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.<\/p>\n
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.<\/p>\n
To install OpenAI CLIP, we can use the This will install the OpenAI library along with the necessary dependencies.<\/p>\n 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:<\/p>\n Make sure to replace Let’s start by importing the necessary libraries for this project. We will be using the Before we can use the OpenAI CLIP API, we need to authenticate ourselves using the API key. Run the following code to authenticate:<\/p>\n Make sure to replace 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 Let’s go through each of the parameters passed to the 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 Now that we have the The pip<\/code> package manager. Open a terminal and run the following command:<\/p>\npip install openai\n<\/code><\/pre>\nGetting the API Key<\/h2>\n
export OPENAI_API_KEY='your-api-key'\n<\/code><\/pre>\nyour-api-key<\/code> with the actual API key you obtained.<\/p>\nImporting the Required Libraries<\/h2>\n
openai<\/code> library to interact with OpenAI CLIP, PIL<\/code> to manipulate images, and matplotlib<\/code> to display the generated images. Run the following code to import the libraries:<\/p>\nimport openai\nfrom PIL import Image\nimport matplotlib.pyplot as plt\n<\/code><\/pre>\nAuthenticating with OpenAI<\/h2>\n
openai.api_key = 'your-api-key'\n<\/code><\/pre>\nyour-api-key<\/code> with your actual API key.<\/p>\nGenerating an Image from a Text Description<\/h2>\n
generate_image_from_text<\/code>. It will take a single parameter, text<\/code>, which represents the textual description of the image:<\/p>\ndef generate_image_from_text(text):\n response = openai.Completion.create(\n engine='davinci',\n prompt=text,\n max_tokens=50,\n temperature=0.7,\n top_p=1.0,\n n=1,\n stop=None,\n temperature_decay=0.0\n )\n\n image_url = response.choices[0]['text']\n image = Image.open(requests.get(image_url, stream=True).raw)\n\n return image\n<\/code><\/pre>\nopenai.Completion.create<\/code> method:<\/p>\n\n
engine<\/code> specifies the model to be used for generating the image. We are using the davinci<\/code> model.<\/li>\nprompt<\/code> is the input text that describes the image.<\/li>\nmax_tokens<\/code> specifies the maximum number of tokens (words or characters) in the response.<\/li>\ntemperature<\/code> 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.<\/li>\ntop_p<\/code> 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.<\/li>\nn<\/code> specifies the number of completions to generate.<\/li>\nstop<\/code> is an optional string to stop generation at. If set, the model will stop generating after encountering this string.<\/li>\ntemperature_decay<\/code> controls the rate at which temperature decreases. Higher values (e.g., 0.6) decay temperature more rapidly, resulting in more focused images.<\/li>\n<\/ul>\nPIL<\/code> library. Finally, we return the generated image.<\/p>\nGenerating and Displaying the Images<\/h2>\n
generate_image_from_text<\/code> function, we can use it to generate and display images. Let’s write a function called synthesize_images<\/code> 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<\/code> function:<\/p>\ndef synthesize_images(descriptions):\n images = []\n for description in descriptions:\n image = generate_image_from_text(description)\n images.append(image)\n\n fig, axs = plt.subplots(1, len(descriptions), figsize=(len(descriptions) * 5, 5))\n for i, image in enumerate(images):\n axs[i].imshow(image)\n axs[i].axis('off')\n axs[i].set_title(descriptions[i])\n\n plt.show()\n<\/code><\/pre>\nsynthesize_images<\/code> function iterates over each description in the given list and calls the generate_image_from_text<\/code> 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<\/code> library and displays them using the imshow<\/code> and axis<\/code> methods. Finally, it shows the plot using the show<\/code> method.<\/p>\nRunning the App<\/h2>\n