On the design fundamentals of diffusion models: A survey
Ziyi Chang, George Alex Koulieris, Hyung Jin Chang, Hubert P. H. Shum
Abstract
Diffusion models are learning pattern-learning systems to model and sample from data distributions with three functional components namely the forward process, the reverse process, and the sampling process. The components of diffusion models have gained significant attention with many design factors being considered in common practice. Existing reviews have primarily focused on higher-level solutions, covering less on the design fundamentals of components. This study seeks to address this gap by providing a comprehensive and coherent review of seminal designable factors within each functional component of diffusion models. This provides a finer-grained perspective of diffusion models, benefiting future studies in the analysis of individual components, the design factors for different purposes, and the implementation of diffusion models. • A literature review of design fundamentals of diffusion models. • Identified the three functional components of diffusion models. • Examined the key factors and their functionalities of the components. • Discussed the popular designs and seminal works for each factor.