UNDERSTANDING DIFFUSION MODELS : HOW AI REALLY CREATES IMAGE FROM NOISE
UNDERSTANDING DIFFUSION MODELS : HOW AI REALLY CREATES IMAGE FROM NOISE CONCEPT BEHIND THIS Imagine you take a clear image and add Gaussian noise to it ( But what is Gaussian noise? Imagine you take a clean, sharp photo. Now, imagine adding a tiny bit of random fuzz — like grain, tiny dots, or static — all over it. That’s noise . Now, Gaussian noise is just a special kind of noise where: Most of the fuzz is small and not very strong. A few spots have slightly more intense fuzz. ) the left side shows a clear coherent image while the left side shows an image with Gaussian noise. We continue this process till the point we get an image where only tiny dots and grain (also called static or noise) are visible, and the image becomes unrecognizable. What if we could reverse this process to start from the pure static and noise form and make our way back to the clear and coherent image? Left shows the pure noise and static image while right sh...