We present CoDi-2, a Multimodal Large Language Model (MLLM) for learning in-context interleaved multi-modal representations. By aligning modalities with language for both encoding and generation, CoDi-2 empowers Large Language Models (LLMs) to understand modality-interleaved instructions and in-context examples and autoregressively generate grounded and coherent multimodal outputs in an any-to-any input-output modality paradigm. 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 and few-shot capabilities for tasks like editing, exemplar learning, composition, reasoning, etc. CoDi-2 surpasses previous domain-specific models on tasks such as subject-driven image generation, vision transformation, and audio editing and showcases a significant advancement for integrating diverse multimodal tasks with sequential generation.<\/p>\n","protected":false},"excerpt":{"rendered":"
We present CoDi-2, a Multimodal Large Language Model (MLLM) for learning in-context interleaved multi-modal representations. By aligning modalities with language for both encoding and generation, CoDi-2 empowers Large Language Models (LLMs) to understand modality-interleaved instructions and in-context examples and autoregressively generate grounded and coherent multimodal outputs in an any-to-any input-output modality paradigm. 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