diff --git a/bayesflow/experimental/stable_consistency_model/stable_consistency_model.py b/bayesflow/experimental/stable_consistency_model/stable_consistency_model.py
index 6ce27fdf4..0f787d44f 100644
--- a/bayesflow/experimental/stable_consistency_model/stable_consistency_model.py
+++ b/bayesflow/experimental/stable_consistency_model/stable_consistency_model.py
@@ -105,7 +105,6 @@ def __init__(
         )
 
         embedding_kwargs = embedding_kwargs or {}
-        self.embedding_kwargs = embedding_kwargs
         self.time_emb = FourierEmbedding(**embedding_kwargs)
         self.time_emb_dim = self.time_emb.embed_dim
 
@@ -123,13 +122,14 @@ def get_config(self):
         config = {
             "subnet": self.subnet,
             "sigma": self.sigma,
-            "embedding_kwargs": self.embedding_kwargs,
+            "time_emb": self.time_emb,
             "concatenate_subnet_input": self._concatenate_subnet_input,
         }
 
         return base_config | serialize(config)
 
-    def _discretize_time(self, num_steps: int, rho: float = 3.5, **kwargs):
+    @staticmethod
+    def _discretize_time(num_steps: int, rho: float = 3.5):
         t = keras.ops.linspace(0.0, pi / 2, num_steps)
         times = keras.ops.exp((t - pi / 2) * rho) * pi / 2
         times = keras.ops.concatenate([keras.ops.zeros((1,)), times[1:]], axis=0)
@@ -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