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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, and runs on consumer hardware.
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`FLUX.2 [klein] 9B Base` is a 9 billion parameter rectified flow transformer capable of generating images from text descriptions and supports multi-reference editing capabilities.
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FLUX.2 [klein] 9B Base is a 9 billion parameter rectified flow transformer capable of generating images from text descriptions and supports multi-reference editing capabilities.
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It's a full-capacity foundation model. Undistilled, preserving complete training signal for maximum flexibility. Ideal for fine-tuning, LoRA training, research, and custom pipelines where control matters more than speed. Higher output diversity than the distilled models.
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This repository holds an FP8 version of FLUX.2 [klein] Base 9B. The main repository of this model (full BF16 weights) can be found [here](https://huggingface.co/black-forest-labs/FLUX.2-klein-base-9B).
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For more information, please read our [blog post](https://bfl.ai/blog/flux2-klein-towards-interactive-visual-intelligence).
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For more information, please read our [blog post](https://bfl.ai/blog/flux2-klein-towards-interactive-visual-intelligence).
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# **Key Features**
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This repository holds an [FP8 version](https://huggingface.co/black-forest-labs/FLUX.2-klein-base-9b-fp8/blob/main/flux-2-klein-base-9b-fp8.safetensors) of FLUX.2 [klein] 9B Base. The main repository of this model (full BF16 weights) can be found [here](https://huggingface.co/black-forest-labs/FLUX.2-klein-base-9B).
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1. Undistilled foundation model for maximum flexibility and control.
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2. Best suited for creative exploration, fine-tuning, LoRA training, research, and custom image generation or editing pipelines.
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3. Trained without step or guidance distillation, making FLUX.2 [klein] 9B Base more efficient and flexible.
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4. Open weights for customization and fine-tuning to drive science, research, and empower artists to iterate with speed.
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# **Usage**
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We provide a reference implementation of FLUX.2 [klein] 9B Base, as well as sampling code, in a dedicated [GitHub repository](https://github.com/black-forest-labs/flux2). Developers and creatives looking to build on top of FLUX.2 [klein] 9B Base are encouraged to use this as a starting point.
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FLUX.2 [klein] 9B Base is also available in both [ComfyUI](https://github.com/comfyanonymous/ComfyUI) and [Diffusers](https://github.com/huggingface/diffusers).
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## **Using with Diffusers 🧨**
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To use FLUX.2 [klein] 9B Base with the 🧨 Diffusers python library, first install or upgrade diffusers:
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```shell
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pip install -U diffusers
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```
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Then you can use Flux2KleinPipeline to run the model:
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```python
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import torch
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from diffusers import Flux2KleinPipeline
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device = "cuda"
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dtype = torch.bfloat16
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pipe = Flux2KleinPipeline.from_pretrained("black-forest-labs/FLUX.2-klein-base-9B", torch_dtype=dtype)
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pipe.enable_model_cpu_offload() # save some VRAM by offloading the model to CPU
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prompt = "A cat holding a sign that says hello world"
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image = pipe(
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prompt,
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height=1024,
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width=1024,
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guidance_scale=4.0,
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num_inference_steps=50,
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generator=torch.Generator(device=device).manual_seed(0)
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).images[0]
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image.save("flux-klein.png")
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```
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---
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---
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Limitations
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Limitations
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