Janus Pro 7B

--- 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} } ``...