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Diffusion Model Sampler

An implementation of a diffusion model sampler using a UNet transformer to generate handwritten digit samples.
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Table of Contents
  1. About The Project
  2. Getting Started
  3. Usage
  4. Results
  5. Roadmap
  6. Contributing
  7. License
  8. Contact
  9. Acknowledgments

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.
MNIST GIF CIFAR-10 GIF

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Built With

AI and Machine Learning Libraries

Python PyTorch NumPy Matplotlib Pillow

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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
    

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Usage

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

python train.py <dataset> <sampler> <epoch> <batch_size>
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Datasets used to train Yavzan/Unet-Stable-Diffusion