885 lines
26 KiB
Python
885 lines
26 KiB
Python
# v1: initial release
|
|
# v2: add open and save folder icons
|
|
# v3: Add new Utilities tab for Dreambooth folder preparation
|
|
# v3.1: Adding captionning of images to utilities
|
|
|
|
import gradio as gr
|
|
import json
|
|
import math
|
|
import os
|
|
import subprocess
|
|
import pathlib
|
|
import argparse
|
|
from library.common_gui import (
|
|
get_folder_path,
|
|
remove_doublequote,
|
|
get_file_path,
|
|
get_any_file_path,
|
|
get_saveasfile_path,
|
|
color_aug_changed,
|
|
save_inference_file,
|
|
gradio_advanced_training,
|
|
run_cmd_advanced_training,
|
|
run_cmd_training,
|
|
gradio_training,
|
|
gradio_config,
|
|
gradio_source_model,
|
|
set_legacy_8bitadam,
|
|
update_optimizer,
|
|
)
|
|
from library.tensorboard_gui import (
|
|
gradio_tensorboard,
|
|
start_tensorboard,
|
|
stop_tensorboard,
|
|
)
|
|
from library.dreambooth_folder_creation_gui import (
|
|
gradio_dreambooth_folder_creation_tab,
|
|
)
|
|
from library.utilities import utilities_tab
|
|
from easygui import msgbox
|
|
|
|
folder_symbol = '\U0001f4c2' # 📂
|
|
refresh_symbol = '\U0001f504' # 🔄
|
|
save_style_symbol = '\U0001f4be' # 💾
|
|
document_symbol = '\U0001F4C4' # 📄
|
|
|
|
|
|
def save_configuration(
|
|
save_as,
|
|
file_path,
|
|
pretrained_model_name_or_path,
|
|
v2,
|
|
v_parameterization,
|
|
logging_dir,
|
|
train_data_dir,
|
|
reg_data_dir,
|
|
output_dir,
|
|
max_resolution,
|
|
learning_rate,
|
|
lr_scheduler,
|
|
lr_warmup,
|
|
train_batch_size,
|
|
epoch,
|
|
save_every_n_epochs,
|
|
mixed_precision,
|
|
save_precision,
|
|
seed,
|
|
num_cpu_threads_per_process,
|
|
cache_latents,
|
|
caption_extension,
|
|
enable_bucket,
|
|
gradient_checkpointing,
|
|
full_fp16,
|
|
no_token_padding,
|
|
stop_text_encoder_training,
|
|
use_8bit_adam,
|
|
xformers,
|
|
save_model_as,
|
|
shuffle_caption,
|
|
save_state,
|
|
resume,
|
|
prior_loss_weight,
|
|
color_aug,
|
|
flip_aug,
|
|
clip_skip,
|
|
vae,
|
|
output_name,
|
|
max_token_length,
|
|
max_train_epochs,
|
|
max_data_loader_n_workers,
|
|
mem_eff_attn,
|
|
gradient_accumulation_steps,
|
|
model_list,
|
|
token_string,
|
|
init_word,
|
|
num_vectors_per_token,
|
|
max_train_steps,
|
|
weights,
|
|
template,
|
|
keep_tokens,
|
|
persistent_data_loader_workers,
|
|
bucket_no_upscale,
|
|
random_crop,
|
|
bucket_reso_steps,
|
|
caption_dropout_every_n_epochs, caption_dropout_rate,
|
|
optimizer,optimizer_args,noise_offset,
|
|
):
|
|
# Get list of function parameters and values
|
|
parameters = list(locals().items())
|
|
|
|
original_file_path = file_path
|
|
|
|
save_as_bool = True if save_as.get('label') == 'True' else False
|
|
|
|
if save_as_bool:
|
|
print('Save as...')
|
|
file_path = get_saveasfile_path(file_path)
|
|
else:
|
|
print('Save...')
