diff --git a/extensions-builtin/Lora/lora_patches.py b/extensions-builtin/Lora/lora_patches.py
index 40291fe4f..7b0916e3a 100644
--- a/extensions-builtin/Lora/lora_patches.py
+++ b/extensions-builtin/Lora/lora_patches.py
@@ -22,12 +22,11 @@ class LoraPatches:
             return
         if "Model" in shared.opts.optimum_quanto_weights or "Text Encoder" in shared.opts.optimum_quanto_weights:
             from optimum import quanto
-            self.Linear_forward = patches.patch(__name__, quanto.nn.QLinear, 'forward', networks.network_QLinear_forward)
-            self.Conv2d_forward = patches.patch(__name__, quanto.nn.QConv2d, 'forward', networks.network_QConv2d_forward)
-        else:
-            self.Linear_forward = patches.patch(__name__, torch.nn.Linear, 'forward', networks.network_Linear_forward)
-            self.Conv2d_forward = patches.patch(__name__, torch.nn.Conv2d, 'forward', networks.network_Conv2d_forward)
+            self.QLinear_forward = patches.patch(__name__, quanto.nn.QLinear, 'forward', networks.network_QLinear_forward)
+            self.QConv2d_forward = patches.patch(__name__, quanto.nn.QConv2d, 'forward', networks.network_QConv2d_forward)
+        self.Linear_forward = patches.patch(__name__, torch.nn.Linear, 'forward', networks.network_Linear_forward)
         self.Linear_load_state_dict = patches.patch(__name__, torch.nn.Linear, '_load_from_state_dict', networks.network_Linear_load_state_dict)
+        self.Conv2d_forward = patches.patch(__name__, torch.nn.Conv2d, 'forward', networks.network_Conv2d_forward)
         self.Conv2d_load_state_dict = patches.patch(__name__, torch.nn.Conv2d, '_load_from_state_dict', networks.network_Conv2d_load_state_dict)
         self.GroupNorm_forward = patches.patch(__name__, torch.nn.GroupNorm, 'forward', networks.network_GroupNorm_forward)
         self.GroupNorm_load_state_dict = patches.patch(__name__, torch.nn.GroupNorm, '_load_from_state_dict', networks.network_GroupNorm_load_state_dict)
@@ -45,12 +44,11 @@ class LoraPatches:
             return
         if "Model" in shared.opts.optimum_quanto_weights or "Text Encoder" in shared.opts.optimum_quanto_weights:
             from optimum import quanto
-            self.Linear_forward = patches.undo(__name__, quanto.nn.QLinear, 'forward') # pylint: disable=E1128
-            self.Conv2d_forward = patches.undo(__name__, quanto.nn.QConv2d, 'forward') # pylint: disable=E1128
-        else:
-            self.Linear_forward = patches.undo(__name__, torch.nn.Linear, 'forward') # pylint: disable=E1128
-            self.Conv2d_forward = patches.undo(__name__, torch.nn.Conv2d, 'forward') # pylint: disable=E1128
+            self.QLinear_forward = patches.undo(__name__, quanto.nn.QLinear, 'forward') # pylint: disable=E1128
+            self.QConv2d_forward = patches.undo(__name__, quanto.nn.QConv2d, 'forward') # pylint: disable=E1128
+        self.Linear_forward = patches.undo(__name__, torch.nn.Linear, 'forward') # pylint: disable=E1128
         self.Linear_load_state_dict = patches.undo(__name__, torch.nn.Linear, '_load_from_state_dict') # pylint: disable=E1128
+        self.Conv2d_forward = patches.undo(__name__, torch.nn.Conv2d, 'forward') # pylint: disable=E1128
         self.Conv2d_load_state_dict = patches.undo(__name__, torch.nn.Conv2d, '_load_from_state_dict') # pylint: disable=E1128
         self.GroupNorm_forward = patches.undo(__name__, torch.nn.GroupNorm, 'forward') # pylint: disable=E1128
         self.GroupNorm_load_state_dict = patches.undo(__name__, torch.nn.GroupNorm, '_load_from_state_dict') # pylint: disable=E1128