diff --git a/README.md b/README.md
index c198d09..caef52e 100644
--- a/README.md
+++ b/README.md
@@ -213,6 +213,7 @@ This will store your a backup file with your current locally installed pip packa
     - Support extensions with upper cases for images for not Windows environment.
     - Fix `resize_lora.py` to work with LoRA with dynamic rank (including `conv_dim != network_dim`). Thanks to toshiaki!
     - Fix issue: https://github.com/bmaltais/kohya_ss/issues/406
+    - Add device support to LoRA extract.
 * 2023/03/19 (v21.2.5):
     - Fix basic captioning logic
     - Add possibility to not train TE in Dreamboot by setting `Step text encoder training` to -1.
diff --git a/dreambooth_gui.py b/dreambooth_gui.py
index c3458be..a72acff 100644
--- a/dreambooth_gui.py
+++ b/dreambooth_gui.py
@@ -338,12 +338,21 @@ def train_model(
         if os.path.isdir(os.path.join(train_data_dir, f)) and not f.startswith('.')
     ]
 
+    # Check if subfolders are present. If not let the user know and return
+    if not subfolders:
+        print('\033[33mNo subfolders were found in', train_data_dir, ' can\'t train\...033[0m')
+        return
+
     total_steps = 0
 
     # Loop through each subfolder and extract the number of repeats
     for folder in subfolders:
         # Extract the number of repeats from the folder name
-        repeats = int(folder.split('_')[0])
+        try:
+            repeats = int(folder.split('_')[0])
+        except ValueError:
+            print('\033[33mSubfolder', folder, 'does not have a proper repeat value, please correct the name or remove it... can\'t train...\033[0m')
+            continue
 
         # Count the number of images in the folder
         num_images = len(
@@ -356,13 +365,20 @@ def train_model(
                 or f.endswith('.webp')
             ]
         )
+        
+        if num_images == 0:
+            print(f'{folder} folder contain no images, skipping...')
+        else:
+            # Calculate the total number of steps for this folder
+            steps = repeats * num_images
+            total_steps += steps
 
-        # Calculate the total number of steps for this folder
-        steps = repeats * num_images
-        total_steps += steps
+            # Print the result
+            print('\033[33mFolder', folder, ':', steps, 'steps\033[0m')
 
-        # Print the result
-        print(f'Folder {folder}: {steps} steps')
+    if total_steps == 0:
+        print('\033[33mNo images were found in folder', train_data_dir, '... please rectify!\033[0m')
+        return
 
     # Print the result
     # print(f"{total_steps} total steps")
@@ -370,9 +386,7 @@ def train_model(
     if reg_data_dir == '':
         reg_factor = 1
     else:
-        print(
-            'Regularisation images are used... Will double the number of steps required...'
-        )
+        print('\033[94mRegularisation images are used... Will double the number of steps required...\033[0m')
         reg_factor = 2
 
     # calculate max_train_steps
diff --git a/library/common_gui.py b/library/common_gui.py
index 14c448c..0d37cc5 100644
--- a/library/common_gui.py
+++ b/library/common_gui.py
@@ -932,7 +932,8 @@ def gradio_advanced_training():
             label='VAE batch size',
             minimum=0,
             maximum=32,
-            value=0
+            value=0,
+            every=1
         )
     with gr.Row():
         save_state = gr.Checkbox(label='Save training state', value=False)
diff --git a/library/extract_lora_gui.py b/library/extract_lora_gui.py
index 6088805..5f48686 100644
--- a/library/extract_lora_gui.py
+++ b/library/extract_lora_gui.py
@@ -23,6 +23,7 @@ def extract_lora(
     dim,
     v2,
     conv_dim,
+    device,
 ):
     # Check for caption_text_input
     if model_tuned == '':
@@ -50,6 +51,7 @@ def extract_lora(
     run_cmd += f' --model_org "{model_org}"'
     run_cmd += f' --model_tuned "{model_tuned}"'
     run_cmd += f' --dim {dim}'
+    run_cmd += f' --device {device}'
     if conv_dim > 0:
         run_cmd += f' --conv_dim {conv_dim}'
     if v2:
@@ -148,6 +150,15 @@ def gradio_extract_lora_tab():
                 interactive=True,
             )
             v2 = gr.Checkbox(label='v2', value=False, interactive=True)
+            device = gr.Dropdown(
+                label='Device',
+                choices=[
+                    'cpu',
+                    'cuda',
+                ],
+                value='cuda',
+                interactive=True,
+            )
 
         extract_button = gr.Button('Extract LoRA model')
 
@@ -161,6 +172,7 @@ def gradio_extract_lora_tab():
                 dim,
                 v2,
                 conv_dim,
+                device
             ],
             show_progress=False,
         )
diff --git a/networks/extract_lora_from_models.py b/networks/extract_lora_from_models.py
index 9aa2848..783fa1b 100644
--- a/networks/extract_lora_from_models.py
+++ b/networks/extract_lora_from_models.py
@@ -11,8 +11,8 @@ import library.model_util as model_util
 import lora
 
 
-CLAMP_QUANTILE = 0.99
-MIN_DIFF = 1e-6
+CLAMP_QUANTILE = 1
+MIN_DIFF = 1e-8
 
 
 def save_to_file(file_name, model, state_dict, dtype):
@@ -121,12 +121,12 @@ def svd(args):
 
       Vh = Vh[:rank, :]
 
-      dist = torch.cat([U.flatten(), Vh.flatten()])
-      hi_val = torch.quantile(dist, CLAMP_QUANTILE)
-      low_val = -hi_val
+      # dist = torch.cat([U.flatten(), Vh.flatten()])
+      # hi_val = torch.quantile(dist, CLAMP_QUANTILE)
+      # low_val = -hi_val
 
-      U = U.clamp(low_val, hi_val)
-      Vh = Vh.clamp(low_val, hi_val)
+      # U = U.clamp(low_val, hi_val)
+      # Vh = Vh.clamp(low_val, hi_val)
 
       if conv2d:
         U = U.reshape(out_dim, rank, 1, 1)