How diffusion models turn noise into images
A simple trick hides behind many smart image tools: tiny steps of messing and fixing.
Diffusion models start by taking something clear, adding random dots and blur, then they practice how to clean it, over and over.
They can from a few words slowly generate images that feel new and real.
The secret is the way they deal with noise, learning the direction from messy to neat, not memorizing pictures.
Each step teaches the net to learn a little more about what a real picture looks like, so when asked it can imagine one from scratch.
You can also nudge the process, with extra hints, so the outcome leans toward a cat, a face, or a bright sunset, that is called guidance.
It’s like teaching by scrubbing a drawing until the right lines appear, and the model gets better each pass.
The method feels simple, but it’s why many new tools make surprising, beautiful images from plain text prompts.
Read article comprehensive review in Paperium.net:
Understanding Diffusion Models: A Unified Perspective
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