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]: 🐳 Due to copyright issues, we have to delay the release of the training code (expected by the end of this year). Please star and stay tuned for our future updates!
- 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
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
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 [input folder]
🖼️ 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 video clips
# Set frame rate of saved video via '--save_video_fps 24'
python inference_codeformer.py --bg_upsampler realesrgan --face_upsample -w 1.0 --input_path [video path] --save_video_fps 24
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 S-Lab License 1.0. Redistribution and use for non-commercial purposes 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 question, please feel free to reach me out at shangchenzhou@gmail.com.