KohyaSS/library/convert_model_gui.py

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import gradio as gr
from easygui import msgbox
import subprocess
import os
import shutil
from .common_gui import get_folder_path, get_file_path
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,
):
# 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):
print('The provided source model is a file')
elif os.path.isdir(source_model_input):
print('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):
print('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:
print('SD v1 model specified. Setting --v1 parameter')
run_cmd += ' --v1'
else:
print('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'
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}"'
print(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'
)
print(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'
)
print(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():
with gr.Tab('Convert model'):
gr.Markdown(
'This utility can be used to convert from one stable diffusion model format to another.'
)
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'
)
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'
)
button_source_model_file.click(
get_file_path,
inputs=[source_model_input],
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'
)
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',
)
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,
],
show_progress=False,
)