This repository serves as a beginner-friendly compilation of resources and PyTorch code implementations for diffusion models and score matching models. It aims to demystify the theory and practical application of these generative models for newcomers to the field.

How It Works

The project curates foundational papers, including the origin of diffusion models and key advancements like DDPM and improved DDPM. It links to PyTorch implementations and provides annotated Jupyter notebooks that bridge theoretical concepts with code, facilitating a deeper understanding of how diffusion models work.

Quick Start & Requirements

• Install: Clone the repository and install dependencies via pip install -r requirements.txt.
• Prerequisites: Python 3.x, PyTorch.
• Resources: Annotated code examples are provided in .ipynb files.
• Links:
  • Awesome Diffusion Models: https://github.com/heejkoo/Awesome-Diffusion-Models
  • Denoising Diffusion PyTorch: https://github.com/lucidrains/denoising-diffusion-pytorch
  • Improved Diffusion PyTorch: https://github.com/openai/improved-diffusion
  • Annotated Deep Learning Paper Implementations: https://github.com/labmlai/annotated_deep_learning_paper_implementations

Highlighted Details

• Curated list of classic and recent diffusion model papers.
• Annotated PyTorch code implementations for key papers.
• Links to relevant blogs and video tutorials for theoretical understanding.
• Includes resources on related topics like VAEs, attention mechanisms, and variational inference.

Maintenance & Community

The project is maintained by yangqy1110. It is a personal compilation, and community engagement signals are not explicitly detailed.

Licensing & Compatibility

The repository states "有侵权的资料联系我删除哈" (Contact me to delete infringing material), suggesting a cautious approach to licensing. Specific licenses for the compiled resources or code are not clearly stated, which may require further investigation for commercial use.

Limitations & Caveats

The repository is a compilation of external resources and personal annotations, not a standalone, runnable diffusion model library. The licensing status of the included materials is unclear, potentially posing risks for commercial applications.