This repo provides PyTorch Implementation of MSG-Net (ours) and Neural Style (Gatys et al. CVPR 2016), which has been included by ModelDepot. We also provide Torch implementation and MXNet implementation.
Tabe of content
Multi-style Generative Network for Real-time Transfer [arXiv] [project] Hang Zhang, Kristin Dana @article{zhang2017multistyle, title={Multi-style Generative Network for Real-time Transfer}, author={Zhang, Hang and Dana, Kristin}, journal={arXiv preprint arXiv:1703.06953}, year={2017} } |
git clone git@github.com:zhanghang1989/PyTorch-Style-Transfer.git
cd PyTorch-Style-Transfer/experiments
bash models/download_model.sh
python camera_demo.py demo --model models/21styles.model
Test the model
python main.py eval --content-image images/content/venice-boat.jpg --style-image images/21styles/candy.jpg --model models/21styles.model --content-size 1024
If you don't have a GPU, simply set --cuda=0
. For a different style, set --style-image path/to/style
.
If you would to stylize your own photo, change the --content-image path/to/your/photo
.
More options:
--content-image
: path to content image you want to stylize.
--style-image
: path to style image (typically covered during the training).
--model
: path to the pre-trained model to be used for stylizing the image.
--output-image
: path for saving the output image.
--content-size
: the content image size to test on.
--cuda
: set it to 1 for running on GPU, 0 for CPU.
bash dataset/download_dataset.sh
python main.py train --epochs 4
--style-folder path/to/your/styles
. More options:--style-folder
: path to the folder style images.--vgg-model-dir
: path to folder where the vgg model will be downloaded.--save-model-dir
: path to folder where trained model will be saved.--cuda
: set it to 1 for running on GPU, 0 for CPU.Image Style Transfer Using Convolutional Neural Networks by Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge.
python main.py optim --content-image images/content/venice-boat.jpg --style-image images/21styles/candy.jpg
--content-image
: path to content image.--style-image
: path to style image.--output-image
: path for saving the output image.--content-size
: the content image size to test on.--style-size
: the style image size to test on.--cuda
: set it to 1 for running on GPU, 0 for CPU.
The code benefits from outstanding prior work and their implementations including: