Diffusion Model From Scratch

This Jupyter notebook explores the implementation of a diffusion model entirely from scratch. The notebook is designed for educational purposes, to help understand the intricacies and the mathematical foundation behind diffusion models, commonly used in generative modeling.

Contents

1. Introduction: Brief overview of diffusion models and their applications.
2. Mathematical Background: Detailed discussion on the theory and mathematics involved in diffusion models.
3. Implementation Steps: Step-by-step implementation of the diffusion model, including data preparation, model architecture, and training routines.
4. Results and Analysis: Visualization of the results and discussion of the model's performance.
5. Conclusion: Summary and conclusions on the implementation and its potential applications.

Prerequisites

• Python 3.8 or above
• Jupyter Notebook or JupyterLab
• Libraries: NumPy, Matplotlib, PyTorch (installation instructions included)

Setup and Running Instructions

1. Clone the Repository:

   git clone <repository-url>
   cd <repository-directory>

2. Install Required Libraries:

   pip install numpy matplotlib torch

3. Launch the Notebook:

   jupyter notebook diffusion_from_scratch.ipynb

4. Follow the instructions within the notebook to run the various cells.

Additional Notes

This notebook is part of a series on understanding and building generative models. For further reading and more complex models, refer to the links provided in the "Resources" section of the notebook.

Acknowledgments

Thanks to the contributors and the community for insights and discussions that have helped shape this implementation.