Feat/add max grad norm dreambooth (#3251)

* I've added a `max_grad_norm` parameter to the Dreambooth GUI.

This change adds support for the `max_grad_norm` parameter to the Dreambooth training GUI.

- The `max_grad_norm` option is now available in the 'Basic' training parameters section.
- The value you set in the GUI for `max_grad_norm` is passed to the training script via the generated TOML configuration file, similar to its existing implementation in the LoRA GUI.

* Fix mising entry for max_grad_norm

---------

Co-authored-by: google-labs-jules[bot] <161369871+google-labs-jules[bot]@users.noreply.github.com>

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
 

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",

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,