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Generate images using text prompts

API reference overview: To view an overview of the API options for image generation and editing, see the imagegeneration model API reference.

You can use Imagen on Vertex AI to generate new images from a text prompt you provide in the Google Cloud console or send in a request to the Vertex AI API .

For more information about writing text prompts for image generation and editing, see the prompt guide.

View Imagen for Generation model card

Try image generation (Vertex AI Studio)

Locations

A location is a region you can specify in a request to control where data is stored at rest. For a list of available regions, see Generative AI on Vertex AI locations.

Safety filtering

Both input data and output content are checked for offensive material when you send an image generation request to Imagen. This means a text prompt input that's offensive can be blocked. Similarly, offensive output images might also be blocked, affecting the number of generated images you get in a response.

For more information about safety filtering and blocked content handling, see Responsible AI and usage guidelines for Imagen.

Performance and limitations

The following limits apply when you use an Imagen model for image generation:

Limits Value (Imagen 3)
Maximum number of API requests per minute per project Imagen 3: 20 Imagen 3 Fast: 200
Maximum number of images returned per request (text-to-image generation) 4
Maximum image size uploaded or sent in a request (MB) 10 MB
Supported returned image resolution (pixels) - 1024x1024 pixels (1:1 aspect ratio) - 896x1280 (3:4 aspect ratio) - 1280x896 (4:3 aspect ratio) - 768x1408 (9:16 aspect ratio) - 1408x768 (16:9 aspect ratio)
Maximum number of input tokens (text-to-image generation prompt text) 480 tokens

Model versions

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

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.

Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

Go to project selector - Make sure 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.

Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

Go to project selector - Make sure 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:

### Console

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

### Java

To use the Java 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.

  1. Install the Google Cloud CLI.
  2. If you're using an external identity provider (IdP), you must first sign in to the gcloud CLI with your federated identity.
  3. To initialize the gcloud CLI, run the following command:

gcloud init
4. 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.

### Node.js

To use the Node.js 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.

  1. Install the Google Cloud CLI.
  2. If you're using an external identity provider (IdP), you must first sign in to the gcloud CLI with your federated identity.
  3. To initialize the gcloud CLI, run the following command:

gcloud init
4. 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.

### 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.

  1. Install the Google Cloud CLI.
  2. If you're using an external identity provider (IdP), you must first sign in to the gcloud CLI with your federated identity.
  3. To initialize the gcloud CLI, run the following command:

gcloud init
4. 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.

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.

Generate images with text

To see examples of image generation using Imagen, run the following Jupyter notebooks in the environment of your choice:

  • "Image Generation with Imagen on Vertex AI":

Open in Colab | Open in Colab Enterprise | Open in Vertex AI Workbench user-managed notebooks | View on GitHub - "Create High Quality Visual Assets with Imagen and Gemini":

Open in Colab | Open in Colab Enterprise | Open in Vertex AI Workbench user-managed notebooks | View on GitHub

You can generate novel images using only descriptive text as an input. The following samples show you basic instructions to generate images, but you can also use additional parameters depending on your use case.

Console

  1. In the Google Cloud console, open the Vertex AI Studio > Media tab in the Vertex AI dashboard.

Go to the Vertex AI Studio tab 2. In the Write your prompt field, enter a description for the images you want to generate. For details about writing effective prompts, see the prompt guide.

  • For example: small boat on water in the morning watercolor illustration
  • Optional. In the Model options box in the Parameters panel, select the model version to use. For more information, see model versions.
  • Optional. Change standard and advanced parameters.

These features are subject to model availability. For more information, see model versions. 5. To generate images, click play_arrowGenerate.

Image generation view of images generated with Imagen on Vertex AI from the prompt: small red boat on water in the morning watercolor illustration muted colors.

REST

For more information about imagegeneration model requests, see the imagegeneration model API reference.

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

  • PROJECT_ID: Your Google Cloud project ID.
  • MODEL_VERSION: The imagegeneration model version to use. Available values:
  • Imagen 3:
  • imagen-3.0-generate-002 (newest model)
  • imagen-3.0-generate-001
  • imagen-3.0-fast-generate-001 - Low latency model version.
  • Default model version:
  • imagegeneration - Uses the default model version v.006. As a best practice, you should always specify a model version, especially in production environments.

For more information about model versions and features, see model versions. - LOCATION: Your project's region. For example, us-central1, europe-west2, or asia-northeast3. For a list of available regions, see Generative AI on Vertex AI locations. - TEXT_PROMPT: The text prompt that guides what images the model generates. This field is required for both generation and editing. - IMAGE_COUNT: The number of generated images. Accepted integer values: 1-8 (imagegeneration@002), 1-4 (all other model versions). Default value: 4.

Additional optional parameters

Use the following optional variables depending on your use case. Add some or all of the following parameters in the "parameters": {} object. This list shows common optional parameters and isn't meant to be exhaustive. For more information about optional parameters, see Imagen API reference: Generate images.

