Virtual Try-On

Try on clothing and accessories in realtime. Stream a live camera feed, describe the outfit or provide a reference image of the garment, and Lucy VTON will realistically dress the subject while preserving motion and body shape — all with minimal latency.

Quick start (Realtime)

import { createDecartClient, models } from "@decartai/sdk";

const model = models.realtime("lucy-vton-latest");

const stream = await navigator.mediaDevices.getUserMedia({
  audio: true,
  video: {
    frameRate: model.fps,
    width: model.width,
    height: model.height,
  },
});

const client = createDecartClient({ apiKey: "your-api-key-here" });

const realtimeClient = await client.realtime.connect(stream, {
  model,
  mirror: "auto",
  onRemoteStream: (editedStream) => {
    document.querySelector("#video-output").srcObject = editedStream;
  },
});

// Try on with a garment image and descriptive prompt
const garmentFile = document.querySelector('input[type="file"]').files[0];
await realtimeClient.set({
  prompt: "Substitute the current top with a navy blue hoodie with a white cross logo on the chest",
  image: garmentFile,
  enhance: false,
});

// Disconnect when done
realtimeClient.disconnect();

from decart import models
from decart.realtime import RealtimeClient, RealtimeConnectOptions
from decart.types import ModelState

model = models.realtime("lucy-vton-latest")

realtime_client = await RealtimeClient.connect(
    api_key="your-api-key-here",
    stream=camera_stream,
    options=RealtimeConnectOptions(
        model=model,
        initial_state=ModelState(
            prompt="Substitute the current top with a red leather jacket with a zip front",
        ),
        on_remote_stream=lambda edited_stream: handle_stream(edited_stream),
    ),
)

# Change outfit on the fly
await realtime_client.set_prompt("Substitute the current top with a blue denim jacket with a relaxed fit")

# Disconnect when done
await realtime_client.disconnect()

import ai.decart.sdk.DecartClient
import ai.decart.sdk.DecartClientConfig
import ai.decart.sdk.RealtimeModels
import ai.decart.sdk.realtime.ConnectOptions
import ai.decart.sdk.realtime.InitialPrompt

val client = DecartClient(context, DecartClientConfig(apiKey = "your-api-key"))
val realtime = client.realtime

realtime.initialize(eglBase)

realtime.connect(
    localVideoTrack = cameraTrack,
    localAudioTrack = null,
    options = ConnectOptions(
        model = RealtimeModels.LUCY_2_1_VTON,
        onRemoteVideoTrack = { track ->
            remoteRenderer.addSink(track)
        },
        initialPrompt = InitialPrompt("Substitute the current top with a red leather jacket with a zip front")
    )
)

// Change outfit on the fly
realtime.setPrompt("Substitute the current top with a blue denim jacket with a relaxed fit", enhance = true)

import DecartSDK

let config = DecartConfiguration(apiKey: "your-api-key-here")
let client = DecartClient(decartConfiguration: config)

let model = Models.realtime(.lucy2_1_vton)

let realtimeManager = try await client.realtime.connect(
    stream: cameraStream,
    model: model,
    initialState: .init(prompt: "Substitute the current top with a red leather jacket with a zip front")
) { editedStream in
    // Display the edited video
    videoView.stream = editedStream
}

// Change outfit on the fly
try await realtimeManager.setPrompt("Substitute the current top with a blue denim jacket with a relaxed fit")

Realtime Parameters

prompt — Text description of the outfit change. Use the substitute or add patterns described in the VTON 3.5 Prompting Guide.
image — A garment reference image to guide the try-on. Supported formats: JPEG, PNG, WebP.
enhance — Whether to auto-enhance the prompt (default: true). Set to false when you provide detailed prompts yourself.

set() replaces the entire state — fields you omit are cleared. Always include every field you want to keep.

Using Reference Images

For the best results, provide a garment reference image paired with a descriptive prompt. This gives the model the clearest signal of what to apply.

