Merge pull request #300 from bmaltais/dev

v21.1.3

README-ja.md

@@ -38,6 +38,18 @@ PowerShellを使う場合、venvを使えるようにするためには以下の
 - 「Set-ExecutionPolicy Unrestricted」と入力し、Yと答えます。
 - 管理者のPowerShellを閉じます。
 
+## Ubuntu環境でのインストール
+
+```
+git clone https://github.com/kohya-ss/sd-scripts.git
+cd sd-scripts
+bash ubuntu_setup.sh
+```
+
+をコマンドプロンプトで実行し、tkをインストールし、accelerateの質問をWindowsと同じように答えます。
+
+`./gui.sh`でGUIを実行します。
+
 ## Windows環境でのインストール
 
 以下の例ではPyTorchは1.12.1／CUDA 11.6版をインストールします。CUDA 11.3版やPyTorch 1.13を使う場合は適宜書き換えください。

README.md

@@ -41,6 +41,19 @@ If you run on Linux and would like to use the GUI, there is now a port of it as
 
 ## Installation
 
+### Ubuntu
+In the terminal, run
+
+```
+git clone https://github.com/bmaltais/kohya_ss.git
+cd kohya_ss
+bash ubuntu_setup.sh
+```
+
+then configure accelerate with the same answers as in the Windows instructions when prompted.
+
+### Windows
+
 Give unrestricted script access to powershell so venv can work:
 
 - Run PowerShell as an administrator
@@ -163,6 +176,9 @@ This will store your a backup file with your current locally installed pip packa
 
 ## Change History
 
+* 2023/03/04 (v21.1.3):
+    - Fix progress bar being displayed when not required.
+    - Add support for linux, thank you @devNegative-asm
 * 2023/03/03 (v21.1.2):
     - Fix issue https://github.com/bmaltais/kohya_ss/issues/277
     - Fix issue https://github.com/bmaltais/kohya_ss/issues/278 introduce by LoCon project switching to pip module. Make sure to run upgrade.ps1 to install the latest pip requirements for LoCon support.

dreambooth_gui.py

@@ -25,7 +25,7 @@ from library.common_gui import (
     gradio_config,
     gradio_source_model,
     set_legacy_8bitadam,
-    my_data,
+    update_my_data,
 )
 from library.tensorboard_gui import (
     gradio_tensorboard,
@@ -214,7 +214,7 @@ def open_configuration(
             my_data = json.load(f)
             print('Loading config...')
             # Update values to fix deprecated use_8bit_adam checkbox and set appropriate optimizer if it is set to True
-            my_data = my_data(my_data)
+            my_data = update_my_data(my_data)
     else:
         file_path = original_file_path  # In case a file_path was provided and the user decide to cancel the open action
         my_data = {}
@@ -456,7 +456,7 @@ def train_model(
 
     print(run_cmd)
     # Run the command
-    subprocess.run(run_cmd)
+    os.system(run_cmd)
 
     # check if output_dir/last is a folder... therefore it is a diffuser model
     last_dir = pathlib.Path(f'{output_dir}/{output_name}')
@@ -500,7 +500,9 @@ def dreambooth_tab(
                 '📂', elem_id='open_folder_small'
             )
             train_data_dir_input_folder.click(
-                get_folder_path, outputs=train_data_dir
+                get_folder_path,
+                outputs=train_data_dir,
+                show_progress=False,
             )
             reg_data_dir = gr.Textbox(
                 label='Regularisation folder',
@@ -510,7 +512,9 @@ def dreambooth_tab(
                 '📂', elem_id='open_folder_small'
             )
             reg_data_dir_input_folder.click(
-                get_folder_path, outputs=reg_data_dir
+                get_folder_path,
+                outputs=reg_data_dir,
+                show_progress=False,
             )
         with gr.Row():
             output_dir = gr.Textbox(
@@ -529,7 +533,9 @@ def dreambooth_tab(
                 '📂', elem_id='open_folder_small'
             )
             logging_dir_input_folder.click(
-                get_folder_path, outputs=logging_dir
+                get_folder_path,
+                outputs=logging_dir,
+                show_progress=False,
             )
         with gr.Row():
             output_name = gr.Textbox(
@@ -610,7 +616,11 @@ def dreambooth_tab(
                     placeholder='(Optiona) path to checkpoint of vae to replace for training',
                 )
                 vae_button = gr.Button('📂', elem_id='open_folder_small')
-                vae_button.click(get_any_file_path, outputs=vae)
+                vae_button.click(
+                    get_any_file_path,
+                    outputs=vae,
+                    show_progress=False,
+                )
             (
                 use_8bit_adam,
                 xformers,
@@ -664,10 +674,12 @@ def dreambooth_tab(
     button_start_tensorboard.click(
         start_tensorboard,
         inputs=logging_dir,
+        show_progress=False,
     )
 
     button_stop_tensorboard.click(
         stop_tensorboard,
+        show_progress=False,
     )
 
     settings_list = [
@@ -730,23 +742,27 @@ def dreambooth_tab(
         open_configuration,
         inputs=[config_file_name] + settings_list,
         outputs=[config_file_name] + settings_list,
+        show_progress=False,
     )
 
     button_save_config.click(
         save_configuration,
         inputs=[dummy_db_false, config_file_name] + settings_list,
         outputs=[config_file_name],
+        show_progress=False,
     )
 
