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# Standard library imports
import os
import subprocess
import sys
import json
# Third-party imports
import gradio as gr
# Local module imports
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 check_model(model):
if not model:
return True
if not os.path.isfile(model):
log.info(f"The provided {model} is not a file")
return False
return True
def verify_conditions(sd_model, lora_models):
lora_models_count = sum(1 for model in lora_models if model)
if sd_model and lora_models_count >= 1:
return True
elif not sd_model and lora_models_count >= 2:
return True
return False
class GradioMergeLoRaTab:
def __init__(self, headless=False):
self.headless = headless
self.build_tab()
def save_inputs_to_json(self, file_path, inputs):
with open(file_path, "w", encoding="utf-8") as file:
json.dump(inputs, file)
log.info(f"Saved inputs to {file_path}")
def load_inputs_from_json(self, file_path):
with open(file_path, "r", encoding="utf-8") as file:
inputs = json.load(file)
log.info(f"Loaded inputs from {file_path}")
return inputs
def build_tab(self):
current_sd_model_dir = os.path.join(scriptdir, "outputs")
current_save_dir = os.path.join(scriptdir, "outputs")
current_a_model_dir = current_sd_model_dir
current_b_model_dir = current_sd_model_dir
current_c_model_dir = current_sd_model_dir
current_d_model_dir = current_sd_model_dir
def list_sd_models(path):
nonlocal current_sd_model_dir
current_sd_model_dir = path
return list(list_files(path, exts=[".ckpt", ".safetensors"], all=True))
def list_a_models(path):
nonlocal current_a_model_dir
current_a_model_dir = path
return list(list_files(path, exts=[".pt", ".safetensors"], all=True))
def list_b_models(path):
nonlocal current_b_model_dir
current_b_model_dir = path
return list(list_files(path, exts=[".pt", ".safetensors"], all=True))
def list_c_models(path):
nonlocal current_c_model_dir
current_c_model_dir = path
return list(list_files(path, exts=[".pt", ".safetensors"], all=True))
def list_d_models(path):
nonlocal current_d_model_dir
current_d_model_dir = path
return list(list_files(path, exts=[".pt", ".safetensors"], all=True))
def list_save_to(path):
nonlocal current_save_dir
current_save_dir = path
return list(list_files(path, exts=[".ckpt", ".safetensors"], all=True))
with gr.Tab("Merge LoRA"):
gr.Markdown(
"This utility can merge up to 4 LoRA together or alternatively merge up to 4 LoRA into a SD checkpoint."
)
lora_ext = gr.Textbox(value="*.safetensors *.pt", visible=False)
lora_ext_name = gr.Textbox(value="LoRA model types", visible=False)
ckpt_ext = gr.Textbox(value="*.safetensors *.ckpt", visible=False)
ckpt_ext_name = gr.Textbox(value="SD model types", visible=False)
with gr.Group(), gr.Row():
sd_model = gr.Dropdown(
label="SD Model (Optional. Stable Diffusion model path, if you want to merge it with LoRA files)",
interactive=True,
choices=[""] + list_sd_models(current_sd_model_dir),
value="",
allow_custom_value=True,
)
create_refresh_button(
sd_model,
lambda: None,
lambda: {"choices": list_sd_models(current_sd_model_dir)},
"open_folder_small",
)
sd_model_file = gr.Button(
folder_symbol,
elem_id="open_folder_small",
elem_classes=["tool"],
visible=(not self.headless),
)
sd_model_file.click(
get_file_path,
inputs=[sd_model, ckpt_ext, ckpt_ext_name],
outputs=sd_model,
show_progress=False,
)
sdxl_model = gr.Checkbox(label="SDXL model", value=False)
sd_model.change(
fn=lambda path: gr.Dropdown(choices=[""] + list_sd_models(path)),
inputs=sd_model,
outputs=sd_model,
show_progress=False,
)
with gr.Group(), gr.Row():
lora_a_model = gr.Dropdown(
label='LoRA model "A" (path to the LoRA A model)',
interactive=True,
choices=[""] + list_a_models(current_a_model_dir),
value="",
allow_custom_value=True,
)
create_refresh_button(
lora_a_model,
lambda: None,
lambda: {"choices": list_a_models(current_a_model_dir)},
"open_folder_small",
)
button_lora_a_model_file = gr.Button(
folder_symbol,
elem_id="open_folder_small",
elem_classes=["tool"],
visible=(not self.headless),
)
button_lora_a_model_file.click(
get_file_path,
inputs=[lora_a_model, lora_ext, lora_ext_name],
outputs=lora_a_model,
show_progress=False,
)
lora_b_model = gr.Dropdown(
label='LoRA model "B" (path to the LoRA B model)',
interactive=True,
choices=[""] + list_b_models(current_b_model_dir),
value="",
allow_custom_value=True,
)
create_refresh_button(
lora_b_model,
lambda: None,
lambda: {"choices": list_b_models(current_b_model_dir)},
"open_folder_small",
)
button_lora_b_model_file = gr.Button(
folder_symbol,
elem_id="open_folder_small",
elem_classes=["tool"],
visible=(not self.headless),
)
button_lora_b_model_file.click(
get_file_path,
inputs=[lora_b_model, lora_ext, lora_ext_name],
outputs=lora_b_model,
show_progress=False,
)
lora_a_model.change(
fn=lambda path: gr.Dropdown(choices=[""] + list_a_models(path)),
inputs=lora_a_model,
outputs=lora_a_model,
