Text-to-Image
Text-to-image is the task of producing an image from a written description: a text encoder converts your prompt into a numeric representation, and an image model uses it as guidance to generate pixels that match the description.
No jargon, no math
In plain words, what is it?
You write a sentence, you get an image. The path has three stations. First the text: your prompt is split into tokens and a text encoder converts them into vectors of meaning, numbers that capture what you intend. Then the guidance: those vectors condition the image model, usually a diffusion model, which starts from noise and cleans it up pass after pass while keeping the prompt's meaning in view. Finally the pixels: the resulting grid is decoded into the file you see. The known weak point: the model paints a plausible depiction of the sentence, not your exact intent. Counts come out wrong, spatial relations get inverted, and any text written inside the image is decoration, not data.
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Prompt in. You write a description; it is split into tokens.
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Encode. A text encoder turns the tokens into numeric meaning.
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Condition. The image model receives the description as guidance.
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Generate. Pixels are built step by step to match the guidance.
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Decode. The finished grid becomes the image file you see.
Terms that live nearby.
Generative AI
Generative AI is software that produces new content, text, images, audio, code, by sampling patterns learned from vast training data, rather than only classifying or retrieving what already exists.
ReadDiffusion Model
A diffusion model is a generative model trained to reverse a gradual noising process: it learns to remove noise step by step, so at generation time it can start from pure static and refine it into an image, sound or other output that matches your prompt.
ReadPrompt
A prompt is everything you hand to a language model in one call: instructions, context and the question itself. The model answers by continuing exactly that text, and nothing else.
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