Awesome
<p align="center"> <img src="./example/IAM.png"> </p>Inpaint Anything: Segment Anything Meets Image Inpainting
Inpaint Anything can inpaint anything in images, videos and 3D scenes!
- Authors: Tao Yu, Runseng Feng, Ruoyu Feng, Jinming Liu, Xin Jin, Wenjun Zeng and Zhibo Chen.
- Institutes: University of Science and Technology of China; Eastern Institute for Advanced Study.
- [Paper] [Website] [Hugging Face Homepage]
TL; DR: Users can select any object in an image by clicking on it. With powerful vision models, e.g., SAM, LaMa and Stable Diffusion (SD), Inpaint Anything is able to remove the object smoothly (i.e., Remove Anything). Further, prompted by user input text, Inpaint Anything can fill the object with any desired content (i.e., Fill Anything) or replace the background of it arbitrarily (i.e., Replace Anything).
π News
[2023/4/30] Remove Anything Video available! You can remove any object from a video!
[2023/4/24] Local web UI supported! You can run the demo website locally!
[2023/4/22] Website available! You can experience Inpaint Anything through the interface!
[2023/4/22] Remove Anything 3D available! You can remove any 3D object from a 3D scene!
[2023/4/13] Technical report on arXiv available!
π Features
- Remove Anything
- Fill Anything
- Replace Anything
- Remove Anything 3D (<span style="color:red">π₯NEW</span>)
- Fill Anything 3D
- Replace Anything 3D
- Remove Anything Video (<span style="color:red">π₯NEW</span>)
- Fill Anything Video
- Replace Anything Video
π‘ Highlights
- Any aspect ratio supported
- 2K resolution supported
- Technical report on arXiv available (<span style="color:red">π₯NEW</span>)
- Website available (<span style="color:red">π₯NEW</span>)
- Local web UI available (<span style="color:red">π₯NEW</span>)
- Multiple modalities (i.e., image, video and 3D scene) supported (<span style="color:red">π₯NEW</span>)
<span id="remove-anything">π Remove Anything</span>
<!-- <table> <tr> <td><img src="./example/remove-anything/dog/with_points.png" width="100%"></td> <td><img src="./example/remove-anything/dog/with_mask.png" width="100%"></td> <td><img src="./example/remove-anything/dog/inpainted_with_mask.png" width="100%"></td> </tr> </table> --> <p align="center"> <img src="./example/GIF/Remove-dog.gif" alt="image" style="width:400px;"> </p>Click on an object in the image, and Inpainting Anything will remove it instantly!
- Click on an object;
- Segment Anything Model (SAM) segments the object out;
- Inpainting models (e.g., LaMa) fill the "hole".
Installation
Requires python>=3.8
python -m pip install torch torchvision torchaudio
python -m pip install -e segment_anything
python -m pip install -r lama/requirements.txt
In Windows, we recommend you to first install miniconda and
open Anaconda Powershell Prompt (miniconda3)
as administrator.
Then pip install ./lama_requirements_windows.txt instead of
./lama/requirements.txt.
Usage
Download the model checkpoints provided in Segment Anything and LaMa (e.g., sam_vit_h_4b8939.pth and big-lama), and put them into ./pretrained_models
. For simplicity, you can also go here, directly download pretrained_models, put the directory into ./
and get ./pretrained_models
.
For MobileSAM, the sam_model_type should use "vit_t", and the sam_ckpt should use "./weights/mobile_sam.pt". For the MobileSAM project, please refer to MobileSAM
bash script/remove_anything.sh
Specify an image and a point, and Remove Anything will remove the object at the point.
python remove_anything.py \
--input_img ./example/remove-anything/dog.jpg \
--coords_type key_in \
--point_coords 200 450 \
--point_labels 1 \
--dilate_kernel_size 15 \
--output_dir ./results \
--sam_model_type "vit_h" \
--sam_ckpt ./pretrained_models/sam_vit_h_4b8939.pth \
--lama_config ./lama/configs/prediction/default.yaml \
--lama_ckpt ./pretrained_models/big-lama
You can change --coords_type key_in
to --coords_type click
if your machine has a display device. If click
is set, after running the above command, the image will be displayed. (1) Use left-click to record the coordinates of the click. It supports modifying points, and only last point coordinates are recorded. (2) Use right-click to finish the selection.
