  Large Language Models have shown remarkable efficacy in generating streaming
data such as text and audio, thanks to their temporally uni-directional
attention mechanism, which models correlations between the current token and
previous tokens. However, video streaming remains much less explored, despite a
growing need for live video processing. State-of-the-art video diffusion models
leverage bi-directional temporal attention to model the correlations between
the current frame and all the surrounding (i.e. including future) frames, which
hinders them from processing streaming videos. To address this problem, we
present Live2Diff, the first attempt at designing a video diffusion model with
uni-directional temporal attention, specifically targeting live streaming video
translation. Compared to previous works, our approach ensures temporal
consistency and smoothness by correlating the current frame with its
predecessors and a few initial warmup frames, without any future frames.
Additionally, we use a highly efficient denoising scheme featuring a KV-cache
mechanism and pipelining, to facilitate streaming video translation at
interactive framerates. Extensive experiments demonstrate the effectiveness of
the proposed attention mechanism and pipeline, outperforming previous methods
in terms of temporal smoothness and/or efficiency.
