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import gradio as gr
from .common_gui import get_folder_path, scriptdir, list_dirs, create_refresh_button
import shutil
import os
from .class_gui_config import KohyaSSGUIConfig
from .custom_logging import setup_logging
# Set up logging
log = setup_logging()
def copy_info_to_Folders_tab(training_folder):
img_folder = gr.Dropdown(value=os.path.join(training_folder, "img"))
if os.path.exists(os.path.join(training_folder, "reg")):
reg_folder = gr.Dropdown(value=os.path.join(training_folder, "reg"))
else:
reg_folder = gr.Dropdown(value="")
model_folder = gr.Dropdown(value=os.path.join(training_folder, "model"))
log_folder = gr.Dropdown(value=os.path.join(training_folder, "log"))
return img_folder, reg_folder, model_folder, log_folder
def dreambooth_folder_preparation(
util_training_images_dir_input,
util_training_images_repeat_input,
util_instance_prompt_input,
util_regularization_images_dir_input,
util_regularization_images_repeat_input,
util_class_prompt_input,
util_training_dir_output,
):
# Check if the input variables are empty
if not len(util_training_dir_output):
log.info(
"Destination training directory is missing... can't perform the required task..."
)
return
else:
# Create the util_training_dir_output directory if it doesn't exist
os.makedirs(util_training_dir_output, exist_ok=True)
# Check for instance prompt
if util_instance_prompt_input == "":
log.error("Instance prompt missing...")
return
# Check for class prompt
if util_class_prompt_input == "":
log.error("Class prompt missing...")
return
# Create the training_dir path
if util_training_images_dir_input == "":
log.info(
"Training images directory is missing... can't perform the required task..."
)
return
else:
training_dir = os.path.join(
util_training_dir_output,
f"img/{int(util_training_images_repeat_input)}_{util_instance_prompt_input} {util_class_prompt_input}",
)
# Remove folders if they exist
if os.path.exists(training_dir):
log.info(f"Removing existing directory {training_dir}...")
shutil.rmtree(training_dir)
# Copy the training images to their respective directories
log.info(f"Copy {util_training_images_dir_input} to {training_dir}...")
shutil.copytree(util_training_images_dir_input, training_dir)
if not util_regularization_images_dir_input == "":
# Create the regularization_dir path
if not util_regularization_images_repeat_input > 0:
log.info("Repeats is missing... not copying regularisation images...")
else:
regularization_dir = os.path.join(
util_training_dir_output,
f"reg/{int(util_regularization_images_repeat_input)}_{util_class_prompt_input}",
)
# Remove folders if they exist
if os.path.exists(regularization_dir):
log.info(f"Removing existing directory {regularization_dir}...")
shutil.rmtree(regularization_dir)
# Copy the regularisation images to their respective directories
log.info(
f"Copy {util_regularization_images_dir_input} to {regularization_dir}..."
)
shutil.copytree(util_regularization_images_dir_input, regularization_dir)
else:
log.info(
"Regularization images directory is missing... not copying regularisation images..."
)
# create log and model folder
# Check if the log folder exists and create it if it doesn't
if not os.path.exists(os.path.join(util_training_dir_output, "log")):
os.makedirs(os.path.join(util_training_dir_output, "log"))
# Check if the model folder exists and create it if it doesn't
if not os.path.exists(os.path.join(util_training_dir_output, "model")):
os.makedirs(os.path.join(util_training_dir_output, "model"))
log.info(
f"Done creating kohya_ss training folder structure at {util_training_dir_output}..."
)
def gradio_dreambooth_folder_creation_tab(
config: KohyaSSGUIConfig,
train_data_dir_input=gr.Dropdown(),
reg_data_dir_input=gr.Dropdown(),
output_dir_input=gr.Dropdown(),
logging_dir_input=gr.Dropdown(),
headless=False,
):
current_train_data_dir = os.path.join(scriptdir, "data")
current_reg_data_dir = os.path.join(scriptdir, "data")
current_train_output_dir = os.path.join(scriptdir, "data")
with gr.Tab("Dreambooth/LoRA Folder preparation"):
gr.Markdown(
"This utility will create the necessary folder structure for the training images and optional regularization images needed for the kohys_ss Dreambooth/LoRA method to function correctly."
