Individual_Incremental vs else in the all_sample function

[Gabriel Maliakal]

In Line 610 of deblurring_diffusion_pytorch.py, the all_sample function is defined to use in test_from_data function during testing.

In here there are two options -

if self.blur_routine == 'Individual_Incremental': img = self.gaussian_kernels[t - 1](img) else: for i in range(t): with torch.no_grad(): img = self.gaussian_kernels[i](img)

One applies the blur kernel of strength at the t-1 -th step to the original image , while the other (which is the "final") applies the kernel of increasing strength to the previous step starting from original image until the last step.

My question is What is the point of this ? Is this just to compare the "power" of the cold diffusion model when the degradation is applied in this schedule ?