Diffusion Models Adapt to Low-Dimensional Structure Under Flexible Coefficient Choices
Diffusion models are known to exploit unknown low-dimensional structure to accelerate sampling. However, existing convergence theory under low-dimensional data structure has largely focused on update rules with narrowly prescribed coefficient choices. This raises a fundamental question: is adaptation to low-dimensional structure sensitive to the precise choice of update coefficients? In this paper, we show that such adaptation is a robust property of diffusion models. For a broad class of update coefficients, we prove that $\widetilde{O}(k/\varepsilon)$ iterations suffice to generate an $\varepsilon$-accurate sample in total variation (TV) distance, independently of the ambient dimension....
| Authors | Changxiao Cai, Yuchen Jiao, Gen Li |
|---|---|
| Primary category | stat.ML |
| Published | 22 June 2026 |
| Last updated at source | 22 June 2026 |
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