Towards Robust Blind Face Restoration with Codebook Lookup Transformer

Paper | Project Page | Video

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

S-Lab, Nanyang Technological University

Updates

• 2022.07.17: The Colab demo of CodeFormer is available now.
• 2022.07.16: Test code for face restoration is released. 😊
• 2022.06.21: This repo is created.

Face Restoration

Face Color Enhancement and Restoration

Face Inpainting

Dependencies and Installation

• Pytorch >= 1.7.1
• CUDA >= 10.1
• Other required packages in requirements.txt

# 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
source activate codeformer

# install python dependencies
pip3 install -r requirements.txt
python basicsr/setup.py develop
conda install -c conda-forge dlib

Quick Inference

Download Pre-trained Models:

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

python scripts/download_pretrained_models.py dlib

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

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.

Testing on Face Restoration:

# For cropped and aligned faces
python inference_codeformer.py --w 0.5 --has_aligned --test_path [input folder]

# For the whole images
python inference_codeformer.py --w 0.7 --test_path [input folder]

NOTE that w is 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.

Citation

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

@article{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},
    journal = {arXiv preprint arXiv:2206.11253},
    year = {2022}
}

License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Acknowledgement

This project is based on BasicSR.