Optimize Your Sampling: Tuned Diffusion Sampling with Bayesian Optimization
Score
77提出了直接优化目标指标的黑盒采样调度新思路，区别于以往代理优化，增量显著。
实验设计较为全面，涵盖多任务、多采样器及人工评估，论证充分。
无需训练、即插即用且推理成本降低10倍，对实际部署极具价值。
扩散模型高效采样是当前热点，论文提出的调度优化方法具有时效性。
来自arXiv预印本，作者团队背景可信但尚未经同行评审。
Base 85 × T2 Source weight ×0.9 = 77
Curation line 70Selected
Why selected
The paper proposes a novel and general sampling schedule optimization method with strong results across tasks and samplers, showing high practical value.
AI commentary
「This paper is worth attention because it breaks away from the usual solver-improvement approach and directly black-box optimizes sampling timesteps, a simple yet surprisingly effective idea. For engineers and researchers working on diffusion inference acceleration, OYS can be plugged into existing pipelines almost for free, especially in low-latency deployment scenarios.」
Quick overview
Sampling from a diffusion model typically requires many forward passes through a large neural network, making generation computationally expensive. While much work has focused on efficient solvers and samplers, comparatively little attention has been paid to selecting the sampling timesteps themselves. A recent line of work optimizes theoretically derived surrogates for sample quality rather than the quality metric itself. This paper proposes Optimizing Your Sampling (OYS), which treats timestep selection as a black-box optimization problem and optimizes the target metric directly with Bayesian optimization. OYS outperforms both default schedules and those from Align Your Steps on text-to-image generation, and improves over default schedules on inpainting and other image tasks, in both quantitative and human evaluations. OYS requires no additional training, is applicable even to distilled models, and improves both simple and sophisticated samplers such as Euler and DPM-Solver++. A 5-step OYS schedule retains 89%-94% of the quality of a 50-step schedule while reducing inference cost by 10x.
- OYS treats timestep selection in diffusion models as a black-box optimization problem, directly optimizing the target metric without extra training.
- OYS-generated sampling schedules outperform default schedules and Align Your Steps on text-to-image, inpainting, and other image tasks.
- A 5-step OYS schedule retains 89%-94% of the quality of a 50-step schedule while reducing inference cost by 10x.
- OYS is applicable to distilled models and improves both simple and sophisticated samplers such as Euler and DPM-Solver++.
Full text
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