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Nov 4, 2022

How to use the DALL·E API

This recipe is archived and may reference outdated models or APIs.
Ted Sanders
Ted Sanders (OpenAI)
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This notebook shows how to use OpenAI’s DALL·E image API endpoints.

There are three API endpoints:

  • Generations: generates an image or images based on an input caption
  • Edits: edits or extends an existing image
  • Variations: generates variations of an input image

Setup

  • Import the packages you’ll need
  • Import your OpenAI API key: You can do this by running `export OPENAI_API_KEY="your API key"` in your terminal.
  • Set a directory to save images to
# imports
from openai import OpenAI  # OpenAI Python library to make API calls
import requests  # used to download images
import os  # used to access filepaths
from PIL import Image  # used to print and edit images

# initialize OpenAI client
client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY", "<your OpenAI API key if not set as env var>"))
# set a directory to save DALL·E images to
image_dir_name = "images"
image_dir = os.path.join(os.curdir, image_dir_name)

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

# print the directory to save to
print(f"{image_dir=}")
image_dir='./images'

Generations

The generation API endpoint creates an image based on a text prompt. API Reference

Required inputs:

  • prompt (str): A text description of the desired image(s). The maximum length is 1000 characters for dall-e-2 and 4000 characters for dall-e-3.

Optional inputs:

  • model (str): The model to use for image generation. Defaults to dall-e-2
  • n (int): The number of images to generate. Must be between 1 and 10. Defaults to 1.
  • quality (str): The quality of the image that will be generated. hd creates images with finer details and greater consistency across the image. This param is only supported for dall-e-3.
  • response_format (str): The format in which the generated images are returned. Must be one of “url” or “b64_json”. Defaults to “url”.
  • size (str): The size of the generated images. Must be one of 256x256, 512x512, or 1024x1024 for dall-e-2. Must be one of 1024x1024, 1792x1024, or 1024x1792 for dall-e-3 models. Defaults to “1024x1024”.
  • style(str | null): The style of the generated images. Must be one of vivid or natural. Vivid causes the model to lean towards generating hyper-real and dramatic images. Natural causes the model to produce more natural, less hyper-real looking images. This param is only supported for dall-e-3.
  • user (str): A unique identifier representing your end-user, which will help OpenAI to monitor and detect abuse. Learn more.
# create an image

# set the prompt
prompt = "A cyberpunk monkey hacker dreaming of a beautiful bunch of bananas, digital art"

# call the OpenAI API
generation_response = client.images.generate(
    model = "dall-e-3",
    prompt=prompt,
    n=1,
    size="1024x1024",
    response_format="url",
)

# print response
print(generation_response)
ImagesResponse(created=1701994117, data=[Image(b64_json=None, revised_prompt=None, url='https://oaidalleapiprodscus.blob.core.windows.net/private/org-9HXYFy8ux4r6aboFyec2OLRf/user-8OA8IvMYkfdAcUZXgzAXHS7d/img-ced13hkOk3lXkccQgW1fAQjm.png?st=2023-12-07T23%3A08%3A37Z&se=2023-12-08T01%3A08%3A37Z&sp=r&sv=2021-08-06&sr=b&rscd=inline&rsct=image/png&skoid=6aaadede-4fb3-4698-a8f6-684d7786b067&sktid=a48cca56-e6da-484e-a814-9c849652bcb3&skt=2023-12-07T16%3A41%3A48Z&ske=2023-12-08T16%3A41%3A48Z&sks=b&skv=2021-08-06&sig=tcD0iyU0ABOvWAKsY89gp5hLVIYkoSXQnrcmH%2Brkric%3D')])
# save the image
generated_image_name = "generated_image.png"  # any name you like; the filetype should be .png
generated_image_filepath = os.path.join(image_dir, generated_image_name)
generated_image_url = generation_response.data[0].url  # extract image URL from response
generated_image = requests.get(generated_image_url).content  # download the image

with open(generated_image_filepath, "wb") as image_file:
    image_file.write(generated_image)  # write the image to the file
# print the image
print(generated_image_filepath)
display(Image.open(generated_image_filepath))

Variations

The variations endpoint generates new images (variations) similar to an input image. API Reference

Here we’ll generate variations of the image generated above.

