Improved Convergence Rate for Diffusion Probabilistic Models

• URL: http://arxiv.org/abs/2410.13738v1
• Date: Thu, 17 Oct 2024 16:37:33 GMT
• Title: Improved Convergence Rate for Diffusion Probabilistic Models
• Authors: Gen Li, Yuchen Jiao,
• Abstract summary: Score-based diffusion models have achieved remarkable empirical performance in the field of machine learning and artificial intelligence. Despite a lot of theoretical attempts, there still exists significant gap between theory and practice. We establish an iteration complexity at the order of $d2/3varepsilon-2/3$, which is better than $d5/12varepsilon-1$. Our theory accommodates $varepsilon$-accurate score estimates, and does not require log-concavity on the target distribution.
• Score: 7.237817437521988
• License:
• Abstract: Score-based diffusion models have achieved remarkable empirical performance in the field of machine learning and artificial intelligence for their ability to generate high-quality new data instances from complex distributions. Improving our understanding of diffusion models, including mainly convergence analysis for such models, has attracted a lot of interests. Despite a lot of theoretical attempts, there still exists significant gap between theory and practice. Towards to close this gap, we establish an iteration complexity at the order of $d^{1/3}\varepsilon^{-2/3}$, which is better than $d^{5/12}\varepsilon^{-1}$, the best known complexity achieved before our work. This convergence analysis is based on a randomized midpoint method, which is first proposed for log-concave sampling (Shen and Lee, 2019), and then extended to diffusion models by Gupta et al. (2024). Our theory accommodates $\varepsilon$-accurate score estimates, and does not require log-concavity on the target distribution. Moreover, the algorithm can also be parallelized to run in only $O(\log^2(d/\varepsilon))$ parallel rounds in a similar way to prior works.

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