diff --git a/quantus/metrics/randomisation/smooth_mprt.py b/quantus/metrics/randomisation/smooth_mprt.py
index cb62a6aa..d79317a2 100644
--- a/quantus/metrics/randomisation/smooth_mprt.py
+++ b/quantus/metrics/randomisation/smooth_mprt.py
@@ -53,8 +53,8 @@ class SmoothMPRT(Metric):
     """
     Implementation of the Smooth MPRT by Hedström et al., 2023.
 
-    The Smooth Model Parameter Randomisation adds a "denoising" preprocessing step to the original MPRT, 
-    where the explanations are averaged over N noisy samples before the similarity between the original- 
+    The Smooth Model Parameter Randomisation adds a "denoising" preprocessing step to the original MPRT,
+    where the explanations are averaged over N noisy samples before the similarity between the original-
     and fully random model's explanations is measured.
 
     References:
@@ -350,7 +350,7 @@ def __call__(
                         model.get_model(),
                         x_full_dataset,
                         y_full_dataset,
-                        **kwargs,
+                        **self.explain_func_kwargs,
                     )
 
                     # Compute the similarity of explanations of the original model.
@@ -384,14 +384,16 @@ def __call__(
                     random_layer_model,
                     x_full_dataset,
                     y_full_dataset,
-                    **kwargs,
+                    **self.explain_func_kwargs,
                 )
 
                 # Compute the similarity of explanations of the perturbed model.
                 for a_batch, a_batch_perturbed in zip(
                     self.generate_a_batches(a_full_dataset), a_perturbed_generator
                 ):
-                    for a_instance, a_instance_perturbed in zip(a_batch, a_batch_perturbed):
+                    for a_instance, a_instance_perturbed in zip(
+                        a_batch, a_batch_perturbed
+                    ):
                         score = self.evaluate_instance(
                             model=random_layer_model,
                             x=None,
@@ -544,7 +546,9 @@ def custom_preprocess(
             return None
 
         a_batch_chunks = []
-        for a_chunk in self.generate_explanations(model, x_batch, y_batch):
+        for a_chunk in self.generate_explanations(
+            model, x_batch, y_batch, **{**kwargs, **self.explain_func_kwargs}
+        ):
             a_batch_chunks.extend(a_chunk)
         return dict(a_batch=np.asarray(a_batch_chunks))
 
diff --git a/tests/metrics/test_randomisation_metrics.py b/tests/metrics/test_randomisation_metrics.py
index 95dd75e6..91c18e1d 100644
--- a/tests/metrics/test_randomisation_metrics.py
+++ b/tests/metrics/test_randomisation_metrics.py
@@ -3,6 +3,7 @@
 import pytest
 from pytest_lazyfixture import lazy_fixture
 import numpy as np
+from zennit import attribution as zattr
 
 from quantus.functions.explanation_func import explain
 from quantus.functions import complexity_func, n_bins_func
@@ -745,6 +746,57 @@ def test_model_parameter_randomisation(
             },
             {"exception": ValueError},
         ),
+        (
+            lazy_fixture("load_mnist_model"),
+            lazy_fixture("load_mnist_images"),
+            {
+                "init": {
+                    "layer_order": "independent",
+                    "similarity_func": correlation_spearman,
+                    "normalise": True,
+                    "abs": True,
+                    "disable_warnings": True,
+                    "return_average_correlation": False,
+                    "return_last_correlation": True,
+                    "skip_layers": True,
+                },
+                "call": {
+                    "explain_func": explain,
+                    "explain_func_kwargs": {
+                        "shape": (8, 1, 28, 28),
+                        "canonizer": None,
+                        "composite": None,
+                        "attributor": zattr.Gradient,
+                        "xai_lib": "zennit",
+                    },
+                },
+            },
+            {"min": -1.0, "max": 1.0},
+        ),
+        (
+            lazy_fixture("load_mnist_model"),
+            lazy_fixture("load_mnist_images"),
+            {
+                "init": {
+                    "layer_order": "independent",
+                    "similarity_func": correlation_spearman,
+                    "normalise": True,
+                    "abs": True,
+                    "disable_warnings": True,
+                    "return_average_correlation": False,
+                    "return_last_correlation": True,
+                    "skip_layers": True,
+                },
+                "call": {
+                    "explain_func": explain,
+                    "explain_func_kwargs": {
+                        "attributor": zattr.IntegratedGradients,
+                        "xai_lib": "zennit",
+                    },
+                },
+            },
+            {"min": -1.0, "max": 1.0},
+        ),
     ],
 )
 def test_smooth_model_parameter_randomisation(