Virtual Try On - Upper Body

Generate images by combining clothing and model images using the Fashion Image Generation API. Customize the image generation process with various parameters.

Python

import requests
import json

url = "https://api.imagepipeline.io/fashion_upper/v1"
headers = {
    "API-Key": "your-api-key",
    "Content-Type": "application/json"
}
data = {
    "cloth_image": "https://example.com/clothing.jpg",
    "model_image": "https://example.com/model.jpg",
    "num_inference_steps": 30,
    "guidance_scale": 15,
    "samples": 4,
    "server_id": "your-server-id",
    "webhook": "https://example.com/webhook",
    "direct_link": true,
    "batch_id": "batch123",
    "file_prefix": "fashion_",
    "queued_image_urls": true
}
response = requests.post(url, headers=headers, json=data)
print(response.json())

JavaScript

const url = "https://api.imagepipeline.io/fashion_upper/v1";
const headers = {
    "API-Key": "your-api-key",
    "Content-Type": "application/json"
};
const data = {
    "cloth_image": "https://example.com/clothing.jpg",
    "model_image": "https://example.com/model.jpg",
    "num_inference_steps": 30,
    "guidance_scale": 15,
    "samples": 4,
    "server_id": "your-server-id",
    "webhook": "https://example.com/webhook",
    "direct_link": true,
    "batch_id": "batch123",
    "file_prefix": "fashion_",
    "queued_image_urls": true
};
fetch(url, {
    method: "POST",
    headers: headers,
    body: JSON.stringify(data)
})
.then(response => response.json())
.then(data => console.log(data))
.catch(error => console.error('Error:', error));

PHP

<?php
$url = "https://api.imagepipeline.io/fashion_upper/v1";
$headers = array(
    "API-Key: your-api-key",
    "Content-Type: application/json"
);
$data = array(
    "cloth_image" => "https://example.com/clothing.jpg",
    "model_image" => "https://example.com/model.jpg",
    "num_inference_steps" => 30,
    "guidance_scale" => 15,
    "samples" => 4,
    "server_id" => "your-server-id",
    "webhook" => "https://example.com/webhook",
    "direct_link" => true,
    "batch_id" => "batch123",
    "file_prefix" => "fashion_",
    "queued_image_urls" => true
);
$ch = curl_init($url);
curl_setopt($ch, CURLOPT_POST, true);
curl_setopt($ch, CURLOPT_RETURNTRANSFER, true);
curl_setopt($ch, CURLOPT_HTTPHEADER, $headers);
curl_setopt($ch, CURLOPT_POSTFIELDS, json_encode($data));
$response = curl_exec($ch);
if($response === FALSE){
    die(curl_error($ch));
}
$responseData = json_decode($response, TRUE);
curl_close($ch);
var_dump($responseData);
?>

Available JSON Parameters for Control

Parameter	Type	Notes
cloth_image	string	required. Public URL for the clothing image.
model_image	string	required. Public URL for the model image.
num_inference_steps	integer	required. Number of inference steps.
guidance_scale	number	required. Max: 20. The guidance scale for the image generation.
samples	integer	required. Max: 4. Number of image samples to generate.
server_id	string	Optional. Provide a server ID for load-balancing or to use an enterprise subscription.
webhook	string	URL to send the generated image to.
direct_link	boolean	Fetches the friendly URL in PNG format.
batch_id	string	Adds a batch ID for the image generation request.
file_prefix	string	Adds a prefix to the generated files.
queued_image_urls	boolean	Returns the links of the images prematurely. The link will have the image once the task is run successfully.

Status

Your response will include a status.

• If the status = SUCCESS, you will also have download_urls that will contain the links to your generated image based on the number of samples you have entered. The maximum number of samples that can be generated is 4.
• If the status = PENDING, you will receive an id. You can use the status endpoint to fetch your image using the id.
• If the status = FAILURE, you will receive only an error message.

Endpoint: [GET]

• https://api.imagepipeline.io/fashion_upper/v1/status/{{id}}

Pass the API-Key as the authorization in the header.