update README

README.md

@@ -12,7 +12,7 @@ tags:
 
 ## 1. Introduction
 
-Janus is a novel autoregressive framework that unifies multimodal understanding and generation. 
+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 surpasses previous unified model and matches or exceeds the performance of task-specific models. 
 The simplicity, high flexibility, and effectiveness of Janus make it a strong candidate for next-generation unified multimodal models.
@@ -20,14 +20,19 @@ The simplicity, high flexibility, and effectiveness of Janus make it a strong ca
 [**Github Repository**](https://github.com/deepseek-ai/Janus)
 
 <div align="center">
-<img alt="image" src="teaser.png" style="width:90%;">
+<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 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.
+Janus-Pro is a unified understanding and generation MLLM, which decouples visual encoding for multimodal understanding and generation. 
+Janus is constructed based on the DeepSeek-LLM-1.5b-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 uses the tokenizer from [here](https://github.com/FoundationVision/LlamaGen) with a downsample rate of 16.
 
 
@@ -43,6 +48,11 @@ This code repository is licensed under [the MIT License](https://github.com/deep
 ## 5. Citation
 
 ```
+@misc{chen2025januspro,
+      title={Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling}, 
+      author={Xiaokang Chen and Zhiyu Wu and Xingchao Liu and Zizheng Pan and Wen Liu and Zhenda Xie and Xingkai Yu and Chong Ruan},
+      year={2025},
+}
 ```
 
 ## 6. Contact