Add authentication support
This commit is contained in:
bmaltais
parent
1bc5089db8
commit
aa5d13f9a7
@ -10,4 +10,9 @@ You can find the dreambooth solution spercific [Dreambooth README](README_dreamb
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## Finetune
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You can find the finetune solution spercific [Finetune README](README_finetune.md)
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You can find the finetune solution spercific [Finetune README](README_finetune.md)
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## Change history
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* 12/21 (v9.7) update:
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- add optional GUI authentication support via: `python fine_tune.py --username=<name> --password=<password>`
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@ -10,6 +10,7 @@ import os
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import subprocess
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import pathlib
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import shutil
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import argparse
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from library.dreambooth_folder_creation_gui import (
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gradio_dreambooth_folder_creation_tab,
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)
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@ -472,491 +473,505 @@ def set_pretrained_model_name_or_path_input(value, v2, v_parameterization):
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return value, v2, v_parameterization
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def UI(username, password):
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css = ''
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css = ''
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if os.path.exists('./style.css'):
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with open(os.path.join('./style.css'), 'r', encoding='utf8') as file:
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print('Load CSS...')
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css += file.read() + '\n'
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if os.path.exists('./style.css'):
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with open(os.path.join('./style.css'), 'r', encoding='utf8') as file:
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print('Load CSS...')
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css += file.read() + '\n'
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interface = gr.Blocks(css=css)
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interface = gr.Blocks(css=css)
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with interface:
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dummy_true = gr.Label(value=True, visible=False)
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dummy_false = gr.Label(value=False, visible=False)
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with gr.Tab('Dreambooth'):
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gr.Markdown('Enter kohya finetuner parameter using this interface.')
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with gr.Accordion('Configuration file', open=False):
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with gr.Row():
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button_open_config = gr.Button('Open 📂', elem_id='open_folder')
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button_save_config = gr.Button('Save 💾', elem_id='open_folder')
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button_save_as_config = gr.Button(
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'Save as... 💾', elem_id='open_folder'
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)
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config_file_name = gr.Textbox(
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label='',
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placeholder="type the configuration file path or use the 'Open' button above to select it...",
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interactive=True,
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)
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# config_file_name.change(
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# remove_doublequote,
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# inputs=[config_file_name],
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# outputs=[config_file_name],
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# )
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with gr.Tab('Source model'):
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# Define the input elements
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with gr.Row():
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pretrained_model_name_or_path_input = gr.Textbox(
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label='Pretrained model name or path',
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placeholder='enter the path to custom model or name of pretrained model',
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)
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pretrained_model_name_or_path_fille = gr.Button(
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document_symbol, elem_id='open_folder_small'
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)
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pretrained_model_name_or_path_fille.click(
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get_file_path,
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with interface:
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dummy_true = gr.Label(value=True, visible=False)
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dummy_false = gr.Label(value=False, visible=False)
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with gr.Tab('Dreambooth'):
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gr.Markdown('Enter kohya finetuner parameter using this interface.')
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with gr.Accordion('Configuration file', open=False):
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with gr.Row():
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button_open_config = gr.Button('Open 📂', elem_id='open_folder')
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button_save_config = gr.Button('Save 💾', elem_id='open_folder')
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button_save_as_config = gr.Button(
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'Save as... 💾', elem_id='open_folder'
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)
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config_file_name = gr.Textbox(
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label='',
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placeholder="type the configuration file path or use the 'Open' button above to select it...",
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interactive=True,
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)
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# config_file_name.change(
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# remove_doublequote,
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# inputs=[config_file_name],
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# outputs=[config_file_name],
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# )
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with gr.Tab('Source model'):
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# Define the input elements
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with gr.Row():
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pretrained_model_name_or_path_input = gr.Textbox(
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label='Pretrained model name or path',
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placeholder='enter the path to custom model or name of pretrained model',
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)
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pretrained_model_name_or_path_fille = gr.Button(
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document_symbol, elem_id='open_folder_small'
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)
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pretrained_model_name_or_path_fille.click(
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get_file_path,
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inputs=[pretrained_model_name_or_path_input],
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outputs=pretrained_model_name_or_path_input,
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)
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pretrained_model_name_or_path_folder = gr.Button(
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folder_symbol, elem_id='open_folder_small'
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)
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pretrained_model_name_or_path_folder.click(
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get_folder_path,
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outputs=pretrained_model_name_or_path_input,
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)
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model_list = gr.Dropdown(
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label='(Optional) Model Quick Pick',
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choices=[
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'custom',
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'stabilityai/stable-diffusion-2-1-base',
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'stabilityai/stable-diffusion-2-base',
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'stabilityai/stable-diffusion-2-1',
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'stabilityai/stable-diffusion-2',
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'runwayml/stable-diffusion-v1-5',
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'CompVis/stable-diffusion-v1-4',
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],
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)
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save_model_as_dropdown = gr.Dropdown(
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label='Save trained model as',
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choices=[
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'same as source model',
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'ckpt',
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'diffusers',
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'diffusers_safetensors',
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'safetensors',
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],
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value='same as source model',
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)
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with gr.Row():
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v2_input = gr.Checkbox(label='v2', value=True)
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v_parameterization_input = gr.Checkbox(
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label='v_parameterization', value=False
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)
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pretrained_model_name_or_path_input.change(
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remove_doublequote,
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inputs=[pretrained_model_name_or_path_input],
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outputs=pretrained_model_name_or_path_input,
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outputs=[pretrained_model_name_or_path_input],
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)
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pretrained_model_name_or_path_folder = gr.Button(
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folder_symbol, elem_id='open_folder_small'
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)
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pretrained_model_name_or_path_folder.click(
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get_folder_path,
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outputs=pretrained_model_name_or_path_input,
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)
