Classifier-free guidance has become a staple for conditional generation with denoising diffusion models. However, a comprehensive understanding of classifier-free guidance is still missing. In this work, we carry out an empirical study to provide a fresh perspective on classifier-free guidance. Concretely, instead of solely focusing on classifier-free guidance, we trace back to the root, i.e., classifier guidance, pinpoint the key assumption for the derivation, and conduct a systematic study to understand the role of the classifier. We find that both classifier guidance and classifier-free guidance achieve conditional generation by pushing the denoising diffusion trajectories away from decision boundaries, i.e., areas where conditional information is usually entangled and is hard to learn. Based on this classifier-centric understanding, we propose a generic postprocessing step built upon flow-matching to shrink the gap between the learned distribution for a pre-trained denoising diffusion model and the real data distribution, majorly around the decision boundaries. Experiments on various datasets verify the effectiveness of the proposed approach.

本研究解决了对无分类器引导理解不足的问题；通过回溯到分类器引导，明确了关键假设并系统研究分类器的作用。研究发现，无论是分类器引导还是无分类器引导，都通过将去噪扩散轨迹推离决策边界来实现条件生成，提出的基于流匹配的后处理步骤有效缩小了预训练的去噪扩散模型学习到的分布与真实数据分布之间的差距。

从分类器视角研究无分类器引导