Demo
<table> <tr> <td><img src="./example/remove-anything/person/with_points.png" width="100%"></td> <td><img src="./example/remove-anything/person/with_mask.png" width="100%"></td> <td><img src="./example/remove-anything/person/inpainted_with_mask.png" width="100%"></td> </tr> </table> <table> <tr> <td><img src="./example/remove-anything/bridge/with_points.png" width="100%"></td> <td><img src="./example/remove-anything/bridge/with_mask.png" width="100%"></td> <td><img src="./example/remove-anything/bridge/inpainted_with_mask.png" width="100%"></td> </tr> </table> <table> <tr> <td><img src="./example/remove-anything/boat/with_points.png" width="100%"></td> <td><img src="./example/remove-anything/boat/with_mask.png" width="100%"></td> <td><img src="./example/remove-anything/boat/inpainted_with_mask.png" width="100%"></td> </tr> </table> <table> <tr> <td><img src="./example/remove-anything/baseball/with_points.png" width="100%"></td> <td><img src="./example/remove-anything/baseball/with_mask.png" width="100%"></td> <td><img src="./example/remove-anything/baseball/inpainted_with_mask.png" width="100%"></td> </tr> </table><span id="fill-anything">π Fill Anything</span>
<!-- <table> <caption align="center">Text prompt: "a teddy bear on a bench"</caption> <tr> <td><img src="./example/fill-anything/sample1/with_points.png" width="100%"></td> <td><img src="./example/fill-anything/sample1/with_mask.png" width="100%"></td> <td><img src="./example/fill-anything/sample1/filled_with_mask.png" width="100%"></td> </tr> </table> --> <p align="center">Text prompt: "a teddy bear on a bench"</p> <p align="center"> <img src="./example/GIF/Fill-sample1.gif" alt="image" style="width:400px;"> </p>Click on an object, type in what you want to fill, and Inpaint Anything will fill it!
- Click on an object;
- SAM segments the object out;
- Input a text prompt;
- Text-prompt-guided inpainting models (e.g., Stable Diffusion) fill the "hole" according to the text.
Installation
Requires python>=3.8
python -m pip install torch torchvision torchaudio
python -m pip install -e segment_anything
python -m pip install diffusers transformers accelerate scipy safetensors
Usage
Download the model checkpoints provided in Segment Anything (e.g., sam_vit_h_4b8939.pth) and put them into ./pretrained_models
. For simplicity, you can also go here, directly download pretrained_models, put the directory into ./
and get ./pretrained_models
.
For MobileSAM, the sam_model_type should use "vit_t", and the sam_ckpt should use "./weights/mobile_sam.pt". For the MobileSAM project, please refer to MobileSAM
bash script/fill_anything.sh
Specify an image, a point and text prompt, and run:
python fill_anything.py \
--input_img ./example/fill-anything/sample1.png \
--coords_type key_in \
--point_coords 750 500 \
--point_labels 1 \
--text_prompt "a teddy bear on a bench" \
--dilate_kernel_size 50 \
--output_dir ./results \
--sam_model_type "vit_h" \
--sam_ckpt ./pretrained_models/sam_vit_h_4b8939.pth
Demo
<table> <caption align="center">Text prompt: "a camera lens in the hand"</caption> <tr> <td><img src="./example/fill-anything/sample2/with_points.png" width="100%"></td> <td><img src="./example/fill-anything/sample2/with_mask.png" width="100%"></td> <td><img src="./example/fill-anything/sample2/filled_with_mask.png" width="100%"></td> </tr> </table> <table> <caption align="center">Text prompt: "a Picasso painting on the wall"</caption> <tr> <td><img src="./example/fill-anything/sample5/with_points.png" width="100%"></td> <td><img src="./example/fill-anything/sample5/with_mask.png" width="100%"></td> <td><img src="./example/fill-anything/sample5/filled_with_mask.png" width="100%"></td> </tr> </table> <table> <caption align="center">Text prompt: "an aircraft carrier on the sea"</caption> <tr> <td><img src="./example/fill-anything/sample3/with_points.png" width="100%"></td> <td><img src="./example/fill-anything/sample3/with_mask.png" width="100%"></td> <td><img src="./example/fill-anything/sample3/filled_with_mask.png" width="100%"></td> </tr> </table> <table> <caption align="center">Text prompt: "a sports car on a road"</caption> <tr> <td><img src="./example/fill-anything/sample4/with_points.png" width="100%"></td> <td><img src="./example/fill-anything/sample4/with_mask.png" width="100%"></td> <td><img src="./example/fill-anything/sample4/filled_with_mask.png" width="100%"></td> </tr> </table><span id="replace-anything">π Replace Anything</span>
<!-- <table> <caption align="center">Text prompt: "a man in office"</caption> <tr> <td><img src="./example/replace-anything/man/with_points.png" width="100%"></td> <td><img src="./example/replace-anything/man/with_mask.png" width="100%"></td> <td><img src="./example/replace-anything/man/replaced_with_mask.png" width="100%"></td> </tr> </table> --> <p align="center">Text prompt: "a man in office"</p> <p align="center"> <img src="./example/GIF/Replace-man.gif" alt="image" style="width:400px;"> </p>Click on an object, type in what background you want to replace, and Inpaint Anything will replace it!
