How AI Turns Noise into Realistic Images

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Издатель Jun 4, 2026

The popular explanation for image generators is that they "remove" noise
until a picture appears. That's true — but almost empty as an explanation.

Here's what actually happens. At every step, a diffusion model estimates a
direction: which way to nudge the current image so it looks slightly more
like real data. That direction is called the score — the gradient of the
log-density. The network learns it everywhere, then follows it, step by step.

Structure isn't erased out of the static. It's guided out, one small step
at a time.

Full breakdown — the forward process, the reverse process, and why predicting
noise works — in the related videos.