| <p align="center"> | |
| <img src="assets/gfpgan_logo.png" height=130> | |
| </p> | |
| ## <div align="center"><b><a href="README.md">English</a> | <a href="README_CN.md">简体中文</a></b></div> | |
| <div align="center"> | |
| <!-- <a href="https://twitter.com/_Xintao_" style="text-decoration:none;"> | |
| <img src="https://user-images.githubusercontent.com/17445847/187162058-c764ced6-952f-404b-ac85-ba95cce18e7b.png" width="4%" alt="" /> | |
| </a> --> | |
| [](https://github.com/TencentARC/GFPGAN/releases) | |
| [](https://pypi.org/project/gfpgan/) | |
| [](https://github.com/TencentARC/GFPGAN/issues) | |
| [](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) | |
| </div> | |
| 1. :boom: **Updated** online demo: [](https://replicate.com/tencentarc/gfpgan). Here is the [backup](https://replicate.com/xinntao/gfpgan). | |
| 1. :boom: **Updated** online demo: [](https://huggingface.co/spaces/Xintao/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>; (Another [Colab Demo](https://colab.research.google.com/drive/1Oa1WwKB4M4l1GmR7CtswDVgOCOeSLChA?usp=sharing) for the original paper model) | |
| <!-- 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** | |
| - :white_check_mark: Add [RestoreFormer](https://github.com/wzhouxiff/RestoreFormer) inference codes. | |
| - :white_check_mark: Add [V1.4 model](https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth), which produces slightly more details and better identity than V1.3. | |
| - :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`. | |