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finetune_gui.py
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finetune_gui.py
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import gradio as gr
import json
import math
import os
import subprocess
import pathlib
import argparse
from library.common_gui import (
get_folder_path,
get_file_path,
get_saveasfile_path,
save_inference_file,
gradio_advanced_training,
run_cmd_advanced_training,
gradio_training,
run_cmd_advanced_training,
gradio_config,
gradio_source_model,
color_aug_changed,
run_cmd_training,
# set_legacy_8bitadam,
update_my_data,
check_if_model_exist,
output_message,
)
from library.tensorboard_gui import (
gradio_tensorboard,
start_tensorboard,
stop_tensorboard,
)
from library.utilities import utilities_tab
from library.sampler_gui import sample_gradio_config, run_cmd_sample
# from easygui import msgbox
folder_symbol = '\U0001f4c2' # 📂
refresh_symbol = '\U0001f504' # 🔄
save_style_symbol = '\U0001f4be' # 💾
document_symbol = '\U0001F4C4' # 📄
PYTHON = 'python3' if os.name == 'posix' else './venv/Scripts/python.exe'
def save_configuration(
save_as,
file_path,
pretrained_model_name_or_path,
v2,
v_parameterization,
train_dir,
image_folder,
output_dir,
logging_dir,
max_resolution,
min_bucket_reso,
max_bucket_reso,
batch_size,
flip_aug,
caption_metadata_filename,
latent_metadata_filename,
full_path,
learning_rate,
lr_scheduler,
lr_warmup,
dataset_repeats,
train_batch_size,
epoch,
save_every_n_epochs,
mixed_precision,
save_precision,
seed,
num_cpu_threads_per_process,
train_text_encoder,
create_caption,
create_buckets,
save_model_as,
caption_extension,
# use_8bit_adam,
xformers,
clip_skip,
save_state,
resume,
gradient_checkpointing,
gradient_accumulation_steps,
mem_eff_attn,
shuffle_caption,
output_name,
max_token_length,
max_train_epochs,
max_data_loader_n_workers,
full_fp16,
color_aug,
model_list,
cache_latents,
cache_latents_to_disk,
use_latent_files,
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_type,noise_offset,adaptive_noise_scale,
multires_noise_iterations,
multires_noise_discount,
sample_every_n_steps,
sample_every_n_epochs,
sample_sampler,
sample_prompts,
additional_parameters,
vae_batch_size,
min_snr_gamma,
weighted_captions,
save_every_n_steps,
save_last_n_steps,
save_last_n_steps_state,
use_wandb,
wandb_api_key,
):
# 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(
ask_for_file,
file_path,
pretrained_model_name_or_path,
v2,
v_parameterization,
train_dir,
image_folder,
output_dir,
logging_dir,
max_resolution,
min_bucket_reso,
max_bucket_reso,
batch_size,
flip_aug,
caption_metadata_filename,
latent_metadata_filename,
full_path,
learning_rate,
lr_scheduler,
lr_warmup,
dataset_repeats,
train_batch_size,
epoch,
save_every_n_epochs,
mixed_precision,
save_precision,
seed,
num_cpu_threads_per_process,
train_text_encoder,
create_caption,
create_buckets,
save_model_as,
caption_extension,
# use_8bit_adam,
xformers,
clip_skip,
save_state,
resume,
gradient_checkpointing,
gradient_accumulation_steps,
mem_eff_attn,
shuffle_caption,
output_name,
max_token_length,
max_train_epochs,
max_data_loader_n_workers,
full_fp16,
color_aug,
model_list,
cache_latents,
cache_latents_to_disk,
use_latent_files,
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_type,noise_offset,adaptive_noise_scale,
multires_noise_iterations,
multires_noise_discount,
sample_every_n_steps,
sample_every_n_epochs,
sample_sampler,
sample_prompts,
additional_parameters,
vae_batch_size,
min_snr_gamma,
weighted_captions,
save_every_n_steps,
