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arXiv Machine Learning By Yuan Cao, Yifu Tang, Hangqi Li, Zeyu Zheng

Leveraging Inference-Time Compute for Diffusion Models via Global Scheduling of Denoising Trajectories

Read the original on arXiv Machine Learning →

The paper studies how to allocate a fixed computational budget across the denoising steps of diffusion models to improve sample quality at deployment. It shows that the expected benefit of evaluating multiple candidates at a step can be decomposed into a step‑specific sensitivity and a universal sample‑size factor, and that the optimal allocation follows a water‑filling structure. Experiments demonstrate that this allocation achieves the same quality as a uniform strategy while reducing function evaluations by 20–50%.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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