SAGA: Stable Acceleration Guidance for Autoregressive Video Generation
This paper proposes SAGA, a training-free stable acceleration guidance method to improve temporal instability in autoregressive video diffusion. By using acceleration-domain spectral guidance and structured noise initialization, it effectively reduces flickering and jitter, enhancing temporal and image quality.
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[Submitted on 9 Jul 2026]
Title:SAGA: Stable Acceleration Guidance for Autoregressive Video Generation
View a PDF of the paper titled SAGA: Stable Acceleration Guidance for Autoregressive Video Generation, by Thanh-Nhan Vo and 4 other authors
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Abstract:Autoregressive video diffusion enables efficient streaming and long-horizon video generation, but repeatedly reusing generated latents as causal context can amplify temporal errors, resulting in flickering, motion jitter, and structural drift. In this paper, we investigate this failure mode from a spectral kinematic perspective and identify discrete latent acceleration as an effective signal for revealing unstable high-frequency temporal perturbations. To this end, we propose SAGA, a training-free \textbf{\textit{s}}table \textbf{\textit{a}}cceleration \textbf{\textit{g}}uidance approach for \textbf{\textit{a}}utoregressive video generation. SAGA integrates an acceleration domain spectral guidance objective based on finite-window Slepian projections with a structured autoregressive noise initialization strategy that suppresses short-range temporal correlations while preserving long-range motion structure. Without retraining or modifying the backbone, SAGA can be directly applied to existing chunk-wise autoregressive diffusion models, which is the prevalent setting for high-quality generation. Extensive experiments show that SAGA consistently improves temporal quality across multiple autoregressive diffusion models. On Self-Forcing, SAGA improves Temporal Quality from 97.30 to 97.91 and Image Quality from 69.60 to 70.51. Moreover, spectral analysis and human preference studies demonstrate that SAGA reduces temporal instability while maintaining visual fidelity.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2607.08020 [cs.CV]
(or arXiv:2607.08020v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.08020
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Trong-Thuan Nguyen [view email] [v1] Thu, 9 Jul 2026 00:55:10 UTC (2,178 KB)
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