## 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) [](https://huggingface.co/spaces/sczhou/CodeFormer) [](https://replicate.com/sczhou/codeformer) [](https://openxlab.org.cn/apps/detail/ShangchenZhou/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 - **2023.07.20**: Integrated to :panda_face: [OpenXLab](https://openxlab.org.cn/apps). Try out online demo! [](https://openxlab.org.cn/apps/detail/ShangchenZhou/CodeFormer) - **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](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) - [**More**](docs/history_changelog.md) ### 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 [](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 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](https://github.com/sczhou/CodeFormer/releases/tag/v0.1.0) | [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 python scripts/download_pretrained_models.py dlib (only for dlib face detector) ``` Download the CodeFormer pretrained models from [[Releases](https://github.com/sczhou/CodeFormer/releases/tag/v0.1.0) | [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. 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 produce a higher-quality result, while larger *w* yields a higher-fidelity result. The results will be saved in the `results` folder. 🧑🏻 Face Restoration (cropped and aligned face) ``` # For cropped and aligned faces (512x512) 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] ``` 🌈 Face Colorization (cropped and aligned face) ``` # For cropped and aligned faces (512x512) # Colorize black and white or faded photo python inference_colorization.py --input_path [image folder]|[image path] ``` 🎨 Face Inpainting (cropped and aligned face) ``` # For cropped and aligned faces (512x512) # Inputs could be masked by white brush using an image editing app (e.g., Photoshop) # (check out the examples in inputs/masked_faces) python inference_inpainting.py --input_path [image folder]|[image path] ``` ### Training: The training commands can be found in the documents: [English](docs/train.md) **|** [简体中文](docs/train_CN.md). ### 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 questions, please feel free to reach me out at `shangchenzhou@gmail.com`.