Text-to-image model

A text-to-image model is a <a href="/facts/Machine_learning_model/e0w0XJTu">machine learning model</a> which takes an input <a href="/facts/Natural_language/B4gLS7Kd">natural language</a> description and produces an image matching that description. 
Text-to-image models began to be developed in the mid-2010s during the beginnings of the <a href="/facts/AI_boom/wAPVI1SN">AI boom</a>, as a result of advances in <a href="/facts/Deep_learning/JLuwD3ea">deep neural networks</a>. In 2022, the output of state-of-the-art text-to-image models—such as OpenAI's <a href="/facts/DALL-E_2/rkWL8P49">DALL-E 2</a>, <a href="/facts/Google_Brain/yp0eeKSX">Google Brain</a>'s <a href="/facts/Imagen_(text-to-image_model)/VmhYsxQU">Imagen</a>, Stability AI's <a href="/facts/Stable_Diffusion/BaXu5Vc8">Stable Diffusion</a>, and <a href="/facts/Midjourney/ETtS6Nhm">Midjourney</a>—began to be considered to approach the quality of <a href="/facts/Photograph/gnq0f1or">real photographs</a> and human-drawn <a href="/facts/Folk_art/gp0bLamL">art</a>.
Text-to-image models are generally <a href="/facts/Latent_diffusion_model/RgyDnrNN">latent diffusion models</a>, which combine a <a href="/facts/Language_model/FntSpg0j">language model</a>, which transforms the input text into a <a href="/facts/Latent_variable_model/FCcgtsmc">latent representation</a>, and a <a href="/facts/Generative_model/JUgExNIP">generative image model</a>, which produces an image conditioned on that representation. The most effective models have generally been trained on massive amounts of image and text data <a href="/facts/Web_scraping/5R4mf1bd">scraped from the web</a>.

Text-to-image model open-in-new

Text-to-image model