---
title: Janus-Pro-1B
canonical_url: "https://www.modelscope.cn/models/deepseek-ai/Janus-Pro-1B"
md_url: "https://www.modelscope.cn/models/deepseek-ai/Janus-Pro-1B.md"
repository: deepseek-ai/Janus-Pro-1B
last_updated: 2025-02-01
license: mit
pipeline_tag: any-to-any
tasks:
  - any-to-any
model_type:
  - multi_modality
architectures:
  - MultiModalityCausalLM
library_name:
  - lora
  - pytorch
frameworks:
  - pytorch
inference_backends:
  - "deploy_task vlm"
  - "lmdeploy 0.9.1"
  - "lmdeploy_turbomind 0.9.1"
  - "sglang 0.5.2"
downloads: 78119
stars: 36
tags:
  - muiltimodal
  - text-to-image
  - unified-model
---

# Janus-Pro-1B

> Janus-Pro-1B - deepseek-ai 在 ModelScope 开源的模型。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…

deepseek-ai/Janus-Pro-1B 是 ModelScope 魔搭社区上的any-to-any模型，采用 mit 许可，可用 deploy_task vlm、lmdeploy 0.9.1、lmdeploy_turbomind 0.9.1 部署。

- **Repository**: deepseek-ai/Janus-Pro-1B
- **License**: mit
- **Tasks**: any-to-any
- **Inference backends**: deploy_task vlm, lmdeploy 0.9.1, lmdeploy_turbomind 0.9.1, sglang 0.5.2
- **Tags**: muiltimodal, text-to-image, unified-model
- **Downloads**: 78119
- **Stars**: 36
- **Last updated**: 2025-02-01

Source: https://www.modelscope.cn/models/deepseek-ai/Janus-Pro-1B

---

## 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).