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Depth as Time in One-Step Generative Models

Paper recorded by Signals 4 on 2026-10-02 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-02 on arXiv · recorded by Signals 4 on 2026-10-05

Category: cs.AI · 人工智能 · first seen 2026-10-05

Abstract

The recent wave of one-step generative models, which compress the multi-step trajectory of diffusion via either distillation or learned flow maps, has reached an inflection point where they can generate high-quality images. Here, we ask a natural question that follows from these advances: what happens to the denoising trajectory of multi-step diffusion when generation is compressed into a single f

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#514 most recent of 1020 cs.AI papers we have recorded · ↑ newer: NeutronGym: Physics-Graded Neutron Instrument Design for LLM Agents · ↓ older: Low-Cost Video--Time Priors as a Strong Baseline for EEG--fNIRS Emotio
Cite this page: Depth as Time in One-Step Generative Models: the #514 most recent of 1020 cs.AI papers we have recorded (as of 2026-10-02). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/depth-as-time-in-one-step-generative-models.html
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