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model_list = gr.Dropdown(
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label='(Optional) Model Quick Pick',
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choices=[
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'custom',
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'stabilityai/stable-diffusion-2-1-base',
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'stabilityai/stable-diffusion-2-base',
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'stabilityai/stable-diffusion-2-1',
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'stabilityai/stable-diffusion-2',
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'runwayml/stable-diffusion-v1-5',
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'CompVis/stable-diffusion-v1-4',
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model_list.change(
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set_pretrained_model_name_or_path_input,
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inputs=[model_list, v2_input, v_parameterization_input],
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outputs=[
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pretrained_model_name_or_path_input,
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v2_input,
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v_parameterization_input,
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],
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)
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save_model_as_dropdown = gr.Dropdown(
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label='Save trained model as',
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choices=[
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'same as source model',
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'ckpt',
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'diffusers',
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'diffusers_safetensors',
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'safetensors',
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],
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value='same as source model',
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)
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with gr.Row():
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v2_input = gr.Checkbox(label='v2', value=True)
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v_parameterization_input = gr.Checkbox(
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label='v_parameterization', value=False
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)
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pretrained_model_name_or_path_input.change(
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remove_doublequote,
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inputs=[pretrained_model_name_or_path_input],
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outputs=[pretrained_model_name_or_path_input],
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)
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model_list.change(
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set_pretrained_model_name_or_path_input,
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inputs=[model_list, v2_input, v_parameterization_input],
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outputs=[
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pretrained_model_name_or_path_input,
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v2_input,
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v_parameterization_input,
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],
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)
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with gr.Tab('Directories'):
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with gr.Row():
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train_data_dir_input = gr.Textbox(
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label='Image folder',
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placeholder='Folder where the training folders containing the images are located',
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)
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train_data_dir_input_folder = gr.Button(
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'📂', elem_id='open_folder_small'
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)
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train_data_dir_input_folder.click(
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get_folder_path, outputs=train_data_dir_input
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)
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reg_data_dir_input = gr.Textbox(
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label='Regularisation folder',
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placeholder='(Optional) Folder where where the regularization folders containing the images are located',
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)
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reg_data_dir_input_folder = gr.Button(
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'📂', elem_id='open_folder_small'
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)
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reg_data_dir_input_folder.click(
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get_folder_path, outputs=reg_data_dir_input
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)
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with gr.Row():
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output_dir_input = gr.Textbox(
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label='Output folder',
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placeholder='Folder to output trained model',
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)
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output_dir_input_folder = gr.Button(
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'📂', elem_id='open_folder_small'
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)
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output_dir_input_folder.click(
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get_folder_path, outputs=output_dir_input
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)
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logging_dir_input = gr.Textbox(
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label='Logging folder',
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placeholder='Optional: enable logging and output TensorBoard log to this folder',
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)
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logging_dir_input_folder = gr.Button(
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'📂', elem_id='open_folder_small'
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)
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logging_dir_input_folder.click(
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get_folder_path, outputs=logging_dir_input
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)
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train_data_dir_input.change(
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remove_doublequote,
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inputs=[train_data_dir_input],
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outputs=[train_data_dir_input],
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)
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reg_data_dir_input.change(
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remove_doublequote,
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inputs=[reg_data_dir_input],
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outputs=[reg_data_dir_input],
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)
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output_dir_input.change(
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remove_doublequote,
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inputs=[output_dir_input],
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outputs=[output_dir_input],
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)
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logging_dir_input.change(
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remove_doublequote,
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inputs=[logging_dir_input],
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outputs=[logging_dir_input],
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)
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with gr.Tab('Training parameters'):
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with gr.Row():
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learning_rate_input = gr.Textbox(
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label='Learning rate', value=1e-6
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)
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lr_scheduler_input = gr.Dropdown(
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label='LR Scheduler',
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choices=[
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'constant',
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'constant_with_warmup',
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'cosine',
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'cosine_with_restarts',
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'linear',
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'polynomial',
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],
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value='constant',
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)
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lr_warmup_input = gr.Textbox(label='LR warmup', value=0)
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with gr.Row():
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train_batch_size_input = gr.Slider(
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minimum=1,
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maximum=32,
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label='Train batch size',
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value=1,
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step=1,
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)
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epoch_input = gr.Textbox(label='Epoch', value=1)
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save_every_n_epochs_input = gr.Textbox(
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label='Save every N epochs', value=1
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)
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with gr.Row():
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mixed_precision_input = gr.Dropdown(
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label='Mixed precision',
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choices=[
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'no',
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'fp16',
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'bf16',
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],
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value='fp16',
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)
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save_precision_input = gr.Dropdown(
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label='Save precision',
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choices=[
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'float',
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'fp16',
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'bf16',
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],
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value='fp16',
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)
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num_cpu_threads_per_process_input = gr.Slider(
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minimum=1,
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maximum=os.cpu_count(),
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step=1,
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label='Number of CPU threads per process',
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value=os.cpu_count(),
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)
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with gr.Row():
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seed_input = gr.Textbox(label='Seed', value=1234)
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max_resolution_input = gr.Textbox(
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label='Max resolution',
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value='512,512',
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placeholder='512,512',
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)
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with gr.Row():
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caption_extention_input = gr.Textbox(
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label='Caption Extension',
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placeholder='(Optional) Extension for caption files. default: .caption',
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)
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stop_text_encoder_training_input = gr.Slider(
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minimum=0,
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maximum=100,
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value=0,
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step=1,
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label='Stop text encoder training',
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)
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with gr.Row():
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enable_bucket_input = gr.Checkbox(
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label='Enable buckets', value=True
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)
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cache_latent_input = gr.Checkbox(
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label='Cache latent', value=True
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)
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use_8bit_adam_input = gr.Checkbox(
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label='Use 8bit adam', value=True
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)
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xformers_input = gr.Checkbox(label='Use xformers', value=True)
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with gr.Accordion('Advanced Configuration', open=False):
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with gr.Tab('Directories'):
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with gr.Row():
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full_fp16_input = gr.Checkbox(
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label='Full fp16 training (experimental)', value=False
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train_data_dir_input = gr.Textbox(
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label='Image folder',
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placeholder='Folder where the training folders containing the images are located',
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)
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no_token_padding_input = gr.Checkbox(
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label='No token padding', value=False
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train_data_dir_input_folder = gr.Button(
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'📂', elem_id='open_folder_small'
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)
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gradient_checkpointing_input = gr.Checkbox(
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label='Gradient checkpointing', value=False
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train_data_dir_input_folder.click(
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get_folder_path, outputs=train_data_dir_input
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)
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shuffle_caption = gr.Checkbox(
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label='Shuffle caption', value=False
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reg_data_dir_input = gr.Textbox(
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label='Regularisation folder',
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placeholder='(Optional) Folder where where the regularization folders containing the images are located',
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)
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reg_data_dir_input_folder = gr.Button(
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'📂', elem_id='open_folder_small'
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)
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reg_data_dir_input_folder.click(
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get_folder_path, outputs=reg_data_dir_input
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)
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save_state = gr.Checkbox(label='Save training state', value=False)
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with gr.Row():
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resume = gr.Textbox(
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label='Resume from saved training state',
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placeholder='path to "last-state" state folder to resume from',
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output_dir_input = gr.Textbox(
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label='Output folder',
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placeholder='Folder to output trained model',
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)
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resume_button = gr.Button('📂', elem_id='open_folder_small')
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resume_button.click(get_folder_path, outputs=resume)
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prior_loss_weight = gr.Number(
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label='Prior loss weight', value=1.0
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output_dir_input_folder = gr.Button(
|
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'📂', elem_id='open_folder_small'
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)
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output_dir_input_folder.click(
|
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get_folder_path, outputs=output_dir_input
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)
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logging_dir_input = gr.Textbox(
|
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label='Logging folder',
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placeholder='Optional: enable logging and output TensorBoard log to this folder',
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)
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logging_dir_input_folder = gr.Button(
|
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'📂', elem_id='open_folder_small'
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)
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logging_dir_input_folder.click(
|
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get_folder_path, outputs=logging_dir_input
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)
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train_data_dir_input.change(
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remove_doublequote,
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inputs=[train_data_dir_input],
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outputs=[train_data_dir_input],
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)
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reg_data_dir_input.change(
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remove_doublequote,
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inputs=[reg_data_dir_input],
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outputs=[reg_data_dir_input],
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)
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output_dir_input.change(
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remove_doublequote,
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inputs=[output_dir_input],
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outputs=[output_dir_input],
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)
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logging_dir_input.change(
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remove_doublequote,
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inputs=[logging_dir_input],
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outputs=[logging_dir_input],
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)
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with gr.Tab('Training parameters'):
|
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with gr.Row():
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learning_rate_input = gr.Textbox(
|
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label='Learning rate', value=1e-6
|
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)
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lr_scheduler_input = gr.Dropdown(
|
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label='LR Scheduler',
|
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choices=[
|
||||
'constant',
|
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'constant_with_warmup',
|
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'cosine',
|
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'cosine_with_restarts',
|
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'linear',
|
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'polynomial',
|
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],
|
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value='constant',
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)
|
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lr_warmup_input = gr.Textbox(label='LR warmup', value=0)
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with gr.Row():
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train_batch_size_input = gr.Slider(
|
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minimum=1,
|
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maximum=32,
|
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label='Train batch size',
|
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value=1,
|
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step=1,
|
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)
|
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epoch_input = gr.Textbox(label='Epoch', value=1)
|
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save_every_n_epochs_input = gr.Textbox(
|
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label='Save every N epochs', value=1
|
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)
|
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with gr.Row():
|
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mixed_precision_input = gr.Dropdown(
|
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label='Mixed precision',
|
||||
choices=[
|
||||
'no',
|
||||
'fp16',
|
||||
'bf16',
|
||||
],
|
||||
value='fp16',
|
||||
)
|
||||
save_precision_input = gr.Dropdown(
|
||||
label='Save precision',
|
||||
choices=[
|
||||
'float',
|
||||
'fp16',
|
||||
'bf16',
|
||||
],
|
||||
value='fp16',
|
||||
)
|
||||
num_cpu_threads_per_process_input = gr.Slider(
|
||||
minimum=1,
|
||||
maximum=os.cpu_count(),
|
||||
step=1,
|
||||
label='Number of CPU threads per process',
|
||||
value=os.cpu_count(),
|
||||
)
|
||||
with gr.Row():
|
||||
seed_input = gr.Textbox(label='Seed', value=1234)
|
||||
max_resolution_input = gr.Textbox(
|
||||
label='Max resolution',
|
||||
value='512,512',
|
||||
placeholder='512,512',
|
||||
)
|
||||
with gr.Row():
|
||||
caption_extention_input = gr.Textbox(
|
||||
label='Caption Extension',
|
||||
placeholder='(Optional) Extension for caption files. default: .caption',
|
||||
)
|
||||
stop_text_encoder_training_input = gr.Slider(
|
||||
minimum=0,
|
||||
maximum=100,
|
||||
value=0,
|
||||
step=1,
|
||||
label='Stop text encoder training',
|
||||
)
|
||||
with gr.Row():
|
||||
enable_bucket_input = gr.Checkbox(
|
||||
label='Enable buckets', value=True
|
||||
)
|
||||
cache_latent_input = gr.Checkbox(
|
||||
label='Cache latent', value=True
|
||||
)
|
||||
use_8bit_adam_input = gr.Checkbox(
|
||||
label='Use 8bit adam', value=True
|
||||
)
|
||||
xformers_input = gr.Checkbox(label='Use xformers', value=True)
|
||||
with gr.Accordion('Advanced Configuration', open=False):
|
||||
with gr.Row():
|
||||
full_fp16_input = gr.Checkbox(
|
||||
label='Full fp16 training (experimental)', value=False
|
||||
)
|
||||
no_token_padding_input = gr.Checkbox(
|
||||
label='No token padding', value=False
|
||||
)
|
||||
|
||||
button_run = gr.Button('Train model')
|
||||
gradient_checkpointing_input = gr.Checkbox(
|
||||
label='Gradient checkpointing', value=False
|
||||
)
|
||||
|
||||
with gr.Tab('Utilities'):
|
||||
with gr.Tab('Captioning'):
|
||||
gradio_basic_caption_gui_tab()
|
||||
gradio_blip_caption_gui_tab()
|
||||
gradio_wd14_caption_gui_tab()
|
||||
gradio_dreambooth_folder_creation_tab(
|
||||
train_data_dir_input,
|
||||
reg_data_dir_input,
|
||||
output_dir_input,
|
||||
logging_dir_input,
|
||||
shuffle_caption = gr.Checkbox(
|
||||
label='Shuffle caption', value=False
|
||||
)
|
||||
save_state = gr.Checkbox(label='Save training state', value=False)
|
||||
with gr.Row():
|
||||
resume = gr.Textbox(
|
||||
label='Resume from saved training state',
|
||||
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)
|
||||
prior_loss_weight = gr.Number(
|
||||
label='Prior loss weight', value=1.0
|
||||
)
|
||||
|
||||
button_run = gr.Button('Train model')
|
||||
|
||||
with gr.Tab('Utilities'):
|
||||
with gr.Tab('Captioning'):
|
||||
gradio_basic_caption_gui_tab()
|
||||
gradio_blip_caption_gui_tab()
|
||||
gradio_wd14_caption_gui_tab()
|
||||
gradio_dreambooth_folder_creation_tab(
|
||||
train_data_dir_input,
|
||||
reg_data_dir_input,
|
||||
output_dir_input,
|
||||
logging_dir_input,
|
||||
)
|
||||
gradio_dataset_balancing_tab()
|
||||
gradio_convert_model_tab()
|
||||
|
||||
button_open_config.click(
|
||||
open_configuration,
|
||||
inputs=[
|
||||
config_file_name,
|
||||
pretrained_model_name_or_path_input,
|
||||
v2_input,
|
||||
v_parameterization_input,
|
||||
logging_dir_input,
|
||||
train_data_dir_input,
|
||||
reg_data_dir_input,
|
||||
output_dir_input,
|
||||
max_resolution_input,
|
||||
learning_rate_input,
|
||||
lr_scheduler_input,
|
||||
lr_warmup_input,
|
||||
train_batch_size_input,
|
||||
epoch_input,
|
||||
save_every_n_epochs_input,
|
||||
mixed_precision_input,
|
||||
save_precision_input,
|
||||
seed_input,
|
||||
num_cpu_threads_per_process_input,
|
||||
cache_latent_input,
|
||||
caption_extention_input,
|
||||
enable_bucket_input,
|
||||
gradient_checkpointing_input,
|
||||
full_fp16_input,
|
||||
no_token_padding_input,
|
||||
stop_text_encoder_training_input,
|
||||
use_8bit_adam_input,
|
||||
xformers_input,
|
||||
save_model_as_dropdown,
|
||||
shuffle_caption,
|
||||
save_state,
|
||||
resume,
|
||||
prior_loss_weight,
|
||||
],
|
||||
outputs=[
|
||||
config_file_name,
|
||||
pretrained_model_name_or_path_input,
|
||||
v2_input,
|
||||
v_parameterization_input,
|
||||
logging_dir_input,
|
||||
train_data_dir_input,
|
||||
reg_data_dir_input,
|
||||
output_dir_input,
|
||||
max_resolution_input,
|
||||
learning_rate_input,
|
||||
lr_scheduler_input,
|
||||
lr_warmup_input,
|
||||
train_batch_size_input,
|
||||
epoch_input,
|
||||
save_every_n_epochs_input,
|
||||
mixed_precision_input,
|
||||
save_precision_input,
|
||||
seed_input,
|
||||
num_cpu_threads_per_process_input,
|
||||
cache_latent_input,
|
||||
caption_extention_input,
|
||||
enable_bucket_input,
|
||||
gradient_checkpointing_input,
|
||||
full_fp16_input,
|
||||
no_token_padding_input,
|
||||
stop_text_encoder_training_input,
|
||||
use_8bit_adam_input,
|
||||
xformers_input,
|
||||
save_model_as_dropdown,
|
||||
shuffle_caption,
|
||||
save_state,
|
||||
resume,
|
||||
prior_loss_weight,
|
||||
],
|
||||
)
|
||||
gradio_dataset_balancing_tab()
|
||||
gradio_convert_model_tab()
|
||||
|
||||
button_open_config.click(
|
||||
open_configuration,
|
||||
inputs=[
|
||||
config_file_name,
|
||||
pretrained_model_name_or_path_input,
|
||||
v2_input,
|
||||
v_parameterization_input,
|
||||
logging_dir_input,
|
||||
train_data_dir_input,
|
||||
reg_data_dir_input,
|
||||
output_dir_input,
|
||||
max_resolution_input,
|
||||
learning_rate_input,
|
||||
lr_scheduler_input,
|
||||
lr_warmup_input,
|
||||
train_batch_size_input,
|
||||
epoch_input,
|
||||
save_every_n_epochs_input,
|
||||
mixed_precision_input,
|
||||
save_precision_input,
|
||||
seed_input,
|
||||
num_cpu_threads_per_process_input,
|
||||
cache_latent_input,
|
||||
caption_extention_input,
|
||||
enable_bucket_input,
|
||||
gradient_checkpointing_input,
|
||||
full_fp16_input,
|
||||
no_token_padding_input,
|
||||
stop_text_encoder_training_input,
|
||||
use_8bit_adam_input,
|
||||
xformers_input,
|
||||
save_model_as_dropdown,
|
||||
shuffle_caption,
|
||||
save_state,
|
||||
resume,
|
||||
prior_loss_weight,
|
||||
],
|
||||
outputs=[
|
||||
config_file_name,
|
||||
pretrained_model_name_or_path_input,
|
||||
v2_input,
|
||||
v_parameterization_input,
|
||||
logging_dir_input,
|
||||
train_data_dir_input,
|
||||
reg_data_dir_input,
|
||||
output_dir_input,
|
||||
max_resolution_input,
|
||||
learning_rate_input,
|
||||
lr_scheduler_input,
|
||||
lr_warmup_input,
|
||||
train_batch_size_input,
|
||||
epoch_input,
|
||||
save_every_n_epochs_input,
|
||||
mixed_precision_input,
|
||||
save_precision_input,
|
||||
seed_input,
|
||||
num_cpu_threads_per_process_input,
|
||||
cache_latent_input,
|
||||
caption_extention_input,
|
||||
enable_bucket_input,
|
||||
gradient_checkpointing_input,
|
||||
full_fp16_input,
|
||||
no_token_padding_input,
|
||||
stop_text_encoder_training_input,
|
||||
use_8bit_adam_input,
|
||||
xformers_input,
|
||||
save_model_as_dropdown,
|
||||
shuffle_caption,
|
||||
save_state,
|
||||
resume,
|
||||
prior_loss_weight,
|
||||
],
|
||||
)
|
||||
save_as = True
|
||||
not_save_as = False
|
||||
button_save_config.click(
|
||||
save_configuration,
|
||||
inputs=[
|
||||
dummy_false,
|
||||
config_file_name,
|
||||
pretrained_model_name_or_path_input,
|
||||
v2_input,
|
||||
v_parameterization_input,
|
||||
logging_dir_input,
|
||||
train_data_dir_input,
|
||||
reg_data_dir_input,
|
||||
output_dir_input,
|
||||
max_resolution_input,
|
||||
learning_rate_input,
|
||||
lr_scheduler_input,
|
||||
lr_warmup_input,
|
||||
train_batch_size_input,
|
||||
epoch_input,
|
||||
save_every_n_epochs_input,
|
||||
mixed_precision_input,
|
||||
save_precision_input,
|
||||
seed_input,
|
||||
num_cpu_threads_per_process_input,
|
||||
cache_latent_input,
|
||||
caption_extention_input,
|
||||
enable_bucket_input,
|
||||
gradient_checkpointing_input,
|
||||
full_fp16_input,
|
||||
no_token_padding_input,
|
||||
stop_text_encoder_training_input,
|
||||
use_8bit_adam_input,
|
||||
xformers_input,
|
||||
save_model_as_dropdown,
|
||||
shuffle_caption,
|
||||
save_state,
|
||||
resume,
|
||||
prior_loss_weight,
|
||||
],
|
||||
outputs=[config_file_name],
|
||||
)
|
||||
|
||||
save_as = True
|
||||
not_save_as = False
|
||||
button_save_config.click(
|
||||
save_configuration,
|
||||
inputs=[
|
||||
dummy_false,
|
||||
config_file_name,
|
||||
pretrained_model_name_or_path_input,
|
||||
v2_input,
|
||||
v_parameterization_input,
|
||||
logging_dir_input,
|
||||
train_data_dir_input,
|
||||
reg_data_dir_input,
|
||||
output_dir_input,
|
||||
max_resolution_input,
|
||||
learning_rate_input,
|
||||
lr_scheduler_input,
|
||||
lr_warmup_input,
|
||||
train_batch_size_input,
|
||||
epoch_input,
|
||||
save_every_n_epochs_input,
|
||||
mixed_precision_input,
|
||||
save_precision_input,
|
||||
seed_input,
|
||||
num_cpu_threads_per_process_input,
|
||||
cache_latent_input,
|
||||
caption_extention_input,
|
||||
enable_bucket_input,
|
||||
gradient_checkpointing_input,
|
||||
full_fp16_input,
|
||||
no_token_padding_input,
|
||||
stop_text_encoder_training_input,
|
||||
use_8bit_adam_input,
|
||||
xformers_input,
|
||||
save_model_as_dropdown,
|
||||
shuffle_caption,
|
||||
save_state,
|
||||
resume,
|
||||
prior_loss_weight,
|
||||
],
|
||||
outputs=[config_file_name],
|
||||
)
|
||||
button_save_as_config.click(
|
||||
save_configuration,
|
||||
inputs=[
|
||||
dummy_true,
|
||||
config_file_name,
|
||||
pretrained_model_name_or_path_input,
|
||||
v2_input,
|
||||
v_parameterization_input,
|
||||
logging_dir_input,
|
||||
train_data_dir_input,
|
||||
reg_data_dir_input,
|
||||
output_dir_input,
|
||||
max_resolution_input,
|
||||
learning_rate_input,
|
||||
lr_scheduler_input,
|
||||
lr_warmup_input,
|
||||
train_batch_size_input,
|
||||
epoch_input,
|
||||
save_every_n_epochs_input,
|
||||
mixed_precision_input,
|
||||
save_precision_input,
|
||||
seed_input,
|
||||
num_cpu_threads_per_process_input,
|
||||
cache_latent_input,
|
||||
caption_extention_input,
|
||||
enable_bucket_input,
|
||||
gradient_checkpointing_input,
|
||||
full_fp16_input,
|
||||
no_token_padding_input,
|
||||
stop_text_encoder_training_input,
|
||||
use_8bit_adam_input,
|
||||
xformers_input,
|
||||
save_model_as_dropdown,
|
||||
shuffle_caption,
|
||||
save_state,
|
||||
resume,
|
||||
prior_loss_weight,
|
||||
],
|
||||
outputs=[config_file_name],
|
||||
)
|
||||
|
||||
button_save_as_config.click(
|
||||
save_configuration,
|
||||
inputs=[
|
||||
dummy_true,
|
||||
config_file_name,
|
||||
pretrained_model_name_or_path_input,
|
||||
v2_input,
|
||||
v_parameterization_input,
|
||||
logging_dir_input,
|
||||
train_data_dir_input,
|
||||
reg_data_dir_input,
|
||||
output_dir_input,
|
||||
max_resolution_input,
|
||||
learning_rate_input,
|
||||
lr_scheduler_input,
|
||||
lr_warmup_input,
|
||||
train_batch_size_input,
|
||||
epoch_input,
|
||||
save_every_n_epochs_input,
|
||||
mixed_precision_input,
|
||||
save_precision_input,
|
||||
seed_input,
|
||||
num_cpu_threads_per_process_input,
|
||||
cache_latent_input,
|
||||
caption_extention_input,
|
||||
enable_bucket_input,
|
||||
gradient_checkpointing_input,
|
||||
full_fp16_input,
|
||||
no_token_padding_input,
|
||||
stop_text_encoder_training_input,
|
||||
use_8bit_adam_input,
|
||||
xformers_input,
|
||||
save_model_as_dropdown,
|
||||
shuffle_caption,
|
||||
save_state,
|
||||
resume,
|
||||
prior_loss_weight,
|
||||
],
|
||||
outputs=[config_file_name],
|
||||
)
|
||||
button_run.click(
|
||||
train_model,
|
||||
inputs=[
|
||||
pretrained_model_name_or_path_input,
|
||||
v2_input,
|
||||
v_parameterization_input,
|
||||
logging_dir_input,
|
||||
train_data_dir_input,
|
||||
reg_data_dir_input,
|
||||
output_dir_input,
|
||||
max_resolution_input,
|
||||
learning_rate_input,
|
||||
lr_scheduler_input,
|
||||
lr_warmup_input,
|
||||
train_batch_size_input,
|
||||
epoch_input,
|
||||
save_every_n_epochs_input,
|
||||
mixed_precision_input,
|
||||
save_precision_input,
|
||||
seed_input,
|
||||
num_cpu_threads_per_process_input,
|
||||
cache_latent_input,
|
||||
caption_extention_input,
|
||||
enable_bucket_input,
|
||||
gradient_checkpointing_input,
|
||||
full_fp16_input,
|
||||
no_token_padding_input,
|
||||
stop_text_encoder_training_input,
|
||||
use_8bit_adam_input,
|
||||
xformers_input,
|
||||
save_model_as_dropdown,
|
||||
shuffle_caption,
|
||||
save_state,
|
||||
resume,
|
||||
prior_loss_weight,
|
||||
],
|
||||
)
|
||||
|
||||
button_run.click(
|
||||
train_model,
|
||||
inputs=[
|
||||
pretrained_model_name_or_path_input,
|
||||
v2_input,
|
||||
v_parameterization_input,
|
||||
logging_dir_input,
|
||||
train_data_dir_input,
|
||||
reg_data_dir_input,
|
||||
output_dir_input,
|
||||
max_resolution_input,
|
||||
learning_rate_input,
|
||||
lr_scheduler_input,
|
||||
lr_warmup_input,
|
||||
train_batch_size_input,
|
||||
epoch_input,
|
||||
save_every_n_epochs_input,
|
||||
mixed_precision_input,
|
||||
save_precision_input,
|
||||
seed_input,
|
||||
num_cpu_threads_per_process_input,
|
||||
cache_latent_input,
|
||||
caption_extention_input,
|
||||
enable_bucket_input,
|
||||
gradient_checkpointing_input,
|
||||
full_fp16_input,
|
||||
no_token_padding_input,
|
||||
stop_text_encoder_training_input,
|
||||
use_8bit_adam_input,
|
||||
xformers_input,
|
||||
save_model_as_dropdown,
|
||||
shuffle_caption,
|
||||
save_state,
|
||||
resume,
|
||||
prior_loss_weight,
|
||||
],
|
||||
)
|
||||
# Show the interface
|
||||
if not username == '':
|
||||
interface.launch(auth=(username, password))
|
||||
else:
|
||||
interface.launch()
|
||||
|
||||
# Show the interface
|
||||
interface.launch()
|
||||
|
||||
if __name__ == '__main__':
|
||||
# torch.cuda.set_per_process_memory_fraction(0.48)
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--username", type=str, default='', help="Username for authentication")
|
||||
parser.add_argument("--password", type=str, default='', help="Password for authentication")
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
UI(username=args.username, password=args.password)
