diff --git a/src/diffusers/guiders/frequency_decoupled_guidance.py b/src/diffusers/guiders/frequency_decoupled_guidance.py
index f1786d0e603d..b0871991f57c 100644
--- a/src/diffusers/guiders/frequency_decoupled_guidance.py
+++ b/src/diffusers/guiders/frequency_decoupled_guidance.py
@@ -275,7 +275,7 @@ def forward(self, pred_cond: torch.Tensor, pred_uncond: torch.Tensor | None = No
                     pred_guided_pyramid.append(pred)
                 else:
                     # Add the current pred_cond_pyramid level as the "non-FDG" prediction
-                    pred_guided_pyramid.append(pred_cond_freq)
+                    pred_guided_pyramid.append(pred_cond_pyramid[level])
 
             # Convert from frequency space back to data (e.g. pixel) space by applying inverse freq transform
             pred = build_image_from_pyramid(pred_guided_pyramid)
diff --git a/src/diffusers/guiders/tangential_classifier_free_guidance.py b/src/diffusers/guiders/tangential_classifier_free_guidance.py
index 497cdc3c463d..75ad5310a541 100644
--- a/src/diffusers/guiders/tangential_classifier_free_guidance.py
+++ b/src/diffusers/guiders/tangential_classifier_free_guidance.py
@@ -101,7 +101,7 @@ def forward(self, pred_cond: torch.Tensor, pred_uncond: torch.Tensor | None = No
 
     @property
     def is_conditional(self) -> bool:
-        return self._num_outputs_prepared == 1
+        return self._count_prepared == 1
 
     @property
     def num_conditions(self) -> int: