FLUX

flux-redux-dev — Image-to-Image AI Model

flux-redux-dev from Black Forest Labs empowers developers to create precise, controlled variations of input images, preserving original structure while enabling prompt-driven restyling for advanced image-to-image workflows. As part of the innovative flux family, this dev model acts as an adapter on FLUX.1 base models, ideal for generating consistent image variants without losing key details—perfect for developers seeking a high-fidelity image-to-image AI model. Whether refining details or experimenting with styles, flux-redux-dev delivers professional-grade results for custom image transformations.

Built for technical precision, flux-redux-dev stands out in Black Forest Labs' lineup by supporting seamless integration into ComfyUI and similar pipelines, making it a go-to for image editing API applications.

What Sets flux-redux-dev Apart

flux-redux-dev excels as a specialized adapter for FLUX.1 base models, focusing on high-fidelity image variations that maintain structural integrity during prompt-based modifications—a key edge over general-purpose image-to-image tools. This enables developers to produce restyled outputs with refined details, such as subtle lighting adjustments or style transfers, while keeping core compositions intact.

Unlike broader generative models, flux-redux-dev integrates natively with advanced pipelines like ComfyUI's TBG Enhanced Tiled Upscaler, supporting high-resolution workflows up to 200MP through tiled processing and ControlNet compatibility for precise regional control. Users gain the ability to handle massive images with per-tile prompt editing and denoising, ideal for Black Forest Labs image-to-image tasks requiring extreme fidelity.

It pairs with models like sigclip_vision for vision encoding, ensuring robust input handling in resource-intensive setups that demand around 28GB VRAM for optimal refinement passes. This setup allows for dynamic structure preservation via parameters like Reassemblance, which anchors outputs to original layouts during multi-step upscaling.

• Controlled variations: Generates prompt-driven restyles while preserving input structure, outperforming standard diffusion models in consistency.
• Tiled high-res support: Enables up to 200MP outputs with tile-specific controls, perfect for large-scale AI image editor API deployments.
• ControlNet integration: Applies unlimited inputs per tile for depth, edge, or pose conditioning, delivering targeted edits unattainable in non-tiled systems.

• Content Sensitivity: Ensure that the input images comply with ethical guidelines and do not contain sensitive or inappropriate content.
• Resource Management: Be mindful of computational resources, especially when generating multiple high-resolution outputs.
• Parameter Tuning: Experiment with different parameter settings to achieve the desired balance between image quality and processing efficiency.

Legal Information

By using this Flux Redux Dev, you agree to:

• Black Forest Labs Privacy Policy
• Black Forest Labs Terms of Service

How to Use flux-redux-dev on Eachlabs

Access flux-redux-dev seamlessly through Eachlabs' Playground for instant testing, API for production-scale image-to-image AI model integrations, or SDK for custom apps. Provide an input image, text prompt for restyling, and optional parameters like denoise strength or tile settings; expect high-fidelity outputs in standard formats with preserved structures and refined details in seconds.

Image Variation: Generates diverse variations of the input image while maintaining core elements.

Customization: Offers adjustable parameters to fine-tune the output according to user preferences.

Use Cases for flux-redux-dev

Developers building automated image editing API for e-commerce can upload product photos and use flux-redux-dev to generate variants like "transform this sneaker onto a urban street background with neon reflections, maintaining exact shape and branding," producing studio-ready composites without manual shoots.

Digital artists experimenting with style transfers feed reference images into ComfyUI workflows, leveraging flux-redux-dev's tiled refinement to upscale and restyle artworks up to 200MP—such as converting a portrait sketch to "oil painting in Rembrandt style with dramatic chiaroscuro lighting"—while preserving facial details across tiles.

Marketers needing consistent asset libraries input brand visuals and prompts like "restyle this logo illustration as a futuristic cyberpunk vector with glowing edges," using the model's structure-preserving adapter to create variant packs for campaigns, integrated via flux-redux-dev API for scalable production.

Game designers utilize its ControlNet pipe for character customization, applying per-tile pose maps to evolve base sprites into "armored warrior in dynamic combat pose on alien terrain," ensuring identity consistency in high-res assets for interactive prototypes.

Experiment with Aspect Ratios – Test different aspect ratios (e.g., 1:1 for social media, 16:9 for cinematic looks) to see how compositions change.

Optimize Inference Steps – Use a higher number of steps (e.g., 30–50) for detailed and refined outputs, or lower steps (e.g., 10–20) for faster results.

Adjust Guidance for Control – Set guidance between 3–7 for balanced creativity; higher values make the output more faithful to the original image.

Improve Image Sharpness with Output Quality – Increase output quality closer to 100 for high-fidelity results; reduce it to save processing time.

Test Different Megapixel Settings – Use 0.25 MP for quick previews and 1 MP for high-resolution outputs.

Input Dependency: The quality of the output is heavily dependent on the quality of the input image.

Computational Load: Generating multiple high-resolution images can be resource-intensive and time-consuming.

Content Restrictions: The Flux Redux Dev may not perform well with images containing complex patterns or excessive noise.

Output Format: PNG,JPG,WEBP