179 lines
5.1 KiB
Python
179 lines
5.1 KiB
Python
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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from .common_gui import (
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get_saveasfilename_path,
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get_any_file_path,
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get_file_path,
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)
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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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PYTHON = 'python3' if os.name == 'posix' else './venv/Scripts/python.exe'
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def extract_lora(
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model_tuned,
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model_org,
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save_to,
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save_precision,
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dim,
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v2,
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conv_dim,
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device,
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):
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# Check for caption_text_input
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if model_tuned == '':
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msgbox('Invalid finetuned model file')
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return
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if model_org == '':
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msgbox('Invalid base model file')
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return
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# Check if source model exist
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if not os.path.isfile(model_tuned):
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msgbox('The provided finetuned model is not a file')
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return
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if not os.path.isfile(model_org):
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msgbox('The provided base model is not a file')
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return
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run_cmd = (
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f'{PYTHON} "{os.path.join("networks","extract_lora_from_models.py")}"'
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)
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run_cmd += f' --save_precision {save_precision}'
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run_cmd += f' --save_to "{save_to}"'
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run_cmd += f' --model_org "{model_org}"'
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run_cmd += f' --model_tuned "{model_tuned}"'
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run_cmd += f' --dim {dim}'
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run_cmd += f' --device {device}'
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if conv_dim > 0:
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run_cmd += f' --conv_dim {conv_dim}'
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if v2:
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run_cmd += f' --v2'
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print(run_cmd)
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# Run the command
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if os.name == 'posix':
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os.system(run_cmd)
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else:
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subprocess.run(run_cmd)
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###
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# Gradio UI
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###
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def gradio_extract_lora_tab():
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with gr.Tab('Extract LoRA'):
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gr.Markdown(
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'This utility can extract a LoRA network from a finetuned model.'
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)
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lora_ext = gr.Textbox(value='*.safetensors *.pt', visible=False)
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lora_ext_name = gr.Textbox(value='LoRA model types', visible=False)
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model_ext = gr.Textbox(value='*.ckpt *.safetensors', visible=False)
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model_ext_name = gr.Textbox(value='Model types', visible=False)
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with gr.Row():
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model_tuned = gr.Textbox(
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label='Finetuned model',
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placeholder='Path to the finetuned model to extract',
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interactive=True,
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)
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button_model_tuned_file = gr.Button(
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folder_symbol, elem_id='open_folder_small'
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)
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button_model_tuned_file.click(
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get_file_path,
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inputs=[model_tuned, model_ext, model_ext_name],
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outputs=model_tuned,
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show_progress=False,
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)
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model_org = gr.Textbox(
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label='Stable Diffusion base model',
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placeholder='Stable Diffusion original model: ckpt or safetensors file',
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interactive=True,
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)
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button_model_org_file = gr.Button(
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folder_symbol, elem_id='open_folder_small'
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)
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button_model_org_file.click(
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get_file_path,
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inputs=[model_org, model_ext, model_ext_name],
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outputs=model_org,
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show_progress=False,
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)
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with gr.Row():
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save_to = gr.Textbox(
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label='Save to',
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placeholder='path where to save the extracted LoRA model...',
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interactive=True,
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)
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button_save_to = gr.Button(
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folder_symbol, elem_id='open_folder_small'
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)
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button_save_to.click(
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get_saveasfilename_path,
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inputs=[save_to, lora_ext, lora_ext_name],
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outputs=save_to,
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show_progress=False,
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)
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save_precision = gr.Dropdown(
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label='Save precision',
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choices=['fp16', 'bf16', 'float'],
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value='float',
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interactive=True,
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)
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with gr.Row():
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dim = gr.Slider(
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minimum=4,
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maximum=1024,
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label='Network Dimension (Rank)',
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value=128,
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step=1,
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interactive=True,
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)
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conv_dim = gr.Slider(
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minimum=0,
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maximum=1024,
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label='Conv Dimension (Rank)',
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value=128,
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step=1,
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interactive=True,
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)
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v2 = gr.Checkbox(label='v2', value=False, interactive=True)
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device = gr.Dropdown(
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label='Device',
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choices=[
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'cpu',
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'cuda',
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],
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value='cuda',
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interactive=True,
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)
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extract_button = gr.Button('Extract LoRA model')
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extract_button.click(
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extract_lora,
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inputs=[
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model_tuned,
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model_org,
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save_to,
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save_precision,
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dim,
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v2,
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conv_dim,
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device
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],
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show_progress=False,
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)
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