Run blue

dreambooth_gui.py

@@ -95,9 +95,11 @@ def save_configuration(
     bucket_no_upscale,
     random_crop,
     bucket_reso_steps,
-    caption_dropout_every_n_epochs, caption_dropout_rate,
+    caption_dropout_every_n_epochs,
+    caption_dropout_rate,
     optimizer,
-    optimizer_args,noise_offset,
+    optimizer_args,
+    noise_offset,
 ):
     # Get list of function parameters and values
     parameters = list(locals().items())
@@ -194,9 +196,11 @@ def open_configuration(
     bucket_no_upscale,
     random_crop,
     bucket_reso_steps,
-    caption_dropout_every_n_epochs, caption_dropout_rate,
+    caption_dropout_every_n_epochs,
+    caption_dropout_rate,
     optimizer,
-    optimizer_args,noise_offset,
+    optimizer_args,
+    noise_offset,
 ):
     # Get list of function parameters and values
     parameters = list(locals().items())
@@ -272,9 +276,11 @@ def train_model(
     bucket_no_upscale,
     random_crop,
     bucket_reso_steps,
-    caption_dropout_every_n_epochs, caption_dropout_rate,
+    caption_dropout_every_n_epochs,
+    caption_dropout_rate,
     optimizer,
-    optimizer_args,noise_offset,
+    optimizer_args,
+    noise_offset,
 ):
     if pretrained_model_name_or_path == '':
         msgbox('Source model information is missing')
@@ -566,7 +572,8 @@ def dreambooth_tab(
             seed,
             caption_extension,
             cache_latents,
-            optimizer,optimizer_args,
+            optimizer,
+            optimizer_args,
         ) = gradio_training(
             learning_rate_value='1e-5',
             lr_scheduler_value='cosine',
@@ -624,7 +631,9 @@ def dreambooth_tab(
                 bucket_no_upscale,
                 random_crop,
                 bucket_reso_steps,
-                caption_dropout_every_n_epochs, caption_dropout_rate,noise_offset,
+                caption_dropout_every_n_epochs,
+                caption_dropout_rate,
+                noise_offset,
             ) = gradio_advanced_training()
             color_aug.change(
                 color_aug_changed,
@@ -648,15 +657,15 @@ def dreambooth_tab(
         )
 
     button_run = gr.Button('Train model', variant='primary')
-    
+
     # Setup gradio tensorboard buttons
     button_start_tensorboard, button_stop_tensorboard = gradio_tensorboard()
-    
+
     button_start_tensorboard.click(
         start_tensorboard,
         inputs=logging_dir,
     )
-    
+
     button_stop_tensorboard.click(
         stop_tensorboard,
     )
@@ -710,8 +719,11 @@ def dreambooth_tab(
         bucket_no_upscale,
         random_crop,
         bucket_reso_steps,
-        caption_dropout_every_n_epochs, caption_dropout_rate,
-        optimizer,optimizer_args,noise_offset,
+        caption_dropout_every_n_epochs,
+        caption_dropout_rate,
+        optimizer,
+        optimizer_args,
+        noise_offset,
     ]
 
     button_open_config.click(
@@ -773,16 +785,20 @@ def UI(**kwargs):
             )
 
     # Show the interface
-    launch_kwargs={}
+    launch_kwargs = {}
     if not kwargs.get('username', None) == '':
-        launch_kwargs["auth"] = (kwargs.get('username', None), kwargs.get('password', None))
+        launch_kwargs['auth'] = (
+            kwargs.get('username', None),
+            kwargs.get('password', None),
+        )
     if kwargs.get('server_port', 0) > 0:
-        launch_kwargs["server_port"] = kwargs.get('server_port', 0)
-    if kwargs.get('inbrowser', False):        
-        launch_kwargs["inbrowser"] = kwargs.get('inbrowser', False)
+        launch_kwargs['server_port'] = kwargs.get('server_port', 0)
+    if kwargs.get('inbrowser', False):
+        launch_kwargs['inbrowser'] = kwargs.get('inbrowser', False)
     print(launch_kwargs)
     interface.launch(**launch_kwargs)
 
