example/gluon/style_transfer/README.md

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# MXNet-Gluon-Style-Transfer

This repo provides MXNet Implementation of **[Neural Style Transfer](#neural-style)** and **[MSG-Net](#real-time-style-transfer)**. 

**Tabe of content**

* [Slow Neural Style Transfer](#neural-style)
* [Real-time Style Transfer](#real-time-style-transfer)
	- [Stylize Images using Pre-trained MSG-Net](#stylize-images-using-pre-trained-msg-net)
	- [Train Your Own MSG-Net Model](#train-your-own-msg-net-model)

## Neural Style

[A Neural Algorithm of Artistic Style](https://arxiv.org/abs/1508.06576) by Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge.


**Download the images**

```bash
python download_images.py 
```

**Neural style transfer**

```bash
python main.py optim --content-image images/content/venice-boat.jpg --style-image images/styles/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.

<img src ="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/example/style_transfer/images/g1.jpg" width="260px" /> <img src ="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/example/style_transfer/images/g2.jpg" width="260px" />
<img src ="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/example/style_transfer/images/g3.jpg" width="260px" />
<img src ="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/example/style_transfer/images/g4.jpg" width="260px" />
<img src ="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/example/style_transfer/images/g5.jpg" width="260px" />
<img src ="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/example/style_transfer/images/g6.jpg" width="260px" />
<img src ="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/example/style_transfer/images/g7.jpg" width="260px" />
<img src ="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/example/style_transfer/images/g8.jpg" width="260px" />
<img src ="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/example/style_transfer/images/g9.jpg" width="260px" />

## Real-time Style Transfer
<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
0. Download the images and pre-trained model
	```bash
    python download_images.py 
	python models/download_model.py
	```
0. Test the model
	```bash
	python main.py eval --content-image images/content/venice-boat.jpg --style-image images/styles/candy.jpg --model models/21styles.params --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.

<img src ="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/example/style_transfer/images/1.jpg" width="260px" /> <img src ="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/example/style_transfer/images/2.jpg" width="260px" />
<img src ="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/example/style_transfer/images/3.jpg" width="260px" />
<img src ="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/example/style_transfer/images/4.jpg" width="260px" />
<img src ="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/example/style_transfer/images/5.jpg" width="260px" />
<img src ="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/example/style_transfer/images/6.jpg" width="260px" />
<img src ="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/example/style_transfer/images/7.jpg" width="260px" />
<img src ="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/example/style_transfer/images/8.jpg" width="260px" />
<img src ="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/example/style_transfer/images/9.jpg" width="260px" />

### Train Your Own MSG-Net Model
0. Download the style images and COCO dataset
	```bash
    python download_images.py 
	python dataset/download_dataset.py
	```
0. Train the model
	```bash
	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.


The code is mainly modified from [PyTorch-Style-Transfer](https://github.com/zhanghang1989/PyTorch-Style-Transfer).