diff --git a/networks/lora.py b/networks/lora.py
index ad8331c8..f60789f8 100644
--- a/networks/lora.py
+++ b/networks/lora.py
@@ -191,18 +191,18 @@ def create_network(multiplier, network_dim, network_alpha, vae, text_encoder, un
         conv_alpha=conv_alpha,
     )
 
-    up_weight=None
-    if 'up_weight' in kwargs:
-        up_weight = kwargs.get('up_weight',None)
-        if "," in up_weight:
-            up_weight = [float(s) for s in up_weight.split(",") if s]
-    down_weight=None
-    if 'down_weight' in kwargs:
-        down_weight = kwargs.get('down_weight',None)
-        if "," in down_weight:
-            down_weight = [float(s) for s in down_weight.split(",") if s]
-
-    network.set_stratified_lr_weight(up_weight,float(kwargs.get('mid_weight', 1.0)),down_weight,float(kwargs.get('stratified_zero_threshold', 0.0)))
+    up_lr_weight=None
+    if 'up_lr_weight' in kwargs:
+        up_lr_weight = kwargs.get('up_lr_weight',None)
+        if "," in up_lr_weight:
+            up_lr_weight = [float(s) for s in up_lr_weight.split(",") if s]
+    down_lr_weight=None
+    if 'down_lr_weight' in kwargs:
+        down_lr_weight = kwargs.get('down_lr_weight',None)
+        if "," in down_lr_weight:
+            down_lr_weight = [float(s) for s in down_lr_weight.split(",") if s]
+    mid_lr_weight=float(kwargs.get('mid_lr_weight', 1.0)) if 'mid_lr_weight' in kwargs else None
+    network.set_stratified_lr_weight(up_lr_weight,mid_lr_weight,down_lr_weight,float(kwargs.get('stratified_zero_threshold', 0.0)))
 
     return network
 
@@ -334,9 +334,9 @@ class LoRANetwork(torch.nn.Module):
             assert lora.lora_name not in names, f"duplicated lora name: {lora.lora_name}"
             names.add(lora.lora_name)
 
-        self.up_weight:list[float] = None
-        self.down_weight:list[float] = None
-        self.mid_weight:float = None
+        self.up_lr_weight:list[float] = None
+        self.down_lr_weight:list[float] = None
+        self.mid_lr_weight:float = None
 
     def set_multiplier(self, multiplier):
         self.multiplier = multiplier
@@ -433,7 +433,7 @@ class LoRANetwork(torch.nn.Module):
         print(f"weights are merged")
 
     # 層別学習率用に層ごとの学習率に対する倍率を定義する
-    def set_stratified_lr_weight(self, up_weight:list[float]|str=None, mid_weight:float=None, down_weight:list[float]|str=None, zero_threshold:float=0.0):
+    def set_stratified_lr_weight(self, up_lr_weight:list[float]|str=None, mid_lr_weight:float=None, down_lr_weight:list[float]|str=None, zero_threshold:float=0.0):
         max_len = 3 # attentions -> attentions -> attentions で3個のModuleに対して定義
         if self.apply_to_conv2d_3x3:
             max_len = 10 # (resnets -> {up,down}sampler -> attentions) x3 -> resnets で10個のModuleに対して定義
@@ -451,33 +451,33 @@ class LoRANetwork(torch.nn.Module):
             elif name=="zeros":
                 return [0.0] * max_len
             else:
-                print("不明なweightの引数 %s が使われました。\n\t有効な引数: cosine, sine, linear, reverse_linear, zeros"%(name))
+                print("不明なlr_weightの引数 %s が使われました。\n\t有効な引数: cosine, sine, linear, reverse_linear, zeros"%(name))
                 return None
 
-        if type(down_weight)==str:
-            down_weight=get_list(down_weight)
-        if type(up_weight)==str:
-            up_weight=get_list(up_weight)
+        if type(down_lr_weight)==str:
+            down_lr_weight=get_list(down_lr_weight)
+        if type(up_lr_weight)==str:
+            up_lr_weight=get_list(up_lr_weight)
 
