diff --git a/library/train_util.py b/library/train_util.py
index 72b5b24db..275143ba2 100644
--- a/library/train_util.py
+++ b/library/train_util.py
@@ -4537,6 +4537,10 @@ def read_config_from_file(args: argparse.Namespace, parser: argparse.ArgumentPar
             ignore_nesting_dict[section_name] = section_dict
             continue
 
+        if section_name == "scale_weight_norms_map":
+            ignore_nesting_dict[section_name] = section_dict
+            continue
+
         # if value is dict, save all key and value into one dict
         for key, value in section_dict.items():
             ignore_nesting_dict[key] = value
diff --git a/networks/lora.py b/networks/lora.py
index 6f33f1a1e..6af1a1f22 100644
--- a/networks/lora.py
+++ b/networks/lora.py
@@ -1366,7 +1366,7 @@ def pre_calculation(self):
             org_module._lora_restored = False
             lora.enabled = False
 
-    def apply_max_norm_regularization(self, max_norm_value, device):
+    def apply_max_norm_regularization(self, max_norm, device, scale_map: dict[str, float]={}):
         downkeys = []
         upkeys = []
         alphakeys = []
@@ -1381,6 +1381,11 @@ def apply_max_norm_regularization(self, max_norm_value, device):
                 alphakeys.append(key.replace("lora_down.weight", "alpha"))
 
         for i in range(len(downkeys)):
+            max_norm_value = max_norm
+            for key in scale_map.keys():
+                if key in downkeys[i]:
+                    max_norm_value = scale_map[key]
+
             down = state_dict[downkeys[i]].to(device)
             up = state_dict[upkeys[i]].to(device)
             alpha = state_dict[alphakeys[i]].to(device)
diff --git a/train_network.py b/train_network.py
index 5e82b307c..a347f8768 100644
--- a/train_network.py
+++ b/train_network.py
@@ -10,6 +10,8 @@
 from typing import Any, List
 import toml
 
+import ast
+
 from tqdm import tqdm
 
 import torch
@@ -1260,8 +1262,9 @@ def remove_model(old_ckpt_name):
                     optimizer.zero_grad(set_to_none=True)
 
                 if args.scale_weight_norms:
+                    scale_map = args.scale_weight_norms_map if args.scale_weight_norms_map else {}
                     keys_scaled, mean_norm, maximum_norm = accelerator.unwrap_model(network).apply_max_norm_regularization(
-                        args.scale_weight_norms, accelerator.device
+                        args.scale_weight_norms, accelerator.device, scale_map=scale_map
                     )
                     max_mean_logs = {"Keys Scaled": keys_scaled, "Average key norm": mean_norm}
                 else:
@@ -1356,6 +1359,14 @@ def remove_model(old_ckpt_name):
 
             logger.info("model saved.")
 
+def parse_dict(input_str):
+    """Convert string input into a dictionary."""
+    try:
+        # Use ast.literal_eval to safely evaluate the string as a Python literal (dict)
+        return ast.literal_eval(input_str)
+    except ValueError:
+        raise argparse.ArgumentTypeError(f"Invalid dictionary format: {input_str}")
+
 
 def setup_parser() -> argparse.ArgumentParser:
     parser = argparse.ArgumentParser()
@@ -1458,6 +1469,12 @@ def setup_parser() -> argparse.ArgumentParser:
         default=None,
         help="Scale the weight of each key pair to help prevent overtraing via exploding gradients. (1 is a good starting point) / 重みの値をスケーリングして勾配爆発を防ぐ（1が初期値としては適当）",
     )
+    parser.add_argument(
+        "--scale_weight_norms_map",
+        type=parse_dict,
+        default="{}",
+        help="Scale the weight of each key pair to help prevent overtraing via exploding gradients. (1 is a good starting point) / 重みの値をスケーリングして勾配爆発を防ぐ（1が初期値としては適当）",
+    )
     parser.add_argument(
         "--base_weights",
         type=str,