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v18.1: Model conversion utility
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‎.gitignore

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@@ -2,4 +2,6 @@ venv
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venv1
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mytraining.ps
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__pycache__
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.vscode
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.vscode
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*.egg-info
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build

‎README.md

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@@ -130,6 +130,8 @@ Drop by the discord server for support: https://discord.com/channels/10415185624
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## Change history
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* 12/18 (v18.1) update:
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- Add Stable Diffusion model conversion utility. Make sure to run `pip upgrade -U -r requirements.txt` after updating to this release as this introduce new pip requirements.
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* 12/17 (v18) update:
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- Save model as option added to train_db_fixed.py
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- Save model as option added to GUI

‎dreambooth_gui.py

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import subprocess
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import pathlib
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import shutil
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from dreambooth_gui.dreambooth_folder_creation import gradio_dreambooth_folder_creation_tab
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from dreambooth_gui.caption_gui import gradio_caption_gui_tab
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from dreambooth_gui.dataset_balancing import gradio_dataset_balancing_tab
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from dreambooth_gui.common_gui import (
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from library.dreambooth_folder_creation_gui import gradio_dreambooth_folder_creation_tab
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from library.caption_gui import gradio_caption_gui_tab
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from library.dataset_balancing_gui import gradio_dataset_balancing_tab
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from library.common_gui import (
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get_folder_path,
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remove_doublequote,
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get_file_path,
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)
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from library.convert_model_gui import gradio_convert_model_tab
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from easygui import filesavebox, msgbox
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folder_symbol = '\U0001f4c2' # 📂
@@ -699,6 +700,7 @@ def set_pretrained_model_name_or_path_input(value, v2, v_parameterization):
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# Captionning tab
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gradio_caption_gui_tab()
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gradio_dataset_balancing_tab()
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gradio_convert_model_tab()
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# with gr.Tab('Model conversion'):
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# convert_to_safetensors_input = gr.Checkbox(
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# label='Convert to SafeTensors', value=True

‎dreambooth_gui/__init__.py renamed to ‎library/__init__.py

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‎dreambooth_gui/caption_gui.py renamed to ‎library/caption_gui.py

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‎dreambooth_gui/common_gui.py renamed to ‎library/common_gui.py

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‎library/convert_model_gui.py

