Instructions to use BarelyFunctionalCode/Janus-Pro-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BarelyFunctionalCode/Janus-Pro-1B with Transformers:
# Load model directly from transformers import MultiModalityCausalLM model = MultiModalityCausalLM.from_pretrained("BarelyFunctionalCode/Janus-Pro-1B", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| license: mit | |
| license_name: deepseek | |
| license_link: LICENSE | |
| pipeline_tag: any-to-any | |
| library_name: transformers | |
| tags: | |
| - muiltimodal | |
| - text-to-image | |
| - unified-model | |
| ## 1. Introduction | |
| Janus-Pro is a novel autoregressive framework that unifies multimodal understanding and generation. | |
| It addresses the limitations of previous approaches by decoupling visual encoding into separate pathways, while still utilizing a single, unified transformer architecture for processing. The decoupling not only alleviates the conflict between the visual encoder’s roles in understanding and generation, but also enhances the framework’s flexibility. | |
| Janus-Pro surpasses previous unified model and matches or exceeds the performance of task-specific models. | |
| The simplicity, high flexibility, and effectiveness of Janus-Pro make it a strong candidate for next-generation unified multimodal models. | |
| [**Github Repository**](https://github.com/deepseek-ai/Janus) | |
| <div align="center"> | |
| <img alt="image" src="janus_pro_teaser1.png" style="width:90%;"> | |
| </div> | |
| <div align="center"> | |
| <img alt="image" src="janus_pro_teaser2.png" style="width:90%;"> | |
| </div> | |
| ### 2. Model Summary | |
| Janus-Pro is a unified understanding and generation MLLM, which decouples visual encoding for multimodal understanding and generation. | |
| Janus-Pro is constructed based on the DeepSeek-LLM-1.5b-base/DeepSeek-LLM-7b-base. | |
| For multimodal understanding, it uses the [SigLIP-L](https://huggingface.co/timm/ViT-L-16-SigLIP-384) as the vision encoder, which supports 384 x 384 image input. For image generation, Janus-Pro uses the tokenizer from [here](https://github.com/FoundationVision/LlamaGen) with a downsample rate of 16. | |
| ## 3. Quick Start | |
| Please refer to [**Github Repository**](https://github.com/deepseek-ai/Janus) | |
| ## 4. License | |
| This code repository is licensed under [the MIT License](https://github.com/deepseek-ai/DeepSeek-LLM/blob/HEAD/LICENSE-CODE). The use of Janus-Pro models is subject to [DeepSeek Model License](https://github.com/deepseek-ai/DeepSeek-LLM/blob/HEAD/LICENSE-MODEL). | |
| ## 5. Citation | |
| ``` | |
| @article{chen2025janus, | |
| title={Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling}, | |
| author={Chen, Xiaokang and Wu, Zhiyu and Liu, Xingchao and Pan, Zizheng and Liu, Wen and Xie, Zhenda and Yu, Xingkai and Ruan, Chong}, | |
| journal={arXiv preprint arXiv:2501.17811}, | |
| year={2025} | |
| } | |
| ``` | |
| ## 6. Contact | |
| If you have any questions, please raise an issue or contact us at [service@deepseek.com](mailto:service@deepseek.com). |