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Bria

FIBO 1.5

fibo-edit-1.5/image-to-image

BRIA's 8B flow-matching DiT distilled image model generates images with just 4 to 6 sampling steps and supports JSON-structured prompts with attribute-decoupled editing

Try fibo-edit-1.5/image-to-image

Input

Output

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

Pricing

fibo-edit-1.5/image-to-image per image

Unit Price $0.0575

Overview

fibo-edit-1.5/image-to-image is an image editing model for developers that modifies existing images via instruction-driven editing. It supports passing in 1 to 4 reference images at a time, which the instructions reference in order; a mask can optionally be used to precisely target the edited region, and a fixed random seed allows results to be reproduced. With multi-image input, the output aspect ratio can be specified; with a single image, the original aspect ratio is preserved. It is suited to product teams that need to embed image modifications into automated pipelines, batch processing, or creative tools.

Key Highlights

  • Multi-image references understood in order — up to 4 images can be passed in, and instructions reference them in upload order, making it easy to combine multiple assets in one edit.
  • Mask-based local editing — when a mask URL is provided, only the white-marked region is edited and the black region is preserved, ideal for changing only part of the image.
  • Reproducible with seeds — the same seed and inputs yield the same output, facilitating debugging, comparison, and batch reruns.

Capabilities

Instruction-driven image editing: describe what to change in natural language and the model directly outputs the modified image, with no manual retouching steps.

Multi-image input and ordered references: pass in 1–4 images at a time; instructions reference them in image order, supporting composition or reference-based editing from multiple assets.

Mask region control: pass in a mask the same size as the image to limit the edit scope — white regions are modified, black regions are preserved.

Controllable output aspect ratio: with multi-image input, one of nine output aspect ratios can be specified; with single-image input, the original aspect ratio is automatically retained, reducing the cost of cropping and fitting.

Use Cases

  • E-commerce teams batch-modifying product image backgrounds or local details — upload product images and editing instructions to produce modified images ready for detail pages or ad creatives.
  • Design tool integration for on-demand retouching — users select a mask and enter modification requirements, and the product returns the local editing result instantly.
  • Multi-asset reference composition — pass in multiple reference images and describe the composition intent in order to generate composite results for concept drafts or marketing images.

Limitations and Selection Advice

Only instruction-based editing of existing images is supported; pure text-to-image generation is not provided. Single-image input cannot customize the output aspect ratio, and mask editing only allows single-image input. To precisely control the scope of changes, a mask the same size as the image must be prepared; multi-image instructions depend on upload order, so callers must ensure the order matches the description. It is suited to developers embedding image modifications into automated pipelines, and not to single-image scenarios that require generating an image from scratch or that impose mandatory output aspect ratios.

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