SAFIN: Arbitrary Style Transfer With Self-Attentive Factorized Instance Normalization

This repository contains the code for our ICME 2021 paper:

Aaditya Singh*, Shreeshail Hingane*, Xinyu Gong and Zhangyang Wang. SAFIN: Arbitrary Style Transfer With Self-Attentive Factorized Instance Normalization, {pdf}).

Requirements

• Python 3.7 should be installed via Conda with conda create --name <ENV_NAME> python=3.7
• Required packages should be installed in this conda environment with pip install -r requirements.txt

Download models

Download vgg_normalized.pth/decoder.pth and put them under models/.

Test

Use --content and --style to provide the respective path to the content and style image.

CUDA_VISIBLE_DEVICES=<gpu_id> python test.py --net_file wave_net --content input/content/cornell.jpg --style input/style/woman_with_hat_matisse.jpg

You can also run the code on directories of content and style images using --content_dir and --style_dir. It will save every possible combination of content and styles to the output directory.

CUDA_VISIBLE_DEVICES=<gpu_id> python test.py --net_file wave_net --content_dir input/content --style_dir input/style

This is an example of mixing four styles by specifying --style and --style_interpolation_weights option.

CUDA_VISIBLE_DEVICES=<gpu_id> python test.py --net_file wave_net --content input/content/avril.jpg --style input/style/picasso_self_portrait.jpg,input/style/impronte_d_artista.jpg,input/style/trial.jpg,input/style/antimonocromatismo.jpg --style_interpolation_weights 1,1,1,1 --content_size 512 --style_size 512 --crop

Some other options:

• --content_size: New (minimum) size for the content image. Keeping the original size if set to 0.
• --style_size: New (minimum) size for the content image. Keeping the original size if set to 0.
• --alpha: Adjust the degree of stylization. It should be a value between 0.0 and 1.0 (default).
• --preserve_color: Preserve the color of the content image.

Train

Use --content_dir and --style_dir to provide the respective directory to the content and style images.

CUDA_VISIBLE_DEVICES=<gpu_id> python train.py --net_file wave_net --content_dir <content_dir> --style_dir <style_dir> --start_iter 0 --save_dir ./save/

For more details and parameters, please refer to --help option.

Bibtex

@INPROCEEDINGS{9428124,
  author={Singh, Aaditya and Hingane, Shreeshail and Gong, Xinyu and Wang, Zhangyang},
  booktitle={2021 IEEE International Conference on Multimedia and Expo (ICME)}, 
  title={SAFIN: Arbitrary Style Transfer with Self-Attentive Factorized Instance Normalization}, 
  year={2021},
  volume={},
  number={},
  pages={1-6},
  doi={10.1109/ICME51207.2021.9428124}}

References

• [1]: PyTorch implementation of AdaIN