PSFR-GAN in PyTorch

We only provide test codes at this time.

Progressive Semantic-Aware Style Transformation for Blind Face Restoration
Chaofeng Chen, Xiaoming Li, Lingbo Yang, Xianhui Lin, Lei Zhang, Kwan-Yee K. Wong

Getting Started

Prerequisites and Installation

Ubuntu 18.04
CUDA 10.1

Clone this repository

git clone https://github.com/chaofengc/PSFR-GAN.git
cd PSFR-GAN

Python 3.7, install required packages by pip3 install -r requirements.txt

Download Pretrain Models and Dataset

Download the pretrained models from the following link and put them to ./pretrain_models

GoogleDrive
BaiduNetDisk, extract code: 4uip

Test single image

Run the following script to enhance face(s) in single input

python test_enhance_single_unalign.py --test_img_path ./test_dir/test_hzgg.jpg --results_dir test_hzgg_results --gpus 1

This script do the following things:

Crop and align all the faces from input image, stored at results_dir/LQ_faces
Parse these faces and then enhance them, results stored at results_dir/ParseMaps and results_dir/HQ
Paste then enhanced faces back to the original image results_dir/hq_final.jpg
You can use --gpus to specify how many GPUs to use, <=0 means running on CPU. The program will use GPU with the most available memory. Set CUDA_VISIBLE_DEVICE to specify the GPU if you do not want automatic GPU selection.

Test image folder

To test multiple images, we first crop out all the faces and align them use the following script.

python align_and_crop_dir.py --src_dir test_dir --results_dir test_dir_align_results

For images (e.g. multiface_test.jpg) contain multiple faces, the aligned faces will be stored as multiface_test_{face_index}.jpg
And then parse the aligned faces and enhance them with

python test_enhance_dir_align.py --src_dir test_dir_align_results --results_dir test_dir_enhance_results

Results will be saved to three folders respectively: results_dir/lq, results_dir/parse, results_dir/hq.

Additional test script

For your convenience, we also provide script to test multiple unaligned images and paste the enhance results back. Note the paste back operation could be quite slow for large size images containing many faces.

python test_enhance_dir_unalign.py --src_dir test_dir --results_dir test_unalign_results

This script basically do the same thing as test_enhance_single_unalign.py for each image in src_dir

Citation

@InProceedings{ChenPSFRGAN,
    author = {Chen, Chaofeng and Li, Xiaoming and Lin, Xianhui and Lingbo, Yang and Zhang, Lei and Wong, KKY},
    title = {Progressive Semantic-Aware Style Transformation for Blind Face Restoration},
    Journal = {arXiv preprint arXiv:2009.08709},
    year = {2020}
}

License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Acknowledgement

This work is inspired by SPADE, and closed related to DFDNet and HiFaceGAN. Our codes largely benefit from CycleGAN.

Rancherzhang/PSFRGAN