InternLM-XComposer2: Mastering Free-form Text-Image Composition and
  Comprehension in Vision-Language Large Model
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.
65b86307da874c1bc094ac1b
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Pan Zhang
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Yuhang Zang
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Yuhang Cao
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Bin Wang
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2024-04-03T09:03:54.001Z
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Bin Wang
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Linke Ouyang
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Xilin Wei
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Songyang Zhang
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HAODONG DUAN
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Maosong Cao
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maosongcao
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Yining Li
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Yining Li
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ly015
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Hang Yan
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Yang Gao
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Yang Gao
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Xinyue Zhang
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Wei Li
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Jingwen Li
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Kai Chen
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Conghui He
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Conghui He
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Xingcheng Zhang
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Yu Qiao
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Dahua Lin
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Dahua Lin
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Jiaqi Wang
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Jiaqi Wang
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myownskyW7
InternLM-XComposer2: Mastering Free-form Text-Image Composition and
  Comprehension in Vision-Language Large Model
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.
InternLM-XComposer2, a vision-language model, excels in free-form text-image creation and understanding using Partial LoRA, surpassing existing models and matching GPT-4V and Gemini Pro in multimodal benchmarks.
vision-language model free-form text-image composition comprehension Partial LoRA (PLoRA) image tokens pre-trained language knowledge multimodal content vision-language understanding GPT-4V Gemini Pro multimodal understanding