FLUX-KONTEXT

multi-image-kontext — Image-to-Image AI Model

multi-image-kontext, part of Black Forest Labs' flux-kontext family, revolutionizes image-to-image AI by enabling precise edits across multiple reference images while preserving character faces, outfits, and visual details for consistent storytelling. This multi-image-kontext tool tackles the common challenge of identity drift in iterative editing, allowing creators to maintain photorealistic consistency in complex scenes like ad campaigns or fashion series. Developed on a Diffusion Transformer architecture, it supports up to 10 input images for seamless multi-reference composition, outputting high-resolution images up to 4MP in any aspect ratio.

What Sets multi-image-kontext Apart

multi-image-kontext excels in the competitive image-to-image AI model landscape through its specialized multi-reference capabilities, handling up to 10 input images—far surpassing the 4-image limit of faster variants like FLUX.2 [klein]—to ensure unwavering character consistency across outputs. This enables users to blend elements from diverse sources, such as combining a model's face from one photo with outfits from others, without losing fine details like fabric textures or facial features. Unlike generic editors prone to artifacts in multi-turn workflows, multi-image-kontext's in-context editing preserves identity through iterative changes, ideal for professional Black Forest Labs image-to-image applications.

• Up to 10 reference images: References multiple sources simultaneously for style transfer and character consistency, producing 4MP outputs with minimum 64x64 input support.
• Advanced in-context preservation: Maintains facial and outfit details across edits, solving degradation issues in prolonged sessions.
• Sub-second to seconds inference: Balances speed (3-5 seconds typical) with quality, supporting PNG/JPEG formats for real-time AI image editor API workflows.

• The quality of the combined image heavily depends on the clarity and specificity of the prompt; ambiguous instructions may lead to unexpected blending or artifacting
• For best results, use high-quality, well-lit source images with clear subjects and minimal background clutter
• The model may occasionally blend features from both images in unintended ways, especially when subjects overlap or are visually similar
• Iterative refinement—generating multiple outputs and adjusting prompts—is often necessary to achieve optimal results
• There is a trade-off between output quality and generation speed, especially at higher resolutions or with complex prompts
• Prompt engineering is critical: explicitly describe the desired relationship between the two images (e.g., "place object A onto background B as a print, making it look natural")

How to Use multi-image-kontext on Eachlabs

Access multi-image-kontext seamlessly on Eachlabs via the Playground for instant testing, API for scalable multi-image-kontext API integrations, or SDK for custom apps. Provide a text prompt, up to 10 reference images (64x64 min), and optional controls like aspect ratio or hex colors; it delivers 4MP PNG/JPEG outputs in seconds with preserved consistency.

• Combines two input images into a single output with context-aware blending and object placement
• Supports prompt-driven control over how images are merged, including overlay, integration, or selective feature transfer
• Maintains high fidelity in object appearance and naturalness of composition when provided with clear instructions
• Adaptable to a variety of creative and professional use cases, including product mockups, scene composition, and visual storytelling
• Capable of handling complex compositional tasks, such as placing characters or objects from one image into the context of another

Use Cases for multi-image-kontext

For content creators building visual narratives, multi-image-kontext ensures a character in a story sequence retains the same face and attire across scenes; upload photos of the actor in different poses, prompt "place this character in a rainy city street wearing the red jacket, dramatic lighting," and generate consistent panels without redrawing.

Marketers using image-to-image AI model for e-commerce can create product mockups by referencing a single item photo across 10 lifestyle scenes, maintaining exact color and shape for variants like "swap background to beach sunset, keep product lighting realistic"—streamlining shoots for catalogs.

Developers integrating multi-image-kontext API into apps for fashion designers reference model shoots to generate editorials: feed face, outfit, and pose images, then edit with "change pose to walking runway, preserve fabric sheen and expression," accelerating design iterations.

Game artists leverage its multi-reference for asset consistency, combining character concept art with environment refs to output variations like "integrate elf warrior into forest battle, match skin tone and armor details from all inputs," enhancing prototype visuals efficiently.

• As an experimental model, multi-image-kontext may exhibit unpredictable behaviors, especially with complex or ambiguous prompts
• Users have reported occasional blending of features between images, leading to artifacts or loss of distinctiveness in subjects
• Performance can vary depending on input image quality, prompt specificity, and hardware resources
• High-resolution outputs may require more computational resources and longer generation times
• Consistency across multiple generations can be improved by refining prompts and iteratively adjusting input parameters
• Positive feedback highlights the model's flexibility and creative potential for compositional tasks
• Some users note challenges in achieving perfect separation of features, especially when merging visually similar elements

• May struggle with precise separation of features when input images have overlapping or similar subjects
• Not optimal for tasks requiring pixel-perfect alignment or photorealistic compositing in all scenarios
• Experimental status means documentation, support, and performance guarantees may be limited compared to mature models