Applying Guidance in a Limited Interval Improves Sample and Distribution
  Quality in Diffusion Models
Tuomas Kynkäänniemi Miika Aittala Tero Karras Samuli Laine Timo Aila Jaakko Lehtinen
Guidance is a crucial technique for extracting the best performance out of
image-generating diffusion models. Traditionally, a constant guidance weight
has been applied throughout the sampling chain of an image. We show that
guidance is clearly harmful toward the beginning of the chain (high noise
levels), largely unnecessary toward the end (low noise levels), and only
beneficial in the middle. We thus restrict it to a specific range of noise
levels, improving both the inference speed and result quality. This limited
guidance interval improves the record FID in ImageNet-512 significantly, from
1.81 to 1.40. We show that it is quantitatively and qualitatively beneficial
across different sampler parameters, network architectures, and datasets,
including the large-scale setting of Stable Diffusion XL. We thus suggest
exposing the guidance interval as a hyperparameter in all diffusion models that
use guidance.