  In this study, we identify the inefficient attention phenomena in Large
Vision-Language Models (LVLMs), notably within prominent models like LLaVA-1.5,
QwenVL-Chat and Video-LLaVA. We find out that the attention computation over
visual tokens is of extreme inefficiency in the deep layers of popular LVLMs,
suggesting a need for a sparser approach compared to textual data handling. To
this end, we introduce FastV, a versatile plug-and-play method designed to
optimize computational efficiency by learning adaptive attention patterns in
early layers and pruning visual tokens in subsequent ones. Our evaluations
demonstrate FastV's ability to dramatically reduce computational costs (e.g., a
45 reduction in FLOPs for LLaVA-1.5-13B) without sacrificing performance in a
wide range of image and video understanding tasks. The computational efficiency
and performance trade-off of FastV are highly customizable and
pareto-efficient. It can compress the FLOPs of a 13B-parameter model to achieve
a lower budget than that of a 7B-parameter model, while still maintaining
superior performance. We believe FastV has practical values for deployment of
LVLMs in edge devices and commercial models. Code is released at
https://github.com/pkunlp-icler/FastV.
