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Quickstart: Generate images with Azure OpenAI Service

  • Članak
Choose your preferred usage method

Napomena

The image generation API creates an image from a text prompt. It doesn't edit or create variations from existing images.

Use this guide to get started generating images with Azure OpenAI in your browser with Azure AI Foundry.

Prerequisites

Go to Azure AI Foundry

Browse to Azure AI Foundry and sign in with the credentials associated with your Azure OpenAI resource. During or after the sign-in workflow, select the appropriate directory, Azure subscription, and Azure OpenAI resource.

From the Azure AI Foundry landing page, create or select a new project. Navigate to the Models + endpoints page on the left nav. Select Deploy model and then choose one of the DALL-E models from the list. Complete the deployment process.

On the model's page, select Open in playground.

Try out image generation

Start exploring Azure OpenAI capabilities with a no-code approach through the Images playground. Enter your image prompt into the text box and select Generate. When the AI-generated image is ready, it appears on the page.

Napomena

The image generation APIs come with a content moderation filter. If Azure OpenAI recognizes your prompt as harmful content, it doesn't return a generated image. For more information, see Content filtering.

In the Images playground, you can also view Python and cURL code samples, which are prefilled according to your settings. Select View code near the top of the page. You can use this code to write an application that completes the same task.

Clean up resources

If you want to clean up and remove an Azure OpenAI resource, you can delete the resource or resource group. Deleting the resource group also deletes any other resources associated with it.

Next steps

Use this guide to get started calling the Azure OpenAI Service image generation REST APIs by using Python.

Prerequisites

Setup

Retrieve key and endpoint

To successfully call the Azure OpenAI APIs, you need the following information about your Azure OpenAI resource:

Variable Name Value
Endpoint api_base The endpoint value is located under Keys and Endpoint for your resource in the Azure portal. You can also find the endpoint via the Deployments page in Azure AI Foundry portal. An example endpoint is: https://docs-test-001.openai.azure.com/.
Key api_key The key value is also located under Keys and Endpoint for your resource in the Azure portal. Azure generates two keys for your resource. You can use either value.

Go to your resource in the Azure portal. On the navigation pane, select Keys and Endpoint under Resource Management. Copy the Endpoint value and an access key value. You can use either the KEY 1 or KEY 2 value. Always having two keys allows you to securely rotate and regenerate keys without causing a service disruption.

Screenshot that shows the Keys and Endpoint page for an Azure OpenAI resource in the Azure portal.

Environment variables

Create and assign persistent environment variables for your key and endpoint.

Važno

We recommend Microsoft Entra ID authentication with managed identities for Azure resources to avoid storing credentials with your applications that run in the cloud.

Use API keys with caution. Don't include the API key directly in your code, and never post it publicly. If using API keys, store them securely in Azure Key Vault, rotate the keys regularly, and restrict access to Azure Key Vault using role based access control and network access restrictions. For more information about using API keys securely in your apps, see API keys with Azure Key Vault.

For more information about AI services security, see Authenticate requests to Azure AI services.

Windows Command Prompt 
setx AZURE_OPENAI_API_KEY "REPLACE_WITH_YOUR_KEY_VALUE_HERE" 
setx AZURE_OPENAI_ENDPOINT "REPLACE_WITH_YOUR_ENDPOINT_HERE"

Create a new Python application

Create a new Python file named quickstart.py. Open the new file in your preferred editor or IDE.

  1. Replace the contents of quickstart.py with the following code. Change the value of prompt to your preferred text.

    You also need to replace <dalle3> in the URL with the deployment name you chose when you deployed the DALL-E 3 model. Entering the model name will result in an error unless you chose a deployment name that is identical to the underlying model name. If you encounter an error, double check to make sure that you don't have a doubling of the / at the separation between your endpoint and /openai/deployments.

    Python 
    import requests
import time
import os
api_base = os.environ['AZURE_OPENAI_ENDPOINT']  # Enter your endpoint here
api_key = os.environ['AZURE_OPENAI_API_KEY']         # Enter your API key here

api_version = '2024-02-01'
url = f"{api_base}/openai/deployments/<dalle3>/images/generations?api-version={api_version}"
headers= { "api-key": api_key, "Content-Type": "application/json" }
body = {
    # Enter your prompt text here
    "prompt": "A multi-colored umbrella on the beach, disposable camera",
    "size": "1024x1024", # supported values are “1792x1024”, “1024x1024” and “1024x1792” 
    "n": 1, #The number of images to generate. Only n=1 is supported for DALL-E 3.
    "quality": "hd", # Options are “hd” and “standard”; defaults to standard 
    "style": "vivid" # Options are “natural” and “vivid”; defaults to “vivid”
}
submission = requests.post(url, headers=headers, json=body)

image_url = submission.json()['data'][0]['url']

print(image_url)

    The script makes a synchronous image generation API call.

