diff --git a/src/diffusers/schedulers/scheduling_ddpm.py b/src/diffusers/schedulers/scheduling_ddpm.py
index e047a553a2..481010fcb7 100644
--- a/src/diffusers/schedulers/scheduling_ddpm.py
+++ b/src/diffusers/schedulers/scheduling_ddpm.py
@@ -214,16 +214,17 @@ class DDPMScheduler(SchedulerMixin, ConfigMixin):
         # and sample from it to get previous sample
         # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
         variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * current_beta_t
+        variance = torch.clamp(variance, min=1e-20)
 
         if variance_type is None:
             variance_type = self.config.variance_type
 
         # hacks - were probably added for training stability
         if variance_type == "fixed_small":
-            variance = torch.clamp(variance, min=1e-20)
+            variance = variance
         # for rl-diffuser https://arxiv.org/abs/2205.09991
         elif variance_type == "fixed_small_log":
-            variance = torch.log(torch.clamp(variance, min=1e-20))
+            variance = torch.log(variance, min=1e-20)
             variance = torch.exp(0.5 * variance)
         elif variance_type == "fixed_large":
             variance = current_beta_t
@@ -234,7 +235,7 @@ class DDPMScheduler(SchedulerMixin, ConfigMixin):
             return predicted_variance
         elif variance_type == "learned_range":
             min_log = torch.log(variance)
-            max_log = torch.log(self.betas[t])
+            max_log = torch.log(current_beta_t)
             frac = (predicted_variance + 1) / 2
             variance = frac * max_log + (1 - frac) * min_log