|
||||
|
||||
791
finetune_gui.py
791
finetune_gui.py
@ -5,6 +5,7 @@ import os
|
||||
import subprocess
|
||||
import pathlib
|
||||
import shutil
|
||||
import argparse
|
||||
|
||||
# from easygui import fileopenbox, filesavebox, diropenbox, msgbox
|
||||
from library.basic_caption_gui import gradio_basic_caption_gui_tab
|
||||
@ -99,9 +100,8 @@ def save_configuration(
|
||||
}
|
||||
|
||||
# Save the data to the selected file
|
||||
# with open(file_path, 'w') as file:
|
||||
# json.dump(variables, file)
|
||||
# msgbox('File was saved...')
|
||||
with open(file_path, 'w') as file:
|
||||
json.dump(variables, file)
|
||||
|
||||
return file_path
|
||||
|
||||
@ -386,410 +386,425 @@ def remove_doublequote(file_path):
|
||||
|
||||
return file_path
|
||||
|
||||
def UI(username, password):
|
||||
|
||||
css = ''
|
||||
css = ''
|
||||
|
||||
if os.path.exists('./style.css'):
|
||||
with open(os.path.join('./style.css'), 'r', encoding='utf8') as file:
|
||||
print('Load CSS...')
|
||||
css += file.read() + '\n'
|
||||
if os.path.exists('./style.css'):
|
||||
with open(os.path.join('./style.css'), 'r', encoding='utf8') as file:
|
||||
print('Load CSS...')
|
||||
css += file.read() + '\n'
|
||||
|
||||
interface = gr.Blocks(css=css)
|
||||
interface = gr.Blocks(css=css)
|
||||
|
||||
with interface:
|
||||
dummy_true = gr.Label(value=True, visible=False)
|
||||
dummy_false = gr.Label(value=False, visible=False)
|
||||
with gr.Tab('Finetuning'):
|
||||
gr.Markdown('Enter kohya finetuner parameter using this interface.')
|
||||
with gr.Accordion('Configuration File Load/Save', open=False):
|
||||
with gr.Row():
|
||||
button_open_config = gr.Button(
|
||||
f'Open {folder_symbol}', elem_id='open_folder'
|
||||
with interface:
|
||||
dummy_true = gr.Label(value=True, visible=False)
|
||||
dummy_false = gr.Label(value=False, visible=False)
|
||||
with gr.Tab('Finetuning'):
|
||||
gr.Markdown('Enter kohya finetuner parameter using this interface.')
|
||||
with gr.Accordion('Configuration File Load/Save', open=False):
|
||||
with gr.Row():
|
||||
button_open_config = gr.Button(
|
||||
f'Open {folder_symbol}', elem_id='open_folder'
|
||||
)
|
||||
button_save_config = gr.Button(
|
||||
f'Save {save_style_symbol}', elem_id='open_folder'
|
||||
)
|
||||
button_save_as_config = gr.Button(
|
||||
f'Save as... {save_style_symbol}', elem_id='open_folder'
|
||||
)
|
||||
config_file_name = gr.Textbox(
|
||||
label='', placeholder='type file path or use buttons...'
|
||||
)
|
||||
button_save_config = gr.Button(
|
||||
f'Save {save_style_symbol}', elem_id='open_folder'
|
||||
config_file_name.change(
|
||||
remove_doublequote,
|
||||
inputs=[config_file_name],
|
||||
outputs=[config_file_name],
|
||||
)
|
||||
button_save_as_config = gr.Button(
|
||||
f'Save as... {save_style_symbol}', elem_id='open_folder'
|
||||
)
|
||||
config_file_name = gr.Textbox(
|
||||
label='', placeholder='type file path or use buttons...'
|
||||
)
|
||||
config_file_name.change(
|
||||
remove_doublequote,
|
||||
inputs=[config_file_name],
|
||||
outputs=[config_file_name],
|
||||
)
|
||||
with gr.Tab('Source model'):
|
||||
# Define the input elements
|
||||
with gr.Row():
|
||||
pretrained_model_name_or_path_input = gr.Textbox(
|
||||
label='Pretrained model name or path',
|
||||
placeholder='enter the path to custom model or name of pretrained model',
|
||||
)
|
||||
pretrained_model_name_or_path_file = gr.Button(
|
||||
document_symbol, elem_id='open_folder_small'
|
||||
)
|
||||
pretrained_model_name_or_path_file.click(
|
||||
get_file_path,
|
||||
inputs=pretrained_model_name_or_path_input,
|
||||
outputs=pretrained_model_name_or_path_input,
|
||||
)
|
||||
pretrained_model_name_or_path_folder = gr.Button(
|
||||
folder_symbol, elem_id='open_folder_small'
|
||||
)
|
||||
pretrained_model_name_or_path_folder.click(
|
||||
get_folder_path,
|
||||
inputs=pretrained_model_name_or_path_input,
|
||||
outputs=pretrained_model_name_or_path_input,
|
||||
)
|
||||
model_list = gr.Dropdown(
|
||||
label='(Optional) Model Quick Pick',
|
||||
choices=[
|
||||
'custom',
|
||||
'stabilityai/stable-diffusion-2-1-base',
|
||||
'stabilityai/stable-diffusion-2-base',
|
||||
'stabilityai/stable-diffusion-2-1',
|
||||
'stabilityai/stable-diffusion-2',
|
||||
'runwayml/stable-diffusion-v1-5',
|
||||
'CompVis/stable-diffusion-v1-4',
|
||||
with gr.Tab('Source model'):
|
||||
# Define the input elements
|
||||
with gr.Row():
|
||||
pretrained_model_name_or_path_input = gr.Textbox(
|
||||
label='Pretrained model name or path',
|
||||
placeholder='enter the path to custom model or name of pretrained model',
|
||||
)
|
||||
pretrained_model_name_or_path_file = gr.Button(
|
||||
document_symbol, elem_id='open_folder_small'
|
||||
)
|
||||
pretrained_model_name_or_path_file.click(
|
||||
get_file_path,
|
||||
inputs=pretrained_model_name_or_path_input,
|
||||
outputs=pretrained_model_name_or_path_input,
|
||||
)
|
||||
pretrained_model_name_or_path_folder = gr.Button(
|
||||
folder_symbol, elem_id='open_folder_small'
|
||||
)
|
||||
pretrained_model_name_or_path_folder.click(
|
||||
get_folder_path,
|
||||
inputs=pretrained_model_name_or_path_input,
|
||||
outputs=pretrained_model_name_or_path_input,
|
||||
)
|
||||
model_list = gr.Dropdown(
|
||||
label='(Optional) Model Quick Pick',
|
||||
choices=[
|
||||
'custom',
|
||||
'stabilityai/stable-diffusion-2-1-base',
|
||||
'stabilityai/stable-diffusion-2-base',
|
||||
'stabilityai/stable-diffusion-2-1',
|
||||
'stabilityai/stable-diffusion-2',
|
||||
'runwayml/stable-diffusion-v1-5',
|
||||
'CompVis/stable-diffusion-v1-4',
|
||||
],
|
||||
)
|
||||
save_model_as_dropdown = gr.Dropdown(
|
||||
label='Save trained model as',
|
||||
choices=[
|
||||
'same as source model',
|
||||
'ckpt',
|
||||
'diffusers',
|
||||
'diffusers_safetensors',
|
||||
'safetensors',
|
||||
],
|
||||
value='same as source model',
|
||||
)
|
||||
|
||||
with gr.Row():
|
||||
v2_input = gr.Checkbox(label='v2', value=True)
|
||||
v_parameterization_input = gr.Checkbox(
|
||||
label='v_parameterization', value=False
|
||||
)
|
||||
model_list.change(
|
||||
set_pretrained_model_name_or_path_input,
