diff --git a/bayesflow/experimental/stable_consistency_model/stable_consistency_model.py b/bayesflow/experimental/stable_consistency_model/stable_consistency_model.py
index 6ce27fdf4..dc092ab4e 100644
--- a/bayesflow/experimental/stable_consistency_model/stable_consistency_model.py
+++ b/bayesflow/experimental/stable_consistency_model/stable_consistency_model.py
@@ -307,7 +307,7 @@ def compute_metrics(
         r = 1.0  # TODO: if consistency distillation training (not supported yet) is unstable, add schedule here
 
         def f_teacher(x, t):
-            o = self._apply_subnet(x / self.sigma, self.time_emb(t), conditions, training=stage == "training")
+            o = self._apply_subnet(x, self.time_emb(t), conditions, training=stage == "training")
             return self.subnet_projector(o)
 
         primals = (xt / self.sigma, t)
@@ -321,7 +321,7 @@ def f_teacher(x, t):
         cos_sin_dFdt = ops.stop_gradient(cos_sin_dFdt)
 
         # calculate output of the network
-        subnet_out = self._apply_subnet(x / self.sigma, self.time_emb(t), conditions, training=stage == "training")
+        subnet_out = self._apply_subnet(xt / self.sigma, self.time_emb(t), conditions, training=stage == "training")
         student_out = self.subnet_projector(subnet_out)
 
         # calculate the tangent