Fix bad merge

kohya_gui/class_tensorboard.py

@@ -113,4 +113,4 @@ def gradio_interface(self):
                 self.stop_tensorboard,
                 outputs=[button_start_tensorboard, button_stop_tensorboard],
                 show_progress=False,
-            )
+            )

kohya_gui/dreambooth_gui.py

@@ -510,12 +510,12 @@ def train_model(
         vae=vae,
         dataset_config=dataset_config,
     ):
-        return
+        return TRAIN_BUTTON_VISIBLE
 
     if not print_only and check_if_model_exist(
         output_name, output_dir, save_model_as, headless=headless
     ):
-        return
+        return TRAIN_BUTTON_VISIBLE
 
     if dataset_config:
         log.info(
@@ -934,25 +934,13 @@ def dreambooth_tab(
                     "Stop training", visible=False, variant="stop"
                 )
 
-            button_print = gr.Button("Print training command")
+        with gr.Column(), gr.Group():
+            with gr.Row():
+                button_print = gr.Button("Print training command")
 
         # Setup gradio tensorboard buttons
         with gr.Column(), gr.Group():
-            (
-                button_start_tensorboard,
-                button_stop_tensorboard,
-            ) = gradio_tensorboard()
-
-        button_start_tensorboard.click(
-            start_tensorboard,
-            inputs=[dummy_headless, folders.logging_dir],
-            show_progress=False,
-        )
-
-        button_stop_tensorboard.click(
-            stop_tensorboard,
-            show_progress=False,
-        )
+            TensorboardManager(headless=headless, logging_dir=folders.logging_dir)
 
         settings_list = [
             source_model.pretrained_model_name_or_path,
@@ -1142,4 +1130,4 @@ def dreambooth_tab(
             folders.reg_data_dir,
             folders.output_dir,
             folders.logging_dir,
-        )
+        )

kohya_gui/finetune_gui.py

@@ -548,12 +548,12 @@ def train_model(
         resume=resume,
         dataset_config=dataset_config,
     ):
-        return
+        return TRAIN_BUTTON_VISIBLE
 
     if not print_only and check_if_model_exist(
         output_name, output_dir, save_model_as, headless
     ):
-        return
+        return TRAIN_BUTTON_VISIBLE
 
     if dataset_config:
         log.info(
@@ -1086,24 +1086,13 @@ def list_presets(path):
                     "Stop training", visible=False, variant="stop"
                 )
 
-            button_print = gr.Button("Print training command")
+        with gr.Column(), gr.Group():
+            with gr.Row():
+                button_print = gr.Button("Print training command")
 
         # Setup gradio tensorboard buttons
         with gr.Column(), gr.Group():
-            (
-                button_start_tensorboard,
-                button_stop_tensorboard,
-            ) = gradio_tensorboard()
-
-        button_start_tensorboard.click(
-            start_tensorboard,
-            inputs=[dummy_headless, logging_dir],
-        )
-
-        button_stop_tensorboard.click(
-            stop_tensorboard,
-            show_progress=False,
-        )
+            TensorboardManager(headless=headless, logging_dir=folders.logging_dir)
 
         settings_list = [
             source_model.pretrained_model_name_or_path,
@@ -1323,4 +1312,4 @@ def list_presets(path):
         if os.path.exists(top_level_path):
             with open(os.path.join(top_level_path), "r", encoding="utf8") as file:
                 guides_top_level = file.read() + "\n"
-            gr.Markdown(guides_top_level)
+            gr.Markdown(guides_top_level)

kohya_gui/lora_gui.py

@@ -55,7 +55,7 @@
 
 button_run = gr.Button("Start training", variant="primary")
 
-button_stop_training = gr.Button("Stop training")
+button_stop_training = gr.Button("Stop training", visible=False)
 
 document_symbol = "\U0001F4C4"  # 📄
 
@@ -713,14 +713,14 @@ def train_model(
         lora_network_weights=lora_network_weights,
         dataset_config=dataset_config,
     ):
-        return
+        return TRAIN_BUTTON_VISIBLE
 
     if int(bucket_reso_steps) < 1:
         output_message(
             msg="Bucket resolution steps need to be greater than 0",
             headless=headless,
         )
-        return
+        return TRAIN_BUTTON_VISIBLE
 
