import gradio as gr from easygui import msgbox import subprocess import os import shutil from .common_gui import get_folder_path, get_file_path from library.custom_logging import setup_logging # Set up logging log = setup_logging() folder_symbol = '\U0001f4c2' # 📂 refresh_symbol = '\U0001f504' # 🔄 save_style_symbol = '\U0001f4be' # 💾 document_symbol = '\U0001F4C4' # 📄 PYTHON = 'python3' if os.name == 'posix' else './venv/Scripts/python.exe' def convert_model( source_model_input, source_model_type, target_model_folder_input, target_model_name_input, target_model_type, target_save_precision_type, unet_use_linear_projection, ): # Check for caption_text_input if source_model_type == '': msgbox('Invalid source model type') return # Check if source model exist if os.path.isfile(source_model_input): log.info('The provided source model is a file') elif os.path.isdir(source_model_input): log.info('The provided model is a folder') else: msgbox('The provided source model is neither a file nor a folder') return # Check if source model exist if os.path.isdir(target_model_folder_input): log.info('The provided model folder exist') else: msgbox('The provided target folder does not exist') return run_cmd = f'{PYTHON} "tools/convert_diffusers20_original_sd.py"' v1_models = [ 'runwayml/stable-diffusion-v1-5', 'CompVis/stable-diffusion-v1-4', ] # check if v1 models if str(source_model_type) in v1_models: log.info('SD v1 model specified. Setting --v1 parameter') run_cmd += ' --v1' else: log.info('SD v2 model specified. Setting --v2 parameter') run_cmd += ' --v2' if not target_save_precision_type == 'unspecified': run_cmd += f' --{target_save_precision_type}' if ( target_model_type == 'diffuser' or target_model_type == 'diffuser_safetensors' ): run_cmd += f' --reference_model="{source_model_type}"' if target_model_type == 'diffuser_safetensors': run_cmd += ' --use_safetensors' # Fix for stabilityAI diffusers format. When saving v2 models in Diffusers format in training scripts and conversion scripts, # it was found that the U-Net configuration is different from those of Hugging Face's stabilityai models (this repository is # "use_linear_projection": false, stabilityai is true). Please note that the weight shapes are different, so please be careful # when using the weight files directly. if unet_use_linear_projection: run_cmd += ' --unet_use_linear_projection' run_cmd += f' "{source_model_input}"' if ( target_model_type == 'diffuser' or target_model_type == 'diffuser_safetensors' ): target_model_path = os.path.join( target_model_folder_input, target_model_name_input ) run_cmd += f' "{target_model_path}"' else: target_model_path = os.path.join( target_model_folder_input, f'{target_model_name_input}.{target_model_type}', ) run_cmd += f' "{target_model_path}"' log.info(run_cmd) # Run the command if os.name == 'posix': os.system(run_cmd) else: subprocess.run(run_cmd) if ( not target_model_type == 'diffuser' or target_model_type == 'diffuser_safetensors' ): v2_models = [ 'stabilityai/stable-diffusion-2-1-base', 'stabilityai/stable-diffusion-2-base', ] v_parameterization = [ 'stabilityai/stable-diffusion-2-1', 'stabilityai/stable-diffusion-2', ] if str(source_model_type) in v2_models: inference_file = os.path.join( target_model_folder_input, f'{target_model_name_input}.yaml' ) log.info(f'Saving v2-inference.yaml as {inference_file}') shutil.copy( f'./v2_inference/v2-inference.yaml', f'{inference_file}', ) if str(source_model_type) in v_parameterization: inference_file = os.path.join( target_model_folder_input, f'{target_model_name_input}.yaml' ) log.info(f'Saving v2-inference-v.yaml as {inference_file}') shutil.copy( f'./v2_inference/v2-inference-v.yaml', f'{inference_file}', ) # parser = argparse.ArgumentParser() # parser.add_argument("--v1", action='store_true', # help='load v1.x model (v1 or v2 is required to load checkpoint) / 1.xのモデルを読み込む') # parser.add_argument("--v2", action='store_true', # help='load v2.0 model (v1 or v2 is required to load checkpoint) / 2.0のモデルを読み込む') # parser.add_argument("--fp16", action='store_true', # 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）') # 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）') # parser.add_argument("--float", action='store_true', # help='save as float (checkpoint only) / float(float32)形式で保存する（checkpointのみ対応）') # parser.add_argument("--epoch", type=int, default=0, help='epoch to write to checkpoint / checkpointに記録するepoch数の値') # parser.add_argument("--global_step", type=int, default=0, # help='global_step to write to checkpoint / checkpointに記録するglobal_stepの値') # parser.add_argument("--reference_model", type=str, default=None, # 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") # parser.add_argument("model_to_load", type=str, default=None, # 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") # parser.add_argument("model_to_save", type=str, default=None, # 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") ### # Gradio UI ### def gradio_convert_model_tab(headless=False): with gr.Tab('Convert model'): gr.Markdown( 'This utility can be used to convert from one stable diffusion model format to another.' ) model_ext = gr.Textbox(value='*.safetensors *.ckpt', visible=False) model_ext_name = gr.Textbox(value='Model types', visible=False) with gr.Row(): source_model_input = gr.Textbox( label='Source model', placeholder='path to source model folder of file to convert...', interactive=True, ) button_source_model_dir = gr.Button( folder_symbol, elem_id='open_folder_small', visible=(not headless), ) button_source_model_dir.click( get_folder_path, outputs=source_model_input, show_progress=False, ) button_source_model_file = gr.Button( document_symbol, elem_id='open_folder_small', visible=(not headless), ) button_source_model_file.click( get_file_path, inputs=[source_model_input, model_ext, model_ext_name], outputs=source_model_input, show_progress=False, ) source_model_type = gr.Dropdown( label='Source model type', choices=[ '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.Row(): target_model_folder_input = gr.Textbox( label='Target model folder', placeholder='path to target model folder of file name to create...', interactive=True, ) button_target_model_folder = gr.Button( folder_symbol, elem_id='open_folder_small', visible=(not headless), ) button_target_model_folder.click( get_folder_path, outputs=target_model_folder_input, show_progress=False, ) target_model_name_input = gr.Textbox( label='Target model name', placeholder='target model name...', interactive=True, ) target_model_type = gr.Dropdown( label='Target model type', choices=[ 'diffuser', 'diffuser_safetensors', 'ckpt', 'safetensors', ], ) target_save_precision_type = gr.Dropdown( label='Target model precision', choices=['unspecified', 'fp16', 'bf16', 'float'], value='unspecified', ) unet_use_linear_projection = gr.Checkbox( label='UNet linear projection', value=False, info="Enable for Hugging Face's stabilityai models", ) convert_button = gr.Button('Convert model') convert_button.click( convert_model, inputs=[ source_model_input, source_model_type, target_model_folder_input, target_model_name_input, target_model_type, target_save_precision_type, unet_use_linear_projection, ], show_progress=False, )