We propose InternLM-XComposer, a vision-language large model that enables advanced image-text comprehension and composition. The innovative nature of our model is highlighted by three appealing properties: 1) Interleaved Text-Image Composition: InternLM-XComposer can effortlessly generate coherent and contextual articles that seamlessly integrate images, providing a more engaging and immersive reading experience. Simply provide a title, and our system will generate the corresponding manuscript. It can intelligently identify the areas in the text where images would enhance the content and automatically insert the most appropriate visual candidates. 2) Comprehension with Rich Multilingual Knowledge: The text-image comprehension is empowered by training on extensive multi-modal multilingual concepts with carefully crafted strategies, resulting in a deep understanding of visual content. 3) State-of-the-art Performance: Our model consistently achieves state-of-the-art results across various mainstream benchmarks for vision-language foundational models, including MME Benchmark, MMBench, MMBench-CN, Seed-Bench, and CCBench (Chinese Cultural Benchmark). Collectively, InternLM-XComposer seamlessly blends advanced text-image comprehension and composition, revolutionizing vision-language interaction and offering new insights and opportunities. The InternLM-XComposer model series with 7B parameters are publicly available at https://github.com/InternLM/InternLM-XComposer.

翻译：我们提出InternLM-XComposer，一个视觉语言大模型，实现了高级图像-文本理解与创作。该模型的创新性体现在三个显著特性上：1）交错式图文创作：InternLM-XComposer能轻松生成连贯且富有语境的文章，自然融入图像，提供更具沉浸感的阅读体验。仅需提供标题，系统即可生成对应稿件，并智能识别文本中需通过图像增强内容的区域，自动插入最合适的视觉候选。2）丰富多语言知识支持的理解能力：通过精心设计的策略，在多模态多语言概念的大规模训练下，模型具备对视觉内容的深度理解能力。3）最先进性能：在多个主流视觉语言基础模型基准测试中，包括MME Benchmark、MMBench、MMBench-CN、Seed-Bench和CCBench（中文文化基准），我们的模型持续取得最先进结果。整体而言，InternLM-XComposer无缝融合了高级图文理解与创作，革新了视觉语言交互方式，提供了新的洞见与机遇。拥有7B参数的InternLM-XComposer模型系列已公开发布于https://github.com/InternLM/InternLM-XComposer。