|
|
if file_path == None or file_path == '':
|
|
file_path = get_saveasfile_path(file_path)
|
|
|
|
# print(file_path)
|
|
|
|
if file_path == None or file_path == '':
|
|
return original_file_path # In case a file_path was provided and the user decide to cancel the open action
|
|
|
|
# Return the values of the variables as a dictionary
|
|
variables = {
|
|
name: value
|
|
for name, value in parameters # locals().items()
|
|
if name
|
|
not in [
|
|
'file_path',
|
|
'save_as',
|
|
]
|
|
}
|
|
|
|
# Extract the destination directory from the file path
|
|
destination_directory = os.path.dirname(file_path)
|
|
|
|
# Create the destination directory if it doesn't exist
|
|
if not os.path.exists(destination_directory):
|
|
os.makedirs(destination_directory)
|
|
|
|
# Save the data to the selected file
|
|
with open(file_path, 'w') as file:
|
|
json.dump(variables, file, indent=2)
|
|
|
|
return file_path
|
|
|
|
|
|
def open_configuration(
|
|
file_path,
|
|
pretrained_model_name_or_path,
|
|
v2,
|
|
v_parameterization,
|
|
logging_dir,
|
|
train_data_dir,
|
|
reg_data_dir,
|
|
output_dir,
|
|
max_resolution,
|
|
learning_rate,
|
|
lr_scheduler,
|
|
lr_warmup,
|
|
train_batch_size,
|
|
epoch,
|
|
save_every_n_epochs,
|
|
mixed_precision,
|
|
save_precision,
|
|
seed,
|
|
num_cpu_threads_per_process,
|
|
cache_latents,
|
|
caption_extension,
|
|
enable_bucket,
|
|
gradient_checkpointing,
|
|
full_fp16,
|
|
no_token_padding,
|
|
stop_text_encoder_training,
|
|
use_8bit_adam,
|
|
xformers,
|
|
save_model_as,
|
|
shuffle_caption,
|
|
save_state,
|
|
resume,
|
|
prior_loss_weight,
|
|
color_aug,
|
|
flip_aug,
|
|
clip_skip,
|
|
vae,
|
|
output_name,
|
|
max_token_length,
|
|
max_train_epochs,
|
|
max_data_loader_n_workers,
|
|
mem_eff_attn,
|
|
gradient_accumulation_steps,
|
|
model_list,
|
|
token_string,
|
|
init_word,
|
|
num_vectors_per_token,
|
|
max_train_steps,
|
|
weights,
|
|
template,
|
|
keep_tokens,
|
|
persistent_data_loader_workers,
|
|
bucket_no_upscale,
|
|
random_crop,
|
|
bucket_reso_steps,
|
|
caption_dropout_every_n_epochs, caption_dropout_rate,
|
|
optimizer,optimizer_args,noise_offset,
|
|
):
|
|
# Get list of function parameters and values
|
|
parameters = list(locals().items())
|
|
|
|
original_file_path = file_path
|
|
file_path = get_file_path(file_path)
|
|
|
|
if not file_path == '' and not file_path == None:
|
|
# load variables from JSON file
|
|
with open(file_path, 'r') as f:
|
|
my_data_db = json.load(f)
|
|
print('Loading config...')
