[](https://replicate.com/sczhou/codeformer) 
[Shangchen Zhou](https://shangchenzhou.com/), [Kelvin C.K. Chan](https://ckkelvinchan.github.io/), [Chongyi Li](https://li-chongyi.github.io/), [Chen Change Loy](https://www.mmlab-ntu.com/person/ccloy/)
S-Lab, Nanyang Technological University
:star: If CodeFormer is helpful to your images or projects, please help star this repo. Thanks! :hugs:
### Update
- **2022.09.09**: Integrated to :rocket: [Replicate](https://replicate.com/). Try out online demo! [](https://replicate.com/sczhou/codeformer)
- **2022.09.04**: Add face upsampling `--face_upsample` for 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](https://github.com/xinntao/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. :blush:
- **2022.06.21**: This repo is created.
### TODO
- [ ] Add checkpoint for face inpainting
- [ ] Add training code and config files
- [x] ~~Add background image enhancement~~
#### 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
```
### Quick Inference
##### Download Pre-trained Models:
Download the facelib pretrained models from [[Google Drive](https://drive.google.com/drive/folders/1b_3qwrzY_kTQh0-SnBoGBgOrJ_PLZSKm?usp=sharing) | [OneDrive](https://entuedu-my.sharepoint.com/:f:/g/personal/s200094_e_ntu_edu_sg/EvDxR7FcAbZMp_MA9ouq7aQB8XTppMb3-T0uGZ_2anI2mg?e=DXsJFo)] 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](https://drive.google.com/drive/folders/1CNNByjHDFt0b95q54yMVp6Ifo5iuU6QS?usp=sharing) | [OneDrive](https://entuedu-my.sharepoint.com/:f:/g/personal/s200094_e_ntu_edu_sg/EoKFj4wo8cdIn2-TY2IV6CYBhZ0pIG4kUOeHdPR_A5nlbg?e=AO8UN9)] 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
```
##### 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.
##### Testing on Face Restoration:
```
# For cropped and aligned faces
python inference_codeformer.py --w 0.5 --has_aligned --test_path [input folder]
# For the whole images
# 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 --test_path [input folder]
```
NOTE that *w* is 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.
### Citation
If our work is useful for your research, please consider citing:
@article{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},
journal = {arXiv preprint arXiv:2206.11253},
year = {2022}
}
### License
