Face Hallucination via Split-Attention in Split-Attention Network (Accepted by ACM MM 2021)

This repository provides the official PyTorch implementation of the following paper:

Paper link: SISN-MM'21

Requirement

Python 3.7
PyTorch >= 1.4.0 (1.5.0 is ok)
numpy
skimage
imageio
matplotlib
tqdm

Dataset

Please download FFHQ dataset from here and CelebA dataset from here. After download all datasets, the folder dataset should be like this (take FFHQ as an example):

    dataset    
    └── FFHQ
        ├── 1024X1024
            ├── HR
            └── LR
                ├── X2
                ├── X4
                └── X8
        └── 256X256
            ├── HR
            └── LR
                ├── X2
                ├── X4
                └── X8

Training Model

First, you need to set the necessary parameters in the option.py such as scale, dataset_root, train_val_range, etc. Training the model on the X4 scale as below:

python train.py --model SISN --scale 4

By default, the trained model will be saved in ./pt directory.

Testing model

python test.py --model SISN --scale 4 --pretrain <path_of_pretrained_model> --dataset_root <path_of_input_image> --save_root <path_of_result>

Citation

If you find the code helpful in your resarch or work, please cite the following paper.

@inproceedings{lu2021face,
  title={Face Hallucination via Split-Attention in Split-Attention Network},
  author={Lu, Tao and Wang, Yuanzhi and Zhang, Yanduo and Wang, Yu and Wei, Liu and Wang, Zhongyuan and Jiang, Junjun},
  booktitle={Proceedings of the 29th ACM International Conference on Multimedia},
  pages={5501--5509},
  year={2021}
}