  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.
