Minimal inference code for running FLUX.1 open-weight models for image generation and editing.
FLUX is the official inference repository for FLUX.1 open-weight models by Black Forest Labs, providing minimal code to run image generation and editing tasks locally. It enables text-to-image synthesis, inpainting, structural conditioning, and image variation using state-of-the-art diffusion models. The project focuses on making advanced AI image models accessible for local deployment and experimentation.
AI researchers, developers, and hobbyists interested in running and experimenting with cutting-edge image generation models locally, especially those who prefer open-weight alternatives to proprietary AI image services.
FLUX offers a streamlined, self-hostable solution for using FLUX.1 models without relying on cloud APIs, with support for commercial licensing and hardware acceleration via TensorRT. Its minimal codebase and open-weight approach provide transparency and control over inference workflows.
Official inference repo for FLUX.1 models
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Provides access to state-of-the-art FLUX.1 models with permissive licenses like Apache-2.0 for some models, enabling local deployment and experimentation as shown in the model table.
Includes built-in usage tracking via the BFL API for commercial applications, with clear CLI examples for enabling tracking with API keys, as detailed in the licensing section.
Offers optional TensorRT installation for accelerated inference on NVIDIA GPUs, though it requires complex setup with enroot and specific PyTorch images.
Features specialized models for tasks like inpainting, structural conditioning, and image variation, enhancing versatility for different image editing workflows.
Installing with TensorRT requires additional tools like enroot and a NVIDIA PyTorch container, adding significant complexity compared to standard pip installations.
Many models, such as FLUX.1 [dev], are under non-commercial licenses, restricting commercial use without separate licensing and mandatory usage tracking.
Key documentation is hosted externally at docs.bfl.ai, which may hinder quick reference and integration compared to having it fully within the repository.