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bria/remove-background-guided

Bria Guided Remove Background removes image backgrounds with optional text guidance to control what should be preserved, producing clean transparent cutouts while retaining the original foreground pixels for product images, e-commerce assets, portraits, and design workflows. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

Image Background Removal
Input

Drag & drop or click to upload

preview
Preserve existing transparency. Disable to make the retained subject fully opaque.

Idle

$0.03per run·~33 / $1

Next:

ExamplesView all

Keep only the astronaut and the helmet she is holding.

Keep only the astronaut and the helmet she is holding.

Related Models

README

Bria Guided Remove Background

Bria Guided Remove Background removes image backgrounds with optional text guidance for more controlled cutouts. Provide an image and, when needed, describe which subjects or objects should be kept or removed.

Use it for product cutouts, people, props, catalog assets, advertising composites, and other workflows that need transparent-background images with more control than standard automatic background removal.

Why Choose This?

  • Prompt-guided background removal
    Use natural-language instructions to describe which subjects or objects should remain in the cutout.

  • Standard background removal
    Leave prompt empty when you want automatic background removal without additional guidance.

  • Selective cutouts
    Guide the model toward specific people, products, props, or other visual elements.

  • Existing alpha control
    Preserve transparency already present in the source image or make retained regions fully opaque.

  • Multiple image input formats
    Supports JPEG, JPG, PNG, and WEBP input images.

Parameters

ParameterRequiredDescription
imageYesSource image provided as a publicly accessible URL. Supported formats: JPEG, JPG, PNG, and WEBP.
promptNoOptional instructions describing what should be kept or removed. Omit or leave blank for standard background removal.
preserve_alphaNoPreserve existing transparency in the input image.

How to Use

  1. Provide an image — Upload the source image or provide a public URL or Base64-encoded image.
  2. Add guidance optional — Use prompt when you want to specify which subject, object, or group of elements should remain.
  3. Configure transparency optional — Keep preserve_alpha=true to retain existing transparency, or disable it to make retained regions fully opaque.
  4. Submit — Generate the background-removed image.
  5. Retrieve the result — Access the generated image from the prediction outputs array.

Pricing

Pricing is fixed at $0.03 per image.

OutputCost
One background-removed image$0.03

prompt and preserve_alpha do not add separate charges.

Best Use Cases

  • E-commerce product cutouts — Remove backgrounds from product images for storefronts and catalogs.
  • Advertising composites — Isolate people, products, or props for placement into new designs.
  • People and portrait cutouts — Keep selected subjects while removing surrounding background content.
  • Catalog preparation — Create clean cutouts for product and asset libraries.
  • Selective background removal — Use prompt guidance when only specific visual elements should remain.

Pro Tips

  • Use a clear prompt when the image contains several people or objects and only some should remain.
  • Describe the subject specifically, such as only the chair or the person with the plant.
  • Leave prompt empty when standard automatic background removal is sufficient.
  • Keep preserve_alpha=true when existing transparency in the source image should remain.
  • Set preserve_alpha=false when the retained subject should be fully opaque.

Notes

  • image is required.
  • prompt is optional.
  • When prompt is omitted or left blank, the endpoint performs standard background removal.
  • Supported input formats are JPEG, JPG, PNG, and WEBP.

Related Models

Note:This website uses AI models provided by third parties. Documentation prices are for reference and may be outdated. The Generate button shows an estimate; the final task charge prevails.

Remove Background Guided API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/bria/remove-background-guided 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 Remove Background Guided 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",
    "preserve_alpha": true
}
JSON
)

# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  -X POST "https://api.wavespeed.ai/api/v3/bria/remove-background-guided" \
  -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="https://api.wavespeed.ai/api/v3/predictions/$PREDICTION_ID/result"

# 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' "$RESULT" | jq '.outputs'; break ;;
    failed|cancelled|timeout|deleted) printf '%s\n' "$RESULT" | jq . >&2; exit 1 ;;
    *) sleep 2 ;;
  esac
done
Node.js example
const submitUrl = "https://api.wavespeed.ai/api/v3/bria/remove-background-guided";
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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
        "preserve_alpha": true
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = `https://api.wavespeed.ai/api/v3/predictions/${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", "deleted"].includes(result.status)) throw new Error(JSON.stringify(result));
  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",
    "preserve_alpha": True
}

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/remove-background-guided", 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 = 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", "deleted"}:
        raise RuntimeError(result)
    time.sleep(2)

Remove Background Guided API — Frequently asked questions

What is the Remove Background Guided API?

Remove Background Guided is a Bria model for image background removal, exposed as a REST API on WaveSpeedAI. Bria Guided Remove Background removes image backgrounds with optional text guidance to control what should be preserved, producing clean transparent cutouts while retaining the original foreground pixels for product images, e-commerce assets, portraits, and design workflows. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing. You can call it programmatically or try it from the playground above.

How do I call the Remove Background Guided 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 Python, JavaScript, and cURL examples for submitting requests and polling results. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/bria/bria-remove-background-guided.

How much does Remove Background Guided cost per run?

Remove Background Guided starts at $0.03 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 Remove Background Guided accept?

Key inputs: `prompt`, `image`, `preserve_alpha`. 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-remove-background-guided.

How do I get started with the Remove Background Guided API?

Sign up for a free WaveSpeedAI account to claim starter credits, copy your API key from /accesskey, then call the endpoint shown in the API tab of the playground. The playground also auto-generates a code sample in Python, JavaScript, or cURL for the parameters you've set.

Can I use Remove Background Guided outputs commercially?

Commercial usage rights depend on the model's license, set by its provider (Bria). Check the provider's applicable terms and WaveSpeedAI's Terms of Service before commercial use.

llms.txt — bria/remove-background-guided for AI agents and LLMs