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arXiv Machine Learning By Travis Zhang, Christian Belardi, Justin Lovelace, Jin Peng Zhou, Saebyeol Shin, Carla P. Gomes, Kilian Q. Weinberger

Optimize Your Sampling: Tuned Diffusion Sampling with Bayesian Optimization

Read the original on arXiv Machine Learning →

The paper introduces Optimizing Your Sampling (OYS), a method that treats diffusion model timestep selection as a black-box optimization problem and uses Bayesian optimization to directly improve the target metric. OYS outperforms default schedules and the Align Your Steps approach on text-to-image generation, inpainting, and other image tasks, as shown by both quantitative and human evaluations. It works without additional training, applies to distilled models, and enhances both simple and sophisticated samplers, achieving 89–94% of a 50‑step schedule’s quality with only 5 steps and a tenfold reduction in inference cost.

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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