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import gradio as gr
from easygui import msgbox
import subprocess
import os
from .common_gui import (
get_saveasfilename_path,
get_any_file_path,
get_file_path,
)
from library.custom_logging import setup_logging
# Set up logging
log = setup_logging()
PYTHON = 'python3' if os.name == 'posix' else './venv/Scripts/python.exe'
folder_symbol = '\U0001f4c2' # 📂
refresh_symbol = '\U0001f504' # 🔄
save_style_symbol = '\U0001f4be' # 💾
document_symbol = '\U0001F4C4' # 📄
def verify_lora(
lora_model,
):
# verify for caption_text_input
if lora_model == '':
msgbox('Invalid model A file')
return
# verify if source model exist
if not os.path.isfile(lora_model):
msgbox('The provided model A is not a file')
return
run_cmd = [
PYTHON,
os.path.join('networks', 'check_lora_weights.py'),
f'{lora_model}',
]
log.info(' '.join(run_cmd))
# Run the command
process = subprocess.Popen(
run_cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE
)
output, error = process.communicate()
return (output.decode(), error.decode())
###
# Gradio UI
###
def gradio_verify_lora_tab(headless=False):
with gr.Tab('Verify LoRA'):
gr.Markdown(
'This utility can verify a LoRA network to make sure it is properly trained.'
)
lora_ext = gr.Textbox(value='*.pt *.safetensors', visible=False)
lora_ext_name = gr.Textbox(value='LoRA model types', visible=False)
with gr.Row():
lora_model = gr.Textbox(
label='LoRA model',
placeholder='Path to the LoRA model to verify',
interactive=True,
)
button_lora_model_file = gr.Button(
folder_symbol,
elem_id='open_folder_small',
visible=(not headless),
)
button_lora_model_file.click(
get_file_path,
inputs=[lora_model, lora_ext, lora_ext_name],
outputs=lora_model,
show_progress=False,
)
verify_button = gr.Button('Verify', variant='primary')
lora_model_verif_output = gr.Textbox(
label='Output',
placeholder='Verification output',
interactive=False,
lines=1,
max_lines=10,
)
lora_model_verif_error = gr.Textbox(
label='Error',
placeholder='Verification error',
interactive=False,
lines=1,
max_lines=10,
)
verify_button.click(
verify_lora,
inputs=[
lora_model,
],
outputs=[lora_model_verif_output, lora_model_verif_error],
show_progress=False,
)
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