Comment by b0a04gl
17 hours ago
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
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.