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! 🤗
[News]: 🐳 We regret to inform you that the release of our code will be postponed from its earlier plan. Nevertheless, we assure you that it will be made available by the end of this April. Thank you for your understanding and patience. Our apologies for any inconvenience this may cause.
- 2023.02.10: Include
dlibas a new face detector option, it produces more accurate face identity. - 2022.10.05: Support video input
--input_path [YOUR_VIDOE.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!
- 2022.09.04: Add face upsampling
--face_upsamplefor 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.
- Add checkpoint for face inpainting
- Add checkpoint for face colorization
- Add training code and config files
-
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 dlib face detector)
Download the facelib and dlib pretrained models from [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 [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.
[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.
🧑🏻 Face Restoration (cropped and aligned face)
# For cropped and aligned faces
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]
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.
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].