Virtual Try-On API

What is a Virtual Try-On API?

A virtual try-on API takes a photo of a person and a photo of a garment, then generates an image of that person wearing the garment — without a physical fitting room, a photoshoot, or a new studio session. It answers a fit-and-look question, not a product-catalog question.

Muapi exposes this through AI Dress Change, a single endpoint that takes model_image_url and garment_image_url and returns the composited result — one API key, one request/poll flow, pay-as-you-go pricing.

Virtual Try-On vs. Product Photography

These are adjacent but distinct intents. Virtual try-on takes an existing person image and swaps in a different garment — the output is a fit/look visualization of that specific person or model. Product photography generates new catalog-ready images of a product from scratch, with prompt-controlled backgrounds and lighting, without necessarily placing it on a fixed person. Use virtual try-on to show a shopper what a garment looks like on them; use product photography to produce a clean listing or lifestyle photo of the product. Compare with Product Photography →

Key Capabilities

Getting Good Input Quality and Pose Results

• Use a clear, front-facing model photo. A single person, good lighting, and a neutral or standing pose gives the model the cleanest reference for where the garment should sit.
• Isolate the garment. A flat-lay or mannequin shot with minimal background clutter produces more accurate texture and shape transfer than a garment photographed on a different person.
• Match garment category to body coverage. Tops, dresses, and outerwear generally transfer more reliably than complex layered outfits; test with your specific catalog before batch-processing.
• Expect some iteration. Like every generative image model, results vary run to run — treat the first output as a draft and re-run with a cleaner source image if fit or texture looks off.

Privacy and Consent

Virtual try-on generates a likeness-based image of whoever appears in the model photo. Only submit photos of people who have agreed to be used this way — yourself, a hired model under a release, or a customer who explicitly opted in to a try-on feature. Do not use someone else's photo without permission, and do not use this endpoint to generate a real person's likeness in a garment or context they have not consented to. Muapi does not verify consent on your behalf; that responsibility sits with the API caller.

How to Call the Virtual Try-On API

1. Host both images. Upload the model photo and garment photo via POST /api/v1/upload_file, or use any publicly reachable URL.
2. Submit the request. Call POST /api/v1/ai-dress-change with model_image_url and garment_image_url.
3. Poll for completion. Check GET /api/v1/predictions/{request_id}/result until status is completed, then download the composited image.

curl -X POST https://api.muapi.ai/api/v1/ai-dress-change \
  -H "Content-Type: application/json" \
  -H "x-api-key: YOUR_API_KEY" \
  -d '{
    "model_image_url": "https://example.com/model-photo.jpg",
    "garment_image_url": "https://example.com/garment-photo.jpg"
  }'

import requests

response = requests.post(
    "https://api.muapi.ai/api/v1/ai-dress-change",
    headers={"x-api-key": "YOUR_API_KEY"},
    json={
        "model_image_url": "https://example.com/model-photo.jpg",
        "garment_image_url": "https://example.com/garment-photo.jpg",
    },
)
request_id = response.json()["request_id"]

result = requests.get(
    f"https://api.muapi.ai/api/v1/predictions/{request_id}/result",
    headers={"x-api-key": "YOUR_API_KEY"},
)
print(result.json())

Frequently Asked Questions