Add new community pipeline for 'Adaptive Mask Inpainting', introduced in [ECCV2024] ComA (#9228)

* Add new community pipeline for 'Adaptive Mask Inpainting', introduced in [ECCV2024] Beyond the Contact: Discovering Comprehensive Affordance for 3D Objects from Pre-trained 2D Diffusion Models

Author: jellyheadandrew

examples/community/README.md

@@ -10,6 +10,7 @@ Please also check out our [Community Scripts](https://github.com/huggingface/dif
 
 | Example                                                                                                                               | Description                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              | Code Example                                                                              | Colab                                                                                                                                                                                                              |                                                        Author |
 |:--------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------:|
+|Adaptive Mask Inpainting|Adaptive Mask Inpainting algorithm from [Beyond the Contact: Discovering Comprehensive Affordance for 3D Objects from Pre-trained 2D Diffusion Models](https://github.com/snuvclab/coma) (ECCV '24, Oral) provides a way to insert human inside the scene image without altering the background, by inpainting with adapting mask.|[Adaptive Mask Inpainting](#adaptive-mask-inpainting)|-|[Hyeonwoo Kim](https://sshowbiz.xyz),[Sookwan Han](https://jellyheadandrew.github.io)|
 |Flux with CFG|[Flux with CFG](https://github.com/ToTheBeginning/PuLID/blob/main/docs/pulid_for_flux.md) provides an implementation of using CFG in [Flux](https://blackforestlabs.ai/announcing-black-forest-labs/).|[Flux with CFG](#flux-with-cfg)|NA|[Linoy Tsaban](https://github.com/linoytsaban), [Apolinário](https://github.com/apolinario), and [Sayak Paul](https://github.com/sayakpaul)|
 |Differential Diffusion|[Differential Diffusion](https://github.com/exx8/differential-diffusion) modifies an image according to a text prompt, and according to a map that specifies the amount of change in each region.|[Differential Diffusion](#differential-diffusion)|[![Hugging Face Space](https://img.shields.io/badge/🤗%20Hugging%20Face-Space-yellow)](https://huggingface.co/spaces/exx8/differential-diffusion) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/exx8/differential-diffusion/blob/main/examples/SD2.ipynb)|[Eran Levin](https://github.com/exx8) and [Ohad Fried](https://www.ohadf.com/)|
 | HD-Painter                                                                                                                            | [HD-Painter](https://github.com/Picsart-AI-Research/HD-Painter) enables prompt-faithfull and high resolution (up to 2k) image inpainting upon any diffusion-based image inpainting method.                                                                                                                                                                                                                                                                                                               | [HD-Painter](#hd-painter)                                                                 | [![Hugging Face Space](https://img.shields.io/badge/🤗%20Hugging%20Face-Space-yellow)](https://huggingface.co/spaces/PAIR/HD-Painter)                                                                              | [Manukyan Hayk](https://github.com/haikmanukyan) and [Sargsyan Andranik](https://github.com/AndranikSargsyan) |
@@ -85,6 +86,161 @@ pipe = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion
 
