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! 🤗

• 2023.04.19: 🐳 Training codes and config files are public available now.
• 2023.04.09: Add features of inpainting and colorization for cropped and aligned face images.
• 2023.02.10: Include dlib as a new face detector option, it produces more accurate face identity.
• 2022.10.05: Support video input --input_path [YOUR_VIDEO.mp4]. Try it to enhance your videos! 🎬
• 2022.09.14: Integrated to 🤗 Hugging Face. Try out online demo!
• 2022.09.09: Integrated to 🚀 Replicate. Try out online demo!
• More

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

• 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
conda install -c conda-forge dlib (only for face detection or cropping with dlib)

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

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]

[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.

🧑🏻 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]

🖼️ 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]

🎬 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]

The training commands can be found in the documents: English | 简体中文.

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}
}

This project is licensed under NTU S-Lab License 1.0. Redistribution and use should follow this license.

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.

If you have any questions, please feel free to reach me out at [email protected].