sungkukpark / codeformer

PyTorch codes for "Towards Robust Blind Face Restoration with Codebook Lookup Transformer" (NeurIPS 2022)

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

Paper | Project Page | Video

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

S-Lab, Nanyang Technological University

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

Update

• 2022.09.14: Integrated to 🤗 Hugging Face. Try out online demo!
• 2022.09.09: Integrated to 🚀 Replicate. Try out online demo!
• 2022.09.04: Add face upsampling --face_upsample for high-resolution AI-created face enhancement.
• 2022.08.23: Some modifications on face detection and fusion for better AI-created face enhancement.
• 2022.08.07: Integrate Real-ESRGAN to support background image enhancement.
• 2022.07.29: Integrate new face detectors of ['RetinaFace'(default), 'YOLOv5'].
• 2022.07.17: Add Colab demo of CodeFormer.
• 2022.07.16: Release inference code for face restoration. 😊
• 2022.06.21: This repo is created.

TODO

• Add checkpoint for face inpainting
• Add training code and config files
• Add background image enhancement

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
conda activate codeformer

# install python dependencies
pip3 install -r requirements.txt
python basicsr/setup.py develop

Quick Inference

Download Pre-trained Models:

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

python scripts/download_pretrained_models.py facelib

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

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
# 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 --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:

@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 S-Lab License 1.0. Redistribution and use for non-commercial purposes 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 question, please feel free to reach me out at [email protected].