Learn · Diffusion
Diffusion: noise to image
Image generators like Stable Diffusion learn to reverse a noising process. Forward: add Gaussian noise step by step until an image is pure static. Reverse: a model removes a little noise at each step to recover an image. Drag the slider to add noise, then hit generate to watch it denoise.
What's really happening
Each pixel follows the diffusion equation xₜ = √ᾱₜ·x₀ + √(1-ᾱₜ)·ε — a shrinking amount of the original image plus growing Gaussian noise, on a cosine schedule. Training a diffusion model means teaching a network to predict the noise ε at any step, so it can subtract a little at a time and walk back from static to a clean image. Text-to-image models add a twist: a text prompt steers which image the denoising heads towards. This demo denoises a known picture; a real model hallucinates a brand-new one from pure noise.
Real diffusion runs over hundreds of steps in a compressed "latent" space, not raw pixels — but the maths of the forward process shown here is exactly the same. For the language-model side of generative AI, see the Token and Sampling demos.