CodeFormer

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

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

Update
- 2023.07.20: Integrated to :panda_face: OpenXLab. Try out online demo! 
- 2023.04.19: :whale: 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! :clapper: 
- 2022.09.14: Integrated to :hugs: Hugging Face. Try out online demo! 
- 2022.09.09: Integrated to :rocket: Replicate. Try out online demo! 
- More

TODO
- [x] Add training code and config files
- [x] Add checkpoint and script for face inpainting
- [x] Add checkpoint and script for face colorization
- [x] ~~Add background image enhancement~~

:panda_face: Try Enhancing Old Photos / Fixing AI-arts

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

Quick Inference

Download Pre-trained Models:
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

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. 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]

Testing:
[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 pro

……（后略，见原文）