  This paper investigates the role of CLIP image embeddings within the Stable
Video Diffusion (SVD) framework, focusing on their impact on video generation
quality and computational efficiency. Our findings indicate that CLIP
embeddings, while crucial for aesthetic quality, do not significantly
contribute towards the subject and background consistency of video outputs.
Moreover, the computationally expensive cross-attention mechanism can be
effectively replaced by a simpler linear layer. This layer is computed only
once at the first diffusion inference step, and its output is then cached and
reused throughout the inference process, thereby enhancing efficiency while
maintaining high-quality outputs. Building on these insights, we introduce the
VCUT, a training-free approach optimized for efficiency within the SVD
architecture. VCUT eliminates temporal cross-attention and replaces spatial
cross-attention with a one-time computed linear layer, significantly reducing
computational load. The implementation of VCUT leads to a reduction of up to
322T Multiple-Accumulate Operations (MACs) per video and a decrease in model
parameters by up to 50M, achieving a 20% reduction in latency compared to the
baseline. Our approach demonstrates that conditioning during the Semantic
Binding stage is sufficient, eliminating the need for continuous computation
across all inference steps and setting a new standard for efficient video
generation.
