diff --git a/gui-uv.bat b/gui-uv.bat
index 337a570..6a3e309 100644
--- a/gui-uv.bat
+++ b/gui-uv.bat
@@ -13,8 +13,6 @@ if %errorlevel% neq 0 (
         echo Okay, please install uv manually from https://astral.sh/uv and then re-run this script. Exiting.
         exit /b 1
     )
-) else (
-    echo uv is already installed.
 )
 endlocal
 
diff --git a/kohya_gui/class_basic_training.py b/kohya_gui/class_basic_training.py
index 7cda94b..883cdb9 100644
--- a/kohya_gui/class_basic_training.py
+++ b/kohya_gui/class_basic_training.py
@@ -230,13 +230,7 @@ class BasicTraining:
         """
         with gr.Row():
             # Initialize the maximum gradient norm slider
-            self.max_grad_norm = gr.Slider(
-                label="Max grad norm",
-                value=self.config.get("basic.max_grad_norm", 1.0),
-                minimum=0.0,
-                maximum=1.0,
-                interactive=True,
-            )
+            self.max_grad_norm = gr.Number(label='Max grad norm', value=1.0, interactive=True)
             # Initialize the learning rate scheduler extra arguments textbox
             self.lr_scheduler_args = gr.Textbox(
                 label="LR scheduler extra arguments",
diff --git a/kohya_gui/dreambooth_gui.py b/kohya_gui/dreambooth_gui.py
index 98c6a8f..b2702e2 100644
--- a/kohya_gui/dreambooth_gui.py
+++ b/kohya_gui/dreambooth_gui.py
@@ -132,6 +132,7 @@ def save_configuration(
     keep_tokens,
     lr_scheduler_num_cycles,
     lr_scheduler_power,
+    max_grad_norm,
     persistent_data_loader_workers,
     bucket_no_upscale,
     random_crop,
@@ -342,6 +343,7 @@ def open_configuration(
     keep_tokens,
     lr_scheduler_num_cycles,
     lr_scheduler_power,
+    max_grad_norm,
     persistent_data_loader_workers,
     bucket_no_upscale,
     random_crop,
@@ -547,6 +549,7 @@ def train_model(
     keep_tokens,
     lr_scheduler_num_cycles,
     lr_scheduler_power,
+    max_grad_norm,
     persistent_data_loader_workers,
     bucket_no_upscale,
     random_crop,
@@ -953,6 +956,7 @@ def train_model(
         "lr_warmup_steps": lr_warmup_steps,
         "masked_loss": masked_loss,
         "max_bucket_reso": max_bucket_reso,
+        "max_grad_norm": max_grad_norm,
         "max_timestep": max_timestep if max_timestep != 0 else None,
         "max_token_length": int(max_token_length),
         "max_train_epochs": (
@@ -1329,6 +1333,7 @@ def dreambooth_tab(
             advanced_training.keep_tokens,
             basic_training.lr_scheduler_num_cycles,
             basic_training.lr_scheduler_power,
+            basic_training.max_grad_norm,
             advanced_training.persistent_data_loader_workers,
             advanced_training.bucket_no_upscale,
             advanced_training.random_crop,