The first modern text-to-image model, alignDRAW, was introduced in 2015 by researchers from the University of Toronto. The inverse task, image captioning, was more tractable and a number of image captioning deep learning models came prior to the first text-to-image models. History īefore the rise of deep learning, attempts to build text-to-image models were limited to collages by arranging existing component images, such as from a database of clip art. The most effective models have generally been trained on massive amounts of image and text data scraped from the web. Text-to-image models generally combine a language model, which transforms the input text into a latent representation, and a generative image model, which produces an image conditioned on that representation. In 2022, the output of state of the art text-to-image models, such as OpenAI's DALL-E 2, Google Brain's Imagen, StabilityAI's Stable Diffusion, and Midjourney began to approach the quality of real photographs and human-drawn art. Such models began to be developed in the mid-2010s during the beginnings of the AI spring, as a result of advances in deep neural networks. Machine learning model An image conditioned on the prompt "an astronaut riding a horse, by Hiroshige", generated by Stable Diffusion, a large-scale text-to-image model released in 2022Ī text-to-image model is a machine learning model which takes an input natural language description and produces an image matching that description.
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