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Towards Robust Blind Face Restoration with Codebook Lookup Transformer (NeurIPS 2022)

Paper | Project Page | Video

<a href="https://colab.research.google.com/drive/1m52PNveE4PBhYrecj34cnpEeiHcC5LTb?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="google colab logo"></a> Hugging Face Replicate OpenXLab Visitors

Shangchen Zhou, Kelvin C.K. Chan, Chongyi Li, Chen Change Loy

S-Lab, Nanyang Technological University

<img src="assets/network.jpg" width="800px"/>

:star: If CodeFormer is helpful to your images or projects, please help star this repo. Thanks! :hugs:

Update

TODO

:panda_face: Try Enhancing Old Photos / Fixing AI-arts

<img src="assets/imgsli_1.jpg" height="226px"/> <img src="assets/imgsli_2.jpg" height="226px"/> <img src="assets/imgsli_3.jpg" height="226px"/>

Face Restoration

<img src="assets/restoration_result1.png" width="400px"/> <img src="assets/restoration_result2.png" width="400px"/> <img src="assets/restoration_result3.png" width="400px"/> <img src="assets/restoration_result4.png" width="400px"/>

Face Color Enhancement and Restoration

<img src="assets/color_enhancement_result1.png" width="400px"/> <img src="assets/color_enhancement_result2.png" width="400px"/>

Face Inpainting

<img src="assets/inpainting_result1.png" width="400px"/> <img src="assets/inpainting_result2.png" width="400px"/>

Dependencies and Installation

# git clone this repository
git clone https://github.com/sczhou/CodeFormer
cd CodeFormer

# create new anaconda env
conda create -n codeformer python=3.8 -y
conda activate codeformer

# install python dependencies
pip3 install -r requirements.txt
python basicsr/setup.py develop
conda install -c conda-forge dlib (only for face detection or cropping with dlib)
<!-- conda install -c conda-forge dlib -->

Quick Inference

Download Pre-trained Models:

Download the facelib and dlib pretrained models from [Releases | Google Drive | OneDrive] to the weights/facelib folder. You can manually download the pretrained models OR download by running the following command:

python scripts/download_pretrained_models.py facelib
python scripts/download_pretrained_models.py dlib (only for dlib face detector)

Download the CodeFormer pretrained models from [Releases | Google Drive | OneDrive] to the weights/CodeFormer folder. You can manually download the pretrained models OR download by running the following command:

python scripts/download_pretrained_models.py CodeFormer

Prepare Testing Data:

You can put the testing images in the inputs/TestWhole folder. If you would like to test on cropped and aligned faces, you can put them in the inputs/cropped_faces folder. You can get the cropped and aligned faces by running the following command:

# you may need to install dlib via: conda install -c conda-forge dlib
python scripts/crop_align_face.py -i [input folder] -o [output folder]

Testing:

[Note] If you want to compare CodeFormer in your paper, please run the following command indicating --has_aligned (for cropped and aligned face), as the command for the whole image will involve a process of face-background fusion that may damage hair texture on the boundary, which leads to unfair comparison.

Fidelity weight w lays in [0, 1]. Generally, smaller w tends to produce a higher-quality result, while larger w yields a higher-fidelity result. The results will be saved in the results folder.

🧑đŸģ Face Restoration (cropped and aligned face)

# For cropped and aligned faces (512x512)
python inference_codeformer.py -w 0.5 --has_aligned --input_path [image folder]|[image path]

:framed_picture: Whole Image Enhancement

# For whole image
# Add '--bg_upsampler realesrgan' to enhance the background regions with Real-ESRGAN
# Add '--face_upsample' to further upsample restorated face with Real-ESRGAN
python inference_codeformer.py -w 0.7 --input_path [image folder]|[image path]

:clapper: Video Enhancement

# For Windows/Mac users, please install ffmpeg first
conda install -c conda-forge ffmpeg

# For video clips
# Video path should end with '.mp4'|'.mov'|'.avi'
python inference_codeformer.py --bg_upsampler realesrgan --face_upsample -w 1.0 --input_path [video path]

🌈 Face Colorization (cropped and aligned face)

# For cropped and aligned faces (512x512)
# Colorize black and white or faded photo
python inference_colorization.py --input_path [image folder]|[image path]

🎨 Face Inpainting (cropped and aligned face)

# For cropped and aligned faces (512x512)
# Inputs could be masked by white brush using an image editing app (e.g., Photoshop) 
# (check out the examples in inputs/masked_faces)
python inference_inpainting.py --input_path [image folder]|[image path]

Training:

The training commands can be found in the documents: English | įŽ€äŊ“中文.

Citation

If our work is useful for your research, please consider citing:

@inproceedings{zhou2022codeformer,
    author = {Zhou, Shangchen and Chan, Kelvin C.K. and Li, Chongyi and Loy, Chen Change},
    title = {Towards Robust Blind Face Restoration with Codebook Lookup TransFormer},
    booktitle = {NeurIPS},
    year = {2022}
}

License

This project is licensed under <a rel="license" href="https://github.com/sczhou/CodeFormer/blob/master/LICENSE">NTU S-Lab License 1.0</a>. Redistribution and use should follow this license.

Acknowledgement

This project is based on BasicSR. Some codes are brought from Unleashing Transformers, YOLOv5-face, and FaceXLib. We also adopt Real-ESRGAN to support background image enhancement. Thanks for their awesome works.

Contact

If you have any questions, please feel free to reach me out at shangchenzhou@gmail.com.