Yu no meme generator1/27/2024 became the first to use generative adversarial networks for the text-to-image task. Įight images generated from the text prompt "A stop sign is flying in blue skies." by AlignDRAW (2015). ![]() Images generated by alignDRAW were blurry and not photorealistic, but the model was able to generalize to objects not represented in the training data (such as a red school bus), and appropriately handled novel prompts such as "a stop sign is flying in blue skies", showing that it was not merely "memorizing" data from the training set. alignDRAW extended the previously-introduced DRAW architecture (which used a recurrent variational autoencoder with an attention mechanism) to be conditioned on text sequences. 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 and StabilityAI's Stable Diffusion began to approach the quality of real photographs and human-drawn art. Such models began to be developed in the mid-2010s, as a result of advances in deep neural networks. 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 as input a natural language description and produces an image matching that description.
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