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

About

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.

Jinheng Xie, Weijia Mao, Zechen Bai, David Junhao Zhang, Weihao Wang, Kevin Qinghong Lin, Yuchao Gu, Zhijie Chen, Zhenheng Yang, Mike Zheng Shou• 2024

Related benchmarks

TaskDatasetResultRank
Object Hallucination EvaluationPOPE
Accuracy84.5
2056
Visual Question AnsweringGQA
Accuracy69.4
1445
Text-to-Image GenerationGenEval
Overall Score69
914
Multimodal EvaluationMME--
902
Multimodal UnderstandingMMBench--
887
Multimodal UnderstandingMM-Vet
MM-Vet Score23.3
664
Visual Question AnsweringChartQA
Accuracy44.7
620
Text-to-Image GenerationGenEval
Overall Score69
581
Multimodal UnderstandingSEED-Bench--
571
Visual Question AnsweringGQA
Accuracy58
524
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Other info

Code

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