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Black Forest Labs

FLUX.1 Fill Pro

flux-1-fill-pro

A professional-grade local inpainting and outpainting model that replaces, removes, or extends parts of an image at precise points according to a binary mask while keeping lighting and perspective consistent; it requires a mask input and is suited for retouching rather than whole-image generation

flux-1-fill-pro

Try flux-1-fill-pro

Input

Output

image
Fill in the parameters and run — results show up here.

Pricing

flux-1-fill-pro per image

Unit Price $0.0715

Overview

flux-1-fill-pro is an image inpainting model built for targeted edits: it takes an original image and a black-and-white mask along with a text prompt, and repaints only the white region of the mask while leaving the black region unchanged. It is suited to scenarios that require surgical modifications on an existing composition, such as product image retouching, swapping parts in e-commerce assets, adjusting renderings, and extending the canvas. Developers call it through the unified HiAPI.ai interface and pay per image, with no need to build their own image editing pipeline.

Key Highlights

  • Mask-driven, targeted repainting — only the white region of the mask is changed while all other pixels stay as they are, making it suitable for fine edits that replace, remove, or add elements.
  • Repair and outpainting in one interface — perform local repair when the structure should not change, or extend the canvas to adjust the aspect ratio when needed; both needs share one set of input parameters.
  • Reproducible generation control — control randomness and prompt adherence via seed, steps, guidance, and prompt_upsampling, which helps with batch generation and regression verification.

Capabilities

Masked local repainting: pass the original image, a same-size black-and-white mask, and a prompt to replace or add content in the specified region while the black region is preserved as-is, which is suitable for swapping local parts and fixing defects.

Canvas extension and composition completion: generative extension beyond the image boundaries can be used to adjust the aspect ratio or complete the canvas, adapting to the size requirements of different delivery channels.

Parameterized quality control: diffusion steps range from 15 to 50 and guidance strength from 1.5 to 100, both adjustable as needed; higher steps yield finer details but take longer, and higher guidance strength stays closer to the prompt.

Reproducible results and format options: a fixed seed reproduces similar results, facilitating batch tasks and effect comparisons; output can be jpg or png and can go directly into subsequent asset workflows.

Prompt auto-rewrite toggle: prompt_upsampling is off by default; when enabled, it automatically rewrites the prompt to increase creativity. Keep it off when generation must strictly follow the original text.

Use Cases

  • E-commerce operations: swapping parts and fixing defects in product images — use a mask to outline the product component or blemish to be replaced, enter a new description, and produce a product main image ready for listing, reducing reshoots and full-image regeneration.
  • Design teams: local adjustment and canvas extension of renderings — modify specific areas or extend the canvas outward on renderings such as architectural and interior ones, producing images that fit different layouts while keeping lighting and perspective consistent.
  • Asset processing: removing distracting elements and filling them in naturally — mark unwanted objects on the original image with a mask and describe the fill content in the prompt to produce a clean image for later layout and delivery.

Limitations and Selection Advice

Before use, you must prepare a black-and-white mask of the same size as the original image, adding an extra region annotation or segmentation step. It is an editing model rather than a general text-to-image model and is not suitable for generating a complete image from scratch; results depend on mask quality and prompt description, and complex text and fine structures may require multiple adjustments. Large-area or high-resolution edits significantly increase processing time and call costs, and scenarios that are budget-sensitive or require self-hosted weights should be evaluated separately.

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Frequently asked questions

View all FAQs →
  • What is FLUX.1 Fill Pro?

    FLUX.1 Fill Pro is a mask-driven image editing model: upload a source image, a same-size black-and-white mask and a prompt, and only the white area is repainted while the black area stays unchanged.

  • What do black and white mean in the mask?

    Black keeps the source and white is repainted by the prompt. The mask must match the source image dimensions.

  • Are both image and mask required?

    Yes. The current public contract requires a source image and a black-and-white mask.

  • What should the prompt describe?

    Describe only the new content inside the white mask and state what must be preserved; do not repeat the area outside the mask.

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