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 sampling method that provides first-principle non-linear correction for classifier-free guided DDPMs. Such correction forces the guided DDPMs to respect the Fokker-Planck equation of their underlying diffusion process, in a way that is training-free, derivative-free, and compatible with existing sampling methods. Experiments show that characteristic guidance enhances control and reduces color and exposure issues in image generation, proving effective in diverse applications ranging from latent space sampling to solving physics problems like magnet phase transitions.

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控制。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。为了解决这一问题，我们提出了特征引导，一种为无分类器引导的DDPM提供基于第一性原理非线性校正的采样方法。这种校正迫使引导的DDPM遵循其底层扩散过程的福克-普朗克方程，且无需训练、无需导数计算，并与现有采样方法兼容。实验表明，特征引导增强了图像生成中的控制能力，减少了颜色和曝光问题，在从潜空间采样到解决磁相变等物理问题的多种应用中均证明了其有效性。