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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, do_append_zero=False, flip=True, idx_range=None):
self.num_idx = num_idx
self.sigmas = instantiate_from_config(discretization_config)(
num_idx, do_append_zero=do_append_zero, flip=flip
)
self.idx_range = idx_range
def idx_to_sigma(self, idx):
# print(self.sigmas[idx])
return self.sigmas[idx]
def __call__(self, n_samples, rand=None):
if self.idx_range is None:
idx = default(
rand,
torch.randint(0, self.num_idx, (n_samples,)),
)
else:
idx = default(
rand,
torch.randint(self.idx_range[0], self.idx_range[1], (n_samples,)),
)
return self.idx_to_sigma(idx)
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