    Važno

    Remember to remove the key from your code when you're done, and never post your key publicly. For production, use a secure way of storing and accessing your credentials. For more information, see Azure Key Vault.

  2. Run the application with the python command:

    Console 
    python quickstart.py

    Wait a few moments to get the response.

Output

The output from a successful image generation API call looks like the following example. The url field contains a URL where you can download the generated image. The URL stays active for 24 hours.

JSON 
{ 
    "created": 1698116662, 
    "data": [ 
        { 
            "url": "<URL_to_generated_image>",
            "revised_prompt": "<prompt_that_was_used>" 
        }
    ]
}

The image generation APIs come with a content moderation filter. If the service recognizes your prompt as harmful content, it doesn't generate an image. For more information, see Content filtering. For examples of error responses, see the DALL-E how-to guide.

The system returns an operation status of Failed and the error.code value in the message is set to contentFilter. Here's an example:

JSON 
{
    "created": 1698435368,
    "error":
    {
        "code": "contentFilter",
        "message": "Your task failed as a result of our safety system."
    }
}

It's also possible that the generated image itself is filtered. In this case, the error message is set to Generated image was filtered as a result of our safety system.. Here's an example:

JSON 
{
    "created": 1698435368,
    "error":
    {
        "code": "contentFilter",
        "message": "Generated image was filtered as a result of our safety system."
    }
}

Clean up resources

If you want to clean up and remove an Azure OpenAI resource, you can delete the resource or resource group. Deleting the resource group also deletes any other resources associated with it.

Next steps

Use this guide to get started generating images with the Azure OpenAI SDK for Python.

Library source code | Package | Samples

Prerequisites

Setup

Retrieve key and endpoint

To successfully call the Azure OpenAI APIs, you need the following information about your Azure OpenAI resource:

Variable Name Value
Endpoint api_base The endpoint value is located under Keys and Endpoint for your resource in the Azure portal. You can also find the endpoint via the Deployments page in Azure AI Foundry portal. An example endpoint is: https://docs-test-001.openai.azure.com/.
Key api_key The key value is also located under Keys and Endpoint for your resource in the Azure portal. Azure generates two keys for your resource. You can use either value.

Go to your resource in the Azure portal. On the navigation pane, select Keys and Endpoint under Resource Management. Copy the Endpoint value and an access key value. You can use either the KEY 1 or KEY 2 value. Always having two keys allows you to securely rotate and regenerate keys without causing a service disruption.

Screenshot that shows the Keys and Endpoint page for an Azure OpenAI resource in the Azure portal.

Environment variables

Create and assign persistent environment variables for your key and endpoint.

Važno

We recommend Microsoft Entra ID authentication with managed identities for Azure resources to avoid storing credentials with your applications that run in the cloud.

Use API keys with caution. Don't include the API key directly in your code, and never post it publicly. If using API keys, store them securely in Azure Key Vault, rotate the keys regularly, and restrict access to Azure Key Vault using role based access control and network access restrictions. For more information about using API keys securely in your apps, see API keys with Azure Key Vault.

For more information about AI services security, see Authenticate requests to Azure AI services.

Windows Command Prompt 
setx AZURE_OPENAI_API_KEY "REPLACE_WITH_YOUR_KEY_VALUE_HERE" 
setx AZURE_OPENAI_ENDPOINT "REPLACE_WITH_YOUR_ENDPOINT_HERE"

Install the Python SDK

Open a command prompt and browse to your project folder. Install the OpenAI Python SDK by using the following command:

Bash 
pip install openai

Install the following libraries as well:

Bash 
pip install requests
pip install pillow

Generate images with DALL-E

Create a new python file, quickstart.py. Open it in your preferred editor or IDE.

Replace the contents of quickstart.py with the following code.

Python 
from openai import AzureOpenAI
import os
import requests
from PIL import Image
import json

client = AzureOpenAI(
    api_version="2024-02-01",  
    api_key=os.environ["AZURE_OPENAI_API_KEY"],  
    azure_endpoint=os.environ['AZURE_OPENAI_ENDPOINT']
)

result = client.images.generate(
    model="dalle3", # the name of your DALL-E 3 deployment
    prompt="a close-up of a bear walking throughthe forest",
    n=1
)

json_response = json.loads(result.model_dump_json())

# Set the directory for the stored image
image_dir = os.path.join(os.curdir, 'images')

# If the directory doesn't exist, create it
if not os.path.isdir(image_dir):
    os.mkdir(image_dir)

# Initialize the image path (note the filetype should be png)
image_path = os.path.join(image_dir, 'generated_image.png')

# Retrieve the generated image
image_url = json_response["data"][0]["url"]  # extract image URL from response
generated_image = requests.get(image_url).content  # download the image
with open(image_path, "wb") as image_file:
    image_file.write(generated_image)

# Display the image in the default image viewer
image = Image.open(image_path)
image.show()
  1. Enter your endpoint URL and key in the appropriate fields.
  2. Change the value of prompt to your preferred text.
  3. Change the value of model to the name of your deployed DALL-E 3 model.

