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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

Real-time Style Transfer using MSG-Net
- Stylize Images using Pre-trained Model
- Train Your Own MSG-Net Model
Slow Neural Style Transfer

MSG-Net

<table width="100%" border="0" cellspacing="15" cellpadding="0"> <tbody> <tr> <td> <b>Multi-style Generative Network for Real-time Transfer</b> [<a href="https://arxiv.org/pdf/1703.06953.pdf">arXiv</a>] [<a href="http://computervisionrutgers.github.io/MSG-Net/">project</a>] <br> <a href="http://hangzh.com/">Hang Zhang</a>, <a href="http://eceweb1.rutgers.edu/vision/dana.html">Kristin Dana</a> <pre> @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} } </pre> </td> <td width="440"><a><img src ="https://raw.githubusercontent.com/zhanghang1989/MSG-Net/master/images/figure1.jpg" width="420px" border="1"></a></td> </tr> </tbody> </table>

Stylize Images Using Pre-trained MSG-Net

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

Camera Demo

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.

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.

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:

Texture Networks: Feed-forward Synthesis of Textures and Stylized Images by Ulyanov et al. ICML 2016. (code)
Perceptual Losses for Real-Time Style Transfer and Super-Resolution by Johnson et al. ECCV 2016 (code) and its pytorch implementation code by Abhishek.
Image Style Transfer Using Convolutional Neural Networks by Gatys et al. CVPR 2016 and its torch implementation code by Johnson.