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Embed Product

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Bria Embed Product seamlessly integrates product images into scene backgrounds with natural lighting and perspective matching. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

image-to-image
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Idle

$0.04per run·~25 / $1

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Bria Embed Product

Bria Embed Product seamlessly embeds product images into background scenes with precise positioning control. Upload a background image and one or more product images with exact placement coordinates — the model intelligently blends products into the scene with natural lighting, shadows, and perspective.

Why Choose This?

  • Precise positioning Specify exact X, Y coordinates and dimensions for pixel-perfect product placement.

  • Multi-product support Embed multiple products into a single scene with individual positioning for each.

  • Intelligent blending Automatically adapts lighting, shadows, and perspective for realistic integration.

  • Simple workflow Define position and size — the model handles the compositing automatically.

Parameters

ParameterRequiredDescription
imageYesBackground image (URL or upload)
productsYesList of products to embed (click "+ Add Item" to add more)
→ imageYesProduct image (URL or upload)
→ xYesX coordinate for product placement (pixels)
→ yYesY coordinate for product placement (pixels)
→ widthYesProduct width in the output (pixels)
→ heightYesProduct height in the output (pixels)
seedNoRandom seed for reproducibility
enable_sync_modeNoWait for result in response (API only)
enable_base64_outputNoReturn base64 instead of URL (API only)

How to Use

  1. Upload background image — provide the scene where products will be placed.
  2. Add products — for each product:
  • Upload the product image
  • Set X and Y coordinates for placement position
  • Set width and height for product size
  1. Add more products (optional) — click "+ Add Item" to embed additional products.
  2. Set seed (optional) — for reproducible results.
  3. Run — submit and download your composite image.

Pricing

OutputCost
Per image$0.04

Best Use Cases

  • E-commerce — Place products in lifestyle scenes for catalog and marketing images.
  • Interior Design — Visualize furniture and decor in room settings.
  • Advertising — Create product placement visuals for campaigns.
  • Virtual Staging — Add products to empty spaces for real estate or retail.
  • Product Photography — Composite products into professional backgrounds without physical shoots.

Pro Tips

  • Use transparent PNG product images for best blending results.
  • Coordinate values (x, y) define the top-left corner of the product placement.
  • Match product size (width, height) to the perspective of the background scene.
  • For multiple products, consider their relative positions and overlap.
  • Use a consistent seed to iterate on placement while maintaining the same blending style.

Notes

  • Both image (background) and products are required fields.
  • Each product requires image, x, y, width, and height values.
  • Coordinates are in pixels relative to the background image.
  • Ensure uploaded image URLs are publicly accessible.

Related Models

Note:This website uses AI models provided by third parties.

Embed Product API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/bria/embed-product 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 Embed Product 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",
    "products": [
        {
            "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
            "x": 0,
            "y": 0,
            "width": 0,
            "height": 0
        }
    ]
}
JSON
)

# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  -X POST "https://api.wavespeed.ai/api/v3/bria/embed-product" \
  -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/embed-product";
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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
        "products": [
                {
                        "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
                        "x": 0,
                        "y": 0,
                        "width": 0,
                        "height": 0
                }
        ]
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${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",
    "products": [
        {
            "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
            "x": 0,
            "y": 0,
            "width": 0,
            "height": 0
        }
    ]
}

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/embed-product", 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)

Embed Product API — Frequently asked questions

What is the Embed Product API?

Embed Product is a Bria model for image editing, exposed as a REST API on WaveSpeedAI. Bria Embed Product seamlessly integrates product images into scene backgrounds with natural lighting and perspective matching. 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 Embed Product 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-embed-product.

How much does Embed Product cost per run?

Embed Product 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 Embed Product accept?

Key inputs: `image`, `seed`, `enable_base64_output`, `enable_sync_mode`, `products`. 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-embed-product.

How do I get started with the Embed Product 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 Embed Product 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.

Embed Product | Fast Image Editing API | WaveSpeedAI