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 .
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext f0f760fe-d883-4bbb-b41f-92a86657ee43Cited by top-tier papers354
- Flow-GRPO: Training Flow Matching Models via Online RLJie Liu, Gongye Liu, Jiajun Liang, Yangguang Li et al.NeurIPS 2025 · 647 citations
- DeepEyes: Incentivizing "Thinking with Images" via Reinforcement LearningZiwei Zheng, Michael Yang, Jack Hong, Chenxiao Zhao et al.ICLR 2026 · 321 citations
- Show-o2: Improved Native Unified Multimodal ModelsJinheng Xie, Zhenheng Yang, Mike Zheng ShouNeurIPS 2025 · 261 citations
- MMaDA: Multimodal Large Diffusion Language ModelsLing Yang, Ye Tian, Bowen Li, Xinchen Zhang et al.NeurIPS 2025 · 255 citations
- OmniGen2: Towards Instruction-Aligned Multimodal GenerationChenyuan Wu, Jiahao Wang, Pengfei Zheng, Ruiran Yan et al.CVPR 2026 · 231 citations
Builds on41
- Language Models are Few-Shot LearnersTom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah et al.NeurIPS 2020 · 64,255 citations
- Learning Transferable Visual Models From Natural Language SupervisionAlec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh et al.ICML 2021 · 47,906 citations
- Denoising Diffusion Probabilistic ModelsJonathan Ho, Ajay Jain, Pieter AbbeelNeurIPS 2020 · 35,902 citations
- Segment AnythingAlexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao et al.ICCV 2023 · 13,211 citations
- High-Resolution Image Synthesis with Latent Diffusion ModelsRobin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser et al.CVPR 2022 · 13,123 citations
Related papers
- Dual Diffusion for Unified Image Generation and UnderstandingZijie Li, Henry Li, Yichun Shi, Amir Barati Farimani et al.CVPR 2025
- Lavida-O: Elastic Large Masked Diffusion Models for Unified Multimodal Understanding and GenerationShufan Li, Jiuxiang Gu, Kangning Liu, Zhe Lin et al.ICLR 2026 · 14 citations
- OneCAT: Decoder-Only Auto-Regressive Model for Unified Understanding and GenerationHan Li, Xinyu Peng, Yaoming Wang, Zelin Peng et al.CVPR 2026 · 47 citations
- Janus: Decoupling Visual Encoding for Unified Multimodal Understanding and GenerationChengyue Wu, Xiaokang Chen, Zhiyu Wu, Yiyang Ma et al.CVPR 2025
- VILA-U: a Unified Foundation Model Integrating Visual Understanding and GenerationYecheng Wu, Zhuoyang Zhang, Junyu Chen, Haotian Tang et al.ICLR 2025
