FLUX

flux-kontext-dev — Image Editing AI Model

flux-kontext-dev, the open-weight developer version of Black Forest Labs' FLUX.1 Kontext, revolutionizes image-to-image AI model workflows by enabling precise text-based edits that preserve character identity and visual details across iterations. Developers and creators using this Black Forest Labs image-to-image tool can perform style transfers, object replacements, background changes, and text edits with unmatched consistency, solving common pain points in iterative image editing. Built on a 12-billion parameter Diffusion Transformer (DiT) architecture with Latent Adversarial Diffusion Distillation (LADD), flux-kontext-dev delivers 8-10x faster inference than models like GPT-4o, making it ideal for research and local deployment in AI image editor API applications.

What Sets flux-kontext-dev Apart

flux-kontext-dev excels in in-context editing, allowing targeted modifications to specific image areas via text prompts while maintaining facial features, poses, and fine details—outperforming Midjourney and DALL·E in character consistency for multi-turn edits. This enables rapid prototyping of edited visuals without quality degradation, perfect for developers integrating flux-kontext-dev API into automated image editing pipelines.

• Superior text rendering: Generates clean, legible text in images, setting a new benchmark over competitors' inconsistent fonts, which supports professional designs like logos or signage directly in edits.
• High-speed inference: Achieves 3-5 second generation times with revolutionary flow matching and LADD, facilitating interactive workflows unlike slower diffusion models.
• Open-weight dev access: 12B parameter model available for local fine-tuning and research via safetensors or GGUF formats, with support up to high-resolution outputs and any aspect ratio (up to 4MP in family).

Technical specs include input images starting at 64x64, multi-reference support (recommended max 6 for dev), and compatibility with ComfyUI workflows for advanced control.

• The model is optimized for prompt-driven image editing, so clear and specific prompts yield the best results
• Supports LoRA fine-tuning for custom styles and character consistency; users can train and apply LoRA adapters for specialized tasks
• Input images should be high quality and under 10MB for optimal performance and reliability
• Quality and speed trade-offs exist between Dev, Pro, and Max variants; Dev is fastest and most cost-effective, while Pro/Max offer higher fidelity
• Prompt engineering is crucial: ambiguous or overly complex prompts may produce less predictable results
• Supports both local and cloud-based workflows, allowing flexibility in deployment and resource management

How to Use flux-kontext-dev on Eachlabs

Access flux-kontext-dev seamlessly on Eachlabs via the Playground for instant testing—upload your input image, craft a descriptive edit prompt, adjust settings like steps or resolution (up to 4MP support), and generate high-quality outputs in seconds. Integrate the flux-kontext-dev API or SDK for production apps, providing image references and text instructions for consistent, photorealistic edits with preserved details.

• Performs high-quality text-based image editing, including style transfer, object/background replacement, and text manipulation
• Maintains character consistency across edits, especially when fine-tuned with LoRA adapters
• Handles both single and multi-image workflows (e.g., combining elements from two images)
• Supports iterative editing, allowing users to build up complex changes step by step
• Open-weight distribution enables local deployment, integration into custom pipelines, and offline use
• Commercial use rights included, allowing integration into products and services without additional licensing

Use Cases for flux-kontext-dev

E-commerce developers building AI photo editing for product catalogs can upload a base image and prompt "replace the plain white background with a luxurious marble kitchen counter under soft morning light, keep product shadows realistic" to generate studio-quality composites instantly, streamlining inventory visuals without photoshoots.

Game designers maintain character consistency by iteratively editing concept art—start with a base character sketch, then add "change outfit to cyberpunk leather jacket with neon accents, preserve facial expression and pose"—leveraging the model's in-context preservation for rapid asset iteration.

Marketers needing quick campaign assets use flux-kontext-dev for style transfers, like transforming a portrait to "oil painting in Van Gogh style with swirling blues, embed promotional text 'Summer Sale 50% Off' legibly," capitalizing on its top-tier text rendering for branded edits.

UI/UX researchers fine-tune prototypes locally with open weights, editing wireframes via prompts for typography tests or layout tweaks, benefiting from fast inference and ComfyUI integration in image-to-image AI model experiments.

• Some experimental features (like advanced LoRA fine-tuning) may require technical expertise and careful dataset preparation
• Users have reported that highly complex or ambiguous prompts can lead to unexpected or inconsistent results
• Performance varies with image size and prompt complexity; larger images or intricate edits may require more processing time
• Local deployment requires sufficient GPU resources for optimal speed; cloud-based workflows offer scalability
• Consistency across edits is generally strong, especially with LoRA, but may degrade with highly diverse or conflicting prompts
• Positive feedback highlights the model’s flexibility, ease of integration, and strong results for style transfer and object editing
• Some users note that the Dev variant prioritizes speed over maximum fidelity; for best quality, consider higher-tier variants
• Occasional concerns about artifacts or loss of detail in highly detailed or high-resolution images, especially with aggressive edits

• May struggle with extremely high-resolution images or highly detailed edits, especially in the Dev variant
• Complex, multi-step edits in a single prompt can sometimes produce artifacts or unintended changes
• Requires careful prompt engineering and, for advanced use, familiarity with LoRA fine-tuning for optimal results