On the Design Fundamentals of Diffusion Models: A Survey

Chang, Ziyi; Koulieris, George Alex; Chang, Hyung Jin; Shum, Hubert P. H.

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.

Citation

Chang, Z., Koulieris, G. A., Chang, H. J., & Shum, H. P. H. (2026). On the Design Fundamentals of Diffusion Models: A Survey. Pattern Recognition, 169, Article 111934. https://doi.org/10.1016/j.patcog.2025.111934

• Journal Article Type: Article
• Acceptance Date: May 29, 2025
• Online Publication Date: Jun 14, 2025
• Publication Date: 2026-01
• Deposit Date: May 30, 2025
• Publicly Available Date: Jul 1, 2025
• Journal: Pattern Recognition
• Print ISSN: 0031-3203
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 169
• Article Number: 111934
• DOI: https://doi.org/10.1016/j.patcog.2025.111934
• Public URL: https://durham-repository.worktribe.com/output/3967465