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BRIA FIBO Edit GenFill API

bria /

BRIA FIBO Edit GenFill is a fast AI generative fill image editing model that fills masked regions in an image from a text prompt using BRIA’s licensed-data image editing API. Ready-to-use REST inference API for inpainting, object replacement, background repair, product image editing, creative retouching, marketing assets, and professional image editing workflows with simple integration, no coldstarts, and affordable pricing.

image-to-image
입력

대기 중

A kind-faced old lady.

$0.04실행당·~25 / $1

다음:

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A kind-faced old lady.

A kind-faced old lady.

관련 모델

README

Bria FIBO Edit GenFill

Bria FIBO Edit GenFill fills masked regions in an image using a natural-language prompt. Upload a source image, provide a mask, and describe what should appear in the masked area to generate a clean, context-aware edit.

Why Choose This?

  • Text-guided generative fill
    Replace or insert content inside a masked region using a prompt.

  • Precise mask-based control
    Limit generation to exactly the area you want to edit.

  • Clean editing workflow
    Ideal for object replacement, scene cleanup, product retouching, and controlled inpainting.

  • Licensed-data Bria pipeline
    Built on Bria’s commercial-friendly image editing workflow.

  • Simple input setup
    Works with direct URLs or uploaded image assets.

Parameters

ParameterRequiredDescription
imageYesSource image to edit.
mask_imageYesMask image that marks the region to fill.
promptYesText description of what to generate inside the masked area.

How to Use

  1. Upload the source image — provide the image you want to edit.
  2. Upload the mask image — clearly mark the region that should be filled or replaced.
  3. Write your prompt — describe the desired content, style, material, color, lighting, or object.
  4. Submit — run the model and download the generated result.

Example Use Case

Fill an empty tabletop region with a realistic ceramic vase that matches the lighting and perspective of the original photo.

Pricing

OutputCost
Per image$0.04

Billing Rules

  • Each generated image costs $0.04
  • Pricing is fixed per image

Best Use Cases

  • Object insertion — Add products, props, or scene elements into a masked area.
  • Object replacement — Swap one item for another while preserving the rest of the composition.
  • Photo cleanup — Remove distractions and regenerate the background naturally.
  • Product retouching — Refine catalog or marketing visuals with controlled edits.
  • Creative compositing — Build prompt-driven image edits with precise spatial control.

Pro Tips

  • Use a clean, accurate mask for better fill quality.
  • Be specific in the prompt about object type, material, color, and lighting.
  • Mention perspective or scene context when realism matters.
  • Smaller, well-defined masked regions usually produce more stable results.
  • Match the prompt to the style of the original image for more natural blending.

Notes

  • image, mask_image, and prompt are all required.
  • The mask should clearly define the area to be filled.
  • Better prompts usually improve control over object appearance and scene consistency.
  • Pricing is fixed per generated image.

Related Models

  • Bria image editing workflows — Useful when you need other prompt-based edit or inpainting styles.
  • Bria fill and replace workflows — Useful for more targeted commercial image editing tasks.
참고:이 웹사이트는 제3자가 제공하는 AI 모델을 사용합니다.

Fibo Edit Genfill API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/bria/fibo-edit/genfill with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Fibo Edit Genfill below.

HTTP example
set -euo pipefail

: "${WAVESPEED_API_KEY:?Set WAVESPEED_API_KEY}"

REQUEST_BODY=$(cat <<'JSON'
{
    "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
    "mask_image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
    "prompt": "A cinematic shot of a city at sunset, soft golden light"
}
JSON
)

# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  -X POST "https://api.wavespeed.ai/api/v3/bria/fibo-edit/genfill" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $WAVESPEED_API_KEY" \
  -d "$REQUEST_BODY")

TASK=$(printf '%s' "$SUBMIT_RESPONSE" | jq 'if has("data") then .data else . end')
PREDICTION_ID=$(printf '%s' "$TASK" | jq -r '.id')
if [ -z "$PREDICTION_ID" ] || [ "$PREDICTION_ID" = "null" ]; then
  printf 'Submission response did not contain a prediction id
' >&2
  exit 1
fi
RESULT_URL=$(printf '%s' "$TASK" | jq -r '.urls.get // empty')
if [ -z "$RESULT_URL" ]; then
  RESULT_URL="https://api.wavespeed.ai/api/v3/predictions/$PREDICTION_ID/result"
fi

