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AI Virtual Outfit Try-On generates videos of a person wearing uploaded clothing. Upload a portrait and clothing images, add an optional prompt, and get a try-on video. Ready-to-use REST inference API, no coldstarts, affordable pricing.

image-to-video
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Idle

$0.195per run·~51 / $10

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AI Virtual Outfit Try-On

AI Virtual Outfit Try-On generates realistic videos of you wearing any outfit. Upload a portrait and clothing images — AI composites the look onto your body and animates it into a natural, wearable video. See how any outfit looks on you before buying, without a fitting room.

Why Choose This?

  • Realistic virtual try-on video Goes beyond static image try-on — generates an animated video showing the outfit in natural motion on your body.

  • Multi-garment support Upload up to 8 clothing images per request to try on complete outfits with multiple pieces.

  • Prompt-guided scene control Optionally describe the desired video scene, background, or mood to customize the visual context of the try-on.

  • Flexible duration Generate clips from 5 to 15 seconds to capture enough movement to evaluate the outfit.

Parameters

ParameterRequiredDescription
imageYesPortrait photo of the person (URL or file upload).
clothes_imagesYesClothing image URLs to try on. Up to 8 images per request.
promptNoText description of the desired video scene, background, or atmosphere.
durationNoVideo length in seconds. Range: 5–15. Default: 5.

How to Use

  1. Upload your portrait — a clear, full-body or upper-body photo works best.
  2. Upload clothing images — provide image URLs for each garment you want to try on (up to 8).
  3. Write a prompt (optional) — describe the scene, setting, or vibe you want for the video.
  4. Set duration — choose between 5 and 15 seconds.
  5. Submit — generate, preview, and download your try-on video.

Pricing

DurationCost
5s$0.195
10s$0.390
15s$0.585

Billing Rules

  • Rate: $0.039 per second
  • Duration range: 5–15 seconds

Best Use Cases

  • E-commerce & fashion retail — Let customers virtually try on outfits before purchasing.
  • Personal styling — See how multiple garments look together as a complete outfit.
  • Social media content — Create fashion try-on videos for Instagram and TikTok.
  • Wardrobe planning — Preview new clothing against your actual appearance before buying.

Pro Tips

  • Use a clear, well-lit portrait with a neutral background for the most accurate outfit compositing.
  • Full-body or upper-body photos give the model more context for realistic garment placement.
  • Upload clean, front-facing clothing images on a plain background for the cleanest try-on results.
  • Use the prompt to set the scene — for example, "walking in a city street, natural daylight" for a lifestyle feel.

Notes

  • Both image and clothes_images are required fields.
  • Up to 8 clothing images can be provided per request.
  • Ensure all image URLs are publicly accessible if using links rather than direct uploads.
  • Please ensure your content complies with WaveSpeed AI's usage policies.
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.

Ai Virtual Outfit Tryon API — Quick start

Grab a WaveSpeedAI API key, then call POST https://api.wavespeed.ai/api/v3/wavespeed-ai/ai-virtual-outfit-tryon 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 Ai Virtual Outfit Tryon 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",
    "clothes_images": [
        "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
    ],
    "duration": 5
}
JSON
)

# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  -X POST "https://api.wavespeed.ai/api/v3/wavespeed-ai/ai-virtual-outfit-tryon" \
  -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/wavespeed-ai/ai-virtual-outfit-tryon";
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 ${apiKey}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
        "image": "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg",
        "clothes_images": [
                "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
        ],
        "duration": 5
}),
});
const task = body.data ?? body;
if (!task.id) throw new Error("Submission response did not contain a prediction id");
const resultUrl = task.urls?.get ||
  `https://api.wavespeed.ai/api/v3/predictions/${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",
    "clothes_images": [
        "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
    ],
    "duration": 5
}

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/wavespeed-ai/ai-virtual-outfit-tryon", 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)

Ai Virtual Outfit Tryon API — Frequently asked questions

What is the Ai Virtual Outfit Tryon API?

Ai Virtual Outfit Tryon is a WaveSpeedAI model for video generation from images, exposed as a REST API on WaveSpeedAI. AI Virtual Outfit Try-On generates videos of a person wearing uploaded clothing. Upload a portrait and clothing images, add an optional prompt, and get a try-on video. Ready-to-use REST inference API, no coldstarts, affordable pricing. You can call it programmatically or try it from the playground above.

How do I call the Ai Virtual Outfit Tryon 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/wavespeed-ai/ai-virtual-outfit-tryon.

How much does Ai Virtual Outfit Tryon cost per run?

Ai Virtual Outfit Tryon starts at $0.20 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 Ai Virtual Outfit Tryon accept?

Key inputs: `prompt`, `image`, `duration`, `clothes_images`. 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/wavespeed-ai/ai-virtual-outfit-tryon.

How long does Ai Virtual Outfit Tryon take to generate?

Median end-to-end generation time on WaveSpeedAI is around 92 seconds per request, based on recent successful runs. Queue time varies with global demand; live status is visible in the prediction record.

Can I use Ai Virtual Outfit Tryon outputs commercially?

Commercial usage rights depend on the model's license, set by its provider (WaveSpeedAI). The license summary appears on the model card above; see WaveSpeedAI's Terms of Service for platform-level conditions.

AI Virtual Outfit Tryon | Fast Image-to-Video API on WaveSpeedAI