Važno

Remember to remove the key from your code when you're done, and never post your key publicly. For production, use a secure way of storing and accessing your credentials. For more information, see Azure Key Vault.

Run the application with the python command:

Console 
python quickstart.py

Wait a few moments to get the response.

Output

Azure OpenAI stores the output image in the generated_image.png file in your specified directory. The script also displays the image in your default image viewer.

The image generation APIs come with a content moderation filter. If the service recognizes your prompt as harmful content, it doesn't generate an image. For more information, see Content filtering.

Clean up resources

If you want to clean up and remove an Azure OpenAI resource, you can delete the resource or resource group. Deleting the resource group also deletes any other resources associated with it.

Next steps

Use this guide to get started generating images with the Azure OpenAI SDK for C#.

Library source code | Package (NuGet) | Samples

Prerequisites

Setup

Retrieve key and endpoint

To successfully make a call against Azure OpenAI, you need an endpoint and a key.

Variable name Value
ENDPOINT The service endpoint can be found in the Keys & Endpoint section when examining your resource from the Azure portal. Alternatively, you can find the endpoint via the Deployments page in Azure AI Foundry portal. An example endpoint is: https://docs-test-001.openai.azure.com/.
API-KEY This value can be found in the Keys & Endpoint section when examining your resource from the Azure portal. You can use either KEY1 or KEY2.

Go to your resource in the Azure portal. The Keys & Endpoint section can be found in the Resource Management section. Copy your endpoint and access key as you'll need both for authenticating your API calls. You can use either KEY1 or KEY2. Always having two keys allows you to securely rotate and regenerate keys without causing a service disruption.

Screenshot of the overview UI for an Azure OpenAI resource in the Azure portal with the endpoint and access keys location circled in red.

Environment variables

Create and assign persistent environment variables for your key and endpoint.

Važno

We recommend Microsoft Entra ID authentication with managed identities for Azure resources to avoid storing credentials with your applications that run in the cloud.

Use API keys with caution. Don't include the API key directly in your code, and never post it publicly. If using API keys, store them securely in Azure Key Vault, rotate the keys regularly, and restrict access to Azure Key Vault using role based access control and network access restrictions. For more information about using API keys securely in your apps, see API keys with Azure Key Vault.

For more information about AI services security, see Authenticate requests to Azure AI services.

Windows Command Prompt 
setx AZURE_OPENAI_API_KEY "REPLACE_WITH_YOUR_KEY_VALUE_HERE" 
setx AZURE_OPENAI_ENDPOINT "REPLACE_WITH_YOUR_ENDPOINT_HERE"

Create a new .NET Core application

In a console window (such as cmd, PowerShell, or Bash), use the dotnet new command to create a new console app with the name azure-openai-quickstart. This command creates a simple "Hello World" project with a single C# source file: Program.cs.

.NET CLI 
dotnet new console -n azure-openai-quickstart

Change your directory to the newly created app folder. You can build the application with:

.NET CLI 
dotnet build

The build output should contain no warnings or errors.

Output 
...
Build succeeded.
 0 Warning(s)
 0 Error(s)
...

Install the OpenAI .NET SDK

Install the client library with:

.NET CLI 
dotnet add package Azure.AI.OpenAI --version 1.0.0-beta.6

Generate images with DALL-E

From the project directory, open the program.cs file and replace the contents with the following code:

C# 
using Azure;
using Azure.AI.OpenAI;
using OpenAI.Images;
using static System.Environment;

string endpoint = GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT");
string key = GetEnvironmentVariable("AZURE_OPENAI_API_KEY");

AzureOpenAIClient azureClient = new(
    new Uri(endpoint),
    new AzureKeyCredential(key));

// This must match the custom deployment name you chose for your model
ImageClient chatClient = azureClient.GetImageClient("dalle-3");

var imageGeneration = await chatClient.GenerateImageAsync(
        "a happy monkey sitting in a tree, in watercolor",
        new ImageGenerationOptions()
        {
            Size = GeneratedImageSize.W1024xH1024
        }
    );

Console.WriteLine(imageGeneration.Value.ImageUri);

Build and run the application from your application directory with these commands:

dotnet 
dotnet build
dotnet run

Output

The URL of the generated image is printed to the console.

Console 
<SAS URL>

Napomena

The image generation APIs come with a content moderation filter. If the service recognizes your prompt as harmful content, it won't return a generated image. For more information, see the content filter article.

