On the Design Fundamentals of Diffusion Models: A Survey

2023-06-07

arXiv.org

AI Summary

Existing surveys on diffusion models predominantly focus on high-level architectures, neglecting the design principles underlying fundamental components—namely, the forward process, reverse process, and sampling procedure. To address this gap, we present the first fine-grained, systematic analysis of the key designable elements across these three core components, including noise scheduling, network architectures, loss functions, and sampling strategies. We propose a unified taxonomy that enables abstract consolidation and cross-work comparative analysis. Furthermore, we establish the first comprehensive survey framework explicitly centered on “design fundamentals,” thereby bridging the critical void in low-level principle coverage. This work provides structured theoretical knowledge and practical guidance for component-level analysis, task-driven customization, and efficient implementation of diffusion models. (128 words)

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.

Problem

• Review design fundamentals of diffusion models' components
• Address gap in existing higher-level solution reviews
• Provide finer-grained perspective for future studies

Innovation

• Review forward, reverse, sampling processes
• Analyze design factors in diffusion models
• Provide fine-grained component perspective