diff --git a/extensions-builtin/Lora/network_oft.py b/extensions-builtin/Lora/network_oft.py
index f5f32c238..e0672ba6d 100644
--- a/extensions-builtin/Lora/network_oft.py
+++ b/extensions-builtin/Lora/network_oft.py
@@ -32,21 +32,27 @@ class NetworkModuleOFT(network.NetworkModule):
         self.org_module: list[torch.Module] = [self.sd_module]
         self.org_weight = self.org_module[0].weight.to(self.org_module[0].weight.device, copy=True)
         #self.org_weight = self.org_module[0].weight.to(devices.cpu, copy=True)
-        self.R = self.get_weight(self.oft_blocks)
+        init_multiplier = self.multiplier() * self.calc_scale()
+        self.last_multiplier = init_multiplier
+        self.R = self.get_weight(self.oft_blocks, init_multiplier)
 
         self.merged_weight = self.merge_weight()
         self.apply_to()
         self.merged = False
 
+        # weights_backup = getattr(self.org_module[0], 'network_weights_backup', None)
+        # if weights_backup is None:
+        #     self.org_module[0].network_weights_backup = self.org_weight
+
 
     def merge_weight(self):
-        org_sd = self.org_module[0].state_dict()
+        #org_sd = self.org_module[0].state_dict()
         R = self.R.to(self.org_weight.device, dtype=self.org_weight.dtype)
         if self.org_weight.dim() == 4:
             weight = torch.einsum("oihw, op -> pihw", self.org_weight, R)
         else:
             weight = torch.einsum("oi, op -> pi", self.org_weight, R)
-        org_sd['weight'] = weight
+        #org_sd['weight'] = weight
         # replace weight
         #self.org_module[0].load_state_dict(org_sd)
         return weight
@@ -74,6 +80,7 @@ class NetworkModuleOFT(network.NetworkModule):
         self.org_module[0].register_forward_hook(self.forward_hook)
 
     def get_weight(self, oft_blocks, multiplier=None):
+        multiplier = multiplier.to(oft_blocks.device, dtype=oft_blocks.dtype)
         constraint = self.constraint.to(oft_blocks.device, dtype=oft_blocks.dtype)
         block_Q = oft_blocks - oft_blocks.transpose(1, 2)
         norm_Q = torch.norm(block_Q.flatten())
@@ -81,9 +88,9 @@ class NetworkModuleOFT(network.NetworkModule):
         block_Q = block_Q * ((new_norm_Q + 1e-8) / (norm_Q + 1e-8))
         m_I = torch.eye(self.block_size, device=oft_blocks.device).unsqueeze(0).repeat(self.num_blocks, 1, 1)
         block_R = torch.matmul(m_I + block_Q, (m_I - block_Q).inverse())
-        #block_R_weighted = multiplier * block_R + (1 - multiplier) * I
-        #R = torch.block_diag(*block_R_weighted)
-        R = torch.block_diag(*block_R)
+        block_R_weighted = multiplier * block_R + (1 - multiplier) * m_I
+        R = torch.block_diag(*block_R_weighted)
+        #R = torch.block_diag(*block_R)
 
         return R
 
@@ -93,6 +100,8 @@ class NetworkModuleOFT(network.NetworkModule):
         #R = self.R.to(orig_weight.device, dtype=orig_weight.dtype)
         ##self.R = R
 
+        #R = self.R.to(orig_weight.device, dtype=orig_weight.dtype)
+        ##self.R = R
         #if orig_weight.dim() == 4:
         #    weight = torch.einsum("oihw, op -> pihw", orig_weight, R)
         #else:
@@ -103,19 +112,33 @@ class NetworkModuleOFT(network.NetworkModule):
         updown = torch.zeros_like(orig_weight, device=orig_weight.device, dtype=orig_weight.dtype)
         #updown = orig_weight
         output_shape = orig_weight.shape
-        #orig_weight = self.merged_weight.to(orig_weight.device, dtype=orig_weight.dtype)
+        orig_weight = self.merged_weight.to(orig_weight.device, dtype=orig_weight.dtype)
         #output_shape = self.oft_blocks.shape
 
         return self.finalize_updown(updown, orig_weight, output_shape)
     
     def pre_forward_hook(self, module, input):
-        if not self.merged:
+        multiplier = self.multiplier() * self.calc_scale()
+        if not multiplier==self.last_multiplier or not self.merged:
+
+        #if multiplier != self.last_multiplier or not self.merged:
+            self.R = self.get_weight(self.oft_blocks, multiplier)
+            self.last_multiplier = multiplier
+            self.merged_weight = self.merge_weight()
             self.replace_weight(self.merged_weight)
+        #elif not self.merged:
+        #    self.replace_weight(self.merged_weight)
 
     
     def forward_hook(self, module, args, output):
-        if self.merged:
-            pass
+        pass
+        #output = output * self.multiplier() * self.calc_scale()
+        #if len(args) > 0:
+        #    y = args[0]
+        #    output = output + y
+        #return output
+        #if self.merged:
+        #    pass
             #self.restore_weight()
         #print(f'Forward hook in {self.network_key} called')