Diffusion Models

Diffusion models (DM) have become the state of the art for sample quality in generative modelling. They work by sequentially corrupting training data with slowly increasing noise and then learning to reverse the corruption. A look at their mathematical foundations reveals how much they borrow from statistical mechanics. By understanding their foundations and recent implementations, practitioners will add a powerful new tool to their ML portfolio.