save_last_n_steps,
save_last_n_steps_state,
use_wandb,
wandb_api_key,
):
# Get list of function parameters and values
parameters = list(locals().items())
ask_for_file = True if ask_for_file.get('label') == 'True' else False
original_file_path = file_path
if ask_for_file:
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 = 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_my_data(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 = {}
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 ['ask_for_file', 'file_path']:
values.append(my_data.get(key, value))
return tuple(values)
def train_model(
headless,
pretrained_model_name_or_path,
v2,
v_parameterization,
train_dir,
image_folder,
output_dir,
logging_dir,
max_resolution,
min_bucket_reso,
max_bucket_reso,
batch_size,
flip_aug,
caption_metadata_filename,
latent_metadata_filename,
full_path,
learning_rate,
lr_scheduler,
lr_warmup,
dataset_repeats,
train_batch_size,
epoch,
save_every_n_epochs,
mixed_precision,
save_precision,
seed,
num_cpu_threads_per_process,
train_text_encoder,
generate_caption_database,
generate_image_buckets,
save_model_as,
caption_extension,
# use_8bit_adam,
xformers,
clip_skip,
save_state,
resume,
gradient_checkpointing,
gradient_accumulation_steps,
mem_eff_attn,
shuffle_caption,
output_name,
max_token_length,
max_train_epochs,
max_data_loader_n_workers,
full_fp16,
color_aug,
model_list, # Keep this. Yes, it is unused here but required given the common list used
cache_latents,
cache_latents_to_disk,
use_latent_files,
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_type,noise_offset,adaptive_noise_scale,
multires_noise_iterations,
multires_noise_discount,
sample_every_n_steps,
sample_every_n_epochs,
sample_sampler,
sample_prompts,
additional_parameters,
vae_batch_size,
min_snr_gamma,
weighted_captions,
save_every_n_steps,
save_last_n_steps,
save_last_n_steps_state,
use_wandb,
wandb_api_key,
):
headless_bool = True if headless.get('label') == 'True' else False
if check_if_model_exist(
output_name, output_dir, save_model_as, headless_bool
):
return
# if float(noise_offset) > 0 and (
# multires_noise_iterations > 0 or multires_noise_discount > 0
# ):
# output_message(
# msg="noise offset and multires_noise can't be set at the same time. Only use one or the other.",
# title='Error',
# headless=headless_bool,
# )
# return
if optimizer == 'Adafactor' and lr_warmup != '0':
output_message(
msg="Warning: lr_scheduler is set to 'Adafactor', so 'LR warmup (% of steps)' will be considered 0.",
title='Warning',
headless=headless_bool,
)
lr_warmup = '0'
# create caption json file
if generate_caption_database:
if not os.path.exists(train_dir):
os.mkdir(train_dir)
run_cmd = f'{PYTHON} finetune/merge_captions_to_metadata.py'
if caption_extension == '':
run_cmd += f' --caption_extension=".caption"'
else:
run_cmd += f' --caption_extension={caption_extension}'
run_cmd += f' "{image_folder}"'
run_cmd += f' "{train_dir}/{caption_metadata_filename}"'
if full_path:
run_cmd += f' --full_path'
print(run_cmd)
# Run the command
if os.name == 'posix':
os.system(run_cmd)
else:
subprocess.run(run_cmd)
# create images buckets
if generate_image_buckets:
run_cmd = f'{PYTHON} finetune/prepare_buckets_latents.py'
run_cmd += f' "{image_folder}"'
run_cmd += f' "{train_dir}/{caption_metadata_filename}"'
run_cmd += f' "{train_dir}/{latent_metadata_filename}"'
run_cmd += f' "{pretrained_model_name_or_path}"'
run_cmd += f' --batch_size={batch_size}'
run_cmd += f' --max_resolution={max_resolution}'
run_cmd += f' --min_bucket_reso={min_bucket_reso}'