Clean up resources

If you want to clean up and remove an Azure OpenAI resource, you can delete the resource. Before deleting the resource, you must first delete any deployed models.

Next steps

Use this guide to get started generating images with the Azure OpenAI SDK for Java.

Library source code | Artifact (Maven) | Samples

Prerequisites

Setup

Retrieve key and endpoint

To successfully make a call against Azure OpenAI, you need an endpoint and a key.

Variable name Value
ENDPOINT The service endpoint can be found in the Keys & Endpoint section when examining your resource from the Azure portal. Alternatively, you can find the endpoint via the Deployments page in Azure AI Foundry portal. An example endpoint is: https://docs-test-001.openai.azure.com/.
API-KEY This value can be found in the Keys & Endpoint section when examining your resource from the Azure portal. You can use either KEY1 or KEY2.

Go to your resource in the Azure portal. The Keys & Endpoint section can be found in the Resource Management section. Copy your endpoint and access key as you'll need both for authenticating your API calls. You can use either KEY1 or KEY2. Always having two keys allows you to securely rotate and regenerate keys without causing a service disruption.

Screenshot of the overview UI for an Azure OpenAI resource in the Azure portal with the endpoint and access keys location circled in red.

Environment variables

Create and assign persistent environment variables for your key and endpoint.

Važno

We recommend Microsoft Entra ID authentication with managed identities for Azure resources to avoid storing credentials with your applications that run in the cloud.

Use API keys with caution. Don't include the API key directly in your code, and never post it publicly. If using API keys, store them securely in Azure Key Vault, rotate the keys regularly, and restrict access to Azure Key Vault using role based access control and network access restrictions. For more information about using API keys securely in your apps, see API keys with Azure Key Vault.

For more information about AI services security, see Authenticate requests to Azure AI services.

Windows Command Prompt 
setx AZURE_OPENAI_API_KEY "REPLACE_WITH_YOUR_KEY_VALUE_HERE" 
setx AZURE_OPENAI_ENDPOINT "REPLACE_WITH_YOUR_ENDPOINT_HERE"

Create a new Java application

Create a new Gradle project.

In a console window (such as cmd, PowerShell, or Bash), create a new directory for your app, and navigate to it.

Console 
mkdir myapp && cd myapp

Run the gradle init command from your working directory. This command will create essential build files for Gradle, including build.gradle.kts, which is used at runtime to create and configure your application.

Console 
gradle init --type basic

When prompted to choose a DSL, select Kotlin.

Install the Java SDK

This quickstart uses the Gradle dependency manager. You can find the client library and information for other dependency managers on the Maven Central Repository.

Locate build.gradle.kts and open it with your preferred IDE or text editor. Then copy in the following build configuration. This configuration defines the project as a Java application whose entry point is the class OpenAIQuickstart. It imports the Azure AI Vision library.

Kotlin 
plugins {
    java
    application
}
application { 
    mainClass.set("OpenAIQuickstart")
}
repositories {
    mavenCentral()
}
dependencies {
    implementation(group = "com.azure", name = "azure-ai-openai", version = "1.0.0-beta.3")
    implementation("org.slf4j:slf4j-simple:1.7.9")
}

Generate images with DALL-E

  1. Create a Java file.

    From your working directory, run the following command to create a project source folder:

    Console 
    mkdir -p src/main/java

    Navigate to the new folder and create a file called OpenAIQuickstart.java.

  2. Open OpenAIQuickstart.java in your preferred editor or IDE and paste in the following code.

    Java 
    import com.azure.ai.openai.OpenAIAsyncClient;
import com.azure.ai.openai.OpenAIClientBuilder;
import com.azure.ai.openai.models.ImageGenerationOptions;
import com.azure.ai.openai.models.ImageLocation;
import com.azure.core.credential.AzureKeyCredential;
import com.azure.core.models.ResponseError;

import java.util.concurrent.TimeUnit;

/**
 * Sample demonstrates how to get the images for a given prompt.
 */
public class OpenAIQuickstart {

    /**
     * Runs the sample algorithm and demonstrates how to get the images for a given prompt.
     *
     * @param args Unused. Arguments to the program.
     */
    public static void main(String[] args) throws InterruptedException {

        // Get key and endpoint from environment variables:
        String azureOpenaiKey = System.getenv("AZURE_OPENAI_API_KEY");
        String endpoint = System.getenv("AZURE_OPENAI_ENDPOINT");

        OpenAIAsyncClient client = new OpenAIClientBuilder()
            .endpoint(endpoint)
            .credential(new AzureKeyCredential(azureOpenaiKey))
            .buildAsyncClient();

