Janus-Pro is DeepSeek's autoregressive multimodal model family for understanding images and generating pictures from text. DeepSeek publishes Janus-Pro-1B and Janus-Pro-7B through its official Hugging Face repositories, with inference code in the Janus GitHub project. To generate locally, download a complete checkpoint and use the Janus runtime and generation example. Repository 1B model card 7B model card
What are the key facts about Janus-Pro downloads?
| Field | Verified information |
|---|---|
| Developer | DeepSeek-AI. Repository |
| Released | January 27, 2025. Repository release entry |
| Type | Autoregressive framework unifying image understanding and generation through separate visual encoding paths. 7B model card |
| Size or parameters | Official checkpoint variants are named Janus-Pro-1B and Janus-Pro-7B. Model download table |
| License and access | Downloadable checkpoints; MIT source code, with model-card prose directing model use to the DeepSeek Model License despite MIT metadata. 1B card 7B card Model license |
| Where it runs | The provided Python, PyTorch, and Transformers implementation; the generation example uses CUDA. Repository |
What does the official Janus-Pro implementation provide?
Janus-Pro puts image understanding and generation into one model framework while separating the visual encoders used for the tasks. For this article, the practical benefit is access to an explicit text-to-image implementation you can inspect and run with the released checkpoints. Technical report
DeepSeek's technical report describes improvements to training strategy, training data, and model scale. Its evaluations cover both tasks, but you should evaluate generated images against a visual brief rather than assume that image-question answering performance predicts your desired artwork. Technical report
Start with a simple generation assignment, such as a blue cup beside a yellow book on a wooden table. List the expected objects, their colors, and their relationship before running the model. This gives the first output an objective setup check: did it generate an image, and can you identify the requested arrangement?
The sibling Janus-Pro-7B multimodal guide covers evaluation across understanding and generation. This page concentrates on obtaining the correct files and reaching a local generation run.
What resolution, licensing, and memory limits affect Janus-Pro?
DeepSeek's text-to-image example generates at 384 by 384 pixels. The dedicated Janus-Pro demo then enlarges generated images to 768 by 768 for display using Lanczos resizing. The displayed dimensions therefore should not be mistaken for native generated detail. Generation example Demo source
Licensing labels need attention. Both model cards show MIT in their metadata, while their license sections direct model use to the DeepSeek Model License. Retain the actual card and linked agreement with your checkpoint records instead of silently choosing the most permissive label. 1B model card 7B model card Model license
The repository's generation example and demo use different parallel batch settings. A model download that fits on disk does not establish whether the complete inference workload fits in GPU memory. Measure the implementation you actually launch before planning repeated or concurrent runs. Repository Demo source
For the cup-and-book exercise, judge the native output before deciding whether further finishing is appropriate. A correct coarse arrangement can still fail if the intended deliverable needs fine lettering or intricate texture. Set those requirements from the project, not from the size of the enlarged preview.
How do you download Janus-Pro and run the official example?
Choose deepseek-ai/Janus-Pro-1B or deepseek-ai/Janus-Pro-7B from DeepSeek's official repositories. Download the selected repository into its own directory and keep its configuration and checkpoint files together. Model download table
Install huggingface_hub in your Python environment. Hugging Face documents snapshot_download for retrieving an entire repository into a local directory, including configuration and supporting files. This example downloads Janus-Pro-1B to an explicit folder. Download guide
from huggingface_hub import snapshot_download
snapshot_download(
repo_id="deepseek-ai/Janus-Pro-1B",
local_dir="models/Janus-Pro-1B",
)
Downloading the repository does not run inference. Keep the returned files together and use the complete folder as the checkpoint location. For an experiment you need to reproduce, record the downloaded repository revision; Hugging Face's guide documents selecting revisions for downloads. Download guide
Next, clone DeepSeek's implementation and install it in a compatible Python and PyTorch environment. The repository documents an editable installation. Repository
git clone https://github.com/deepseek-ai/Janus.git
cd Janus
python -m pip install -e .
Open the Janus-Pro section of the README and use its complete Text-to-Image Generation example. Set model_path to the absolute path of your downloaded checkpoint directory for both the processor and model loader. Retain the example's prompt formatting and image-token generation procedure. Generation example
That full procedure matters: the example prepares a conversation, applies the Janus template, generates image tokens, decodes them, and saves images. A short text-only call to the language model would not reproduce those steps. Start from the working structure before adapting it to your application. Generation example
Replace the sample prompt with the cup-and-book brief. Leave the initial settings otherwise unchanged unless your hardware requires reducing the number of parallel images. Keep a record of any such change so later output comparisons are interpretable. Generation example
Confirm the setup in stages. First verify that the processor and checkpoint load. Then check that generation reaches the decoding step and writes an image. Finally, inspect the saved picture for the requested objects and arrangement. This separates runtime failures from unsuccessful visual results.
Once a baseline works, introduce a new requirement, such as moving the book behind the cup or changing the tabletop material. Keep the successful baseline and compare the revised output against a written checklist. Avoid adjusting the prompt, batch size, and sampling controls all at once.
For repeated use, store checkpoint identity, code revision, environment details, prompt, and native output together. A useful local result includes enough information to understand how it was produced, especially when you later compare checkpoint variants.
How does Janus-Pro setup compare with SDXL-Lightning?
SDXL-Lightning is ByteDance's distilled text-to-image model project, published with Diffusers and ComfyUI instructions. Janus-Pro uses its own autoregressive image-token procedure and also covers image understanding. The two therefore require different loading and inference setups. SDXL-Lightning card 7B model card
| Your question | Comparison to make |
|---|---|
| Which produces the desired picture? | Use the same visual requirements and inspect native outputs. |
| Which fits an existing runtime? | Compare the Janus implementation with the documented SDXL-Lightning workflow. |
Read the ComfyUI guide for broader workflow planning. Keep this Janus-Pro setup tied to DeepSeek's implementation rather than assuming that a Stable Diffusion checkpoint loader is interchangeable.
What should you know before running Janus-Pro locally?
Where can I download Janus-Pro?
Download Janus-Pro from DeepSeek's official Janus-Pro-1B or Janus-Pro-7B repositories on Hugging Face. Download the complete checkpoint repository and use it with the Janus implementation. 1B card 7B card
Is Janus-Pro a Stable Diffusion derivative?
Janus-Pro is an autoregressive multimodal framework with separate visual encoding paths for understanding and generation. Use the Janus runtime and generation example for that architecture. 7B model card
Does the larger demo preview mean higher native resolution?
DeepSeek's Janus-Pro demo generates at 384 by 384 pixels and resizes the result to 768 by 768 for display. Inspect the native generation before evaluating how much detail a finished asset contains. Demo source
What should I do after the download finishes?
After downloading Janus-Pro, install the official Janus runtime and point its processor and model loader at the complete checkpoint directory. Run the complete generation example and verify the saved output before changing the workflow. Repository
Sources
- DeepSeek Janus repository and generation examples
- Janus-Pro-1B model card
- Janus-Pro-7B model card
- DeepSeek model license
- Official Janus-Pro demo source
- Hugging Face repository download guide
- ByteDance SDXL-Lightning model card
- DeepSeek Janus-Pro technical report
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