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We present CoDi-2, a versatile and interactive Multimodal Large LanguageModel (MLLM) that can follow complex multimodal interleaved instructions,conduct in-context learning (ICL), reason, chat, edit, etc., in an any-to-anyinput-output modality paradigm. By aligning modalities with language for bothencoding and generation, CoDi-2 empowers Large Language Models (LLMs) to notonly understand complex modality-interleaved instructions and in-contextexamples, but also autoregressively generate grounded and coherent multimodaloutputs in the continuous feature space. To train CoDi-2, we build alarge-scale generation dataset encompassing in-context multimodal instructionsacross text, vision, and audio. CoDi-2 demonstrates a wide range of zero-shotcapabilities for multimodal generation, such as in-context learning, reasoning,and compositionality of any-to-any modality generation through multi-roundinteractive conversation. CoDi-2 surpasses previous domain-specific models ontasks such as subject-driven image generation, vision transformation, and audioediting. CoDi-2 signifies a substantial breakthrough in developing acomprehensive multimodal foundation model adept at interpreting in-contextlanguage-vision-audio interleaved instructions and producing multimodaloutputs.

CoDi-2: In-Context, Interleaved, and Interactive Any-to-Any Generation