+
 if __name__ == '__main__':
     # torch.cuda.set_per_process_memory_fraction(0.48)
     parser = argparse.ArgumentParser()
@@ -793,10 +809,20 @@ if __name__ == '__main__':
         '--password', type=str, default='', help='Password for authentication'
     )
     parser.add_argument(
-        '--server_port', type=int, default=0, help='Port to run the server listener on'
+        '--server_port',
+        type=int,
+        default=0,
+        help='Port to run the server listener on',
+    )
+    parser.add_argument(
+        '--inbrowser', action='store_true', help='Open in browser'
     )
-    parser.add_argument("--inbrowser", action="store_true", help="Open in browser")
 
     args = parser.parse_args()
 
-    UI(username=args.username, password=args.password, inbrowser=args.inbrowser, server_port=args.server_port)
+    UI(
+        username=args.username,
+        password=args.password,
+        inbrowser=args.inbrowser,
+        server_port=args.server_port,
+    )

finetune_gui.py

@@ -91,8 +91,11 @@ def save_configuration(
     bucket_no_upscale,
     random_crop,
     bucket_reso_steps,
-    caption_dropout_every_n_epochs, caption_dropout_rate,
-    optimizer,optimizer_args,noise_offset,
+    caption_dropout_every_n_epochs,
+    caption_dropout_rate,
+    optimizer,
+    optimizer_args,
+    noise_offset,
 ):
     # Get list of function parameters and values
     parameters = list(locals().items())
@@ -195,8 +198,11 @@ def open_config_file(
     bucket_no_upscale,
     random_crop,
     bucket_reso_steps,
-    caption_dropout_every_n_epochs, caption_dropout_rate,
-    optimizer,optimizer_args,noise_offset,
+    caption_dropout_every_n_epochs,
+    caption_dropout_rate,
+    optimizer,
+    optimizer_args,
+    noise_offset,
 ):
     # Get list of function parameters and values
     parameters = list(locals().items())
@@ -278,8 +284,11 @@ def train_model(
     bucket_no_upscale,
     random_crop,
     bucket_reso_steps,
-    caption_dropout_every_n_epochs, caption_dropout_rate,
-    optimizer,optimizer_args,noise_offset,
+    caption_dropout_every_n_epochs,
+    caption_dropout_rate,
+    optimizer,
+    optimizer_args,
+    noise_offset,
 ):
     # create caption json file
     if generate_caption_database:
@@ -585,7 +594,8 @@ def finetune_tab():
             seed,
             caption_extension,
             cache_latents,
-            optimizer,optimizer_args,
+            optimizer,
+            optimizer_args,
         ) = gradio_training(learning_rate_value='1e-5')
         with gr.Row():
             dataset_repeats = gr.Textbox(label='Dataset repeats', value=40)
@@ -617,7 +627,9 @@ def finetune_tab():
                 bucket_no_upscale,
                 random_crop,
                 bucket_reso_steps,
-                caption_dropout_every_n_epochs, caption_dropout_rate,noise_offset,
+                caption_dropout_every_n_epochs,
+                caption_dropout_rate,
+                noise_offset,
             ) = gradio_advanced_training()
             color_aug.change(
                 color_aug_changed,
@@ -631,15 +643,15 @@ def finetune_tab():
         )
 
     button_run = gr.Button('Train model', variant='primary')
-    
+
     # Setup gradio tensorboard buttons
     button_start_tensorboard, button_stop_tensorboard = gradio_tensorboard()
-    
+
     button_start_tensorboard.click(
         start_tensorboard,
         inputs=logging_dir,
     )
-    
+
     button_stop_tensorboard.click(
         stop_tensorboard,
     )
@@ -699,8 +711,11 @@ def finetune_tab():
         bucket_no_upscale,
         random_crop,
         bucket_reso_steps,
-        caption_dropout_every_n_epochs, caption_dropout_rate,
-        optimizer,optimizer_args,noise_offset,
+        caption_dropout_every_n_epochs,
+        caption_dropout_rate,
+        optimizer,
+        optimizer_args,
+        noise_offset,
     ]
 
     button_run.click(train_model, inputs=settings_list)
@@ -742,16 +757,19 @@ def UI(**kwargs):
             utilities_tab(enable_dreambooth_tab=False)
 
