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import gradio as gr
import os
import subprocess
import sys
from .common_gui import (
get_saveasfilename_path,
get_file_path,
scriptdir,
list_files,
create_refresh_button, setup_environment
)
from .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 = sys.executable
def convert_lcm(
name,
model_path,
lora_scale,
model_type,
):
# Check if source model exist
if not os.path.isfile(model_path):
log.error("The provided DyLoRA model is not a file")
return
if os.path.dirname(name) == "":
# only filename given. prepend dir
name = os.path.join(os.path.dirname(model_path), name)
if os.path.isdir(name):
# only dir name given. set default lcm name
name = os.path.join(name, "lcm.safetensors")
if os.path.normpath(model_path) == os.path.normpath(name):
# same path. silently ignore but rename output
path, ext = os.path.splitext(save_to)
save_to = f"{path}_lcm{ext}"
# Construct the command to run the script
run_cmd = [
rf"{PYTHON}",
rf"{scriptdir}/tools/lcm_convert.py",
"--lora-scale",
str(lora_scale),
"--model",
rf"{model_path}",
"--name",
str(name),
]
if model_type == "SDXL":
run_cmd.append("--sdxl")
if model_type == "SSD-1B":
run_cmd.append("--ssd-1b")
# Set up the environment
env = setup_environment()
# Reconstruct the safe command string for display
command_to_run = " ".join(run_cmd)
log.info(f"Executing command: {command_to_run}")
# Run the command in the sd-scripts folder context
subprocess.run(run_cmd, env=env, shell=False)
# Return a success message
log.info("Done extracting...")
def gradio_convert_lcm_tab(headless=False):
"""
Creates a Gradio tab for converting a model to an LCM model.
Args:
headless (bool): If True, the tab will be created without any visible elements.
Returns:
None
"""
current_model_dir = os.path.join(scriptdir, "outputs")
current_save_dir = os.path.join(scriptdir, "outputs")
def list_models(path):
"""
Lists all model files in the given directory.
Args:
path (str): The directory path to search for model files.
Returns:
list: A list of model file paths.
"""
nonlocal current_model_dir
current_model_dir = path
return list(list_files(path, exts=[".safetensors"], all=True))
def list_save_to(path):
"""
Lists all save-to options for the given directory.
Args:
path (str): The directory path to search for save-to options.
Returns:
list: A list of save-to options.
"""
nonlocal current_save_dir
current_save_dir = path
return list(list_files(path, exts=[".safetensors"], all=True))
with gr.Tab("Convert to LCM"):
gr.Markdown("This utility convert a model to an LCM model.")
lora_ext = gr.Textbox(value="*.safetensors", visible=False)
lora_ext_name = gr.Textbox(value="LCM model types", visible=False)
model_ext = gr.Textbox(value="*.safetensors", visible=False)
model_ext_name = gr.Textbox(value="Model types", visible=False)
with gr.Group(), gr.Row():
model_path = gr.Dropdown(
label="Stable Diffusion model to convert to LCM",
interactive=True,
choices=[""] + list_models(current_model_dir),
value="",
allow_custom_value=True,
)
create_refresh_button(
model_path,
lambda: None,
lambda: {"choices": list_models(current_model_dir)},
"open_folder_small",
)
button_model_path_file = gr.Button(
folder_symbol,
elem_id="open_folder_small",
elem_classes=["tool"],
visible=(not headless),
)
button_model_path_file.click(
get_file_path,
inputs=[model_path, model_ext, model_ext_name],
outputs=model_path,
show_progress=False,
)
name = gr.Dropdown(
label="Name of the new LCM model",
interactive=True,
choices=[""] + list_save_to(current_save_dir),
value="",
allow_custom_value=True,
)
create_refresh_button(
name,
lambda: None,
lambda: {"choices": list_save_to(current_save_dir)},
"open_folder_small",
)
button_name = gr.Button(
folder_symbol,
elem_id="open_folder_small",
elem_classes=["tool"],
visible=(not headless),
)
button_name.click(
get_saveasfilename_path,
inputs=[name, lora_ext, lora_ext_name],
outputs=name,
show_progress=False,
)
model_path.change(
fn=lambda path: gr.Dropdown(choices=[""] + list_models(path)),
inputs=model_path,
outputs=model_path,
show_progress=False,
)
name.change(
fn=lambda path: gr.Dropdown(choices=[""] + list_save_to(path)),
inputs=name,
outputs=name,
show_progress=False,
)
with gr.Row():
lora_scale = gr.Slider(
label="Strength of the LCM",
minimum=0.0,
maximum=2.0,
step=0.1,
value=1.0,
interactive=True,
)
# with gr.Row():
# no_half = gr.Checkbox(label="Convert the new LCM model to FP32", value=False)
model_type = gr.Radio(
label="Model type", choices=["SD15", "SDXL", "SD-1B"], value="SD15"
)
extract_button = gr.Button("Extract LCM")
extract_button.click(
convert_lcm,
inputs=[
name,
model_path,
lora_scale,
model_type,
],
show_progress=False,
)
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