Controlled Customization

Controlled Customization lets you to create new images based on a source image or a source image signal (canny edge or scribble). This page shows you how to send two types of Controlled Customization requests:

1. Imagen 3 Controlled Customization (GA feature)

   View Imagen for Editing and Customization model card

2. Imagen 2 Controlled Customization - canny edge or scribble source image signal (Preview feature)

Use cases

Imagen 3 Controlled Customization offers free-style prompting, which can give you the impression that the model can do more than it is trained to do. The following sections describe use cases and examples for Imagen 3 Controlled Customization.

The model has been trained on use cases that we provide, and we expect good results when you use Imagen 3 Controlled Customization. If you push the model to respond in unexpected ways, we don't expect good results.

Examples of intended use cases

The following are use cases that Imagen 3 Controlled Customization is trained to do and produces good results for:

• Generate an image that follows the prompt and the canny edge control images.

• Generate an image that follows the prompt and the scribble images.

• Stylize a photo of a person while preserving the facial expression.

Examples of unintended use cases

The following are use cases that Imagen 3 Controlled Customization isn't trained to do and produces poor results for:

• Generate an image using a style specified in the prompt.

• Generate an image from text that follows a specific style provided by a reference image, with some level of control on the image composition using a control image.

• Generate an image from text that follows a specific style provided by a reference image, with some level of control on the image composition using a control scribble.

• Generate an image from text that follows a specific style provided by the reference image, with some level of control on the image composition using a control image. The person in the image has a specific facial expression.

• Stylize a photo of two or more people, and preserve their facial expressions.

• Stylize a photo of a pet, and turn it into a drawing. Preserve or specify the composition of the image (for example, watercolor).

Before you begin

1. 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.

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

   Go to project selector

3. Make sure that billing is enabled for your Google Cloud project.

4. Enable the Vertex AI API.

   Enable the API

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

   Go to project selector

6. Make sure that billing is enabled for your Google Cloud project.

7. Enable the Vertex AI API.

   Enable the API

8. Set up authentication for your environment.

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

   Console

   When you use the Google Cloud console to access Google Cloud services and APIs, you don't need to set up authentication.

   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.

   After installing the Google Cloud CLI, initialize it by running the following command:

   gcloud init

   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.

Customization prompt writing

The prompt you use with Imagen 3 Customization might affect the quality of your generated images. Use the following prompt templates as a starting point for writing customization prompts. You might need to send several requests to get your targeted output.

Send a Imagen 3 Controlled Customization request

Use the following samples to send a Imagen 3 Controlled Customization request:

POST https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/publishers/google/models/imagen-3.0-capability-001:predict

{
  "instances": [
    {
      "prompt": "TEXT_PROMPT",
      "referenceImages": [
        {
          "referenceType": "REFERENCE_TYPE_CONTROL",
          "referenceId": 1,
          "referenceImage": {
            "bytesBase64Encoded": "BASE64_CONTROL_IMAGE"
          },
          "controlImageConfig": {
            "controlType": "CONTROL_TYPE",
            "enableControlImageComputation": false
          }
        }
      ]
    }
  ],
  "parameters": {
    "sampleCount": IMAGE_COUNT
  }
}

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/publishers/google/models/imagen-3.0-capability-001: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://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/publishers/google/models/imagen-3.0-capability-001:predict" | Select-Object -Expand Content

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

from google import genai
from google.genai.types import ControlReferenceConfig, ControlReferenceImage, EditImageConfig, Image

client = genai.Client()

# TODO(developer): Update and un-comment below line
# output_gcs_uri = "gs://your-bucket/your-prefix"

# Create a reference image out of an existing scribble image signal
# using https://storage.googleapis.com/cloud-samples-data/generative-ai/image/car_scribble.png
control_reference_image = ControlReferenceImage(
    reference_id=1,
    reference_image=Image(gcs_uri="gs://cloud-samples-data/generative-ai/image/car_scribble.png"),
    config=ControlReferenceConfig(control_type="CONTROL_TYPE_SCRIBBLE"),
)

image = client.models.edit_image(
    model="imagen-3.0-capability-001",
    prompt="an oil painting showing the side of a red car[1]",
    reference_images=[control_reference_image],
    config=EditImageConfig(
        edit_mode="EDIT_MODE_CONTROLLED_EDITING",
        number_of_images=1,
        seed=1,
        safety_filter_level="BLOCK_MEDIUM_AND_ABOVE",
        person_generation="ALLOW_ADULT",
        output_gcs_uri=output_gcs_uri,
    ),
)

