Jupyter Notebook Python
Switch branches/tags
Nothing to show
Clone or download
Latest commit 17d42f7 May 15, 2018
Permalink
Failed to load latest commit information.
experiments Update utils.py May 15, 2018
images updates Oct 6, 2017
.editorconfig v1.0 Apr 25, 2017
.gitignore v1.0 Apr 25, 2017
LICENSE Update LICENSE Nov 9, 2017
README.md Update README.md Mar 15, 2018
msgnet.ipynb add ipynb example Mar 9, 2018

README.md

PyTorch-Style-Transfer

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

MSG-Net

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}
}

Stylize Images Using Pre-trained MSG-Net

  1. Download the pre-trained model
    git clone git@github.com:zhanghang1989/PyTorch-Style-Transfer.git
    cd PyTorch-Style-Transfer/experiments
    bash models/download_model.sh
  2. Camera Demo
    python camera_demo.py demo --model models/21styles.model
  3. 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.

Train Your Own MSG-Net Model

  1. Download the COCO dataset
    bash dataset/download_dataset.sh
  2. 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.

Neural Style

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.

Acknowledgement

The code benefits from outstanding prior work and their implementations including: