  Classifier-Free Guidance (CFG) enhances the quality and condition adherence
of text-to-image diffusion models. It operates by combining the conditional and
unconditional predictions using a fixed weight. However, recent works vary the
weights throughout the diffusion process, reporting superior results but
without providing any rationale or analysis. By conducting comprehensive
experiments, this paper provides insights into CFG weight schedulers. Our
findings suggest that simple, monotonically increasing weight schedulers
consistently lead to improved performances, requiring merely a single line of
code. In addition, more complex parametrized schedulers can be optimized for
further improvement, but do not generalize across different models and tasks.
