On the Design Fundamentals of Diffusion Models: A Survey
It provides a detailed survey on design fundamentals for researchers in machine learning, but it is incremental as it builds on existing reviews by focusing on finer-grained components.
This survey addresses the gap in existing reviews by providing a comprehensive and coherent review of seminal designable factors within each functional component of diffusion models, offering a finer-grained perspective to benefit future studies in analysis, design, and implementation.
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.