     button_save_as_config.click(
         save_configuration,
         inputs=[dummy_db_true, config_file_name] + settings_list,
         outputs=[config_file_name],
+        show_progress=False,
     )
 
     button_run.click(
         train_model,
         inputs=settings_list,
+        show_progress=False,
     )
 
     return (

finetune_gui.py

@@ -19,7 +19,7 @@ from library.common_gui import (
     color_aug_changed,
     run_cmd_training,
     set_legacy_8bitadam,
-    my_data,
+    update_my_data,
 )
 from library.tensorboard_gui import (
     gradio_tensorboard,
@@ -33,6 +33,8 @@ refresh_symbol = '\U0001f504'  # 🔄
 save_style_symbol = '\U0001f4be'  # 💾
 document_symbol = '\U0001F4C4'   # 📄
 
+PYTHON = 'python3' if os.name == 'posix' else './venv/Scripts/python.exe'
+
 
 def save_configuration(
     save_as,
@@ -216,7 +218,7 @@ def open_config_file(
             my_data_db = json.load(f)
             print('Loading config...')
             # Update values to fix deprecated use_8bit_adam checkbox and set appropriate optimizer if it is set to True
-            my_data = my_data(my_data)
+            my_data = update_my_data(my_data)
     else:
         file_path = original_file_path  # In case a file_path was provided and the user decide to cancel the open action
         my_data_db = {}
@@ -295,9 +297,7 @@ def train_model(
         if not os.path.exists(train_dir):
             os.mkdir(train_dir)
 
-        run_cmd = (
-            f'./venv/Scripts/python.exe finetune/merge_captions_to_metadata.py'
-        )
+        run_cmd = f'{PYTHON} finetune/merge_captions_to_metadata.py'
         if caption_extension == '':
             run_cmd += f' --caption_extension=".caption"'
         else:
@@ -310,13 +310,11 @@ def train_model(
         print(run_cmd)
 
         # Run the command
-        subprocess.run(run_cmd)
+        os.system(run_cmd)
 
     # create images buckets
     if generate_image_buckets:
-        run_cmd = (
-            f'./venv/Scripts/python.exe finetune/prepare_buckets_latents.py'
-        )
+        run_cmd = f'{PYTHON} finetune/prepare_buckets_latents.py'
         run_cmd += f' "{image_folder}"'
         run_cmd += f' "{train_dir}/{caption_metadata_filename}"'
         run_cmd += f' "{train_dir}/{latent_metadata_filename}"'
@@ -334,7 +332,7 @@ def train_model(
         print(run_cmd)
 
         # Run the command
-        subprocess.run(run_cmd)
+        os.system(run_cmd)
 
     image_num = len(
         [
@@ -444,7 +442,7 @@ def train_model(
 
     print(run_cmd)
     # Run the command
-    subprocess.run(run_cmd)
+    os.system(run_cmd)
 
     # check if output_dir/last is a folder... therefore it is a diffuser model
     last_dir = pathlib.Path(f'{output_dir}/{output_name}')
@@ -490,7 +488,11 @@ def finetune_tab():
             train_dir_folder = gr.Button(
                 folder_symbol, elem_id='open_folder_small'
             )
-            train_dir_folder.click(get_folder_path, outputs=train_dir)
+            train_dir_folder.click(
+                get_folder_path,
+                outputs=train_dir,
+                show_progress=False,
+            )
 
             image_folder = gr.Textbox(
                 label='Training Image folder',
@@ -500,7 +502,9 @@ def finetune_tab():
                 folder_symbol, elem_id='open_folder_small'
             )
             image_folder_input_folder.click(
-                get_folder_path, outputs=image_folder
+                get_folder_path,
+                outputs=image_folder,
+                show_progress=False,
             )
         with gr.Row():
             output_dir = gr.Textbox(
@@ -510,7 +514,11 @@ def finetune_tab():
             output_dir_input_folder = gr.Button(
                 folder_symbol, elem_id='open_folder_small'
             )
-            output_dir_input_folder.click(get_folder_path, outputs=output_dir)
+            output_dir_input_folder.click(
+                get_folder_path,
+                outputs=output_dir,
+                show_progress=False,
+            )
 
             logging_dir = gr.Textbox(
                 label='Logging folder',
@@ -520,7 +528,9 @@ def finetune_tab():
                 folder_symbol, elem_id='open_folder_small'
             )
             logging_dir_input_folder.click(
-                get_folder_path, outputs=logging_dir
+                get_folder_path,
+                outputs=logging_dir,
+                show_progress=False,
             )
         with gr.Row():
             output_name = gr.Textbox(
@@ -654,6 +664,7 @@ def finetune_tab():
 
     button_stop_tensorboard.click(
         stop_tensorboard,
+        show_progress=False,
     )
 
     settings_list = [
@@ -724,18 +735,21 @@ def finetune_tab():
         open_config_file,
         inputs=[config_file_name] + settings_list,
         outputs=[config_file_name] + settings_list,
+        show_progress=False,
     )
 
     button_save_config.click(
         save_configuration,
         inputs=[dummy_ft_false, config_file_name] + settings_list,
         outputs=[config_file_name],
+        show_progress=False,
     )
 
     button_save_as_config.click(
         save_configuration,
         inputs=[dummy_ft_true, config_file_name] + settings_list,
         outputs=[config_file_name],
+        show_progress=False,
     )
 
