InternLM-XComposer2 is a leading visual language model proficient in free-form text-to-image synthesis and understanding. It not only comprehends traditional visual languages but also adeptly constructs interwoven text-image content from various inputs, including outlines, detailed text specifications, and reference images, enabling highly customizable content creation. InternLM-XComposer2 proposes a Partial LoRA (PLoRA) method, specifically applying additional LoRA parameters to image tokens to preserve the integrity of pre-trained language knowledge, achieving a balance between precise visual understanding and literary-quality text generation. Experimental results demonstrate that InternLM-XComposer2, based on InternLM2-7B, excels in generating high-quality long-form multimodal content and exhibits outstanding visual language understanding performance in various benchmark tests. It significantly surpasses existing multimodal models and even rivals or surpasses GPT-4V and Gemini Pro in some evaluations, highlighting its exceptional capabilities in the field of multimodal understanding. InternLM-XComposer2 models, with 7B parameters, are publicly available on https://github.com/InternLM/InternLM-XComposer.