| # GFPGAN (CVPR 2021) | |
| [**Paper**](https://arxiv.org/abs/2101.04061) **|** [**Project Page**](https://xinntao.github.io/projects/gfpgan)    [English](README.md) **|** [简体中文](README_CN.md) | |
| GFPGAN is a blind face restoration algorithm towards real-world face images. | |
| <a href="https://colab.research.google.com/drive/1sVsoBd9AjckIXThgtZhGrHRfFI6UUYOo"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="google colab logo"></a> | |
| [Colab Demo](https://colab.research.google.com/drive/1sVsoBd9AjckIXThgtZhGrHRfFI6UUYOo) | |
| ### :book: GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior | |
| > [[Paper](https://arxiv.org/abs/2101.04061)]   [[Project Page](https://xinntao.github.io/projects/gfpgan)]   [Demo] <br> | |
| > [Xintao Wang](https://xinntao.github.io/), [Yu Li](https://yu-li.github.io/), [Honglun Zhang](https://scholar.google.com/citations?hl=en&user=KjQLROoAAAAJ), [Ying Shan](https://scholar.google.com/citations?user=4oXBp9UAAAAJ&hl=en) <br> | |
| > Applied Research Center (ARC), Tencent PCG | |
| #### Abstract | |
| Blind face restoration usually relies on facial priors, such as facial geometry prior or reference prior, to restore realistic and faithful details. However, very low-quality inputs cannot offer accurate geometric prior while high-quality references are inaccessible, limiting the applicability in real-world scenarios. In this work, we propose GFP-GAN that leverages **rich and diverse priors encapsulated in a pretrained face GAN** for blind face restoration. This Generative Facial Prior (GFP) is incorporated into the face restoration process via novel channel-split spatial feature transform layers, which allow our method to achieve a good balance of realness and fidelity. Thanks to the powerful generative facial prior and delicate designs, our GFP-GAN could jointly restore facial details and enhance colors with just a single forward pass, while GAN inversion methods require expensive image-specific optimization at inference. Extensive experiments show that our method achieves superior performance to prior art on both synthetic and real-world datasets. | |
| #### BibTeX | |
| @InProceedings{wang2021gfpgan, | |
| author = {Xintao Wang and Yu Li and Honglun Zhang and Ying Shan}, | |
| title = {Towards Real-World Blind Face Restoration with Generative Facial Prior}, | |
| booktitle={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, | |
| year = {2021} | |
| } | |
| <p align="center"> | |
| <img src="https://xinntao.github.io/projects/GFPGAN_src/gfpgan_teaser.jpg"> | |
| </p> | |
| --- | |
| ## :wrench: Dependencies and Installation | |
| - Python >= 3.7 (Recommend to use [Anaconda](https://www.anaconda.com/download/#linux) or [Miniconda](https://docs.conda.io/en/latest/miniconda.html)) | |
| - [PyTorch >= 1.7](https://pytorch.org/) | |
| - NVIDIA GPU + [CUDA](https://developer.nvidia.com/cuda-downloads) | |
| ### Installation | |
| 1. Clone repo | |
| ```bash | |
| git clone https://github.com/xinntao/GFPGAN.git | |
| cd GFPGAN | |
| ``` | |
| 1. Install dependent packages | |
| ```bash | |
| # Install basicsr - https://github.com/xinntao/BasicSR | |
| # We use BasicSR for both training and inference | |
| # Set BASICSR_EXT=True to compile the cuda extensions in the BasicSR - It may take several minutes to compile, please be patient | |
| BASICSR_EXT=True pip install basicsr | |
| # Install facexlib - https://github.com/xinntao/facexlib | |
| # We use face detection and face restoration helper in the facexlib package | |
| pip install facexlib | |
| pip install -r requirements.txt | |
| ``` | |
| ## :zap: Quick Inference | |
| Download pre-trained models: [GFPGANv1.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/GFPGANv1.pth) | |
| ```bash | |
| wget https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/GFPGANv1.pth -P experiments/pretrained_models | |
| ``` | |
| ```bash | |
| python inference_gfpgan_full.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/whole_imgs | |
| # for aligned images | |
| python inference_gfpgan_full.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/cropped_faces --aligned | |
| ``` | |
| ## :computer: Training | |
| We provide complete training codes for GFPGAN. <br> | |
| You could improve it according to your own needs. | |
| 1. Dataset preparation: [FFHQ](https://github.com/NVlabs/ffhq-dataset) | |
| 1. Download pre-trained models and other data. Put them in the `experiments/pretrained_models` folder. | |
| 1. [Pretrained StyleGAN2 model: StyleGAN2_512_Cmul1_FFHQ_B12G4_scratch_800k.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/StyleGAN2_512_Cmul1_FFHQ_B12G4_scratch_800k.pth) | |
| 1. [Component locations of FFHQ: FFHQ_eye_mouth_landmarks_512.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/FFHQ_eye_mouth_landmarks_512.pth) | |
| 1. [A simple ArcFace model: arcface_resnet18.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/arcface_resnet18.pth) | |
| 1. Modify the configuration file `train_gfpgan_v1.yml` accordingly. | |
| 1. Training | |
| > python -m torch.distributed.launch --nproc_per_node=4 --master_port=22021 train.py -opt train_gfpgan_v1.yml --launcher pytorch | |
| ## :scroll: License and Acknowledgement | |
| GFPGAN is realeased under Apache License Version 2.0. | |
| ## :e-mail: Contact | |
| If you have any question, please email `xintao.wang@outlook.com` or `xintaowang@tencent.com`. | |