Pith Number

pith:425FAKAT

pith:2026:425FAKATCEINVWPTAU2YBOM7E4

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Covariance-aware sampling for Diffusion Models

Andrea Schioppa, Tim Salimans

Modeling the full reverse-process covariance improves few-step sampling in pixel-space diffusion models.

arxiv:2605.13910 v1 · 2026-05-13 · stat.ML · cs.CV · cs.LG

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Claims

C1strongest claim

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).

C2weakest assumption

The hypothesis that samplers fail in the few-step regime solely because they rely only on the predicted mean of the reverse distribution, and that explicitly modeling the covariance via Tweedie's formula plus Fourier decomposition will reliably fix it without introducing new instabilities.

C3one line summary

A covariance-aware extension of DDIM sampling for pixel-space diffusion models that uses Tweedie's formula and Fourier decomposition to model reverse-process covariance and improves sample quality at low NFE.

References

22 extracted · 22 resolved · 2 Pith anchors

[1] doi: 10.1109/T-C.1974.223784. A. Blattmann, T. Dockhorn, S. Kulal, D. Mendelevitch, M. Kilian, D. Lorenz, Y. Levi, Z. English, V. Voleti, A. Letts, et al. Stable video diffusion: Scaling latent video 1974 · doi:10.1109/t-c.1974.223784

[2] Tweedie moment projected diffusions for inverse problems 2009

[3] URLhttps://sander.ai/2024/09/02/ spectral-autoregression.html. T. Dockhorn, A. Vahdat, and K. Kreis. Genie: Higher-order denoising diffusion solvers. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, 2024

[4] URL https://proceedings.neurips.cc/paper_files/paper/2022/file/ c281c5a17ad2e55e1ac1ca825071f991-Paper-Conference.pdf. B. Efron. Tweedie’s formula and selection bias.Journal of the American Statistica 2022

[5] Tweedie’s Formula and Selection Bias.Journal of the American Statistical Association, 106(496):1602–1614

Formal links

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Receipt and verification

First computed	2026-05-17T23:39:18.824218Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

e6ba5028131110dad9f3053580b99f272ca257b9452f50fd6c0d38b931e65e75

Aliases

arxiv: 2605.13910 · arxiv_version: 2605.13910v1 · doi: 10.48550/arxiv.2605.13910 · pith_short_12: 425FAKATCEIN · pith_short_16: 425FAKATCEINVWPT · pith_short_8: 425FAKAT

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curl -sH 'Accept: application/ld+json' https://pith.science/pith/425FAKATCEINVWPTAU2YBOM7E4 \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: e6ba5028131110dad9f3053580b99f272ca257b9452f50fd6c0d38b931e65e75

Canonical record JSON

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    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "stat.ML",
    "submitted_at": "2026-05-13T07:46:06Z",
    "title_canon_sha256": "cf0392d79de7e1d5f4e6f7649d50b3c88c6ead065fc6365eb9586b3a19714a62"
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