 

gui.sh

@@ -0,0 +1,3 @@
+#!/bin/bash
+source venv/bin/activate
+python kohya_gui.py 

library/basic_caption_gui.py

@@ -2,6 +2,7 @@ import gradio as gr
 from easygui import msgbox
 import subprocess
 from .common_gui import get_folder_path, add_pre_postfix, find_replace
+import os
 
 
 def caption_images(
@@ -38,7 +39,7 @@ def caption_images(
         print(run_cmd)
 
         # Run the command
-        subprocess.run(run_cmd)
+        os.system(run_cmd)
 
     if overwrite_input:
         if not prefix == '' or not postfix == '':
@@ -85,7 +86,9 @@ def gradio_basic_caption_gui_tab():
                 '📂', elem_id='open_folder_small'
             )
             button_images_dir_input.click(
-                get_folder_path, outputs=images_dir_input
+                get_folder_path,
+                outputs=images_dir_input,
+                show_progress=False,
             )
             caption_file_ext = gr.Textbox(
                 label='Caption file extension',
@@ -139,4 +142,5 @@ def gradio_basic_caption_gui_tab():
                 find,
                 replace,
             ],
+            show_progress=False,
         )

library/blip_caption_gui.py

@@ -4,6 +4,8 @@ import subprocess
 import os
 from .common_gui import get_folder_path, add_pre_postfix
 
+PYTHON = 'python3' if os.name == 'posix' else './venv/Scripts/python.exe'
+
 
 def caption_images(
     train_data_dir,
@@ -32,7 +34,7 @@ def caption_images(
         return
 
     print(f'Captioning files in {train_data_dir}...')
-    run_cmd = f'.\\venv\\Scripts\\python.exe "finetune/make_captions.py"'
+    run_cmd = f'{PYTHON} "finetune/make_captions.py"'
     run_cmd += f' --batch_size="{int(batch_size)}"'
     run_cmd += f' --num_beams="{int(num_beams)}"'
     run_cmd += f' --top_p="{top_p}"'
@@ -48,7 +50,7 @@ def caption_images(
     print(run_cmd)
 
     # Run the command
-    subprocess.run(run_cmd)
+    os.system(run_cmd)
 
     # Add prefix and postfix
     add_pre_postfix(
@@ -81,7 +83,9 @@ def gradio_blip_caption_gui_tab():
                 '📂', elem_id='open_folder_small'
             )
             button_train_data_dir_input.click(
-                get_folder_path, outputs=train_data_dir
+                get_folder_path,
+                outputs=train_data_dir,
+                show_progress=False,
             )
         with gr.Row():
             caption_file_ext = gr.Textbox(
@@ -138,4 +142,5 @@ def gradio_blip_caption_gui_tab():
                 prefix,
                 postfix,
             ],
+            show_progress=False,
         )

library/common_gui.py

@@ -10,11 +10,11 @@ save_style_symbol = '\U0001f4be'  # 💾
 document_symbol = '\U0001F4C4'   # 📄
 
 
-def my_data(my_data):
+def update_my_data(my_data):
     if my_data.get('use_8bit_adam', False):
         my_data['optimizer'] = 'AdamW8bit'
         my_data['use_8bit_adam'] = False
-    
+
     if my_data.get('model_list', 'custom') == []:
         print('Old config with empty model list. Setting to custom...')
         my_data['model_list'] = 'custom'
@@ -276,7 +276,9 @@ def save_inference_file(output_dir, v2, v_parameterization, output_name):
                     )
 
 
-def set_pretrained_model_name_or_path_input(model_list, pretrained_model_name_or_path, v2, v_parameterization):
+def set_pretrained_model_name_or_path_input(
+    model_list, pretrained_model_name_or_path, v2, v_parameterization
+):
     # define a list of substrings to search for
     substrings_v2 = [
         'stabilityai/stable-diffusion-2-1-base',
@@ -320,7 +322,12 @@ def set_pretrained_model_name_or_path_input(model_list, pretrained_model_name_or
         return model_list, v2, v_parameterization
 
