First Hitting Diffusion Models for Generating Manifold, Graph and Categorical Data

Understand

We propose a family of First Hitting Diffusion Models (FHDM), deep generative models that generate data with a diffusion process that terminates at a random first hitting time.

• This yields an extension of the standard fixed-time diffusion models that terminate at a pre-specified deterministic time.
• Although standard diffusion models are designed for continuous unconstrained data, FHDM is naturally designed to learn distributions on continuous as well as a range of discrete and structure domains.
• Moreover, FHDM enables instance-dependent terminate time and accelerates the diffusion process to sample higher quality data with fewer diffusion steps.