Question about parameter tuning

[xiong-creator]

1. The paper outlines the hyperparameters sigma_min and sigma_max during deterministic sampling, as well as P_mean, P_std, and sigma_data during training. Could you kindly elaborate on how these hyperparameters are determined and shed light on their impact on both the training and sampling processes?
2. If I intend to apply the EDM framework to my own dataset, how would you recommend adjusting the aforementioned hyperparameters based on the characteristics of my dataset? Are there any guidelines or considerations for fine-tuning these parameters for optimal performance?
   Thank you very much for considering my inquiry.

[meetcfd]

I too want to tweak these hyperparameters for my dataset. Did you get any insights on how to modify them?

Thanks in advance!

[Declan-Curran1]

Wanted to follow up on this issue. The hyperparameters of the noise process (in training & sampling) seems quite arbitrary with no further explanation???

The rest of the architecture decisions/additions are very carefully explored so seems like a bit of shame that there is no mention of this outside table 1 in the paper where they are stated.

Surely the team did an ablation study of sorts to come to these figures? Would be great if some guidance could be shared on this.