Janus Pro

Janus Pro is an open-source unified multimodal AI model developed by DeepSeek that combines image understanding and text-to-image generation within a single autoregressive framework. Available in 1 billion and 7 billion parameter variants, it is designed for researchers, developers, and organizations building vision-language applications that require both visual comprehension and image creation capabilities.

Key Features

• Decoupled visual encoding with separate pathways for understanding and generation tasks, operating through a unified Transformer architecture for flexible multimodal processing without architectural conflicts.
• SigLIP-L vision encoder supporting 384x384 image resolution for multimodal understanding inputs, paired with DeepSeek-LLM base models for language processing.
• Text-to-image generation using a LlamaGen-based tokenizer with 16x downsample rate, enabling instruction-following image creation from natural language descriptions.
• GenEval benchmark score of 0.80, outperforming DALL-E 3 (0.67) and Stable Diffusion in text-to-image instruction-following tasks according to published evaluation results.
• Available in two model sizes: Janus Pro 1B (1.5 billion parameters) for lightweight deployment including browser-based inference, and Janus Pro 7B (7 billion parameters) for higher accuracy across benchmarks.
• MIT licensed code with permissive model license allowing commercial use, modification, and redistribution.

How It Works

Janus Pro processes multimodal inputs through decoupled visual encoding pathways within a unified autoregressive Transformer backbone. For image understanding, it encodes visual features using the SigLIP-L encoder and projects them into the language model embedding space. For image generation, it uses a separate tokenizer pathway that produces image tokens autoregressively. This architectural decoupling resolves conflicts between the visual encoder dual roles in understanding and generation while maintaining a single unified model. The 7B variant requires approximately 22 GB of GPU memory for inference.

Use Cases

• Multimodal understanding: answering questions about images, converting visual content to structured outputs such as LaTeX code, and extracting information from diagrams, screenshots, and documents.
• Text-to-image generation: creating images from descriptive prompts with classifier-free guidance for controlled instruction following and style adherence.
• Research and experimentation: serving as a foundation model for fine-tuning and adapting to custom vision-language tasks in academic and commercial research settings.
• Browser-based inference: the 1B parameter variant can run locally in web browsers via WebGPU and Transformers.js, enabling client-side multimodal AI without server infrastructure.

Intended Users

Janus Pro targets AI researchers exploring unified multimodal architectures, developers building applications that require both image understanding and generation, and organizations seeking cost-effective open-source alternatives to proprietary multimodal APIs such as DALL-E 3 and GPT-4V. The 1B variant suits resource-constrained and edge deployments while the 7B variant delivers higher benchmark performance for production workloads.

Pricing

Janus Pro is released as open-source software under the MIT License for code and the DeepSeek Model License for model weights. There is no licensing fee. Usage costs are limited to the infrastructure required for self-hosted deployment, which varies by model size and inference scale.

Privacy and Security

As a self-hosted open-source model, Janus Pro processes all data locally on the user infrastructure. No data is transmitted to external servers for inference. Users maintain full control over input data, generated outputs, and model deployment.