|
|
# Update values to fix deprecated use_8bit_adam checkbox and set appropriate optimizer if it is set to True
|
|
my_data = update_optimizer(my_data)
|
|
else:
|
|
file_path = original_file_path # In case a file_path was provided and the user decide to cancel the open action
|
|
my_data_db = {}
|
|
|
|
values = [file_path]
|
|
for key, value in parameters:
|
|
# Set the value in the dictionary to the corresponding value in `my_data`, or the default value if not found
|
|
if not key in ['file_path']:
|
|
values.append(my_data_db.get(key, value))
|
|
return tuple(values)
|
|
|
|
|
|
def train_model(
|
|
pretrained_model_name_or_path,
|
|
v2,
|
|
v_parameterization,
|
|
logging_dir,
|
|
train_data_dir,
|
|
reg_data_dir,
|
|
output_dir,
|
|
max_resolution,
|
|
learning_rate,
|
|
lr_scheduler,
|
|
lr_warmup,
|
|
train_batch_size,
|
|
epoch,
|
|
save_every_n_epochs,
|
|
mixed_precision,
|
|
save_precision,
|
|
seed,
|
|
num_cpu_threads_per_process,
|
|
cache_latents,
|
|
caption_extension,
|
|
enable_bucket,
|
|
gradient_checkpointing,
|
|
full_fp16,
|
|
no_token_padding,
|
|
stop_text_encoder_training_pct,
|
|
use_8bit_adam,
|
|
xformers,
|
|
save_model_as,
|
|
shuffle_caption,
|
|
save_state,
|
|
resume,
|
|
prior_loss_weight,
|
|
color_aug,
|
|
flip_aug,
|
|
clip_skip,
|
|
vae,
|
|
output_name,
|
|
max_token_length,
|
|
max_train_epochs,
|
|
max_data_loader_n_workers,
|
|
mem_eff_attn,
|
|
gradient_accumulation_steps,
|
|
model_list, # Keep this. Yes, it is unused here but required given the common list used
|
|
token_string,
|
|
init_word,
|
|
num_vectors_per_token,
|
|
max_train_steps,
|
|
weights,
|
|
template,
|
|
keep_tokens,
|
|
persistent_data_loader_workers,
|
|
bucket_no_upscale,
|
|
random_crop,
|
|
bucket_reso_steps,
|
|
caption_dropout_every_n_epochs, caption_dropout_rate,
|
|
optimizer,optimizer_args,noise_offset,
|
|
):
|
|
if pretrained_model_name_or_path == '':
|
|
msgbox('Source model information is missing')
|
|
return
|
|
|
|
if train_data_dir == '':
|
|
msgbox('Image folder path is missing')
|
|
return
|
|
|
|
if not os.path.exists(train_data_dir):
|
|
msgbox('Image folder does not exist')
|
|
return
|
|
|
|
if reg_data_dir != '':
|
|
if not os.path.exists(reg_data_dir):
|
|
msgbox('Regularisation folder does not exist')
|
|
return
|
|
|
|
if output_dir == '':
|
|
msgbox('Output folder path is missing')
|
|
return
|
|
|
|
if token_string == '':
|
|
msgbox('Token string is missing')
|
|
return
|
|
|
|
if init_word == '':
|
|
msgbox('Init word is missing')
|
|
return
|
|
|
|
if not os.path.exists(output_dir):
|
|
os.makedirs(output_dir)
|
|
|
|
# Get a list of all subfolders in train_data_dir
|
|
subfolders = [
|
|
f
|
|
for f in os.listdir(train_data_dir)
|
|
if os.path.isdir(os.path.join(train_data_dir, f))
|
|
]
|
|
|
|
total_steps = 0
|
|
|
|
# Loop through each subfolder and extract the number of repeats
|
|
for folder in subfolders:
|
|
# Extract the number of repeats from the folder name
|
|
repeats = int(folder.split('_')[0])
|
|
|
|
# Count the number of images in the folder
|
|
num_images = len(
|
|
[
|
|
f
|
|
for f in os.listdir(os.path.join(train_data_dir, folder))
|
|
if f.endswith('.jpg')
|
|
or f.endswith('.jpeg')
|
|
or f.endswith('.png')
|
|
or f.endswith('.webp')
|
|
]
|
|
)
|
|
|
|
# Calculate the total number of steps for this folder
|
|
steps = repeats * num_images
|
|
total_steps += steps
|
|
|
|
# Print the result
|
|
print(f'Folder {folder}: {steps} steps')
|
|
|
|
# Print the result
|
|
# print(f"{total_steps} total steps")
|
|
|
|
if reg_data_dir == '':
|
|
reg_factor = 1
|
|
else:
|
|
print(
|
|
'Regularisation images are used... Will double the number of steps required...'
