Forward diffusion adds noise to a picture in small steps until nothing is left. A real model learns to run this backwards; here you can watch the forward half and see why the schedule matters. Everything runs in your browser: the bundled photo and any picture you load stay on your device and are never uploaded.
The denoise toggle applies a small box blur to the noised image as a rough illustration of "removing noise". It is not a trained model and does not reconstruct the original picture.
Image at step t
alpha-bar(t) for the chosen schedule
What to notice: at step 0 the image is unchanged; by step 1000 it is indistinguishable from static. The cosine schedule keeps more signal in the middle steps than the linear schedule, so a cosine-trained model spends more of its steps on fine detail.