"parameters": {
 "sampleCount": IMAGE_COUNT,
 "addWatermark": ADD_WATERMARK,
 "aspectRatio": "ASPECT_RATIO",
 "enhancePrompt": ENABLE_PROMPT_REWRITING,
 "includeRaiReason": INCLUDE_RAI_REASON,
 "includeSafetyAttributes": INCLUDE_SAFETY_ATTRIBUTES,
 "outputOptions": {
 "mimeType": "MIME_TYPE",
 "compressionQuality": COMPRESSION_QUALITY
 },
 "personGeneration": "PERSON_SETTING",
 "safetySetting": "SAFETY_SETTING",
 "seed": SEED_NUMBER,
 "storageUri": "OUTPUT_STORAGE_URI"
}
  • ADD_WATERMARK: boolean. Optional. Whether to enable a watermark for generated images. Any image generated when the field is set to true contains a digital SynthID that you can use to verify a watermarked image. If you omit this field, the default value of true is used; you must set the value to false to disable this feature. You can use the seed field to get deterministic output only when this field is set to false.
  • ASPECT_RATIO: string. Optional. A generation mode parameter that controls aspect ratio. Supported ratio values and their intended use:
  • 1:1 (default, square)
  • 3:4 (Ads, social media)
  • 4:3 (TV, photography)
  • 16:9 (landscape)
  • 9:16 (portrait)
  • ENABLE_PROMPT_REWRITING: boolean. Optional. A parameter to use an LLM-based prompt rewriting feature to deliver higher quality images that better reflect the original prompt's intent. Disabling this feature may impact image quality and prompt adherence. Default value: true.
  • INCLUDE_RAI_REASON: boolean. Optional. Whether to enable the Responsible AI filtered reason code in responses with blocked input or output. Default value: false.
  • INCLUDE_SAFETY_ATTRIBUTES: boolean. Optional. Whether to enable rounded Responsible AI scores for a list of safety attributes in responses for unfiltered input and output. Safety attribute categories: "Death, Harm & Tragedy", "Firearms & Weapons", "Hate", "Health", "Illicit Drugs", "Politics", "Porn", "Religion & Belief", "Toxic", "Violence", "Vulgarity", "War & Conflict". Default value: false.
  • MIME_TYPE: string. Optional. The MIME type of the content of the image. Available values:
  • image/jpeg
  • image/gif
  • image/png
  • image/webp
  • image/bmp
  • image/tiff
  • image/vnd.microsoft.icon
  • COMPRESSION_QUALITY: integer. Optional. Only applies to JPEG output files. The level of detail the model preserves for images generated in JPEG file format. Values: 0 to 100, where a higher number means more compression. Default: 75.
  • PERSON_SETTING: string. Optional. The safety setting that controls the type of people or face generation the model allows. Available values:
  • allow_adult (default): Allow generation of adults only, except for celebrity generation. Celebrity generation is not allowed for any setting.
  • dont_allow: Disable the inclusion of people or faces in generated images.
  • SAFETY_SETTING: string. Optional. A setting that controls safety filter thresholds for generated images. Available values:
  • block_low_and_above: The highest safety threshold, resulting in the largest amount of generated images that are filtered. Previous value: block_most.
  • block_medium_and_above (default): A medium safety threshold that balances filtering for potentially harmful and safe content. Previous value: block_some.
  • block_only_high: A safety threshold that reduces the number of requests blocked due to safety filters. This setting might increase objectionable content generated by Imagen. Previous value: block_few.
  • SEED_NUMBER: integer. Optional. Any non-negative integer you provide to make output images deterministic. Providing the same seed number always results in the same output images. If the model you're using supports digital watermarking, you must set "addWatermark": false to use this field. Accepted integer values: 1 - 2147483647.
  • OUTPUT_STORAGE_URI: string. Optional. The Cloud Storage bucket to store the output images. If not provided, base64-encoded image bytes are returned in the response. Sample value: gs://image-bucket/output/.

HTTP method and URL:

POST https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/publishers/google/models/MODEL_VERSION:predict

Request JSON body:

{
 "instances": [
 {
 "prompt": "TEXT_PROMPT"
 }
 ],
 "parameters": {
 "sampleCount": IMAGE_COUNT
 }
}

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://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/publishers/google/models/MODEL_VERSION: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://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/publishers/google/models/MODEL_VERSION:predict" | Select-Object -Expand Content

The following sample response is for a request with "sampleCount": 2. The response returns two prediction objects, with the generated image bytes base64-encoded.

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

If you use a model that supports prompt enhancement, the response includes an additional prompt field with the enhanced prompt used for generation:

{
 "predictions": [
 {
 "mimeType": "MIME_TYPE",
 "prompt": "ENHANCED_PROMPT_1",
 "bytesBase64Encoded": "BASE64_IMG_BYTES_1"
 },
 {
 "mimeType": "MIME_TYPE",
 "prompt": "ENHANCED_PROMPT_2",
 "bytesBase64Encoded": "BASE64_IMG_BYTES_2"
 }
 ]
}

Python

Before trying this sample, follow the Python setup instructions in the Vertex AI quickstart using client libraries. For more information, see the Vertex AI Python API reference documentation.

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

In this sample you call the generate_images method on the ImageGenerationModel and save generated images locally. You then can optionally use the show() method in a notebook to show you the generated images. For more information on model versions and features, see model versions.

import vertexai
from vertexai.preview.vision_models import ImageGenerationModel

# TODO(developer): Update and un-comment below lines
# PROJECT_ID = "your-project-id"
# output_file = "input-image.png"
# prompt = "" # The text prompt describing what you want to see.

vertexai.init(project=PROJECT_ID, location="us-central1")

model = ImageGenerationModel.from_pretrained("imagen-3.0-generate-002")

images = model.generate_images(
 prompt=prompt,
 # Optional parameters
 number_of_images=1,
 language="en",
 # You can't use a seed value and watermark at the same time.
 # add_watermark=False,
 # seed=100,
 aspect_ratio="1:1",
 safety_filter_level="block_some",
 person_generation="allow_adult",
)

images[0].save(location=output_file, include_generation_parameters=False)

# Optional. View the generated image in a notebook.
# images[0].show()

print(f"Created output image using {len(images[0]._image_bytes)} bytes")
# Example response:
# Created output image using 1234567 bytes

Java

Before trying this sample, follow the Java setup instructions in the Vertex AI quickstart using client libraries. For more information, see the Vertex AI Java API reference documentation.

