Merge dfe1da4d36 into fa53f71ec0

library/train_util.py

@@ -4816,6 +4816,10 @@ 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

networks/lora.py

@@ -5,6 +5,7 @@
 
 import math
 import os
+from fnmatch import fnmatch
 from typing import Dict, List, Optional, Tuple, Type, Union
 from diffusers import AutoencoderKL
 from transformers import CLIPTextModel
@@ -1366,7 +1367,8 @@ class LoRANetwork(torch.nn.Module):
             org_module._lora_restored = False
             lora.enabled = False
 
-    def apply_max_norm_regularization(self, max_norm_value, device):
+    @torch.no_grad()
+    def apply_max_norm_regularization(self, max_norm, device, scale_map: dict[str, float]={}):
         downkeys = []
         upkeys = []
         alphakeys = []
@@ -1381,6 +1383,11 @@ class LoRANetwork(torch.nn.Module):
                 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 fnmatch(downkeys[i], key):
+                    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)
@@ -1404,7 +1411,7 @@ class LoRANetwork(torch.nn.Module):
                 keys_scaled += 1
                 state_dict[upkeys[i]] *= sqrt_ratio
                 state_dict[downkeys[i]] *= sqrt_ratio
-            scalednorm = updown.norm() * ratio
+            scalednorm: torch.Tensor = updown.norm() * ratio
             norms.append(scalednorm.item())
 
         return keys_scaled, sum(norms) / len(norms), max(norms)

train_network.py

@@ -12,6 +12,8 @@ import json
 from multiprocessing import Value
 import numpy as np
 
+import ast
+
 from tqdm import tqdm
 
 import torch
@@ -1444,8 +1446,9 @@ class NetworkTrainer:
                     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
                     )
                     mean_grad_norm = None
                     mean_combined_norm = None
@@ -1713,6 +1716,14 @@ class NetworkTrainer:
 
             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()
@@ -1816,6 +1827,12 @@ 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,