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<img src="assets/gfpgan_logo.png" height=130> | |
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## <div align="center"><b><a href="README.md">English</a> | <a href="README_CN.md">简体中文</a></b></div> | |
[![download](https://img.shields.io/github/downloads/TencentARC/GFPGAN/total.svg)](https://github.com/TencentARC/GFPGAN/releases) | |
[![PyPI](https://img.shields.io/pypi/v/gfpgan)](https://pypi.org/project/gfpgan/) | |
[![Open issue](https://img.shields.io/github/issues/TencentARC/GFPGAN)](https://github.com/TencentARC/GFPGAN/issues) | |
[![Closed issue](https://img.shields.io/github/issues-closed/TencentARC/GFPGAN)](https://github.com/TencentARC/GFPGAN/issues) | |
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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>; (Another [Colab Demo](https://colab.research.google.com/drive/1Oa1WwKB4M4l1GmR7CtswDVgOCOeSLChA?usp=sharing) for the original paper model) | |
2. Online demo: [Huggingface](https://huggingface.co/spaces/akhaliq/GFPGAN) (return only the cropped face) | |
3. Online demo: [Replicate.ai](https://replicate.com/xinntao/gfpgan) (may need to sign in, return the whole image) | |
4. Online demo: [Baseten.co](https://app.baseten.co/applications/Q04Lz0d/operator_views/8qZG6Bg) (backed by GPU, returns the whole image) | |
5. We provide a *clean* version of GFPGAN, which can run without CUDA extensions. So that it can run in **Windows** or on **CPU mode**. | |
> :rocket: **Thanks for your interest in our work. You may also want to check our new updates on the *tiny models* for *anime images and videos* in [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN/blob/master/docs/anime_video_model.md)** :blush: | |
GFPGAN aims at developing a **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. | |
:question: Frequently Asked Questions can be found in [FAQ.md](FAQ.md). | |
:triangular_flag_on_post: **Updates** | |
- :fire::fire::white_check_mark: Add **[V1.3 model](https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth)**, which produces **more natural** restoration results, and better results on *very low-quality* / *high-quality* inputs. See more in [Model zoo](#european_castle-model-zoo), [Comparisons.md](Comparisons.md) | |
- :white_check_mark: Integrated to [Huggingface Spaces](https://huggingface.co/spaces) with [Gradio](https://github.com/gradio-app/gradio). See [Gradio Web Demo](https://huggingface.co/spaces/akhaliq/GFPGAN). | |
- :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 or recommend it to your friends. Thanks:blush: | |
Other recommended projects:<br> | |
:arrow_forward: [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN): A practical algorithm for general image restoration<br> | |
:arrow_forward: [BasicSR](https://github.com/xinntao/BasicSR): An open-source image and video restoration toolbox<br> | |
:arrow_forward: [facexlib](https://github.com/xinntao/facexlib): A collection that provides useful face-relation functions<br> | |
:arrow_forward: [HandyView](https://github.com/xinntao/HandyView): A PyQt5-based image viewer that is handy for view and comparison<br> | |
--- | |
### :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 | |
<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/) | |
- 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. <br> | |
If you 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 | |
We take the v1.3 version for an example. More models can be found [here](#european_castle-model-zoo). | |
Download pre-trained models: [GFPGANv1.3.pth](https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth) | |
```bash | |
wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth -P experiments/pretrained_models | |
``` | |
**Inference!** | |
```bash | |
python inference_gfpgan.py -i inputs/whole_imgs -o results -v 1.3 -s 2 | |
``` | |
```console | |
Usage: python inference_gfpgan.py -i inputs/whole_imgs -o results -v 1.3 -s 2 [options]... | |
-h show this help | |
-i input Input image or folder. Default: inputs/whole_imgs | |
-o output Output folder. Default: results | |
-v version GFPGAN model version. Option: 1 | 1.2 | 1.3. Default: 1.3 | |
-s upscale The final upsampling scale of the image. Default: 2 | |
-bg_upsampler background upsampler. Default: realesrgan | |
-bg_tile Tile size for background sampler, 0 for no tile during testing. Default: 400 | |
-suffix Suffix of the restored faces | |
-only_center_face Only restore the center face | |
-aligned Input are aligned faces | |
-ext Image extension. Options: auto | jpg | png, auto means using the same extension as inputs. Default: auto | |
``` | |
If you want to use the original model in our paper, please see [PaperModel.md](PaperModel.md) for installation and inference. | |
## :european_castle: Model Zoo | |
| Version | Model Name | Description | | |
| :---: | :---: | :---: | | |
| V1.3 | [GFPGANv1.3.pth](https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth) | Based on V1.2; **more natural** restoration results; better results on very low-quality / high-quality inputs. | | |
| V1.2 | [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. Trained with more data with pre-processing. | | |
| V1 | [GFPGANv1.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/GFPGANv1.pth) | The paper model, with colorization. | | |
The comparisons are in [Comparisons.md](Comparisons.md). | |
Note that V1.3 is not always better than V1.2. You may need to select different models based on your purpose and inputs. | |
| Version | Strengths | Weaknesses | | |
| :---: | :---: | :---: | | |
|V1.3 | ✓ natural outputs<br> ✓better results on very low-quality inputs <br> ✓ work on relatively high-quality inputs <br>✓ can have repeated (twice) restorations | ✗ not very sharp <br> ✗ have a slight change on identity | | |
|V1.2 | ✓ sharper output <br> ✓ with beauty makeup | ✗ some outputs are unnatural | | |
You can find **more models (such as the discriminators)** here: [[Google Drive](https://drive.google.com/drive/folders/17rLiFzcUMoQuhLnptDsKolegHWwJOnHu?usp=sharing)], OR [[Tencent Cloud 腾讯微云](https://share.weiyun.com/ShYoCCoc)] | |
## :computer: Training | |
We provide the training codes for GFPGAN (used in our paper). <br> | |
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. [Pre-trained 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`. | |