Guidance in the Frequency Domain Enables High-Fidelity Sampling at Low
  CFG Scales
Authors: name: Seyedmorteza Sadat, name: Tobias Vontobel, name: Farnood Salehi, name: Romann M. Weber

Classifier-free guidance (CFG) has become an essential component of modern
conditional diffusion models. Although highly effective in practice, the
underlying mechanisms by which CFG enhances quality, detail, and prompt
alignment are not fully understood. We present a novel perspective on CFG by
analyzing its effects in the frequency domain, showing that low and high
frequencies have distinct impacts on generation quality. Specifically,
low-frequency guidance governs global structure and condition alignment, while
high-frequency guidance mainly enhances visual fidelity. However, applying a
uniform scale across all frequencies -- as is done in standard CFG -- leads to
oversaturation and reduced diversity at high scales and degraded visual quality
at low scales. Based on these insights, we propose frequency-decoupled guidance
(FDG), an effective approach that decomposes CFG into low- and high-frequency
components and applies separate guidance strengths to each component. FDG
improves image quality at low guidance scales and avoids the drawbacks of high
CFG scales by design. Through extensive experiments across multiple datasets
and models, we demonstrate that FDG consistently enhances sample fidelity while
preserving diversity, leading to improved FID and recall compared to CFG,
establishing our method as a plug-and-play alternative to standard
classifier-free guidance.