        ImageGenerationOptions imageGenerationOptions = new ImageGenerationOptions(
            "A drawing of the Seattle skyline in the style of Van Gogh");
        client.getImages(imageGenerationOptions).subscribe(
            images -> {
                for (ImageLocation imageLocation : images.getData()) {
                    ResponseError error = imageLocation.getError();
                    if (error != null) {
                        System.out.printf("Image generation operation failed. Error code: %s, error message: %s.%n",
                            error.getCode(), error.getMessage());
                    } else {
                        System.out.printf(
                            "Image location URL that provides temporary access to download the generated image is %s.%n",
                            imageLocation.getUrl());
                    }
                }
            },
            error -> System.err.println("There was an error getting images." + error),
            () -> System.out.println("Completed getImages."));

        // The .subscribe() creation and assignment is not a blocking call. For the purpose of this example, we sleep
        // the thread so the program does not end before the send operation is complete. Using .block() instead of
        // .subscribe() will turn this into a synchronous call.
        TimeUnit.SECONDS.sleep(10);
    }
}

  3. Navigate back to the project root folder, and build the app with:

    Console 
    gradle build

    Then, run it with the gradle run command:

    Console 
    gradle run

Output

The URL of the generated image is printed to the console.

Console 
Image location URL that provides temporary access to download the generated image is <SAS URL>.
Completed getImages.

Napomena

The image generation APIs come with a content moderation filter. If the service recognizes your prompt as harmful content, it won't return a generated image. For more information, see the content filter article.

Clean up resources

If you want to clean up and remove an Azure OpenAI resource, you can delete the resource. Before deleting the resource, you must first delete any deployed models.

Next steps

Use this guide to get started generating images with the Azure OpenAI SDK for JavaScript.

Reference documentation | Source code | Package (npm) | Samples

Prerequisites

Microsoft Entra ID prerequisites

For the recommended keyless authentication with Microsoft Entra ID, you need to:

  • Install the Azure CLI used for keyless authentication with Microsoft Entra ID.
  • Assign the Cognitive Services User role to your user account. You can assign roles in the Azure portal under Access control (IAM) > Add role assignment.

Set up

  1. Create a new folder image-quickstart to contain the application and open Visual Studio Code in that folder with the following command:

    shell 
    mkdir image-quickstart && cd image-quickstart

  2. Create the package.json with the following command:

    shell 
    npm init -y

  3. Install the OpenAI client library for JavaScript with:

    Console 
    npm install openai

  4. For the recommended passwordless authentication:

    Console 
    npm install @azure/identity

Retrieve resource information

You need to retrieve the following information to authenticate your application with your Azure OpenAI resource:

Variable name Value
AZURE_OPENAI_ENDPOINT This value can be found in the Keys and Endpoint section when examining your resource from the Azure portal.
AZURE_OPENAI_DEPLOYMENT_NAME This value will correspond to the custom name you chose for your deployment when you deployed a model. This value can be found under Resource Management > Model Deployments in the Azure portal.
OPENAI_API_VERSION Learn more about API Versions.

You can change the version in code or use an environment variable.

Learn more about keyless authentication and setting environment variables.

Oprez

To use the recommended keyless authentication with the SDK, make sure that the AZURE_OPENAI_API_KEY environment variable isn't set.

Generate images with DALL-E

  1. Create the index.js file with the following code:

    JavaScript 
    const { AzureOpenAI } = require("openai");
const { 
    DefaultAzureCredential, 
    getBearerTokenProvider 
} = require("@azure/identity");

// You will need to set these environment variables or edit the following values
const endpoint = process.env.AZURE_OPENAI_ENDPOINT || "Your endpoint";

// Required Azure OpenAI deployment name and API version
const apiVersion = process.env.OPENAI_API_VERSION || "2024-07-01";
const deploymentName = process.env.AZURE_OPENAI_DEPLOYMENT_NAME || "dall-e-3";

// The prompt to generate images from
const prompt = "a monkey eating a banana";
const numberOfImagesToGenerate = 1;

// keyless authentication    
const credential = new DefaultAzureCredential();
const scope = "https://cognitiveservices.azure.com/.default";
const azureADTokenProvider = getBearerTokenProvider(credential, scope);

function getClient(): AzureOpenAI {
  return new AzureOpenAI({
    endpoint,
    azureADTokenProvider,
    apiVersion,
    deployment: deploymentName,
  });
}
async function main() {
  console.log("== Image Generation ==");

  const client = getClient();

  const results = await client.images.generate({
    prompt,
    size: "1024x1024",
    n: numberOfImagesToGenerate,
    model: "",
    style: "vivid", // or "natural"
  });

  for (const image of results.data) {
    console.log(`Image generation result URL: ${image.url}`);
  }
}

main().catch((err) => {
  console.error("The sample encountered an error:", err);
});

  2. Sign in to Azure with the following command:

    shell 
    az login

  3. Run the JavaScript file.

    shell 
    node index.js

Output

The URL of the generated image is printed to the console.

