@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}
}
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.
Train Your Own MSG-Net Model
Download the COCO dataset
bash dataset/download_dataset.sh
Train the model
python main.py train --epochs 4
If you would like to customize styles, set --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.