PSFR-GAN in PyTorch

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

Changelog

2021.04.26: Add pytorch vgg19 model to GoogleDrive and remove --distributed option which causes training error.
2021.03.22: Update new model at 15 epoch (52.5k iterations).
2021.03.19: Add train codes for PSFRGAN and FPN.

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

Quick Test

Download Pretrain Models and Dataset

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

GoogleDrive
BaiduNetDisk, extract code: gj2r

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 (dlib takes time to detect faces in large image).

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

Train the Model

Data Preparation

Download FFHQ and put the images to ../datasets/FFHQ/imgs1024
Download parsing masks (512x512) HERE generated by the pretrained FPN and put them to ../datasets/FFHQ/masks512.

Note: you may change ../datasets/FFHQ to your own path. But images and masks must be stored under your_own_path/imgs1024 and your_own_path/masks512 respectively.

Train Script for PSFRGAN

Here is an example train script for PSFRGAN:

python train.py --gpus 2 --model enhance --name PSFRGAN_v001 \
    --g_lr 0.0001 --d_lr 0.0004 --beta1 0.5 \
    --gan_mode 'hinge' --lambda_pix 10 --lambda_fm 10 --lambda_ss 1000 \
    --Dinput_nc 22 --D_num 3 --n_layers_D 4 \
    --batch_size 2 --dataset ffhq  --dataroot ../datasets/FFHQ \
    --visual_freq 100 --print_freq 10 #--continue_train

Please change the --name option for different experiments. Tensorboard records with the same name will be moved to check_points/log_archive, and the weight directory will only store weight history of latest experiment with the same name.
--gpus specify number of GPUs used to train. The script will use GPUs with more available memory first. To specify the GPU index, use export CUDA_VISIBLE_DEVICES=your_gpu_ids before the script.
Uncomment --continue_train to resume training. Current codes do not resume the optimizer state.
It needs at least 8GB memory to train with batch_size=1.

Scripts for FPN

You may also train your own FPN and generate masks for the HQ images by yourself with the following steps:

Download CelebAHQ-Mask dataset. Generate CelebAMask-HQ-mask and CelebAMask-HQ-mask-color with the provided scripts in CelebAMask-HQ/face_parsing/Data_preprocessing/.
Train FPN with the following commmand

python train.py --gpus 1 --model parse --name FPN_v001 \
    --lr 0.0002 --batch_size 8 \
    --dataset celebahqmask --dataroot ../datasets/CelebAMask-HQ \
    --visual_freq 100 --print_freq 10 #--continue_train

Generate parsing masks with your own FPN using the following command:

python generate_masks.py --save_masks_dir ../datasets/FFHQ/masks512 --batch_size 8 --parse_net_weight path/to/your/own/FPN

Citation

@inproceedings{ChenPSFRGAN,
    author = {Chen, Chaofeng and Li, Xiaoming and Lingbo, Yang and Lin, Xianhui and Zhang, Lei and Wong, KKY},
    title = {Progressive Semantic-Aware Style Transformation for Blind Face Restoration},
    Journal = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
    year = {2021}
}

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.

fanyongkang87/PSFRGAN