图像生成 Beta

Learn how to generate or manipulate images with our DALL·E models

Introduction

The Images API provides three methods for interacting with images:

Creating images from scratch based on a text prompt Creating edits of an existing image based on a new text prompt Creating variations of an existing image This guide covers the basics of using these three API endpoints with useful code samples. To see them in action, check out our DALL·E preview app.

The Images API is in beta. During this time the API and models will evolve based on your feedback. To ensure all users can prototype comfortably, the default rate limit is 50 images per minute. If you would like to increase your rate limit, please review this help center article. We will increase the default rate limit as we learn more about usage and capacity requirements. Usage Generations The image generations endpoint allows you to create an original image given a text prompt. Generated images can have a size of 256x256, 512x512, or 1024x1024 pixels. Smaller sizes are faster to generate. You can request 1-10 images at a time using the n parameter.

Generate an image node.js

node.js

const response = await openai.createImage({
  prompt: "a white siamese cat",
  n: 1,
  size: "1024x1024",
});
image_url = response.data.data[0].url;

The more detailed the description, the more likely you are to get the result that you or your end user want. You can explore the examples in the DALL·E preview app for more prompting inspiration. Here's a quick example:

PROMPT GENERATION a white siamese cat a close up, studio photographic portrait of a white siamese cat that looks curious, backlit ears Each image can be returned as either a URL or Base64 data, using the response_format parameter. URLs will expire after an hour.

Edits The image edits endpoint allows you to edit and extend an image by uploading a mask. The transparent areas of the mask indicate where the image should be edited, and the prompt should describe the full new image, not just the erased area. This endpoint can enable experiences like the editor in our DALL·E preview app.

Edit an image node.js

node.js

const response = await openai.createImageEdit(
  fs.createReadStream("sunlit_lounge.png"),
  fs.createReadStream("mask.png"),
  "A sunlit indoor lounge area with a pool containing a flamingo",
  1,
  "1024x1024"
);
image_url = response.data.data[0].url;

IMAGE MASK OUTPUT Prompt: a sunlit indoor lounge area with a pool containing a flamingo

The uploaded image and mask must both be square PNG images less than 4MB in size, and also must have the same dimensions as each other. The non-transparent areas of the mask are not used when generating the output, so they don’t necessarily need to match the original image like the example above.

Variations The image variations endpoint allows you to generate a variation of a given image.

Generate an image variation node.js

node.js

const response = await openai.createImageVariation(
  fs.createReadStream("corgi_and_cat_paw.png"),
  1,
  "1024x1024"
);
image_url = response.data.data[0].url;

IMAGE OUTPUT Similar to the edits endpoint, the input image must be a square PNG image less than 4MB in size.

Content moderation Prompts and images are filtered based on our content policy, returning an error when a prompt or image is flagged. If you have any feedback on false positives or related issues, please contact us through our help center.

Language-specific tips

NODE.JS

PYTHON Using in-memory image data The Node.js examples in the guide above use the fs module to read image data from disk. In some cases, you may have your image data in memory instead. Here's an example API call that uses image data stored in a Node.js Buffer object:

// This is the Buffer object that contains your image data
const buffer = [your image data];
// Set a `name` that ends with .png so that the API knows it's a PNG image
buffer.name = "image.png";
const response = await openai.createImageVariation(
buffer,
1,
"1024x1024"
);

Working with TypeScript

If you're using TypeScript, you may encounter some quirks with image file arguments. Here's an example of working around the type mismatch by explicitly casting the argument:

// Cast the ReadStream to `any` to appease the TypeScript compiler
const response = await openai.createImageVariation(
  fs.createReadStream("image.png") as any,
  1,
  "1024x1024"
);

And here's a similar example for in-memory image data:

// This is the Buffer object that contains your image data
const buffer: Buffer = [your image data];
// Cast the buffer to `any` so that we can set the `name` property
const file: any = buffer;
// Set a `name` that ends with .png so that the API knows it's a PNG image
file.name = "image.png";
const response = await openai.createImageVariation(
file,
1,
"1024x1024"
);

Error handling

API requests can potentially return errors due to invalid inputs, rate limits, or other issues. These errors can be handled with a try...catch statement, and the error details can be found in either error.response or error.message:

try {
  const response = await openai.createImageVariation(
    fs.createReadStream("image.png"),
    1,
    "1024x1024"
  );
  console.log(response.data.data[0].url);
} catch (error) {
  if (error.response) {
    console.log(error.response.status);
    console.log(error.response.data);
  } else {
    console.log(error.message);
  }
}