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--- |
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base_model: |
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- Efficient-Large-Model/Sana_1600M_1024px_BF16 |
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- VIPL-GENUN/Jodi |
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tags: |
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- Diffusion |
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- Text-to-Image |
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- Controllable-Generation |
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- Image-Perception |
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--- |
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# Jodi |
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We introduce Jodi, a diffusion framework that unifies visual generation and understanding by jointly modeling the image domain and multiple label domains. |
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- **arXiv**: <https://arxiv.org/abs/2505.19084> |
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- **Project page**: <https://VIPL-GENUN.github.io/Project-Jodi> |
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- **GitHub**: <https://github.com/VIPL-GENUN/Jodi> |
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- **Joint-1.6M Dataset**: <https://huggingface.co/datasets/VIPL-GENUN/Joint-1.6M-1024px> |
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<br> |
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# Citation |
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If you find this project helpful, please consider citing: |
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```bibtex |
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@article{xu2025jodi, |
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title={Jodi: Unification of Visual Generation and Understanding via Joint Modeling}, |
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author={Xu, Yifeng and He, Zhenliang and Kan, Meina and Shan, Shiguang and Chen, Xilin}, |
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journal={arXiv preprint arXiv:2505.19084}, |
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year={2025} |
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} |
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``` |
