deepseek-community
/

Janus-Pro-7B

@@ -1,199 +1,149 @@
 ---
 library_name: transformers
-tags: []
 ---
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 ---
+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
+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.
+## 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]).