Awesome-Diffusion-Models

Why we included this project

This index collects the best starting points for anyone moving from curiosity about diffusion models to actually building with them. It leads with introductory posts, papers, videos, lectures, and Jupyter notebooks, then branches into a deep bibliography organized by domain: vision, audio, natural language, tabular and time series, graphs, and reinforcement learning. That layout lets you pick the right entry material for your background and follow the research thread that matches your problem, whether you care about image generation, text-to-speech, or molecular design. Since it is a hand-maintained list rather than software, its worth is in the curation and the links it points to, not in running anything itself. Teams scoping a diffusion project can use it to survey the field quickly and decide which papers and tutorials deserve a close read before committing to an implementation.