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dfd1909 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 | from torch import Tensor,device
import torch
import numpy as np
class DiffusionUtil:
@staticmethod
def extract(array:Tensor, t, x_shape):
batch_size, *_ = t.shape
out = array.gather(dim = -1, index = t).contiguous()
return out.reshape(batch_size, *((1,) * (len(x_shape) - 1))).contiguous()
@staticmethod
def noise_like(shape:tuple, device:device, repeat:bool = False):
repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1)))
noise = lambda: torch.randn(shape, device=device)
return repeat_noise() if repeat else noise()
@staticmethod
def discretized_gaussian_log_likelihood(x, means, log_scales):
"""
Compute the log-likelihood of a Gaussian distribution discretizing to a
given image.
:param x: the target images. It is assumed that this was uint8 values,
rescaled to the range [-1, 1].
:param means: the Gaussian mean Tensor.
:param log_scales: the Gaussian log stddev Tensor.
:return: a tensor like x of log probabilities (in nats).
"""
assert x.shape == means.shape == log_scales.shape
centered_x = x - means
inv_stdv = torch.exp(-log_scales)
plus_in = inv_stdv * (centered_x + 1.0 / 255.0)
cdf_plus = DiffusionUtil.approx_standard_normal_cdf(plus_in)
min_in = inv_stdv * (centered_x - 1.0 / 255.0)
cdf_min = DiffusionUtil.approx_standard_normal_cdf(min_in)
log_cdf_plus = torch.log(cdf_plus.clamp(min=1e-12))
log_one_minus_cdf_min = torch.log((1.0 - cdf_min).clamp(min=1e-12))
cdf_delta = cdf_plus - cdf_min
log_probs = torch.where(
x < -0.999,
log_cdf_plus,
torch.where(x > 0.999, log_one_minus_cdf_min, torch.log(cdf_delta.clamp(min=1e-12))),
)
assert log_probs.shape == x.shape
return log_probs
@staticmethod
def approx_standard_normal_cdf(x):
"""
A fast approximation of the cumulative distribution function of the
standard normal.
"""
return 0.5 * (1.0 + torch.tanh(np.sqrt(2.0 / np.pi) * (x + 0.044715 * torch.pow(x, 3))))
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