FLUX.2-klein-9B

The FLUX.2 [klein] model family are our fastest image models to date. FLUX.2 [klein] unifies generation and editing in a single compact architecture, delivering state-of-the-art quality with end-to-end inference in as low as under a second. Built for applications that require real-time image generation without sacrificing quality. FLUX.2 [klein] 9B is a 9 billion parameter rectified flow transformer capable of generating images from text descriptions and supports multi-reference editing capabilities. Our flagship small model. Defines the Pareto frontier for quality vs. latency across text-to-image, single-reference editing, and multi-reference generation. Matches or exceeds models 5x its size—in under half a second. Built on a 9B flow model with 8B Qwen3 text embedder, step-distilled to 4 inference steps. For more information, please read our blog post. Key Features A distilled model for sub-second image generation with outstanding quality. Text-to-image and image-to-image multi-reference editing in a single unified model. Great for real-time generation and integration into applications. Ideal for creative exploration with excellent prompt adherence and output diversity. Available for non-commercial use. Usage We provide a reference implementation of FLUX.2 [klein] 9B, as well as sampling code, in a dedicated GitHub repository. Developers and creatives looking to build on top of FLUX.2 [klein] 9B are encouraged to use this as a starting point. API Endpoints The FLUX.2 [klein] 9B model is available via the BFL API: bfl.ai FLUX.2 [klein] 9B is also available in both ComfyUI and Diffusers. Using with Diffusers 🧨 To use FLUX.2 [klein] 9B with the 🧨 Diffusers python library, first install or upgrade diffusers: pip install git+https://github.com/huggingface/diffusers.git Then you can use Flux2KleinPipeline to run the model: import torch from diffusers import Flux2KleinPipeline device = "cuda" dtype = torch.bfloat16 pipe = Flux2KleinPipeline.from_pretrained("black-forest-l