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import gradio as gr
import subprocess
import os
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 resize_lora(
model,
new_rank,
save_to,
save_precision,
device,
dynamic_method,
dynamic_param,
verbose,
):
# Check for caption_text_input
if model == "":
log.info("Invalid model file")
return
# Check if source model exist
if not os.path.isfile(model):
log.info("The provided model is not a file")
return
if dynamic_method == "sv_ratio":
if float(dynamic_param) < 2:
log.info(
f"Dynamic parameter for {dynamic_method} need to be 2 or greater..."
)
return
if dynamic_method == "sv_fro" or dynamic_method == "sv_cumulative":
if float(dynamic_param) < 0 or float(dynamic_param) > 1:
log.info(
f"Dynamic parameter for {dynamic_method} need to be between 0 and 1..."
)
return
# Check if save_to end with one of the defines extension. If not add .safetensors.
if not save_to.endswith((".pt", ".safetensors")):
save_to += ".safetensors"
if device == "":
device = "cuda"
run_cmd = [
rf"{PYTHON}",
rf"{scriptdir}/sd-scripts/networks/resize_lora.py",
"--save_precision",
save_precision,
"--save_to",
rf"{save_to}",
"--model",
rf"{model}",
"--new_rank",
str(new_rank),
"--device",
device,
]
# Conditional checks for dynamic parameters
if dynamic_method != "None":
run_cmd.append("--dynamic_method")
run_cmd.append(dynamic_method)
run_cmd.append("--dynamic_param")
run_cmd.append(str(dynamic_param))
# Check for verbosity
if verbose:
run_cmd.append("--verbose")
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)
log.info("Done resizing...")
###
# Gradio UI
###
def gradio_resize_lora_tab(
headless=False,
):
current_model_dir = os.path.join(scriptdir, "outputs")
current_save_dir = os.path.join(scriptdir, "outputs")
def list_models(path):
nonlocal current_model_dir
current_model_dir = path
return list(list_files(path, exts=[".ckpt", ".safetensors"], all=True))
def list_save_to(path):
nonlocal current_save_dir
current_save_dir = path
return list(list_files(path, exts=[".pt", ".safetensors"], all=True))
with gr.Tab("Resize LoRA"):
gr.Markdown("This utility can resize a LoRA.")
lora_ext = gr.Textbox(value="*.safetensors *.pt", visible=False)
lora_ext_name = gr.Textbox(value="LoRA model types", visible=False)
with gr.Group(), gr.Row():
model = gr.Dropdown(
label="Source LoRA (path to the LoRA to resize)",
interactive=True,
choices=[""] + list_models(current_model_dir),
value="",
allow_custom_value=True,
)
create_refresh_button(
model,
lambda: None,
lambda: {"choices": list_models(current_model_dir)},
"open_folder_small",
)
button_lora_a_model_file = gr.Button(
folder_symbol,
elem_id="open_folder_small",
elem_classes=["tool"],
visible=(not headless),
)
button_lora_a_model_file.click(
get_file_path,
inputs=[model, lora_ext, lora_ext_name],
outputs=model,
show_progress=False,
)
save_to = gr.Dropdown(
label="Save to (path for the LoRA file to save...)",
interactive=True,
choices=[""] + list_save_to(current_save_dir),
value="",
allow_custom_value=True,
)
create_refresh_button(
save_to,
lambda: None,
lambda: {"choices": list_save_to(current_save_dir)},
"open_folder_small",
)
button_save_to = gr.Button(
folder_symbol,
elem_id="open_folder_small",
elem_classes=["tool"],
visible=(not headless),
)
button_save_to.click(
get_saveasfilename_path,
inputs=[save_to, lora_ext, lora_ext_name],
outputs=save_to,
show_progress=False,
)
model.change(
fn=lambda path: gr.Dropdown(choices=[""] + list_models(path)),
inputs=model,
outputs=model,
show_progress=False,
)
save_to.change(
fn=lambda path: gr.Dropdown(choices=[""] + list_save_to(path)),
inputs=save_to,
outputs=save_to,
show_progress=False,
)
with gr.Row():
new_rank = gr.Slider(
label="Desired LoRA rank",
minimum=1,
maximum=1024,
step=1,
value=4,
interactive=True,
)
dynamic_method = gr.Radio(
choices=["None", "sv_ratio", "sv_fro", "sv_cumulative"],
value="sv_fro",
label="Dynamic method",
interactive=True,
)
dynamic_param = gr.Textbox(
label="Dynamic parameter",
value="0.9",
interactive=True,
placeholder="Value for the dynamic method selected.",
)
with gr.Row():
verbose = gr.Checkbox(label="Verbose logging", value=True)
save_precision = gr.Radio(
label="Save precision",
choices=["fp16", "bf16", "float"],
value="fp16",
interactive=True,
)
device = gr.Radio(
label="Device",
choices=[
"cpu",
"cuda",
],
value="cuda",
interactive=True,
)
convert_button = gr.Button("Resize model")
convert_button.click(
resize_lora,
inputs=[
model,
new_rank,
save_to,
save_precision,
device,
dynamic_method,
dynamic_param,
verbose,
],
show_progress=False,
)
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