diff --git a/extensions-builtin/Lora/network_oft.py b/extensions-builtin/Lora/network_oft.py
index e4aa082b7..93402bb28 100644
--- a/extensions-builtin/Lora/network_oft.py
+++ b/extensions-builtin/Lora/network_oft.py
@@ -2,7 +2,6 @@ import torch
 import network
 from lyco_helpers import factorization
 from einops import rearrange
-from modules import devices
 
 
 class ModuleTypeOFT(network.ModuleType):
@@ -54,58 +53,12 @@ class NetworkModuleOFT(network.NetworkModule):
             raise ValueError("sd_module must be Linear or Conv")
 
         if self.is_kohya:
-            #self.num_blocks = self.dim
-            #self.block_size = self.out_dim // self.num_blocks
-            #self.block_size = self.dim
-            #self.num_blocks = self.out_dim // self.block_size
             self.constraint = self.alpha * self.out_dim
             self.num_blocks, self.block_size = factorization(self.out_dim, self.dim)
         else:
             self.constraint = None
             self.block_size, self.num_blocks = factorization(self.out_dim, self.dim)
 
-        if is_other_linear:
-            self.lin_module = self.create_module(weights.w, "oft_diag", none_ok=True)
-
-
-    def create_module(self, weights, key, none_ok=False):
-        weight = weights.get(key)
-
-        if weight is None and none_ok:
-            return None
-
-        is_linear = type(self.sd_module) in [torch.nn.Linear, torch.nn.modules.linear.NonDynamicallyQuantizableLinear, torch.nn.MultiheadAttention]
-        is_conv = type(self.sd_module) in [torch.nn.Conv2d]
-
-        if is_linear:
-            weight = weight.reshape(weight.shape[0], -1)
-            module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
-        elif is_conv and key == "lora_down.weight" or key == "dyn_up":
-            if len(weight.shape) == 2:
-                weight = weight.reshape(weight.shape[0], -1, 1, 1)
-
-            if weight.shape[2] != 1 or weight.shape[3] != 1:
-                module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], self.sd_module.kernel_size, self.sd_module.stride, self.sd_module.padding, bias=False)
-            else:
-                module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False)
-        elif is_conv and key == "lora_mid.weight":
-            module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], self.sd_module.kernel_size, self.sd_module.stride, self.sd_module.padding, bias=False)
-        elif is_conv and key == "lora_up.weight" or key == "dyn_down":
-            module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False)
-        else:
-            raise AssertionError(f'Lora layer {self.network_key} matched a layer with unsupported type: {type(self.sd_module).__name__}')
-
-        with torch.no_grad():
-            if weight.shape != module.weight.shape:
-                weight = weight.reshape(module.weight.shape)
-            module.weight.copy_(weight)
-
-        module.to(device=devices.cpu, dtype=devices.dtype)
-        module.weight.requires_grad_(False)
-
-        return module
-
-
     def merge_weight(self, R_weight, org_weight):
         R_weight = R_weight.to(org_weight.device, dtype=org_weight.dtype)
         if org_weight.dim() == 4: