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flows make sense here not just for size but cuz they're fully invertible and deterministic. imagine running same gen on 3 iphones, same output. means apple can kinda ensure same input gives same output across devices, chips, runs. no weird variance or sampling noise. good for caching, testing, user trust all that. fits apple's whole determinism dna and more of predictable gen at scale

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Normalizing flows generate samples by starting from Gaussian noise and passing it through a series of invertible transformations. Diffusion models generate samples by starting from Gaussian noise and running it through an inverse diffusion process.

To get deterministic results, you fix the seed for your pseudorandom number generator and make sure not to execute any operations that produce different results on different hardware. There's no difference between the approaches in that respect.

Agree. I am a image gen laymen, but when I was running stable diffusion in 2022 it seemed like I could get the same image if I used the same seed and parameters. Seemed easy to get same image when you have full control of the inputs. The randomness is a choice

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