// Best approach: reference image + descriptive prompt
await realtimeClient.set({
  prompt: "Substitute the current top with a black bomber jacket with a blue logo on the chest and a zip front",
  image: garmentFile,
  enhance: false,
});

// Reference image only — the model dresses the person to match
await realtimeClient.set({ image: garmentFile });

Reference image tips:

Clean garment images work best — just the clothing item, no person wearing it
White or plain backgrounds are ideal
At least 512×512 pixels — the model reproduces what it sees, so a clear image produces better results
If your source image shows a person wearing the garment, consider using an image editing model to extract just the clothing item first

Dynamic Outfit Changes

Switch outfits instantly during a live session — call setPrompt() or set() again at any time without reconnecting:

// Start with a casual look
realtimeClient.setPrompt("Substitute the current top with a grey crewneck sweatshirt with a large blue logo on the front");

// Switch to formal
realtimeClient.setPrompt("Substitute the current top with a navy blue blazer, worn over a light blue dress shirt");

// Try an accessory
realtimeClient.setPrompt("Add a black baseball cap with a blue logo to the person's head");

Client Token Security

In production, never expose your permanent API key (dct_*) to the browser. Instead, create a short-lived client token on your server and pass it to the frontend:

// Server: app/api/tokens/route.ts
import { createDecartClient } from "@decartai/sdk";

export async function POST() {
  const client = createDecartClient({ apiKey: process.env.DECART_API_KEY });
  const token = await client.tokens.create();
  return Response.json(token);
}

// Client: fetch token, then connect
const res = await fetch("/api/tokens", { method: "POST" });
const { apiKey } = await res.json();

const client = createDecartClient({ apiKey }); // short-lived ephemeral token
const rtClient = await client.realtime.connect(stream, { model, onRemoteStream });

Client tokens have a 10-minute TTL. Create a new token each time a user opens a try-on session. Active WebRTC sessions continue working even after the token expires. See the Client Tokens guide for details.

Prompting

For how to write effective try-on prompts — patterns, examples, and common mistakes — see the VTON 3.5 Prompting Guide.

Generating Prompts with an LLM

For user-uploaded garment images where you don’t have a pre-written prompt, you can use any vision LLM (GPT-4o-mini, Claude, Gemini) to auto-generate a descriptive prompt from the garment image:

// Send the garment image (and optionally a camera frame) to your LLM
const formData = new FormData();
formData.append("image", garmentBlob);
formData.append("personFrame", cameraFrameBlob); // optional — improves accuracy

const res = await fetch("/api/enhance-prompt", { method: "POST", body: formData });
const { prompt } = await res.json();
// → "Substitute the grey crewneck sweater with a blue and pink flame print hoodie with an oversized fit"

Sending a camera frame of the person gives the LLM context about what they’re currently wearing, so it generates more accurate prompts (e.g. “Substitute the grey sweater” instead of generic “Substitute the current top”).

Extracting Clothing from Model Photos

When users upload photos from fashion websites where a model is wearing the garment, the image contains a person and background rather than a clean garment shot. Use an image editing model to extract just the clothing item on a white background before sending it to the try-on model.

Batch API

Lucy VTON is also available as a batch/queue model for processing pre-recorded videos asynchronously. Submit a job, poll for completion, then download the result.

# Submit job
JOB_ID=$(curl -s -X POST https://api.decart.ai/v1/jobs/lucy-vton-3.5 \
  -H "X-API-KEY: $DECART_API_KEY" \
  -F "data=@person.mp4" \
  -F "prompt=Substitute the current top with a red leather jacket with a zip front" \
  -F "reference_image=@garment.jpg" | jq -r '.job_id')

# Poll until completed
while true; do
  STATUS=$(curl -s -H "X-API-KEY: $DECART_API_KEY" \
    https://api.decart.ai/v1/jobs/$JOB_ID | jq -r '.status')
  echo "Status: $STATUS"
  [ "$STATUS" = "completed" ] && break
  [ "$STATUS" = "failed" ] && exit 1
  sleep 2
done