     # Show the interface
-    launch_kwargs={}
+    launch_kwargs = {}
     if not kwargs.get('username', None) == '':
-        launch_kwargs["auth"] = (kwargs.get('username', None), kwargs.get('password', None))
+        launch_kwargs['auth'] = (
+            kwargs.get('username', None),
+            kwargs.get('password', None),
+        )
     if kwargs.get('server_port', 0) > 0:
-        launch_kwargs["server_port"] = kwargs.get('server_port', 0)
-    if kwargs.get('inbrowser', False):        
-        launch_kwargs["inbrowser"] = kwargs.get('inbrowser', False)
+        launch_kwargs['server_port'] = kwargs.get('server_port', 0)
+    if kwargs.get('inbrowser', False):
+        launch_kwargs['inbrowser'] = kwargs.get('inbrowser', False)
     print(launch_kwargs)
     interface.launch(**launch_kwargs)
-        
+
 
 if __name__ == '__main__':
     # torch.cuda.set_per_process_memory_fraction(0.48)
@@ -763,10 +781,20 @@ if __name__ == '__main__':
         '--password', type=str, default='', help='Password for authentication'
     )
     parser.add_argument(
-        '--server_port', type=int, default=0, help='Port to run the server listener on'
+        '--server_port',
+        type=int,
+        default=0,
+        help='Port to run the server listener on',
+    )
+    parser.add_argument(
+        '--inbrowser', action='store_true', help='Open in browser'
     )
-    parser.add_argument("--inbrowser", action="store_true", help="Open in browser")
 
     args = parser.parse_args()
 
-    UI(username=args.username, password=args.password, inbrowser=args.inbrowser, server_port=args.server_port)
+    UI(
+        username=args.username,
+        password=args.password,
+        inbrowser=args.inbrowser,
+        server_port=args.server_port,
+    )

kohya_gui.py

@@ -53,15 +53,16 @@ def UI(**kwargs):
     inbrowser = kwargs.get('inbrowser', False)
     share = kwargs.get('share', False)
     if username and password:
-        launch_kwargs["auth"] = (username, password)
+        launch_kwargs['auth'] = (username, password)
     if server_port > 0:
-        launch_kwargs["server_port"] = server_port
+        launch_kwargs['server_port'] = server_port
     if inbrowser:
-        launch_kwargs["inbrowser"] = inbrowser
+        launch_kwargs['inbrowser'] = inbrowser
     if share:
-        launch_kwargs["share"] = share
+        launch_kwargs['share'] = share
     interface.launch(**launch_kwargs)
 
+
 if __name__ == '__main__':
     # torch.cuda.set_per_process_memory_fraction(0.48)
     parser = argparse.ArgumentParser()
@@ -72,11 +73,24 @@ if __name__ == '__main__':
         '--password', type=str, default='', help='Password for authentication'
     )
     parser.add_argument(
-        '--server_port', type=int, default=0, help='Port to run the server listener on'
+        '--server_port',
+        type=int,
+        default=0,
+        help='Port to run the server listener on',
+    )
+    parser.add_argument(
+        '--inbrowser', action='store_true', help='Open in browser'
+    )
+    parser.add_argument(
+        '--share', action='store_true', help='Share the gradio UI'
     )
-    parser.add_argument("--inbrowser", action="store_true", help="Open in browser")
-    parser.add_argument("--share", action="store_true", help="Share the gradio UI")
 
     args = parser.parse_args()
 
