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import gradio as gr
from easygui import msgbox
import subprocess
import os
from .common_gui import (
get_saveasfilename_path,
get_file_path,
is_file_writable
)
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 extract_lora(
model_tuned,
model_org,
save_to,
save_precision,
dim,
v2,
sdxl,
conv_dim,
clamp_quantile,
min_diff,
device,
):
# Check for caption_text_input
if model_tuned == '':
log.info('Invalid finetuned model file')
return
if model_org == '':
log.info('Invalid base model file')
return
# Check if source model exist
if not os.path.isfile(model_tuned):
log.info('The provided finetuned model is not a file')
return
if not os.path.isfile(model_org):
log.info('The provided base model is not a file')
return
if not is_file_writable(save_to):
return
run_cmd = (
f'{PYTHON} "{os.path.join("networks","extract_lora_from_models.py")}"'
)
run_cmd += f' --save_precision {save_precision}'
run_cmd += f' --save_to "{save_to}"'
run_cmd += f' --model_org "{model_org}"'
run_cmd += f' --model_tuned "{model_tuned}"'
run_cmd += f' --dim {dim}'
run_cmd += f' --device {device}'
if conv_dim > 0:
run_cmd += f' --conv_dim {conv_dim}'
if v2:
run_cmd += f' --v2'
if sdxl:
run_cmd += f' --sdxl'
run_cmd += f' --clamp_quantile {clamp_quantile}'
run_cmd += f' --min_diff {min_diff}'
log.info(run_cmd)
# Run the command
if os.name == 'posix':
os.system(run_cmd)
else:
subprocess.run(run_cmd)
###
# Gradio UI
###
def gradio_extract_lora_tab(headless=False):
with gr.Tab('Extract LoRA'):
gr.Markdown(
'This utility can extract a LoRA network from a finetuned model.'
)
lora_ext = gr.Textbox(value='*.safetensors *.pt', visible=False)
lora_ext_name = gr.Textbox(value='LoRA model types', visible=False)
model_ext = gr.Textbox(value='*.ckpt *.safetensors', visible=False)
model_ext_name = gr.Textbox(value='Model types', visible=False)
with gr.Row():
model_tuned = gr.Textbox(
label='Finetuned model',
placeholder='Path to the finetuned model to extract',
interactive=True,
)
button_model_tuned_file = gr.Button(
folder_symbol,
elem_id='open_folder_small',
visible=(not headless),
)
button_model_tuned_file.click(
get_file_path,
inputs=[model_tuned, model_ext, model_ext_name],
outputs=model_tuned,
show_progress=False,
)
model_org = gr.Textbox(
label='Stable Diffusion base model',
placeholder='Stable Diffusion original model: ckpt or safetensors file',
interactive=True,
)
button_model_org_file = gr.Button(
folder_symbol,
elem_id='open_folder_small',
visible=(not headless),
)
button_model_org_file.click(
get_file_path,
inputs=[model_org, model_ext, model_ext_name],
outputs=model_org,
show_progress=False,
)
with gr.Row():
save_to = gr.Textbox(
label='Save to',
placeholder='path where to save the extracted LoRA model...',
interactive=True,
)
button_save_to = gr.Button(
folder_symbol,
elem_id='open_folder_small',
visible=(not headless),
)
button_save_to.click(
get_saveasfilename_path,
inputs=[save_to, lora_ext, lora_ext_name],
outputs=save_to,
show_progress=False,
)
save_precision = gr.Dropdown(
label='Save precision',
choices=['fp16', 'bf16', 'float'],
value='float',
interactive=True,
)
with gr.Row():
dim = gr.Slider(
minimum=4,
maximum=1024,
label='Network Dimension (Rank)',
value=128,
step=1,
interactive=True,
)
conv_dim = gr.Slider(
minimum=0,
maximum=1024,
label='Conv Dimension (Rank)',
value=128,
step=1,
interactive=True,
)
clamp_quantile = gr.Number(
label='Clamp Quantile',
value=1,
interactive=True,
)
min_diff = gr.Number(
label='Minimum difference',
value=0.01,
interactive=True,
)
with gr.Row():
v2 = gr.Checkbox(label='v2', value=False, interactive=True)
sdxl = gr.Checkbox(label='SDXL', value=False, interactive=True)
device = gr.Dropdown(
label='Device',
choices=[
'cpu',
'cuda',
],
value='cuda',
interactive=True,
)
extract_button = gr.Button('Extract LoRA model')
extract_button.click(
extract_lora,
inputs=[
model_tuned,
model_org,
save_to,
save_precision,
dim,
v2,
sdxl,
conv_dim,
clamp_quantile,
min_diff,
device,
],
show_progress=False,
)
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