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

In this sample, you specify the imagen-3.0-generate-001 model as part of an EndpointName. The EndpointName is passed to the predict method which is called on a PredictionServiceClient. The service generates images which are then saved locally. For more information on model versions and features, see model versions.

import com.google.api.gax.rpc.ApiException;
import com.google.cloud.aiplatform.v1.EndpointName;
import com.google.cloud.aiplatform.v1.PredictResponse;
import com.google.cloud.aiplatform.v1.PredictionServiceClient;
import com.google.cloud.aiplatform.v1.PredictionServiceSettings;
import com.google.gson.Gson;
import com.google.protobuf.InvalidProtocolBufferException;
import com.google.protobuf.Value;
import com.google.protobuf.util.JsonFormat;
import java.io.IOException;
import java.nio.file.Files;
import java.nio.file.Path;
import java.util.Base64;
import java.util.Collections;
import java.util.HashMap;
import java.util.Map;

public class GenerateImageSample {

 public static void main(String[] args) throws IOException {
 // TODO(developer): Replace these variables before running the sample.
 String projectId = "my-project-id";
 String location = "us-central1";
 String prompt = ""; // The text prompt describing what you want to see.

 generateImage(projectId, location, prompt);
 }

 // Generate an image using a text prompt using an Imagen model
 public static PredictResponse generateImage(String projectId, String location, String prompt)
 throws ApiException, IOException {
 final String endpoint = String.format("%s-aiplatform.googleapis.com:443", location);
 PredictionServiceSettings predictionServiceSettings =
 PredictionServiceSettings.newBuilder().setEndpoint(endpoint).build();

 // Initialize client that will be used to send requests. This client only needs to be created
 // once, and can be reused for multiple requests.
 try (PredictionServiceClient predictionServiceClient =
 PredictionServiceClient.create(predictionServiceSettings)) {

 final EndpointName endpointName =
 EndpointName.ofProjectLocationPublisherModelName(
 projectId, location, "google", "imagen-3.0-generate-001");

 Map<String, Object> instancesMap = new HashMap<>();
 instancesMap.put("prompt", prompt);
 Value instances = mapToValue(instancesMap);

 Map<String, Object> paramsMap = new HashMap<>();
 paramsMap.put("sampleCount", 1);
 // You can't use a seed value and watermark at the same time.
 // paramsMap.put("seed", 100);
 // paramsMap.put("addWatermark", false);
 paramsMap.put("aspectRatio", "1:1");
 paramsMap.put("safetyFilterLevel", "block_some");
 paramsMap.put("personGeneration", "allow_adult");
 Value parameters = mapToValue(paramsMap);

 PredictResponse predictResponse =
 predictionServiceClient.predict(
 endpointName, Collections.singletonList(instances), parameters);

 for (Value prediction : predictResponse.getPredictionsList()) {
 Map<String, Value> fieldsMap = prediction.getStructValue().getFieldsMap();
 if (fieldsMap.containsKey("bytesBase64Encoded")) {
 String bytesBase64Encoded = fieldsMap.get("bytesBase64Encoded").getStringValue();
 Path tmpPath = Files.createTempFile("imagen-", ".png");
 Files.write(tmpPath, Base64.getDecoder().decode(bytesBase64Encoded));
 System.out.format("Image file written to: %s\n", tmpPath.toUri());
 }
 }
 return predictResponse;
 }
 }

 private static Value mapToValue(Map<String, Object> map) throws InvalidProtocolBufferException {
 Gson gson = new Gson();
 String json = gson.toJson(map);
 Value.Builder builder = Value.newBuilder();
 JsonFormat.parser().merge(json, builder);
 return builder.build();
 }
}

Node.js

Before trying this sample, follow the Node.js setup instructions in the Vertex AI quickstart using client libraries. For more information, see the Vertex AI Node.js API reference documentation.

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

In this sample, you call the predict method on a PredictionServiceClient. The service generates images which are then saved locally. For more information on model versions and features, see model versions.

/**
 * TODO(developer): Update these variables before running the sample.
 */
const projectId = process.env.CAIP_PROJECT_ID;
const location = 'us-central1';
const prompt = 'a dog reading a newspaper';

const aiplatform = require('@google-cloud/aiplatform');

// Imports the Google Cloud Prediction Service Client library
const {PredictionServiceClient} = aiplatform.v1;

// Import the helper module for converting arbitrary protobuf.Value objects
const {helpers} = aiplatform;

// Specifies the location of the api endpoint
const clientOptions = {
 apiEndpoint: `${location}-aiplatform.googleapis.com`,
};

// Instantiates a client
const predictionServiceClient = new PredictionServiceClient(clientOptions);

async function generateImage() {
 const fs = require('fs');
 const util = require('util');
 // Configure the parent resource
 const endpoint = `projects/${projectId}/locations/${location}/publishers/google/models/imagen-3.0-generate-001`;

 const promptText = {
 prompt: prompt, // The text prompt describing what you want to see
 };
 const instanceValue = helpers.toValue(promptText);
 const instances = [instanceValue];

 const parameter = {
 sampleCount: 1,
 // You can't use a seed value and watermark at the same time.
 // seed: 100,
 // addWatermark: false,
 aspectRatio: '1:1',
 safetyFilterLevel: 'block_some',
 personGeneration: 'allow_adult',
 };
 const parameters = helpers.toValue(parameter);

 const request = {
 endpoint,
 instances,
 parameters,
 };

 // Predict request
 const [response] = await predictionServiceClient.predict(request);
 const predictions = response.predictions;
 if (predictions.length === 0) {
 console.log(
 'No image was generated. Check the request parameters and prompt.'
 );
 } else {
 let i = 1;
 for (const prediction of predictions) {
 const buff = Buffer.from(
 prediction.structValue.fields.bytesBase64Encoded.stringValue,
 'base64'
 );
 // Write image content to the output file
 const writeFile = util.promisify(fs.writeFile);
 const filename = `output${i}.png`;
 await writeFile(filename, buff);
 console.log(`Saved image ${filename}`);
 i++;
 }
 }
}
await generateImage();

Use parameters to generate images

When you generate images there are several standard and advanced parameters you can set depending on your use case.

