deepseek-ai/janus

★ 17,728PythonAudience · researcherComplexity · 4/5LicenseSetup · hard

Mindmap

mindmap
  root((repo))
    What it does
      Understand images
      Generate images
    Model variants
      Janus 1.3B
      JanusFlow 1.3B
      Janus-Pro 1B and 7B
    How it works
      Dual encoding paths
      Shared transformer
    Setup
      Python
      Hugging Face download

mindmap root((repo)) What it does Understand images Generate images Model variants Janus 1.3B JanusFlow 1.3B Janus-Pro 1B and 7B How it works Dual encoding paths Shared transformer Setup Python Hugging Face download

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Things people build with this

USE CASE 1

Build an app that can answer questions about photos and also create images from text descriptions using a single downloaded model.

USE CASE 2

Research unified multimodal architectures by experimenting with separate encoding pathways for image understanding vs. generation.

USE CASE 3

Download and run a 1B or 7B parameter model locally from Hugging Face to test image-to-text and text-to-image capabilities.

Tech stack

PythonHugging Face

Getting it running

Difficulty · hard Time to first run · 1h+

Requires downloading large model weights from Hugging Face, model weights have a separate license from the code.

The code is MIT licensed, use freely for any purpose. Model weights have a separate model license that may carry additional restrictions.

In plain English

Janus is a series of AI models from DeepSeek that can both understand images and generate images from text, using a single unified architecture. Most AI models are specialized, a model that answers questions about images is separate from a model that creates images from text prompts. Janus attempts to do both within the same framework. The core research insight, as described in the README, is that understanding and generating images benefit from different visual processing strategies. Rather than forcing one visual encoder (the part of the model that processes images) to serve both purposes, Janus uses separate encoding pathways for understanding and generation while still sharing a single underlying transformer, the type of neural network that powers modern AI models. This separation is claimed to reduce conflict between the two tasks and improve performance on both. The series currently has three variants. The original Janus model (1.3 billion parameters) focuses on the decoupled encoding approach. JanusFlow (1.3B) integrates a generation technique called rectified flow into the language model framework. Janus-Pro (available in 1B and 7B sizes) is an improved version with more training data and refined training strategy. You would use Janus if you are an AI researcher or developer working with multimodal models and want a single model that can describe what is in an image (visual understanding) and also create images from text instructions (image generation). Models are available to download from Hugging Face and are written in Python. The code license is MIT and the model weights have a separate model license. The full README is longer than what was provided.

Copy-paste prompts

Prompt 1

I want to use the Janus model from deepseek-ai to describe what is in an image and then generate a new image based on that description. Show me the Python code to load it from Hugging Face and run both tasks.

Prompt 2

Compare Janus-Pro 7B and JanusFlow 1.3B for a project needing both image understanding and generation. Which should I use and what are the trade-offs?

Prompt 3

Walk me through fine-tuning the Janus-Pro model on my own image-caption dataset. What Python setup and training configuration should I start with?

Prompt 4

How does Janus use separate encoding pathways for understanding and generating images while sharing one transformer? Explain the architecture in plain terms.

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