     if model_list == 'custom':
-        if str(pretrained_model_name_or_path) in substrings_v1_model or str(pretrained_model_name_or_path) in substrings_v2 or str(pretrained_model_name_or_path) in substrings_v_parameterization:
+        if (
+            str(pretrained_model_name_or_path) in substrings_v1_model
+            or str(pretrained_model_name_or_path) in substrings_v2
+            or str(pretrained_model_name_or_path)
+            in substrings_v_parameterization
+        ):
             pretrained_model_name_or_path = ''
             v2 = False
             v_parameterization = False
@@ -359,7 +366,7 @@ def gradio_source_model():
             pretrained_model_name_or_path = gr.Textbox(
                 label='Pretrained model name or path',
                 placeholder='enter the path to custom model or name of pretrained model',
-                value='runwayml/stable-diffusion-v1-5'
+                value='runwayml/stable-diffusion-v1-5',
             )
             pretrained_model_name_or_path_file = gr.Button(
                 document_symbol, elem_id='open_folder_small'
@@ -368,6 +375,7 @@ def gradio_source_model():
                 get_any_file_path,
                 inputs=pretrained_model_name_or_path,
                 outputs=pretrained_model_name_or_path,
+                show_progress=False,
             )
             pretrained_model_name_or_path_folder = gr.Button(
                 folder_symbol, elem_id='open_folder_small'
@@ -376,6 +384,7 @@ def gradio_source_model():
                 get_folder_path,
                 inputs=pretrained_model_name_or_path,
                 outputs=pretrained_model_name_or_path,
+                show_progress=False,
             )
             model_list = gr.Dropdown(
                 label='Model Quick Pick',
@@ -388,7 +397,7 @@ def gradio_source_model():
                     'runwayml/stable-diffusion-v1-5',
                     'CompVis/stable-diffusion-v1-4',
                 ],
-                value='runwayml/stable-diffusion-v1-5'
+                value='runwayml/stable-diffusion-v1-5',
             )
             save_model_as = gr.Dropdown(
                 label='Save trained model as',
@@ -409,7 +418,12 @@ def gradio_source_model():
             )
         model_list.change(
             set_pretrained_model_name_or_path_input,
-            inputs=[model_list, pretrained_model_name_or_path, v2, v_parameterization],
+            inputs=[
+                model_list,
+                pretrained_model_name_or_path,
+                v2,
+                v_parameterization,
+            ],
             outputs=[
                 pretrained_model_name_or_path,
                 v2,
@@ -661,7 +675,11 @@ def gradio_advanced_training():
             placeholder='path to "last-state" state folder to resume from',
         )
         resume_button = gr.Button('📂', elem_id='open_folder_small')
-        resume_button.click(get_folder_path, outputs=resume)
+        resume_button.click(
+            get_folder_path,
+            outputs=resume,
+            show_progress=False,
+        )
         max_train_epochs = gr.Textbox(
             label='Max train epoch',
             placeholder='(Optional) Override number of epoch',

library/convert_model_gui.py

@@ -9,6 +9,7 @@ folder_symbol = '\U0001f4c2'  # 📂
 refresh_symbol = '\U0001f504'  # 🔄
 save_style_symbol = '\U0001f4be'  # 💾
 document_symbol = '\U0001F4C4'   # 📄
+PYTHON = 'python3' if os.name == 'posix' else './venv/Scripts/python.exe'
 
 
 def convert_model(
@@ -40,7 +41,7 @@ def convert_model(
         msgbox('The provided target folder does not exist')
         return
 
-    run_cmd = f'.\\venv\Scripts\python.exe "tools/convert_diffusers20_original_sd.py"'
+    run_cmd = f'{PYTHON} "tools/convert_diffusers20_original_sd.py"'
 
     v1_models = [
         'runwayml/stable-diffusion-v1-5',
@@ -87,7 +88,7 @@ def convert_model(
     print(run_cmd)
 
     # Run the command
-    subprocess.run(run_cmd)
+    os.system(run_cmd)
 
     if (
         not target_model_type == 'diffuser'
@@ -166,7 +167,9 @@ def gradio_convert_model_tab():
                 folder_symbol, elem_id='open_folder_small'
             )
             button_source_model_dir.click(
-                get_folder_path, outputs=source_model_input
+                get_folder_path,
+                outputs=source_model_input,
+                show_progress=False,
             )
 
             button_source_model_file = gr.Button(
@@ -176,6 +179,7 @@ def gradio_convert_model_tab():
                 get_file_path,
                 inputs=[source_model_input],
                 outputs=source_model_input,
+                show_progress=False,
             )
 
             source_model_type = gr.Dropdown(
@@ -199,7 +203,9 @@ def gradio_convert_model_tab():
                 folder_symbol, elem_id='open_folder_small'
             )
             button_target_model_folder.click(
-                get_folder_path, outputs=target_model_folder_input
+                get_folder_path,
+                outputs=target_model_folder_input,
+                show_progress=False,
             )
 
             target_model_name_input = gr.Textbox(
@@ -234,4 +240,5 @@ def gradio_convert_model_tab():
                 target_model_type,
                 target_save_precision_type,
             ],
+            show_progress=False,
         )

library/dataset_balancing_gui.py

@@ -118,7 +118,9 @@ def gradio_dataset_balancing_tab():
                 '📂', elem_id='open_folder_small'
             )
             select_dataset_folder_button.click(
-                get_folder_path, outputs=select_dataset_folder_input
+                get_folder_path,
+                outputs=select_dataset_folder_input,
+                show_progress=False,
             )
 
             total_repeats_number = gr.Number(
@@ -140,4 +142,5 @@ def gradio_dataset_balancing_tab():
                 select_dataset_folder_input,
                 insecure,
             ],
+            show_progress=False,
         )

