GFP-GAN: Blind Face Restoration with Generative Facial Prior

Output

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Installation

1. Clone repo

   git clone https://github.com/saba99/Real-world-Face-Restoration-GFPGAN.git
   cd Real-world-Face-Restoration-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

Quick Inference

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

Model

Version	Model Name	Description
V1.3	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	No colorization; no CUDA extensions are required. Trained with more data with pre-processing.
V1	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.

Procedures

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