-    UI(username=args.username, password=args.password, inbrowser=args.inbrowser, server_port=args.server_port, share=args.share)
+    UI(
+        username=args.username,
+        password=args.password,
+        inbrowser=args.inbrowser,
+        server_port=args.server_port,
+        share=args.share,
+    )

library/common_gui.py

@@ -9,6 +9,7 @@ refresh_symbol = '\U0001f504'  # 🔄
 save_style_symbol = '\U0001f4be'  # 💾
 document_symbol = '\U0001F4C4'   # 📄
 
+
 def update_optimizer(my_data):
     if my_data.get('use_8bit_adam', False):
         my_data['optimizer'] = 'AdamW8bit'
@@ -86,13 +87,18 @@ def remove_doublequote(file_path):
 
     return file_path
 
+
 def set_legacy_8bitadam(optimizer, use_8bit_adam):
     if optimizer == 'AdamW8bit':
         # use_8bit_adam = True
-        return gr.Dropdown.update(value=optimizer), gr.Checkbox.update(value=True, interactive=False, visible=True)
+        return gr.Dropdown.update(value=optimizer), gr.Checkbox.update(
+            value=True, interactive=False, visible=True
+        )
     else:
         # use_8bit_adam = False
-        return gr.Dropdown.update(value=optimizer), gr.Checkbox.update(value=False, interactive=False, visible=True)
+        return gr.Dropdown.update(value=optimizer), gr.Checkbox.update(
+            value=False, interactive=False, visible=True
+        )
 
 
 def get_folder_path(folder_path=''):
@@ -489,14 +495,15 @@ def gradio_training(
                 'DAdaptation',
                 'Lion',
                 'SGDNesterov',
-                'SGDNesterov8bit'
+                'SGDNesterov8bit',
             ],
-            value="AdamW8bit",
+            value='AdamW8bit',
             interactive=True,
         )
     with gr.Row():
         optimizer_args = gr.Textbox(
-            label='Optimizer extra arguments', placeholder='(Optional) eg: relative_step=True scale_parameter=True warmup_init=True'
+            label='Optimizer extra arguments',
+            placeholder='(Optional) eg: relative_step=True scale_parameter=True warmup_init=True',
         )
     return (
         learning_rate,
@@ -549,11 +556,14 @@ def run_cmd_training(**kwargs):
         ' --cache_latents' if kwargs.get('cache_latents') else '',
         # ' --use_lion_optimizer' if kwargs.get('optimizer') == 'Lion' else '',
         f' --optimizer_type="{kwargs.get("optimizer", "AdamW")}"',
-        f' --optimizer_args {kwargs.get("optimizer_args", "")}' if not kwargs.get('optimizer_args') == '' else '',
+        f' --optimizer_args {kwargs.get("optimizer_args", "")}'
+        if not kwargs.get('optimizer_args') == ''
+        else '',
     ]
     run_cmd = ''.join(options)
     return run_cmd
 
+
 # # This function takes a dictionary of keyword arguments and returns a string that can be used to run a command-line training script
 # def run_cmd_training(**kwargs):
 #     arg_map = {
@@ -611,7 +621,9 @@ def gradio_advanced_training():
         )
     with gr.Row():
         # This use_8bit_adam element should be removed in a future release as it is no longer used
-        use_8bit_adam = gr.Checkbox(label='Use 8bit adam', value=False, visible=False)
+        use_8bit_adam = gr.Checkbox(
+            label='Use 8bit adam', value=False, visible=False
+        )
         xformers = gr.Checkbox(label='Use xformers', value=True)
         color_aug = gr.Checkbox(label='Color augmentation', value=False)
         flip_aug = gr.Checkbox(label='Flip augmentation', value=False)
@@ -628,17 +640,13 @@ def gradio_advanced_training():
         noise_offset = gr.Textbox(
             label='Noise offset (0 - 1)', placeholder='(Oprional) eg: 0.1'
         )
-        
+
     with gr.Row():
         caption_dropout_every_n_epochs = gr.Number(
-            label="Dropout caption every n epochs",
-            value=0
+            label='Dropout caption every n epochs', value=0
         )
         caption_dropout_rate = gr.Slider(
-            label="Rate of caption dropout",
-            value=0,
-            minimum=0,
-            maximum=1
+            label='Rate of caption dropout', value=0, minimum=0, maximum=1
         )
     with gr.Row():
         save_state = gr.Checkbox(label='Save training state', value=False)
@@ -676,7 +684,9 @@ def gradio_advanced_training():
         bucket_no_upscale,
         random_crop,
         bucket_reso_steps,
-        caption_dropout_every_n_epochs, caption_dropout_rate,noise_offset,
+        caption_dropout_every_n_epochs,
+        caption_dropout_rate,
+        noise_offset,
     )
 
