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Show-o: One Single Transformer to Unify Multimodal Understanding and Generation

Jinheng Xie
Weijia Mao
Zechen Bai
David Junhao Zhang
Weihao Wang
Kevin Qinghong Lin
Yuchao Gu
Zhijie Chen
Zhenheng Yang
Mike Zheng Shou
Abstract

We present a unified transformer, i.e., Show-o, that unifies multimodal understanding and generation. Unlike fully autoregressive models, Show-o unifies autoregressive and (discrete) diffusion modeling to adaptively handle inputs and outputs of various and mixed modalities. The unified model flexibly supports a wide range of vision-language tasks including visual question-answering, text-to-image generation, text-guided inpainting/extrapolation, and mixed-modality generation. Across various benchmarks, it demonstrates comparable or superior performance to existing individual models with an equivalent or larger number of parameters tailored for understanding or generation. This significantly highlights its potential as a next-generation foundation model. Code and models are released at https://github.com/showlab/Show-o.

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