|
|
)
|
|
reg_factor = 2
|
|
|
|
# calculate max_train_steps
|
|
if max_train_steps == '':
|
|
max_train_steps = int(
|
|
math.ceil(
|
|
float(total_steps)
|
|
/ int(train_batch_size)
|
|
* int(epoch)
|
|
* int(reg_factor)
|
|
)
|
|
)
|
|
else:
|
|
max_train_steps = int(max_train_steps)
|
|
|
|
print(f'max_train_steps = {max_train_steps}')
|
|
|
|
# calculate stop encoder training
|
|
if stop_text_encoder_training_pct == None:
|
|
stop_text_encoder_training = 0
|
|
else:
|
|
stop_text_encoder_training = math.ceil(
|
|
float(max_train_steps) / 100 * int(stop_text_encoder_training_pct)
|
|
)
|
|
print(f'stop_text_encoder_training = {stop_text_encoder_training}')
|
|
|
|
lr_warmup_steps = round(float(int(lr_warmup) * int(max_train_steps) / 100))
|
|
print(f'lr_warmup_steps = {lr_warmup_steps}')
|
|
|
|
run_cmd = f'accelerate launch --num_cpu_threads_per_process={num_cpu_threads_per_process} "train_textual_inversion.py"'
|
|
if v2:
|
|
run_cmd += ' --v2'
|
|
if v_parameterization:
|
|
run_cmd += ' --v_parameterization'
|
|
if enable_bucket:
|
|
run_cmd += ' --enable_bucket'
|
|
if no_token_padding:
|
|
run_cmd += ' --no_token_padding'
|
|
run_cmd += (
|
|
f' --pretrained_model_name_or_path="{pretrained_model_name_or_path}"'
|
|
)
|
|
run_cmd += f' --train_data_dir="{train_data_dir}"'
|
|
if len(reg_data_dir):
|
|
run_cmd += f' --reg_data_dir="{reg_data_dir}"'
|
|
run_cmd += f' --resolution={max_resolution}'
|
|
run_cmd += f' --output_dir="{output_dir}"'
|
|
run_cmd += f' --logging_dir="{logging_dir}"'
|
|
if not stop_text_encoder_training == 0:
|
|
run_cmd += (
|
|
f' --stop_text_encoder_training={stop_text_encoder_training}'
|
|
)
|
|
if not save_model_as == 'same as source model':
|
|
run_cmd += f' --save_model_as={save_model_as}'
|
|
# if not resume == '':
|
|
# run_cmd += f' --resume={resume}'
|
|
if not float(prior_loss_weight) == 1.0:
|
|
run_cmd += f' --prior_loss_weight={prior_loss_weight}'
|
|
if not vae == '':
|
|
run_cmd += f' --vae="{vae}"'
|
|
if not output_name == '':
|
|
run_cmd += f' --output_name="{output_name}"'
|
|
if int(max_token_length) > 75:
|
|
run_cmd += f' --max_token_length={max_token_length}'
|
|
if not max_train_epochs == '':
|
|
run_cmd += f' --max_train_epochs="{max_train_epochs}"'
|
|
if not max_data_loader_n_workers == '':
|
|
run_cmd += (
|
|
f' --max_data_loader_n_workers="{max_data_loader_n_workers}"'
|
|
)
|
|
if int(gradient_accumulation_steps) > 1:
|
|
run_cmd += f' --gradient_accumulation_steps={int(gradient_accumulation_steps)}'