Add or verify an image watermark

By default, a digital watermark is added to any images generated by a model version that supports watermark generation. This features adds a non-visible digital watermark—called a SynthID—to images. You can then verify if an image contains a digital watermark or not.

Generate watermarked images

Use the following samples to generate images with a digital watermark.

Console

Model versions 006 and greater automatically add a digital watermark when you Generate images. You can't disable digital watermark for image generation using the Google Cloud console.

REST

For more information about imagegeneration model requests, see the imagegeneration model API reference.

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

  • PROJECT_ID: Your Google Cloud project ID.
  • MODEL_VERSION: The imagegeneration model version to use. Available values:
  • imagen-3.0-generate-002 (newest model)
  • imagen-3.0-generate-001
  • imagen-3.0-fast-generate-001 - Low latency model version.
  • imagegeneration@006

For more information about model versions and features, see model versions. - LOCATION: Your project's region. For example, us-central1, europe-west2, or asia-northeast3. For a list of available regions, see Generative AI on Vertex AI locations. - TEXT_PROMPT: The text prompt that guides what images the model generates. This field is required for both generation and editing. - IMAGE_COUNT: The number of generated images. Accepted integer values: 1-8 (imagegeneration@002), 1-4 (all other model versions). Default value: 4. - ADD_WATERMARK: boolean. Optional. Whether to enable a watermark for generated images. Any image generated when the field is set to true contains a digital SynthID that you can use to verify a watermarked image. If you omit this field, the default value of true is used; you must set the value to false to disable this feature. You can use the seed field to get deterministic output only when this field is set to false.

HTTP method and URL:

POST https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/publishers/google/models/MODEL_VERSION:predict

Request JSON body:

{
 "instances": [
 {
 "prompt": "TEXT_PROMPT"
 }
 ],
 "parameters": {
 "sampleCount": IMAGE_COUNT,
 "addWatermark": ADD_WATERMARK
 }
}

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://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/publishers/google/models/MODEL_VERSION: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://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/publishers/google/models/MODEL_VERSION:predict" | Select-Object -Expand Content

The following sample response is for a request with "sampleCount": 2. The response returns two prediction objects, with the generated image bytes base64-encoded. The digital watermark is automatically added to images, so the response is the same as a non-watermarked response.

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

Vertex AI SDK for Python

import vertexai
from vertexai.preview.vision_models import ImageGenerationModel

# TODO(developer): Update and un-comment below lines
# PROJECT_ID = "your-project-id"
# output_file = "input-image.png"
# prompt = "" # The text prompt describing what you want to see.

vertexai.init(project=PROJECT_ID, location="us-central1")

model = ImageGenerationModel.from_pretrained("imagen-3.0-generate-002")

images = model.generate_images(
 prompt=prompt,
 # Optional parameters
 number_of_images=1,
 language="en",
 # You can't use a seed value and watermark at the same time.
 # add_watermark=False,
 # seed=100,
 aspect_ratio="1:1",
 safety_filter_level="block_some",
 person_generation="allow_adult",
)

images[0].save(location=output_file, include_generation_parameters=False)

# Optional. View the generated image in a notebook.
# images[0].show()

print(f"Created output image using {len(images[0]._image_bytes)} bytes")
# Example response:
# Created output image using 1234567 bytes

Node.js

/**
 * TODO(developer): Update these variables before running the sample.
 */
const projectId = process.env.CAIP_PROJECT_ID;
const location = 'us-central1';
const prompt = 'a dog reading a newspaper';

const aiplatform = require('@google-cloud/aiplatform');

// Imports the Google Cloud Prediction Service Client library
const {PredictionServiceClient} = aiplatform.v1;

// Import the helper module for converting arbitrary protobuf.Value objects
const {helpers} = aiplatform;

// Specifies the location of the api endpoint
const clientOptions = {
 apiEndpoint: `${location}-aiplatform.googleapis.com`,
};

// Instantiates a client
const predictionServiceClient = new PredictionServiceClient(clientOptions);

async function generateImage() {
 const fs = require('fs');
 const util = require('util');
 // Configure the parent resource
 const endpoint = `projects/${projectId}/locations/${location}/publishers/google/models/imagen-3.0-generate-001`;

 const promptText = {
 prompt: prompt, // The text prompt describing what you want to see
 };
 const instanceValue = helpers.toValue(promptText);
 const instances = [instanceValue];

 const parameter = {
 sampleCount: 1,
 // You can't use a seed value and watermark at the same time.
 // seed: 100,
 // addWatermark: false,
 aspectRatio: '1:1',
 safetyFilterLevel: 'block_some',
 personGeneration: 'allow_adult',
 };
 const parameters = helpers.toValue(parameter);

 const request = {
 endpoint,
 instances,
 parameters,
 };

 // Predict request
 const [response] = await predictionServiceClient.predict(request);
 const predictions = response.predictions;
 if (predictions.length === 0) {
 console.log(
 'No image was generated. Check the request parameters and prompt.'
 );
 } else {
 let i = 1;
 for (const prediction of predictions) {
 const buff = Buffer.from(
 prediction.structValue.fields.bytesBase64Encoded.stringValue,
 'base64'
 );
 // Write image content to the output file
 const writeFile = util.promisify(fs.writeFile);
 const filename = `output${i}.png`;
 await writeFile(filename, buff);
 console.log(`Saved image ${filename}`);
 i++;
 }
 }
}
await generateImage();

Verify a watermarked image

Do the following:

  1. In the Google Cloud console, open the Vertex AI Studio > Media tab in the Vertex AI dashboard.

Go to the Vertex AI Studio tab 2. In the lower panel, click local_policeVerify. 3. Click Upload image. 4. Select a locally-saved generated image.

