Update README.md

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README.md

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 ---
+license: mit
+license_name: deepseek
+license_link: LICENSE
+pipeline_tag: any-to-any
 library_name: transformers
-tags: []
+tags:
+- muiltimodal
+- text-to-image
+- unified-model
 ---
 
-# Model Card for Model ID
+## 1. Introduction
 
-<!-- Provide a quick summary of what the model is/does. -->
+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="https://huggingface.co/deepseek-community/Janus-Pro-1B/resolve/main/janus_pro_teaser1.png" style="width:90%;">
+</div>
+
+<div align="center">
+<img alt="image" src="https://huggingface.co/deepseek-community/Janus-Pro-1B/resolve/main/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.
 
-
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+## 3. Usage Examples
+
+### Single Image Inference
+
+Here is an example of visual understanding with a single image.
+
+```python
+import torch  
+from PIL import Image  
+import requests  
+from transformers import JanusForConditionalGeneration, JanusProcessor  
+
+model_id = "deepseek-community/Janus-Pro-7B"
+
+# Prepare input for generation
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {'type': 'image', 'url': 'http://images.cocodataset.org/val2017/000000039769.jpg'},
+            {'type': 'text', 'text': "What do you see in this image?"}
+        ]
+    },
+]
+
+# Set generation mode to 'text' to perform text generation
+processor = JanusProcessor.from_pretrained(model_id)
+model = JanusForConditionalGeneration.from_pretrained(
+    model_id, torch_dtype=torch.bfloat16, device_map="auto"
+)
+
+inputs = processor.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    generation_mode="text",
+    tokenize=True,
+    return_dict=True,
+    return_tensors="pt"
+).to(model.device, dtype=torch.bfloat16)
+
+output = model.generate(**inputs, max_new_tokens=40, generation_mode='text', do_sample=True)
+text = processor.decode(output[0], skip_special_tokens=True)
+print(text)
+```
+
+## Text to Image generation
+Janus can also generate images from prompts by simply setting the generation mode to `image` as shown below.
+
+```python
+import torch
+from transformers import JanusForConditionalGeneration, JanusProcessor
+
+model_id = "deepseek-community/Janus-Pro-7B"
+
+# Load processor and model
+processor = JanusProcessor.from_pretrained(model_id)
+model = JanusForConditionalGeneration.from_pretrained(
+    model_id, torch_dtype=torch.bfloat16, device_map="auto"
+)
+
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "text", "text": "A dog running under the rain."}
+        ]
+    }
+]
+
+# Apply chat template
+prompt = processor.apply_chat_template(messages, add_generation_prompt=True)
+inputs = processor(
+    text=prompt,
+    generation_mode="image",
+    return_tensors="pt"
+).to(model.device, dtype=torch.bfloat16)
+
+# Set number of images to generate
+model.generation_config.num_return_sequences = 2
+
+outputs = model.generate(
+    **inputs,
+    generation_mode="image",
+    do_sample=True,
+    use_cache=True
+)
+
+# Decode and save images
+decoded_image = model.decode_image_tokens(outputs)
+images = processor.postprocess(list(decoded_image.float()), return_tensors="PIL.Image.Image")
+
+for i, image in enumerate(images["pixel_values"]):
+    image.save(f"image{i}.png")
+```
+
+## 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 [[email protected]](mailto:[email protected]).