Video diffusion has long been tethered to massive compute budgets, with PF‑ODE trajectory collection alone consuming “tens of thousands of GPU‑hours” [1]. Glance Forcing shatters that paradigm by partitioning the denoising process into slow and fast experts, trimming the entire trajectory‑collection stage down to roughly a single A100‑GPU hour.
Prior pipelines distilled bidirectional diffusion models into causal students but relied on exhaustive offline PF‑ODE sampling, a step that dominated the overall training cost and required substantial compute resources [1]. The high‑noise regime governs global layout and motion, while the low‑noise regime refines texture; collapsing both into one monolithic student caused severe interference and quality collapse.
Using only one PF‑ODE trajectory, Glance Forcing already surpasses the fully‑trained state‑of‑the‑art baseline, raising the VBench Total score from 84.04 to 84.22 while cutting trajectory‑collection cost from thousands of GPU‑hours to about one A100‑GPU‑hour [1]. The slow‑LoRA and fast‑LoRA checkpoints are trained with stage‑specific objectives, preserving the frozen teacher’s knowledge yet allowing each expert to specialize in its curvature regime.
The method still hinges on a pretrained bidirectional teacher that must remain frozen, and the decoupling introduces two LoRA modules that need to be stored and swapped during inference [1]. This suggests that while compute for trajectory generation is negligible, the overall training pipeline retains a modest overhead for expert‑specific fine‑tuning, an aspect future work could streamline further.
If high‑quality video generators can now be trained on a single workstation, the community should retire benchmarks that implicitly assume “GPU‑weeks” as a baseline requirement and re‑evaluate past results under this new compute budget.
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