diff --git a/networks/resize_lora.py b/networks/resize_lora.py
index 49b71481..d8a37da2 100644
--- a/networks/resize_lora.py
+++ b/networks/resize_lora.py
@@ -24,6 +24,7 @@ MIN_SV = 1e-6
 LORAFMT1 = ["lora_down", "lora_up"]
 LORAFMT2 = ["lora.down", "lora.up"]
 LORAFMT3 = ["lora_A", "lora_B"]
+LORAFMT4 = ["down", "up"]
 LORAFMT = LORAFMT1
 
 # Model save and load functions
@@ -209,13 +210,15 @@ def resize_lora_model(lora_sd, new_rank, new_conv_rank, save_dtype, device, dyna
         if network_alpha is None and "alpha" in key:
             network_alpha = value
         if (network_dim is None and len(value.size()) == 2
-            and (LORAFMT1[0] in key or LORAFMT2[0] in key or LORAFMT3[0] in key)):
+            and (LORAFMT1[0] in key or LORAFMT2[0] in key or LORAFMT3[0] in key or LORAFMT4[0] in key)):
             if LORAFMT1[0] in key:
                 LORAFMT = LORAFMT1
             elif LORAFMT2[0] in key:
                 LORAFMT = LORAFMT2
             elif LORAFMT3[0] in key:
                 LORAFMT = LORAFMT3
+            elif LORAFMT4[0] in key:
+                LORAFMT = LORAFMT4
             network_dim = value.size()[0]
         if network_alpha is not None and network_dim is not None:
             break
@@ -241,14 +244,17 @@ def resize_lora_model(lora_sd, new_rank, new_conv_rank, save_dtype, device, dyna
             weight_name = None
             if LORAFMT[0] in key:
                 block_down_name = key.rsplit(f".{LORAFMT[0]}", 1)[0]
-                weight_name = key.rsplit(".", 1)[-1]
+                if key.endswith(f".{LORAFMT[0]}"):
+                    weight_name = ""
+                else:
+                    weight_name = key.rsplit(f".{LORAFMT[0]}", 1)[-1]
                 lora_down_weight = value
             else:
                 continue
 
             # find corresponding lora_up and alpha
             block_up_name = block_down_name
-            lora_up_weight = lora_sd.get(block_up_name + f".{LORAFMT[1]}." + weight_name, None)
+            lora_up_weight = lora_sd.get(block_up_name + f".{LORAFMT[1]}" + weight_name, None)
             lora_alpha = lora_sd.get(block_down_name + ".alpha", None)
 
             weights_loaded = lora_down_weight is not None and lora_up_weight is not None
@@ -286,8 +292,8 @@ def resize_lora_model(lora_sd, new_rank, new_conv_rank, save_dtype, device, dyna
                     verbose_str += "\n"
 
                 new_alpha = param_dict["new_alpha"]
-                o_lora_sd[block_down_name + f".{LORAFMT[0]}.weight"] = param_dict[LORAFMT[0]].to(save_dtype).contiguous()
-                o_lora_sd[block_up_name + f".{LORAFMT[1]}.weight"] = param_dict[LORAFMT[1]].to(save_dtype).contiguous()
+                o_lora_sd[block_down_name + f".{LORAFMT[0]}" + weight_name] = param_dict[LORAFMT[0]].to(save_dtype).contiguous()
+                o_lora_sd[block_up_name + f".{LORAFMT[1]}" + weight_name] = param_dict[LORAFMT[1]].to(save_dtype).contiguous()
                 o_lora_sd[block_up_name + ".alpha"] = torch.tensor(param_dict["new_alpha"]).to(save_dtype)
 
                 block_down_name = None