Diffusion LLMs are still residual networks. You can Google that, but it means that they don't generate the whole text at once. Every layer generates corrections to be made to the whole text at once.

Think of it like writing a text by forcing your teacher to write for you by entering in the assignment 100 times. You begin by generating completely inaccurate text, almost random, that leans perhaps a little bit towards the answer. Then you systematically begin to correct small parts of the text. The teacher that sees the text, and uses red the red pen to correct a bunch of things. Then the corrected text is copied onto a fresh page, and resubmitted to the teacher. And again. And again. And again. And again. 50 times. 100 times. That's how diffusion models work.

Technically, it adds your corrections to the text, but that's mathematical addition, not adding at the end. Also technically every layer is a teacher that's slightly different from the previous one. And and and ... but this is the basic principle. The big advantage is that this makes neural networks slowly lean towards the answer. First they decide to have 3 sections, one about X, Y and one about Z, then they decide on what sentences to put, then they start thinking about individual words, then they start worrying about things like grammar, and finally about spelling and pronouns and ...

So to answer your question: diffusion networks can at any time decide to send out a correction that effectively erases the text (in several ways). So they can always start over by just correcting everything all at once back to randomness.