[Bug] scheduling_ddpm: fix variance in the case of learned_range type. (huggingface#2090)

scheduling_ddpm: fix variance in the case of learned_range type.

In the case of learned_range variance type, there are missing logs
and exponent comparing to the theory (see "Improved Denoising Diffusion
Probabilistic Models" section 3.1 equation 15:
https://arxiv.org/pdf/2102.09672.pdf).

Author: dudulightricks

src/diffusers/schedulers/scheduling_ddpm.py

@@ -218,8 +218,8 @@ def _get_variance(self, t, predicted_variance=None, variance_type=None):
         elif variance_type == "learned":
             return predicted_variance
         elif variance_type == "learned_range":
-            min_log = variance
-            max_log = self.betas[t]
+            min_log = torch.log(variance)
+            max_log = torch.log(self.betas[t])
             frac = (predicted_variance + 1) / 2
             variance = frac * max_log + (1 - frac) * min_log
 
@@ -304,6 +304,9 @@ def step(
             )
             if self.variance_type == "fixed_small_log":
                 variance = self._get_variance(t, predicted_variance=predicted_variance) * variance_noise
+            elif self.variance_type == "learned_range":
+                variance = self._get_variance(t, predicted_variance=predicted_variance)
+                variance = torch.exp(0.5 * variance) * variance_noise
             else:
                 variance = (self._get_variance(t, predicted_variance=predicted_variance) ** 0.5) * variance_noise