Stable Diffusion in PyTorch

This repository contains a PyTorch implementation of Stable Diffusion, a state-of-the-art text-to-image generation model. This implementation aims to provide a clear, educational, and efficient version of the Stable Diffusion architecture.

🌟 Features

• Pure PyTorch implementation of Stable Diffusion
• Clean and well-documented code
• Support for text-to-image generation
• Efficient implementation with modern PyTorch features
• Educational comments and explanations throughout the codebase

🚀 Getting Started

Prerequisites

• Python 3.8 or higher
• PyTorch 2.0 or higher
• CUDA-capable GPU (recommended)

Installation

1. Clone the repository:

git clone https://github.com/yourusername/stable_diffusion_in_pytorch.git
cd stable_diffusion_in_pytorch

2. Create and activate a virtual environment:

python -m venv .venv
# On Windows
.venv\Scripts\activate
# On Unix or MacOS
source .venv/bin/activate

3. Install the required dependencies:

pip install -e .

📖 Usage

To generate images using the model:

from stable_diffusion import StableDiffusion

model = StableDiffusion()
image = model.generate(
    prompt="A beautiful sunset over mountains, digital art",
    num_inference_steps=50,
    guidance_scale=7.5
)
image.save("output.png")

🏗️ Project Structure

stable_diffusion_in_pytorch/
├── stable_diffusion/         # Main implementation directory
│   ├── models/              # Neural network architectures
│   ├── pipelines/           # Generation pipelines
│   └── utils/              # Utility functions
├── main.py                  # Example usage script
├── pyproject.toml           # Project configuration
└── README.md               # This file

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

📝 License

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

🙏 Acknowledgments

• Stability AI for the original Stable Diffusion model
• The PyTorch team for their excellent deep learning framework
• The open-source community for their continuous support and contributions

⚠️ Disclaimer

This is an implementation for educational purposes. Please ensure you comply with the model's license terms and usage restrictions when using it for any purpose.

📧 Contact

For questions and feedback, please open an issue in the GitHub repository.