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[![LICENSE](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://github.com/TencentARC/GFPGAN/blob/master/LICENSE)
[![python lint](https://github.com/TencentARC/GFPGAN/actions/workflows/pylint.yml/badge.svg)](https://github.com/TencentARC/GFPGAN/blob/master/.github/workflows/pylint.yml)
[**Paper**](https://arxiv.org/abs/2101.04061) **|** [**Project Page**](https://xinntao.github.io/projects/gfpgan)
1. [Colab Demo](https://colab.research.google.com/drive/1sVsoBd9AjckIXThgtZhGrHRfFI6UUYOo) for GFPGAN <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>
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.
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**.<br>
It leverages rich and diverse priors encapsulated in a pretrained face GAN (*e.g.*, StyleGAN2) for blind face restoration.
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### Installation
We now provide a *clean* version of GFPGAN, which does not require customized CUDA extensions. <br>
If you want want to use the original model in our paper, please see [PaperModel.md](Installation.md) for installation.
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/xinntao/GFPGAN.git
git clone https://github.com/TencentARC/GFPGAN.git
cd GFPGAN
```
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We provide the training codes for GFPGAN (used in our paper). <br>
You could improve it according to your own needs.
Tips:
**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**:<br>
**Procedures**
(You can try a simple version that does not require face component landmarks.)
(You can try a simple version ( `train_gfpgan_v1_simple.yml`) that does not require face component landmarks.)
1. Dataset preparation: [FFHQ](https://github.com/NVlabs/ffhq-dataset)