Pytorch_WCT

Unofficial Pytorch(1.0+) implementation of nips paper Universal Style Transfer via Feature Transforms.

Original torch implementation from the author can be found here.

Other implementations such as Pytorch_implementation1 , Pytorch_implementation2 or Pytorch_implementation3 are also available.

This repository provides a pre-trained model for you to generate your own image given content image and style image.

If you have any question, please feel free to contact me. (Language in English/Japanese/Chinese will be ok!)

Notice

I propose a structure-emphasized multimodal style transfer(SEMST), feel free to use it here.

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Requirements

• Python 3.7
• PyTorch 1.0+
• TorchVision
• Pillow

Anaconda environment recommended here!

(optional)

• GPU environment

Usage

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test

1. Clone this repository

   git clone https://github.com/irasin/Pytorch_WCT
   cd Pytorch_WCT

2. Prepare your content image and style image. I provide some in the content and style and you can try to use them easily.

3. Download the pretrained model here and put them under the directory named model_state

4. Generate the output image. A transferred output image and a content_output_pair image and a NST_demo_like image will be generated.

   python test.py -c content_image_path -s style_image_path

   usage: test.py [-h] 
                  [--content CONTENT] 
                  [--style STYLE]
                  [--output_name OUTPUT_NAME] 
                  [--alpha ALPHA] 
                  [--gpu GPU]
                  [--model_state_path MODEL_STATE_PATH]
   
   
   

   If output_name is not given, it will use the combination of content image name and style image name.

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Result

Some results of content image and my cat (called Sora) will be shown here.