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.
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Visual Question Answering | GQA | Accuracy69.4 | 963 | |
| Object Hallucination Evaluation | POPE | Accuracy84.5 | 935 | |
| Multimodal Evaluation | MME | -- | 557 | |
| Text-to-Image Generation | GenEval | Overall Score69 | 467 | |
| Multimodal Understanding | MM-Vet | MM-Vet Score20.9 | 418 | |
| Visual Question Answering | GQA | Accuracy58 | 374 | |
| Text-to-Image Generation | GenEval | GenEval Score68 | 277 | |
| Multimodal Understanding | MMMU | Accuracy30.7 | 275 | |
| Multi-discipline Multimodal Understanding | MMMU | -- | 266 | |
| Visual Question Answering | ChartQA | Accuracy44.7 | 239 |