|
||||
inputs=[model_list, v2_input, v_parameterization_input],
|
||||
outputs=[
|
||||
pretrained_model_name_or_path_input,
|
||||
v2_input,
|
||||
v_parameterization_input,
|
||||
],
|
||||
)
|
||||
save_model_as_dropdown = gr.Dropdown(
|
||||
label='Save trained model as',
|
||||
choices=[
|
||||
'same as source model',
|
||||
'ckpt',
|
||||
'diffusers',
|
||||
'diffusers_safetensors',
|
||||
'safetensors',
|
||||
],
|
||||
value='same as source model',
|
||||
)
|
||||
with gr.Tab('Directories'):
|
||||
with gr.Row():
|
||||
train_dir_input = gr.Textbox(
|
||||
label='Training config folder',
|
||||
placeholder='folder where the training configuration files will be saved',
|
||||
)
|
||||
train_dir_folder = gr.Button(
|
||||
folder_symbol, elem_id='open_folder_small'
|
||||
)
|
||||
train_dir_folder.click(
|
||||
get_folder_path, outputs=train_dir_input
|
||||
)
|
||||
|
||||
with gr.Row():
|
||||
v2_input = gr.Checkbox(label='v2', value=True)
|
||||
v_parameterization_input = gr.Checkbox(
|
||||
label='v_parameterization', value=False
|
||||
image_folder_input = gr.Textbox(
|
||||
label='Training Image folder',
|
||||
placeholder='folder where the training images are located',
|
||||
)
|
||||
image_folder_input_folder = gr.Button(
|
||||
folder_symbol, elem_id='open_folder_small'
|
||||
)
|
||||
image_folder_input_folder.click(
|
||||
get_folder_path, outputs=image_folder_input
|
||||
)
|
||||
with gr.Row():
|
||||
output_dir_input = gr.Textbox(
|
||||
label='Output folder',
|
||||
placeholder='folder where the model will be saved',
|
||||
)
|
||||
output_dir_input_folder = gr.Button(
|
||||
folder_symbol, elem_id='open_folder_small'
|
||||
)
|
||||
output_dir_input_folder.click(
|
||||
get_folder_path, outputs=output_dir_input
|
||||
)
|
||||
|
||||
logging_dir_input = gr.Textbox(
|
||||
label='Logging folder',
|
||||
placeholder='Optional: enable logging and output TensorBoard log to this folder',
|
||||
)
|
||||
logging_dir_input_folder = gr.Button(
|
||||
folder_symbol, elem_id='open_folder_small'
|
||||
)
|
||||
logging_dir_input_folder.click(
|
||||
get_folder_path, outputs=logging_dir_input
|
||||
)
|
||||
train_dir_input.change(
|
||||
remove_doublequote,
|
||||
inputs=[train_dir_input],
|
||||
outputs=[train_dir_input],
|
||||
)
|
||||
model_list.change(
|
||||
set_pretrained_model_name_or_path_input,
|
||||
inputs=[model_list, v2_input, v_parameterization_input],
|
||||
outputs=[
|
||||
image_folder_input.change(
|
||||
remove_doublequote,
|
||||
inputs=[image_folder_input],
|
||||
outputs=[image_folder_input],
|
||||
)
|
||||
output_dir_input.change(
|
||||
remove_doublequote,
|
||||
inputs=[output_dir_input],
|
||||
outputs=[output_dir_input],
|
||||
)
|
||||
with gr.Tab('Training parameters'):
|
||||
with gr.Row():
|
||||
learning_rate_input = gr.Textbox(
|
||||
label='Learning rate', value=1e-6
|
||||
)
|
||||
lr_scheduler_input = gr.Dropdown(
|
||||
label='LR Scheduler',
|
||||
choices=[
|
||||
'constant',
|
||||
'constant_with_warmup',
|
||||
'cosine',
|
||||
'cosine_with_restarts',
|
||||
'linear',
|
||||
'polynomial',
|
||||
],
|
||||
value='constant',
|
||||
)
|
||||
lr_warmup_input = gr.Textbox(label='LR warmup', value=0)
|
||||
with gr.Row():
|
||||
dataset_repeats_input = gr.Textbox(
|
||||
label='Dataset repeats', value=40
|
||||
)
|
||||
train_batch_size_input = gr.Slider(
|
||||
minimum=1,
|
||||
maximum=32,
|
||||
label='Train batch size',
|
||||
value=1,
|
||||
step=1,
|
||||
)
|
||||
epoch_input = gr.Textbox(label='Epoch', value=1)
|
||||
save_every_n_epochs_input = gr.Textbox(
|
||||
label='Save every N epochs', value=1
|
||||
)
|
||||
with gr.Row():
|
||||
mixed_precision_input = gr.Dropdown(
|
||||
label='Mixed precision',
|
||||
choices=[
|
||||
'no',
|
||||
'fp16',
|
||||
'bf16',
|
||||
],
|
||||
value='fp16',
|
||||
)
|
||||
save_precision_input = gr.Dropdown(
|
||||
label='Save precision',
|
||||
choices=[
|
||||
'float',
|
||||
'fp16',
|
||||
'bf16',
|
||||
],
|
||||
value='fp16',
|
||||
)
|
||||
num_cpu_threads_per_process_input = gr.Slider(
|
||||
minimum=1,
|
||||
maximum=os.cpu_count(),
|
||||
step=1,
|
||||
label='Number of CPU threads per process',
|
||||
value=os.cpu_count(),
|
||||
)
|
||||
with gr.Row():
|
||||
seed_input = gr.Textbox(label='Seed', value=1234)
|
||||
max_resolution_input = gr.Textbox(
|
||||
label='Max resolution', value='512,512'
|
||||
)
|
||||
with gr.Row():
|
||||
caption_extention_input = gr.Textbox(
|
||||
label='Caption Extension',
|
||||
placeholder='(Optional) Extension for caption files. default: .txt',
|
||||
)
|
||||
train_text_encoder_input = gr.Checkbox(
|
||||
label='Train text encoder', value=True
|
||||
)
|
||||
with gr.Box():
|
||||
with gr.Row():
|
||||
create_caption = gr.Checkbox(
|
||||
label='Generate caption database', value=True
|
||||
)
|
||||
create_buckets = gr.Checkbox(
|
||||
label='Generate image buckets', value=True
|
||||
)
|
||||
train = gr.Checkbox(label='Train model', value=True)
|
||||
|
||||
button_run = gr.Button('Run')
|
||||
|
||||
button_run.click(
|
||||
train_model,
|
||||
inputs=[
|
||||
create_caption,
|
||||
create_buckets,
|
||||
train,
|
||||
pretrained_model_name_or_path_input,
|
||||
v2_input,
|
||||
v_parameterization_input,
|
||||
train_dir_input,
|
||||
image_folder_input,
|
||||
output_dir_input,
|
||||
logging_dir_input,
|
||||
max_resolution_input,
|
||||
learning_rate_input,
|
||||
lr_scheduler_input,
|
||||
lr_warmup_input,
|
||||
dataset_repeats_input,
|
||||
train_batch_size_input,
|
||||
epoch_input,
|
||||
save_every_n_epochs_input,
|
||||
mixed_precision_input,
|
||||
save_precision_input,
|
||||
seed_input,
|
||||
num_cpu_threads_per_process_input,
|
||||
train_text_encoder_input,
|
||||
save_model_as_dropdown,
|
||||
caption_extention_input,
|
||||
],
|
||||
)
|
||||
with gr.Tab('Directories'):
|
||||
with gr.Row():
|
||||
train_dir_input = gr.Textbox(
|
||||
label='Training config folder',
|
||||
placeholder='folder where the training configuration files will be saved',
|
||||
)
|
||||
train_dir_folder = gr.Button(
|
||||
folder_symbol, elem_id='open_folder_small'
|
||||
)
|
||||
train_dir_folder.click(
|
||||