run_cmd += f' --max_bucket_reso={max_bucket_reso}'
run_cmd += f' --mixed_precision={mixed_precision}'
# if flip_aug:
# run_cmd += f' --flip_aug'
if full_path:
run_cmd += f' --full_path'
print(run_cmd)
# Run the command
if os.name == 'posix':
os.system(run_cmd)
else:
subprocess.run(run_cmd)
image_num = len(
[
f
for f, lower_f in (
(file, file.lower()) for file in os.listdir(image_folder)
)
if lower_f.endswith(('.jpg', '.jpeg', '.png', '.webp'))
]
)
print(f'image_num = {image_num}')
repeats = int(image_num) * int(dataset_repeats)
print(f'repeats = {str(repeats)}')
# calculate max_train_steps
max_train_steps = int(
math.ceil(
float(repeats)
/ int(train_batch_size)
/ int(gradient_accumulation_steps)
* int(epoch)
)
)
# Divide by two because flip augmentation create two copied of the source images
if flip_aug:
max_train_steps = int(math.ceil(float(max_train_steps) / 2))
print(f'max_train_steps = {max_train_steps}')
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} "./fine_tune.py"'
if v2:
run_cmd += ' --v2'
if v_parameterization:
run_cmd += ' --v_parameterization'
if train_text_encoder:
run_cmd += ' --train_text_encoder'
if weighted_captions:
run_cmd += ' --weighted_captions'
run_cmd += (
f' --pretrained_model_name_or_path="{pretrained_model_name_or_path}"'
)
if use_latent_files == 'Yes':
run_cmd += f' --in_json="{train_dir}/{latent_metadata_filename}"'
else:
run_cmd += f' --in_json="{train_dir}/{caption_metadata_filename}"'
run_cmd += f' --train_data_dir="{image_folder}"'
run_cmd += f' --output_dir="{output_dir}"'
if not logging_dir == '':
run_cmd += f' --logging_dir="{logging_dir}"'
run_cmd += f' --dataset_repeats={dataset_repeats}'
run_cmd += f' --learning_rate={learning_rate}'
run_cmd += ' --enable_bucket'
run_cmd += f' --resolution={max_resolution}'
run_cmd += f' --min_bucket_reso={min_bucket_reso}'
run_cmd += f' --max_bucket_reso={max_bucket_reso}'
if not save_model_as == 'same as source model':
run_cmd += f' --save_model_as={save_model_as}'
if int(gradient_accumulation_steps) > 1:
run_cmd += f' --gradient_accumulation_steps={int(gradient_accumulation_steps)}'
# if save_state:
# run_cmd += ' --save_state'
# if not resume == '':
# run_cmd += f' --resume={resume}'
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}'
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,
cache_latents_to_disk=cache_latents_to_disk,
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_type=noise_offset_type,
noise_offset=noise_offset,
adaptive_noise_scale=adaptive_noise_scale,
multires_noise_iterations=multires_noise_iterations,
multires_noise_discount=multires_noise_discount,
additional_parameters=additional_parameters,
vae_batch_size=vae_batch_size,
min_snr_gamma=min_snr_gamma,
save_every_n_steps=save_every_n_steps,
save_last_n_steps=save_last_n_steps,
save_last_n_steps_state=save_last_n_steps_state,
use_wandb=use_wandb,
wandb_api_key=wandb_api_key,
)
run_cmd += run_cmd_sample(
sample_every_n_steps,
sample_every_n_epochs,
sample_sampler,
sample_prompts,
output_dir,
)
print(run_cmd)
# Run the command
if os.name == 'posix':
os.system(run_cmd)
else:
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 remove_doublequote(file_path):
if file_path != None:
file_path = file_path.replace('"', '')
return file_path
def finetune_tab(headless=False):
dummy_db_true = gr.Label(value=True, visible=False)
dummy_db_false = gr.Label(value=False, visible=False)
dummy_headless = gr.Label(value=headless, visible=False)
gr.Markdown('Train a custom model using kohya finetune python code...')
