readme: # GFPGAN (CVPR 2021)
  
  [**Paper**](https://arxiv.org/abs/2101.04061) **|** [**Project Page**](https://xinntao.github.io/projects/gfpgan) &emsp;&emsp; [English](README.md) **|** [简体中文](README_CN.md)
  
  GitHub: https://github.com/TencentARC/GFPGAN
  
  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)] &emsp; [[Project Page](https://xinntao.github.io/projects/gfpgan)] &emsp; [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.ym