|
|
|
|
run_cmd += run_cmd_training(
|
|
learning_rate=learning_rate,
|
|
lr_scheduler=lr_scheduler,
|
|
lr_warmup_steps=lr_warmup_steps,
|
|
train_batch_size=train_batch_size,
|
|
max_train_steps=max_train_steps,
|
|
save_every_n_epochs=save_every_n_epochs,
|
|
mixed_precision=mixed_precision,
|
|
save_precision=save_precision,
|
|
seed=seed,
|
|
caption_extension=caption_extension,
|
|
cache_latents=cache_latents,
|
|
optimizer=optimizer,
|
|
optimizer_args=optimizer_args,
|
|
)
|
|
|
|
run_cmd += run_cmd_advanced_training(
|
|
max_train_epochs=max_train_epochs,
|
|
max_data_loader_n_workers=max_data_loader_n_workers,
|
|
max_token_length=max_token_length,
|
|
resume=resume,
|
|
save_state=save_state,
|
|
mem_eff_attn=mem_eff_attn,
|
|
clip_skip=clip_skip,
|
|
flip_aug=flip_aug,
|
|
color_aug=color_aug,
|
|
shuffle_caption=shuffle_caption,
|
|
gradient_checkpointing=gradient_checkpointing,
|
|
full_fp16=full_fp16,
|
|
xformers=xformers,
|
|
use_8bit_adam=use_8bit_adam,
|
|
keep_tokens=keep_tokens,
|
|
persistent_data_loader_workers=persistent_data_loader_workers,
|
|
bucket_no_upscale=bucket_no_upscale,
|
|
random_crop=random_crop,
|
|
bucket_reso_steps=bucket_reso_steps,
|
|
caption_dropout_every_n_epochs=caption_dropout_every_n_epochs,
|
|
caption_dropout_rate=caption_dropout_rate,
|
|
noise_offset=noise_offset,
|
|
)
|
|
run_cmd += f' --token_string="{token_string}"'
|
|
run_cmd += f' --init_word="{init_word}"'
|
|
run_cmd += f' --num_vectors_per_token={num_vectors_per_token}'
|
|
if not weights == '':
|
|
run_cmd += f' --weights="{weights}"'
|
|
if template == 'object template':
|
|
run_cmd += f' --use_object_template'
|
|
elif template == 'style template':
|
|
run_cmd += f' --use_style_template'
|
|
|
|
print(run_cmd)
|
|
# Run the command
|
|
subprocess.run(run_cmd)
|
|
|
|
# check if output_dir/last is a folder... therefore it is a diffuser model
|
|
last_dir = pathlib.Path(f'{output_dir}/{output_name}')
|
|
|
|
if not last_dir.is_dir():
|
|
# Copy inference model for v2 if required
|
|
save_inference_file(output_dir, v2, v_parameterization, output_name)
|
|
|
|
|
|
def ti_tab(
|
|
train_data_dir=gr.Textbox(),
|
|
reg_data_dir=gr.Textbox(),
|
|
output_dir=gr.Textbox(),
|
|
logging_dir=gr.Textbox(),
|
|
):
|
|
dummy_db_true = gr.Label(value=True, visible=False)
|
|
dummy_db_false = gr.Label(value=False, visible=False)
|
|
gr.Markdown('Train a TI using kohya textual inversion python code...')
