diff --git a/api/onnx_web/convert/diffusion/lora.py b/api/onnx_web/convert/diffusion/lora.py
index d10157ee..f2dbf75e 100644
--- a/api/onnx_web/convert/diffusion/lora.py
+++ b/api/onnx_web/convert/diffusion/lora.py
@@ -452,13 +452,16 @@ def blend_loras(
     else:
         lora_prefix = f"lora_{model_type}_"
 
-    blended: Dict[str, np.ndarray] = {}
+    layers = []
     for (lora_name, lora_weight), lora_model in zip(loras, lora_models):
         logger.debug("blending LoRA from %s with weight of %s", lora_name, lora_weight)
         if lora_model is None:
             logger.warning("unable to load tensor for LoRA")
             continue
 
+        blended: Dict[str, np.ndarray] = {}
+        layers.append(blended)
+
         for key in lora_model.keys():
             if ".hada_w1_a" in key and lora_prefix in key:
                 # LoHA
@@ -466,38 +469,36 @@ def blend_loras(
                     key, lora_prefix, lora_model, dtype
                 )
                 np_weights = np_weights * lora_weight
-                if base_key in blended:
-                    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:
-                    logger.trace(
-                        "adding LoHA weights: %s",
-                        np_weights.shape,
-                    )
-                    blended[base_key] = np_weights
+                logger.trace(
+                    "adding LoHA weights: %s",
+                    np_weights.shape,
+                )
+                blended[base_key] = np_weights
             elif ".lora_down" in key and lora_prefix in key:
                 # LoRA or LoCON
                 base_key, np_weights = blend_weights_lora(
                     key, lora_prefix, lora_model, dtype
                 )
                 np_weights = np_weights * lora_weight
-                if base_key in blended:
-                    logger.trace(
-                        "summing LoRA weights: %s + %s",
-                        blended[base_key].shape,
-                        np_weights.shape,
-                    )
-                    blended[base_key] = sum_weights(blended[base_key], np_weights)
-                else:
-                    logger.trace(
-                        "adding LoRA weights: %s",
-                        np_weights.shape,
-                    )
-                    blended[base_key] = np_weights
+                logger.trace(
+                    "adding LoRA weights: %s",
+                    np_weights.shape,
+                )
+                blended[base_key] = np_weights
+
+    # rewrite node names for XL and flatten layers
+    weights = Dict[str, np.ndarray] = {}
+
+    for blended in layers:
+        if xl:
+            nodes = list(base_model.graph.node)
+            blended = fix_xl_names(blended, nodes)
+
+        for key, value in blended.items():
+            if key in weights:
+                weights[key] = sum_weights(weights[key], value)
+            else:
+                weights[key] = value
 
     # fix node names once
     fixed_initializer_names = [
@@ -505,19 +506,14 @@ def blend_loras(
     ]
     fixed_node_names = [fix_node_name(node.name) for node in base_model.graph.node]
 
-    # rewrite node names for XL
-    if xl:
-        nodes = list(base_model.graph.node)
-        blended = fix_xl_names(blended, nodes)
-
     logger.debug(
         "updating %s of %s initializers",
-        len(blended.keys()),
+        len(weights.keys()),
         len(base_model.graph.initializer),
     )
 
     unmatched_keys = []
-    for base_key, weights in blended.items():
+    for base_key, weights in weights.items():
         conv_key = base_key + "_Conv"
         gemm_key = base_key + "_Gemm"
         matmul_key = base_key + "_MatMul"
@@ -579,7 +575,7 @@ def blend_loras(
         else:
             unmatched_keys.append(base_key)
 
-    logger.debug(
+    logger.trace(
         "node counts: %s -> %s, %s -> %s",
         len(fixed_initializer_names),
         len(base_model.graph.initializer),
@@ -588,7 +584,7 @@ def blend_loras(
     )
 
     if len(unmatched_keys) > 0:
-        logger.warning("could not find nodes for some keys: %s", unmatched_keys)
+        logger.warning("could not find nodes for some LoRA keys: %s", unmatched_keys)
 
     return base_model