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

Stylize Images Using Pre-trained MSG-Net

0. Download the pre-trained model

   git clone [email protected]:zhanghang1989/PyTorch-Style-Transfer.git
   cd PyTorch-Style-Transfer/experiments
   bash models/download_model.sh
   

1. Camera Demo

   python camera_demo.py demo --model models/21styles.model
   

   
2. 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

0. Download the COCO dataset

   bash dataset/download_dataset.sh
   

1. 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.