 
@@ -706,11 +716,9 @@ def run_cmd_advanced_training(**kwargs):
         f' --caption_dropout_rate="{kwargs.get("caption_dropout_rate", "")}"'
         if float(kwargs.get('caption_dropout_rate', 0)) > 0
         else '',
-        
         f' --bucket_reso_steps={int(kwargs.get("bucket_reso_steps", 1))}'
         if int(kwargs.get('bucket_reso_steps', 64)) >= 1
         else '',
-        
         ' --save_state' if kwargs.get('save_state') else '',
         ' --mem_eff_attn' if kwargs.get('mem_eff_attn') else '',
         ' --color_aug' if kwargs.get('color_aug') else '',
@@ -734,6 +742,7 @@ def run_cmd_advanced_training(**kwargs):
     run_cmd = ''.join(options)
     return run_cmd
 
+
 # def run_cmd_advanced_training(**kwargs):
 #     arg_map = {
 #         'max_train_epochs': ' --max_train_epochs="{}"',
@@ -763,4 +772,4 @@ def run_cmd_advanced_training(**kwargs):
 
 #     cmd = ''.join(options)
 
-#     return cmd
+#     return cmd

library/tensorboard_gui.py

@@ -4,43 +4,49 @@ from easygui import msgbox
 import subprocess
 import time
 
-tensorboard_proc = None # I know... bad but heh
+tensorboard_proc = None   # I know... bad but heh
+
 
 def start_tensorboard(logging_dir):
     global tensorboard_proc
-    
+
     if not os.listdir(logging_dir):
-        print("Error: log folder is empty")
-        msgbox(msg="Error: log folder is empty")
+        print('Error: log folder is empty')
+        msgbox(msg='Error: log folder is empty')
         return
-    
+
     run_cmd = f'tensorboard.exe --logdir "{logging_dir}"'
-    
+
     print(run_cmd)
     if tensorboard_proc is not None:
-        print("Tensorboard is already running. Terminating existing process before starting new one...")
+        print(
+            'Tensorboard is already running. Terminating existing process before starting new one...'
+        )
         stop_tensorboard()
-    
+
     # Start background process
-    print('Starting tensorboard...')    
+    print('Starting tensorboard...')
     tensorboard_proc = subprocess.Popen(run_cmd)
-    
+
     # Wait for some time to allow TensorBoard to start up
     time.sleep(5)
-    
+
     # Open the TensorBoard URL in the default browser
     print('Opening tensorboard url in browser...')
     import webbrowser
+
     webbrowser.open('http://localhost:6006')
-    
+
+
 def stop_tensorboard():
     print('Stopping tensorboard process...')
     tensorboard_proc.kill()
     print('...process stopped')
-    
+
+
 def gradio_tensorboard():
     with gr.Row():
         button_start_tensorboard = gr.Button('Start tensorboard')
         button_stop_tensorboard = gr.Button('Stop tensorboard')
-        
-    return(button_start_tensorboard, button_stop_tensorboard)
+
+    return (button_start_tensorboard, button_stop_tensorboard)

library/utilities.py

@@ -50,16 +50,19 @@ def UI(**kwargs):
         utilities_tab()
 
