Face Super-resolution Using Stochastic Differential Equations

Input - LR

Output - SR

This project was built using a fork of Score-SDE

Prepare conda environment

conda create -n srsde python=3.8.2

Install requirements

pip3 install -r requirements.txt

Also install jax+cuda

pip install --upgrade jax==0.2.8 jaxlib==0.1.59+cuda110 -f https://storage.googleapis.com/jax-releases/jax_releases.html

Activate conda environment

conda activate srsde

Train the models

python3 main.py --config 'configs/ve/sr_ve.py' --mode 'train' --workdir VESDE

SR images

To generate Super-Resolution images from CelebaHQ without training, download the pre-trained SDE-VE model here, copy to ./VESDE/checkpoints and run

python3 main.py --config 'configs/ve/sr_ve.py' --mode 'sr' --workdir VESDE

Tfrecords

The algorithm receives images in tfrecords format. In the tfrecords folder there is a sample of 32 images from the CelebAHQ dataset.

Adjust settings and path in files config/default_ve_configs.py and configs/ve/sr_ve.py.

Citation

• M. dos Santos, R. Laroca, R. O. Ribeiro, J. Neves, H. Proença, D. Menotti, “Face Super-Resolution Using Stochastic Differential Equations”, in Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 216-221, Oct. 2022. [IEEE Xplore] [arXiv]

@inproceedings{santos2022face,
  title = {Face Super-Resolution Using Stochastic Differential Equations},
  author = {M. {dos Santos} and R. {Laroca} and R. O. {Ribeiro} and J. {Neves} and H. {Proen\c{c}a} and D. {Menotti}},
  year = {2022},
  month = {Oct},
  booktitle = {Conference on Graphics, Patterns and Images (SIBGRAPI)},
  volume = {},
  number = {},
  pages = {216-221},
  doi = {10.1109/SIBGRAPI55357.2022.9991799},
  issn = {1530-1834},
}