VisionLLaMA: A Unified LLaMA Backbone for Vision Tasks

Xiangxiang Chu*, Jianlin Su, Bo Zhang*, Chunhua Shen ;

"We all know that large language models are built on top of a transformer-based architecture to process textual inputs. For example, the LLaMA family of models stands out among many open-source implementations. Can the same transformer be used to process 2D images? In this paper, we answer this question by unveiling a LLaMA-like vision transformer in plain and pyramid forms, termed , which is tailored for this purpose. is a unified and generic modeling framework for solving most vision tasks. We extensively evaluate its effectiveness using typical pre-training paradigms in a good portion of downstream tasks of image perception and especially image generation. In many cases, has exhibited substantial gains over the previous state-of-the-art vision transformers. It is our hope that researchers in computer vision can apply presented here to solve various specific image generation and perception tasks. Code is at: https://github.com/Meituan-AutoML/VisionLLaMA"