Paper | Project Page | Video
Shangchen Zhou, Kelvin C.K. Chan, Chongyi Li, Chen Change Loy
S-Lab, Nanyang Technological University
โญ If CodeFormer is helpful to your images or projects, please help star this repo. Thanks! ๐ค
- 2023.04.19: ๐ณ 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
dlibas 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! ๐ฌ - 2022.09.14: Integrated to ๐ค Hugging Face. Try out online demo!
- 2022.09.09: Integrated to ๐ 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
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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.
๐ง๐ป 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]
๐ผ๏ธ 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]
๐ฌ 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]
๐ 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]
๐จ 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 | ็ฎไฝไธญๆ.
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 shangchenzhou@gmail.com.