StyTR-2
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StyTr2 : Image Style Transfer with Transformers
StyTr^2 : Image Style Transfer with Transformers（CVPR2022）
Authors: Yingying Deng, Fan Tang, XingjiaPan, Weiming Dong, Chongyang Ma, Changsheng Xu
This paper is proposed to achieve unbiased image style transfer based on the transformer model. We can promote the stylization effect compared with state-of-the-art methods. This repository is the official implementation of SyTr^2 : Image Style Transfer with Transformers.
Results presentation
Framework
Experiment
Requirements
- python 3.6
- pytorch 1.4.0
- PIL, numpy, scipy
- tqdm
Testing
Pretrained models: vgg-model, vit_embedding, decoder, Transformer_module
Please download them and put them into the floder ./experiments/
python test.py --content_dir input/content/ --style_dir input/style/ --output out
Training
Style dataset is WikiArt collected from WIKIART
content dataset is COCO2014
python train.py --style_dir ../../datasets/Images/ --content_dir ../../datasets/train2014 --save_dir models/ --batch_size 8
Reference
If you find our work useful in your research, please cite our paper using the following BibTeX entry ~ Thank you ^ . ^. Paper Link pdf
@inproceedings{deng2021stytr2,
title={StyTr^2: Image Style Transfer with Transformers},
author={Yingying Deng and Fan Tang and Weiming Dong and Chongyang Ma and Xingjia Pan and Lei Wang and Changsheng Xu},
booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2022},
}
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