InternLM-XComposer2: Mastering Free-form Text-Image Composition and
  Comprehension in Vision-Language Large Model
Xiaoyi Dong Pan Zhang Yuhang Zang Yuhang Cao Bin Wang Linke Ouyang Xilin Wei Songyang Zhang Haodong Duan Maosong Cao Wenwei Zhang Yining Li Hang Yan Yang Gao Xinyue Zhang Wei Li Jingwen Li Kai Chen Conghui He Xingcheng Zhang Yu Qiao Dahua Lin Jiaqi Wang
We introduce InternLM-XComposer2, a cutting-edge vision-language model
excelling in free-form text-image composition and comprehension. This model
goes beyond conventional vision-language understanding, adeptly crafting
interleaved text-image content from diverse inputs like outlines, detailed
textual specifications, and reference images, enabling highly customizable
content creation. InternLM-XComposer2 proposes a Partial LoRA (PLoRA) approach
that applies additional LoRA parameters exclusively to image tokens to preserve
the integrity of pre-trained language knowledge, striking a balance between
precise vision understanding and text composition with literary talent.
Experimental results demonstrate the superiority of InternLM-XComposer2 based
on InternLM2-7B in producing high-quality long-text multi-modal content and its
exceptional vision-language understanding performance across various
benchmarks, where it not only significantly outperforms existing multimodal
models but also matches or even surpasses GPT-4V and Gemini Pro in certain
assessments. This highlights its remarkable proficiency in the realm of
multimodal understanding. The InternLM-XComposer2 model series with 7B
parameters are publicly available at
https://github.com/InternLM/InternLM-XComposer.