- Click on an object;
- SAM segments the object out;
- Input a text prompt;
- Text-prompt-guided inpainting models (e.g., Stable Diffusion) replace the background according to the text.
Installation
Requires python>=3.8
python -m pip install torch torchvision torchaudio
python -m pip install -e segment_anything
python -m pip install diffusers transformers accelerate scipy safetensors
Usage
Download the model checkpoints provided in Segment Anything (e.g. sam_vit_h_4b8939.pth) and put them into ./pretrained_models
. For simplicity, you can also go here, directly download pretrained_models, put the directory into ./
and get ./pretrained_models
.
For MobileSAM, the sam_model_type should use "vit_t", and the sam_ckpt should use "./weights/mobile_sam.pt". For the MobileSAM project, please refer to MobileSAM
bash script/replace_anything.sh
Specify an image, a point and text prompt, and run:
python replace_anything.py \
--input_img ./example/replace-anything/dog.png \
--coords_type key_in \
--point_coords 750 500 \
--point_labels 1 \
--text_prompt "sit on the swing" \
--output_dir ./results \
--sam_model_type "vit_h" \
--sam_ckpt ./pretrained_models/sam_vit_h_4b8939.pth
Demo
<table> <caption align="center">Text prompt: "sit on the swing"</caption> <tr> <td><img src="./example/replace-anything/dog/with_points.png" width="100%"></td> <td><img src="./example/replace-anything/dog/with_mask.png" width="100%"></td> <td><img src="./example/replace-anything/dog/replaced_with_mask.png" width="100%"></td> </tr> </table> <table> <caption align="center">Text prompt: "a bus, on the center of a country road, summer"</caption> <tr> <td><img src="./example/replace-anything/bus/with_points.png" width="100%"></td> <td><img src="./example/replace-anything/bus/with_mask.png" width="100%"></td> <td><img src="./example/replace-anything/bus/replaced_with_mask.png" width="100%"></td> </tr> </table> <table> <caption align="center">Text prompt: "breakfast"</caption> <tr> <td><img src="./example/replace-anything/000000029675/with_points.png" width="100%"></td> <td><img src="./example/replace-anything/000000029675/with_mask.png" width="100%"></td> <td><img src="./example/replace-anything/000000029675/replaced_with_mask.png" width="100%"></td> </tr> </table> <table> <caption align="center">Text prompt: "crossroad in the city"</caption> <tr> <td><img src="./example/replace-anything/000000000724/with_points.png" width="100%"></td> <td><img src="./example/replace-anything/000000000724/with_mask.png" width="100%"></td> <td><img src="./example/replace-anything/000000000724/replaced_with_mask.png" width="100%"></td> </tr> </table><span id="remove-anything-3d">π Remove Anything 3D</span>
Remove Anything 3D can remove any object from a 3D scene! We release some results below. (Code and implementation details will be released soon.)
<table> <tr> <td><img src="./example/remove-anything-3d/horns/org.gif" width="100%"></td> <td><img src="./example/remove-anything-3d/horns/mask.gif" width="100%"></td> <td><img src="./example/remove-anything-3d/horns/result.gif" width="100%"></td> </tr> </table> <table> <tr> <td><img src="./example/remove-anything-3d/room/org.gif" width="100%"></td> <td><img src="./example/remove-anything-3d/room/mask.gif" width="100%"></td> <td><img src="./example/remove-anything-3d/room/result.gif" width="100%"></td> </tr> </table><span id="remove-anything-video">π Remove Anything Video</span>
<table> <tr> <td><img src="./example/remove-anything-video/paragliding/original.gif" width="100%"></td> <td><img src="./example/remove-anything-video/paragliding/mask.gif" width="100%"></td> <td><img src="./example/remove-anything-video/paragliding/removed.gif" width="100%"></td> </tr> </table>With a single click on an object in the first video frame, Remove Anything Video can remove the object from the whole video!
