发表机构
California Institute of Technology（加州理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究扩散模型推理时间缩放问题，提出渐进式种子剪枝方法，通过早期评估多种子并剪枝，有效利用固定计算预算，在扩散和流匹配主干上比其他基线方法在奖励引导选择及提示对齐评估上表现更优。
AI 中文摘要
扩散模型和流匹配模型在条件图像生成中占主导地位，但其推理时间缩放远不如自回归语言模型成熟。由于最终质量对初始噪声种子高度敏感，许多方法在种子搜索或重采样上花费额外计算。我们表明，放宽这一约束能实现未充分探索的推理时间缩放轴：通过早期加载探索、评估多个种子并积极剪枝，可更有效地使用固定计算预算。渐进式种子剪枝（PSP）对中间去噪估计进行评分并逐步缩小候选集，在保持模型评估总数固定的情况下，仅对有希望的轨迹进行完全去噪。在扩散和流匹配主干上，PSP始终改进奖励引导选择，在匹配计算时比最佳N、重要性采样和树搜索基线获得更高的GenEval分数（自动化）和更好的人类提示对齐评估。
英文摘要
Diffusion and flow-matching models dominate conditional image generation, yet inference-time scaling for these models is far less developed than for autoregressive language models. Because final quality is highly sensitive to the initial noise seed, many approaches spend extra compute on seed search or resampling under a black-box reward, but typically maintaining a constant memory footprint throughout inference. We show that relaxing this constraint enables an underexplored inference-time scaling axis: by front-loading exploration, evaluating many seeds early, and pruning aggressively, we can use a fixed compute budget more effectively. \emph{Progressive Seed Pruning} (\PSP) scores intermediate denoised estimates and progressively narrows the candidate set so that only promising trajectories are fully denoised, while keeping the total number of model evaluations fixed. Across diffusion and flow-matching backbones, \PSP \ consistently improves reward-guided selection and achieves higher GenEval scores (automated) and better human evaluation on prompt-alignment than best-of-$N$, importance-sampling, and tree-search baselines at matched compute. Project page: https://www.vision.caltech.edu/psp. Code: https://github.com/rogerioagjr/psp.
CommentsProject page: https://www.vision.caltech.edu/psp. Code: https://github.com/rogerioagjr/psp
