Virtual Try-On API

Preview

This product or feature is a Generative AI Preview offering, subject to the "Pre-GA Offerings Terms" of the Google Cloud Service Specific Terms, as well as the Additional Terms for Generative AI Preview Products. For this Generative AI Preview offering, Customers may elect to use it for production or commercial purposes, or disclose Generated Output to third-parties, and may process personal data as outlined in the Cloud Data Processing Addendum, subject to the obligations and restrictions described in the agreement under which you access Google Cloud. Pre-GA products are available "as is" and might have limited support. For more information, see the launch stage descriptions.

Virtual Try-On lets you generate images of people modeling clothing products. You provide an image of a person and a sample clothing product, and then you use Virtual Try-On to generate images of the person wearing the product.

Supported model versions

Virtual Try-On supports the following models:

• virtual-try-on-preview-08-04

For more information about the features that the model supports, see Imagen models.

HTTP request

curl -X POST \
  -H "Authorization: Bearer $(gcloud auth print-access-token)" \
  -H "Content-Type: application/json" \
https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/publishers/google/models/MODEL_ID:predict \

-d '{
  "instances": [
    {
      "personImage": {
        "image": {
          // Union field can be only one of the following:
          "bytesBase64Encoded": string,
          "gcsUri": string,
        }
      },
      "productImages": [
        {
          "image": {
            // Union field can be only one of the following:
            "bytesBase64Encoded": string,
            "gcsUri": string,
          }
        }
      ]
    }
  ],
  "parameters": {
    "addWatermark": boolean,
    "baseSteps": integer,
    "personGeneration": string,
    "safetySetting": string,
    "sampleCount": integer,
    "seed": integer,
    "storageUri": string,
    "outputOptions": {
      "mimeType": string,
      "compressionQuality": integer
    }
  }
}'

Instances
personImage	string Required. An image of a person to try-on the clothing product, which can be either of the following: A bytesBase64Encoded string that encodes an image. A gcsUri string URI to a Cloud Storage bucket location.
productImages	string Required. An image of a product to try-on a person, which can be either of the following: A bytesBase64Encoded string that encodes an image. A gcsUri string URI to a Cloud Storage bucket location.

Parameters
addWatermark	bool Optional. Add an invisible watermark to the generated images. The default value is true.
baseSteps	int Required. An integer that controls image generation, with higher steps trading higher quality for increased latency. Integer values greater than 0. The default is 32.
personGeneration	string Optional. Allow generation of people by the model. The following values are supported: "dont_allow": Disallow the inclusion of people or faces in images. "allow_adult": Allow generation of adults only. "allow_all": Allow generation of people of all ages. The default value is "allow_adult".
safetySetting	string Optional. Adds a filter level to safety filtering. The following values are supported: "block_low_and_above": Strongest filtering level, most strict blocking. Deprecated value: "block_most". "block_medium_and_above": Block some problematic prompts and responses. Deprecated value: "block_some". "block_only_high": Reduces the number of requests blocked due to safety filters. May increase objectionable content generated by Imagen. Deprecated value: "block_few". "block_none": Block very few problematic prompts and responses. Access to this feature is restricted. Previous field value: "block_fewest". The default value is "block_medium_and_above".
sampleCount	int Required. The number of images to generate. An integer value between 1 and 4, inclusive. The default value is 1.
seed	Uint32 Optional. The random seed for image generation. This isn't available when addWatermark is set to true.
storageUri	string Optional. A string URI to a Cloud Storage bucket location to store the generated images.
outputOptions	outputOptions Optional. Describes the output image format in an outputOptions object.

Output options object

The outputOptions object describes the image output.

Parameters
outputOptions.mimeType	Optional: string The image output format.. The following values are supported: "image/png": Save as a PNG image "image/jpeg": Save as a JPEG image The default value is "image/png".
outputOptions.compressionQuality	Optional: int The level of compression if the output type is "image/jpeg". Accepted values are 0 through 100. The default value is 75.

Sample request

REST

Before using any of the request data, make the following replacements:

• REGION: The region that your project is located in. For more information about supported regions, see Generative AI on Vertex AI locations.
• PROJECT_ID: Your Google Cloud project ID.
• BASE64_PERSON_IMAGE: The Base64-encoded image of the person image.
• BASE64_PRODUCT_IMAGE: The Base64-encoded image of the product image.
• IMAGE_COUNT: The number of images to generate. The accepted range of values is 1 to 4.
• GCS_OUTPUT_PATH: The Cloud Storage path to store the virtual try-on output to.

HTTP method and URL:

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

Request JSON body:

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

To send your request, choose one of these options:

curl

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login , or by using Cloud Shell, which automatically logs you into the gcloud CLI . You can check the currently active account by running gcloud auth list.

Save the request body in a file named request.json, and execute the following command:

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"

PowerShell

Note: The following command assumes that you have logged in to the gcloud CLI with your user account by running gcloud init or gcloud auth login . You can check the currently active account by running gcloud auth list.

Save the request body in a file named request.json, and execute the following command:

$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

The request returns image objects. In this example, two image objects are returned, with two prediction objects as base64-encoded images.

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