library/dreambooth_folder_creation_gui.py

@@ -140,7 +140,9 @@ def gradio_dreambooth_folder_creation_tab(
                 '📂', elem_id='open_folder_small'
             )
             button_util_training_images_dir_input.click(
-                get_folder_path, outputs=util_training_images_dir_input
+                get_folder_path,
+                outputs=util_training_images_dir_input,
+                show_progress=False,
             )
             util_training_images_repeat_input = gr.Number(
                 label='Repeats',
@@ -158,7 +160,9 @@ def gradio_dreambooth_folder_creation_tab(
                 '📂', elem_id='open_folder_small'
             )
             button_util_regularization_images_dir_input.click(
-                get_folder_path, outputs=util_regularization_images_dir_input
+                get_folder_path,
+                outputs=util_regularization_images_dir_input,
+                show_progress=False,
             )
             util_regularization_images_repeat_input = gr.Number(
                 label='Repeats',
@@ -190,6 +194,7 @@ def gradio_dreambooth_folder_creation_tab(
                 util_class_prompt_input,
                 util_training_dir_output,
             ],
+            show_progress=False,
         )
         button_copy_info_to_Folders_tab = gr.Button('Copy info to Folders Tab')
         button_copy_info_to_Folders_tab.click(
@@ -201,4 +206,5 @@ def gradio_dreambooth_folder_creation_tab(
                 output_dir_input,
                 logging_dir_input,
             ],
+            show_progress=False,
         )

library/extract_lora_gui.py

@@ -12,6 +12,7 @@ folder_symbol = '\U0001f4c2'  # 📂
 refresh_symbol = '\U0001f504'  # 🔄
 save_style_symbol = '\U0001f4be'  # 💾
 document_symbol = '\U0001F4C4'   # 📄
+PYTHON = 'python3' if os.name == 'posix' else './venv/Scripts/python.exe'
 
 
 def extract_lora(
@@ -41,7 +42,7 @@ def extract_lora(
         return
 
     run_cmd = (
-        f'.\\venv\Scripts\python.exe "networks\extract_lora_from_models.py"'
+        f'{PYTHON} "{os.path.join("networks","extract_lora_from_models.py")}"'
     )
     run_cmd += f' --save_precision {save_precision}'
     run_cmd += f' --save_to "{save_to}"'
@@ -54,7 +55,7 @@ def extract_lora(
     print(run_cmd)
 
     # Run the command
-    subprocess.run(run_cmd)
+    os.system(run_cmd)
 
 
 ###
@@ -85,6 +86,7 @@ def gradio_extract_lora_tab():
                 get_file_path,
                 inputs=[model_tuned, model_ext, model_ext_name],
                 outputs=model_tuned,
+                show_progress=False,
             )
 
             model_org = gr.Textbox(
@@ -99,6 +101,7 @@ def gradio_extract_lora_tab():
                 get_file_path,
                 inputs=[model_org, model_ext, model_ext_name],
                 outputs=model_org,
+                show_progress=False,
             )
         with gr.Row():
             save_to = gr.Textbox(
@@ -113,6 +116,7 @@ def gradio_extract_lora_tab():
                 get_saveasfilename_path,
                 inputs=[save_to, lora_ext, lora_ext_name],
                 outputs=save_to,
+                show_progress=False,
             )
             save_precision = gr.Dropdown(
                 label='Save precision',
@@ -136,4 +140,5 @@ def gradio_extract_lora_tab():
         extract_button.click(
             extract_lora,
             inputs=[model_tuned, model_org, save_to, save_precision, dim, v2],
+            show_progress=False,
         )

library/git_caption_gui.py

@@ -4,6 +4,8 @@ import subprocess
 import os
 from .common_gui import get_folder_path, add_pre_postfix
 
+PYTHON = 'python3' if os.name == 'posix' else './venv/Scripts/python.exe'
+
 
 def caption_images(
     train_data_dir,
@@ -73,7 +75,9 @@ def gradio_git_caption_gui_tab():
                 '📂', elem_id='open_folder_small'
             )
             button_train_data_dir_input.click(
-                get_folder_path, outputs=train_data_dir
+                get_folder_path,
+                outputs=train_data_dir,
+                show_progress=False,
             )
         with gr.Row():
             caption_ext = gr.Textbox(
@@ -126,4 +130,5 @@ def gradio_git_caption_gui_tab():
                 prefix,
                 postfix,
             ],
+            show_progress=False,
         )

library/merge_lora_gui.py

@@ -12,6 +12,7 @@ folder_symbol = '\U0001f4c2'  # 📂
 refresh_symbol = '\U0001f504'  # 🔄
 save_style_symbol = '\U0001f4be'  # 💾
 document_symbol = '\U0001F4C4'   # 📄
+PYTHON = 'python3' if os.name == 'posix' else './venv/Scripts/python.exe'
 
 
 def merge_lora(
@@ -43,7 +44,7 @@ def merge_lora(
     ratio_a = ratio
     ratio_b = 1 - ratio
 
-    run_cmd = f'.\\venv\Scripts\python.exe "networks\merge_lora.py"'
+    run_cmd = f'{PYTHON} "{os.path.join("networks","merge_lora.py")}"'
     run_cmd += f' --save_precision {save_precision}'
     run_cmd += f' --precision {precision}'
     run_cmd += f' --save_to "{save_to}"'
@@ -53,7 +54,7 @@ def merge_lora(
     print(run_cmd)
 
