Spectral Prior for Reducing Exposure Bias in Diffusion Models

Hugging Face Daily Papers Papers
diffusion-models exposure-bias spectral-prior guidance image-generation

Summary

This paper proposes Spectral Alignment (SPA), a lightweight guidance-based method that reduces exposure bias in diffusion models by calibrating the power spectrum of intermediate predictions, showing consistent improvements across pixel-space, latent, and flow-matching models with minimal computational overhead.

Diffusion models typically suffer from error accumulation during iterative sampling, commonly referred to as exposure bias. We reveal systematic frequency-dependent discrepancies between training and inference, which can be interpreted as frequency-dependent SNR error. Crucially, the direction of this mismatch varies across models and timesteps, indicating that fixed correction rules do not generalize. We propose Spectral Alignment (SPA), a lightweight, guidance-based method that calibrates the power spectrum of intermediate predictions to a pre-computed prior. Our approach consists of two stages: (1) offline fitting of a parametric spectrum model from training data, and (2) inference-time guidance via efficient FFT-based gradient computation. SPA introduces minimal computational overhead (3-4\%) and is complementary to Classifier-Free Guidance (CFG). We demonstrate consistent improvements across diverse architectures, from pixel-space models (DDPM, ADM) to latent diffusion models (SD2.0, SDXL) and flow-matching models (SD3.5, FLUX). Our implementation is available at https://github.com/SonyResearch/SPA.
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Cached at: 07/27/26, 09:43 AM

Paper page - Spectral Prior for Reducing Exposure Bias in Diffusion Models

Source: https://huggingface.co/papers/2607.22091

Abstract

Diffusionmodelstypicallysufferfromerroraccumulationduringiterativesampling,commonlyreferredtoasexposurebias.Werevealsystematicfrequency-dependentdiscrepanciesbetweentrainingandinference,whichcanbeinterpretedasfrequency-dependentSNRerror.Crucially,thedirectionofthismismatchvariesacrossmodelsandtimesteps,indicatingthatfixedcorrectionrulesdonotgeneralize.WeproposeSpectralAlignment(SPA),alightweight,guidance-basedmethodthatcalibratesthepowerspectrumofintermediatepredictionstoapre-computedprior.Ourapproachconsistsoftwostages:(1)offlinefittingofaparametricspectrummodelfromtrainingdata,and(2)inference-timeguidanceviaefficientFFT-basedgradientcomputation.SPAintroducesminimalcomputationaloverhead(3-4\%)andiscomplementarytoClassifier-FreeGuidance(CFG).Wedemonstrateconsistentimprovementsacrossdiversearchitectures,frompixel-spacemodels(DDPM,ADM)tolatentdiffusionmodels(SD2.0,SDXL)andflow-matchingmodels(SD3.5,FLUX).Ourimplementationisavailableathttps://github.com/SonyResearch/SPA.

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