- Click on an object in the first frame of a video;
- SAM segments the object out (with three possible masks);
- Select one mask;
- A tracking model such as OSTrack is ultilized to track the object in the video;
- SAM segments the object out in each frame according to tracking results;
- A video inpainting model such as STTN is ultilized to inpaint the object in each frame.
Installation
Requires python>=3.8
python -m pip install torch torchvision torchaudio
python -m pip install -e segment_anything
python -m pip install -r lama/requirements.txt
python -m pip install jpeg4py lmdb
Usage
Download the model checkpoints provided in Segment Anything and STTN (e.g., sam_vit_h_4b8939.pth and sttn.pth), and put them into ./pretrained_models
. Further, download OSTrack pretrained model from here (e.g., vitb_384_mae_ce_32x4_ep300.pth) and put it into ./pytracking/pretrain
. For simplicity, you can also go here, directly download pretrained_models, put the directory into ./
and get ./pretrained_models
. Additionally, download pretrain, put the directory into ./pytracking
and get ./pytracking/pretrain
.
For MobileSAM, the sam_model_type should use "vit_t", and the sam_ckpt should use "./weights/mobile_sam.pt". For the MobileSAM project, please refer to MobileSAM
bash script/remove_anything_video.sh
Specify a video, a point, video FPS and mask index (indicating using which mask result of the first frame), and Remove Anything Video will remove the object from the whole video.
python remove_anything_video.py \
--input_video ./example/video/paragliding/original_video.mp4 \
--coords_type key_in \
--point_coords 652 162 \
--point_labels 1 \
--dilate_kernel_size 15 \
--output_dir ./results \
--sam_model_type "vit_h" \
--sam_ckpt ./pretrained_models/sam_vit_h_4b8939.pth \
--lama_config lama/configs/prediction/default.yaml \
--lama_ckpt ./pretrained_models/big-lama \
--tracker_ckpt vitb_384_mae_ce_32x4_ep300 \
--vi_ckpt ./pretrained_models/sttn.pth \
--mask_idx 2 \
--fps 25
The --mask_idx
is usually set to 2, which typically is the most confident mask result of the first frame. If the object is not segmented out well, you can try other masks (0 or 1).
Demo
<table> <tr> <td><img src="./example/remove-anything-video/drift-chicane/original.gif" width="100%"></td> <td><img src="./example/remove-anything-video/drift-chicane/mask.gif" width="100%"></td> <td><img src="./example/remove-anything-video/drift-chicane/removed.gif" width="100%"></td> </tr> </table> <table> <tr> <td><img src="./example/remove-anything-video/surf/original.gif" width="100%"></td> <td><img src="./example/remove-anything-video/surf/mask.gif" width="100%"></td> <td><img src="./example/remove-anything-video/surf/removed.gif" width="100%"></td> </tr> </table> <table> <tr> <td><img src="./example/remove-anything-video/tennis-vest/original.gif" width="100%"></td> <td><img src="./example/remove-anything-video/tennis-vest/mask.gif" width="100%"></td> <td><img src="./example/remove-anything-video/tennis-vest/removed.gif" width="100%"></td> </tr> </table> <table> <tr> <td><img src="./example/remove-anything-video/dog-gooses/original.gif" width="100%"></td> <td><img src="./example/remove-anything-video/dog-gooses/mask.gif" width="100%"></td> <td><img src="./example/remove-anything-video/dog-gooses/removed.gif" width="100%"></td> </tr> </table>Acknowledgments
Other Interesting Repositories
Citation
If you find this work useful for your research, please cite us:
@article{yu2023inpaint,
title={Inpaint Anything: Segment Anything Meets Image Inpainting},
author={Yu, Tao and Feng, Runseng and Feng, Ruoyu and Liu, Jinming and Jin, Xin and Zeng, Wenjun and Chen, Zhibo},
journal={arXiv preprint arXiv:2304.06790},
year={2023}
}
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