Classifier-Free Guidance is a Predictor-Corrector

We investigate the theoretical foundations of classifier-free guidance (CFG). CFG is the dominant method of conditional sampling for text-to-image diffusion models, yet unlike other aspects of diffusion, it remains on shaky theoretical footing. In this paper, we disprove common misconceptions, by showing that CFG interacts differently with DDPM (Ho et al., 2020) and DDIM (Song et al., 2021), and neither sampler with CFG generates the gamma-powered distribution p(x|c)^γp(x)^{1−γ}. Then, we clarify the behavior of CFG by showing that it is a kind of predictor-corrector method (Song et al., 2020)…