Required inputs:

  • image (str): The image to use as the basis for the variation(s). Must be a valid PNG file, less than 4MB, and square.

Optional inputs:

  • model (str): The model to use for image variations. Only dall-e-2 is supported at this time.
  • n (int): The number of images to generate. Must be between 1 and 10. Defaults to 1.
  • size (str): The size of the generated images. Must be one of “256x256”, “512x512”, or “1024x1024”. Smaller images are faster. Defaults to “1024x1024”.
  • response_format (str): The format in which the generated images are returned. Must be one of “url” or “b64_json”. Defaults to “url”.
  • user (str): A unique identifier representing your end-user, which will help OpenAI to monitor and detect abuse. Learn more.
# create variations

# call the OpenAI API, using `create_variation` rather than `create`
variation_response = client.images.create_variation(
    image=generated_image,  # generated_image is the image generated above
    n=2,
    size="1024x1024",
    response_format="url",
)

# print response
print(variation_response)
ImagesResponse(created=1701994139, data=[Image(b64_json=None, revised_prompt=None, url='https://oaidalleapiprodscus.blob.core.windows.net/private/org-9HXYFy8ux4r6aboFyec2OLRf/user-8OA8IvMYkfdAcUZXgzAXHS7d/img-noNRGgwaaotRGIe6Y2GVeSpr.png?st=2023-12-07T23%3A08%3A59Z&se=2023-12-08T01%3A08%3A59Z&sp=r&sv=2021-08-06&sr=b&rscd=inline&rsct=image/png&skoid=6aaadede-4fb3-4698-a8f6-684d7786b067&sktid=a48cca56-e6da-484e-a814-9c849652bcb3&skt=2023-12-07T16%3A39%3A11Z&ske=2023-12-08T16%3A39%3A11Z&sks=b&skv=2021-08-06&sig=ER5RUglhtIk9LWJXw1DsolorT4bnEmFostfnUjY21ns%3D'), Image(b64_json=None, revised_prompt=None, url='https://oaidalleapiprodscus.blob.core.windows.net/private/org-9HXYFy8ux4r6aboFyec2OLRf/user-8OA8IvMYkfdAcUZXgzAXHS7d/img-oz952tL11FFhf9iXXJVIRUZX.png?st=2023-12-07T23%3A08%3A59Z&se=2023-12-08T01%3A08%3A59Z&sp=r&sv=2021-08-06&sr=b&rscd=inline&rsct=image/png&skoid=6aaadede-4fb3-4698-a8f6-684d7786b067&sktid=a48cca56-e6da-484e-a814-9c849652bcb3&skt=2023-12-07T16%3A39%3A11Z&ske=2023-12-08T16%3A39%3A11Z&sks=b&skv=2021-08-06&sig=99rJOQwDKsfIeerlMXMHholhAhrHfYaQRFJBF8FKv74%3D')])
# save the images
variation_urls = [datum.url for datum in variation_response.data]  # extract URLs
variation_images = [requests.get(url).content for url in variation_urls]  # download images
variation_image_names = [f"variation_image_{i}.png" for i in range(len(variation_images))]  # create names
variation_image_filepaths = [os.path.join(image_dir, name) for name in variation_image_names]  # create filepaths
for image, filepath in zip(variation_images, variation_image_filepaths):  # loop through the variations
    with open(filepath, "wb") as image_file:  # open the file
        image_file.write(image)  # write the image to the file
# print the original image
print(generated_image_filepath)
display(Image.open(generated_image_filepath))

# print the new variations
for variation_image_filepaths in variation_image_filepaths:
    print(variation_image_filepaths)
    display(Image.open(variation_image_filepaths))

Edits

The edit endpoint uses DALL·E to generate a specified portion of an existing image. Three inputs are needed: the image to edit, a mask specifying the portion to be regenerated, and a prompt describing the desired image. API Reference

Required inputs:

  • image (str): The image to edit. Must be a valid PNG file, less than 4MB, and square. If mask is not provided, image must have transparency, which will be used as the mask.
  • prompt (str): A text description of the desired image(s). The maximum length is 1000 characters.