)
with gr.Row():
util_instance_prompt_input = gr.Textbox(
label="Instance prompt",
placeholder="Eg: asd",
interactive=True,
value=config.get(key="dataset_preparation.instance_prompt", default=""),
)
util_class_prompt_input = gr.Textbox(
label="Class prompt",
placeholder="Eg: person",
interactive=True,
value=config.get(key="dataset_preparation.class_prompt", default=""),
)
with gr.Group(), gr.Row():
def list_train_data_dirs(path):
nonlocal current_train_data_dir
current_train_data_dir = path
return list(list_dirs(path))
util_training_images_dir_input = gr.Dropdown(
label="Training images (directory containing the training images)",
interactive=True,
choices=[
config.get(key="dataset_preparation.images_folder", default="")
]
+ list_train_data_dirs(current_train_data_dir),
value=config.get(key="dataset_preparation.images_folder", default=""),
allow_custom_value=True,
)
create_refresh_button(
util_training_images_dir_input,
lambda: None,
lambda: {"choices": list_train_data_dirs(current_train_data_dir)},
"open_folder_small",
)
button_util_training_images_dir_input = gr.Button(
"📂",
elem_id="open_folder_small",
elem_classes=["tool"],
visible=(not headless),
)
button_util_training_images_dir_input.click(
get_folder_path,
outputs=util_training_images_dir_input,
show_progress=False,
)
util_training_images_repeat_input = gr.Number(
label="Repeats",
value=config.get(key="dataset_preparation.util_training_images_repeat_input", default=40),
interactive=True,
elem_id="number_input",
)
util_training_images_dir_input.change(
fn=lambda path: gr.Dropdown(choices=[config.get(key="dataset_preparation.images_folder", default="")] + list_train_data_dirs(path)),
inputs=util_training_images_dir_input,
outputs=util_training_images_dir_input,
show_progress=False,
)
with gr.Group(), gr.Row():
def list_reg_data_dirs(path):
nonlocal current_reg_data_dir
current_reg_data_dir = path
return list(list_dirs(path))
util_regularization_images_dir_input = gr.Dropdown(
label="Regularisation images (Optional. directory containing the regularisation images)",
interactive=True,
choices=[
config.get(key="dataset_preparation.reg_images_folder", default="")
]
+ list_reg_data_dirs(current_reg_data_dir),
value=config.get(
key="dataset_preparation.reg_images_folder", default=""
),
allow_custom_value=True,
)
create_refresh_button(
util_regularization_images_dir_input,
lambda: None,
lambda: {"choices": list_reg_data_dirs(current_reg_data_dir)},
"open_folder_small",
)
button_util_regularization_images_dir_input = gr.Button(
"📂",
elem_id="open_folder_small",
elem_classes=["tool"],
visible=(not headless),
)
button_util_regularization_images_dir_input.click(
get_folder_path,
outputs=util_regularization_images_dir_input,
show_progress=False,
)
util_regularization_images_repeat_input = gr.Number(
label="Repeats",
value=config.get(
key="dataset_preparation.util_regularization_images_repeat_input",
default=1
),
interactive=True,
elem_id="number_input",
)
util_regularization_images_dir_input.change(
fn=lambda path: gr.Dropdown(choices=[""] + list_reg_data_dirs(path)),
inputs=util_regularization_images_dir_input,
outputs=util_regularization_images_dir_input,
show_progress=False,
)
with gr.Group(), gr.Row():
def list_train_output_dirs(path):
nonlocal current_train_output_dir
current_train_output_dir = path
return list(list_dirs(path))
util_training_dir_output = gr.Dropdown(
label="Destination training directory (where formatted training and regularisation folders will be placed)",
interactive=True,
choices=[config.get(key="train_data_dir", default="")]
+ list_train_output_dirs(current_train_output_dir),
value=config.get(key="train_data_dir", default=""),
allow_custom_value=True,
)
create_refresh_button(
util_training_dir_output,
lambda: None,
lambda: {"choices": list_train_output_dirs(current_train_output_dir)},
"open_folder_small",
)
button_util_training_dir_output = gr.Button(
"📂",
elem_id="open_folder_small",
elem_classes=["tool"],
visible=(not headless),
)
button_util_training_dir_output.click(
get_folder_path, outputs=util_training_dir_output
)
util_training_dir_output.change(
fn=lambda path: gr.Dropdown(
choices=[config.get(key="train_data_dir", default="")] + list_train_output_dirs(path)
),
inputs=util_training_dir_output,
outputs=util_training_dir_output,
show_progress=False,
)
button_prepare_training_data = gr.Button("Prepare training data")
button_prepare_training_data.click(
dreambooth_folder_preparation,
inputs=[
util_training_images_dir_input,
util_training_images_repeat_input,
util_instance_prompt_input,
util_regularization_images_dir_input,
util_regularization_images_repeat_input,
util_class_prompt_input,
util_training_dir_output,
],
show_progress=False,
)
button_copy_info_to_Folders_tab = gr.Button('Copy info to respective fields')
button_copy_info_to_Folders_tab.click(
copy_info_to_Folders_tab,
inputs=[util_training_dir_output],
outputs=[
train_data_dir_input,
reg_data_dir_input,
output_dir_input,
logging_dir_input,
],
show_progress=False,
)
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