# GFPGAN (CVPR 2021)
[](https://github.com/TencentARC/GFPGAN/releases)
[](https://pypi.org/project/gfpgan/)
[](https://github.com/TencentARC/GFPGAN/issues)
[](https://github.com/TencentARC/GFPGAN/blob/master/LICENSE)
[](https://github.com/TencentARC/GFPGAN/blob/master/.github/workflows/pylint.yml)
[](https://github.com/TencentARC/GFPGAN/blob/master/.github/workflows/publish-pip.yml)
1. [Colab Demo](https://colab.research.google.com/drive/1sVsoBd9AjckIXThgtZhGrHRfFI6UUYOo) for GFPGAN 
; (Another [Colab Demo](https://colab.research.google.com/drive/1Oa1WwKB4M4l1GmR7CtswDVgOCOeSLChA?usp=sharing) for the original paper model)
1. We provide a *clean* version of GFPGAN, which can run without CUDA extensions. So that it can run in **Windows** or on **CPU mode**.
GFPGAN aims at developing **Practical Algorithm for Real-world Face Restoration**.
It leverages rich and diverse priors encapsulated in a pretrained face GAN (*e.g.*, StyleGAN2) for blind face restoration.
:triangular_flag_on_post: **Updates**
- :white_check_mark: Support enhancing non-face regions (background) with [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN).
- :white_check_mark: We provide a *clean* version of GFPGAN, which does not require CUDA extensions.
- :white_check_mark: We provide an updated model without colorizing faces.
If GFPGAN is helpful in your photos/projects, please help to :star: this repo. Thanks:blush:
Other recommended projects: :arrow_forward: [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN) :arrow_forward: [BasicSR](https://github.com/xinntao/BasicSR) :arrow_forward: [facexlib](https://github.com/xinntao/facexlib)
---
### :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]
> [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)
> Applied Research Center (ARC), Tencent PCG
---
## :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/)
- Option: NVIDIA GPU + [CUDA](https://developer.nvidia.com/cuda-downloads)
- Option: Linux
### Installation
We now provide a *clean* version of GFPGAN, which does not require customized CUDA extensions.
If you want want to use the original model in our paper, please see [PaperModel.md](PaperModel.md) for installation.
1. Clone repo
```bash
git clone https://github.com/TencentARC/GFPGAN.git
cd GFPGAN
```
1. Install dependent packages
```bash
# Install basicsr - https://github.com/xinntao/BasicSR
# We use BasicSR for both training and inference
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
python setup.py develop
# If you want to enhance the background (non-face) regions with Real-ESRGAN,
# you also need to install the realesrgan package
pip install realesrgan
```
## :zap: Quick Inference
Download pre-trained models: [GFPGANCleanv1-NoCE-C2.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth)
```bash
wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P experiments/pretrained_models
```
**Inference!**
```bash
python inference_gfpgan.py --upscale 2 --test_path inputs/whole_imgs --save_root results
```
## :european_castle: Model Zoo
- [GFPGANCleanv1-NoCE-C2.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth): No colorization; no CUDA extensions are required. It is still in training. Trained with more data with pre-processing.
- [GFPGANv1.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/GFPGANv1.pth): The paper model, with colorization.
## :computer: Training
We provide the training codes for GFPGAN (used in our paper).
You could improve it according to your own needs.
**Tips**
1. More high quality faces can improve the restoration quality.
2. You may need to perform some pre-processing, such as beauty makeup.
**Procedures**
(You can try a simple version ( `options/train_gfpgan_v1_simple.yml`) that does not require face component landmarks.)
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 `options/train_gfpgan_v1.yml` accordingly.
1. Training
> python -m torch.distributed.launch --nproc_per_node=4 --master_port=22021 gfpgan/train.py -opt options/train_gfpgan_v1.yml --launcher pytorch
## :scroll: License and Acknowledgement
GFPGAN is released under Apache License Version 2.0.
## 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}
}
## :e-mail: Contact
If you have any question, please email `xintao.wang@outlook.com` or `xintaowang@tencent.com`.