(
button_open_config,
button_save_config,
button_save_as_config,
config_file_name,
button_load_config,
) = gradio_config(headless=headless)
(
pretrained_model_name_or_path,
v2,
v_parameterization,
save_model_as,
model_list,
) = gradio_source_model(headless=headless)
with gr.Tab('Folders'):
with gr.Row():
train_dir = gr.Textbox(
label='Training config folder',
placeholder='folder where the training configuration files will be saved',
)
train_dir_folder = gr.Button(
folder_symbol,
elem_id='open_folder_small',
visible=(not headless),
)
train_dir_folder.click(
get_folder_path,
outputs=train_dir,
show_progress=False,
)
image_folder = gr.Textbox(
label='Training Image folder',
placeholder='folder where the training images are located',
)
image_folder_input_folder = gr.Button(
folder_symbol,
elem_id='open_folder_small',
visible=(not headless),
)
image_folder_input_folder.click(
get_folder_path,
outputs=image_folder,
show_progress=False,
)
with gr.Row():
output_dir = gr.Textbox(
label='Model output folder',
placeholder='folder where the model will be saved',
)
output_dir_input_folder = gr.Button(
folder_symbol,
elem_id='open_folder_small',
visible=(not headless),
)
output_dir_input_folder.click(
get_folder_path,
outputs=output_dir,
show_progress=False,
)
logging_dir = gr.Textbox(
label='Logging folder',
placeholder='Optional: enable logging and output TensorBoard log to this folder',
)
logging_dir_input_folder = gr.Button(
folder_symbol,
elem_id='open_folder_small',
visible=(not headless),
)
logging_dir_input_folder.click(
get_folder_path,
outputs=logging_dir,
show_progress=False,
)
with gr.Row():
output_name = gr.Textbox(
label='Model output name',
placeholder='Name of the model to output',
value='last',
interactive=True,
)
train_dir.change(
remove_doublequote,
inputs=[train_dir],
outputs=[train_dir],
)
image_folder.change(
remove_doublequote,
inputs=[image_folder],
outputs=[image_folder],
)
output_dir.change(
remove_doublequote,
inputs=[output_dir],
outputs=[output_dir],
)
with gr.Tab('Dataset preparation'):
with gr.Row():
max_resolution = gr.Textbox(
label='Resolution (width,height)', value='512,512'
)
min_bucket_reso = gr.Textbox(
label='Min bucket resolution', value='256'
)
max_bucket_reso = gr.Textbox(
label='Max bucket resolution', value='1024'
)
batch_size = gr.Textbox(label='Batch size', value='1')
with gr.Row():
create_caption = gr.Checkbox(
label='Generate caption metadata', value=True
)
create_buckets = gr.Checkbox(
label='Generate image buckets metadata', value=True
)
use_latent_files = gr.Dropdown(
label='Use latent files',
choices=[
'No',
'Yes',
],
value='Yes',
)
with gr.Accordion('Advanced parameters', open=False):
with gr.Row():
caption_metadata_filename = gr.Textbox(
label='Caption metadata filename', value='meta_cap.json'
)
latent_metadata_filename = gr.Textbox(
label='Latent metadata filename', value='meta_lat.json'
)
with gr.Row():
full_path = gr.Checkbox(label='Use full path', value=True)
weighted_captions = gr.Checkbox(
label='Weighted captions', value=False
)
with gr.Tab('Training parameters'):
(
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,
cache_latents_to_disk,
optimizer,
optimizer_args,
) = gradio_training(learning_rate_value='1e-5')
with gr.Row():
dataset_repeats = gr.Textbox(label='Dataset repeats', value=40)
train_text_encoder = gr.Checkbox(
label='Train text encoder', value=True
)
with gr.Accordion('Advanced parameters', open=False):
with gr.Row():
gradient_accumulation_steps = gr.Number(
label='Gradient accumulate steps', value='1'
)
(
# 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_type,noise_offset,adaptive_noise_scale,
multires_noise_iterations,
