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.
Idle

$0.04per run·~25 / $1


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.
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.
| Parameter | Required | Description |
|---|---|---|
| image | Yes | Person photo to edit. Public image URL or Base64-encoded image. JPEG, JPG, PNG, and WEBP are supported. |
| reference_images | Yes | One to three garment or accessory reference images, supplied as public URLs or Base64-encoded images. These are separate from image. |
| prompt | No | Optional instructions to guide the edit. Omit to use the model's built-in instructions. |
| aspect_ratio | No | Output 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_format | No | Output image format: png or jpeg. Default: png. |
| seed | No | Random seed for generation. Omit for a random seed. |
image.reference_images.prompt for placement or styling details.Pricing is fixed at $0.04 per generated image. Each successful request returns one image.
| Output | Price |
|---|---|
| One generated image | $0.04 |
One to three reference images are included at the same price. prompt, aspect_ratio, output_format, and seed do not add separate charges.
aspect_ratio unset when you want to preserve the original framing.image and reference_images are required; prompt is optional.Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/bria/virtual-try-on with your input as JSON. The endpoint returns a prediction id. Start polling the result endpoint around every 2 seconds, increase the interval for long-running tasks, and stop on any terminal status. On completed, read output values from data.outputs. Examples for Virtual Try On below.
set -euo pipefail
: "${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
doneconst 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));
}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 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.
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.
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.
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.
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.
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.