Rolling Forcing: Autoregressive Long Video Diffusion in Real Time
Streaming video generation, as one fundamental component in interactive world
models and neural game engines, aims to generate high-quality, low-latency, and
temporally coherent long video streams. However, most existing work suffers
from severe error accumulation that often significantly degrades the generated
stream videos over long horizons. We design Rolling Forcing, a novel video
generation technique that enables streaming long videos with minimal error
accumulation. Rolling Forcing comes with three novel designs. First, instead of
iteratively sampling individual frames, which accelerates error propagation, we
design a joint denoising scheme that simultaneously denoises multiple frames
with progressively increasing noise levels. This design relaxes the strict
causality across adjacent frames, effectively suppressing error growth. Second,
we introduce the attention sink mechanism into the long-horizon stream video
generation task, which allows the model to keep key value states of initial
frames as a global context anchor and thereby enhances long-term global
consistency. Third, we design an efficient training algorithm that enables
few-step distillation over largely extended denoising windows. This algorithm
operates on non-overlapping windows and mitigates exposure bias conditioned on
self-generated histories. Extensive experiments show that Rolling Forcing
enables real-time streaming generation of multi-minute videos on a single GPU,
with substantially reduced error accumulation.
68db4f7bd2bf1f4b15ec749d
Kunhao Liu
claimed_verified
2025-09-30T10:18:01.327Z
660b79aa822b119f215c3488
https://cdn-avatars.huggingface.co/v1/production/uploads/660b79aa822b119f215c3488/le_EuzAz6mhW0FQ9J0knV.jpeg
Kunhao Liu
user
KunhaoLiu
68db4f7bd2bf1f4b15ec749e
Wenbo Hu
claimed_verified
2025-09-30T06:00:54.201Z
657a7458afbb0117ba15c59f
https://cdn-avatars.huggingface.co/v1/production/uploads/657a7458afbb0117ba15c59f/8_iwTS1UG_mKnfylFbLsY.jpeg
Wenbo Hu
user
wbhu-tc
68db4f7bd2bf1f4b15ec749f
Jiale Xu
68db4f7bd2bf1f4b15ec74a0
Ying Shan
68db4f7bd2bf1f4b15ec74a1
Shijian Lu
Rolling Forcing: Autoregressive Long Video Diffusion in Real Time
Streaming video generation, as one fundamental component in interactive world
models and neural game engines, aims to generate high-quality, low-latency, and
temporally coherent long video streams. However, most existing work suffers
from severe error accumulation that often significantly degrades the generated
stream videos over long horizons. We design Rolling Forcing, a novel video
generation technique that enables streaming long videos with minimal error
accumulation. Rolling Forcing comes with three novel designs. First, instead of
iteratively sampling individual frames, which accelerates error propagation, we
design a joint denoising scheme that simultaneously denoises multiple frames
with progressively increasing noise levels. This design relaxes the strict
causality across adjacent frames, effectively suppressing error growth. Second,
we introduce the attention sink mechanism into the long-horizon stream video
generation task, which allows the model to keep key value states of initial
frames as a global context anchor and thereby enhances long-term global
consistency. Third, we design an efficient training algorithm that enables
few-step distillation over largely extended denoising windows. This algorithm
operates on non-overlapping windows and mitigates exposure bias conditioned on
self-generated histories. Extensive experiments show that Rolling Forcing
enables real-time streaming generation of multi-minute videos on a single GPU,
with substantially reduced error accumulation.
Rolling Forcing is a novel video generation technique that reduces error accumulation in long video streams by using joint denoising, attention sink mechanism, and efficient training with non-overlapping windows.
joint denoising attention sink mechanism error accumulation long-horizon stream video generation global context anchor long-term global consistency efficient training algorithm few-step distillation denoising windows exposure bias