Neural Style Transfer using PyTorch (GPU version)

Created By: Swastika Kar & Siddharth Sen

Try the web app on this link https://nst-v01.streamlit.app/

Table of Contents

• Introduction
• Features
• Important
• Examples
• Initialization
• Installation
• Contributing
• License

Introduction

This project is based on the Neural-Style algorithm developed by Leon A. Gatys, Alexander S. Ecker and Matthias Bethge. Neural-Style, or Neural-Transfer, allows you to take an image and reproduce it with a new artistic style. The algorithm takes three images, an input image, a content-image, and a style-image, and changes the input to resemble the content of the content-image and the artistic style of the style-image.

We have referred to the paper Gatys_Image_Style_Transfer_CVPR_2016_paper.pdf for understanding the underlying principles.

Features

• Apply the style of famous artworks to your photos.
• Adjustable style strength.
• Supports multiple image formats.
• Easy to use command-line interface.

Important :

1. The notebook demonstrates the PyTorch version utilizing the CUDA GPU available on Google Colab for executing the neural style transfer.
2. The app version runs on PyTorch version , which required patience for up to 10-15 minutes of loading.
3. We have referred to the paper Gatys_Image_Style_Transfer_CVPR_2016_paper.pdf for understanding the underlying principles.

Examples

Here are a few examples of the style transfer in action:

TensorFlow Version

PyTorch Version

Features

• Apply the style of famous artworks to your photos.
• Adjustable style strength.
• Supports multiple image formats.
• Easy to use command-line interface.

Different kinds of initializations

Initialization using Gaussian Noise

Initialization using White Noise

Initialization using Style image

Initialization using Content image

Installation

Prerequisites

• Python 3.6 or higher
• Git
• Virtual environment (optional but recommended)

Steps

1. Clone the repository:

   git clone https://github.com/swas-kar/Neural_Style_Transfer.git
   cd Neural_Style_Transfer

2. Create and activate a virtual environment:

   python3 -m venv venv
   source venv/bin/activate   # On Windows use `venv\Scripts\activate`

3. Install the required dependencies:

   pip install -r requirements.txt

Arguments

• --content: Path to the content image.
• --style: Path to the style image.
• --output: Path to save the output image.
• --iterations: Number of iterations to run (default: 500).
• --style-weight: Weight of the style (default: 1e6).
• --content-weight: Weight of the content (default: 1).

Contributing

We welcome contributions! If you find a bug or want to add a new feature, feel free to open an issue or submit a pull request. Please follow the guidelines below:

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Commit your changes (git commit -am 'Add new feature').
4. Push to the branch (git push origin feature-branch).
5. Create a new pull request.

License

This project is licensed under the MIT License - see the MIT LICENSE file for details.