[Submitted on 13 Sep 2026]
Title:Rethinking Streaming Video Diffusion Model: Context, Execution, and Training
View a PDF of the paper titled Rethinking Streaming Video Diffusion Model: Context, Execution, and Training, by Hongchen Zhang (University of Chinese Academy of Sciences)
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Abstract:Understanding the design space of streaming video diffusion is essential to exploring its potential for generation quality and computational efficiency. We develop a unified analytical framework that relates model and sampler choices, historical conditioning, execution scheduling, and training strategies. The framework accommodates a broad family of causal context-selection policies and makes their computational dependencies and training-inference alignment explicit. Within this design space, we study three representative policies: clean, same-level, and progressive history. On the full VBench prompt set, same-level and progressive history achieve aggregate scores of 85.24 and 85.60, respectively, compared with 84.45 for the clean-history reference. Long-video comparisons further show improved subject consistency and more coherent motion with progressive history. By allowing multiple denoising nodes to be processed together, progressive-history pipelining achieves $1.57$-$2.83\times$ steady-state DiT speedups under our evaluated conditions. We additionally find that LoRA adaptation of the DMD fake-score network improves generation quality using only 2.15% as many trainable fake-score parameters as full-parameter adaptation. Together, these findings show that fully denoised history is not a prerequisite for high-quality streaming generation and motivate the joint design of historical conditioning, execution, and training.
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as: arXiv:2609.22283 [cs.CV]
(or arXiv:2609.22283v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2609.22283
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Hongchen Zhang [view email] [v1] Sun, 13 Sep 2026 03:49:06 UTC (13,236 KB)
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