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An image conditioned on the prompt "an astronaut riding a horse, by Hiroshige", generated by Stable Diffusion 3.5, a large-scale text-to-image model first released in 2022. A text-to-image model is a machine learning model which takes an input natural language description and produces an image matching that description.
Image makers on Picrew are 600 by 600 pixels with the square 1:1 aspect ratio. Since October 31 2023, image makers are now also available in Tatanaga size, [7] which refers to the aspect ratio of 9:16 for a canvas size of 540 by 960 pixels. Each image maker may have up to 50 layers and parts, and up to 750 items in total.
For example: the word "been" might be depicted by a rebus showing an illustrated bumblebee next to a plus sign (+) and the letter "n". It was a favourite form of heraldic expression used in the Middle Ages to denote surnames. For example, in its basic form, three salmon (fish) are used to denote the surname "Salmon".
Similarly, an image model prompted with the text "a photo of a CEO" might disproportionately generate images of white male CEOs, [112] if trained on a racially biased data set. A number of methods for mitigating bias have been attempted, such as altering input prompts [ 113 ] and reweighting training data.
In computer vision, the bag-of-words model (BoW model) sometimes called bag-of-visual-words model [1] [2] can be applied to image classification or retrieval, by treating image features as words. In document classification , a bag of words is a sparse vector of occurrence counts of words; that is, a sparse histogram over the vocabulary.
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