     # Run the command
-    subprocess.run(run_cmd)
+    os.system(run_cmd)
 
 
 ###
@@ -81,6 +82,7 @@ def gradio_merge_lora_tab():
                 get_file_path,
                 inputs=[lora_a_model, lora_ext, lora_ext_name],
                 outputs=lora_a_model,
+                show_progress=False,
             )
 
             lora_b_model = gr.Textbox(
@@ -95,6 +97,7 @@ def gradio_merge_lora_tab():
                 get_file_path,
                 inputs=[lora_b_model, lora_ext, lora_ext_name],
                 outputs=lora_b_model,
+                show_progress=False,
             )
         with gr.Row():
             ratio = gr.Slider(
@@ -119,6 +122,7 @@ def gradio_merge_lora_tab():
                 get_saveasfilename_path,
                 inputs=[save_to, lora_ext, lora_ext_name],
                 outputs=save_to,
+                show_progress=False,
             )
             precision = gr.Dropdown(
                 label='Merge precision',
@@ -145,4 +149,5 @@ def gradio_merge_lora_tab():
                 precision,
                 save_precision,
             ],
+            show_progress=False,
         )

library/resize_lora_gui.py

@@ -4,6 +4,7 @@ import subprocess
 import os
 from .common_gui import get_saveasfilename_path, get_file_path
 
+PYTHON = 'python3' if os.name == 'posix' else './venv/Scripts/python.exe'
 folder_symbol = '\U0001f4c2'  # 📂
 refresh_symbol = '\U0001f504'  # 🔄
 save_style_symbol = '\U0001f4be'  # 💾
@@ -30,7 +31,7 @@ def resize_lora(
     if device == '':
         device = 'cuda'
 
-    run_cmd = f'.\\venv\Scripts\python.exe "networks\\resize_lora.py"'
+    run_cmd = f'{PYTHON} "{os.path.join("networks","resize_lora.py")}"'
     run_cmd += f' --save_precision {save_precision}'
     run_cmd += f' --save_to {save_to}'
     run_cmd += f' --model {model}'
@@ -40,7 +41,7 @@ def resize_lora(
     print(run_cmd)
 
     # Run the command
-    subprocess.run(run_cmd)
+    os.system(run_cmd)
 
 
 ###
@@ -68,6 +69,7 @@ def gradio_resize_lora_tab():
                 get_file_path,
                 inputs=[model, lora_ext, lora_ext_name],
                 outputs=model,
+                show_progress=False,
             )
         with gr.Row():
             new_rank = gr.Slider(
@@ -92,6 +94,7 @@ def gradio_resize_lora_tab():
                 get_saveasfilename_path,
                 inputs=[save_to, lora_ext, lora_ext_name],
                 outputs=save_to,
+                show_progress=False,
             )
             save_precision = gr.Dropdown(
                 label='Save precision',
@@ -116,4 +119,5 @@ def gradio_resize_lora_tab():
                 save_precision,
                 device,
             ],
+            show_progress=False,
         )

library/tensorboard_gui.py

@@ -5,6 +5,7 @@ import subprocess
 import time
 
 tensorboard_proc = None   # I know... bad but heh
+TENSORBOARD = 'tensorboard' if os.name == 'posix' else 'tensorboard.exe'
 
 
 def start_tensorboard(logging_dir):
@@ -15,7 +16,7 @@ def start_tensorboard(logging_dir):
         msgbox(msg='Error: log folder is empty')
         return
 
-    run_cmd = f'tensorboard.exe --logdir "{logging_dir}"'
+    run_cmd = [f'{TENSORBOARD}', '--logdir', f'{logging_dir}']
 
     print(run_cmd)
     if tensorboard_proc is not None:

library/verify_lora_gui.py

@@ -8,6 +8,7 @@ from .common_gui import (
     get_file_path,
 )
 
+PYTHON = 'python3' if os.name == 'posix' else './venv/Scripts/python.exe'
 folder_symbol = '\U0001f4c2'  # 📂
 refresh_symbol = '\U0001f504'  # 🔄
 save_style_symbol = '\U0001f4be'  # 💾
@@ -27,13 +28,15 @@ def verify_lora(
         msgbox('The provided model A is not a file')
         return
 
-    run_cmd = f'.\\venv\Scripts\python.exe "networks\check_lora_weights.py"'
-    run_cmd += f' {lora_model}'
+    run_cmd = [
+        PYTHON,
+        os.path.join('networks', 'check_lora_weights.py'),
+        f'{lora_model}',
+    ]
 
-    print(run_cmd)
+    print(' '.join(run_cmd))
 
     # Run the command
-    subprocess.run(run_cmd)
     process = subprocess.Popen(
         run_cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE
     )
@@ -69,6 +72,7 @@ def gradio_verify_lora_tab():
                 get_file_path,
                 inputs=[lora_model, lora_ext, lora_ext_name],
                 outputs=lora_model,
+                show_progress=False,
             )
             verify_button = gr.Button('Verify', variant='primary')
 
@@ -94,4 +98,5 @@ def gradio_verify_lora_tab():
                 lora_model,
             ],
             outputs=[lora_model_verif_output, lora_model_verif_error],
+            show_progress=False,
         )

library/wd14_caption_gui.py

@@ -2,6 +2,7 @@ import gradio as gr
 from easygui import msgbox
 import subprocess
 from .common_gui import get_folder_path
+import os
 
 
 def caption_images(train_data_dir, caption_extension, batch_size, thresh):
@@ -30,7 +31,7 @@ def caption_images(train_data_dir, caption_extension, batch_size, thresh):
     print(run_cmd)
 
     # Run the command
-    subprocess.run(run_cmd)
+    os.system(run_cmd)
 
     print('...captioning done')
 
