diff --git a/networks/extract_control_net_lora.py b/networks/extract_control_net_lora.py
index d2c460ba..1c25b486 100644
--- a/networks/extract_control_net_lora.py
+++ b/networks/extract_control_net_lora.py
@@ -19,16 +19,16 @@ CLAMP_QUANTILE = 0.99
 MIN_DIFF = 1e-6
 
 
-def save_to_file(file_name, model, state_dict, dtype):
+def save_to_file(file_name, state_dict, dtype):
   if dtype is not None:
     for key in list(state_dict.keys()):
       if type(state_dict[key]) == torch.Tensor:
         state_dict[key] = state_dict[key].to(dtype)
 
   if os.path.splitext(file_name)[1] == '.safetensors':
-    save_file(model, file_name)
+    save_file(state_dict, file_name)
   else:
-    torch.save(model, file_name)
+    torch.save(state_dict, file_name)
 
 
 def svd(args):
@@ -45,15 +45,15 @@ def svd(args):
 
   # Diffusersのキーに変換するため、original sdとcontrol sdからU-Netに重みを読み込む ###############
 
-  # original sdをDiffusersに読み込む
+  # original sdをDiffusersのU-Netに読み込む
   print(f"loading original SD model : {args.model_org}")
-  org_text_encoder, _, org_unet = model_util.load_models_from_stable_diffusion_checkpoint(args.v2, args.model_org)
+  _, _, org_unet = model_util.load_models_from_stable_diffusion_checkpoint(args.v2, args.model_org)
 
   org_sd = torch.load(args.model_org, map_location='cpu')
   if 'state_dict' in org_sd:
     org_sd = org_sd['state_dict']
 
-  # control sdからキー変換しつつU-Netに対応する部分のみ取り出す
+  # control sdからキー変換しつつU-Netに対応する部分のみ取り出し、DiffusersのuU-Netに読み込む
   print(f"loading control SD model : {args.model_tuned}")
 
   ctrl_sd = torch.load(args.model_tuned, map_location='cpu')
@@ -65,8 +65,8 @@ def svd(args):
         continue
       ctrl_unet_sd[unet_key] = ctrl_sd[key]
 
-  unet_config = model_util.create_unet_diffusers_config(False)
-  ctrl_unet_sd_du = model_util.convert_ldm_unet_checkpoint(False, ctrl_unet_sd, unet_config)
+  unet_config = model_util.create_unet_diffusers_config(args.v2)
+  ctrl_unet_sd_du = model_util.convert_ldm_unet_checkpoint(args.v2, ctrl_unet_sd, unet_config)
 
   # load weights to U-Net
   ctrl_unet = UNet2DConditionModel(**unet_config)
@@ -75,8 +75,6 @@ def svd(args):
 
   # LoRAに対応する部分のU-Netの重みを読み込む #################################
 
-  org_unet_sd_du = org_unet.state_dict()
-
   diffs = {}
   for (org_name, org_module), (ctrl_name, ctrl_module) in zip(org_unet.named_modules(), ctrl_unet.named_modules()):
     if org_module.__class__.__name__ != "Linear" and org_module.__class__.__name__ != "Conv2d":
@@ -172,6 +170,7 @@ def svd(args):
     if 'zero_convs' in key or 'input_hint_block' in key or 'middle_block_out' in key:
       ctrl_lora_sd[key] = value
 
+  # verify state dict by loading it
   info = lora_network.load_state_dict(ctrl_lora_sd)
   print(f"loading control lora sd: {info}")
 
@@ -183,7 +182,7 @@ def svd(args):
   # metadata = {"ss_network_dim": str(args.dim), "ss_network_alpha": str(args.dim)}
 
   # lora_network.save_weights(args.save_to, save_dtype, metadata)
-  save_file(ctrl_lora_sd, args.save_to)
+  save_to_file(args.save_to, ctrl_lora_sd, save_dtype)
   print(f"LoRA weights are saved to: {args.save_to}")