| title | Spectral Prior for Reducing Exposure Bias in Diffusion Models |
|---|---|
| authors | Yuya Kobayashi, Masato Ishii, Yuhta Takida, Takashi Shibuya, Yuki Mitsufuji |
| date | 2026-07-24 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2607.22091 |
| affiliation | Sony |
| upvotes | 5 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | 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. |
cite
@misc{kobayashi2026spectral,
title={Spectral Prior for Reducing Exposure Bias in Diffusion Models},
author={Yuya Kobayashi and Masato Ishii and Yuhta Takida and Takashi Shibuya and Yuki Mitsufuji},
year={2026},
eprint={2607.22091},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2607.22091}
}