get_folder_path, outputs=train_dir_input
|
||||
)
|
||||
|
||||
image_folder_input = gr.Textbox(
|
||||
label='Training Image folder',
|
||||
placeholder='folder where the training images are located',
|
||||
)
|
||||
image_folder_input_folder = gr.Button(
|
||||
folder_symbol, elem_id='open_folder_small'
|
||||
)
|
||||
image_folder_input_folder.click(
|
||||
get_folder_path, outputs=image_folder_input
|
||||
)
|
||||
with gr.Row():
|
||||
output_dir_input = gr.Textbox(
|
||||
label='Output folder',
|
||||
placeholder='folder where the model will be saved',
|
||||
)
|
||||
output_dir_input_folder = gr.Button(
|
||||
folder_symbol, elem_id='open_folder_small'
|
||||
)
|
||||
output_dir_input_folder.click(
|
||||
get_folder_path, outputs=output_dir_input
|
||||
)
|
||||
|
||||
logging_dir_input = gr.Textbox(
|
||||
label='Logging folder',
|
||||
placeholder='Optional: enable logging and output TensorBoard log to this folder',
|
||||
)
|
||||
logging_dir_input_folder = gr.Button(
|
||||
folder_symbol, elem_id='open_folder_small'
|
||||
)
|
||||
logging_dir_input_folder.click(
|
||||
get_folder_path, outputs=logging_dir_input
|
||||
)
|
||||
train_dir_input.change(
|
||||
remove_doublequote,
|
||||
inputs=[train_dir_input],
|
||||
outputs=[train_dir_input],
|
||||
button_open_config.click(
|
||||
open_config_file,
|
||||
inputs=[
|
||||
config_file_name,
|
||||
pretrained_model_name_or_path_input,
|
||||
v2_input,
|
||||
v_parameterization_input,
|
||||
train_dir_input,
|
||||
image_folder_input,
|
||||
output_dir_input,
|
||||
logging_dir_input,
|
||||
max_resolution_input,
|
||||
learning_rate_input,
|
||||
lr_scheduler_input,
|
||||
lr_warmup_input,
|
||||
dataset_repeats_input,
|
||||
train_batch_size_input,
|
||||
epoch_input,
|
||||
save_every_n_epochs_input,
|
||||
mixed_precision_input,
|
||||
save_precision_input,
|
||||
seed_input,
|
||||
num_cpu_threads_per_process_input,
|
||||
train_text_encoder_input,
|
||||
create_buckets,
|
||||
create_caption,
|
||||
train,
|
||||
save_model_as_dropdown,
|
||||
caption_extention_input,
|
||||
],
|
||||
outputs=[
|
||||
config_file_name,
|
||||
pretrained_model_name_or_path_input,
|
||||
v2_input,
|
||||
v_parameterization_input,
|
||||
train_dir_input,
|
||||
image_folder_input,
|
||||
output_dir_input,
|
||||
logging_dir_input,
|
||||
max_resolution_input,
|
||||
learning_rate_input,
|
||||
lr_scheduler_input,
|
||||
lr_warmup_input,
|
||||
dataset_repeats_input,
|
||||
train_batch_size_input,
|
||||
epoch_input,
|
||||
save_every_n_epochs_input,
|
||||
mixed_precision_input,
|
||||
save_precision_input,
|
||||
seed_input,
|
||||
num_cpu_threads_per_process_input,
|
||||
train_text_encoder_input,
|
||||
create_buckets,
|
||||
create_caption,
|
||||
train,
|
||||
save_model_as_dropdown,
|
||||
caption_extention_input,
|
||||
],
|
||||
)
|
||||
image_folder_input.change(
|
||||
remove_doublequote,
|
||||
inputs=[image_folder_input],
|
||||
outputs=[image_folder_input],
|
||||
|
||||
button_save_config.click(
|
||||
save_configuration,
|
||||
inputs=[
|
||||
dummy_false,
|
||||
config_file_name,
|
||||
pretrained_model_name_or_path_input,
|
||||
v2_input,
|
||||
v_parameterization_input,
|
||||
train_dir_input,
|
||||
image_folder_input,
|
||||
output_dir_input,
|
||||
logging_dir_input,
|
||||
max_resolution_input,
|
||||
learning_rate_input,
|
||||
lr_scheduler_input,
|
||||
lr_warmup_input,
|
||||
dataset_repeats_input,
|
||||
train_batch_size_input,
|
||||
epoch_input,
|
||||
save_every_n_epochs_input,
|
||||
mixed_precision_input,
|
||||
save_precision_input,
|
||||
seed_input,
|
||||
num_cpu_threads_per_process_input,
|
||||
train_text_encoder_input,
|
||||
create_buckets,
|
||||
create_caption,
|
||||
train,
|
||||
save_model_as_dropdown,
|
||||
caption_extention_input,
|
||||
],
|
||||
outputs=[config_file_name],
|
||||
)
|
||||
output_dir_input.change(
|
||||
remove_doublequote,
|
||||
inputs=[output_dir_input],
|
||||
outputs=[output_dir_input],
|
||||
|
||||
button_save_as_config.click(
|
||||
save_configuration,
|
||||
inputs=[
|
||||
dummy_true,
|
||||
config_file_name,
|
||||
pretrained_model_name_or_path_input,
|
||||
v2_input,
|
||||
v_parameterization_input,
|
||||
train_dir_input,
|
||||
image_folder_input,
|
||||
output_dir_input,
|
||||
logging_dir_input,
|
||||
max_resolution_input,
|
||||
learning_rate_input,
|
||||
lr_scheduler_input,
|
||||
lr_warmup_input,
|
||||
dataset_repeats_input,
|
||||
train_batch_size_input,
|
||||
epoch_input,
|
||||
save_every_n_epochs_input,
|
||||
mixed_precision_input,
|
||||
save_precision_input,
|
||||
seed_input,
|
||||
num_cpu_threads_per_process_input,
|
||||
train_text_encoder_input,
|
||||
create_buckets,
|
||||
create_caption,
|
||||
train,
|
||||
save_model_as_dropdown,
|
||||
caption_extention_input,
|
||||
],
|
||||
outputs=[config_file_name],
|
||||
)
|
||||
with gr.Tab('Training parameters'):
|
||||
with gr.Row():
|
||||
learning_rate_input = gr.Textbox(
|
||||
label='Learning rate', value=1e-6
|
||||
)
|
||||
lr_scheduler_input = gr.Dropdown(
|
||||
label='LR Scheduler',
|
||||
choices=[
|
||||
'constant',
|
||||
'constant_with_warmup',
|
||||
'cosine',
|
||||
'cosine_with_restarts',
|
||||
'linear',
|
||||
'polynomial',
|
||||
],
|
||||
value='constant',
|
||||
)
|
||||
lr_warmup_input = gr.Textbox(label='LR warmup', value=0)
|
||||
with gr.Row():
|
||||
dataset_repeats_input = gr.Textbox(
|
||||
label='Dataset repeats', value=40
|
||||
)
|
||||
train_batch_size_input = gr.Slider(
|
||||
minimum=1,
|
||||
maximum=32,
|
||||
label='Train batch size',
|
||||
value=1,
|
||||
step=1,
|
||||
)
|
||||
epoch_input = gr.Textbox(label='Epoch', value=1)
|
||||
save_every_n_epochs_input = gr.Textbox(
|
||||
label='Save every N epochs', value=1
|
||||
)
|
||||
with gr.Row():
|
||||
mixed_precision_input = gr.Dropdown(
|
||||
label='Mixed precision',
|
||||
choices=[
|
||||
'no',
|
||||
'fp16',
|
||||
'bf16',
|
||||
],
|
||||
value='fp16',
|
||||
)
|
||||
save_precision_input = gr.Dropdown(
|
||||
label='Save precision',
|
||||
choices=[
|
||||
'float',
|
||||
'fp16',
|
||||
'bf16',
|
||||
],
|
||||
value='fp16',
|
||||
)
|
||||
