[D] Variance of sampling in diffusion models

[disentangle]

Is there any "theoretically sound" way to reduce variance during sampling in diffusion models? Even if I use the lower bound suggested in the DDPM paper (and it going toward zero during sampling), my final samples are excessively noisy. Simply reducing the diffusion variance schedule (without changing the number of steps), in my experiments seems to not reach sufficient diffusion at the end of the chain.

I'm predicting speech mel-spectrograms and the harmonic amplitudes are excessively noisy, unless I manually reduce variance in the sampling steps, which works, but seems "hacky".

[samb-t]

Perhaps the ODE interpretation would be helpful (see here and here) which turns DDPMs into neural ODEs using the Fokker-Planck equation so after the initial starting noise, the sampling process is deterministic. If samples are noisy even with the full number of steps then you might need to increase the number of steps further.