Text-to-image

Text-to-Image Models

Introduction

Machine learning models known as text-to-image models are designed to create an image that corresponds to a given natural language description. These models have evolved, particularly since the mid-2010s, thanks to the growth in deep neural network technology. By 2022, cutting-edge examples began delivering outputs nearing the quality of actual photographs or artwork crafted by humans.

Evolution and Examples

Among the leading examples are OpenAI's DALL-E 2, Google Brain's Imagen, StabilityAI's Stable Diffusion, and Midjourney. These advancements have been fueled by the explosion in available data and computational resources.

Structure and Functionality

Typically, a text-to-image model functions by integrating two main components: a language model that translates the textual input into a latent form, and a generative image model that then takes this latent form to generate an image. The most powerful of these models are commonly the result of training on substantial quantities of text and image data found on the internet.

Prompts and Expanded Capabilities

The model functions by accepting text inputs, referred to as prompts, which can be either positive or negative, and then generates an image based on those inputs. Stable Diffusion's capabilities have expanded beyond merely processing text inputs, as it now also considers numerous other parameters. Nevertheless, the text inputs remain the essential cornerstone of the Stable Diffusion model.