Generate Virtual Try-On Images

Virtual Try-On lets you generate images of people to virtually try-on clothing products. You provide an image of a person and an image of a clothing product, and then Virtual Try-On generates an image of the person wearing that product.

Try Virtual Try-On in a Colab

Before you begin

Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

Go to project selector

Verify that billing is enabled for your Google Cloud project .

Enable the Vertex AI API.

Enable the API

In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

Go to project selector

Verify that billing is enabled for your Google Cloud project .

Enable the Vertex AI API.

Enable the API

1. Set up authentication for your environment.

   Select the tab for how you plan to use the samples on this page:

   Python

   To use the Python samples on this page in a local development environment, install and initialize the gcloud CLI, and then set up Application Default Credentials with your user credentials.

   Install the Google Cloud CLI.

   If you're using an external identity provider (IdP), you must first sign in to the gcloud CLI with your federated identity .

   If you're using a local shell, then create local authentication credentials for your user account:

   gcloud  
   auth  
   application-default  
   login

   You don't need to do this if you're using Cloud Shell.

   If an authentication error is returned, and you are using an external identity provider (IdP), confirm that you have signed in to the gcloud CLI with your federated identity .

   For more information, see Set up ADC for a local development environment in the Google Cloud authentication documentation.

   REST

   To use the REST API samples on this page in a local development environment, you use the credentials you provide to the gcloud CLI.

   Install the Google Cloud CLI.

   If you're using an external identity provider (IdP), you must first sign in to the gcloud CLI with your federated identity .

   For more information, see Authenticate for using REST in the Google Cloud authentication documentation.

Generate images

pip install --upgrade google-genai

 # Replace the `GOOGLE_CLOUD_PROJECT` and `GOOGLE_CLOUD_LOCATION` values 
 # with appropriate values for your project. 
 export 
  
 GOOGLE_CLOUD_PROJECT 
 = 
 GOOGLE_CLOUD_PROJECT 
 export 
  
 GOOGLE_CLOUD_LOCATION 
 = 
 global 
 export 
  
 GOOGLE_GENAI_USE_VERTEXAI 
 = 
True

  from 
  
 google 
  
 import 
 genai 
 from 
  
 google.genai.types 
  
 import 
 RecontextImageSource 
 , 
 ProductImage 
 client 
 = 
 genai 
 . 
 Client 
 () 
 # TODO(developer): Update and un-comment below line 
 # output_file = "output-image.png" 
 image 
 = 
 client 
 . 
 models 
 . 
 recontext_image 
 ( 
 model 
 = 
 "virtual-try-on-preview-08-04" 
 , 
 source 
 = 
 RecontextImageSource 
 ( 
 person_image 
 = 
 Image 
 . 
 from_file 
 ( 
 location 
 = 
 "test_resources/man.png" 
 ), 
 product_images 
 = 
 [ 
 ProductImage 
 ( 
 product_image 
 = 
 Image 
 . 
 from_file 
 ( 
 location 
 = 
 "test_resources/sweater.jpg" 
 )) 
 ], 
 ), 
 ) 
 image 
 . 
 generated_images 
 [ 
 0 
 ] 
 . 
 image 
 . 
 save 
 ( 
 output_file 
 ) 
 print 
 ( 
 f 
 "Created output image using 
 { 
 len 
 ( 
 image 
 . 
 generated_images 
 [ 
 0 
 ] 
 . 
 image 
 . 
 image_bytes 
 ) 
 } 
 bytes" 
 ) 
 # Example response: 
 # Created output image using 1234567 bytes 
 

POST https:// REGION 
-aiplatform.googleapis.com/v1/projects/ PROJECT_ID 
/locations/ REGION 
/publishers/google/models/virtual-try-on-preview-08-04:predict

{
  "instances": [
    {
      "personImage": {
        "image": {
          "bytesBase64Encoded": " BASE64_PERSON_IMAGE 
"
        }
      },
      "productImages": [
        {
          "image": {
            "bytesBase64Encoded": " BASE64_PRODUCT_IMAGE 
"
          }
        }
      ]
    }
  ],
  "parameters": {
    "sampleCount": IMAGE_COUNT 
,
    "storageUri": " GCS_OUTPUT_PATH 
"
  }
}

curl -X POST \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json; charset=utf-8" \
-d @request.json \
"https:// REGION 
-aiplatform.googleapis.com/v1/projects/ PROJECT_ID 
/locations/ REGION 
/publishers/google/models/virtual-try-on-preview-08-04:predict"

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
-Method POST `
-Headers $headers `
-ContentType: "application/json; charset=utf-8" `
-InFile request.json `
-Uri "https:// REGION 
-aiplatform.googleapis.com/v1/projects/ PROJECT_ID 
/locations/ REGION 
/publishers/google/models/virtual-try-on-preview-08-04:predict" | Select-Object -Expand Content

{
  "predictions": [
    {
      "mimeType": "image/png",
      "bytesBase64Encoded": " BASE64_IMG_BYTES 
"
    },
    {
      "bytesBase64Encoded": " BASE64_IMG_BYTES 
",
      "mimeType": "image/png"
    }
  ]
}