diff --git a/onnxruntime/python/tools/transformers/fusion_group_norm.py b/onnxruntime/python/tools/transformers/fusion_group_norm.py
index a4491d29b3..cd7dc7017c 100644
--- a/onnxruntime/python/tools/transformers/fusion_group_norm.py
+++ b/onnxruntime/python/tools/transformers/fusion_group_norm.py
@@ -88,13 +88,17 @@ class FusionGroupNorm(Fusion):
         if instance_norm_bias is None:
             return
 
-        if not (
-            len(instance_norm_scale.shape) == 1
-            and len(instance_norm_bias.shape) == 1
-            and instance_norm_scale.shape == instance_norm_bias.shape
-            and instance_norm_scale.shape[0] == 32
-        ):
-            logger.info("InstanceNormalization groups=%d", instance_norm_scale.shape[0])
+        # Only groups=32 is supported in GroupNorm kernel. Check the scale and bias is 1D tensor with shape [32].
+        if not (len(instance_norm_scale.shape) == 1 and instance_norm_scale.shape[0] == 32):
+            logger.debug(
+                "Skip GroupNorm fusion since scale shape is expected to be [32], Got %s", str(instance_norm_scale.shape)
+            )
+            return
+
+        if not (len(instance_norm_bias.shape) == 1 and instance_norm_bias.shape[0] == 32):
+            logger.debug(
+                "Skip GroupNorm fusion since bias shape is expected to be [32], Got %s", str(instance_norm_bias.shape)
+            )
             return
 
         if not np.allclose(np.ones_like(instance_norm_scale), instance_norm_scale):
@@ -105,7 +109,8 @@ class FusionGroupNorm(Fusion):
         group_norm_name = self.model.create_node_name("GroupNorm", name_prefix="GroupNorm")
 
         if weight_elements not in [320, 640, 960, 1280, 1920, 2560, 128, 256, 512]:
-            logger.info("GroupNorm channels=%d", weight_elements)
+            logger.info("Skip GroupNorm fusion since channels=%d is not supported.", weight_elements)
+            return
 
         self.add_initializer(
             name=group_norm_name + "_gamma",