     # if noise_offset == "":
     #     noise_offset = 0
@@ -730,7 +730,7 @@ def train_model(
             msg="Noise offset need to be a value between 0 and 1",
             headless=headless,
         )
-        return
+        return TRAIN_BUTTON_VISIBLE
 
     if output_dir != "":
         if not os.path.exists(output_dir):
@@ -746,7 +746,7 @@ def train_model(
     if not print_only and check_if_model_exist(
         output_name, output_dir, save_model_as, headless=headless
     ):
-        return
+        return TRAIN_BUTTON_VISIBLE
 
     # If string is empty set string to 0.
     # if text_encoder_lr == "":
@@ -1015,7 +1015,7 @@ def train_model(
     # Sets flags for training specific components based on the provided learning rates.
     if float(learning_rate) == unet_lr_float == text_encoder_lr_float == 0:
         output_message(msg="Please input learning rate values.", headless=headless)
-        return
+        return TRAIN_BUTTON_VISIBLE
     # Flag to train text encoder only if its learning rate is non-zero and unet's is zero.
     network_train_text_encoder_only = text_encoder_lr_float != 0 and unet_lr_float == 0
     # Flag to train unet only if its learning rate is non-zero and text encoder's is zero.
@@ -2081,25 +2081,13 @@ def update_LoRA_settings(
                     "Stop training", visible=False, variant="stop"
                 )
 
-            button_print = gr.Button("Print training command")
+        with gr.Column(), gr.Group():
+            with gr.Row():
+                button_print = gr.Button("Print training command")
 
         # Setup gradio tensorboard buttons
         with gr.Column(), gr.Group():
-            (
-                button_start_tensorboard,
-                button_stop_tensorboard,
-            ) = gradio_tensorboard()
-
-        button_start_tensorboard.click(
-            start_tensorboard,
-            inputs=[dummy_headless, folders.logging_dir],
-            show_progress=False,
-        )
-
-        button_stop_tensorboard.click(
-            stop_tensorboard,
-            show_progress=False,
-        )
+            TensorboardManager(headless=headless, logging_dir=folders.logging_dir)
 
         settings_list = [
             source_model.pretrained_model_name_or_path,
@@ -2352,4 +2340,4 @@ def update_LoRA_settings(
         folders.reg_data_dir,
         folders.output_dir,
         folders.logging_dir,
-    )
+    )

kohya_gui/textual_inversion_gui.py

@@ -507,20 +507,20 @@ def train_model(
         vae=vae,
         dataset_config=dataset_config,
     ):
-        return
+        return TRAIN_BUTTON_VISIBLE
 
     if token_string == "":
         output_message(msg="Token string is missing", headless=headless)
-        return
+        return TRAIN_BUTTON_VISIBLE
 
     if init_word == "":
         output_message(msg="Init word is missing", headless=headless)
-        return
+        return TRAIN_BUTTON_VISIBLE
 
     if not print_only and check_if_model_exist(
         output_name, output_dir, save_model_as, headless
     ):
-        return
+        return TRAIN_BUTTON_VISIBLE
 
     if dataset_config:
         log.info(
@@ -1046,25 +1046,13 @@ def list_embedding_files(path):
                     "Stop training", visible=False, variant="stop"
                 )
 
-            button_print = gr.Button("Print training command")
+        with gr.Column(), gr.Group():
+            with gr.Row():
+                button_print = gr.Button("Print training command")
 
         # Setup gradio tensorboard buttons
         with gr.Column(), gr.Group():
-            (
-                button_start_tensorboard,
-                button_stop_tensorboard,
-            ) = gradio_tensorboard()
-
-        button_start_tensorboard.click(
-            start_tensorboard,
-            inputs=[dummy_headless, folders.logging_dir],
-            show_progress=False,
-        )
-
-        button_stop_tensorboard.click(
-            stop_tensorboard,
-            show_progress=False,
-        )
+            TensorboardManager(headless=headless, logging_dir=folders.logging_dir)
 
         settings_list = [
             source_model.pretrained_model_name_or_path,
@@ -1246,4 +1234,4 @@ def list_embedding_files(path):
             folders.reg_data_dir,
             folders.output_dir,
             folders.logging_dir,
-        )
+        )

requirements_windows.txt

@@ -1,5 +1,5 @@
 bitsandbytes==0.43.0
 tensorboard
-tensorflow
+tensorflow>=2.16.1
 onnxruntime-gpu==1.17.1
 -r requirements.txt