diff --git a/networks/lora_fa.py b/networks/lora_fa.py
index 919222ce..fcc503e8 100644
--- a/networks/lora_fa.py
+++ b/networks/lora_fa.py
@@ -1033,22 +1033,43 @@ class LoRANetwork(torch.nn.Module):
         return lr_weight
 
     # 二つのText Encoderに別々の学習率を設定できるようにするといいかも
-    def prepare_optimizer_params(self, text_encoder_lr, unet_lr, default_lr):
+    def prepare_optimizer_params(self, text_encoder_lr, unet_lr, default_lr, , unet_lora_plus_ratio=None, text_encoder_lora_plus_ratio=None):
         self.requires_grad_(True)
         all_params = []
 
-        def enumerate_params(loras: List[LoRAModule]):
-            params = []
+        def assemble_params(loras: List[LoRAModule], lr, lora_plus_ratio):
+            param_groups = {"lora": {}, "plus": {}}
             for lora in loras:
-                # params.extend(lora.parameters())
-                params.extend(lora.get_trainable_params())
+                for name, param in lora.get_trainable_named_params():
+                    if lora_plus_ratio is not None and "lora_up" in name:
+                        param_groups["plus"][f"{lora.lora_name}.{name}"] = param
+                    else:
+                        param_groups["lora"][f"{lora.lora_name}.{name}"] = param
+
+            # assigned_param_groups = ""
+            # for group in param_groups:
+            #     assigned_param_groups += f"{group}\n {list(param_groups[group].keys())}\n\n"
+            # logger.info(assigned_param_groups)
+
+            params = []
+            for key in param_groups.keys():
+                param_data = {"params": param_groups[key].values()}
+                if lr is not None:
+                    if key == "plus":
+                        param_data["lr"] = lr * lora_plus_ratio
+                    else:
+                        param_data["lr"] = lr
+
+                if ("lr" in param_data) and (param_data["lr"] == 0):
+                    continue
+
+                params.append(param_data)
+
             return params
 
         if self.text_encoder_loras:
-            param_data = {"params": enumerate_params(self.text_encoder_loras)}
-            if text_encoder_lr is not None:
-                param_data["lr"] = text_encoder_lr
-            all_params.append(param_data)
+            params = assemble_params(self.text_encoder_loras, text_encoder_lr, text_encoder_lora_plus_ratio)
+            all_params.extend(params)
 
         if self.unet_loras:
             if self.block_lr:
@@ -1062,21 +1083,15 @@ class LoRANetwork(torch.nn.Module):
 
                 # blockごとにパラメータを設定する
                 for idx, block_loras in block_idx_to_lora.items():
-                    param_data = {"params": enumerate_params(block_loras)}
-
                     if unet_lr is not None:
-                        param_data["lr"] = unet_lr * self.get_lr_weight(block_loras[0])
+                        params = assemble_params(block_loras, unet_lr * self.get_lr_weight(block_loras[0]), unet_lora_plus_ratio)
                     elif default_lr is not None:
-                        param_data["lr"] = default_lr * self.get_lr_weight(block_loras[0])
-                    if ("lr" in param_data) and (param_data["lr"] == 0):
-                        continue
-                    all_params.append(param_data)
+                        params = assemble_params(block_loras, default_lr * self.get_lr_weight(block_loras[0]), unet_lora_plus_ratio)
+                    all_params.extend(params)
 
             else:
-                param_data = {"params": enumerate_params(self.unet_loras)}
-                if unet_lr is not None:
-                    param_data["lr"] = unet_lr
-                all_params.append(param_data)
+                params = assemble_params(self.unet_loras, unet_lr, unet_lora_plus_ratio)
+                all_params.extend(params)
 
         return all_params
 
@@ -1093,6 +1108,9 @@ class LoRANetwork(torch.nn.Module):
     def get_trainable_params(self):
         return self.parameters()
 
+    def get_trainable_named_params(self):
+        return self.named_parameters()
+
     def save_weights(self, file, dtype, metadata):
         if metadata is not None and len(metadata) == 0:
             metadata = None