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Bria Virtual Try-On dresses a person in reference garments using a person photo, one to three garment or accessory reference images, and optional text instructions for virtual try-on, fashion visualization, e-commerce, and styling workflows. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

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Bria Virtual Try-On

Bria Virtual Try-On lets you visualize garments and accessories on a person using a portrait or full-body photo and one to three reference images. Upload the person image separately from the clothing or accessory references, then optionally add instructions to guide how the items should appear in the final result.

The model is designed for fashion visualization, apparel previews, accessory try-on, and e-commerce workflows where you want to place referenced items onto an existing person image.

Why Choose This?

  • Purpose-built editing
    Visualize garments and accessories on a person from reference images.

  • Multi-reference support
    Combine one person photo with one to three garment or accessory reference images.

  • Optional prompt guidance
    Images alone are sufficient. Add a prompt only when you want to direct a specific edit.

  • Original framing by default
    Omit aspect_ratio to retain the person photo's aspect ratio.

  • Standard image output
    Receive a PNG or JPEG image URL in the standard WaveSpeed prediction response.

Parameters

ParameterRequiredDescription
imageYesPerson photo to edit. Public image URL or Base64-encoded image. JPEG, JPG, PNG, and WEBP are supported.
reference_imagesYesOne to three garment or accessory reference images, supplied as public URLs or Base64-encoded images. These are separate from image.
promptNoOptional instructions to guide the edit. Omit to use the model's built-in instructions.
aspect_ratioNoOutput aspect ratio: 1:1, 2:3, 3:2, 3:4, 4:3, 4:5, 5:4, 9:16, or 16:9. Omit to preserve the person photo's aspect ratio.
output_formatNoOutput image format: png or jpeg. Default: png.
seedNoRandom seed for generation. Omit for a random seed.

How to Use

  1. Upload a person photo - Provide the base person image in image.
  2. Add reference images - Supply one to three garment or accessory images in reference_images.
  3. Guide the edit optionally - Add a concise prompt for placement or styling details.
  4. Choose output settings - Optionally set the aspect ratio, output format, and seed.
  5. Submit - Poll the prediction result and retrieve the generated image URL.

Pricing

Pricing is fixed at $0.04 per generated image. Each successful request returns one image.

OutputPrice
One generated image$0.04

One to three reference images are included at the same price. prompt, aspect_ratio, output_format, and seed do not add separate charges.

Best Use Cases

  • Virtual clothing try-on
  • Fashion catalog variations
  • Multi-item outfit visualization

Pro Tips

  • Use a clear person photo with the relevant body area visible.
  • Use clear garment references with visible fabric, patterns, and closures. Use the prompt to explain layering or which existing items to retain.
  • Leave aspect_ratio unset when you want to preserve the original framing.
  • Keep all input image URLs publicly accessible while the request is processing.
  • Use a fixed seed when comparing prompt changes.

Notes

  • image and reference_images are required; prompt is optional.
  • The limit is one person photo plus one to three reference images, for two to four images in total.

Related Models

  • Bria Virtual Try-On — Apply garment or accessory references to an existing person photo for virtual try-on workflows.
  • Bria Product Holding — Generate images of a person naturally holding or presenting a referenced product.
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.

Virtual Try On API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/bria/virtual-try-on 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 Virtual Try On 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",
    "reference_images": [
        "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
    ],
    "aspect_ratio": "1:1",
    "output_format": "png"
}
JSON
)

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

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/virtual-try-on", 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)

Virtual Try On API — Frequently asked questions

What is the Virtual Try On API?

Virtual Try On is a Bria model for image editing, exposed as a REST API on WaveSpeedAI. Bria Virtual Try-On dresses a person in reference garments using a person photo, one to three garment or accessory reference images, and optional text instructions for virtual try-on, fashion visualization, e-commerce, and styling 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 Virtual Try On 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-virtual-try-on.

How much does Virtual Try On cost per run?

Virtual Try On 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 Virtual Try On accept?

Key inputs: `prompt`, `image`, `aspect_ratio`, `seed`, `reference_images`, `output_format`. 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-virtual-try-on.

How do I get started with the Virtual Try On 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 Virtual Try On 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.

Bria Virtual Try-On API on WaveSpeedAI