diff --git a/src/diffusers/schedulers/scheduling_ddim.py b/src/diffusers/schedulers/scheduling_ddim.py
index 04c4a2c6..29a79d39 100644
--- a/src/diffusers/schedulers/scheduling_ddim.py
+++ b/src/diffusers/schedulers/scheduling_ddim.py
@@ -301,12 +301,13 @@ class DDIMScheduler(SchedulerMixin, ConfigMixin):
         # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
         if self.config.prediction_type == "epsilon":
             pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
+            pred_epsilon = model_output
         elif self.config.prediction_type == "sample":
             pred_original_sample = model_output
+            pred_epsilon = (sample - alpha_prod_t ** (0.5) * pred_original_sample) / beta_prod_t ** (0.5)
         elif self.config.prediction_type == "v_prediction":
             pred_original_sample = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
-            # predict V
-            model_output = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
+            pred_epsilon = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
         else:
             raise ValueError(
                 f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"
@@ -328,17 +329,16 @@ class DDIMScheduler(SchedulerMixin, ConfigMixin):
         std_dev_t = eta * variance ** (0.5)
 
         if use_clipped_model_output:
-            # the model_output is always re-derived from the clipped x_0 in Glide
-            model_output = (sample - alpha_prod_t ** (0.5) * pred_original_sample) / beta_prod_t ** (0.5)
+            # the pred_epsilon is always re-derived from the clipped x_0 in Glide
+            pred_epsilon = (sample - alpha_prod_t ** (0.5) * pred_original_sample) / beta_prod_t ** (0.5)
 
         # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
-        pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * model_output
+        pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * pred_epsilon
 
         # 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
         prev_sample = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction
 
         if eta > 0:
-            device = model_output.device
             if variance_noise is not None and generator is not None:
                 raise ValueError(
                     "Cannot pass both generator and variance_noise. Please make sure that either `generator` or"
@@ -347,7 +347,7 @@ class DDIMScheduler(SchedulerMixin, ConfigMixin):
 
             if variance_noise is None:
                 variance_noise = randn_tensor(
-                    model_output.shape, generator=generator, device=device, dtype=model_output.dtype
+                    model_output.shape, generator=generator, device=model_output.device, dtype=model_output.dtype
                 )
             variance = std_dev_t * variance_noise
 
diff --git a/src/diffusers/schedulers/scheduling_ddim_flax.py b/src/diffusers/schedulers/scheduling_ddim_flax.py
index 14a374d4..db248c33 100644
--- a/src/diffusers/schedulers/scheduling_ddim_flax.py
+++ b/src/diffusers/schedulers/scheduling_ddim_flax.py
@@ -254,12 +254,13 @@ class FlaxDDIMScheduler(FlaxSchedulerMixin, ConfigMixin):
         # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
         if self.config.prediction_type == "epsilon":
             pred_original_sample = (sample - beta_prod_t ** (0.5) * model_output) / alpha_prod_t ** (0.5)
+            pred_epsilon = model_output
         elif self.config.prediction_type == "sample":
             pred_original_sample = model_output
+            pred_epsilon = (sample - alpha_prod_t ** (0.5) * pred_original_sample) / beta_prod_t ** (0.5)
         elif self.config.prediction_type == "v_prediction":
             pred_original_sample = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
-            # predict V
-            model_output = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
+            pred_epsilon = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
         else:
             raise ValueError(
                 f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"
@@ -272,7 +273,7 @@ class FlaxDDIMScheduler(FlaxSchedulerMixin, ConfigMixin):
         std_dev_t = eta * variance ** (0.5)
 
         # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
-        pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * model_output
+        pred_sample_direction = (1 - alpha_prod_t_prev - std_dev_t**2) ** (0.5) * pred_epsilon
 
         # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
         prev_sample = alpha_prod_t_prev ** (0.5) * pred_original_sample + pred_sample_direction