@@ -55,7 +56,9 @@ def gradio_wd14_caption_gui_tab():
                 '📂', elem_id='open_folder_small'
             )
             button_train_data_dir_input.click(
-                get_folder_path, outputs=train_data_dir
+                get_folder_path,
+                outputs=train_data_dir,
+                show_progress=False,
             )
 
             caption_extension = gr.Textbox(
@@ -75,4 +78,5 @@ def gradio_wd14_caption_gui_tab():
         caption_button.click(
             caption_images,
             inputs=[train_data_dir, caption_extension, batch_size, thresh],
+            show_progress=False,
         )

lora_gui.py

@@ -25,7 +25,7 @@ from library.common_gui import (
     gradio_source_model,
     run_cmd_training,
     set_legacy_8bitadam,
-    my_data,
+    update_my_data,
 )
 from library.dreambooth_folder_creation_gui import (
     gradio_dreambooth_folder_creation_tab,
@@ -239,7 +239,7 @@ def open_configuration(
             my_data = json.load(f)
             print('Loading config...')
             # Update values to fix deprecated use_8bit_adam checkbox and set appropriate optimizer if it is set to True
-            my_data = my_data(my_data)
+            my_data = update_my_data(my_data)
     else:
         file_path = original_file_path  # In case a file_path was provided and the user decide to cancel the open action
         my_data = {}
@@ -462,7 +462,9 @@ def train_model(
         try:
             import locon.locon_kohya
         except ModuleNotFoundError:
-            print("\033[1;31mError:\033[0m The required module 'locon' is not installed. Please install by running \033[33mupgrade.ps1\033[0m before running this program.")
+            print(
+                "\033[1;31mError:\033[0m The required module 'locon' is not installed. Please install by running \033[33mupgrade.ps1\033[0m before running this program."
+            )
             return
         run_cmd += f' --network_module=locon.locon_kohya'
         run_cmd += (
@@ -544,7 +546,7 @@ def train_model(
 
     print(run_cmd)
     # Run the command
-    subprocess.run(run_cmd)
+    os.system(run_cmd)
 
     # check if output_dir/last is a folder... therefore it is a diffuser model
     last_dir = pathlib.Path(f'{output_dir}/{output_name}')
@@ -588,27 +590,41 @@ def lora_tab(
             )
             train_data_dir_folder = gr.Button('📂', elem_id='open_folder_small')
             train_data_dir_folder.click(
-                get_folder_path, outputs=train_data_dir
+                get_folder_path,
+                outputs=train_data_dir,
+                show_progress=False,
             )
             reg_data_dir = gr.Textbox(
                 label='Regularisation folder',
                 placeholder='(Optional) Folder where where the regularization folders containing the images are located',
             )
             reg_data_dir_folder = gr.Button('📂', elem_id='open_folder_small')
-            reg_data_dir_folder.click(get_folder_path, outputs=reg_data_dir)
+            reg_data_dir_folder.click(
+                get_folder_path,
+                outputs=reg_data_dir,
+                show_progress=False,
+            )
         with gr.Row():
             output_dir = gr.Textbox(
                 label='Output folder',
                 placeholder='Folder to output trained model',
             )
             output_dir_folder = gr.Button('📂', elem_id='open_folder_small')
-            output_dir_folder.click(get_folder_path, outputs=output_dir)
+            output_dir_folder.click(
+                get_folder_path,
+                outputs=output_dir,
+                show_progress=False,
+            )
             logging_dir = gr.Textbox(
                 label='Logging folder',
                 placeholder='Optional: enable logging and output TensorBoard log to this folder',
             )
             logging_dir_folder = gr.Button('📂', elem_id='open_folder_small')
-            logging_dir_folder.click(get_folder_path, outputs=logging_dir)
+            logging_dir_folder.click(
+                get_folder_path,
+                outputs=logging_dir,
+                show_progress=False,
+            )
         with gr.Row():
             output_name = gr.Textbox(
                 label='Model output name',
@@ -662,6 +678,7 @@ def lora_tab(
                 get_any_file_path,
                 inputs=[lora_network_weights],
                 outputs=lora_network_weights,
+                show_progress=False,
             )
         (
             learning_rate,
@@ -736,7 +753,7 @@ def lora_tab(
                 return gr.Group.update(visible=True)
             else:
                 return gr.Group.update(visible=False)
-            
+
         LoRA_type.change(
             LoRA_type_change, inputs=[LoRA_type], outputs=[LoCon_row]
         )
@@ -834,10 +851,12 @@ def lora_tab(
     button_start_tensorboard.click(
         start_tensorboard,
         inputs=logging_dir,
+        show_progress=False,
     )
 
     button_stop_tensorboard.click(
         stop_tensorboard,
+        show_progress=False,
     )
 
     settings_list = [
@@ -910,23 +929,27 @@ def lora_tab(
         open_configuration,
         inputs=[config_file_name] + settings_list,
         outputs=[config_file_name] + settings_list + [LoCon_row],
+        show_progress=False,
     )
 
     button_save_config.click(
         save_configuration,
         inputs=[dummy_db_false, config_file_name] + settings_list,
         outputs=[config_file_name],
+        show_progress=False,
     )
 
     button_save_as_config.click(
         save_configuration,
         inputs=[dummy_db_true, config_file_name] + settings_list,
         outputs=[config_file_name],
+        show_progress=False,
     )
 
     button_run.click(
         train_model,
         inputs=settings_list,
+        show_progress=False,
     )
 
