Chameleon: Mixed-Modal Early-Fusion Foundation Models
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.
6646b4a06d5f0538ee0176cc
Chameleon Team
Chameleon: Mixed-Modal Early-Fusion Foundation Models
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.
Chameleon is a mixed-modal early-fusion token-based model that achieves state-of-the-art performance across various tasks, including image captioning, text generation, and long-form mixed-modal generation, using a unified architecture.
early-fusion token-based mixed-modal models visual question answering image captioning text generation image generation long-form mixed modal generation Llama-2 Mixtral 8x7B Gemini-Pro GPT-4V full multimodal documents