     # Show the interface
-    launch_kwargs={}
+    launch_kwargs = {}
     if not kwargs.get('username', None) == '':
-        launch_kwargs["auth"] = (kwargs.get('username', None), kwargs.get('password', None))
+        launch_kwargs['auth'] = (
+            kwargs.get('username', None),
+            kwargs.get('password', None),
+        )
     if kwargs.get('server_port', 0) > 0:
-        launch_kwargs["server_port"] = kwargs.get('server_port', 0)
-    if kwargs.get('inbrowser', False):        
-        launch_kwargs["inbrowser"] = kwargs.get('inbrowser', False)
+        launch_kwargs['server_port'] = kwargs.get('server_port', 0)
+    if kwargs.get('inbrowser', False):
+        launch_kwargs['inbrowser'] = kwargs.get('inbrowser', False)
     print(launch_kwargs)
     interface.launch(**launch_kwargs)
-        
+
 
 if __name__ == '__main__':
     # torch.cuda.set_per_process_memory_fraction(0.48)
@@ -71,10 +74,20 @@ if __name__ == '__main__':
         '--password', type=str, default='', help='Password for authentication'
     )
     parser.add_argument(
-        '--server_port', type=int, default=0, help='Port to run the server listener on'
+        '--server_port',
+        type=int,
+        default=0,
+        help='Port to run the server listener on',
+    )
+    parser.add_argument(
+        '--inbrowser', action='store_true', help='Open in browser'
     )
-    parser.add_argument("--inbrowser", action="store_true", help="Open in browser")
 
     args = parser.parse_args()
 
-    UI(username=args.username, password=args.password, inbrowser=args.inbrowser, server_port=args.server_port)
+    UI(
+        username=args.username,
+        password=args.password,
+        inbrowser=args.inbrowser,
+        server_port=args.server_port,
+    )

lora_gui.py

@@ -47,6 +47,7 @@ refresh_symbol = '\U0001f504'  # 🔄
 save_style_symbol = '\U0001f4be'  # 💾
 document_symbol = '\U0001F4C4'   # 📄
 
+
 def save_configuration(
     save_as,
     file_path,
@@ -105,9 +106,11 @@ def save_configuration(
     bucket_no_upscale,
     random_crop,
     bucket_reso_steps,
-    caption_dropout_every_n_epochs, caption_dropout_rate,
+    caption_dropout_every_n_epochs,
+    caption_dropout_rate,
     optimizer,
-    optimizer_args,noise_offset,
+    optimizer_args,
+    noise_offset,
 ):
     # Get list of function parameters and values
     parameters = list(locals().items())
@@ -211,9 +214,11 @@ def open_configuration(
     bucket_no_upscale,
     random_crop,
     bucket_reso_steps,
-    caption_dropout_every_n_epochs, caption_dropout_rate,
+    caption_dropout_every_n_epochs,
+    caption_dropout_rate,
     optimizer,
-    optimizer_args,noise_offset,
+    optimizer_args,
+    noise_offset,
 ):
     # Get list of function parameters and values
     parameters = list(locals().items())
@@ -237,7 +242,7 @@ def open_configuration(
         # Set the value in the dictionary to the corresponding value in `my_data`, or the default value if not found
         if not key in ['file_path']:
             values.append(my_data.get(key, value))
-            
+
     return tuple(values)
 
 
@@ -297,10 +302,12 @@ def train_model(
     bucket_no_upscale,
     random_crop,
     bucket_reso_steps,
-    caption_dropout_every_n_epochs, caption_dropout_rate,
+    caption_dropout_every_n_epochs,
+    caption_dropout_rate,
     optimizer,
-    optimizer_args,noise_offset,
-):  
+    optimizer_args,
+    noise_offset,
+):
     if pretrained_model_name_or_path == '':
         msgbox('Source model information is missing')
         return
@@ -723,14 +730,16 @@ def lora_tab(
                 bucket_no_upscale,
                 random_crop,
                 bucket_reso_steps,
-                caption_dropout_every_n_epochs, caption_dropout_rate,noise_offset,
+                caption_dropout_every_n_epochs,
+                caption_dropout_rate,
+                noise_offset,
             ) = gradio_advanced_training()
             color_aug.change(
                 color_aug_changed,
                 inputs=[color_aug],
                 outputs=[cache_latents],
             )
-        
+
         optimizer.change(
             set_legacy_8bitadam,
             inputs=[optimizer, use_8bit_adam],
@@ -753,15 +762,15 @@ def lora_tab(
         gradio_verify_lora_tab()
 
