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Black Forest Labs Launches FLUX.1 Kontext, a Breakthrough in Context-aware Image Generation and Editing

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Black Forest Labs, Europe’s fast-rising frontier AI research lab, announced today the release of FLUX.1 Kontext, a new suite of image generation models that introduce powerful in-context generating and editing capabilities. Kontext enables users to generate, retouch, and iteratively refine images from both text and visual inputs, without the need for finetuning or complex editing workflows.

FLUX.1 Kontext is designed for creators, developers, and enterprises seeking precision and creative control. The model can understand and extract visual concepts from images, maintain stylistic and character consistency across multiple scenes, and apply localized edits with exceptional fidelity. This enables seamless visual storytelling, fast ideation, and highly targeted content generation, all at inference speeds up to 8x faster than leading alternatives.

"FLUX.1 Kontext represents a fundamental shift from traditional editing approaches by unifying image generation and editing in a single flow matching architecture," said Robin Rombach, CEO and co-founder of Black Forest Labs. "Using simple flow matching training, we have achieved state-of-the-art character consistency across multi-turn edits while maintaining interactive inference speeds of 3-5 seconds at 1MP resolution. This enables truly iterative creative workflows that were previously impossible due to visual drift and latency constraints."

A Complete Suite for In-Context Generation

The FLUX.1 Kontext suite introduces two distinct models: Kontext [Pro] and Kontext [Max], each designed to meet the demands of advanced image generation and editing. Kontext [Pro] is built for iterative editing workflows. It generates images from text or reference images and supports local edits, scene transformations, and multi-step refinements while preserving character and stylistic consistency.

Kontext [Max] offers maximum performance, combining exceptional prompt adherence, advanced typography handling, and premium rendering quality with no compromise on speed. Together, these models deliver photorealistic outputs with minimal latency and support a wide range of applications from rapid ideation to high-end creative production.

A B2B Playground for Enterprise Evaluation

As part of the rollout, Black Forest Labs is introducing the Playground, a browser-based demo environment designed for business users to explore the full capabilities of the FLUX.1 model suite. Tailored for enterprise evaluation, the Playground allows teams to validate internal use cases, demonstrate functionality to stakeholders, and experiment with advanced image generation in real time. Playground enables risk-free exploration and rapid demonstration of value, making it easy to assess the power of FLUX before committing to full integration.

Additional components of the Kontext model suite will launch on a rolling basis over the coming weeks, including:

  • Open-weights, distilled variant of Flux.1 Kontext
  • Access of FLUX open-weights models for self-deployment with transparent pricing and instant commercial use rights.

Kontext follows the success of FLUX.1 Dev, one of Hugging Face’s most popular models with over 30 million downloads. To date, the FLUX.1 family of models have generated 700 million images via API.

To learn more, visit https://bfl.ai/announcements/flux-1-kontext

About Black Forest Labs

Black Forest Labs is a European frontier AI lab advancing the future of generative media. Founded by leading AI researchers, BFL develops state-of-the-art image and video generation systems that are performant, accessible, and responsibly deployed. With deep roots in foundational research, the company continuously builds advanced infrastructure to empower creators, developers, and forward-thinking enterprises to bring their imagination to life.

View source version on businesswire.com: https://www.businesswire.com/news/home/20250529605562/en/

Contacts

Media Contact:
Morgan Malone
blackforestlabs@bulleitgroup.com

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