![]() ![]() ![]() Text-to-image models are trained on large datasets of (text, image) pairs, often scraped from the web. Rather than directly training a model to output a high-resolution image conditioned on a text embedding, a popular technique is to train a model to generate low-resolution images, and use one or more auxiliary deep learning models to upscale it, filling in finer details. For the image generation step, conditional generative adversarial networks have been commonly used, with diffusion models also becoming a popular option in recent years. The text encoding step may be performed with a recurrent neural network such as a long short-term memory (LSTM) network, though transformer models have since become a more popular option. Text-to-image models have been built using a variety of architectures. Architecture and training High level architecture showing the state of AI art machine learning models, the larger or more notable models and applications in the AI art landscape, and pertinent relationships and dependencies as a clickable SVG image map įollowing other text-to-image models, language model-powered text-to-video platforms such as Runway, Make-A-Video, Imagen Video, Midjourney, and Phenaki can generate video from text and/or text/image prompts. A successor capable of generating more complex and realistic images, DALL-E 2, was unveiled in April 2022, followed by Stable Diffusion publicly released in August 2022. ![]() One of the first text-to-image models to capture widespread public attention was OpenAI's DALL-E, a transformer system announced in January 2021. Later systems include VQGAN+CLIP, XMC-GAN, and GauGAN2. encouraging", but which lacked coherence in their details. A model trained on the more diverse COCO dataset produced images which were "from a distance. With models trained on narrow, domain-specific datasets, they were able to generate "visually plausible" images of birds and flowers from text captions like "an all black bird with a distinct thick, rounded bill". 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, 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, 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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