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）的常用引导方法通过线性组合不同条件模型来增强对生成样本的控制。然而，该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，我们提出特征引导方法，为无分类器引导提供基于第一性原理的非线性校正。该校正迫使被引导的DDPM遵循扩散过程的福克-普朗克（FP）方程，且无需额外训练，并与现有采样方法兼容。实验表明，特征引导能增强文本提示的语义特征并改善图像生成中的不规则现象，在从模拟磁体相变到潜在空间采样的多种应用中均证明有效。