diff --git a/bayesflow/approximators/continuous_approximator.py b/bayesflow/approximators/continuous_approximator.py
index 9911d6920..5c389b329 100644
--- a/bayesflow/approximators/continuous_approximator.py
+++ b/bayesflow/approximators/continuous_approximator.py
@@ -11,7 +11,7 @@
 from bayesflow.adapters import Adapter
 from bayesflow.networks import InferenceNetwork, SummaryNetwork
 from bayesflow.types import Tensor
-from bayesflow.utils import logging, split_arrays
+from bayesflow.utils import filter_kwargs, logging, split_arrays
 from .approximator import Approximator
 
 
@@ -141,7 +141,7 @@ def sample(
     ) -> dict[str, np.ndarray]:
         conditions = self.adapter(conditions, strict=False, stage="inference", **kwargs)
         conditions = keras.tree.map_structure(keras.ops.convert_to_tensor, conditions)
-        conditions = {"inference_variables": self._sample(num_samples=num_samples, **conditions)}
+        conditions = {"inference_variables": self._sample(num_samples=num_samples, **conditions, **kwargs)}
         conditions = keras.tree.map_structure(keras.ops.convert_to_numpy, conditions)
         conditions = self.adapter(conditions, inverse=True, strict=False, **kwargs)
 
@@ -154,6 +154,7 @@ def _sample(
         num_samples: int,
         inference_conditions: Tensor = None,
         summary_variables: Tensor = None,
+        **kwargs,
     ) -> Tensor:
         if self.summary_network is None:
             if summary_variables is not None:
@@ -162,7 +163,9 @@ def _sample(
             if summary_variables is None:
                 raise ValueError("Summary variables are required when a summary network is present.")
 
-            summary_outputs = self.summary_network(summary_variables)
+            summary_outputs = self.summary_network(
+                summary_variables, **filter_kwargs(kwargs, self.summary_network.call)
+            )
 
             if inference_conditions is None:
                 inference_conditions = summary_outputs
@@ -180,18 +183,26 @@ def _sample(
         else:
             batch_shape = (num_samples,)
 
-        return self.inference_network.sample(batch_shape, conditions=inference_conditions)
+        return self.inference_network.sample(
+            batch_shape,
+            conditions=inference_conditions,
+            **filter_kwargs(kwargs, self.inference_network.sample),
+        )
 
-    def log_prob(self, data: dict[str, np.ndarray]) -> np.ndarray:
-        data = self.adapter(data, strict=False, stage="inference")
+    def log_prob(self, data: dict[str, np.ndarray], **kwargs) -> np.ndarray:
+        data = self.adapter(data, strict=False, stage="inference", **kwargs)
         data = keras.tree.map_structure(keras.ops.convert_to_tensor, data)
-        log_prob = self._log_prob(**data)
+        log_prob = self._log_prob(**data, **kwargs)
         log_prob = keras.ops.convert_to_numpy(log_prob)
 
         return log_prob
 
     def _log_prob(
-        self, inference_variables: Tensor, inference_conditions: Tensor = None, summary_variables: Tensor = None
+        self,
+        inference_variables: Tensor,
+        inference_conditions: Tensor = None,
+        summary_variables: Tensor = None,
+        **kwargs,
     ) -> Tensor:
         if self.summary_network is None:
             if summary_variables is not None:
@@ -200,11 +211,17 @@ def _log_prob(
             if summary_variables is None:
                 raise ValueError("Summary variables are required when a summary network is present.")
 
-            summary_outputs = self.summary_network(summary_variables)
+            summary_outputs = self.summary_network(
+                summary_variables, **filter_kwargs(kwargs, self.summary_network.call)
+            )
 
             if inference_conditions is None:
                 inference_conditions = summary_outputs
             else:
                 inference_conditions = keras.ops.concatenate([inference_conditions, summary_outputs], axis=-1)
 
-        return self.inference_network.log_prob(inference_variables, conditions=inference_conditions)
+        return self.inference_network.log_prob(
+            inference_variables,
+            conditions=inference_conditions,
+            **filter_kwargs(kwargs, self.inference_network.log_prob),
+        )