Introduction

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.

Benchmarking

Training

Parameters

Authors

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Srinivasan Iyer, Bernie Huang, Lili Yu, Arun Babu, Chunting Zhou, Kushal Tirumala, Xi Victoria Lin, Hu Xu, Xian Li, Akshat Shrivastava, Omer Levy, Armen Aghajanyan, Ram Pasunuru, Andrew Cohen, Aram H. Markosyan, Koustuv Sinha, Xiaoqing Ellen Tan, Ivan Evtimov, Ping Yu, Tianlu Wang, Olga Golovneva, Asli Celikyilmaz, Pedro Rodriguez, Leonid Shamis, Vasu Sharma, Christine Jou, Karthik Padthe, Ching-Feng Yeh, Mingda Chen, Bapi Akula, Jacob Kahn, Daniel Li, Scott Yih, Barlas Oguz, Morteza Behrooz, Benjamin Muller, Carleigh Wood, Mary Williamson, Ramya Raghavendra, Barbara Usher, William Ngan, Nikolay Bashlykov, Lukas Blecher, Sony Theakanath, Ammar Rizvi, Gargi Ghosh, Luke Zettlemoyer