FLUX.2-klein

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

1. A distilled model for sub-second image generation with outstanding quality.
2. Text-to-image and image-to-image multi-reference editing in a single unified model.
3. Great for real-time generation and integration into applications.
4. Ideal for creative exploration with excellent prompt adherence and output diversity.
5. 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 -U diffusers

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-labs/FLUX.2-klein-9B", torch_dtype=dtype)
pipe.enable_model_cpu_offload()  # save some VRAM by offloading the model to CPU

prompt = "A cat holding a sign that says hello world"
image = pipe(
    prompt,
    height=1024,
    width=1024,
    guidance_scale=1.0,
    num_inference_steps=4,
    generator=torch.Generator(device=device).manual_seed(0)
).images[0]
image.save("flux-klein.png")

Limitations

• This model is not intended or able to provide factual information.
• While the model can output text, text rendered may be inaccurate or subject to distortion.
• As a statistical model, this checkpoint may represent or amplify biases observed in the training data.
• The model may fail to generate output that matches the prompts.
• Prompt following is heavily influenced by the prompting style.

Out-of-Scope Use

The model and its derivatives may not be used:

• In any way that violates applicable law.
• For the purpose of exploiting, harming or attempting to exploit or harm minors in any way; including but not limited to the solicitation, creation, acquisition, or dissemination of child exploitative content.
• To generate or disseminate deceptive, fraudulent, misleading or otherwise harmful content.
• To generate or disseminate personal identifiable information that can be used to harm an individual.
• To harass, abuse, threaten, stalk, or bully individuals or groups of individuals.
• To create non-consensual intimate imagery or illegal pornographic content.
• For fully automated decision making or high risk applications that adversely impact an individual's legal rights or otherwise create or modify a binding, enforceable obligation.

Nothing contained in this Model Card should be interpreted as or deemed a restriction or modification to the license the model is released under.

Hardware

The FLUX.2 [klein] 9B model fits in ~29GB VRAM and is accessible on NVIDIA RTX 4090 and above.

Responsible AI Development

Black Forest Labs is committed to the responsible development and deployment of our models. Prior to releasing the FLUX.2 family of models, we evaluated and mitigated a number of risks in our model checkpoints and hosted services, including the generation of unlawful content, including child sexual abuse material (CSAM) and nonconsensual intimate imagery (NCII). We implemented a series of pre-release mitigations to help prevent misuse by third parties, with additional post-release mitigations to help address residual risks:

1. Pre-training mitigation. We filtered pre-training data for multiple categories of "not safe for work" (NSFW) and known child sexual abuse material (CSAM) to help prevent a user generating unlawful content in response to text prompts or uploaded images. We have partnered with the https://www.iwf.org.uk/, an independent nonprofit organization dedicated to preventing online abuse, to filter known CSAM from the training data.
2. Post-training mitigation. Subsequently, we undertook multiple rounds of targeted fine-tuning to provide additional mitigation against potential abuse, including both text-to-image (T2I) and image-to-image (I2I) attacks. By inhibiting certain behaviors and suppressing certain concepts in the trained model, these techniques can help to prevent a user generating synthetic CSAM or NCII from a text prompt, or transforming an uploaded image into synthetic CSAM or NCII.
3. Ongoing evaluation. Throughout this process, we conducted multiple internal and external third-party evaluations of model checkpoints to identify further opportunities for mitigation. External third-party evaluations focused on eliciting CSAM and NCII through adversarial testing with (i) text-only prompts, (ii) a single uploaded refer