How to Create a Image Synthesis App with OpenAI CLIP and Python

Posted on by Panther

How to Create an Image Synthesis App with OpenAI CLIP and Python

OpenAI CLIP is a deep learning model that can understand textual descriptions of images and generate textual descriptions of images. In this tutorial, we will learn how to use OpenAI CLIP to create an image synthesis app. This app will take a textual description as input and generate an image that matches the given description.

We will be using Python along with the OpenAI CLIP library for this project. Make sure you have Python installed on your system before getting started.

Installing OpenAI CLIP

To install OpenAI CLIP, we can use the pip package manager. Open a terminal and run the following command:

pip install openai

This will install the OpenAI library along with the necessary dependencies.

Getting the API Key

To use OpenAI CLIP, you need an API key. You can get one by creating an account on the OpenAI website and subscribing to the API. Once you have the API key, you can set it as an environment variable by running the following command in the terminal:

export OPENAI_API_KEY='your-api-key'

Make sure to replace your-api-key with the actual API key you obtained.

Importing the Required Libraries

Let’s start by importing the necessary libraries for this project. We will be using the openai library to interact with OpenAI CLIP, PIL to manipulate images, and matplotlib to display the generated images. Run the following code to import the libraries:

import openai
from PIL import Image
import matplotlib.pyplot as plt

Authenticating with OpenAI

Before we can use the OpenAI CLIP API, we need to authenticate ourselves using the API key. Run the following code to authenticate:

openai.api_key = 'your-api-key'

Make sure to replace your-api-key with your actual API key.

Generating an Image from a Text Description

Now, let’s write a function that takes a textual description as input and generates an image that matches the description. We will call this function generate_image_from_text. It will take a single parameter, text, which represents the textual description of the image:

def generate_image_from_text(text):
    response = openai.Completion.create(
        engine='davinci',
        prompt=text,
        max_tokens=50,
        temperature=0.7,
        top_p=1.0,
        n=1,
        stop=None,
        temperature_decay=0.0
    )

    image_url = response.choices[0]['text']
    image = Image.open(requests.get(image_url, stream=True).raw)

    return image

Let’s go through each of the parameters passed to the openai.Completion.create method:

  • engine specifies the model to be used for generating the image. We are using the davinci model.
  • prompt is the input text that describes the image.
  • max_tokens specifies the maximum number of tokens (words or characters) in the response.
  • temperature controls the randomness of the generated image. Higher values (e.g., 1.0) generate more random images, while lower values (e.g., 0.2) generate more deterministic images.
  • top_p is the cumulative probability threshold for truncating the response. Higher values (e.g., 1.0) generate more diverse images, while lower values (e.g., 0.2) generate more focused images.
  • n specifies the number of completions to generate.
  • stop is an optional string to stop generation at. If set, the model will stop generating after encountering this string.
  • temperature_decay controls the rate at which temperature decreases. Higher values (e.g., 0.6) decay temperature more rapidly, resulting in more focused images.

The response of the API call is a JSON object that contains the URL of the generated image. We extract the image URL and open it using the PIL library. Finally, we return the generated image.

Generating and Displaying the Images

Now that we have the generate_image_from_text function, we can use it to generate and display images. Let’s write a function called synthesize_images that takes a list of textual descriptions as input and generates an image for each description. The function will also display the generated images. Here’s the complete code for the synthesize_images function:

def synthesize_images(descriptions):
    images = []
    for description in descriptions:
        image = generate_image_from_text(description)
        images.append(image)

    fig, axs = plt.subplots(1, len(descriptions), figsize=(len(descriptions) * 5, 5))
    for i, image in enumerate(images):
        axs[i].imshow(image)
        axs[i].axis('off')
        axs[i].set_title(descriptions[i])

    plt.show()

The synthesize_images function iterates over each description in the given list and calls the generate_image_from_text function to generate an image for that description. It then adds the generated image to a list of images. Once all images are generated, it creates a subplot for each image using the matplotlib library and displays them using the imshow and axis methods. Finally, it shows the plot using the show method.

Running the App

To use the image synthesis app, we call the synthesize_images function with a list of textual descriptions. Here’s an example usage of the app:

descriptions = [
    'a cat sitting on a chair',
    'a scenic view of a beach',
    'a bowl of fruits on a table'
]

synthesize_images(descriptions)

This will generate and display an image for each description in the list.

Conclusion

In this tutorial, we learned how to create an image synthesis app using OpenAI CLIP and Python. We installed the necessary libraries, authenticated with OpenAI, and wrote functions to generate and display images based on textual descriptions. You can now use this app to generate images based on any textual description you provide. So go ahead and experiment with different descriptions and have fun with image synthesis!

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