Neural Style Transfer: Create AI-Generated Art 🎨🖌️

Transform your photos into stunning digital art using the power of Neural Style Transfer!

🌟 Overview

Neural Style Transfer (NST) is an innovative AI technique that combines the content of one image with the artistic style of another. This project implements a state-of-the-art NST model using TensorFlow and Keras, allowing you to create unique artworks from your photos.

🎯 Objectives

• Explore and implement Neural Style Transfer techniques
• Develop a user-friendly interface for easy art creation
• Provide a platform for creating potential NFT artworks

🧠 How It Works

NST leverages the power of Convolutional Neural Networks (CNNs) to separate and recombine the content and style of different images. The process involves:

1. Using a pre-trained feature extractor (typically a VGG network)
2. Implementing a transfer network with an encoder-decoder architecture
3. Optimizing the output image to minimize both content and style losses

Our implementation uses a pre-trained "Arbitrary Neural Artistic Stylization Network," capable of applying various artistic styles to any input image in a single, efficient pass.

🚀 Getting Started

Prerequisites

• Python 3.7+
• TensorFlow 2.x
• Keras
• Matplotlib

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/neural-style-transfer.git
   cd neural-style-transfer
   

2. Create and activate a virtual environment:

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

3. Install the required dependencies:

   pip install -r requirements.txt
   

4. Download the pre-trained model from this link and place it in the model directory.

Usage

You can use the Neural Style Transfer model in two ways:

1. Through the Python API:

import matplotlib.pyplot as plt
from API import transfer_style

model_path = "path/to/model/directory"
content_image_path = "path/to/your/content/image.jpg"
style_image_path = "path/to/your/style/image.jpg"

stylized_image = transfer_style(content_image_path, style_image_path, model_path)
plt.imsave('stylized_image.jpg', stylized_image)
plt.imshow(stylized_image)
plt.show()

2. Through the provided application:

python app.py

📚 Learn More

To dive deeper into Neural Style Transfer, check out these resources:

• A Neural Algorithm of Artistic Style (original paper)
• Neural Style Transfer with Keras
• Exploring the structure of a real-time, arbitrary neural artistic stylization network

🎨 Custom Models

The project includes two custom Neural Style Transfer (NST) models, located in the notebooks directory:

1. Full NST Model (VGG19): This model implements the original Neural Style Transfer algorithm. It uses VGG19 to the style from a given style image and applies it to a content image. This process can be computationally intensive but offers high flexibility in style application.

2. Pretrained Model: This is a fast Neural Style Transfer model that uses a pretrained network. It can apply style transfer much more quickly than the Pull NST model, making it suitable for real-time applications or processing large numbers of images.

🖼️ Gallery

Here are some examples of our Neural Style Transfer in action:

Original Image

Generated Artworks

Original Image

Generated Artworks

MISC 🎨

Each of these images demonstrates a unique style transfer, showcasing the versatility of our Neural Style Transfer model.

Acknowledgements

• TensorFlow Hub for the pre-trained model
• The authors of the original NST papers and implementations
• Aleksa Gordić Pytorch Implementation

---

Happy styling! Create, innovate, and share your AI-generated masterpieces with the world! 🎨🚀