# 2. Poll until the prediction finishes.
while true; do
  RESPONSE=$(curl --silent --show-error --fail-with-body "$RESULT_URL" \
    -H "Authorization: Bearer $WAVESPEED_API_KEY")
  RESULT=$(printf '%s' "$RESPONSE" | jq 'if has("data") then .data else . end')
  STATUS=$(printf '%s' "$RESULT" | jq -r '.status')
  case "$STATUS" in
    completed) printf '%s\n' "$RESULT" | jq '.outputs'; break ;;
    failed|cancelled|timeout) printf '%s\n' "$RESULT" | jq . >&2; exit 1 ;;
    created|processing) sleep 2 ;;
    *) printf 'Unexpected status: %s
' "$STATUS" >&2; exit 1 ;;
  esac
done
Node.js example
const submitUrl = "https://api.wavespeed.ai/api/v3/bria/fibo-edit/genfill";
const apiKey = process.env.WAVESPEED_API_KEY;
if (!apiKey) throw new Error('Set WAVESPEED_API_KEY');

async function requestJson(url, options = {}) {
  const response = await fetch(url, options);
  if (!response.ok) throw new Error(await response.text());
  return response.json();
}

// 1. Submit the prediction.
const body = await requestJson(submitUrl, {
  method: "POST",
  headers: {
    "Authorization": `Bearer ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
        "mask_image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
        "prompt": "A cinematic shot of a city at sunset, soft golden light"
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${task.id}/result`;

// 2. Poll until the prediction finishes.
while (true) {
  const resultBody = await requestJson(resultUrl, {
    headers: { "Authorization": `Bearer ${apiKey}` },
  });
  const result = resultBody.data ?? resultBody;
  if (result.status === "completed") {
    console.log(result.outputs);
    break;
  }
  if (["failed", "cancelled", "timeout"].includes(result.status)) throw new Error(JSON.stringify(result));
  if (!["created", "processing"].includes(result.status)) throw new Error("Unexpected status: " + result.status);
  await new Promise(resolve => setTimeout(resolve, 2000));
}
Python example
import json
import os
import time
from urllib.request import Request, urlopen

api_key = os.environ["WAVESPEED_API_KEY"]
headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
payload = {
    "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
    "mask_image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
    "prompt": "A cinematic shot of a city at sunset, soft golden light"
}

def request_json(url, data=None):
    request = Request(url, data=data, headers=headers, method="POST" if data else "GET")
    with urlopen(request) as response:
        return json.load(response)

# 1. Submit the prediction.
body = request_json("https://api.wavespeed.ai/api/v3/bria/fibo-edit/genfill", json.dumps(payload).encode())
task = body.get("data", body)
if not task.get("id"):
    raise RuntimeError("Submission response did not contain a prediction id")
result_url = task.get("urls", {}).get("get") or f"https://api.wavespeed.ai/api/v3/predictions/{task['id']}/result"

# 2. Poll until the prediction finishes.
while True:
    result_body = request_json(result_url)
    result = result_body.get("data", result_body)
    status = result.get("status")
    if status == "completed":
        print(result.get("outputs", []))
        break
    if status in {"failed", "cancelled", "timeout"}:
        raise RuntimeError(result)
    if status not in {"created", "processing"}:
        raise RuntimeError(f"Unexpected status: {status}")
    time.sleep(2)

Fibo Edit Genfill API — Frequently asked questions

What is the Fibo Edit Genfill API?

Fibo Edit Genfill is a Bria model for image editing, exposed as a REST API on WaveSpeedAI. BRIA FIBO Edit GenFill is a fast AI generative fill image editing model that fills masked regions in an image from a text prompt using BRIA’s licensed-data image editing API. Ready-to-use REST inference API for inpainting, object replacement, background repair, product image editing, creative retouching, marketing assets, and professional image editing workflows with simple integration, no coldstarts, and affordable pricing. You can call it programmatically or try it from the playground above.

How do I call the Fibo Edit Genfill API?

POST your input parameters to the model's REST endpoint (shown in the API tab of this playground) with your WaveSpeedAI API key in the Authorization header. Submission returns a prediction ID. Poll the result endpoint starting around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. The playground generates production-oriented Python, JavaScript, and cURL examples with timeouts, transient-error handling, and safe GET retries. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/bria/bria-fibo-edit-genfill.

How much does Fibo Edit Genfill cost per run?

Fibo Edit Genfill starts at $0.040 per run. That figure is the base price — the final charge scales with the parameters you set in the form (output size, length, count, references, or whatever knobs this model exposes), so a higher-quality or larger output costs more than a minimal one. The exact cost for your current input is shown live next to the Generate button before you submit, and the actual per-call charge is recorded on the prediction afterwards.

What inputs does Fibo Edit Genfill accept?

Key inputs: `prompt`, `image`, `mask_image`. The full JSON schema (types, defaults, allowed values) is rendered above the Generate button and mirrored in the API reference at https://wavespeed.ai/docs/docs-api/bria/bria-fibo-edit-genfill.

How long does Fibo Edit Genfill take to generate?

Median end-to-end generation time on WaveSpeedAI is around 28 seconds per request, based on recent successful runs. Queue time varies with global demand; live status is visible in the prediction record.

Can I use Fibo Edit Genfill outputs commercially?

Commercial usage rights depend on the model's license, set by its provider (Bria). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.

BRIA FIBO Edit GenFill API | WaveSpeedAI