     button_run = gr.Button('Train model', variant='primary')
-    
+
     # Setup gradio tensorboard buttons
     button_start_tensorboard, button_stop_tensorboard = gradio_tensorboard()
-    
+
     button_start_tensorboard.click(
         start_tensorboard,
         inputs=logging_dir,
     )
-    
+
     button_stop_tensorboard.click(
         stop_tensorboard,
     )
@@ -822,9 +831,11 @@ def lora_tab(
         bucket_no_upscale,
         random_crop,
         bucket_reso_steps,
-        caption_dropout_every_n_epochs, caption_dropout_rate,
+        caption_dropout_every_n_epochs,
+        caption_dropout_rate,
         optimizer,
-        optimizer_args,noise_offset,
+        optimizer_args,
+        noise_offset,
     ]
 
     button_open_config.click(
@@ -886,16 +897,19 @@ def UI(**kwargs):
             )
 
     # Show the interface
-    launch_kwargs={}
+    launch_kwargs = {}
     if not kwargs.get('username', None) == '':
-        launch_kwargs["auth"] = (kwargs.get('username', None), kwargs.get('password', None))
+        launch_kwargs['auth'] = (
+            kwargs.get('username', None),
+            kwargs.get('password', None),
+        )
     if kwargs.get('server_port', 0) > 0:
-        launch_kwargs["server_port"] = kwargs.get('server_port', 0)
-    if kwargs.get('inbrowser', False):        
-        launch_kwargs["inbrowser"] = kwargs.get('inbrowser', False)
+        launch_kwargs['server_port'] = kwargs.get('server_port', 0)
+    if kwargs.get('inbrowser', False):
+        launch_kwargs['inbrowser'] = kwargs.get('inbrowser', False)
     print(launch_kwargs)
     interface.launch(**launch_kwargs)
-        
+
 
 if __name__ == '__main__':
     # torch.cuda.set_per_process_memory_fraction(0.48)
@@ -907,10 +921,20 @@ if __name__ == '__main__':
         '--password', type=str, default='', help='Password for authentication'
     )
     parser.add_argument(
-        '--server_port', type=int, default=0, help='Port to run the server listener on'
+        '--server_port',
+        type=int,
+        default=0,
+        help='Port to run the server listener on',
+    )
+    parser.add_argument(
+        '--inbrowser', action='store_true', help='Open in browser'
     )
-    parser.add_argument("--inbrowser", action="store_true", help="Open in browser")
 
     args = parser.parse_args()
 
-    UI(username=args.username, password=args.password, inbrowser=args.inbrowser, server_port=args.server_port)
+    UI(
+        username=args.username,
+        password=args.password,
+        inbrowser=args.inbrowser,
+        server_port=args.server_port,
+    )

