Efficient Diffusion Models: A Survey

• URL: http://arxiv.org/abs/2502.06805v1
• Date: Mon, 03 Feb 2025 10:15:08 GMT
• Title: Efficient Diffusion Models: A Survey
• Authors: Hui Shen, Jingxuan Zhang, Boning Xiong, Rui Hu, Shoufa Chen, Zhongwei Wan, Xin Wang, Yu Zhang, Zixuan Gong, Guangyin Bao, Chaofan Tao, Yongfeng Huang, Ye Yuan, Mi Zhang,
• Abstract summary: Diffusion models have emerged as powerful generative models capable of producing high-quality contents. These capabilities come at the cost of their significant computational resources and lengthy generation time. We provide a systematic and comprehensive review of research on efficient diffusion models.
• Score: 33.6141754415072
• License:
• Abstract: 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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