提出基于随机最优控制的自适应扩散引导方法，提升生成质量与条件一致性。
Adaptive Diffusion Guidance via Stochastic Optimal Control
- 将引导调度建模为动态优化问题，根据时间、样本和类别自适应调整强度。
- 理论揭示引导强度与分类器置信度的精确关系，突破传统启发式设计。
- 适用于需要高质量生成的场景，如图像编辑与条件生成任务。
引导是现代扩散模型的核心，对条件生成和无条件样本质量提升至关重要。然而，现有引导调度方法（确定引导权重）多依赖启发式策略，缺乏坚实的理论基础。本文从两方面解决该问题：首先，提供了引导强度与分类器置信度之间精确关系的理论形式化；其次，基于此洞察，提出一种随机最优控制框架，将引导调度视为自适应优化问题。在此框架中，引导强度不固定，而是根据时间、当前样本及条件类别独立或联合动态选择。通过求解相应控制问题，为扩散模型中的更有效引导建立了原则性基础。
原文摘要 · Abstract (English)
Guidance is a cornerstone of modern diffusion models, playing a pivotal role in conditional generation and enhancing the quality of unconditional samples. However, current approaches to guidance scheduling--determining the appropriate guidance weight--are largely heuristic and lack a solid theoretical foundation. This work addresses these limitations on two fronts. First, we provide a theoretical formalization that precisely characterizes the relationship between guidance strength and classifier confidence. Second, building on this insight, we introduce a stochastic optimal control framework that casts guidance scheduling as an adaptive optimization problem. In this formulation, guidance strength is not fixed but dynamically selected based on time, the current sample, and the conditioning class, either independently or in combination. By solving the resulting control problem, we establish a principled foundation for more effective guidance in diffusion models.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。