Image GenerationActive

P-Image Try-On (Virtual Outfit Swap)

Pruna’s p-image-try-on creates virtual try-on images by applying one or more garment photos to a person photo. Generate up to 11 outfit variants in one run.

Image to ImageFast Inference

Model ID

p-image-try-on

Provider

pruna

Updated

Aug 24, 2026, 20:54

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wiro playground—pruna/p-image-try-on

Updated Aug 24, 2026, 20:54

Overview

P-image-try-on by Pruna is an image-to-image virtual try-on model. You provide a person photo and one or more garment images. It generates new images that show the person wearing each garment. This helps teams preview looks without a full photoshoot. It also helps shoppers see outfits on a real person photo.

What you can build

A virtual fitting-room widget for product pages.
On-model image generation for catalogs from flat-lay garment photos.
Outfit A/B tests for ads by generating multiple clothing options on the same person photo.
Styling previews for creators who want quick wardrobe mockups.
Batch try-on workflows that return multiple outfit results from one submission.

Inputs

One person image. Use a clear, well-lit photo where the full outfit area is visible.
One to 11 garment images. If you submit more than 11, only the first 11 are processed.
The output image format you want (JPG, PNG, or WebP).
An output quality value from 0 to 100 when you choose JPG or WebP.
An option to keep the original person image dimensions, or to resize the final result back to the model’s capped person-image size.
An optional numeric seed for repeatable results. If you leave it at zero, the run randomizes the seed.

Outputs

A set of generated images that show the person wearing the provided garment(s).
The platform returns the images in the format you selected (JPG, PNG, or WebP).
When you submit multiple garment images, you typically receive one result per garment image, up to 11 outputs.

Limitations

The model processes a maximum of 11 garment images per run.
Output size behavior depends on whether you choose to preserve the input size. If you don’t, the result may be resized to a capped size.
Results depend heavily on input photo quality. Low-resolution images, heavy compression, blur, or harsh shadows can reduce realism.
Complex poses, strong occlusions (arms crossing the torso), and busy backgrounds can cause garment artifacts.
Garment images with cluttered backgrounds, folds that hide key features, or unclear silhouettes can reduce fit accuracy.

Safety & compliance

Only upload person photos you have rights to use and consent to process.
Don’t use copyrighted garment imagery unless you have permission.
Don’t use the model to create misleading images for fraud, impersonation, or harassment.
Follow your platform’s content rules for sexual content, minors, and non-consensual imagery.

API quick start

Run p-image-try-on with a single API call.

POST https://api.wiro.ai/v1/Run/pruna/p-image-try-on

{
  "prompt": "Your prompt here",
  "inputImagePerson": "https://your-cdn.com/input.png",
  "inputImageGarments": "https://your-cdn.com/input.png",
  "inputImagePose": "https://your-cdn.com/input.png"
}

curl

curl -X POST "https://api.wiro.ai/v1/Run/pruna/p-image-try-on" \
  -H "Content-Type: application/json" \
  -H "x-api-key: YOUR_WIRO_API_KEY" \
  --data-binary @- <<'JSON'
{
  "prompt": "Your prompt here",
  "inputImagePerson": "https://your-cdn.com/input.png",
  "inputImageGarments": "https://your-cdn.com/input.png",
  "inputImagePose": "https://your-cdn.com/input.png"
}
JSON