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