Merge pull request #2312 from kohya-ss/dev

Merge dev to main

README-ja.md

@@ -50,6 +50,9 @@ Stable Diffusion等の画像生成モデルの学習、モデルによる画像
 
 ### 更新履歴
 
+- 次のリリースに含まれる予定の主な変更点は以下の通りです。リリース前の変更点は予告なく変更される可能性があります。
+    - Intel GPUの互換性を向上しました。[PR #2307](https://github.com/kohya-ss/sd-scripts/pull/2307) WhitePr氏に感謝します。
+
 - **Version 0.10.3 (2026-04-02):**
     - Animaでfp16で学習する際の安定性をさらに改善しました。[PR #2302](https://github.com/kohya-ss/sd-scripts/pull/2302) 問題をご報告いただいた方々に深く感謝します。
 

README.md

@@ -47,6 +47,9 @@ If you find this project helpful, please consider supporting its development via
 
 ### Change History
 
+- The following are the main changes planned for the next release. Please note that these changes may be subject to change without notice before the release.
+    - Improved compatibility with Intel GPUs. Thanks to WhitePr for [PR #2307](https://github.com/kohya-ss/sd-scripts/pull/2307).
+
 - **Version 0.10.3 (2026-04-02):**
     - Stability when training with fp16 on Anima has been further improved. See [PR #2302](https://github.com/kohya-ss/sd-scripts/pull/2302) for details. We deeply appreciate those who reported the issue.
 

library/ipex/__init__.py

@@ -1,6 +1,7 @@
 import os
 import sys
 import torch
+from packaging import version
 try:
     import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import
     has_ipex = True
@@ -8,7 +9,7 @@ except Exception:
     has_ipex = False
 from .hijacks import ipex_hijacks
 
-torch_version = float(torch.__version__[:3])
+torch_version = version.parse(torch.__version__)
 
 # pylint: disable=protected-access, missing-function-docstring, line-too-long
 
@@ -56,7 +57,6 @@ def ipex_init(): # pylint: disable=too-many-statements
             torch.cuda.__path__ = torch.xpu.__path__
             torch.cuda.set_stream = torch.xpu.set_stream
             torch.cuda.torch = torch.xpu.torch
-            torch.cuda.Union = torch.xpu.Union
             torch.cuda.__annotations__ = torch.xpu.__annotations__
             torch.cuda.__package__ = torch.xpu.__package__
             torch.cuda.__builtins__ = torch.xpu.__builtins__
@@ -64,14 +64,12 @@ def ipex_init(): # pylint: disable=too-many-statements
             torch.cuda.StreamContext = torch.xpu.StreamContext
             torch.cuda._lazy_call = torch.xpu._lazy_call
             torch.cuda.random = torch.xpu.random
-            torch.cuda._device = torch.xpu._device
             torch.cuda.__name__ = torch.xpu.__name__
-            torch.cuda._device_t = torch.xpu._device_t
             torch.cuda.__spec__ = torch.xpu.__spec__
             torch.cuda.__file__ = torch.xpu.__file__
             # torch.cuda.is_current_stream_capturing = torch.xpu.is_current_stream_capturing
 
-            if torch_version < 2.3:
+            if torch_version < version.parse("2.3"):
                 torch.cuda._initialization_lock = torch.xpu.lazy_init._initialization_lock
                 torch.cuda._initialized = torch.xpu.lazy_init._initialized
                 torch.cuda._is_in_bad_fork = torch.xpu.lazy_init._is_in_bad_fork
@@ -114,17 +112,22 @@ def ipex_init(): # pylint: disable=too-many-statements
                 torch.cuda.threading = torch.xpu.threading
                 torch.cuda.traceback = torch.xpu.traceback
 
-            if torch_version < 2.5:
+            if torch_version < version.parse("2.5"):
                 torch.cuda.os = torch.xpu.os
                 torch.cuda.Device = torch.xpu.Device
                 torch.cuda.warnings = torch.xpu.warnings
                 torch.cuda.classproperty = torch.xpu.classproperty
                 torch.UntypedStorage.cuda = torch.UntypedStorage.xpu
 
-            if torch_version < 2.7:
+            if torch_version < version.parse("2.7"):
                 torch.cuda.Tuple = torch.xpu.Tuple
                 torch.cuda.List = torch.xpu.List
 
+            if torch_version < version.parse("2.11"):
+                torch.cuda._device_t = torch.xpu._device_t
+                torch.cuda._device = torch.xpu._device
+                torch.cuda.Union = torch.xpu.Union
+
 
             # Memory:
             if 'linux' in sys.platform and "WSL2" in os.popen("uname -a").read():
@@ -160,7 +163,7 @@ def ipex_init(): # pylint: disable=too-many-statements
             torch.cuda.initial_seed = torch.xpu.initial_seed
 
             # C
-            if torch_version < 2.3:
+            if torch_version < version.parse("2.3"):
                 torch._C._cuda_getCurrentRawStream = ipex._C._getCurrentRawStream
                 ipex._C._DeviceProperties.multi_processor_count = ipex._C._DeviceProperties.gpu_subslice_count
                 ipex._C._DeviceProperties.major = 12

library/train_util.py

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

@@ -5,7 +5,6 @@
 
 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
@@ -1367,8 +1366,7 @@ class LoRANetwork(torch.nn.Module):
             org_module._lora_restored = False
             lora.enabled = False
 
-    @torch.no_grad()
-    def apply_max_norm_regularization(self, max_norm, device, scale_map: dict[str, float]={}):
+    def apply_max_norm_regularization(self, max_norm_value, device):
         downkeys = []
         upkeys = []
         alphakeys = []
@@ -1383,11 +1381,6 @@ 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)
@@ -1411,7 +1404,7 @@ class LoRANetwork(torch.nn.Module):
                 keys_scaled += 1
                 state_dict[upkeys[i]] *= sqrt_ratio
                 state_dict[downkeys[i]] *= sqrt_ratio
-            scalednorm: torch.Tensor = updown.norm() * ratio
+            scalednorm = updown.norm() * ratio
             norms.append(scalednorm.item())
 
         return keys_scaled, sum(norms) / len(norms), max(norms)

train_network.py

@@ -12,8 +12,6 @@ import json
 from multiprocessing import Value
 import numpy as np
 
-import ast
-
 from tqdm import tqdm
 
 import torch
@@ -1446,9 +1444,8 @@ 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, scale_map=scale_map
+                        args.scale_weight_norms, accelerator.device
                     )
                     mean_grad_norm = None
                     mean_combined_norm = None
@@ -1716,14 +1713,6 @@ 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()
@@ -1827,12 +1816,6 @@ 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,