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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
2019
Visual Question AnsweringGQA
Accuracy69.4
1425
Multimodal UnderstandingMMBench--
847
Multimodal EvaluationMME--
727
Text-to-Image GenerationGenEval
Overall Score68
704
Multimodal UnderstandingMM-Vet
MM-Vet Score23.3
631
Visual Question AnsweringGQA
Accuracy58
524
Visual Question AnsweringChartQA
Accuracy44.7
519
Text-to-Image GenerationGenEval
Overall Score69
517
Multimodal UnderstandingSEED-Bench--
516
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Code

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