diff --git a/tests/model_hub_tests/tf_hub_tests/precommit_models b/tests/model_hub_tests/tf_hub_tests/precommit_models
index 7b663b5046d..2b89ebfe5e3 100644
--- a/tests/model_hub_tests/tf_hub_tests/precommit_models
+++ b/tests/model_hub_tests/tf_hub_tests/precommit_models
@@ -7,6 +7,7 @@ efficientnet/lite0/classification,https://tfhub.dev/tensorflow/efficientnet/lite
 movenet/multipose/lightning,https://tfhub.dev/google/movenet/multipose/lightning/1?tf-hub-format=compressed
 imagenet/efficientnet_v2_imagenet1k_b0/feature_vector,https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet1k_b0/feature_vector/2?tf-hub-format=compressed
 imagenet/mobilenet_v1_100_224/classification,https://tfhub.dev/google/imagenet/mobilenet_v1_100_224/classification/5?tf-hub-format=compressed,skip,119718 - Accuracy issue
+magenta/arbitrary-image-stylization-v1-256,https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2?tf-hub-format=compressed
 # secure notebook models
 unet/industrial/class_1,https://tfhub.dev/nvidia/unet/industrial/class_1/1?tf-hub-format=compressed
 movenet/singlepose/thunder,https://tfhub.dev/google/movenet/singlepose/thunder/4?tf-hub-format=compressed
diff --git a/tests/model_hub_tests/tf_hub_tests/test_tf_hub_convert_model.py b/tests/model_hub_tests/tf_hub_tests/test_tf_hub_convert_model.py
index 64d276d8cc7..94fe5bba83e 100644
--- a/tests/model_hub_tests/tf_hub_tests/test_tf_hub_convert_model.py
+++ b/tests/model_hub_tests/tf_hub_tests/test_tf_hub_convert_model.py
@@ -36,11 +36,16 @@ class TestTFHubConvertModel(TestConvertModel):
         for input_name, input_info in model_obj.structured_input_signature[1].items():
             input_shape = []
             try:
-                for dim in input_info.shape.as_list():
-                    if dim is None:
-                        input_shape.append(1)
-                    else:
-                        input_shape.append(dim)
+                if input_info.shape.as_list() == [None, None, None, 3] and input_info.dtype == tf.float32:
+                    # image classification case, let us imitate an image
+                    # that helps to avoid compute output size issue
+                    input_shape = [1, 200, 200, 3]
+                else:
+                    for dim in input_info.shape.as_list():
+                        if dim is None:
+                            input_shape.append(1)
+                        else:
+                            input_shape.append(dim)
             except ValueError:
                 # unknown rank case
                 pass