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royalemate357 t1_j60xuup wrote

there's an implementation of score-based models from the paper that showed how score based models and diffusion models are the same here: https://github.com/yang-song/score_sde_pytorch

imo their implementation is more or less the same as a diffusion model, except score based models would use a numerical ODE/SDE solver to generate samples instead of using the DDPM based sampling method. it might also train on continuous time, so rather than choosing t ~ randint(0, 1000) it would be t ~ rand_uniform(0, 1.)

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Individual-Cause-616 OP t1_j60yi8b wrote

So do you think it makes a difference in practice, I.e. sampling speed and quality, convergence etc

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royalemate357 t1_j61k9vy wrote

the speed and quality of score based/diffusion depends on what sampler you use. If youre using euler's method to solve the ODE for example, that might be slower than some of the newer methods developed for diffusion models, like tero karass' ODE solvers. AFAIK there isnt consensus on what the best sampler to use is though.

i dont think it affects training convergence much though since its more or less the same objective.

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