Evidence record 13771 · automatically gathered

Spectral Prior for Reducing Exposure Bias in Diffusion Models

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

Record details

Published: 23 July 2026
Source: HuggingFace Daily Papers
Category: Research
Topics: Bias & fairness · Safety & alignment
Retrieved: 28 July 2026

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ethics.ai (23 July 2026), “Spectral Prior for Reducing Exposure Bias in Diffusion Models,” evidence record 13771, https://ethics.ai/record/13771 (originally published by HuggingFace Daily Papers).

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