show_progress=False,
)
lora_b_model.change(
fn=lambda path: gr.Dropdown(choices=[""] + list_b_models(path)),
inputs=lora_b_model,
outputs=lora_b_model,
show_progress=False,
)
with gr.Row():
ratio_a = gr.Slider(
label="Model A merge ratio (eg: 0.5 mean 50%)",
minimum=0,
maximum=1,
step=0.01,
value=0.0,
interactive=True,
)
ratio_b = gr.Slider(
label="Model B merge ratio (eg: 0.5 mean 50%)",
minimum=0,
maximum=1,
step=0.01,
value=0.0,
interactive=True,
)
with gr.Group(), gr.Row():
lora_c_model = gr.Dropdown(
label='LoRA model "C" (path to the LoRA C model)',
interactive=True,
choices=[""] + list_c_models(current_c_model_dir),
value="",
allow_custom_value=True,
)
create_refresh_button(
lora_c_model,
lambda: None,
lambda: {"choices": list_c_models(current_c_model_dir)},
"open_folder_small",
)
button_lora_c_model_file = gr.Button(
folder_symbol,
elem_id="open_folder_small",
elem_classes=["tool"],
visible=(not self.headless),
)
button_lora_c_model_file.click(
get_file_path,
inputs=[lora_c_model, lora_ext, lora_ext_name],
outputs=lora_c_model,
show_progress=False,
)
lora_d_model = gr.Dropdown(
label='LoRA model "D" (path to the LoRA D model)',
interactive=True,
choices=[""] + list_d_models(current_d_model_dir),
value="",
allow_custom_value=True,
)
create_refresh_button(
lora_d_model,
lambda: None,
lambda: {"choices": list_d_models(current_d_model_dir)},
"open_folder_small",
)
button_lora_d_model_file = gr.Button(
folder_symbol,
elem_id="open_folder_small",
elem_classes=["tool"],
visible=(not self.headless),
)
button_lora_d_model_file.click(
get_file_path,
inputs=[lora_d_model, lora_ext, lora_ext_name],
outputs=lora_d_model,
show_progress=False,
)
lora_c_model.change(
fn=lambda path: gr.Dropdown(choices=[""] + list_c_models(path)),
inputs=lora_c_model,
outputs=lora_c_model,
show_progress=False,
)
lora_d_model.change(
fn=lambda path: gr.Dropdown(choices=[""] + list_d_models(path)),
inputs=lora_d_model,
outputs=lora_d_model,
show_progress=False,
)
with gr.Row():
ratio_c = gr.Slider(
label="Model C merge ratio (eg: 0.5 mean 50%)",
minimum=0,
maximum=1,
step=0.01,
value=0.0,
interactive=True,
)
ratio_d = gr.Slider(
label="Model D merge ratio (eg: 0.5 mean 50%)",
minimum=0,
maximum=1,
step=0.01,
value=0.0,
interactive=True,
)
with gr.Group(), gr.Row():
save_to = gr.Dropdown(
label="Save to (path for the file to save...)",
interactive=True,
choices=[""] + list_save_to(current_d_model_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 self.headless),
)
button_save_to.click(
get_saveasfilename_path,
inputs=[save_to, lora_ext, lora_ext_name],
outputs=save_to,
show_progress=False,
)
precision = gr.Radio(
label="Merge precision",
choices=["fp16", "bf16", "float"],
value="float",
interactive=True,
)
save_precision = gr.Radio(
label="Save precision",
choices=["fp16", "bf16", "float"],
value="fp16",
interactive=True,
)
save_to.change(
fn=lambda path: gr.Dropdown(choices=[""] + list_save_to(path)),
inputs=save_to,
outputs=save_to,
show_progress=False,
)
merge_button = gr.Button("Merge model")
merge_button.click(
self.merge_lora,
inputs=[
sd_model,
sdxl_model,
lora_a_model,
lora_b_model,
lora_c_model,
lora_d_model,
ratio_a,
ratio_b,
ratio_c,
ratio_d,
save_to,
precision,
save_precision,
],
show_progress=False,
)
def merge_lora(
self,
sd_model,
sdxl_model,
lora_a_model,
lora_b_model,
lora_c_model,
lora_d_model,
ratio_a,
ratio_b,
ratio_c,
ratio_d,
save_to,
precision,
save_precision,
):
log.info("Merge model...")
models = [
sd_model,
lora_a_model,
lora_b_model,
lora_c_model,
lora_d_model,
]
lora_models = models[1:]
ratios = [ratio_a, ratio_b, ratio_c, ratio_d]
if not verify_conditions(sd_model, lora_models):
log.info(
"Warning: Either provide at least one LoRa model along with the sd_model or at least two LoRa models if no sd_model is provided."
)
return
for model in models:
if not check_model(model):
return
if not sdxl_model:
run_cmd = [rf"{PYTHON}", rf"{scriptdir}/sd-scripts/networks/merge_lora.py"]
else:
run_cmd = [
rf"{PYTHON}",
rf"{scriptdir}/sd-scripts/networks/sdxl_merge_lora.py",
]
if sd_model:
run_cmd.append("--sd_model")
run_cmd.append(rf"{sd_model}")
run_cmd.append("--save_precision")
run_cmd.append(save_precision)
run_cmd.append("--precision")
run_cmd.append(precision)
run_cmd.append("--save_to")
run_cmd.append(rf"{save_to}")
# Prepare model and ratios command as lists, including only non-empty models
valid_models = [model for model in lora_models if model]
valid_ratios = [ratios[i] for i, model in enumerate(lora_models) if model]
if valid_models:
run_cmd.append("--models")
run_cmd.extend(valid_models) # Each model is a separate argument
run_cmd.append("--ratios")
run_cmd.extend(
map(str, valid_ratios)
) # Convert ratios to strings and include them as separate arguments
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 merging...")
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