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

Jinheng Xie, Weijia Mao, Zechen Bai, David Junhao Zhang, Weihao Wang +5 more
2/12/2026

No official code found. Best implementation: showlab/Show-o (1,974 stars, Apache-2.0, last commit 2026-01-08).

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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Code Implementations(1)

showlab/Show-oCommunity73%
Apache-2.0

[ICLR & NeurIPS 2025] Repository for Show-o series, One Single Transformer to Unify Multimodal Understanding and Generation.

1,97494Shell, PythonAug 9, 20249 months agoApache-2.0
diffusion-modelslarge-language-modelsmultimodal

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Cite this paper

@article{xie2026showo,
  title  = {Show-o: One Single Transformer to Unify Multimodal Understanding and Generation},
  author = {Jinheng Xie and Weijia Mao and Zechen Bai and David Junhao Zhang and Weihao Wang and Kevin Qinghong Lin and Yuchao Gu and Zhijie Chen and Zhenheng Yang and Mike Zheng Shou},
  year   = {2026},
  eprint = {2408.12528},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/2408.12528},
  journal = {ICLR 2024 2024}
}

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