Skip to main content
The FreSca node applies frequency-dependent scaling to the guidance during the sampling process. It separates the guidance signal into low-frequency and high-frequency components using Fourier filtering, then applies different scaling factors to each frequency range before recombining them. This allows for more nuanced control over how guidance affects different aspects of the generated output.

Inputs

ParameterDescriptionData TypeRequiredRange
modelThe model to apply frequency scaling toMODELYes-
scale_lowScaling factor for low-frequency components (default: 1.0)FLOATNo0 - 10
scale_highScaling factor for high-frequency components (default: 1.25)FLOATNo0 - 10
freq_cutoffNumber of frequency indices around center to consider as low-frequency (default: 20)INTNo1 - 10000

Outputs

Output NameDescriptionData Type
modelThe modified model with frequency-dependent scaling applied to its guidance functionMODEL
This documentation was AI-generated. If you find any errors or have suggestions for improvement, please feel free to contribute! Edit on GitHub

Source fingerprint (SHA-256): 2ff3517619d856db68a9091ae7c87af8c56a23420fa2176b089bf9700475a7a9