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[](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) 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) 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**.
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:
:arrow_forward: [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN): A practical algorithm for general image restoration
:arrow_forward: [BasicSR](https://github.com/xinntao/BasicSR): An open-source image and video restoration toolbox
:arrow_forward: [facexlib](https://github.com/xinntao/facexlib): A collection that provides useful face-relation functions
:arrow_forward: [HandyView](https://github.com/xinntao/HandyView): A PyQt5-based image viewer that is handy for view and comparison
--- ### :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 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
✓better results on very low-quality inputs
✓ work on relatively high-quality inputs
✓ can have repeated (twice) restorations | ✗ not very sharp
✗ have a slight change on identity | |V1.2 | ✓ sharper output
✓ 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).
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`.