Unet-Stable-Diffusion

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<!-- PROJECT SHIELDS --> <!-- PROJECT LOGO --> <br /> <div align="center"> <a href="https://github.com/Yavuzhan-Baykara/Stable-Diffusion"> </a> <h3 align="center">Diffusion Model Sampler</h3> <p align="center"> An implementation of a diffusion model sampler using a UNet transformer to generate handwritten digit samples. <br /> <a href="https://github.com/Yavuzhan-Baykara/Stable-Diffusion"><strong>Explore the docs »</strong></a> <br /> <br /> <a href="https://github.com/Yavuzhan-Baykara/Stable-Diffusion">View Demo</a> · <a href="https://github.com/Yavuzhan-Baykara/Stable-Diffusion/issues/new?labels=bug&template=bug-report---.md">Report Bug</a> · <a href="https://github.com/Yavuzhan-Baykara/Stable-Diffusion/issues/new?labels=enhancement&template=feature-request---.md">Request Feature</a> </p> </div> <!-- TABLE OF CONTENTS --> <details> <summary>Table of Contents</summary> <ol> <li> <a href="#about-the-project">About The Project</a> <ul> <li><a href="#built-with">Built With</a></li> </ul> </li> <li> <a href="#getting-started">Getting Started</a> <ul> <li><a href="#prerequisites">Prerequisites</a></li> <li><a href="#installation">Installation</a></li> </ul> </li> <li><a href="#usage">Usage</a></li> <li><a href="#results">Results</a></li> <li><a href="#roadmap">Roadmap</a></li> <li><a href="#contributing">Contributing</a></li> <li><a href="#license">License</a></li> <li><a href="#contact">Contact</a></li> <li><a href="#acknowledgments">Acknowledgments</a></li> </ol> </details> <!-- ABOUT THE PROJECT -->

About The Project

Diffusion models have shown great promise in generating high-quality samples in various domains. In this project, we utilize a UNet transformer-based diffusion model to generate samples of handwritten digits. The process involves:

1. Setting up the model and loading pre-trained weights.
2. Generating samples for each digit.
3. Creating a GIF to visualize the generated samples.

<div align="center"> <img src="./digit_samples.gif" alt="MNIST GIF" width="200" height="200" style="display:inline-block;"> <img src="./digit_samples_cifar.gif" alt="CIFAR-10 GIF" width="200" height="200" style="display:inline-block;"> </div> <p align="right">(<a href="#readme-top">back to top</a>)</p>

Built With

AI and Machine Learning Libraries

<div align="center"> <img src="https://icon.icepanel.io/Technology/svg/TensorFlow.svg" alt="Python" width="40" height="40" style="display:inline-block;"> <img src="https://icon.icepanel.io/Technology/svg/PyTorch.svg" alt="PyTorch" width="40" height="40" style="display:inline-block;"> <img src="https://icon.icepanel.io/Technology/svg/NumPy.svg" alt="NumPy" width="40" height="40" style="display:inline-block;"> <img src="https://icon.icepanel.io/Technology/svg/Matplotlib.svg" alt="Matplotlib" width="40" height="40" style="display:inline-block;"> <img src="https://img.shields.io/badge/Pillow-5A9?style=for-the-badge&logo=pillow&logoColor=white" alt="Pillow" width="40" height="40" style="display:inline-block;"> </div> <p align="right">(<a href="#readme-top">back to top</a>)</p> <!-- GETTING STARTED -->

Getting Started

To get a local copy up and running follow these simple example steps.

Prerequisites

Ensure you have the following prerequisites installed:

• Python 3.8 or higher
• CUDA-enabled GPU (optional but recommended)
• The following Python libraries:
  • torch
  • torchvision
  • numpy
  • Pillow
  • matplotlib

Installation

1. Clone the repository:

   git clone https://github.com/Yavuzhan-Baykara/Stable-Diffusion.git
   cd Stable-Diffusion
   

2. Install the required Python libraries:

   pip install torch torchvision numpy Pillow matplotlib
   

<p align="right">(<a href="#readme-top">back to top</a>)</p> <!-- USAGE -->

Usage

To train the UNet transformer with different datasets and samplers, use the following command:

python train.py <dataset> <sampler> <epoch> <batch_size>