FLUX.2-klein-9B

FLUX.2-klein-9B is a 9 billion parameter rectified flow transformer model designed for high-speed image generation and editing. It unifies text-to-image generation and multi-reference image editing into a single compact architecture, achieving state-of-the-art quality with end-to-end inference in under half a second. The model leverages an 8 billion parameter Qwen3 text embedder and is step-distilled to 4 inference steps, enabling real-time performance while matching or exceeding the quality of models five times its size.

Key Features:

• Supports both text-to-image generation and image-to-image editing with multi-reference inputs.
• Optimized for real-time applications without sacrificing output quality. Achieves results in under half a second using 4 inference steps.
• Fits in ~29GB VRAM, compatible with NVIDIA RTX 4090 or higher GPUs.

Limitations:

• Non-commercial use only (licensed under FLUX Non-Commercial License).
• May generate inaccurate or distorted text in outputs.
• Susceptible to biases present in training data.
• Output may not strictly align with prompts in all cases.

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The model is a component of the image generation pipeline, consisting of:

• Text encoder: ~8B parameters,
• Transformer: ~9B parameters,
• VAE: ~84M parameters.

Total: ~17B parameters

Announce Date: 14.01.2026
Parameters: 9B
Developer: Black Forest Labs
Diffusers Version: 0.37.0.dev0
vLLM-Omni Version: 0.14.0
License: FLUX Non-Commercial License v2.1

Use our pre-built public endpoints for free to test inference and explore FLUX.2-klein-9B capabilities. You can obtain an API access token on the token management page after registration and verification.

Rent your own physically dedicated instance with hourly or long-term monthly billing.

We recommend deploying private instances in the following scenarios:

• maximize endpoint performance,
• enable full context for long sequences,
• ensure top-tier security for data processing in an isolated, dedicated environment,
• use custom weights, such as fine-tuned models or LoRA adapters.

Contact our dedicated neural networks support team at nn@immers.cloud or send your request to the sales department at sale@immers.cloud.