README.md

<p align="center">
  <img src="assets/CodeFormer_logo.png" height=110>
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## Towards Robust Blind Face Restoration with Codebook Lookup Transformer (NeurIPS 2022)

[Paper](https://arxiv.org/abs/2206.11253) | [Project Page](https://shangchenzhou.com/projects/CodeFormer/) | [Video](https://youtu.be/d3VDpkXlueI)


<a href="https://colab.research.google.com/drive/1m52PNveE4PBhYrecj34cnpEeiHcC5LTb?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="google colab logo"></a> [![Hugging Face](https://img.shields.io/badge/Demo-%F0%9F%A4%97%20Hugging%20Face-blue)](https://huggingface.co/spaces/sczhou/CodeFormer) [![Replicate](https://img.shields.io/badge/Demo-%F0%9F%9A%80%20Replicate-blue)](https://replicate.com/sczhou/codeformer) ![visitors](https://visitor-badge-sczhou.glitch.me/badge?page_id=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

<img src="assets/network.jpg" width="800px"/>


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

**[<font color=#d1585d>News</font>]**: :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! [![Hugging Face](https://img.shields.io/badge/Demo-%F0%9F%A4%97%20Hugging%20Face-blue)](https://huggingface.co/spaces/sczhou/CodeFormer)
- **2022.09.09**: Integrated to :rocket: [Replicate](https://replicate.com/explore). Try out online demo! [![Replicate](https://img.shields.io/badge/Demo-%F0%9F%9A%80%20Replicate-blue)](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. <a href="https://colab.research.google.com/drive/1m52PNveE4PBhYrecj34cnpEeiHcC5LTb?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="google colab logo"></a>
- **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
[<img src="assets/imgsli_1.jpg" height="226px"/>](https://imgsli.com/MTI3NTE2) [<img src="assets/imgsli_2.jpg" height="226px"/>](https://imgsli.com/MTI3NTE1) [<img src="assets/imgsli_3.jpg" height="226px"/>](https://imgsli.com/MTI3NTIw) 

#### Face Restoration

<img src="assets/restoration_result1.png" width="400px"/> <img src="assets/restoration_result2.png" width="400px"/>
<img src="assets/restoration_result3.png" width="400px"/> <img src="assets/restoration_result4.png" width="400px"/>

#### Face Color Enhancement and Restoration

<img src="assets/color_enhancement_result1.png" width="400px"/> <img src="assets/color_enhancement_result2.png" width="400px"/>

#### Face Inpainting

<img src="assets/inpainting_result1.png" width="400px"/> <img src="assets/inpainting_result2.png" width="400px"/>



### 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 -->

### 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 [image folder]|[image path]
```

: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 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]
```


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 <a rel="license" href="https://github.com/sczhou/CodeFormer/blob/master/LICENSE">NTU S-Lab License 1.0</a>. 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 questions, please feel free to reach me out at `[email protected]`.