系统梳理扩散模型高效化研究，助力快速生成高质量内容
Efficient Diffusion Models: A Survey
- 按算法、系统、框架三维度分类整理高效扩散模型方法
- 涵盖降低计算开销与生成时延的核心技术进展
- 适合关注生成效率的科研与工程人员参考
扩散模型已成为生成高质量图像、视频和音频等数字内容的强大工具，展现出变革内容创作的潜力。然而，其高性能伴随着巨大的计算资源消耗和漫长的生成时间，迫切需要高效的实现技术以支持实际部署。本文系统综述了高效扩散模型的研究进展，构建了一个包含算法级、系统级和框架级三个维度的分类体系，全面覆盖相关核心技术。我们还维护一个 GitHub 资源库（https://github.com/AIoT-MLSys-Lab/Efficient-Diffusion-Model-Survey），汇集本调查中涉及的论文，旨在为研究人员和从业者提供系统性理解，推动该重要且充满活力领域的持续发展。
原文摘要 · Abstract (English)
Diffusion models have emerged as powerful generative models capable of producing high-quality contents such as images, videos, and audio, demonstrating their potential to revolutionize digital content creation. However, these capabilities come at the cost of their significant computational resources and lengthy generation time, underscoring the critical need to develop efficient techniques for practical deployment. In this survey, we provide a systematic and comprehensive review of research on efficient diffusion models. We organize the literature in a taxonomy consisting of three main categories, covering distinct yet interconnected efficient diffusion model topics from algorithm-level, system-level, and framework perspective, respectively. We have also created a GitHub repository where we organize the papers featured in this survey at https://github.com/AIoT-MLSys-Lab/Efficient-Diffusion-Model-Survey. We hope our survey can serve as a valuable resource to help researchers and practitioners gain a systematic understanding of efficient diffusion model research and inspire them to contribute to this important and exciting field.
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