Watermarked images display a local_policeSynthID detected badge.

Configure Responsible AI (RAI) safety settings

Note: The available settings depend on the model version that you use. For more information, see Imagen models and the API reference.

There are several Responsible AI (RAI) filtering parameters you can use with an image generation model. For example, you can let the model report RAI filter codes for blocked content, disable people or face generation using RAI filters, set the level of content filtering, or return rounded RAI scores of list of safety attributes for input and output.

For more detailed information about Responsible AI (RAI), its associated parameters, and their sample output, see Understand and configure Responsible AI for Imagen.

The following samples show you how to set available RAI parameters for image generation.

Console

  1. In the Google Cloud console, open the Vertex AI Studio > Media tab in the Vertex AI dashboard.

Go to the Vertex AI Studio tab 2. Add your text prompt and choose your input parameters. 3. If not expanded, click Advanced options. 4. Click Safety settings. 5. Choose your safety settings:

  • Person/face generation: Choose a setting:
  • Allow (All ages)
  • Allow (Adults only)
  • Don't allow
  • Safety filter threshold: Choose a setting:
  • Block low and above
  • Block medium and above
  • Block only high
  • Click Save.
  • Click play_arrowGenerate.

REST

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

  • PROJECT_ID: Your Google Cloud project ID.
  • LOCATION: Your project's region. For example, us-central1, europe-west2, or asia-northeast3. For a list of available regions, see Generative AI on Vertex AI locations.
  • TEXT_PROMPT: The text prompt that guides what images the model generates. This field is required for both generation and editing.
  • IMAGE_COUNT: The number of generated images. Accepted integer values: 1-8 (imagegeneration@002), 1-4 (all other model versions). Default value: 4.
  • SAFETY_SETTING: string. Optional. A setting that controls safety filter thresholds for generated images. Available values:
  • block_low_and_above: The highest safety threshold, resulting in the largest amount of generated images that are filtered. Previous value: block_most.
  • block_medium_and_above (default): A medium safety threshold that balances filtering for potentially harmful and safe content. Previous value: block_some.
  • block_only_high: A safety threshold that reduces the number of requests blocked due to safety filters. This setting might increase objectionable content generated by Imagen. Previous value: block_few.
  • PERSON_SETTING: string. Optional. The safety setting that controls the type of people or face generation the model allows. Available values:
  • allow_adult (default): Allow generation of adults only, except for celebrity generation. Celebrity generation is not allowed for any setting.
  • dont_allow: Disable the inclusion of people or faces in generated images.
  • INCLUDE_RAI_REASON: boolean. Optional. Whether to enable the Responsible AI filtered reason code in responses with blocked input or output. Default value: false.
  • INCLUDE_SAFETY_ATTRIBUTES: boolean. Optional. Whether to enable rounded Responsible AI scores for a list of safety attributes in responses for unfiltered input and output. Safety attribute categories: "Death, Harm & Tragedy", "Firearms & Weapons", "Hate", "Health", "Illicit Drugs", "Politics", "Porn", "Religion & Belief", "Toxic", "Violence", "Vulgarity", "War & Conflict". Default value: false.

HTTP method and URL:

POST https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/publishers/google/models/imagegeneration@006:predict

Request JSON body:

{
 "instances": [
 {
 "prompt": "TEXT_PROMPT"
 }
 ],
 "parameters": {
 "sampleCount": IMAGE_COUNT,
 "safetySetting": "SAFETY_SETTING",
 "personGeneration": "PERSON_SETTING",
 "includeRaiReason": INCLUDE_RAI_REASON,
 "includeSafetyAttributes": INCLUDE_SAFETY_ATTRIBUTES
 }
}

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://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/publishers/google/models/imagegeneration@006: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://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/publishers/google/models/imagegeneration@006:predict" | Select-Object -Expand Content

The response you get depends on the safety settings you set. For more information, see Understand and configure Responsible AI (RAI) for Imagen.

Prompt enhancement using prompt rewriter

Feature availability: This feature is available only for the Imagen 3 model version 002 (imagen-3.0-generate-002).

The Imagen 3 model version 002 includes a prompt rewriter feature that uses an LLM-based prompt rewriting tool. This tool generally adds more detail to the provided prompt to deliver higher quality images that better reflect the prompt provided. If you disable this feature, the quality and prompt-adherence of the images you receive may be impacted. This feature is enabled by default.

The rewritten prompt is only delivered by API response if the original prompt is fewer than 30 words long.

Console

  1. In the Google Cloud console, open the Vertex AI Studio > Media tab in the Vertex AI dashboard.

Go to the Vertex AI Studio tab 2. Add your text prompt and choose your input parameters. 3. In the Parameters panel, use the toggle_onEnable prompt enhancement toggle option to leave prompt enhancement enabled or to disable this feature. 4. Click play_arrowGenerate.

REST

For more information about imagegeneration model requests, see the imagegeneration model API reference.

