A Survey of Diffusion Models: Methods and Applications

Diffusion models have emerged as the state-of-the-art generative paradigm, surpassing GANs in synthesizing high-fidelity images, videos, and audio. However, their reliance on iterative denoising processes imposes substantial computational burdens and memory overheads, creating a significant barrier to their deployment on resource-constrained edge devices. Unlike existing surveys that broadly cover general methodologies, this paper provides a focused review with a specific emphasis on efficient and lightweight diffusion models. We systematically analyze the trade-offs between generation quality and computational cost, categorizing acceleration techniques into sampling optimization, architectural compression, and knowledge distillation. Furthermore, we explore the integration of diffusion models with emerging architectures (e.g., Mamba) and their evolution towards general-purpose world simulators. This survey aims to provide a roadmap for “Green AI,” bridging the gap between high-end academic research and practical, real-world applications.