Rethinking Streaming Video Diffusion Model: Context, Execution, and Training

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 VBench evaluation set, the three policies use rank-64 fake-score LoRA, with same-level and progressive history achieving aggregate scores of 85.24 and 85.60, respectively, compared with 85.16 for clean history. 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 under clean and progressive history 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.