num_cpu_threads_per_process_input = gr.Slider(
|
||||
minimum=1,
|
||||
maximum=os.cpu_count(),
|
||||
step=1,
|
||||
label='Number of CPU threads per process',
|
||||
value=os.cpu_count(),
|
||||
)
|
||||
with gr.Row():
|
||||
seed_input = gr.Textbox(label='Seed', value=1234)
|
||||
max_resolution_input = gr.Textbox(
|
||||
label='Max resolution', value='512,512'
|
||||
)
|
||||
with gr.Row():
|
||||
caption_extention_input = gr.Textbox(
|
||||
label='Caption Extension',
|
||||
placeholder='(Optional) Extension for caption files. default: .txt',
|
||||
)
|
||||
train_text_encoder_input = gr.Checkbox(
|
||||
label='Train text encoder', value=True
|
||||
)
|
||||
with gr.Box():
|
||||
with gr.Row():
|
||||
create_caption = gr.Checkbox(
|
||||
label='Generate caption database', value=True
|
||||
)
|
||||
create_buckets = gr.Checkbox(
|
||||
label='Generate image buckets', value=True
|
||||
)
|
||||
train = gr.Checkbox(label='Train model', value=True)
|
||||
|
||||
button_run = gr.Button('Run')
|
||||
|
||||
button_run.click(
|
||||
train_model,
|
||||
inputs=[
|
||||
create_caption,
|
||||
create_buckets,
|
||||
train,
|
||||
pretrained_model_name_or_path_input,
|
||||
v2_input,
|
||||
v_parameterization_input,
|
||||
train_dir_input,
|
||||
image_folder_input,
|
||||
output_dir_input,
|
||||
logging_dir_input,
|
||||
max_resolution_input,
|
||||
learning_rate_input,
|
||||
lr_scheduler_input,
|
||||
lr_warmup_input,
|
||||
dataset_repeats_input,
|
||||
train_batch_size_input,
|
||||
epoch_input,
|
||||
save_every_n_epochs_input,
|
||||
mixed_precision_input,
|
||||
save_precision_input,
|
||||
seed_input,
|
||||
num_cpu_threads_per_process_input,
|
||||
train_text_encoder_input,
|
||||
save_model_as_dropdown,
|
||||
caption_extention_input,
|
||||
],
|
||||
)
|
||||
|
||||
button_open_config.click(
|
||||
open_config_file,
|
||||
inputs=[
|
||||
config_file_name,
|
||||
pretrained_model_name_or_path_input,
|
||||
v2_input,
|
||||
v_parameterization_input,
|
||||
train_dir_input,
|
||||
image_folder_input,
|
||||
output_dir_input,
|
||||
logging_dir_input,
|
||||
max_resolution_input,
|
||||
learning_rate_input,
|
||||
lr_scheduler_input,
|
||||
lr_warmup_input,
|
||||
dataset_repeats_input,
|
||||
train_batch_size_input,
|
||||
epoch_input,
|
||||
save_every_n_epochs_input,
|
||||
mixed_precision_input,
|
||||
save_precision_input,
|
||||
seed_input,
|
||||
num_cpu_threads_per_process_input,
|
||||
train_text_encoder_input,
|
||||
create_buckets,
|
||||
create_caption,
|
||||
train,
|
||||
save_model_as_dropdown,
|
||||
caption_extention_input,
|
||||
],
|
||||
outputs=[
|
||||
config_file_name,
|
||||
pretrained_model_name_or_path_input,
|
||||
v2_input,
|
||||
v_parameterization_input,
|
||||
train_dir_input,
|
||||
image_folder_input,
|
||||
output_dir_input,
|
||||
logging_dir_input,
|
||||
max_resolution_input,
|
||||
learning_rate_input,
|
||||
lr_scheduler_input,
|
||||
lr_warmup_input,
|
||||
dataset_repeats_input,
|
||||
train_batch_size_input,
|
||||
epoch_input,
|
||||
save_every_n_epochs_input,
|
||||
mixed_precision_input,
|
||||
save_precision_input,
|
||||
seed_input,
|
||||
num_cpu_threads_per_process_input,
|
||||
train_text_encoder_input,
|
||||
create_buckets,
|
||||
create_caption,
|
||||
train,
|
||||
save_model_as_dropdown,
|
||||
caption_extention_input,
|
||||
],
|
||||
)
|
||||
|
||||
button_save_config.click(
|
||||
save_configuration,
|
||||
inputs=[
|
||||
dummy_false,
|
||||
config_file_name,
|
||||
pretrained_model_name_or_path_input,
|
||||
v2_input,
|
||||
v_parameterization_input,
|
||||
train_dir_input,
|
||||
image_folder_input,
|
||||
output_dir_input,
|
||||
logging_dir_input,
|
||||
max_resolution_input,
|
||||
learning_rate_input,
|
||||
lr_scheduler_input,
|
||||
lr_warmup_input,
|
||||
dataset_repeats_input,
|
||||
train_batch_size_input,
|
||||
epoch_input,
|
||||
save_every_n_epochs_input,
|
||||
mixed_precision_input,
|
||||
save_precision_input,
|
||||
seed_input,
|
||||
num_cpu_threads_per_process_input,
|
||||
train_text_encoder_input,
|
||||
create_buckets,
|
||||
create_caption,
|
||||
train,
|
||||
save_model_as_dropdown,
|
||||
caption_extention_input,
|
||||
],
|
||||
outputs=[config_file_name],
|
||||
)
|
||||
|
||||
button_save_as_config.click(
|
||||
save_configuration,
|
||||
inputs=[
|
||||
dummy_true,
|
||||
config_file_name,
|
||||
pretrained_model_name_or_path_input,
|
||||
v2_input,
|
||||
v_parameterization_input,
|
||||
train_dir_input,
|
||||
image_folder_input,
|
||||
output_dir_input,
|
||||
logging_dir_input,
|
||||
max_resolution_input,
|
||||
learning_rate_input,
|
||||
lr_scheduler_input,
|
||||
lr_warmup_input,
|
||||
dataset_repeats_input,
|
||||
train_batch_size_input,
|
||||
epoch_input,
|
||||
save_every_n_epochs_input,
|
||||
mixed_precision_input,
|
||||
save_precision_input,
|
||||
seed_input,
|
||||
num_cpu_threads_per_process_input,
|
||||
train_text_encoder_input,
|
||||
create_buckets,
|
||||
create_caption,
|
||||
train,
|
||||
save_model_as_dropdown,
|
||||
caption_extention_input,
|
||||
],
|
||||
outputs=[config_file_name],
|
||||
)
|
||||
|
||||
with gr.Tab('Utilities'):
|
||||
gradio_basic_caption_gui_tab()
|
||||
gradio_blip_caption_gui_tab()
|
||||
gradio_wd14_caption_gui_tab()
|
||||
gradio_convert_model_tab()
|
||||
with gr.Tab('Utilities'):
|
||||
gradio_basic_caption_gui_tab()
|
||||
gradio_blip_caption_gui_tab()
|
||||
gradio_wd14_caption_gui_tab()
|
||||
gradio_convert_model_tab()
|
||||
|
||||
|
||||
# Show the interface
|
||||
interface.launch()
|
||||
# Show the interface
|
||||
if not username == '':
|
||||
interface.launch(auth=(username, password))
|
||||
else:
|
||||
interface.launch()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
# torch.cuda.set_per_process_memory_fraction(0.48)
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--username", type=str, default='', help="Username for authentication")
|
||||
parser.add_argument("--password", type=str, default='', help="Password for authentication")
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
UI(username=args.username, password=args.password)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user