GLStyleNet
[update 1/12/2022]
paper: GLStyleNet: Exquisite Style Transfer Combining Global and Local Pyramid Features, published in IET Computer Vision 2020.
Arxiv paper: GLStyleNet: Higher Quality Style Transfer Combining Global and Local Pyramid Features.
Environment Required:
- Python 3.6
- TensorFlow 1.4.0
- CUDA 8.0
Getting Started:
Step 1: clone this repo
git clone https://github.com/EndyWon/GLStyleNet
cd GLStyleNet
Step 2: download pre-trained vgg19 model
bash download_vgg19.sh
Step 3: run style transfer
- Script Parameters
--content: content image path--content-mask: content image semantic mask--style: style image path--style-mask: style image semantic mask--content-weight: weight of content, default=10--local-weight: weight of local style loss--semantic-weight: weight of semantic map constraint--global-weight: weight of global style loss--output: output image path--smoothness: weight of image smoothing scheme--init: image type to initialize, value='noise' or 'content' or 'style', default='content'--iterations: number of iterations, default=500--device: devices, value='gpu'(all available GPUs) or 'gpui'(e.g. gpu0) or 'cpu', default='gpu'--class-num: count of semantic mask classes, default=5
- portrait style transfer (an example)
python GLStyleNet.py --content portrait/Seth.jpg --content-mask portrait/Seth_sem.png --style portrait/Gogh.jpg --style-mask portrait/Gogh_sem.png --content-weight 10 --local-weight 500 --semantic-weight 10 --global-weight 1 --init style --device gpu
！！！You can find all the iteration results in folder 'outputs'！！！
- Chinese ancient painting style transfer (an example)
python GLStyleNet.py --content Chinese/content.jpg --content-mask Chinese/content_sem.png --style Chinese/style.jpg --style-mask Chinese/style_sem.png --content-weight 10 --local-weight 500 --semantic-weight 2.5 --global-weight 0.5 --init content --device gpu
- artistic and photo-realistic style transfer
artistic:
photo-realistic:
Citation:
If you find this code useful for your research, please cite the paper:
@article{wang2020glstylenet,
title={GLStyleNet: exquisite style transfer combining global and local pyramid features},
author={Wang, Zhizhong and Zhao, Lei and Lin, Sihuan and Mo, Qihang and Zhang, Huiming and Xing, Wei and Lu, Dongming},
journal={IET Computer Vision},
volume={14},
number={8},
pages={575--586},
year={2020},
publisher={IET}
}
Acknowledgement:
The code was written based on Champandard's code.