add UniAnimate (see notes)

@ d7814fa44a0a1154524b92fce0e3133a2604d333

https://github.com/ali-vilab/UniAnimate

animation/UniAnimate/tools/modules/diffusions/losses.py

@@ -0,0 +1,28 @@
+import torch
+import math
+
+__all__ = ['kl_divergence', 'discretized_gaussian_log_likelihood']
+
+def kl_divergence(mu1, logvar1, mu2, logvar2):
+    return 0.5 * (-1.0 + logvar2 - logvar1 + torch.exp(logvar1 - logvar2) + ((mu1 - mu2) ** 2) * torch.exp(-logvar2))
+
+def standard_normal_cdf(x):
+    r"""A fast approximation of the cumulative distribution function of the standard normal.
+    """
+    return 0.5 * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
+
+def discretized_gaussian_log_likelihood(x0, mean, log_scale):
+    assert x0.shape == mean.shape == log_scale.shape
+    cx = x0 - mean
+    inv_stdv = torch.exp(-log_scale)
+    cdf_plus = standard_normal_cdf(inv_stdv * (cx + 1.0 / 255.0))
+    cdf_min = standard_normal_cdf(inv_stdv * (cx - 1.0 / 255.0))
+    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(
+        x0 < -0.999,
+        log_cdf_plus,
+        torch.where(x0 > 0.999, log_one_minus_cdf_min, torch.log(cdf_delta.clamp(min=1e-12))))
+    assert log_probs.shape == x0.shape
+    return log_probs