Chameleon: Mixed-Modal Early-Fusion Foundation Models

• URL: http://arxiv.org/abs/2405.09818v2
• Date: Fri, 21 Mar 2025 05:54:00 GMT
• Title: Chameleon: Mixed-Modal Early-Fusion Foundation Models
• Authors: Chameleon Team,
• Abstract summary: We present a family of early-fusion token-based mixed-modal models capable of understanding and generating images and text in any arbitrary sequence.<n>The models are evaluated on a comprehensive range of tasks, including visual question answering, image captioning, text generation, image generation, and long-form mixed modal generation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present Chameleon, a family of early-fusion token-based mixed-modal models capable of understanding and generating images and text in any arbitrary sequence. We outline a stable training approach from inception, an alignment recipe, and an architectural parameterization tailored for the early-fusion, token-based, mixed-modal setting. The models are evaluated on a comprehensive range of tasks, including visual question answering, image captioning, text generation, image generation, and long-form mixed modal generation. Chameleon demonstrates broad and general capabilities, including state-of-the-art performance in image captioning tasks, outperforms Llama-2 in text-only tasks while being competitive with models such as Mixtral 8x7B and Gemini-Pro, and performs non-trivial image generation, all in a single model. It also matches or exceeds the performance of much larger models, including Gemini Pro and GPT-4V, according to human judgments on a new long-form mixed-modal generation evaluation, where either the prompt or outputs contain mixed sequences of both images and text. Chameleon marks a significant step forward in a unified modeling of full multimodal documents.

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