揭示扩散模型引导机制在一般数据分布下的理论有效性
Provable Efficiency of Guidance in Diffusion Models for General Data Distribution
- 从一般数据分布出发，分析引导机制的理论作用
- 证明引导可降低平均分类器概率倒数，提升整体生成质量
- 突破以往仅限于简单分布的分析框架，适合理论研究者
扩散模型已成为强大的生成建模框架，引导技术在提升样本质量方面起关键作用。尽管其在实践中表现优异，但对引导效应的系统性理论理解仍不充分。现有研究仅限于特定情形，如每类数据服从各向同性高斯分布或定义在一条一维区间上且满足额外条件。本文尝试在更一般的数据分布下分析扩散模型的引导效果。不同于以往宣称统一提升样本质量的观点（该结论在某些分布中不成立），我们证明：引入引导后，平均分类器概率的倒数会下降，这与引导设计初衷一致，表明引导能有效改善整体样本质量。
原文摘要 · Abstract (English)
Diffusion models have emerged as a powerful framework for generative modeling, with guidance techniques playing a crucial role in enhancing sample quality. Despite their empirical success, a comprehensive theoretical understanding of the guidance effect remains limited. Existing studies only focus on case studies, where the distribution conditioned on each class is either isotropic Gaussian or supported on a one-dimensional interval with some extra conditions. How to analyze the guidance effect beyond these case studies remains an open question. Towards closing this gap, we make an attempt to analyze diffusion guidance under general data distributions. Rather than demonstrating uniform sample quality improvement, which does not hold in some distributions, we prove that guidance can improve the whole sample quality, in the sense that the average reciprocal of the classifier probability decreases with the existence of guidance. This aligns with the motivation of introducing guidance.
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