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

  • PROJECT_ID: Your Google Cloud project ID.
  • MODEL_VERSION: The image generation model version to use. Available values that support prompt enhancement:
  • imagen-3.0-generate-002

For more information about model versions and features, see model versions. - LOCATION: Your project's region. For example, us-central1, europe-west2, or asia-northeast3. For a list of available regions, see Generative AI on Vertex AI locations. - TEXT_PROMPT: The text prompt that guides what images the model generates. Before images are generated, this base prompt is enhanced with more detail and descripitive language using the LLM-based prompt rewriting tool. - IMAGE_COUNT: The number of generated images. Accepted integer values: 1-4. Default value: 4. - enhancePrompt - A boolean to enable LLM-based prompt enhancement. By default, this value is set to true.

HTTP method and URL:

POST https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/publishers/google/models/MODEL_VERSION:predict

Request JSON body:

{
 "instances": [
 {
 "prompt": "TEXT_PROMPT"
 }
 ],
 "parameters": {
 "sampleCount": IMAGE_COUNT,
 "enhancePrompt": true
 }
}

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://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/publishers/google/models/MODEL_VERSION: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://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/publishers/google/models/MODEL_VERSION:predict" | Select-Object -Expand Content

With prompt enhancement enabled, the response includes an additional prompt field that shows the enhanced prompt and its associated generated image:

 {
 "predictions": [
 {
 "mimeType": "MIME_TYPE",
 "prompt": "ENHANCED_PROMPT_1",
 "bytesBase64Encoded": "BASE64_IMG_BYTES_1"
 },
 {
 "mimeType": "MIME_TYPE",
 "prompt": "ENHANCED_PROMPT_2",
 "bytesBase64Encoded": "BASE64_IMG_BYTES_2"
 }
 ]
 }

For example, the following sample response is for a request with "sampleCount": 2 and "prompt": "A raccoon wearing formal clothes, wearing a top hat. Oil painting in the style of Vincent Van Gogh.". The response returns two prediction objects, each with their enhanced prompt and the generated image bytes base64-encoded.

{
 "predictions": [
 {
 "mimeType": "image/png",
 "prompt": "An oil painting in the style of Vincent van Gogh, depicting a raccoon adorned
 in a finely tailored tuxedo, complete with a crisp white shirt and a bow tie. The raccoon
 also sports a classic top hat, perched jauntily on its head. The painting uses thick,
 swirling brushstrokes characteristic of van Gogh, with vibrant hues of blue, yellow, and
 green in the background, contrasting with the dark tones of the raccoon's attire. The light
 source is subtly placed, casting a dramatic shadow of the raccoon's attire onto the surface
 it sits upon, further enhancing the depth and dimensionality of the composition. The
 overall impression is one of a whimsical and sophisticated character, a raccoon elevated to
 a higher class through its formal attire, rendered in van Gogh's iconic style.",
 "bytesBase64Encoded": "BASE64_IMG_BYTES"
 },
 {
 "mimeType": "image/png",
 "prompt": "An oil painting in the style of Vincent van Gogh featuring a raccoon in a
 dapper suit, complete with a black jacket, crisp white shirt, and a black bow tie. The
 raccoon is wearing a black top hat, adding a touch of elegance to its ensemble. The
 painting is rendered with characteristic van Gogh brushwork, utilizing thick, impasto
 strokes of color. The background is a swirl of blues, greens, and yellows, creating a
 vibrant yet slightly chaotic atmosphere that contrasts with the raccoon's formal attire.
 The lighting is dramatic, casting sharp shadows and highlighting the textures of the fabric
 and the raccoon's fur, enhancing the sense of realism within the fantastical scene. The
 composition focuses on the raccoon's proud posture, highlighting the whimsical contrast of
 a wild animal dressed in formal attire, captured in the unique artistic language of van
 Gogh. ",
 "bytesBase64Encoded": "BASE64_IMG_BYTES"
 }
 ]
}

Text prompt language

Preview

This Imagen feature is a Preview offering, subject to the "Pre-GA Offerings Terms" of the Google Cloud Service Specific Terms. Pre-GA products and features may have limited support, and changes to pre-GA products and features may not be compatible with other pre-GA versions. For more information, see the launch stage descriptions. Further, by using this Imagen feature, you agree to the Generative AI Preview terms and conditions ("Preview Terms").

This optional parameter lets you set the language of the input text for image generation or image editing.
Image generated from prompt: ऊपर से देखा गया किताबों का ढेर। सबसे ऊपरी पुस्तक में एक पक्षी का जलरंग चित्रण है। किताब पर VERTEX AI मोटे अक्षरों में लिखा हुआ है 1 1 A pile of books seen from above. The topmost book contains a watercolor illustration of a bird. VERTEX AI is written in bold letters on the book. Image generated from prompt: 어두운 노란색과 청록색으로 이루어진 밝은 색의 옷을입고 귀걸이를 끼고있는 여자 포스트 모던 패션 사진 2 2 Woman wearing bright colors, in the style of dark yellow and dark cyan, wearing earrings, postmodern fashion photography.

Before you begin

Before you use this feature, complete the following steps:

  1. To create a service identity for Vertex AI to use in your project, use the following command:

gcloud beta services identity create --service=aiplatform.googleapis.com --project=PROJECT_ID
2. Request feature access. To request access, send an email to the Google Cloud Trusted Testers Access: GenApp Builder group. Reference Multi-Lingual Prompts in your message, and include your project number. The approval process usually takes several hours.

Set text prompt language

The following input values are supported for the text-prompt lanague:

  • Chinese (simplified) (zh/zh-CN)
  • Chinese (traditional) (zh-TW)
  • English (en, default value)
  • Hindi (hi)
  • Japanese (ja)
  • Korean (ko)
  • Portuguese (pt)
  • Spanish (es)

Console

If your prompt is in one of the supported languages, Imagen detects and translates your text and returns your generated or edited images.