Console 
== Batch Image Generation ==
Image generation result URL: <SAS URL>
Image generation result URL: <SAS URL>

Napomena

The image generation APIs come with a content moderation filter. If the service recognizes your prompt as harmful content, it won't return a generated image. For more information, see the content filter article.

Clean up resources

If you want to clean up and remove an Azure OpenAI resource, you can delete the resource. Before deleting the resource, you must first delete any deployed models.

Next steps

Use this guide to get started generating images with the Azure OpenAI SDK for JavaScript.

Reference documentation | Source code | Package (npm) | Samples

Prerequisites

Microsoft Entra ID prerequisites

For the recommended keyless authentication with Microsoft Entra ID, you need to:

  • Install the Azure CLI used for keyless authentication with Microsoft Entra ID.
  • Assign the Cognitive Services User role to your user account. You can assign roles in the Azure portal under Access control (IAM) > Add role assignment.

Set up

  1. Create a new folder image-quickstart to contain the application and open Visual Studio Code in that folder with the following command:

    shell 
    mkdir image-quickstart && cd image-quickstart

  2. Create the package.json with the following command:

    shell 
    npm init -y

  3. Update the package.json to ECMAScript with the following command:

    shell 
    npm pkg set type=module

  4. Install the OpenAI client library for JavaScript with:

    Console 
    npm install openai

  5. For the recommended passwordless authentication:

    Console 
    npm install @azure/identity

Retrieve resource information

You need to retrieve the following information to authenticate your application with your Azure OpenAI resource:

Variable name Value
AZURE_OPENAI_ENDPOINT This value can be found in the Keys and Endpoint section when examining your resource from the Azure portal.
AZURE_OPENAI_DEPLOYMENT_NAME This value will correspond to the custom name you chose for your deployment when you deployed a model. This value can be found under Resource Management > Model Deployments in the Azure portal.
OPENAI_API_VERSION Learn more about API Versions.

You can change the version in code or use an environment variable.

Learn more about keyless authentication and setting environment variables.

Oprez

To use the recommended keyless authentication with the SDK, make sure that the AZURE_OPENAI_API_KEY environment variable isn't set.

Generate images with DALL-E

  1. Create the index.ts file with the following code:

    TypeScript 
    import { AzureOpenAI } from "openai";
import { 
    DefaultAzureCredential, 
    getBearerTokenProvider 
} from "@azure/identity";

// You will need to set these environment variables or edit the following values
const endpoint = process.env.AZURE_OPENAI_ENDPOINT || "Your endpoint";

// Required Azure OpenAI deployment name and API version
const apiVersion = process.env.OPENAI_API_VERSION || "2024-07-01";
const deploymentName = process.env.AZURE_OPENAI_DEPLOYMENT_NAME || "dall-e-3";

// keyless authentication    
const credential = new DefaultAzureCredential();
const scope = "https://cognitiveservices.azure.com/.default";
const azureADTokenProvider = getBearerTokenProvider(credential, scope);

function getClient(): AzureOpenAI {
  return new AzureOpenAI({
    endpoint,
    azureADTokenProvider,
    apiVersion,
    deployment: deploymentName,
  });
}
async function main() {
  console.log("== Image Generation ==");

  const client = getClient();

  const results = await client.images.generate({
    prompt,
    size: "1024x1024",
    n: numberOfImagesToGenerate,
    model: "",
    style: "vivid", // or "natural"
  });

  for (const image of results.data) {
    console.log(`Image generation result URL: ${image.url}`);
  }
}

main().catch((err) => {
  console.error("The sample encountered an error:", err);
});

  2. Create the tsconfig.json file to transpile the TypeScript code and copy the following code for ECMAScript.

    JSON 
    {
    "compilerOptions": {
      "module": "NodeNext",
      "target": "ES2022", // Supports top-level await
      "moduleResolution": "NodeNext",
      "skipLibCheck": true, // Avoid type errors from node_modules
      "strict": true // Enable strict type-checking options
    },
    "include": ["*.ts"]
}

  3. Transpile from TypeScript to JavaScript.

    shell 
    tsc

  4. Sign in to Azure with the following command:

    shell 
    az login

  5. Run the code with the following command:

    shell 
    node index.js

Output

The URL of the generated image is printed to the console.

Console 
== Batch Image Generation ==
Image generation result URL: <SAS URL>
Image generation result URL: <SAS URL>

Napomena

The image generation APIs come with a content moderation filter. If the service recognizes your prompt as harmful content, it won't return a generated image. For more information, see the content filter article.

Clean up resources

If you want to clean up and remove an Azure OpenAI resource, you can delete the resource. Before deleting the resource, you must first delete any deployed models.

Next steps

Use this guide to get started generating images with the Azure OpenAI SDK for Go.

Library source code | Package | Samples

Prerequisites

Setup

Retrieve key and endpoint

To successfully make a call against Azure OpenAI, you need an endpoint and a key.

