GFPGAN (CVPR 2021)

Colab Demo for GFPGAN ; (Another Colab Demo for the original paper model)
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.

🚩 Updates

✅ Support enhancing non-face regions (background) with Real-ESRGAN.
✅ We provide a clean version of GFPGAN, which does not require CUDA extensions.
✅ We provide an updated model without colorizing faces.

If GFPGAN is helpful in your photos/projects, please help to ⭐ this repo. Thanks😊
Other recommended projects: ▶️ Real-ESRGAN ▶️ BasicSR ▶️ facexlib

📖 GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior

[Paper] [Project Page] [Demo]
Xintao Wang, Yu Li, Honglun Zhang, Ying Shan
Applied Research Center (ARC), Tencent PCG

🔧 Dependencies and Installation

Python >= 3.7 (Recommend to use Anaconda or Miniconda)
PyTorch >= 1.7
Option: NVIDIA GPU + CUDA
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 for installation.

Clone repo

git clone https://github.com/TencentARC/GFPGAN.git
cd GFPGAN

Install dependent packages

# 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

⚡ Quick Inference

Download pre-trained models: GFPGANCleanv1-NoCE-C2.pth

wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P experiments/pretrained_models

Inference!

python inference_gfpgan.py --upscale_factor 2 --test_path inputs/whole_imgs --save_root results

🏰 Model Zoo

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: The paper model, with colorization.

💻 Training

We provide the training codes for GFPGAN (used in our paper).
You could improve it according to your own needs.

Tips

More high quality faces can improve the restoration quality.
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.)

Dataset preparation: FFHQ
Download pre-trained models and other data. Put them in the experiments/pretrained_models folder.
Modify the configuration file options/train_gfpgan_v1.yml accordingly.
Training

python -m torch.distributed.launch --nproc_per_node=4 --master_port=22021 gfpgan/train.py -opt options/train_gfpgan_v1.yml --launcher pytorch

📜 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}
}

📧 Contact

If you have any question, please email xintao.wang@outlook.com or xintaowang@tencent.com.

zhoushiwei/GFPGAN-