Generate Image with OpenAI API and Node.js


DALL-E, developed by OpenAI, is an AI model that can generate images from textual descriptions. It combines the power of deep learning and generative modeling to create unique and imaginative visual outputs based on the provided text prompts. This technology has sparked excitement among researchers, artists, and developers alike.

To make the capabilities of DALL-E accessible to developers, OpenAI has released the DALL-E API. This API allows developers to interact with the DALL-E model programmatically; by using this, developers can generate images by giving text prompts. By using Node.js and  DALL-E, we can generate images programmatically. 

Note. If you want to achieve this using Python, Please read my previous article on how to Generate Image Using DALL-E API and Python.


It is essential to have a basic understanding of the following concepts and technologies.


Node.js is a JavaScript runtime environment that allows you to run JavaScript code outside of a web browser. To use Node.js, you'll need to install it on your machine. Install Node.js from the official website.

Basic API Concepts

A basic understanding of API (Application Programming Interface) concepts is crucial. APIs provide a way for different software applications to communicate and exchange data. Familiarize yourself with concepts like endpoints, HTTP methods (GET, POST, etc.), request/response structure, and how to handle API responses using JavaScript. 

Setting Up the Project

Open your terminal or command prompt and follow these steps.

  1. Create a new directory for your Project.

    mkdir dalle-image
    cd dalle-image
  2. Initialize a new Node.js project by running the following command.

    npm init -y

This will create a package.json file, keeping track of your Project's dependencies.

Install Required Dependencies

Install the Axios library to make API requests to the DALL-E API. Axios is a popular JavaScript library for making HTTP requests. It simplifies the process of sending asynchronous requests and handling responses. To install it, use the following command

npm install axios

Now, Axios is added to your Project's dependencies, and you can start using it to interact with the DALL-E API.

Writing the Node.js Code

Write the code to interact with the DALL-E API using Node.js. Follow the steps below.

Setting up the API Endpoint and Authorization

Navigate to the dalle-image directory and create a new JavaScript file, for example, dalle-api.js.

Require the Axios module at the top.

const axios = require('axios');

Next, declare a constant variable to store your DALL-E API key.


Implementing the API Call using Axios

Implement the API call using Axios.

const axios = require('axios');

const API_KEY = 'YOUR_API_KEY';   //replace with your key

const generateImages = async () => {
  try {
    const response = await
        prompt: 'A beautiful sunset over a serene lake',
        n: 1,                                //define the number of images
        size: '512x512',                     //define the resolution of image
        headers: {
          'Content-Type': 'application/json',
          'Authorization': `Bearer ${API_KEY}`,

    // Handle the response here, e.g., extract image data and display or save it.
  } catch (error) {


In the code above, we use the Axios. post method to make a POST request to the API endpoint. We pass the request payload as the second argument to the method and include the API key in the request headers.

Now run the Project using the following command.

node dalle-api.js

Check the console output to see the API response or any errors that occurred during the process.



Paste this obtained URL in your web browser, and it will show you the generated image as follows; your image will be different than this.


Advanced Options

The DALL-E API offers various advanced options and parameters that allow developers to have more control over the image generation process. These options enable customization and refinement of the generated images according to specific requirements. In this section, we will explore some of these advanced options and features.

Controlling Image Resolution

One important aspect of image generation is controlling the resolution or size of the output image. The DALL-E API provides a parameter that allows you to specify the desired resolution. However, you can adjust this parameter to generate higher or lower-resolution images based on your needs. For example, you can set the resolution to 1024x1024 pixels for more detailed and larger images or reduce it to 256x256 pixels for small images.

Handling Response Format

DALL-E API offers two types of response formats url and b64_json​​.

Best Practices

When working with the DALL-E API, it's important to follow some best practices to ensure efficient and cost-effective usage. Here are some best practices to consider.

  • Optimize Textual Descriptions: Write clear and concise textual descriptions that accurately capture the desired image. Avoid using ambiguous or vague language that could lead to unexpected or undesired results. Experiment with different descriptions to fine-tune the generated images.

  • Start with Low-Resolution Images: Start with generating low-resolution images to iterate quickly and reduce API usage. Once you are satisfied with the results, gradually increase the resolution to generate higher-quality images.

  • Batch Requests: If you have multiple image generation requests, consider batching them together. Sending multiple requests in a single batch can be more efficient and cost-effective than making individual API calls for each request.

  • Monitor API Usage: Keep track of your API usage to stay within the usage limits and avoid unexpected costs. OpenAI provides usage statistics and rate limits that can help you monitor and manage your usage effectively.


While the DALL-E API is a powerful tool for generating images, it has some limitations, which are the following.

  • Limited Image Resolution: The maximum image resolution supported by DALL-E is currently limited.

  • Interpreting Complex Descriptions: DALL-E may struggle with interpreting complex or abstract textual descriptions. To improve results, break down complex descriptions into simpler, more concrete terms.

  • Response Time and Rate Limits: The DALL-E API has certain rate limits and response time constraints. If you encounter rate limit errors or slow responses, you may need to adjust your usage patterns, such as batching requests or implementing retries with exponential backoff.

  • Unpredictable Output: Due to the nature of AI models, the generated images may sometimes exhibit unpredictable or surprising characteristics. It's important to review and validate the generated images to ensure they align with your expectations.


The DALL-E API, in combination with the versatility of Node.js, offers developers a gateway to unleash the creative potential of AI-generated images. Throughout this article, we have explored the fundamental steps involved in setting up a Node.js project, acquiring an API key from OpenAI, and implementing the code necessary to interact with the DALL-E API. Armed with this knowledge, developers can now embark on a journey of integrating AI-generated images seamlessly into their projects, applications, or artistic pursuits. The boundless possibilities offered by DALL-E and Node.js await exploration and experimentation.


Q. What is DALL-E?

A. DALL-E is an innovative AI model developed by OpenAI that can generate images from textual descriptions. It combines deep learning and generative modeling to create unique visual outputs based on provided text prompts.

Q. How can I control the image resolution when generating images?

A. The DALL-E API allows you to specify the desired resolution of the output image. You can adjust the resolution parameter to generate higher or lower-resolution images based on your needs.

Q. What does the DALL-E API offer the response formats?

A. The DALL-E API offers two response formats: URL and b64_json. The URL format provides a URL that can be used to access the generated image, while the b64_json format provides the image data in base64-encoded JSON format.

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