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 models with 7B parameters are publicly available at https://github.com/InternLM/InternLM-XComposer.

翻译：我们提出InternLM-XComposer，一个支持高级图文理解与合成的视觉语言大模型。该模型的创新性体现在三个引人注目的特性上：1）交织式图文合成：InternLM-XComposer能够轻松生成连贯且富有上下文关联的文章，并自然融入图像，提供更具沉浸感的阅读体验。只需提供一个标题，系统即可生成相应稿件。它能智能识别文本中需要插入图像以强化内容的区域，并自动选取最合适的视觉候选对象。2）丰富的多语言知识理解：通过在多模态多语言海量概念上采用精心设计的策略进行训练，模型实现了对视觉内容的深度理解。3）最先进的性能：该模型在多项视觉语言基础模型主流基准测试中持续取得最先进结果，包括MME Benchmark、MMBench、MMBench-CN、Seed-Bench及CCBench（中文文化基准测试）。总之，InternLM-XComposer将高级图文理解与合成无缝融合，革新了视觉语言交互方式，带来了新的见解与机遇。拥有70亿参数的InternLM-XComposer模型已公开发布于https://github.com/InternLM/InternLM-XComposer。