TencentARC/GFPGAN

1. 💥 Updated online demo: . Here is the backup.
2. 💥 Updated online demo:
3. Colab Demo for GFPGAN ; (Another Colab Demo for the original paper model)

🚀 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 😊

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.

❓ Frequently Asked Questions can be found in FAQ.md.

🚩 Updates

• ✅ Add RestoreFormer inference codes.
• ✅ Add V1.4 model, which produces slightly more details and better identity than V1.3.
• ✅ Add V1.3 model, which produces more natural restoration results, and better results on very low-quality / high-quality inputs. See more in Model zoo, Comparisons.md
• ✅ Integrated to Huggingface Spaces with Gradio. See Gradio Web Demo.
• ✅ 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 or recommend it to your friends. Thanks😊 Other recommended projects:
▶️ Real-ESRGAN: A practical algorithm for general image restoration
▶️ BasicSR: An open-source image and video restoration toolbox
▶️ facexlib: A collection that provides useful face-relation functions
▶️ HandyView: A PyQt5-based image viewer that is handy for view and comparison

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

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

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 for installation.

1. Clone repo

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

2. 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

We take the v1.3 version for an example. More models can be found here.

Download pre-trained models: GFPGANv1.3.pth

wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth -P experiments/pretrained_models

Inference!

python inference_gfpgan.py -i inputs/whole_imgs -o results -v 1.3 -s 2

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 for installation and inference.

The comparisons are in 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.

You can find more models (such as the discriminators) here: [Google Drive], OR [Tencent Cloud 腾讯微云]

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

2. 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
   2. Component locations of FFHQ: FFHQ_eye_mouth_landmarks_512.pth
   3. A simple ArcFace model: arcface_resnet18.pth

3. Modify the configuration file options/train_gfpgan_v1.yml accordingly.

4. Training

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

GFPGAN is released under Apache License Version 2.0.

@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}
}

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