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bria/expand

Bria Expand grows images beyond borders by generating new pixels to fit any aspect ratio; trained on licensed data for commercial use. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

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Preserve existing transparency in the input image.

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$0.04每次運行·~25 / $1

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README

Bria Expand

Bria Expand extends an image beyond its borders to a selected aspect ratio. Generate surrounding content that matches the scene, with optional text guidance and transparency preservation.

Parameters

ParameterRequiredDescription
imageYesInput image URL. Supported formats: JPEG, JPG, PNG, and WEBP.
aspect_ratioYesTarget ratio: 1:1, 2:3, 3:2, 3:4, 4:3, 4:5, 5:4, 9:16, or 16:9. Default: 1:1.
promptNoEnglish description of the expanded area. Omit or leave empty for automatic guidance based on the input image.
negative_promptNoEnglish description of content to avoid.
preserve_alphaNoPreserve existing input transparency. Default: true. Set to false for an opaque result. Has no effect on images without an alpha channel.
seedNoRandom seed for reproducible generation. Omit for random results.

How to Use

  1. Upload an image or provide its URL.
  2. Choose the target aspect ratio.
  3. Optionally describe the desired surroundings and any content to avoid.
  4. Keep preserve_alpha enabled to retain existing transparency, or disable it for an opaque result.
  5. Optionally set a seed, then submit and retrieve the expanded image.

Pricing

$0.04 per generation.

Notes

  • The image is centered within the target aspect ratio. If the target ratio matches the source ratio, the image is unchanged.
  • Large inputs may be resized to keep the final image within 25 million pixels.
  • Keep the main subject above roughly 15% of the canvas area for best results; this is a quality recommendation.
  • Prompts are optional. Use English and avoid special characters.
  • When preserving transparency, generated regions inherit alpha values from nearby original pixels along the same row or column.
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Expand API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/bria/expand 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 Expand 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",
    "aspect_ratio": "1:1",
    "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/expand" \
  -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/expand";
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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
        "aspect_ratio": "1:1",
        "preserve_alpha": true
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = `https://api.wavespeed.ai/api/v3/predictions/${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",
    "aspect_ratio": "1:1",
    "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/expand", 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)

Expand API — Frequently asked questions

What is the Expand API?

Expand is a Bria model for image processing, exposed as a REST API on WaveSpeedAI. Bria Expand grows images beyond borders by generating new pixels to fit any aspect ratio; trained on licensed data for commercial use. 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 Expand 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-expand.

How much does Expand cost per run?

Expand starts at $0.04 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 Expand accept?

Key inputs: `prompt`, `image`, `aspect_ratio`, `seed`, `negative_prompt`, `enable_base64_output`. 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-expand.

How long does Expand take to generate?

Reported generation time on WaveSpeedAI is around 12 seconds per request. This is an estimate, not a latency guarantee; queue time and input settings can change the total wait. live status is visible in the prediction record.

Can I use Expand 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/expand for AI agents and LLMs