[update 11/18/2018] The code was written by Zhizhong Wang and Dongjing Liu based on Champandard's code.
paper: GLStyleNet: Higher Quality Style Transfer Combining Global and Local Pyramid Features
- Python 3.6
- TensorFlow 1.4.0
- CUDA 8.0
If you want to run on your GPU, make sure that the memory of your GPU is large enough, otherwise you may not be able to process large enough pictures.
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
