soon is now

sgm/modules/diffusionmodules/sigma_sampling.py

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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):
+        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):
+        idx = default(
+            rand,
+            torch.randint(0, self.num_idx, (n_samples,)),
+        )
+        return self.idx_to_sigma(idx)