatTensor` or `PIL.Image.Image`):
+                The original image to inpaint on.
+            mask_image (`torch.FloatTensor` or `PIL.Image.Image`):
+                The mask_image where 0.0 values define which part of the original image to inpaint (change).
+            num_inference_steps (`int`, *optional*, defaults to 1000):
+                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
+                expense of slower inference.
+            eta (`float`):
+                The weight of noise for added noise in a diffusion step. Its value is between 0.0 and 1.0 - 0.0 is DDIM
+                and 1.0 is DDPM scheduler respectively.
+            jump_length (`int`, *optional*, defaults to 10):
+                The number of steps taken forward in time before going backward in time for a single jump ("j" in
+                RePaint paper). Take a look at Figure 9 and 10 in https://arxiv.org/pdf/2201.09865.pdf.
+            jump_n_sample (`int`, *optional*, defaults to 10):
+                The number of times we will make forward time jump for a given chosen time sample. Take a look at
+                Figure 9 and 10 in https://arxiv.org/pdf/2201.09865.pdf.
+            generator (`torch.Generator`, *optional*):
+                A [torch generator](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 [`~pipeline_utils.ImagePipelineOutput`] instead of a plain tuple.
+
+        Returns:
+            [`~pipeline_utils.ImagePipelineOutput`] or `tuple`: [`~pipelines.utils.ImagePipelineOutput`] if
+            `return_dict` is True, otherwise a `tuple. When returning a tuple, the first element is a list with the
+            generated images.
+        """
+
+        if not isinstance(original_image, torch.FloatTensor):
+            original_image = _preprocess_image(original_image)
+        original_image = original_image.to(self.device)
+        if not isinstance(mask_image, torch.FloatTensor):
+            mask_image = _preprocess_mask(mask_image)
+        mask_image = mask_image.to(self.device)
+
+        # sample gaussian noise to begin the loop
+        image = torch.randn(
+            original_image.shape,
+            generator=generator,
+            device=self.device,
+        )
+        image = image.to(self.device)
+
+        # set step values
+        self.scheduler.set_timesteps(num_inference_steps, jump_length, jump_n_sample, self.device)
+        self.scheduler.eta = eta
+
+        t_last = self.scheduler.timesteps[0] + 1
+        for i, t in enumerate(tqdm(self.scheduler.timesteps)):
+            if t < t_last:
+                # predict the noise residual
+                model_output = self.unet(image, t).sample
+                # compute previous image: x_t -> x_t-1
+                image = self.scheduler.step(model_output, t, image, original_image, mask_image, generator).prev_sample
+
+            else:
+                # compute the reverse: x_t-1 -> x_t
+                image = self.scheduler.undo_step(image, t_last, generator)
+            t_last = t
+
+        image = (image / 2 + 0.5).clamp(0, 1)
+        image = image.cpu().permute(0, 2, 3, 1).numpy()
+        if output_type == "pil":
+            image = self.numpy_to_pil(image)
+
+        if not return_dict:
+            return (image,)
+
+        return ImagePipelineOutput(images=image)
diff --git a/src/diffusers/schedulers/__init__.py b/src/diffusers/schedulers/__init__.py
@@ -24,6 +24,7 @@
     from .scheduling_ipndm import IPNDMScheduler
     from .scheduling_karras_ve import KarrasVeScheduler
     from .scheduling_pndm import PNDMScheduler
+    from .scheduling_repaint import RePaintScheduler
     from .scheduling_sde_ve import ScoreSdeVeScheduler
     from .scheduling_sde_vp import ScoreSdeVpScheduler
     from .scheduling_utils import SchedulerMixin