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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