Janus-Pro-7B
Go to file
xxl 2cb1b3c57a first commit 2025-02-20 15:46:52 +08:00
.gitattributes Add .gitattributes 2025-02-20 13:39:25 +08:00
README.md first commit 2025-02-20 15:46:52 +08:00
config.json first commit 2025-02-20 15:46:52 +08:00
janus_pro_teaser1.png first commit 2025-02-20 15:46:52 +08:00
janus_pro_teaser2.png first commit 2025-02-20 15:46:52 +08:00
preprocessor_config.json first commit 2025-02-20 15:46:52 +08:00
processor_config.json first commit 2025-02-20 15:46:52 +08:00
pytorch_model-00001-of-00002.bin first commit 2025-02-20 15:46:52 +08:00
pytorch_model-00002-of-00002.bin first commit 2025-02-20 15:46:52 +08:00
pytorch_model.bin.index.json first commit 2025-02-20 15:46:52 +08:00
special_tokens_map.json first commit 2025-02-20 15:46:52 +08:00
tokenizer.json first commit 2025-02-20 15:46:52 +08:00
tokenizer_config.json first commit 2025-02-20 15:46:52 +08:00

README.md

license license_name license_link pipeline_tag library_name tags
mit deepseek LICENSE any-to-any transformers
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 encoders roles in understanding and generation, but also enhances the frameworks 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

image
image

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 service@deepseek.com.