Noise Schedule Design for Diffusion Models: An Optimal Control Perspective
We develop a principled framework for analyzing and designing noise schedules in diffusion models. We show that one can recast this design problem as an optimal control problem, whose state is the Fisher information of the diffusion process, which evolves according to an ODE, and whose control input is the noise schedule. The objective of the optimal control problem is a functional involving the Fisher information, which is shown to be an upper bound on the Kullback-Leibler sampling error. By solving this optimal control problem, we obtain sufficient conditions on noise schedules under which $\tilde{\mathcal{O}}(d/n)$ sampling error is achievable, where $d$ is the data dimension and $n$ i...
| Authors | Seo Taek Kong, Weina Wang, R. Srikant |
|---|---|
| Primary category | cs.LG |
| Published | 21 May 2026 |
| Last updated at source | 10 October 2026 |
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