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import gradio as gr
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from easygui import msgbox
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import subprocess
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import os
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import shutil
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from .common_gui import get_folder_path, get_file_path
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folder_symbol = '\U0001f4c2' # 📂
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refresh_symbol = '\U0001f504' # 🔄
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save_style_symbol = '\U0001f4be' # 💾
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document_symbol = '\U0001F4C4' # 📄
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def convert_model(source_model_input, source_model_type, target_model_folder_input, target_model_name_input, target_model_type, target_save_precision_type):
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# Check for caption_text_input
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if source_model_type == "":
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msgbox("Invalid source model type")
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return
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# Check if source model exist
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if os.path.isfile(source_model_input):
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print('The provided source model is a file')
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elif os.path.isdir(source_model_input):
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print('The provided model is a folder')
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else:
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msgbox("The provided source model is neither a file nor a folder")
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return
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# Check if source model exist
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if os.path.isdir(target_model_folder_input):
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print('The provided model folder exist')
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else:
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msgbox("The provided target folder does not exist")
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return
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run_cmd = f'.\\venv\Scripts\python.exe "tools/convert_diffusers20_original_sd.py"'
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v1_models = [
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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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# check if v1 models
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if str(source_model_type) in v1_models:
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print('SD v1 model specified. Setting --v1 parameter')
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run_cmd += ' --v1'
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else:
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print('SD v2 model specified. Setting --v2 parameter')
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run_cmd += ' --v2'
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if not target_save_precision_type == 'unspecified':
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run_cmd += f' --{target_save_precision_type}'
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if target_model_type == "diffuser":
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run_cmd += f' --reference_model="{source_model_type}"'
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run_cmd += f' "{source_model_input}"'
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if target_model_type == "diffuser":
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target_model_path = os.path.join(target_model_folder_input, target_model_name_input)
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run_cmd += f' "{target_model_path}"'
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else:
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target_model_path = os.path.join(target_model_folder_input, f'{target_model_name_input}.{target_model_type}')
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run_cmd += f' "{target_model_path}"'
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print(run_cmd)
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# Run the command
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subprocess.run(run_cmd)
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if not target_model_type == "diffuser":
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v2_models = ['stabilityai/stable-diffusion-2-1-base',
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'stabilityai/stable-diffusion-2-base',]
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v_parameterization =[
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'stabilityai/stable-diffusion-2-1',
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'stabilityai/stable-diffusion-2',]
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if str(source_model_type) in v2_models:
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inference_file = os.path.join(target_model_folder_input, f'{target_model_name_input}.yaml')
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print(f'Saving v2-inference.yaml as {inference_file}')
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shutil.copy(
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f'./v2_inference/v2-inference.yaml',
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f'{inference_file}',
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)
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if str(source_model_type) in v_parameterization:
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inference_file = os.path.join(target_model_folder_input, f'{target_model_name_input}.yaml')
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print(f'Saving v2-inference-v.yaml as {inference_file}')
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shutil.copy(
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f'./v2_inference/v2-inference-v.yaml',
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f'{inference_file}',
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)
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# parser = argparse.ArgumentParser()
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# parser.add_argument("--v1", action='store_true',
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# help='load v1.x model (v1 or v2 is required to load checkpoint) / 1.xのモデルを読み込む')
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# parser.add_argument("--v2", action='store_true',
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# help='load v2.0 model (v1 or v2 is required to load checkpoint) / 2.0のモデルを読み込む')
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# parser.add_argument("--fp16", action='store_true',
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# help='load as fp16 (Diffusers only) and save as fp16 (checkpoint only) / fp16形式で読み込み（Diffusers形式のみ対応）、保存する（checkpointのみ対応）')
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# parser.add_argument("--bf16", action='store_true', help='save as bf16 (checkpoint only) / bf16形式で保存する（checkpointのみ対応）')
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# parser.add_argument("--float", action='store_true',
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# help='save as float (checkpoint only) / float(float32)形式で保存する（checkpointのみ対応）')
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# parser.add_argument("--epoch", type=int, default=0, help='epoch to write to checkpoint / checkpointに記録するepoch数の値')
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# parser.add_argument("--global_step", type=int, default=0,
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# help='global_step to write to checkpoint / checkpointに記録するglobal_stepの値')
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# parser.add_argument("--reference_model", type=str, default=None,
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# help="reference model for schduler/tokenizer, required in saving Diffusers, copy schduler/tokenizer from this / scheduler/tokenizerのコピー元のDiffusersモデル、Diffusers形式で保存するときに必要")
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# parser.add_argument("model_to_load", type=str, default=None,
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# help="model to load: checkpoint file or Diffusers model's directory / 読み込むモデル、checkpointかDiffusers形式モデルのディレクトリ")
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# parser.add_argument("model_to_save", type=str, default=None,
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# help="model to save: checkpoint (with extension) or Diffusers model's directory (without extension) / 変換後のモデル、拡張子がある場合はcheckpoint、ない場合はDiffusesモデルとして保存")
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###
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# Gradio UI
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###
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def gradio_convert_model_tab():
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with gr.Tab('Convert model'):
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gr.Markdown(
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'This utility can be used to convert from one stable diffusion model format to another.'
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)
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with gr.Row():
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source_model_input = gr.Textbox(
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label='Source model',
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placeholder='path to source model folder of file to convert...',
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interactive=True,
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)
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button_source_model_dir = gr.Button(
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folder_symbol, elem_id='open_folder_small'
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)
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button_source_model_dir.click(
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get_folder_path, outputs=source_model_input
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)
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button_source_model_file = gr.Button(
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document_symbol, elem_id='open_folder_small'
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)
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button_source_model_file.click(
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get_file_path, inputs=[source_model_input], outputs=source_model_input
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)
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source_model_type = gr.Dropdown(label="Source model type", choices=[
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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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with gr.Row():
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target_model_folder_input = gr.Textbox(
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label='Target model folder',
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placeholder='path to target model folder of file name to create...',
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interactive=True,
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)
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button_target_model_folder = gr.Button(
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folder_symbol, elem_id='open_folder_small'
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)
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button_target_model_folder.click(
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get_folder_path, outputs=target_model_folder_input
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)
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target_model_name_input = gr.Textbox(
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label='Target model name',
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placeholder='target model name...',
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interactive=True,
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)
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target_model_type = gr.Dropdown(label="Target model type", choices=[
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'diffuser',
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'ckpt',
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'safetensors',
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],)
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target_save_precision_type = gr.Dropdown(label="Target model precison", choices=[
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'unspecified',
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'fp16',
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'bf16',
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'float'
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], value='unspecified')
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convert_button = gr.Button('Convert model')
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convert_button.click(
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convert_model,
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inputs=[source_model_input, source_model_type, target_model_folder_input, target_model_name_input, target_model_type, target_save_precision_type
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],
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)

‎dreambooth_gui/dataset_balancing.py renamed to ‎library/dataset_balancing_gui.py

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@@ -102,7 +102,6 @@ def gradio_dataset_balancing_tab():
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total_repeats_number = gr.Number(
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value=1000,
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min=1,
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interactive=True,
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label='Training steps per concept per epoch',
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)

‎dreambooth_gui/dreambooth_folder_creation.py renamed to ‎library/dreambooth_folder_creation_gui.py

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‎model_util.py renamed to ‎library/model_util.py

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