# Noise → Structure: Diffusion Intuition

**Track:** 3D, Shaders & AI Intuition — Creative Coding — the existing 50
**Framework / surface:** p5.js
**Level:** Advanced
**Prerequisites:** Perlin Noise Fields, Arrays of Objects

**In one line:** How image generators work in spirit — denoise toward structure.

## Theory, aesthetics & inspiration

Diffusion models build images by reversing decay. Training corrupts data with successive additions of noise until structure dissolves into static; generation learns to undo each step, walking backward from noise toward coherence. The framing comes from Jascha Sohl-Dickstein's 2015 work borrowing nonequilibrium thermodynamics, made practical by Jonathan Ho, Ajay Jain, and Pieter Abbeel in Denoising Diffusion Probabilistic Models (2020). The aesthetic is sculptural in the Michelangelo sense — form already latent in the marble, revealed by removing what is not the figure. Each denoising pass is a small act of resolution, randomness condensing into recognizable structure through many gentle corrections rather than one decisive stroke.