English(EN) Variational Optimal Control for Diffusion Guidance

新的变分控制框架统一了引导和模型适应

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-27 12:42

研究人员引入了一个变分控制框架，该框架统一了采样时间引导和基于奖励的模型适应。该框架基于共享的雅可比回拉，使用吉布斯变分原理和杜布变换来校正生成动力学。由此产生的控制，即变分最优引导（VOG）和蒸馏变分最优控制（DVOC），在ImageNet等基准测试中显示出改进，降低了FID分数并提高了评估器分数。 AI

影响通过统一的引导和适应技术，引入了一种提高生成模型性能的新颖方法。

排序理由该集群包含一篇详细介绍生成模型新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 Hugging Face Daily Papers 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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Hugging Face Daily Papers TIER_1 English(EN) · 2026-10-27 12:42

变分最优控制用于扩散引导

We present a variational control framework that connects sampling-time guidance and reward-based model adaptation through a shared Jacobian pullback. Starting from terminal reward maximization with a path-space KL penalty, the Gibbs variational principle and Doob transform yield …

本文如何被排名

报道来源 [1]

变分最优控制用于扩散引导

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