Face Restoration with GFPGAN
Project Setup
This project uses the GFPGAN repository available on Github:
git clone https://github.com/mpolinowski/GFPGAN
And we will be using a pre-trained model that can be downloaded from here. There are different models available - one of them does not rely on a CUDA-Extensions (can be run on a CPU). But the following one does require an nVidia graphics card:
cd GFPGAN
wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth -P experiments/pretrained_models
Install Dependencies
All dependencies are listed in the requirements.txt file:
torch>=1.7
numpy<1.21 # numba requires numpy<1.21,>=1.17
opencv-python
torchvision
scipy
tqdm
basicsr>=1.3.4.0
facexlib>=0.2.0.3
lmdb
pyyaml
tb-nightly
yapf
pip3 install -r requirements.txt
If you want to enhance the background (non-face) regions with Real-ESRGAN, you also need to install the realesrgan package:
pip3 install realesrgan
And run the setup script:
python setup.py develop
Run Up-Sampling
We can run the following script included inside the repository to feed images from the ./inputs/whole_imgs directory into our GAN and output the up-sampled images into the ./results folder:
python inference_gfpgan.py -i inputs/whole_imgs -o results -v 1.3 -s 2
Results
