support older type hint

networks/lora.py

@@ -5,7 +5,7 @@
 
 import math
 import os
-from typing import List
+from typing import List, Union
 import numpy as np
 import torch
 import re
@@ -247,7 +247,7 @@ def create_network_from_weights(multiplier, file, vae, text_encoder, unet, weigh
 
 
 class LoRANetwork(torch.nn.Module):
-    # is it possible to apply conv_in and conv_out?
+    # is it possible to apply conv_in and conv_out? -> yes, newer LoCon supports it (^^;)
     UNET_TARGET_REPLACE_MODULE = ["Transformer2DModel", "Attention"]
     UNET_TARGET_REPLACE_MODULE_CONV2D_3X3 = ["ResnetBlock2D", "Downsample2D", "Upsample2D"]
     TEXT_ENCODER_TARGET_REPLACE_MODULE = ["CLIPAttention", "CLIPMLP"]
@@ -333,8 +333,8 @@ class LoRANetwork(torch.nn.Module):
 
         self.weights_sd = None
 
-        self.up_lr_weight: list[float] = None
-        self.down_lr_weight: list[float] = None
+        self.up_lr_weight: List[float] = None
+        self.down_lr_weight: List[float] = None
         self.mid_lr_weight: float = None
         self.block_lr = False
 
@@ -445,9 +445,9 @@ class LoRANetwork(torch.nn.Module):
     # 層別学習率用に層ごとの学習率に対する倍率を定義する
     def set_block_lr_weight(
         self,
-        up_lr_weight: list[float] | str = None,
+        up_lr_weight: Union[List[float], str] = None,
         mid_lr_weight: float = None,
-        down_lr_weight: list[float] | str = None,
+        down_lr_weight: Union[List[float], str] = None,
         zero_threshold: float = 0.0,
     ):
         # バラメータ未指定時は何もせず、今までと同じ動作とする
@@ -456,7 +456,7 @@ class LoRANetwork(torch.nn.Module):
 
         max_len = 12  # フルモデル相当でのup,downの層の数
 
-        def get_list(name) -> list[float]:
+        def get_list(name) -> List[float]:
             import math
 
             if name == "cosine":