Variable name Value
ENDPOINT The service endpoint can be found in the Keys & Endpoint section when examining your resource from the Azure portal. Alternatively, you can find the endpoint via the Deployments page in Azure AI Foundry portal. An example endpoint is: https://docs-test-001.openai.azure.com/.
API-KEY This value can be found in the Keys & Endpoint section when examining your resource from the Azure portal. You can use either KEY1 or KEY2.

Go to your resource in the Azure portal. The Keys & Endpoint section can be found in the Resource Management section. Copy your endpoint and access key as you'll need both for authenticating your API calls. You can use either KEY1 or KEY2. Always having two keys allows you to securely rotate and regenerate keys without causing a service disruption.

Screenshot of the overview UI for an Azure OpenAI resource in the Azure portal with the endpoint and access keys location circled in red.

Environment variables

Create and assign persistent environment variables for your key and endpoint.

Važno

We recommend Microsoft Entra ID authentication with managed identities for Azure resources to avoid storing credentials with your applications that run in the cloud.

Use API keys with caution. Don't include the API key directly in your code, and never post it publicly. If using API keys, store them securely in Azure Key Vault, rotate the keys regularly, and restrict access to Azure Key Vault using role based access control and network access restrictions. For more information about using API keys securely in your apps, see API keys with Azure Key Vault.

For more information about AI services security, see Authenticate requests to Azure AI services.

Windows Command Prompt 
setx AZURE_OPENAI_API_KEY "REPLACE_WITH_YOUR_KEY_VALUE_HERE" 
setx AZURE_OPENAI_ENDPOINT "REPLACE_WITH_YOUR_ENDPOINT_HERE"

Create a new Go application

Open the command prompt and navigate to your project folder. Create a new file sample.go.

Install the Go SDK

Install the OpenAI Go SDK using the following command:

Console 
go get github.com/Azure/azure-sdk-for-go/sdk/ai/azopenai@latest

Or, if you use dep, within your repo run:

Console 
dep ensure -add github.com/Azure/azure-sdk-for-go/sdk/ai/azopenai

Generate images with DALL-E

Open sample.go in your preferred code editor.

Add the following code to your script:

Go 
package main

import (
	"context"
	"fmt"
	"net/http"
	"os"

	"github.com/Azure/azure-sdk-for-go/sdk/ai/azopenai"
	"github.com/Azure/azure-sdk-for-go/sdk/azcore"
	"github.com/Azure/azure-sdk-for-go/sdk/azcore/to"
)

func main() {
	azureOpenAIKey := os.Getenv("AZURE_OPENAI_API_KEY")

	// Ex: "https://<your-azure-openai-host>.openai.azure.com"
	azureOpenAIEndpoint := os.Getenv("AZURE_OPENAI_ENDPOINT")

	if azureOpenAIKey == "" || azureOpenAIEndpoint == "" {
		fmt.Fprintf(os.Stderr, "Skipping example, environment variables missing\n")
		return
	}

	keyCredential := azcore.NewKeyCredential(azureOpenAIKey)

	client, err := azopenai.NewClientWithKeyCredential(azureOpenAIEndpoint, keyCredential, nil)

	if err != nil {
		// handle error
	}

	resp, err := client.GetImageGenerations(context.TODO(), azopenai.ImageGenerationOptions{
		Prompt:         to.Ptr("a painting of a cat in the style of Dali"),
		ResponseFormat: to.Ptr(azopenai.ImageGenerationResponseFormatURL),
	}, nil)

	if err != nil {
		// handle error
	}

	for _, generatedImage := range resp.Data {
		// the underlying type for the generatedImage is dictated by the value of
		// ImageGenerationOptions.ResponseFormat. In this example we used `azopenai.ImageGenerationResponseFormatURL`,
		// so the underlying type will be ImageLocation.

		resp, err := http.Head(*generatedImage.URL)

		if err != nil {
			// handle error
		}

		fmt.Fprintf(os.Stderr, "Image generated, HEAD request on URL returned %d\nImage URL: %s\n", resp.StatusCode, *generatedImage.URL)
	}
}

Run the script using the go run command:

Console 
go run sample.go

Output

The URL of the generated image is printed to the console.

Console 
Image generated, HEAD request on URL returned 200
Image URL: <SAS URL>

Napomena

The image generation APIs come with a content moderation filter. If the service recognizes your prompt as harmful content, it won't return a generated image. For more information, see the content filter article.

Clean up resources

If you want to clean up and remove an Azure OpenAI resource, you can delete the resource or resource group. Deleting the resource group also deletes any other resources associated with it.

Next steps

Use this guide to get started calling the Azure OpenAI Service image generation APIs with PowerShell.

