We present CoDi-2, a versatile and interactive Multimodal Large Language
Model (MLLM) that can follow complex multimodal interleaved instructions,
conduct in-context learning (ICL), reason, chat, edit, etc., in an any-to-any
input-output modality paradigm. By aligning modalities with language for both
encoding and generation, CoDi-2 empowers Large Language Models (LLMs) to not
only understand complex modality-interleaved instructions and in-context
examples, but also autoregressively generate grounded and coherent multimodal
outputs in the continuous feature space. To train CoDi-2, we build a
large-scale generation dataset encompassing in-context multimodal instructions
across text, vision, and audio. CoDi-2 demonstrates a wide range of zero-shot
capabilities for multimodal generation, such as in-context learning, reasoning,
and compositionality of any-to-any modality generation through multi-round
interactive conversation. CoDi-2 surpasses previous domain-specific models on
tasks such as subject-driven image generation, vision transformation, and audio
editing. CoDi-2 signifies a substantial breakthrough in developing a
comprehensive multimodal foundation model adept at interpreting in-context
language-vision-audio interleaved instructions and producing multimodal
outputs.

CoDi-2 是一种多功能且互动的多模态大型语言模型，能够在任意输入 - 输出的模态范式下遵循复杂的多模态交织指令、进行上下文学习、推理、聊天、编辑等。通过对语言和模态进行编码和生成的对齐，CoDi-2 赋予大型语言模型不仅理解复杂的模态交织指令和上下文示例的能力，还能在连续特征空间中自动生成基于现实且连贯的多模态输出。