-        if (up_weight != None and len(up_weight)>max_len) or (down_weight != None and len(down_weight)>max_len):
+        if (up_lr_weight != None and len(up_lr_weight)>max_len) or (down_lr_weight != None and len(down_lr_weight)>max_len):
             print("down_weightもしくはup_weightが長すぎます。%d個目以降のパラメータは無視されます。"%max_len)
-        if (up_weight != None and len(up_weight)<max_len) or (down_weight != None and len(down_weight)<max_len):
+        if (up_lr_weight != None and len(up_lr_weight)<max_len) or (down_lr_weight != None and len(down_lr_weight)<max_len):
             print("down_weightもしくはup_weightが短すぎます。%d個目までの不足したパラメータは1で補われます。"%max_len)
-            if down_weight != None and len(down_weight)<max_len:
-                down_weight = down_weight + [1.0] * (max_len - len(down_weight))
-            if up_weight != None and len(up_weight)<max_len:
-                up_weight = up_weight + [1.0] * (max_len - len(up_weight))
-        if (up_weight != None) or (mid_weight != None) or (down_weight != None):
+            if down_lr_weight != None and len(down_lr_weight)<max_len:
+                down_lr_weight = down_lr_weight + [1.0] * (max_len - len(down_lr_weight))
+            if up_lr_weight != None and len(up_lr_weight)<max_len:
+                up_lr_weight = up_lr_weight + [1.0] * (max_len - len(up_lr_weight))
+        if (up_lr_weight != None) or (mid_lr_weight != None) or (down_lr_weight != None):
             print("層別学習率を適用します。")
-            if (down_weight != None):
-                self.down_weight = [w if w > zero_threshold else 0 for w in down_weight[:max_len]]
-                print("down_weight(浅い層->深い層):",self.down_weight)
-            if (mid_weight != None):
-                self.mid_weight = mid_weight if mid_weight > zero_threshold else 0
-                print("mid_weight:",self.mid_weight)
-            if (up_weight != None):
-                self.up_weight = [w if w > zero_threshold else 0 for w in up_weight[:max_len]]
-                print("up_weight(深い層->浅い層):",self.up_weight)
+            if (down_lr_weight != None):
+                self.down_lr_weight = [w if w > zero_threshold else 0 for w in down_lr_weight[:max_len]]
+                print("down_lr_weight(浅い層->深い層):",self.down_lr_weight)
+            if (mid_lr_weight != None):
+                self.mid_lr_weight = mid_lr_weight if mid_lr_weight > zero_threshold else 0
+                print("mid_lr_weight:",self.mid_lr_weight)
+            if (up_lr_weight != None):
+                self.up_lr_weight = [w if w > zero_threshold else 0 for w in up_lr_weight[:max_len]]
+                print("up_lr_weight(深い層->浅い層):",self.up_lr_weight)
         return
 
     def get_stratified_lr_weight(self, lora:LoRAModule) -> float:
@@ -501,12 +501,12 @@ class LoRANetwork(torch.nn.Module):
                 if g[0]=="up":
                     idx=i-1
 
-            if (g[0]=="up") and (self.up_weight != None):
-                return self.up_weight[idx]
-            elif (g[0]=="down") and (self.down_weight != None):
-                return self.down_weight[idx]
-        elif ("mid_block_" in lora.lora_name) and (self.mid_weight != None):
-            return self.mid_weight
+            if (g[0]=="up") and (self.up_lr_weight != None):
+                return self.up_lr_weight[idx]
+            elif (g[0]=="down") and (self.down_lr_weight != None):
+                return self.down_lr_weight[idx]
+        elif ("mid_block_" in lora.lora_name) and (self.mid_lr_weight != None):
+            return self.mid_lr_weight
         # print({'params': lora.parameters(), 'lr':alpha*lr})
         return 1