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, )