Common

gaussian	Samples from gaussian with specified shape, dtype, device using torch.randn
gaussian_like	Samples from gaussian like the tensor x using torch.randn_like
uniform_int	Samples ints from uniform distribution using torch.randint
norm	Normalize input to \([-1, 1]\) linearly
denorm	Denormalize input normalized to \([-1, 1]\) linearly back to \([0, 1]\)
make_history	Visualize diffusion process given an array of histories
set_default	helper for setting default values inside __init__ for LightningCLI

dmme.common.gaussian(shape, dtype=None, device=None)

Samples from gaussian with specified shape, dtype, device using torch.randn

dmme.common.gaussian_like(x)

Samples from gaussian like the tensor x using torch.randn_like

dmme.common.uniform_int(min, max, count=1, device=None)

Samples ints from uniform distribution using torch.randint

dmme.common.norm(x)

Normalize input to \([-1, 1]\) linearly

dmme.common.denorm(x)

Denormalize input normalized to \([-1, 1]\) linearly back to \([0, 1]\)

dmme.common.make_history(history)

Visualize diffusion process given an array of histories

Parameters:

history (List[torch.Tensor]) – list of diffusion history with each item as a tensor of shape \((N, C, H, W)\)

dmme.common.set_default(arg, default_value)

helper for setting default values inside __init__ for LightningCLI