If your prompt is in an unsupported language, Imagen uses the text verbatim for the request. This might result in unexpected output.

REST

For more information about imagegeneration model requests, see the imagegeneration model API reference.

Important: imagen-3.0-generate-002 doesn't support the text prmopt language feature.

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

  • PROJECT_ID: Your Google Cloud project ID.
  • TEXT_PROMPT: The text prompt that guides what images the model generates. This field is required for both generation and editing.
  • PROMPT_LANGUAGE: string. Optional. The language code that corresponds to your text prompt language. In this example it would be hi. Available values:
  • auto - Automatic detection. If Imagen detects a supported language, the prompt (and optionally, a negative prompt), are translated to English. If the language detected is not supported, Imagen uses the input text verbatim, which might result in unexpected output. No error code is returned.
  • en - English (default value if omitted)
  • es - Spanish
  • hi - Hindi
  • ja - Japanese
  • ko - Korean
  • pt - Portuguese
  • zh-TW - Chinese (traditional)
  • zh or zh-CN - Chinese (simplified)

HTTP method and URL:

POST https://us-central1-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/publishers/google/models/imagegeneration@005:predict

Request JSON body:

{
 "instances": [
 {
 "prompt": "सूर्यास्त के समय एक समुद्र तट। उड़ते पक्षी, हवा में लहराते नारियल के पेड़। लोग समुद्र तट पर सैर का आनंद ले रहे हैं।"
 }
 ],
 "parameters": {
 "language": "PROMPT_LANGUAGE"
 }
}

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://us-central1-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/publishers/google/models/imagegeneration@005: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://us-central1-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/us-central1/publishers/google/models/imagegeneration@005:predict" | Select-Object -Expand Content

The following sample response is for a request with "sampleCount": 2. The response returns two prediction objects, with the generated image bytes base64-encoded.

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

Aspect ratio

Note: You can only set certain aspect ratios using specific model versions. For aspect ratio availability by version, see the model versions section.

Depending on how you plan to use your generated images, some aspect ratios may work better than others. Choose the aspect ratio that best suits your use case.

Supported aspect ratios and their intended use:

Aspect ratio Intended use Image resolution (pixels) Sample image
1:1 default, square, general use 1024x1024 (Imagen v.002) 1536x1536 (Imagen 2 v.005, v.006) 1024x1024 (Imagen 3) Prompt: overhead shot of a pasta dinner, studio photo in the style of food magazine cover.
3:4 TV, media, film 1344x1792 (Imagen 2 v.006) 896x1280 (Imagen 3) Prompt: commercial photoshoot, fragrance ad, lavender vanilla scented bottle on a light colored background.
4:3 TV, media, film 1792x1344 (Imagen 2 v.006) 1280x896 (Imagen 3) Prompt: commercial photoshoot, green and gray high top sneakers, 4k, dramatic angles.
9:16 portrait, tall objects, mobile devices 1134x2016 (Imagen 2 v.005, v.006) 768x1408 (Imagen 3) Prompt: skyscrapers in new york city, futuristic rendering, concept, digital art.
16:9 landscape 2016x1134 (Imagen 2 v.006) 1408x768 (Imagen 3) Prompt: nature photography, a beach in hawaii with the ocean in the background, lens flare, sunset.

Console

  1. Follow the generate image with text instructions to open the Vertex AI Studio and enter your text prompt.
  2. In the Parameters panel, select an aspect ratio from the Aspect ratio menu.
  3. Click play_arrowGenerate.

REST

Aspect ratio is an optional field in the parameters object of a JSON request body.

  1. Follow the generate image with text instructions to replace other request body variables.

  2. Replace the following:

  3. ASPECT_RATIO: string. Optional. A generation mode parameter that controls aspect ratio. Supported ratio values and their intended use:

  4. 1:1 (default, square)
  5. 3:4 (Ads, social media)
  6. 4:3 (TV, photography)
  7. 16:9 (landscape)
  8. 9:16 (portrait)

{
"instances": [
...
],
"parameters": {
"sampleCount": IMAGE_COUNT,
"aspectRatio": "ASPECT_RATIO"
}
}
3. Follow the generate image with text instructions to send your REST request.

Number of results

Use the number of results parameter to limit the amount of images returned for each request (generate or edit) you send.

Console

  1. Follow the generate image with text instructions to open the Vertex AI Studio and enter your text prompt.
  2. In the Parameters panel, select a valid integer value in the Number of results field.
  3. Click play_arrowGenerate.

REST

For more information about imagegeneration model requests, see the imagegeneration model API reference.

Number of results is a field in the parameters object of a JSON request body.

  1. Follow the generate image with text instructions to replace other request body variables.

  2. Replace the following:

  3. IMAGE_COUNT: The number of generated images. Accepted integer values: 1-8 (imagegeneration@002), 1-4 (all other model versions). Default value: 4.

{
"instances": [
...
],
"parameters": { 
"sampleCount": IMAGE_COUNT
}
}
3. Follow the generate image with text instructions to send your REST request.

Negative prompt

Caution: Negative prompt isn't supported with the imagen-3.0-generate-002 model.

A negative prompt is a description of what you want to omit in generated images. For example, consider the prompt "a rainy city street at night with no people". The model may interpret "people" as a directive of what include instead of omit. To generate better results, you could use the prompt "a rainy city street at night" with a negative prompt "people".

Imagen generates these images with and without a negative prompt:

Text prompt only

  • Text prompt: "a pizza"

Text prompt and negative prompt

  • Text prompt: "a pizza"
  • Negative prompt: "pepperoni"

Console

  1. Follow the generate image with text instructions to open the Vertex AI Studio and enter your text prompt.
  2. In the Parameters panel, enter a negative prompt in the Negative prompt field.
  3. Click play_arrowGenerate.