# Example response:
# gs://your-bucket/your-prefix
print(image.generated_images[0].image.gcs_uri)

from google import genai
from google.genai.types import ControlReferenceConfig, ControlReferenceImage, EditImageConfig, Image

client = genai.Client()

# TODO(developer): Update and un-comment below line
# output_gcs_uri = "gs://your-bucket/your-prefix"

# Create a reference image out of an existing canny edge image signal
# using https://storage.googleapis.com/cloud-samples-data/generative-ai/image/car_canny.png
control_reference_image = ControlReferenceImage(
    reference_id=1,
    reference_image=Image(gcs_uri="gs://cloud-samples-data/generative-ai/image/car_canny.png"),
    config=ControlReferenceConfig(control_type="CONTROL_TYPE_CANNY"),
)

image = client.models.edit_image(
    model="imagen-3.0-capability-001",
    prompt="a watercolor painting of a red car[1] driving on a road",
    reference_images=[control_reference_image],
    config=EditImageConfig(
        edit_mode="EDIT_MODE_CONTROLLED_EDITING",
        number_of_images=1,
        seed=1,
        safety_filter_level="BLOCK_MEDIUM_AND_ABOVE",
        person_generation="ALLOW_ADULT",
        output_gcs_uri=output_gcs_uri,
    ),
)

# Example response:
# gs://your-bucket/your-prefix
print(image.generated_images[0].image.gcs_uri)

Send an Imagen 2 Controlled Customization request

Input image	Other parameters	Output image
	Prompt: "digital art version" Negative prompt: "black and white" Guide image type: Standard RGB Control condition: canny edge Imagen Control scale: 0.95
	Prompt: "digital art version" Negative prompt: "black and white" Guide image type: canny edge Imagen Control scale: 0.95

Use the following samples to send a Imagen 2 Controlled Customization request:

POST https://us-central1-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/publishers/google/models/imagen-2.0-edit-preview-0627:predict

{
  "instances": [
    {
      "prompt": "TEXT_PROMPT",
      "image": {
        "bytesBase64Encoded": "B64_BASE_IMAGE"
      }
    }
  ],
  "parameters": {
    "sampleCount": EDIT_IMAGE_COUNT,
    "negativePrompt": "NEGATIVE_PROMPT",
    "controlPluginConfig":  {
      "conditions": [
        {
          "conditionName": "CONDITION",
          "controlScale": CONTROL_SCALE,
          "samplingSteps": SAMPLING_STEPS,
          "computeConditionMap": COMPUTE_CONDITION_MAP
        }
      ]
    }
  }
}

curl -X POST \
     -H "Authorization: Bearer $(gcloud auth print-access-token)" \
     -H "Content-Type: application/json; charset=utf-8" \
     -d @request.json \
     "https://us-central1-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/publishers/google/models/imagen-2.0-edit-preview-0627: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://us-central1-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/publishers/google/models/imagen-2.0-edit-preview-0627:predict" | Select-Object -Expand Content

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

Product usage

To view usage standards and content restrictions associated with Imagen on Vertex AI, see the usage guidelines.

Model versions

There are multiple image generation models that you can use. For more information, see Imagen models.

What's next

Read articles about Imagen and other Generative AI on Vertex AI products:

• A developer's guide to getting started with Imagen 3 on Vertex AI
• New generative media models and tools, built with and for creators
• New in Gemini: Custom Gems and improved image generation with Imagen 3
• Google DeepMind: Imagen 3 - Our highest quality text-to-image model