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BFL modelsimage generation api

bfl/flux-differential-diffusion

Flux Differential Diffusion is BFL's intelligent image editing model. Transform, retouch, and reimagine existing images using text prompts - from background replacement to artistic style conversion.

Input
The prompt to generate an image from.

PNG, JPEG, WebP, or GIF · 20 MiB maximum

URL of image to use as initial image.
0.011
The strength to use for image-to-image. 1.0 is completely remakes the image while 0.0 preserves the original. Range: 0.01 to 1.
URL of change map.
The same seed and the same prompt given to the same version of the model will output the same image every time.
020
Range: 0 to 20.
150
Range: 1 to 50.
Idle

Example output — click Run to generate your own

API README

Flux Differential Diffusion

Flux Differential Diffusion belongs at the graduated revision across a single image stage of a visual workflow. Rather than treating the model as an isolated demonstration, teams can place it inside briefing, review, selection, and handoff practices where creative teams can plan a continuum between untouched and substantially changed areas instead of reducing every decision to a binary selection. This positioning clarifies why the model earns a place in a real creative pipeline and what kind of decision it helps people make.

A practical use of Flux Differential Diffusion is portrait retouching, atmosphere shifts, material changes, and localized art direction that should taper naturally into preserved content. Begin by agreeing on the creative objective and review criteria, prepare only the source material needed for that objective, and compare results against audience, brand, editorial, and production needs. Technical request choices remain documented below so the prose can stay focused on planning and creative value.

Highlights

Spatially varying edit strength. Allows strong regeneration in selected areas while other regions change only subtly.

Smooth transition zones. Blends different denoising intensities without hard visual seams between regions.

Fine-grained creative control. Supports nuanced retouching, emphasis, and localized restyling beyond a binary mask.

Context-preserving synthesis. Uses surrounding image information to keep altered regions visually integrated. This configuration emphasizes the model family's characteristic balance of prompt accuracy, visual detail, and creative flexibility.

Pricing

ConfigurationBilling unitPrice
Base generationPer request$0.05

When to Use

✅ Good fit❌ Consider alternatives
The project needs this exact FLUX capabilityThe intended task belongs to a different media workflow
Required source and control media are availableNecessary assets or usage rights are unavailable
Creative direction can state change and preservation goalsOutput must be deterministic at pixel or frame level
Supported dimensions and formats match final deliveryPlacement requires unsupported specifications
An asynchronous generated result fits productionA live frame-synchronous response is mandatory

Prompt Guide

Lead with the main subject and action, then specify composition, context, lighting, materials, style, typography, and atmosphere. For editing, identify each source and clearly separate the requested transformation from the subjects, regions, geometry, or identity that must remain unchanged.

{
  "guidance_scale": 3.5,
  "image": "https://example.com/start-frame.png",
  "prompt": "A cinematic, precisely composed result with a clear subject, controlled camera or viewpoint, realistic lighting, exact materials, and intentional atmosphere",
  "seed": 1,
  "strength": 0.85
}

Technical Specs

SpecValue
Model IDbfl/flux-differential-diffusion
Input fieldsseed (integer)<br>image (string)<br>prompt (string)<br>strength (number; 0.01–1)<br>guidance_scale (number; 0–20)<br>num_inference_steps (integer; 1–50)<br>change_map_image_url (string)
Required inputprompt, image
Output fieldsurl, content_type
ExecutionAsynchronous job

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