Vidu Q3 और Q3 Pro मॉडल पर 50% छूट · केवल WaveSpeedAI | 20 मई – 2 जून

Bria Remove Background

bria /

Bria remove-background is a powerful AI image background removal API that produces clean, precise cutouts for photos and graphics. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

ai-remover
Input

Drag & drop करें या upload के लिए click करें

preview
If set to true, the function will wait for the result to be generated and uploaded before returning the response. It allows you to get the result directly in the response. This property is only available through the API.
If enabled, the output will be encoded into a BASE64 string instead of a URL. This property is only available through the API.

Idle

$0.018per run·~55 / $1

ExamplesView all

Related Models

README

Bria Remove Background

Bria Remove Background extracts clean subjects from images with a high-quality alpha matte. It’s fast, consistent, and production-ready for e-commerce listings, ad creatives, social posts, and compositing. The model is trained on licensed data, supporting safe, compliant commercial use.

Highlights

  • Clean cutouts with detailed edges (hair, fur, semi-transparent areas).
  • Alpha workflow: preserve or drop the alpha channel to match your pipeline.

Parameters

  • image* (required) Source image (URL or upload). Missing image will fail.

  • preserve_alpha (checkbox) Keep the alpha channel in the returned asset for immediate compositing.

Pricing

  • Per run: $0.018

How to Use

  1. Upload/Paste image (required).
  2. Toggle preserve_alpha if you need a premultiplied cutout for direct compositing.
  3. Click Run to generate your cutout and download the result.

Output & Workflow Tips

  • Post background: Place the cutout over solid color, gradient, or generated scenes (e.g., with Bria Background Generation).
  • Edge finesse: For extremely fine hair or translucent fabric, a light feather/refine-edge pass can achieve pixel-perfect results.
  • Consistent catalogs: Use identical lighting shots and batch the same settings for uniform store pages.
Accessibility:This website uses AI models provided by third parties.

Remove Background API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/bria/remove-background with your input as JSON. The endpoint returns a prediction id; poll the prediction endpoint until status flips to completed, then read the output URL from data.outputs[0]. Examples for Remove Background below.

HTTP example
# Submit the prediction
curl -X POST "https://api.wavespeed.ai/api/v3/bria/remove-background" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $WAVESPEED_API_KEY" \
  -d '{
    "image": "https://example.com/your-input.jpg",
    "enable_sync_mode": false,
    "enable_base64_output": false
}'

# Response includes a prediction id. Poll for the result:
curl -X GET "https://api.wavespeed.ai/api/v3/predictions/{request_id}/result" \
  -H "Authorization: Bearer $WAVESPEED_API_KEY"

# When status is "completed", read the output from data.outputs[0].
Node.js example
// npm install wavespeed
const WaveSpeed = require('wavespeed');

const client = new WaveSpeed(); // reads WAVESPEED_API_KEY from env

const result = await client.run("bria/remove-background", {
        "image": "https://example.com/your-input.jpg",
        "enable_sync_mode": false,
        "enable_base64_output": false
});

console.log(result.outputs[0]); // → URL of the generated output
Python example
# pip install wavespeed
import wavespeed

output = wavespeed.run(
    "bria/remove-background",
    {
    "image": "https://example.com/your-input.jpg",
    "enable_sync_mode": false,
    "enable_base64_output": false
}
)

print(output["outputs"][0])  # → URL of the generated output

Remove Background API — Frequently asked questions

What is the Remove Background API?

Remove Background is a Bria model for object / watermark removal, exposed as a REST API on WaveSpeedAI. Bria remove-background is a powerful AI image background removal API that produces clean, precise cutouts for photos and graphics. 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 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 prediction endpoint until status flips to "completed", then read the output URL from the result. The playground generates a ready-to-paste code sample in Python, JavaScript, or cURL for whatever inputs you've set. Full request/response shape is documented at https://wavespeed.ai/docs/docs-api/bria/bria-remove-background.

How much does Remove Background cost per run?

Remove Background starts at $0.018 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 accept?

Key inputs: `image`, `enable_base64_output`, `enable_sync_mode`. 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.

How long does Remove Background take to generate?

Average end-to-end generation time on WaveSpeedAI is around 8 seconds per request — measured across recent runs. Queue time scales with global demand; live status is visible in the prediction record.

Can I use Remove Background 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.