Image Generation
Image generation is the process of creating new pictures from a text description or another image: a generative model, usually a diffusion model, turns a prompt into pixels step by step rather than copying existing photos.
No jargon, no math
In plain words, what is it?
The workhorse is the diffusion model. You write a prompt and a text encoder turns it into a numeric brief the model can steer by. Generation starts from pure noise, a random field of pixels. In a loop of 20 to 50 steps the model predicts what the noise hides and removes a slice of it, nudged each step by your prompt: first rough shapes and palette, then edges, then texture. A decoder upscales the cleaned image to full resolution. What comes out is new pixels, not a collage: nothing was copied. It breaks predictably. Text inside images, hands and fine brand details come out garbled, because the model learned statistics, not spelling. It also reflects its training data, so styles skew toward over-represented images unless you steer hard. Professional use is a loop: brief, generate, correct, regenerate, until it matches the spec.
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Prompt. You write the brief: subject, style, lighting, format.
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Encoding. The prompt becomes a numeric description the model steers by.
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Denoising. The model starts from noise and removes it in 20 to 50 guided steps.
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Decoding. The cleaned image is upscaled to full resolution.
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Refinement. You tweak the prompt or regenerate until it matches the spec.
Terms that live nearby.
Diffusion 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.
ReadText-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.
ReadGAN
A GAN (Generative Adversarial Network) is a pair of neural networks trained against each other: a generator creates synthetic samples while a discriminator judges them against real data, and each improves by trying to beat the other.
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