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FLUX.1 open weights

Black Forest Labs' official inference code for the open FLUX.1 models, with mixed weight licences.

FLUX.1: official open-weight inference code

Apache-2.0 code; schnell weights Apache-2.0, dev weights non-commercial unless you license them.

Free forever · open source · Apache-2.0 (code); FLUX.1 [dev] weights under the FLUX.1-dev Non-Commercial License
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5.7
ZEKAI
Official code, mixed licences
Free & open source · Graphic Design
Ease of setup3
Usefulness7
Maintenance3.5
Community9.2
★ 26,060
GITHUB
Jul 2025
UPDATED
Apache-2.0 (code); FLUX.1 [dev] weights under the FLUX.1-dev Non-Commercial License
LICENSE
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⚡ Quick answer

This is Black Forest Labs' reference repository for running the open FLUX.1 text-to-image models yourself. It suits developers who want the original weights and inference code, and who check each model's licence first.

Runs on
Cross-platform
Hosting
Self-host
Languages
Python
License
Apache-2.0 (code); FLUX.1 [dev] weights under the FLUX.1-dev Non-Commercial License

What it is

You clone the repository, create a Python 3.10 environment and run pip install -e ".[all]". The README lists FLUX.1 [schnell] plus [dev], Fill, Canny, Depth, Redux, Kontext and Krea [dev] models on Hugging Face, with an optional TensorRT setup inside NVIDIA's PyTorch container.

Who it's for

It suits developers and researchers comfortable with Python and Hugging Face downloads. It does not suit anyone who wants a point-and-click app, or who needs to use [dev] weights commercially without paying for a licence.

Key features

schnell weights

Released under Apache-2.0 on Hugging Face.

dev model family

Fill, Canny, Depth, Redux, Kontext and Krea variants.

Image editing models

Fill for in and outpainting, Kontext for editing.

Structural conditioning

Canny and Depth models with LoRA versions.

TensorRT option

Optional setup in NVIDIA's PyTorch container.

Usage tracking reference

Sample code reports usage for commercial licensing of dev weights.

How to start

Up and running in 3 steps

1
Clone https://github.com/black-forest-labs/flux and create a Python 3.10 virtual environment.
2
Activate it and run pip install -e ".[all]".
3
Download the weights you need from the Hugging Face repos listed in the README, then follow the docs for text-to-image.
Real talk

Our honest verdict

What's great

  • The code is Apache-2.0 and the [schnell] weights are Apache-2.0 too.
  • One repository covers text-to-image, fill, structural conditioning, image variation and editing models.
  • Autoencoder weights are also Apache-2.0, per the README.
  • BFL documents a commercial licensing route for the non-commercial [dev] weights.

Where it falls short

  • FLUX.1 [dev] and its Fill, Canny, Depth, Redux, Kontext and Krea variants use a non-commercial licence.
  • The README gives no VRAM figure, so check each Hugging Face model card before you buy a GPU.
  • Install is from source in a Python 3.10 environment; it is a code repository, not an app.
  • Commercial use of open [dev] weights expects usage reporting via the reference code or a paid licence.
Cost

Pricing & license

The code is free under Apache-2.0 and the schnell weights are free. Commercial use of [dev] weights needs a licence from Black Forest Labs, shown at bfl.ai/pricing/licensing.

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Questions

FAQ

Can I use FLUX.1 commercially?

FLUX.1 [schnell] weights are Apache-2.0. The [dev] family uses a non-commercial licence, and BFL offers commercial licensing at bfl.ai/pricing/licensing.

What GPU do I need to run FLUX.1?

The README does not state a VRAM figure. Check the Hugging Face model card for the variant you choose.

Is there a UI for these models?

This repository is inference code. Tools such as ComfyUI, InvokeAI and SwarmUI list FLUX support in their own docs.

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