diff --git a/api/onnx_web/convert/diffusion/lora.py b/api/onnx_web/convert/diffusion/lora.py
index 5ae5ea14a..38be5f78a 100644
--- a/api/onnx_web/convert/diffusion/lora.py
+++ b/api/onnx_web/convert/diffusion/lora.py
@@ -20,6 +20,25 @@
 logger = getLogger(__name__)
 
 
+def sum_weights(a: np.ndarray, b: np.ndarray) -> np.ndarray:
+    logger.trace("summing weights with shapes: %s + %s", a.shape, b.shape)
+
+    # get the kernel size from the tensor with the higher rank
+    if len(a.shape) > len(b.shape):
+        kernel = a.shape[-2:]
+        hr = a
+        lr = b
+    else:
+        kernel = b.shape[-2:]
+        hr = b
+        lr = a
+
+    if kernel == (1, 1):
+        lr = np.expand_dims(lr, axis=(2, 3))
+
+    return hr + lr
+
+
 def buffer_external_data_tensors(
     model: ModelProto,
 ) -> Tuple[ModelProto, List[Tuple[str, OrtValue]]]:
@@ -150,14 +169,8 @@ def blend_loras(
 
                 np_weights *= lora_weight
                 if base_key in blended:
-                    blended_weights = blended[base_key]
-                    logger.trace("summing LoHA weights: %s + %s", blended_weights.shape, np_weights.shape)
-
-                    if blended_weights.shape != np_weights.shape and kernel == (1, 1):
-                        logger.debug("expanding mismatched weights for 1x1 kernel")
-                        blended[base_key] = np.expand_dims(blended_weights, axis=(2, 3))
-
-                    blended[base_key] += np_weights
+                    logger.trace("summing LoHA weights: %s + %s", blended[base_key].shape, np_weights.shape)
+                    blended[base_key] += sum_weights(blended[base_key], np_weights)
                 else:
                     blended[base_key] = np_weights
             elif ".lora_down" in key and lora_prefix in key:
@@ -265,14 +278,8 @@ def blend_loras(
 
                 np_weights *= lora_weight
                 if base_key in blended:
-                    blended_weights = blended[base_key]
-                    logger.trace("summing weights: %s + %s", blended_weights.shape, np_weights.shape)
-
-                    if blended_weights.shape != np_weights.shape and kernel == (1, 1):
-                        logger.debug("expanding mismatched weights for 1x1 kernel")
-                        blended[base_key] = np.expand_dims(blended_weights, axis=(2, 3))
-
-                    blended[base_key] += np_weights
+                    logger.trace("summing weights: %s + %s", blended[base_key].shape, np_weights.shape)
+                    blended[base_key] = sum_weights(blended[base_key], np_weights)
                 else:
                     blended[base_key] = np_weights