https://github.com/bchao1/style-transfer

Neural style transfer implementation.
https://github.com/bchao1/style-transfer

neural-transfer pytorch style-transfer vgg19

Last synced: 7 months ago
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Neural style transfer implementation.

• Host: GitHub
• URL: https://github.com/bchao1/style-transfer
• Owner: bchao1
• Created: 2018-09-20T03:51:47.000Z (about 7 years ago)
• Default Branch: master
• Last Pushed: 2018-09-26T16:54:32.000Z (about 7 years ago)
• Last Synced: 2025-01-28T03:14:40.665Z (9 months ago)
• Topics: neural-transfer, pytorch, style-transfer, vgg19
• Language: Python
• Homepage:
• Size: 21.7 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Style-Transfer

Simple implementation of the classic paper by Gatys et al. Please refer to the original paper for more detail: https://arxiv.org/abs/1508.0657.

## Usage 

```

>> python3 style_transfer.py -h

usage: style_transfer.py [-h] [-d {cpu,cuda}] [-i ITERATIONS] [-c CONTENT]

                         [-s STYLE] [-o OUTPUT] [--output_dir OUTPUT_DIR]

                         [--content_dir CONTENT_DIR] [--style_dir STYLE_DIR]

                         [--style_weight STYLE_WEIGHT]

                         [--content_weight CONTENT_WEIGHT]

                         [--image_size IMAGE_SIZE]

optional arguments:

  -h, --help            show this help message and exit

  -d {cpu,cuda}, --device {cpu,cuda}

                        Device to run style transfer on.

  -i ITERATIONS, --iterations ITERATIONS

                        Number of style transfer iterations

  -c CONTENT, --content CONTENT

                        Content image

  -s STYLE, --style STYLE

                        Style image

  -o OUTPUT, --output OUTPUT

                        Output image

  --output_dir OUTPUT_DIR, --od OUTPUT_DIR

                        Where your output images are stored

  --content_dir CONTENT_DIR, --cd CONTENT_DIR

                        Where your content images are stored

  --style_dir STYLE_DIR, --sd STYLE_DIR

                        Where your style images are stored

  --style_weight STYLE_WEIGHT, --sw STYLE_WEIGHT

                        Weight of style image

  --content_weight CONTENT_WEIGHT, --cw CONTENT_WEIGHT

                        Weight of content image

  --image_size IMAGE_SIZE, --sz IMAGE_SIZE

                        Weight of content image

```

## Overview

![VGG19](./vgg19.png)

We extract `conv1_1, conv2_1, conv3_1, conv4_1, conv5_1` in VGG19 as the style layers, and `conv4_2` in VGG19 as the content layer. Our goal is to minimize the style and content losses of the generated image.

## Results

**Original image**

![original image](./images/contents/city.png)

***

**Van gough style**

![city + van gough](./images/samples/city+van_gough.png)

***

**Kanagawa style**

![city + kanagawa](./images/samples/city_kanagawa.png)

***

**Picasso style**

![city + picasso](./images/samples/city+picasso.png)

***

**Mondrian style**

![city + mondrian](./images/samples/city_mondrian.png)