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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.
A text-to-image prompt commonly includes a description of the subject of the art, the desired medium (such as digital painting or photography), style (such as hyperrealistic or pop-art), lighting (such as rim lighting or crepuscular rays), color, and texture. [51] Word order also affects the output of a text-to-image prompt.
Smaller generative AI models with up to a few billion parameters can run on smartphones, embedded devices, and personal computers. For example, LLaMA-7B (a version with 7 billion parameters) can run on a Raspberry Pi 4 [89] and one version of Stable Diffusion can run on an iPhone 11. [90]
An example of prompt usage for text-to-image generation, using Fooocus. Prompts for some text-to-image models can also include images and keywords and configurable parameters, such as artistic style, which is often used via keyphrases like "in the style of [name of an artist]" in the prompt [97] and/or selection of a broad aesthetic/art style.
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Image and video generators like DALL-E (2021), Stable Diffusion 3 (2024), [45] and Sora (2024), use Transformers to analyse input data (like text prompts) by breaking it down into "tokens" and then calculating the relevance between each token using self-attention, which helps the model understand the context and relationships within the data.
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frameless – displays the image as an unframed at the user's default size; upright – scales the image to approximately 75% of the user's default size (25% smaller) upright=0.8 – scales the image to approximately 80% of the user's default size (20% smaller) upright=1.2 – scales the image to approximately 120% of the user's default size ...
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