Popular guidance for denoising diffusion probabilistic model (DDPM) linearly combines distinct conditional models together to provide enhanced control over samples. However, this approach overlooks nonlinear effects that become significant when guidance scale is large. To address this issue, we propose characteristic guidance, a guidance method that provides first-principle non-linear correction for classifier-free guidance. Such correction forces the guided DDPMs to respect the Fokker-Planck (FP) equation of diffusion process, in a way that is training-free and compatible with existing sampling methods. Experiments show that characteristic guidance enhances semantic characteristics of prompts and mitigate irregularities in image generation, proving effective in diverse applications ranging from simulating magnet phase transitions to latent space sampling.

翻译：去噪扩散概率模型（DDPM）的流行引导方法通过线性组合不同条件模型来增强对样本的可控性。然而，这种方法忽略了在引导尺度较大时变得显著的非线性效应。为解决此问题，我们提出特征引导，这是一种为无分类器引导提供第一性原理非线性校正的引导方法。该校正以无需训练且兼容现有采样方式的形式，强制引导后的DDPM遵循扩散过程的福克-普朗克（FP）方程。实验表明，特征引导能够增强提示的语义特征并抑制图像生成中的不规则性，在从模拟磁相变到潜空间采样的多种应用中均展现出有效性。