|
|
(
|
|
button_open_config,
|
|
button_save_config,
|
|
button_save_as_config,
|
|
config_file_name,
|
|
) = gradio_config()
|
|
|
|
(
|
|
pretrained_model_name_or_path,
|
|
v2,
|
|
v_parameterization,
|
|
save_model_as,
|
|
model_list,
|
|
) = gradio_source_model()
|
|
|
|
with gr.Tab('Folders'):
|
|
with gr.Row():
|
|
train_data_dir = gr.Textbox(
|
|
label='Image folder',
|
|
placeholder='Folder where the training folders containing the images are located',
|
|
)
|
|
train_data_dir_input_folder = gr.Button(
|
|
'📂', elem_id='open_folder_small'
|
|
)
|
|
train_data_dir_input_folder.click(
|
|
get_folder_path, outputs=train_data_dir
|
|
)
|
|
reg_data_dir = gr.Textbox(
|
|
label='Regularisation folder',
|
|
placeholder='(Optional) Folder where where the regularization folders containing the images are located',
|
|
)
|
|
reg_data_dir_input_folder = gr.Button(
|
|
'📂', elem_id='open_folder_small'
|
|
)
|
|
reg_data_dir_input_folder.click(
|
|
get_folder_path, outputs=reg_data_dir
|
|
)
|
|
with gr.Row():
|
|
output_dir = gr.Textbox(
|
|
label='Model output folder',
|
|
placeholder='Folder to output trained model',
|
|
)
|
|
output_dir_input_folder = gr.Button(
|
|
'📂', elem_id='open_folder_small'
|
|
)
|
|
output_dir_input_folder.click(get_folder_path, outputs=output_dir)
|
|
logging_dir = gr.Textbox(
|
|
label='Logging folder',
|
|
placeholder='Optional: enable logging and output TensorBoard log to this folder',
|
|
)
|
|
logging_dir_input_folder = gr.Button(
|
|
'📂', elem_id='open_folder_small'
|
|
)
|
|
logging_dir_input_folder.click(
|
|
get_folder_path, outputs=logging_dir
|
|
)
|
|
with gr.Row():
|
|
output_name = gr.Textbox(
|
|
label='Model output name',
|
|
placeholder='Name of the model to output',
|
|
value='last',
|
|
interactive=True,
|
|
)
|
|
train_data_dir.change(
|
|
remove_doublequote,
|
|
inputs=[train_data_dir],
|
|
outputs=[train_data_dir],
|
|
)
|
|
reg_data_dir.change(
|
|
remove_doublequote,
|
|
inputs=[reg_data_dir],
|
|
outputs=[reg_data_dir],
|
|
)
|
|
output_dir.change(
|
|
remove_doublequote,
|
|
inputs=[output_dir],
|
|
outputs=[output_dir],
|
|
)
|
|
logging_dir.change(
|
|
remove_doublequote,
|
|
inputs=[logging_dir],
|
|
outputs=[logging_dir],
|
|
)
|
|
with gr.Tab('Training parameters'):
|
|
with gr.Row():
|
|
weights = gr.Textbox(
|
|
label='Resume TI training',
|
|
placeholder='(Optional) Path to existing TI embeding file to keep training',
|
|
)
|
|
weights_file_input = gr.Button('📂', elem_id='open_folder_small')
|
|
weights_file_input.click(get_file_path, outputs=weights)
|
|
with gr.Row():
|
|
token_string = gr.Textbox(
|
|
label='Token string',
|
|
placeholder='eg: cat',
|
|
)
|
|
init_word = gr.Textbox(
|
|
label='Init word',
|
|
value='*',
|
|
)
|
|
num_vectors_per_token = gr.Slider(
|
|
minimum=1,
|
|
maximum=75,
|
|
value=1,
|
|
step=1,
|
|
label='Vectors',
|
|
)
|
|
max_train_steps = gr.Textbox(
|
|
label='Max train steps',
|
|
placeholder='(Optional) Maximum number of steps',
|
|
)
|
|
template = gr.Dropdown(
|
|
label='Template',
|
|
choices=[
|
|
'caption',
|
|
'object template',
|
|
'style template',
|
|
],
|
|
value='caption',
|
|
)
|
|
(
|
|
learning_rate,
|
|
lr_scheduler,
|
|