     return (

textual_inversion_gui.py

@@ -25,7 +25,7 @@ from library.common_gui import (
     gradio_config,
     gradio_source_model,
     set_legacy_8bitadam,
-    my_data,
+    update_my_data,
 )
 from library.tensorboard_gui import (
     gradio_tensorboard,
@@ -226,7 +226,7 @@ def open_configuration(
             my_data_db = json.load(f)
             print('Loading config...')
             # Update values to fix deprecated use_8bit_adam checkbox and set appropriate optimizer if it is set to True
-            my_data = my_data(my_data)
+            my_data = update_my_data(my_data)
     else:
         file_path = original_file_path  # In case a file_path was provided and the user decide to cancel the open action
         my_data_db = {}
@@ -498,7 +498,7 @@ def train_model(
 
     print(run_cmd)
     # Run the command
-    subprocess.run(run_cmd)
+    os.system(run_cmd)
 
     # check if output_dir/last is a folder... therefore it is a diffuser model
     last_dir = pathlib.Path(f'{output_dir}/{output_name}')
@@ -542,7 +542,9 @@ def ti_tab(
                 '📂', elem_id='open_folder_small'
             )
             train_data_dir_input_folder.click(
-                get_folder_path, outputs=train_data_dir
+                get_folder_path,
+                outputs=train_data_dir,
+                show_progress=False,
             )
             reg_data_dir = gr.Textbox(
                 label='Regularisation folder',
@@ -552,7 +554,9 @@ def ti_tab(
                 '📂', elem_id='open_folder_small'
             )
             reg_data_dir_input_folder.click(
-                get_folder_path, outputs=reg_data_dir
+                get_folder_path,
+                outputs=reg_data_dir,
+                show_progress=False,
             )
         with gr.Row():
             output_dir = gr.Textbox(
@@ -562,7 +566,11 @@ def ti_tab(
             output_dir_input_folder = gr.Button(
                 '📂', elem_id='open_folder_small'
             )
-            output_dir_input_folder.click(get_folder_path, outputs=output_dir)
+            output_dir_input_folder.click(
+                get_folder_path,
+                outputs=output_dir,
+                show_progress=False,
+            )
             logging_dir = gr.Textbox(
                 label='Logging folder',
                 placeholder='Optional: enable logging and output TensorBoard log to this folder',
@@ -571,7 +579,9 @@ def ti_tab(
                 '📂', elem_id='open_folder_small'
             )
             logging_dir_input_folder.click(
-                get_folder_path, outputs=logging_dir
+                get_folder_path,
+                outputs=logging_dir,
+                show_progress=False,
             )
         with gr.Row():
             output_name = gr.Textbox(
@@ -607,7 +617,11 @@ def ti_tab(
                 placeholder='(Optional) Path to existing TI embeding file to keep training',
             )
             weights_file_input = gr.Button('📂', elem_id='open_folder_small')
-            weights_file_input.click(get_file_path, outputs=weights)
+            weights_file_input.click(
+                get_file_path,
+                outputs=weights,
+                show_progress=False,
+            )
         with gr.Row():
             token_string = gr.Textbox(
                 label='Token string',
@@ -688,7 +702,11 @@ def ti_tab(
                     placeholder='(Optiona) path to checkpoint of vae to replace for training',
                 )
                 vae_button = gr.Button('📂', elem_id='open_folder_small')
-                vae_button.click(get_any_file_path, outputs=vae)
+                vae_button.click(
+                    get_any_file_path,
+                    outputs=vae,
+                    show_progress=False,
+                )
             (
                 use_8bit_adam,
                 xformers,
@@ -742,10 +760,12 @@ def ti_tab(
     button_start_tensorboard.click(
         start_tensorboard,
         inputs=logging_dir,
+        show_progress=False,
     )
 
     button_stop_tensorboard.click(
         stop_tensorboard,
+        show_progress=False,
     )
 
     settings_list = [
@@ -814,23 +834,27 @@ def ti_tab(
         open_configuration,
         inputs=[config_file_name] + settings_list,
         outputs=[config_file_name] + settings_list,
+        show_progress=False,
     )
 
     button_save_config.click(
         save_configuration,
         inputs=[dummy_db_false, config_file_name] + settings_list,
         outputs=[config_file_name],
+        show_progress=False,
     )
 
     button_save_as_config.click(
         save_configuration,
         inputs=[dummy_db_true, config_file_name] + settings_list,
         outputs=[config_file_name],
+        show_progress=False,
     )
 
     button_run.click(
         train_model,
         inputs=settings_list,
+        show_progress=False,
     )
 
     return (

ubuntu_setup.sh

@@ -0,0 +1,12 @@
+#!/bin/bash
+echo installing tk
+sudo apt install python3-tk
+python3 -m venv venv
+source venv/bin/activate
+pip install torch==1.12.1+cu116 torchvision==0.13.1+cu116 --extra-index-url https://download.pytorch.org/whl/cu116
+pip install --use-pep517 --upgrade -r requirements.txt
+pip install -U -I --no-deps https://github.com/C43H66N12O12S2/stable-diffusion-webui/releases/download/linux/xformers-0.0.14.dev0-cp310-cp310-linux_x86_64.whl
+
+accelerate config
+
+echo -e "setup finished! run \e[0;92m./gui.sh\e[0m to start"