Update README.md

README.md

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----
-license: mit
----
+---
+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
+
+We present JanusFlow, a powerful framework that unifies image understanding and generation in a single model. 
+JanusFlow introduces a minimalist architecture that integrates autoregressive
+language models with rectified flow, a state-of-the-art method in generative modeling. Our
+key finding demonstrates that rectified flow can be straightforwardly trained within the large
+language model framework, eliminating the need for complex architectural modifications.
+To further improve the performance of our unified model, we adopt two key strategies: (i)
+decoupling the understanding and generation encoders, and (ii) aligning their representations
+during unified training. Extensive experiments show that JanusFlow achieves comparable or
+superior performance to specialized models in their respective domains, while significantly
+outperforming existing unified approaches across standard benchmarks. This work represents
+a step toward more efficient and versatile vision-language models.
+
+[JanusFlow: Harmonizing Autoregression and Rectified Flow for Unified Multimodal Understanding and Generation](TBD)
+
+[**Github Repository**](https://github.com/deepseek-ai/Janus)
+
+<div align="center">
+<img alt="image" src="teaser.png" style="width:90%;">
+</div>
+
+
+### 2. Model Summary
+
+Janus is a unified understanding and generation MLLM, which decouples visual encoding for multimodal understanding and generation. 
+Janus is constructed based on the DeepSeek-LLM-1.3b-base which is trained on an approximate corpus of 500B text tokens.
+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 uses the tokenizer from [here](https://github.com/FoundationVision/LlamaGen) with a downsample rate of 16.
+
+<div align="center">
+<img alt="image" src="arch.jpg" style="width:90%;">
+</div>
+
+## 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 models is subject to [DeepSeek Model License](https://github.com/deepseek-ai/DeepSeek-LLM/blob/HEAD/LICENSE-MODEL).
+## 5. Citation
+
+```
+@misc{wu2024janus,
+      title={Janus: Decoupling Visual Encoding for Unified Multimodal Understanding and Generation}, 
+      author={Chengyue Wu and Xiaokang Chen and Zhiyu Wu and Yiyang Ma and Xingchao Liu and Zizheng Pan and Wen Liu and Zhenda Xie and Xingkai Yu and Chong Ruan and Ping Luo},
+      year={2024},
+      eprint={2410.13848},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2410.13848}, 
+}
+```
+
+## 6. Contact
+
+If you have any questions, please raise an issue or contact us at [[email protected]](mailto:[email protected]).