[](https://huggingface.co/spaces/sczhou/CodeFormer) [](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:
**[News]**: :whale: *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!*
### Update
- **2022.10.05**: Support video input `--input_path [YOUR_VIDOE.mp4]`. Try it to enhance your videos! :clapper:
- **2022.09.14**: Integrated to :hugs: [Hugging Face](https://huggingface.co/spaces). Try out online demo! [](https://huggingface.co/spaces/sczhou/CodeFormer)
- **2022.09.09**: Integrated to :rocket: [Replicate](https://replicate.com/explore). 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 checkpoint for face colorization
- [ ] Add training code and config files
- [x] ~~Add background image enhancement~~
#### :panda_face: Try Enhancing Old Photos / Fixing AI-arts
[
](https://imgsli.com/MTI3NTE2) [
](https://imgsli.com/MTI3NTE1) [
](https://imgsli.com/MTI3NTIw)
#### 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 running 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 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.
#### Testing on Face Restoration:
[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]
```
:framed_picture: 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]
```
:clapper: Video Enhancement
```
# For video clips
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.
### Citation
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}
}
### License
This project is licensed under NTU S-Lab License 1.0. Redistribution and use should follow this license.
### Acknowledgement
This project is based on [BasicSR](https://github.com/XPixelGroup/BasicSR). Some codes are brought from [Unleashing Transformers](https://github.com/samb-t/unleashing-transformers), [YOLOv5-face](https://github.com/deepcam-cn/yolov5-face), and [FaceXLib](https://github.com/xinntao/facexlib). We also adopt [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN) to support background image enhancement. Thanks for their awesome works.
### Contact
If you have any question, please feel free to reach me out at `shangchenzhou@gmail.com`.