lr_warmup,
|
|
train_batch_size,
|
|
epoch,
|
|
save_every_n_epochs,
|
|
mixed_precision,
|
|
save_precision,
|
|
num_cpu_threads_per_process,
|
|
seed,
|
|
caption_extension,
|
|
cache_latents,
|
|
optimizer,optimizer_args,
|
|
) = gradio_training(
|
|
learning_rate_value='1e-5',
|
|
lr_scheduler_value='cosine',
|
|
lr_warmup_value='10',
|
|
)
|
|
with gr.Row():
|
|
max_resolution = gr.Textbox(
|
|
label='Max resolution',
|
|
value='512,512',
|
|
placeholder='512,512',
|
|
)
|
|
stop_text_encoder_training = gr.Slider(
|
|
minimum=0,
|
|
maximum=100,
|
|
value=0,
|
|
step=1,
|
|
label='Stop text encoder training',
|
|
)
|
|
enable_bucket = gr.Checkbox(label='Enable buckets', value=True)
|
|
with gr.Accordion('Advanced Configuration', open=False):
|
|
with gr.Row():
|
|
no_token_padding = gr.Checkbox(
|
|
label='No token padding', value=False
|
|
)
|
|
gradient_accumulation_steps = gr.Number(
|
|
label='Gradient accumulate steps', value='1'
|
|
)
|
|
with gr.Row():
|
|
prior_loss_weight = gr.Number(
|
|
label='Prior loss weight', value=1.0
|
|
)
|
|
vae = gr.Textbox(
|
|
label='VAE',
|
|
placeholder='(Optiona) path to checkpoint of vae to replace for training',
|
|
)
|
|
vae_button = gr.Button('📂', elem_id='open_folder_small')
|
|
vae_button.click(get_any_file_path, outputs=vae)
|
|
(
|
|
use_8bit_adam,
|
|
xformers,
|
|
full_fp16,
|
|
gradient_checkpointing,
|
|
shuffle_caption,
|
|
color_aug,
|
|
flip_aug,
|
|
clip_skip,
|
|
mem_eff_attn,
|
|
save_state,
|
|
resume,
|
|
max_token_length,
|
|
max_train_epochs,
|
|
max_data_loader_n_workers,
|
|
keep_tokens,
|
|
persistent_data_loader_workers,
|
|
bucket_no_upscale,
|
|
random_crop,
|
|
bucket_reso_steps,
|
|
caption_dropout_every_n_epochs, caption_dropout_rate,noise_offset,
|
|
) = gradio_advanced_training()
|
|
color_aug.change(
|
|
color_aug_changed,
|
|
inputs=[color_aug],
|
|
outputs=[cache_latents],
|
|
)
|
|
optimizer.change(
|
|
set_legacy_8bitadam,
|
|
inputs=[optimizer, use_8bit_adam],
|
|
outputs=[optimizer, use_8bit_adam],
|
|
)
|
|
with gr.Tab('Tools'):
|
|
gr.Markdown(
|
|
'This section provide Dreambooth tools to help setup your dataset...'
|
|
)
|
|
gradio_dreambooth_folder_creation_tab(
|
|
train_data_dir_input=train_data_dir,
|
|
reg_data_dir_input=reg_data_dir,
|
|
output_dir_input=output_dir,
|
|
logging_dir_input=logging_dir,
|
|
)
|
|
|
|
button_run = gr.Button('Train model', variant='primary')
|
|
|
|
# Setup gradio tensorboard buttons
|
|
button_start_tensorboard, button_stop_tensorboard = gradio_tensorboard()
|
|
|
|
button_start_tensorboard.click(
|
|
start_tensorboard,
|
|
inputs=logging_dir,
|
|
)
|
|
|
|
button_stop_tensorboard.click(
|
|
stop_tensorboard,
|
|
)
|
|
|
|
settings_list = [
|
|
pretrained_model_name_or_path,
|
|
v2,
|
|
v_parameterization,
|
|
logging_dir,
|
|
train_data_dir,
|
|
reg_data_dir,
|
|
output_dir,
|
|
max_resolution,
|
|
learning_rate,
|
|
lr_scheduler,
|
|
lr_warmup,
|
|
train_batch_size,
|
|
epoch,
|
|
save_every_n_epochs,
|
|
mixed_precision,
|
|
save_precision,
|
|
seed,
|
|
num_cpu_threads_per_process,
|
|
cache_latents,
|
|
caption_extension,
|
|
enable_bucket,
|
|
gradient_checkpointing,
|
|
full_fp16,
|
|
no_token_padding,
|