Napomena

The image generation API creates an image from a text prompt. It doesn't edit or create variations of existing images.

Prerequisites

Setup

Retrieve key and endpoint

To successfully make a call against Azure OpenAI, you need an endpoint and a key.

Variable name Value
ENDPOINT The service endpoint can be found in the Keys & Endpoint section when examining your resource from the Azure portal. Alternatively, you can find the endpoint via the Deployments page in Azure AI Foundry portal. An example endpoint is: https://docs-test-001.openai.azure.com/.
API-KEY This value can be found in the Keys & Endpoint section when examining your resource from the Azure portal. You can use either KEY1 or KEY2.

Go to your resource in the Azure portal. The Keys & Endpoint section can be found in the Resource Management section. Copy your endpoint and access key as you'll need both for authenticating your API calls. You can use either KEY1 or KEY2. Always having two keys allows you to securely rotate and regenerate keys without causing a service disruption.

Screenshot of the overview UI for an Azure OpenAI resource in the Azure portal with the endpoint and access keys location circled in red.

Environment variables

Create and assign persistent environment variables for your key and endpoint.

Važno

We recommend Microsoft Entra ID authentication with managed identities for Azure resources to avoid storing credentials with your applications that run in the cloud.

Use API keys with caution. Don't include the API key directly in your code, and never post it publicly. If using API keys, store them securely in Azure Key Vault, rotate the keys regularly, and restrict access to Azure Key Vault using role based access control and network access restrictions. For more information about using API keys securely in your apps, see API keys with Azure Key Vault.

For more information about AI services security, see Authenticate requests to Azure AI services.

Windows Command Prompt 
setx AZURE_OPENAI_API_KEY "REPLACE_WITH_YOUR_KEY_VALUE_HERE" 
setx AZURE_OPENAI_ENDPOINT "REPLACE_WITH_YOUR_ENDPOINT_HERE"

Generate images with DALL-E 2

  1. Create a new PowerShell file named quickstart.ps1. Open the new file in your preferred editor or IDE.

  2. Replace the contents of quickstart.ps1 with the following code. Enter your endpoint URL and key in the appropriate fields. Change the value of prompt to your preferred text.

    PowerShell 
    # Azure OpenAI metadata variables
$openai = @{
  api_key     = $Env:AZURE_OPENAI_API_KEY
  api_base    = $Env:AZURE_OPENAI_ENDPOINT # your endpoint should look like the following https://YOUR_RESOURCE_NAME.openai.azure.com/
  api_version = '2023-06-01-preview' # this may change in the future
}

# Text to describe image
$prompt = 'A painting of a dog'

# Header for authentication
$headers = [ordered]@{
  'api-key' = $openai.api_key
}

# Adjust these values to fine-tune completions
$body = [ordered]@{
   prompt = $prompt
   size   = '1024x1024'
   n      = 1
} | ConvertTo-Json

 # Call the API to generate the image and retrieve the response
$url = "$($openai.api_base)/openai/images/generations:submit?api-version=$($openai.api_version)"

$submission = Invoke-RestMethod -Uri $url -Headers $headers -Body $body -Method Post -ContentType 'application/json' -ResponseHeadersVariable submissionHeaders

 $operation_location = $submissionHeaders['operation-location'][0]
 $status = ''
 while ($status -ne 'succeeded') {
     Start-Sleep -Seconds 1
     $response = Invoke-RestMethod -Uri $operation_location -Headers $headers
     $status   = $response.status
 }

# Set the directory for the stored image
$image_dir = Join-Path -Path $pwd -ChildPath 'images'

# If the directory doesn't exist, create it
if (-not(Resolve-Path $image_dir -ErrorAction Ignore)) {
    New-Item -Path $image_dir -ItemType Directory
}

# Initialize the image path (note the filetype should be png)
$image_path = Join-Path -Path $image_dir -ChildPath 'generated_image.png'

# Retrieve the generated image
$image_url = $response.result.data[0].url  # extract image URL from response
$generated_image = Invoke-WebRequest -Uri $image_url -OutFile $image_path  # download the image
return $image_path

    Važno

    For production, use a secure way of storing and accessing your credentials like The PowerShell Secret Management with Azure Key Vault. For more information about credential security, see the Azure AI services security article.

  3. Run the script using PowerShell:

    PowerShell 
    ./quickstart.ps1

    The script loops until the generated image is ready.

Output

PowerShell requests the image from Azure OpenAI and stores the output image in the generated_image.png file in your specified directory. For convenience, the full path for the file is returned at the end of the script.

The image generation APIs come with a content moderation filter. If the service recognizes your prompt as harmful content, it doesn't generate an image. For more information, see Content filtering.

Clean up resources

If you want to clean up and remove an Azure OpenAI resource, you can delete the resource or resource group. Deleting the resource group also deletes any other resources associated with it.

Next steps