FLUX.2-klein-4B

It is a 4 billion parameter rectified flow transformer model designed for fast image generation and editing. It unifies text-to-image generation and multi-reference image editing into a single compact architecture, enabling end-to-end inference in under a second. Optimized for real-time applications without compromising quality, it runs on consumer-grade GPUs such as NVIDIA RTX 3090/4070 with approximately 13GB VRAM.

Key features:

• It supports text-to-image synthesis and image-to-image editing with a unified model architecture.
• Sub-second image generation for interactive workflows, production deployments, and latency-sensitive applications.
• The model is fully open-source under the Apache 2.0 license, allowing commercial use.

Limitations:

• It does not provide factual information and may not always match input prompts precisely.
• The model may generate inaccurate text or amplify biases from training data.
• The model’s development prioritizes safety, with collaboration on data filtering (e.g., IWF) and third-party tools (e.g., TheHive.ai) to address risks. 

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

• Text encoder: ~4B parameters,
• Transformer: ~4B parameters,
• VAE: ~84M parameters.

Total: ~8B parameters

Announce Date: 14.01.2026
Parameters: 4B
Developer: Black Forest Labs
Diffusers Version: 0.37.0.dev0
vLLM-Omni Version: 0.14.0
License: Apache 2.0

Use our pre-built public endpoints for free to test inference and explore FLUX.2-klein-4B 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.