 ## Example usages
 
+### Adaptive Mask Inpainting
+
+**Hyeonwoo Kim\*, Sookwan Han\*, Patrick Kwon, Hanbyul Joo**
+
+**Seoul National University, Naver Webtoon**
+
+Adaptive Mask Inpainting, presented in the ECCV'24 oral paper [*Beyond the Contact: Discovering Comprehensive Affordance for 3D Objects from Pre-trained 2D Diffusion Models*](https://snuvclab.github.io/coma), is an algorithm designed to insert humans into scene images without altering the background. Traditional inpainting methods often fail to preserve object geometry and details within the masked region, leading to false affordances. Adaptive Mask Inpainting addresses this issue by progressively specifying the inpainting region over diffusion timesteps, ensuring that the inserted human integrates seamlessly with the existing scene.
+
+Here is the demonstration of Adaptive Mask Inpainting:
+
+<video controls>
+  <source src="https://snuvclab.github.io/coma/static/videos/adaptive_mask_inpainting_vis.mp4" type="video/mp4">
+  Your browser does not support the video tag.
+</video>
+
+![teaser-img](https://snuvclab.github.io/coma/static/images/example_result_adaptive_mask_inpainting.png)
+
+
+You can find additional information about Adaptive Mask Inpainting in the [paper](https://arxiv.org/pdf/2401.12978) or in the [project website](https://snuvclab.github.io/coma).
+
+#### Usage example
+First, clone the diffusers github repository, and run the following command to set environment.
+```Shell
+git clone https://github.com/huggingface/diffusers.git
+cd diffusers
+
+conda create --name ami python=3.9 -y
+conda activate ami
+
+conda install pytorch==1.10.1 torchvision==0.11.2 torchaudio==0.10.1 cudatoolkit=11.3 -c pytorch -c conda-forge -y
+python -m pip install detectron2==0.6 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu113/torch1.10/index.html
+pip install easydict
+pip install diffusers==0.20.2 accelerate safetensors transformers
+pip install setuptools==59.5.0
+pip install opencv-python
+pip install numpy==1.24.1
+```
+Then, run the below code under 'diffusers' directory.
+```python
+import numpy as np
+import torch
+from PIL import Image
+
+from diffusers import DDIMScheduler
+from diffusers import DiffusionPipeline
+from diffusers.utils import load_image
+
+from examples.community.adaptive_mask_inpainting import download_file, AdaptiveMaskInpaintPipeline, AMI_INSTALL_MESSAGE
+
+print(AMI_INSTALL_MESSAGE)
+
+from easydict import EasyDict
+
+
+
+if __name__ == "__main__":    
+    """
+    Download Necessary Files
+    """
+    download_file(
+        url = "https://huggingface.co/datasets/jellyheadnadrew/adaptive-mask-inpainting-test-images/resolve/main/model_final_edd263.pkl?download=true",
+        output_file = "model_final_edd263.pkl",
+        exist_ok=True,
+    )
+    download_file(
+        url = "https://huggingface.co/datasets/jellyheadnadrew/adaptive-mask-inpainting-test-images/resolve/main/pointrend_rcnn_R_50_FPN_3x_coco.yaml?download=true",
+        output_file = "pointrend_rcnn_R_50_FPN_3x_coco.yaml",
+        exist_ok=True,
+    )
+    download_file(
+        url = "https://huggingface.co/datasets/jellyheadnadrew/adaptive-mask-inpainting-test-images/resolve/main/input_img.png?download=true",
+        output_file = "input_img.png",
+        exist_ok=True,
+    )
+    download_file(
+        url = "https://huggingface.co/datasets/jellyheadnadrew/adaptive-mask-inpainting-test-images/resolve/main/input_mask.png?download=true",
+        output_file = "input_mask.png",
+        exist_ok=True,
+    )
+    download_file(
+        url = "https://huggingface.co/datasets/jellyheadnadrew/adaptive-mask-inpainting-test-images/resolve/main/Base-PointRend-RCNN-FPN.yaml?download=true",
+        output_file = "Base-PointRend-RCNN-FPN.yaml",
+        exist_ok=True,
+    )
+    download_file(
+        url = "https://huggingface.co/datasets/jellyheadnadrew/adaptive-mask-inpainting-test-images/resolve/main/Base-RCNN-FPN.yaml?download=true",
+        output_file = "Base-RCNN-FPN.yaml",
+        exist_ok=True,
+    )
+    
+    """ 
+    Prepare Adaptive Mask Inpainting Pipeline
+    """
+    # device
+    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+    num_steps = 50
+    
+    # Scheduler
+    scheduler = DDIMScheduler(
+        beta_start=0.00085, 
+        beta_end=0.012, 
+        beta_schedule="scaled_linear", 
+        clip_sample=False, 
+        set_alpha_to_one=False
+    )
+    scheduler.set_timesteps(num_inference_steps=num_steps)
+
+    ## load models as pipelines
+    pipeline = AdaptiveMaskInpaintPipeline.from_pretrained(
+        "Uminosachi/realisticVisionV51_v51VAE-inpainting", 
+        scheduler=scheduler, 
+        torch_dtype=torch.float16, 
+        requires_safety_checker=False
+    ).to(device)
+
+    ## disable safety checker
+    enable_safety_checker = False
+    if not enable_safety_checker:
+        pipeline.safety_checker = None
+    
+    """ 
+    Run Adaptive Mask Inpainting 
+    """
+    default_mask_image = Image.open("./input_mask.png").convert("L")
+    init_image = Image.open("./input_img.png").convert("RGB")
+    
+    
+    seed = 59
+    generator = torch.Generator(device=device)
+    generator.manual_seed(seed)
+    
+    image = pipeline(
+        prompt="a man sitting on a couch",
+        negative_prompt="worst quality, normal quality, low quality, bad anatomy, artifacts, blurry, cropped, watermark, greyscale, nsfw",
+        image=init_image,
+        default_mask_image=default_mask_image,
+        guidance_scale=11.0,
+        strength=0.98,
+        use_adaptive_mask=True,
+        generator=generator,
+        enforce_full_mask_ratio=0.0,
+        visualization_save_dir="./ECCV2024_adaptive_mask_inpainting_demo", # DON'T CHANGE THIS!!!
+        human_detection_thres=0.015,
+    ).images[0]
+
+    
+    image.save(f'final_img.png')
+```
+#### [Troubleshooting]
+
+If you run into an error `cannot import name 'cached_download' from 'huggingface_hub'` (issue [1851](https://github.com/easydiffusion/easydiffusion/issues/1851)), remove `cached_download` from the import line in the file `diffusers/utils/dynamic_modules_utils.py`. 
+
+For example, change the import line from `.../env/lib/python3.8/site-packages/diffusers/utils/dynamic_modules_utils.py`.
+
+
 ### Flux with CFG
 
 Know more about Flux [here](https://blackforestlabs.ai/announcing-black-forest-labs/). Since Flux doesn't use CFG, this implementation provides one, inspired by the [PuLID Flux adaptation](https://github.com/ToTheBeginning/PuLID/blob/main/docs/pulid_for_flux.md).