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
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.
Multimodal understanding and generation, large language model
One single transformer to unify multimodal understanding and generation