Why minmize l1(\hat{x_0}, x_0)+l1(\hat{x_1}, x_0) when optimizing aux model?

[caisikai]

Hi, keonlee.
Thanks for sharing code!
I found that when training aux model, we get \hat{x_0} from G, then diffuse it to \hat{x_1}, finally get a prediciton list [ \hat{x_0}, \hat{x_1}]. When calculating mel loss, add l1 loss of them with target. It confuse me. I understand l1(x_0, \hat{x_0}). But why not l1(x_1, \hat{x_1}).