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Janus

Janus is a series of multimodal models that process visual inputs for understanding and generate images. The repository provides model downloads and Python inference examples for image question answering and text-to-image generation.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Janus — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Janus when you want to explore a model family that supports both image understanding and text-to-image generation, or reproduce the repository's documented inference workflows.

Who it is for

  • Developers building or evaluating vision-language and image-generation workflows.
  • Researchers exploring unified multimodal models.

Documented uses

  • Ask a text question about an image and obtain a generated response.
  • Generate an image from a text prompt.
  • Compare the Janus, Janus-Pro and JanusFlow model variants.
Before you choose

Requirements & limits

The repository links online demos and model downloads on Hugging Face. Local examples require Python 3.8 or later; they use CUDA and bfloat16. The code license is MIT, while model use is governed by a separate model agreement.

  • Four listed model options range from 1B to 7B parameters and each has a stated sequence length of 4096.
  • The documented local inference example moves the model to CUDA in bfloat16.
  • Code and model licensing are separate; review the model agreement for intended use.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install the Janus-Pro repository package

Install the project package in a Python environment before following its inference examples.

Before you start

  • Python 3.8 or later
  • A local checkout of the Janus repository

Steps

  1. Open a terminal in the repository directory.

  2. Run the command below.

    Documented command
    pip install -e .

Expected outcome

The repository package and its declared dependencies are installed in editable mode.

Guide limitations

  • This covers installation only; the README's inference examples also use PyTorch, Transformers, CUDA and bfloat16.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Model inference and serving

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
JanusAsk a text question about an image and obtain a generated response.Cloud, Self-hostedThe repository links online demos and model downloads on Hugging Face. Local examples require Python 3.8 or later; they use CUDA and bfloat16. The code license is MIT, while model use is governed by a separate model agreement.
Pulse Of MotionEstimate a video's physical frame rate from its visual dynamics.LinuxThe repository is public. Its README describes a released checkpoint that downloads from Hugging Face on first inference, with a manual download option. The strongest internal model is not fully released; the README directs interested users to contact the project about access. No license information is provided in the repository evidence.
Neural Image AssessmentTrain the model using AVA images and rating annotations.Cloud, Linux, macOS, Windows
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
13 October 2026
Original website title
deepseek-ai/Janus · Official homepage title · retained in its source language
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