Covariance-aware sampling for Diffusion Models

• URL: http://arxiv.org/abs/2605.13910v1
• Date: Wed, 13 May 2026 07:46:06 GMT
• Title: Covariance-aware sampling for Diffusion Models
• Abstract summary: We present a co-aware sampler that improves the quality of pixelspace Diffusion Model (DM) sampling in the few-step regime.<n>We demonstrate that for pixel-based DMs, our method consistently produces superior samples compared to state-of-the-art second order samplers.
• Score: 18.719332889440675
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a covariance-aware sampler that improves the quality of pixel-space Diffusion Model (DM) sampling in the few-step regime. We hypothesize that in the few-step regime samplers fail because they rely solely on the predicted mean of the reverse distribution, while our solution explicitly models the reverse-process covariance. Our method combines Tweedie's formula to estimate the covariance with an efficient, structured Fourier-space decomposition of the covariance matrix. Implemented as an extension of DDIM, our method requires only a minimal overhead: one extra Jacobian-Vector Product (JVP) per step. We demonstrate that for pixel-based DMs, our method consistently produces superior samples compared to state-of-the-art second order samplers (Heun, DPM-Solver++) and the recent aDDIM sampler, at an identical number of function evaluations (NFE).

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