DALL·E: The Wonders of Natural Language Processing

Uncovering the capabilities of DALL·E: a neural network that can turn any natural language input into an AI-generated image

Computism, by Grace Magny-Fokam
3 min readAug 20, 2022
Source: Analytics India Magazine

DALL·E is a machine learning model developed by OpenAI that uses natural language processing to generate digital images from simple descriptions. DALL·E has 12 billion parameters and was trained using a dataset of text-image pairs.

DALL·E is a version of GPT-3, an autoregressive language model developed by OpenAI in 2020. The GPT-3 uses deep learning to produce human-like text and created the original idea that DALL·E would soon follow a year later.

Source: Anjali on Medium

The capabilities of DALL·E are seemingly endless, as its capable of even producing images in different artistic styles, including surrealism and emoji. Not to mention, its huge reservoir of text and image data allows it to auto-generate almost any structure, no matter how arbitrary. In the example below, a very peculiar sentence is inputted, and DALL·E is still able to generate a slew of AI-Generated images that are scarily accurate.

Source: DALL·E blog post from OpenAI

Having launched in early 2022, DALL·E’s successor DALL·E 2 is even more powerful than its predecessor and produces even more realistic-looking images. It increases realism by incorporating aspects like shadows, reflections, and textures in its generation.

Despite the fascinating nature of DALL·E, you may still be wondering how it works.

First, an important and useful thing to not is that OpenAI has already provided the general public with the most interesting and objective information there is to know about DALL·E. A paper detailing the construction and training process of DALL·E’s text-to-image generation model has already been published and is publicly accessible. The code behind DALL·E’s execution is also readily available on GitHub.

But enough general details, let’s get into the nitty-gritty.

Source: Deconstruction of DALL·E 2 model by TechTalks

DALL·E 2 uses Contrastive Learning-Image Pre-Training (CLIP) and diffusion models in order to produce its beautiful graphic designs. As aforementioned, in order for DALL·E 2 to function, it requires a very hefty amount of training image and text data. CLIP trains two neural networks in parallel to images and their associated text. The first network learns the visual representations in an image while the other learns the associated text. DALL·E 2 then uses a diffusion model to create images by gradually noising and denoising the training data.

All in all, I believe it’s extremely fascinating to see a practical example of natural language processing and how AI can be so effective at mimicking the human brain that it can seamlessly generate artwork.