What DALL-E Dataset Did OpenAI Use?

Creating artwork and images is now easy for both professional and amateur artists, thanks to the incorporation of artificial intelligence (AI) into the art industry. With so many AI art-generating programs now available, you don’t have to be a professional, experienced artist to generate original images and other pieces of art. One of the latest innovations in this sector is the DALL-E 2 ai image generator.

DALL-E has revolutionized the art industry by allowing artists to generate images from natural language prompts, such as text. You simply input your image description and wait for the program to generate a corresponding image. But one of the main questions that everyone is asking is: What dataset does DALL-E use? This article discusses this and other related concerns.

What is DALL-E?

DALL-E is a neural network created by OpenAI to enable its users to generate images from text prompts for various concepts that can be expressed in a natural language. It is a 12-billion parameter version of the GPT-3 version. Because DALL-E is a fairly new creation, many people are asking: Is the DALL-E AI open-source?

Unfortunately, DALL-E is a limited version of the GPT-3 model, which is only available to people who made it to the waitlist. Although OpenAI has already released a “controller” of this neural network, it hasn’t launched a source code for any of its models. According to OpenAI, DALL-E can generate photo-realistic images using text prompts, and also comes with a simple editor to enable you to modify your outputs and combine concepts, features, and styles.

Images generated by DALL-E are curated by a separate model known as Contrastive Language-Image Pre-training (CLIP). This model understands and ranks the outputs, thus presenting pieces of art that are of the highest quality for every prompt. To go more in depth regarding how this works, check out a DALL-E AI demo.

What Dataset Does DALL-E Use?

The short answer is: we don’t know. OpenAI revealed that it used an implementation of GPT-3 and trained it with sets of text and image pairs. Official sources haven’t revealed the exact dataset used, but judging by the quality and breadth of images created, we can assume that it was extensive. 

As artificial intelligence continues to take root in different sectors, policymakers are faced with the major task of determining what is needed to create, train, and deploy AI algorithms. This is because many details about gathering data, creating a dataset, and footnote specifics are viewed as less-demanding tasks. However, working with datasets is a laborious part of any AI art generation; therefore, it requires experience and understanding of how to use the actual data collected.

DALL-E’s dataset probably contains millions of text-image pairs that “taught” DALL-E’s transformer how to generate accurate images. And, since there are so much data readily available on the internet, and within OpenAI’s own databases, it’s very easy for DALL-E to collect them for learning.

The availability of these pieces of data has also made it possible for DALL-E to scale its learning algorithms as real products that can add value to your art, instead of using them as by-products of its key processes. With DALL-E, you’ll use images and videos collected from the internet to train its algorithms, test them, and use them to create your desired images and pieces of art.