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import torch
from ...util import default, instantiate_from_config
class EDMSampling:
def __init__(self, p_mean=-1.2, p_std=1.2):
self.p_mean = p_mean
self.p_std = p_std
def __call__(self, n_samples, rand=None):
log_sigma = self.p_mean + self.p_std * default(rand, torch.randn((n_samples,)))
return log_sigma.exp()
class DiscreteSampling:
def __init__(self, discretization_config, num_idx, num_idx_start=0, do_append_zero=False, flip=True):
self.num_idx = num_idx
self.num_idx_start = num_idx_start
self.sigmas = instantiate_from_config(discretization_config)(
num_idx, do_append_zero=do_append_zero, flip=flip
)
def idx_to_sigma(self, idx):
return self.sigmas[idx]
def __call__(self, n_samples, rand=None):
idx = default(
rand,
torch.randint(self.num_idx_start, self.num_idx, (n_samples,)),
)
return self.idx_to_sigma(idx)
class CubicSampling:
def __init__(self, discretization_config, num_idx, do_append_zero=False, flip=True):
self.num_idx = num_idx
self.sigmas = instantiate_from_config(discretization_config)(
num_idx, do_append_zero=do_append_zero, flip=flip
)
def idx_to_sigma(self, idx):
return self.sigmas[idx]
def __call__(self, n_samples, rand=None):
t = torch.rand((n_samples,))
t = (1 - t ** 3) * (self.num_idx-1)
t = t.long()
idx = default(
rand,
t,
)
return self.idx_to_sigma(idx)
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