REST

For more information about imagegeneration model requests, see the imagegeneration model API reference.

Negative prompt is an optional field in the parameters object of a JSON request body.

  1. Follow the generate image with text instructions to replace other request body variables.

  2. Replace the following:

  3. NEGATIVE_PROMPT: A negative prompt to help generate the images. For example: "animals" (removes animals), "blurry" (makes the image clearer), "text" (removes text), or "cropped" (removes cropped images).

{
"instances": [
...
],
"parameters": {
"sampleCount": IMAGE_COUNT,
"negativePrompt": "NEGATIVE_PROMPT"
}
}
3. Follow the generate image with text instructions to send your REST request.

Seed number

Caution: Model version 006 and greater only. To use the seed field, you must also disable the watermark feature using "addWatermark": false.

A seed number is a number you add to a request to make generated images deterministic. Adding a seed number with your request is a way to assure you get the same generated images each time. For example, you can provide a prompt, set the number of results to 1, and use a seed number to get the same image each time you use all those same input values. If you send the same request with the number of results set to 8, you will get the same eight images. However, the images aren't necessarily returned in the same order.

Console

  1. Follow the generate image with text instructions to open the Vertex AI Studio and enter your text prompt.
  2. In the Parameters panel, click the expand_moreAdvanced options expandable section.
  3. In the Seed field, enter a seed number.
  4. Click play_arrowGenerate.

REST

For more information about imagegeneration model requests, see the imagegeneration model API reference.

Seed number is an optional field in the parameters object of a JSON request body.

  1. Follow the generate image with text instructions to replace other request body variables.

  2. Replace the following:

  3. SEED_NUMBER: integer. Optional. Any non-negative integer you provide to make output images deterministic. Providing the same seed number always results in the same output images. If the model you're using supports digital watermarking, you must set "addWatermark": false to use this field. Accepted integer values: 1 - 2147483647.

{
"instances": [
...
],
"parameters": {
"sampleCount": IMAGE_COUNT,
"seed": SEED_NUMBER,
// required for model version 006 and greater only when using a seed number
"addWatermark": false
}
}
3. Follow the generate image with text instructions to send your REST request.

Predefined style

Feature availability: This feature is available only for model version 002 (imagegeneration@002).

The style of image you are looking to generate. You can use this feature to create images in popular styles such as digital art, watercolor, or cyberpunk.

Console

  1. Follow the generate image with text instructions to open the Vertex AI Studio and enter your text prompt.
  2. In Style section of the Parameters panel, chose a style from the menu.
  3. Click play_arrowGenerate.

REST

For more information about imagegeneration model requests, see the imagegeneration model API reference.

Predefined style is an optional field in the parameters object of a JSON request body.

  1. Follow the generate image with text instructions to replace other request body variables.

  2. Replace the following:

  3. IMAGE_STYLE: One of the available predefined styles:

  4. photograph
  5. digital_art
  6. landscape
  7. sketch
  8. watercolor
  9. cyberpunk
  10. pop_art

{
"instances": [
...
],
"parameters": {
"sampleCount": IMAGE_COUNT,
"sampleImageStyle": "IMAGE_STYLE"
}
}
3. Follow the generate image with text instructions to send your REST request.

Upscale an image

Use upscaling to increase the size of existing, generated, or edited images without losing quality.

Console

  1. Follow the generate image with text instructions to generate images.
  2. Select the image to upscale.
  3. Click downloadExport.
  4. Select Upscale images.
  5. Choose a value from the Scale factor.
  6. Click downloadExport to save the upscaled image.

REST

For more information about imagegeneration model requests, see the imagegeneration model API reference.

Upscaling mode is an optional field in the parameters object of a JSON request body. When you upscale an image using the API, specify "mode": "upscale" and upscaleConfig.

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

  • LOCATION: Your project's region. For example, us-central1, europe-west2, or asia-northeast3. For a list of available regions, see Generative AI on Vertex AI locations.
  • PROJECT_ID: Your Google Cloud project ID.
  • B64_BASE_IMAGE: The base image to edit or upscale. The image must be specified as a base64-encoded byte string. Size limit: 10 MB.
  • IMAGE_SOURCE: The Cloud Storage location of the image you want to edit or upscale. For example: gs://output-bucket/source-photos/photo.png.
  • UPSCALE_FACTOR: Optional. The factor to which the image will be upscaled. If not specified, the upscale factor will be determined from the longer side of the input image and sampleImageSize. Available values: x2 or x4 .

HTTP method and URL:

POST https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/publishers/google/models/imagegeneration@002:predict

Request JSON body:

{
 "instances": [
 {
 "prompt": "",
 "image": {
 // use one of the following to specify the image to upscale
 "bytesBase64Encoded": "B64_BASE_IMAGE"
 "gcsUri": "IMAGE_SOURCE"
 // end of base image input options
 },
 }
 ],
 "parameters": {
 "sampleCount": 1,
 "mode": "upscale",
 "upscaleConfig": {
 "upscaleFactor": "UPSCALE_FACTOR"
 }
 }
}

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://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/publishers/google/models/imagegeneration@002: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://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/publishers/google/models/imagegeneration@002:predict" | Select-Object -Expand Content

You should receive a JSON response similar to the following:

{
 "predictions": [
 {
 "mimeType": "image/png",
 "bytesBase64Encoded": "iVBOR..[base64-encoded-upscaled-image]...YII="
 }
 ]
}

What's next

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

[Previous

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Design image generation prompts](https://cloud.google.com/vertex-ai/generative-ai/docs/image/img-gen-prompt-guide)

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Generate images

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