# Download result
curl -H "X-API-KEY: $DECART_API_KEY" \
  https://api.decart.ai/v1/jobs/$JOB_ID/content --output output.mp4

import { createDecartClient, models } from "@decartai/sdk";
import { readFileSync, writeFileSync } from "fs";

const client = createDecartClient({ apiKey: "your-api-key-here" });

const videoBuffer = readFileSync("person.mp4");
const videoBlob = new Blob([videoBuffer], { type: "video/mp4" });

const imageBuffer = readFileSync("garment.jpg");
const imageBlob = new Blob([imageBuffer], { type: "image/jpeg" });

const result = await client.queue.submitAndPoll({
  model: models.video("lucy-vton-3.5"),
  prompt: "Substitute the current top with a red leather jacket with a zip front",
  data: videoBlob,
  reference_image: imageBlob,
  onStatusChange: (job) => console.log(`Status: ${job.status}`),
});

if (result.status === "completed") {
  const buffer = Buffer.from(await result.data.arrayBuffer());
  writeFileSync("output.mp4", buffer);
}

import asyncio
from decart import DecartClient, models

async def virtual_try_on():
    async with DecartClient(api_key="your-api-key-here") as client:
        with open("person.mp4", "rb") as video_file:
            with open("garment.jpg", "rb") as garment_image:
                result = await client.queue.submit_and_poll({
                    "model": models.video("lucy-vton-3.5"),
                    "prompt": "Substitute the current top with a red leather jacket with a zip front",
                    "data": video_file,
                    "reference_image": garment_image,
                    "on_status_change": lambda job: print(f"Status: {job.status}"),
                })

        if result.status == "completed":
            with open("output.mp4", "wb") as f:
                f.write(result.data)

asyncio.run(virtual_try_on())

Batch Parameters

data (required) — Input video of the person to dress.
prompt (optional) — Text description of the outfit change. Use the substitute/add patterns from the prompting guide. Defaults to empty string.
reference_image (optional) — An image of the garment or accessory to try on.
resolution (optional) — Output resolution: 720p (default).
seed (optional) — Seed for reproducible generation (0–4294967295).
enhance_prompt (optional) — Whether to enhance the prompt (default: true).

Video Requirements

Format: MP4 (H.264 or VP8 codec)
Aspect Ratio: 16:9 (landscape) or 9:16 (portrait) — a 2:1 (landscape) / 1:2 (portrait) shape is also supported
File Size: Maximum 200MB
Output Resolution: 1280×720 / 720×1280 (16:9 / 9:16) or 1280×640 / 640×1280 (2:1 / 1:2)

VTON infers the aspect ratio from the input video stream and maps it to the nearest supported generation shape.

Next steps

Try in the Platform

Try virtual try-on in the interactive platform.

Batch API Reference

Full API documentation for the batch virtual try-on endpoint.

Streaming Best Practices

Optimize your realtime integration for the best experience.

Try-On Examples

Production-ready examples: e-commerce, digital mirror, outfit builder, and more.

Getting started

Realtime

Non-realtime

Provider Integrations

Libraries & SDKs

Frameworks

Resources

Quick start (Realtime)

Realtime Parameters

Using Reference Images

Dynamic Outfit Changes

Client Token Security

Prompting

Generating Prompts with an LLM

Extracting Clothing from Model Photos

Batch API

Batch Parameters

Video Requirements

Next steps

Try in the Platform

Batch API Reference

Streaming Best Practices

Try-On Examples

​Quick start (Realtime)

​Realtime Parameters

​Using Reference Images

​Dynamic Outfit Changes

​Client Token Security

​Prompting

​Generating Prompts with an LLM

​Extracting Clothing from Model Photos

​Batch API

​Batch Parameters

​Video Requirements

​Next steps

Try in the Platform

Batch API Reference

Streaming Best Practices

Try-On Examples

Quick start (Realtime)

Realtime Parameters

Using Reference Images

Dynamic Outfit Changes

Client Token Security

Prompting

Generating Prompts with an LLM

Extracting Clothing from Model Photos

Batch API

Batch Parameters

Video Requirements

Next steps