|
stop_text_encoder_training,
|
|
use_8bit_adam,
|
|
xformers,
|
|
save_model_as,
|
|
shuffle_caption,
|
|
save_state,
|
|
resume,
|
|
prior_loss_weight,
|
|
color_aug,
|
|
flip_aug,
|
|
clip_skip,
|
|
vae,
|
|
output_name,
|
|
max_token_length,
|
|
max_train_epochs,
|
|
max_data_loader_n_workers,
|
|
mem_eff_attn,
|
|
gradient_accumulation_steps,
|
|
model_list,
|
|
token_string,
|
|
init_word,
|
|
num_vectors_per_token,
|
|
max_train_steps,
|
|
weights,
|
|
template,
|
|
keep_tokens,
|
|
persistent_data_loader_workers,
|
|
bucket_no_upscale,
|
|
random_crop,
|
|
bucket_reso_steps,
|
|
caption_dropout_every_n_epochs, caption_dropout_rate,
|
|
optimizer,optimizer_args,noise_offset,
|
|
]
|
|
|
|
button_open_config.click(
|
|
open_configuration,
|
|
inputs=[config_file_name] + settings_list,
|
|
outputs=[config_file_name] + settings_list,
|
|
)
|
|
|
|
button_save_config.click(
|
|
save_configuration,
|
|
inputs=[dummy_db_false, config_file_name] + settings_list,
|
|
outputs=[config_file_name],
|
|
)
|
|
|
|
button_save_as_config.click(
|
|
save_configuration,
|
|
inputs=[dummy_db_true, config_file_name] + settings_list,
|
|
outputs=[config_file_name],
|
|
)
|
|
|
|
button_run.click(
|
|
train_model,
|
|
inputs=settings_list,
|
|
)
|
|
|
|
return (
|
|
train_data_dir,
|
|
reg_data_dir,
|
|
output_dir,
|
|
logging_dir,
|
|
)
|
|
|
|
|
|
def UI(**kwargs):
|
|
css = ''
|
|
|
|
if os.path.exists('./style.css'):
|
|
with open(os.path.join('./style.css'), 'r', encoding='utf8') as file:
|
|
print('Load CSS...')
|
|
css += file.read() + '\n'
|
|
|
|
interface = gr.Blocks(css=css)
|
|
|
|
with interface:
|
|
with gr.Tab('Dreambooth TI'):
|
|
(
|
|
train_data_dir_input,
|
|
reg_data_dir_input,
|
|
output_dir_input,
|
|
logging_dir_input,
|
|
) = ti_tab()
|
|
with gr.Tab('Utilities'):
|
|
utilities_tab(
|
|
train_data_dir_input=train_data_dir_input,
|
|
reg_data_dir_input=reg_data_dir_input,
|
|
output_dir_input=output_dir_input,
|
|
logging_dir_input=logging_dir_input,
|
|
enable_copy_info_button=True,
|
|
)
|
|
|
|
# Show the interface
|
|
launch_kwargs={}
|
|
if not kwargs.get('username', None) == '':
|
|
launch_kwargs["auth"] = (kwargs.get('username', None), kwargs.get('password', None))
|
|
if kwargs.get('server_port', 0) > 0:
|
|
launch_kwargs["server_port"] = kwargs.get('server_port', 0)
|
|
if kwargs.get('inbrowser', False):
|
|
launch_kwargs["inbrowser"] = kwargs.get('inbrowser', False)
|
|
print(launch_kwargs)
|
|
interface.launch(**launch_kwargs)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
# torch.cuda.set_per_process_memory_fraction(0.48)
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument(
|
|
'--username', type=str, default='', help='Username for authentication'
|
|
)
|
|
parser.add_argument(
|
|
'--password', type=str, default='', help='Password for authentication'
|
|
)
|
|
parser.add_argument(
|
|
'--server_port', type=int, default=0, help='Port to run the server listener on'
|
|
)
|
|
parser.add_argument("--inbrowser", action="store_true", help="Open in browser")
|
|
|
|
args = parser.parse_args()
|
|
|
|
UI(username=args.username, password=args.password, inbrowser=args.inbrowser, server_port=args.server_port)
|