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https://github.com/hwalsuklee/tensorflow-style-transfer
A simple, concise tensorflow implementation of style transfer (neural style)
https://github.com/hwalsuklee/tensorflow-style-transfer
change-style neural-style online style-transfer tensorflow
Last synced: 1 day ago
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A simple, concise tensorflow implementation of style transfer (neural style)
- Host: GitHub
- URL: https://github.com/hwalsuklee/tensorflow-style-transfer
- Owner: hwalsuklee
- Created: 2017-02-03T14:35:03.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2017-02-17T17:12:57.000Z (almost 8 years ago)
- Last Synced: 2024-12-25T12:06:02.861Z (8 days ago)
- Topics: change-style, neural-style, online, style-transfer, tensorflow
- Language: Python
- Homepage:
- Size: 3.52 MB
- Stars: 297
- Watchers: 17
- Forks: 108
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
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README
# Style Transfer (Neural Style)
A tensorflow implementation of style transfer (neural style) described in the papers:
* [A Neural Algorithm of Artistic Style](https://arxiv.org/pdf/1508.06576v2.pdf) : *submitted version*
* [Image Style Transfer Using Convolutional Neural Networks](http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Gatys_Image_Style_Transfer_CVPR_2016_paper.pdf) : *published version*
by Leon A. Gatys, Alexander S. Ecker, Matthias Bethge
The implementation is coincided with the paper both in variable-names and algorithms so that a reader of the paper can understand the code without too much effort.
## Usage
### Prerequisites
1. Tensorflow
2. Python packages : numpy, scipy, PIL(or Pillow), matplotlib
3. Pretrained VGG19 file : [imagenet-vgg-verydeep-19.mat](http://www.vlfeat.org/matconvnet/models/imagenet-vgg-verydeep-19.mat)
* Please download the file from link above.
* Save the file under `pre_trained_model`
### Running
```
python run_main.py --content --style --output <output file>
```
*Example*:
`python run_main.py --content images/tubingen.jpg --style images/starry-night.jpg --output result.jpg`
#### Arguments
*Required* :
* `--content`: Filename of the content image. *Default*: `images/tubingen.jpg`
* `--style`: Filename of the style image. *Default*: `images/starry-night.jpg`
* `--output`: Filename of the output image. *Default*: `result.jpg`
*Optional* :
* `--model_path`: Relative or absolute directory path to pre trained model. *Default*: `pre_trained_model`
* `--loss_ratio`: Weight of content-loss relative to style-loss. Alpha over beta in the paper. *Default*: `1e-3`
* `--content_layers`: *Space-separated* VGG-19 layer names used for content loss computation. *Default*: `conv4_2`
* `--style_layers`: *Space-separated* VGG-19 layer names used for style loss computation. *Default*: `relu1_1 relu2_1 relu3_1 relu4_1 relu5_1`
* `--content_layer_weights`: *Space-separated* weights of each content layer to the content loss. *Default*: `1.0`
* `--style_layer_weights`: *Space-separated* weights of each style layer to loss. *Default*: `0.2 0.2 0.2 0.2 0.2`
* `--max_size`: Maximum width or height of the input images. *Default*: `512`
* `--num_iter`: The number of iterations to run. *Default*: `1000`
* `--initial_type`: The initial image for optimization. (notation in the paper : x) *Choices*: content, style, random. *Default*: `'content'`
* `--content_loss_norm_type`: Different types of normalization for content loss. *Choices*: [1](https://arxiv.org/pdf/1508.06576v2.pdf), [2](https://arxiv.org/abs/1604.08610), [3](https://github.com/cysmith/neural-style-tf). *Default*: `3`
## Sample results
### The Neckarfront in Tübingen, Germany
Results were obtained from default setting.
An image was rendered approximately after 4 mins on GTX 980 ti.
<p align="center">
<img src="images/tubingen.jpg" height="192px">
<img src="samples/tubingen_shipwreck.jpg" height="192px">
<img src="samples/tubingen_starry-night.jpg" height="192px">
<img src="samples/tubingen_seated-nude.jpg" height="192px">
<img src="samples/tubingen_the_scream.jpg" height="192px">
<img src="samples/tubingen_kandinsky.jpg" height="192px">
</p>
### The Gyeongbokgung Palace in Seoul, South Korea
Results were obtained from default setting except `--max_size 1200`.
An image was rendered approximately after 19.5 mins on GTX 980 ti.
<p align="center">
<img src="images/gyeongbokgung.jpg" height="172px">
<img src="samples/gyeongbokgung_shipwreck.jpg" height="172px">
<img src="samples/gyeongbokgung_starry-night.jpg" height="172px">
<img src="samples/gyeongbokgung_seated-nude.jpg" height="172px">
<img src="samples/gyeongbokgung_the_scream.jpg" height="172px">
<img src="samples/gyeongbokgung_kandinsky.jpg" height="172px">
</p>
## References
The implementation is based on the projects:
#### https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/15_Style_Transfer.ipynb
* This is a tutorial version. Comments on code are well provided. Some exercises are given to check what you learn.
#### https://github.com/anishathalye/neural-style
* This is a simple and well written implemetation, but some parts like optimizer are not conincided with the paper.
#### https://github.com/cysmith/neural-style-tf
* There are other implementations related to style transfer like video style transfer, color-preserving style transfer etc.
I went through these implementations and found some differences from each other.
1. Style image shape : there are some variations how to resize a style image.
*In this implementation, a style image is resized to the shape of a content image.*
2. Optimizer : gradient descent, Adam, L-BFGS.
*In this implementation, only L-BFGS is provided.*
3. Scale factor of loss : scale factors for content-loss and style-loss are different.
*In this implementation, style loss is implemented as in the paper.*
*About content loss, there are 3 choices.*
* *Choice 1 : as in* [A Neural Algorithm of Artistic Style](https://arxiv.org/pdf/1508.06576v2.pdf)
* *Choice 2 : as in* [Artistic style transfer for videos](https://arxiv.org/abs/1604.08610)
* *Choice 3 : as in* https://github.com/cysmith/neural-style-tf
4. Total variance denoising : implementation details for total variance denoising are slightly different.
*In this implementation, total variance denoising is not provided since the paper does not use it.*
## Acknowledgements
This implementation has been tested with Tensorflow r0.12 on Windows 10 and Ubuntu 14.04.