This repository is a curated collection of resources, papers, and tutorials on Diffusion Models, serving as a comprehensive knowledge base for researchers, engineers, and practitioners in the field of generative AI. It aims to consolidate the rapidly evolving landscape of diffusion models, providing a structured overview of their theoretical foundations, diverse applications, and recent advancements.

How It Works

The repository categorizes resources into introductory materials, survey papers, and specific application areas such as vision generation, natural language processing, and graph generation. It links to a wide array of academic papers, blog posts, video lectures, and code repositories, offering a broad yet organized entry point into diffusion model research.

Highlighted Details

• Extensive categorization of diffusion model research across various domains.
• Links to introductory materials, survey papers, and specific application research.
• Covers a wide range of modalities including vision, audio, text, and graphs.
• Includes links to tutorials, Jupyter notebooks, and code repositories for practical learning.

Maintenance & Community

This is a community-driven project, with contributions from various researchers and practitioners in the AI field. The README does not specify active community channels or a roadmap.

Licensing & Compatibility

The repository itself is a collection of links and does not host code or data that would require a specific license. Compatibility is dependent on the licenses of the linked resources.

Limitations & Caveats

The repository is a static collection of links and does not provide active development or support. The sheer volume of resources may be overwhelming for newcomers, and the organization, while broad, might benefit from more granular sub-categorization.