Janus-Pro-1B-rk3588-1.2.0

Janus-Pro-1B-RK3588-1.2.0

This version of Janus-Pro-1B has been converted to run on the RK3588 NPU using w8a8 quantization.

This model has been optimized with the following LoRA:

Compatible with RKLLM version: 1.2.0

Useful links:

Official RKLLM GitHub

RockhipNPU Reddit

EZRKNN-LLM

Pretty much anything by these folks: marty1885 and happyme531

Converted using https://github.com/c0zaut/ez-er-rkllm-toolkit

Original Model Card for base model, Janus-Pro-1B, below:

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

<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 as the vision encoder, which supports 384 x 384 image input. For image generation, Janus-Pro uses the tokenizer from here with a downsample rate of 16.

3. Quick Start

Please refer to Github Repository

4. License

This code repository is licensed under the MIT License. The use of Janus-Pro models is subject to DeepSeek Model License.

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 [email protected].