Optional inputs:

  • mask (file): An additional image whose fully transparent areas (e.g. where alpha is zero) indicate where image should be edited. Must be a valid PNG file, less than 4MB, and have the same dimensions as image.
  • model (str): The model to use for edit image. Only dall-e-2 is supported at this time.
  • n (int): The number of images to generate. Must be between 1 and 10. Defaults to 1.
  • size (str): The size of the generated images. Must be one of “256x256”, “512x512”, or “1024x1024”. Smaller images are faster. Defaults to “1024x1024”.
  • response_format (str): The format in which the generated images are returned. Must be one of “url” or “b64_json”. Defaults to “url”.
  • user (str): A unique identifier representing your end-user, which will help OpenAI to monitor and detect abuse. Learn more.

Set Edit Area

An edit requires a “mask” to specify which portion of the image to regenerate. Any pixel with an alpha of 0 (transparent) will be regenerated. The code below creates a 1024x1024 mask where the bottom half is transparent.

# create a mask
width = 1024
height = 1024
mask = Image.new("RGBA", (width, height), (0, 0, 0, 1))  # create an opaque image mask

# set the bottom half to be transparent
for x in range(width):
    for y in range(height // 2, height):  # only loop over the bottom half of the mask
        # set alpha (A) to zero to turn pixel transparent
        alpha = 0
        mask.putpixel((x, y), (0, 0, 0, alpha))

# save the mask
mask_name = "bottom_half_mask.png"
mask_filepath = os.path.join(image_dir, mask_name)
mask.save(mask_filepath)

Perform Edit

Now we supply our image, caption and mask to the API to get 5 examples of edits to our image

# edit an image

# call the OpenAI API
edit_response = client.images.edit(
    image=open(generated_image_filepath, "rb"),  # from the generation section
    mask=open(mask_filepath, "rb"),  # from right above
    prompt=prompt,  # from the generation section
    n=1,
    size="1024x1024",
    response_format="url",
)

# print response
print(edit_response)
ImagesResponse(created=1701994167, data=[Image(b64_json=None, revised_prompt=None, url='https://oaidalleapiprodscus.blob.core.windows.net/private/org-9HXYFy8ux4r6aboFyec2OLRf/user-8OA8IvMYkfdAcUZXgzAXHS7d/img-9UOVGC7wB8MS2Q7Rwgj0fFBq.png?st=2023-12-07T23%3A09%3A27Z&se=2023-12-08T01%3A09%3A27Z&sp=r&sv=2021-08-06&sr=b&rscd=inline&rsct=image/png&skoid=6aaadede-4fb3-4698-a8f6-684d7786b067&sktid=a48cca56-e6da-484e-a814-9c849652bcb3&skt=2023-12-07T16%3A40%3A37Z&ske=2023-12-08T16%3A40%3A37Z&sks=b&skv=2021-08-06&sig=MsRMZ1rN434bVdWr%2B9kIoqu9CIrvZypZBfkQPTOhCl4%3D')])
# save the image
edited_image_name = "edited_image.png"  # any name you like; the filetype should be .png
edited_image_filepath = os.path.join(image_dir, edited_image_name)
edited_image_url = edit_response.data[0].url  # extract image URL from response
edited_image = requests.get(edited_image_url).content  # download the image

with open(edited_image_filepath, "wb") as image_file:
    image_file.write(edited_image)  # write the image to the file
# print the original image
print(generated_image_filepath)
display(Image.open(generated_image_filepath))

# print edited image
print(edited_image_filepath)
display(Image.open(edited_image_filepath))