Monocular depth estimation algorithms are deterministic in the same sense: using the same recipe on the same input produces the same result.
I'm not sure how you define algorithms, but I have a feeling it's not a universal definition. I would argue that diffusion is an algorithm like HDR compensation. Both describe well defined and repeatable processes that can be broken down into low-level components with different potential implementations.
How does your HDR algorithm select a exposure curve for tone-mapping? How does it handle noise rejection? Does it apply any non-local corrections or smoothing? Does the existence of these questions mean that HDR is less of an algorithm? Where do we draw that line?
To sum up: Marigold V2 is just as deterministic as Bayer demosaicing. But Marigold has about 20 billion magic numbers in it, thoroughly dwarfing the source image. It feels less like transforming the image with Marigold, and more like steering Marigold with the image. And who knows what's in that thing or how it works...
(note: I see the same confusion often in conversations about LLMs. People call them "nondeterministic", as if that were some new category of software. But, with temp 0, they're no less deterministic than any other pile of floating point math - and nobody complains that fluid simulations aren't deterministic. No, what they are is unpredictable.)