Paper (Arxiv) | Supplementary Material | Project Page

I have trained and tested the codes on

• Ubuntu 20.04
• CUDA 11.1
• Python 3.8, install required packages by pip install -r requirements.txt

Download Our Pretrain Models and Test Dataset. Additionally, we offer our FSR results in orginal paper.

• Pretrain_Models
• Test_Datasets
• FSR_Results_in_Orginal_Paper

# On CelebA Test set
python test.py --gpus 1 --model wfen --name wfen \
    --load_size 128 --dataset_name single --dataroot /path/to/datasets/test_datasets/CelebA1000/LR_x8_up/ \
    --pretrain_model_path ./pretrain_models/wfen/wfen_best.pth \
    --save_as_dir results_celeba/wfen

# On Helen Test set
python test.py --gpus 1 --model wfen --name wfen \
    --load_size 128 --dataset_name single --dataroot /path/to/datasets/test_datasets/Helen50/LR_x8_up/ \
    --pretrain_model_path ./pretrain_models/wfen/wfen_best.pth \
    --save_as_dir results_helen/wfen

We provide evaluation codes in script test.sh for calculate PSNR/SSIM/LPIPS/VIF/Parmas/FLOPs scores.

The commands used to train the released models are provided in script train.sh. Here are some train tips:

• You should download CelebA to train WFEN. Please change the --dataroot to the path where your training images are stored.
• To train WFEN, we simply crop out faces from CelebA without pre-alignment, because for ultra low resolution face SR, it is difficult to pre-align the LR images.
• 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.
• If there's not enough memory, you can turn down the --batch_size.
• --gpus specify number of GPUs used to train. The script will use GPUs with more available memory first. To specify the GPU index, uncomment the export CUDA_VISIBLE_DEVICES=.

# Train Code
CUDA_VISIBLE_DEVICES=0,1 python train.py --gpus 2 --name wfen --model wfen \
    --Gnorm "bn" --lr 0.0002 --beta1 0.9 --scale_factor 8 --load_size 128 \
    --dataroot /path/to/datasets/CelebA --dataset_name celeba --batch_size 32 --total_epochs 150 \
    --visual_freq 100 --print_freq 10 --save_latest_freq 500

This code is built on Face-SPARNet. We thank the authors for sharing their codes.

If you have any question, please email [email protected] or [email protected]