Image-to-Text
Diffusers
Safetensors
English
uniar
image-generation
image-understanding
image-editing
multimodal
autoregressive
text-to-image
unified-model
Instructions to use ShareLab-SII/UniAR-RL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ShareLab-SII/UniAR-RL with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ShareLab-SII/UniAR-RL", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
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## Citation
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```bibtex
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@
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title={Unified Multimodal Autoregressive Modeling with Shared Context
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author={Peng, Wujian and Meng, Lingchen and Cai, Yuxuan and Zhuang, Xianwei and Yang, Yuhuan and Fang, Rongyao and Wu, Chenfei and Lin, Junyang and Wu, Zuxuan and Bai, Shuai},
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year={2026}
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}
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```
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## Citation
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```bibtex
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@article{peng2026unified,
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title={Unified Multimodal Autoregressive Modeling with Shared Context-Visual Tokenizer is Key to Unification},
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author={Peng, Wujian and Meng, Lingchen and Cai, Yuxuan and Zhuang, Xianwei and Yang, Yuhuan and Fang, Rongyao and Wu, Chenfei and Lin, Junyang and Wu, Zuxuan and Bai, Shuai},
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journal={arXiv preprint arXiv:2606.18249},
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year={2026}
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}
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```
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