textual_inversion_gui.py

@@ -101,8 +101,11 @@ def save_configuration(
     bucket_no_upscale,
     random_crop,
     bucket_reso_steps,
-    caption_dropout_every_n_epochs, caption_dropout_rate,
-    optimizer,optimizer_args,noise_offset,
+    caption_dropout_every_n_epochs,
+    caption_dropout_rate,
+    optimizer,
+    optimizer_args,
+    noise_offset,
 ):
     # Get list of function parameters and values
     parameters = list(locals().items())
@@ -205,8 +208,11 @@ def open_configuration(
     bucket_no_upscale,
     random_crop,
     bucket_reso_steps,
-    caption_dropout_every_n_epochs, caption_dropout_rate,
-    optimizer,optimizer_args,noise_offset,
+    caption_dropout_every_n_epochs,
+    caption_dropout_rate,
+    optimizer,
+    optimizer_args,
+    noise_offset,
 ):
     # Get list of function parameters and values
     parameters = list(locals().items())
@@ -288,8 +294,11 @@ def train_model(
     bucket_no_upscale,
     random_crop,
     bucket_reso_steps,
-    caption_dropout_every_n_epochs, caption_dropout_rate,
-    optimizer,optimizer_args,noise_offset,
+    caption_dropout_every_n_epochs,
+    caption_dropout_rate,
+    optimizer,
+    optimizer_args,
+    noise_offset,
 ):
     if pretrained_model_name_or_path == '':
         msgbox('Source model information is missing')
@@ -641,7 +650,8 @@ def ti_tab(
             seed,
             caption_extension,
             cache_latents,
-            optimizer,optimizer_args,
+            optimizer,
+            optimizer_args,
         ) = gradio_training(
             learning_rate_value='1e-5',
             lr_scheduler_value='cosine',
@@ -699,7 +709,9 @@ def ti_tab(
                 bucket_no_upscale,
                 random_crop,
                 bucket_reso_steps,
-                caption_dropout_every_n_epochs, caption_dropout_rate,noise_offset,
+                caption_dropout_every_n_epochs,
+                caption_dropout_rate,
+                noise_offset,
             ) = gradio_advanced_training()
             color_aug.change(
                 color_aug_changed,
@@ -723,15 +735,15 @@ def ti_tab(
         )
 
     button_run = gr.Button('Train model', variant='primary')
-    
+
     # Setup gradio tensorboard buttons
     button_start_tensorboard, button_stop_tensorboard = gradio_tensorboard()
-    
+
     button_start_tensorboard.click(
         start_tensorboard,
         inputs=logging_dir,
     )
-    
+
     button_stop_tensorboard.click(
         stop_tensorboard,
     )
@@ -791,8 +803,11 @@ def ti_tab(
         bucket_no_upscale,
         random_crop,
         bucket_reso_steps,
-        caption_dropout_every_n_epochs, caption_dropout_rate,
-        optimizer,optimizer_args,noise_offset,
+        caption_dropout_every_n_epochs,
+        caption_dropout_rate,
+        optimizer,
+        optimizer_args,
+        noise_offset,
     ]
 
     button_open_config.click(
@@ -854,16 +869,19 @@ def UI(**kwargs):
             )
 
     # Show the interface
-    launch_kwargs={}
+    launch_kwargs = {}
     if not kwargs.get('username', None) == '':
-        launch_kwargs["auth"] = (kwargs.get('username', None), kwargs.get('password', None))
+        launch_kwargs['auth'] = (
+            kwargs.get('username', None),
+            kwargs.get('password', None),
+        )
     if kwargs.get('server_port', 0) > 0:
-        launch_kwargs["server_port"] = kwargs.get('server_port', 0)
-    if kwargs.get('inbrowser', False):        
-        launch_kwargs["inbrowser"] = kwargs.get('inbrowser', False)
+        launch_kwargs['server_port'] = kwargs.get('server_port', 0)
+    if kwargs.get('inbrowser', False):
+        launch_kwargs['inbrowser'] = kwargs.get('inbrowser', False)
     print(launch_kwargs)
     interface.launch(**launch_kwargs)
-        
+
 
 if __name__ == '__main__':
     # torch.cuda.set_per_process_memory_fraction(0.48)
@@ -875,10 +893,20 @@ if __name__ == '__main__':
         '--password', type=str, default='', help='Password for authentication'
     )
     parser.add_argument(
-        '--server_port', type=int, default=0, help='Port to run the server listener on'
+        '--server_port',
+        type=int,
+        default=0,
+        help='Port to run the server listener on',
+    )
+    parser.add_argument(
+        '--inbrowser', action='store_true', help='Open in browser'
     )
-    parser.add_argument("--inbrowser", action="store_true", help="Open in browser")
 
     args = parser.parse_args()
 
-    UI(username=args.username, password=args.password, inbrowser=args.inbrowser, server_port=args.server_port)
+    UI(
+        username=args.username,
+        password=args.password,
+        inbrowser=args.inbrowser,
+        server_port=args.server_port,
+    )