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<p align="center"> |
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<img src="assets/CodeFormer_logo.png" height=110> |
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</p> |
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## Towards Robust Blind Face Restoration with Codebook Lookup Transformer |
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[Paper](https://arxiv.org/abs/2206.11253) | [Project Page](https://shangchenzhou.com/projects/CodeFormer/) | [Video](https://youtu.be/d3VDpkXlueI) |
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<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> [![Replicate](https://img.shields.io/badge/Demo-%F0%9F%9A%80%20Replicate-blue)](https://replicate.com/sczhou/codeformer) ![visitors](https://visitor-badge.glitch.me/badge?page_id=sczhou/CodeFormer) |
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[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/) |
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S-Lab, Nanyang Technological University |
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<img src="assets/network.jpg" width="800px"/> |
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:star: If CodeFormer is helpful to your images or projects, please help star this repo. Thanks! :hugs: |
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### Update |
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- **2022.09.09**: Integrated to :rocket: [Replicate](https://replicate.com/). Try out online demo! [![Replicate](https://img.shields.io/badge/Demo-%F0%9F%9A%80%20Replicate-blue)](https://replicate.com/sczhou/codeformer) |
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- **2022.09.04**: Add face upsampling `--face_upsample` for high-resolution AI-created face enhancement. |
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- **2022.08.23**: Some modifications on face detection and fusion for better AI-created face enhancement. |
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- **2022.08.07**: Integrate [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN) to support background image enhancement. |
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- **2022.07.29**: Integrate new face detectors of `['RetinaFace'(default), 'YOLOv5']`. |
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- **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> |
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- **2022.07.16**: Release inference code for face restoration. :blush: |
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- **2022.06.21**: This repo is created. |
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### TODO |
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- [ ] Add checkpoint for face inpainting |
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- [ ] Add training code and config files |
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- [x] ~~Add background image enhancement~~ |
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#### Face Restoration |
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<img src="assets/restoration_result1.png" width="400px"/> <img src="assets/restoration_result2.png" width="400px"/> |
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<img src="assets/restoration_result3.png" width="400px"/> <img src="assets/restoration_result4.png" width="400px"/> |
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#### Face Color Enhancement and Restoration |
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<img src="assets/color_enhancement_result1.png" width="400px"/> <img src="assets/color_enhancement_result2.png" width="400px"/> |
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#### Face Inpainting |
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<img src="assets/inpainting_result1.png" width="400px"/> <img src="assets/inpainting_result2.png" width="400px"/> |
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### Dependencies and Installation |
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- Pytorch >= 1.7.1 |
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- CUDA >= 10.1 |
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- Other required packages in `requirements.txt` |
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``` |
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# git clone this repository |
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git clone https://github.com/sczhou/CodeFormer |
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cd CodeFormer |
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# create new anaconda env |
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conda create -n codeformer python=3.8 -y |
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conda activate codeformer |
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# install python dependencies |
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pip3 install -r requirements.txt |
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python basicsr/setup.py develop |
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``` |
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<!-- conda install -c conda-forge dlib --> |
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### Quick Inference |
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##### Download Pre-trained Models: |
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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. |
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``` |
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python scripts/download_pretrained_models.py facelib |
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``` |
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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. |
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``` |
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python scripts/download_pretrained_models.py CodeFormer |
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``` |
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##### Prepare Testing Data: |
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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. |
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##### Testing on Face Restoration: |
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``` |
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# For cropped and aligned faces |
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python inference_codeformer.py --w 0.5 --has_aligned --test_path [input folder] |
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# For the whole images |
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# Add '--bg_upsampler realesrgan' to enhance the background regions with Real-ESRGAN |
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# Add '--face_upsample' to further upsample restorated face with Real-ESRGAN |
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python inference_codeformer.py --w 0.7 --test_path [input folder] |
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``` |
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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. |
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The results will be saved in the `results` folder. |
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### Citation |
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If our work is useful for your research, please consider citing: |
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@article{zhou2022codeformer, |
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author = {Zhou, Shangchen and Chan, Kelvin C.K. and Li, Chongyi and Loy, Chen Change}, |
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title = {Towards Robust Blind Face Restoration with Codebook Lookup TransFormer}, |
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journal = {arXiv preprint arXiv:2206.11253}, |
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year = {2022} |
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} |
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### License |
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<a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>. |
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### Acknowledgement |
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This project is based on [BasicSR](https://github.com/XPixelGroup/BasicSR). We also borrow some codes 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). Thanks for their awesome works. |
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### Contact |
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If you have any question, please feel free to reach me out at `shangchenzhou@gmail.com`. |