multires_noise_discount,
additional_parameters,
vae_batch_size,
min_snr_gamma,
save_every_n_steps,
save_last_n_steps,
save_last_n_steps_state,
use_wandb,
wandb_api_key,
) = gradio_advanced_training(headless=headless)
color_aug.change(
color_aug_changed,
inputs=[color_aug],
outputs=[cache_latents], # Not applicable to fine_tune.py
)
(
sample_every_n_steps,
sample_every_n_epochs,
sample_sampler,
sample_prompts,
) = sample_gradio_config()
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,
show_progress=False,
)
settings_list = [
pretrained_model_name_or_path,
v2,
v_parameterization,
train_dir,
image_folder,
output_dir,
logging_dir,
max_resolution,
min_bucket_reso,
max_bucket_reso,
batch_size,
flip_aug,
caption_metadata_filename,
latent_metadata_filename,
full_path,
learning_rate,
lr_scheduler,
lr_warmup,
dataset_repeats,
train_batch_size,
epoch,
save_every_n_epochs,
mixed_precision,
save_precision,
seed,
num_cpu_threads_per_process,
train_text_encoder,
create_caption,
create_buckets,
save_model_as,
caption_extension,
# use_8bit_adam,
xformers,
clip_skip,
save_state,
resume,
gradient_checkpointing,
gradient_accumulation_steps,
mem_eff_attn,
shuffle_caption,
output_name,
max_token_length,
max_train_epochs,
max_data_loader_n_workers,
full_fp16,
color_aug,
model_list,
cache_latents,
cache_latents_to_disk,
use_latent_files,
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_type,noise_offset,adaptive_noise_scale,
multires_noise_iterations,
multires_noise_discount,
sample_every_n_steps,
sample_every_n_epochs,
sample_sampler,
sample_prompts,
additional_parameters,
vae_batch_size,
min_snr_gamma,
weighted_captions,
save_every_n_steps,
save_last_n_steps,
save_last_n_steps_state,
use_wandb,
wandb_api_key,
]
button_run.click(train_model, inputs=[dummy_headless] + settings_list)
button_open_config.click(
open_configuration,
inputs=[dummy_db_true, config_file_name] + settings_list,
outputs=[config_file_name] + settings_list,
show_progress=False,
)
button_load_config.click(
open_configuration,
inputs=[dummy_db_false, config_file_name] + settings_list,
outputs=[config_file_name] + settings_list,
show_progress=False,
)
button_save_config.click(
save_configuration,
inputs=[dummy_db_false, config_file_name] + settings_list,
outputs=[config_file_name],
show_progress=False,
)
button_save_as_config.click(
save_configuration,
inputs=[dummy_db_true, config_file_name] + settings_list,
outputs=[config_file_name],
show_progress=False,
)
def UI(**kwargs):
css = ''
headless = kwargs.get('headless', False)
print(f'headless: {headless}')
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, title='Kohya_ss GUI', theme=gr.themes.Default()
)
with interface:
with gr.Tab('Finetune'):
finetune_tab(headless=headless)
with gr.Tab('Utilities'):
utilities_tab(enable_dreambooth_tab=False, headless=headless)
# Show the interface
launch_kwargs = {}
username = kwargs.get('username')
password = kwargs.get('password')
server_port = kwargs.get('server_port', 0)
inbrowser = kwargs.get('inbrowser', False)
share = kwargs.get('share', False)
server_name = kwargs.get('listen')
launch_kwargs['server_name'] = server_name
if username and password:
launch_kwargs['auth'] = (username, password)
if server_port > 0:
launch_kwargs['server_port'] = server_port
if inbrowser:
launch_kwargs['inbrowser'] = inbrowser
if share:
launch_kwargs['share'] = share
interface.launch(**launch_kwargs)
if __name__ == '__main__':
# torch.cuda.set_per_process_memory_fraction(0.48)
parser = argparse.ArgumentParser()
parser.add_argument(
'--listen',
type=str,
default='127.0.0.1',
help='IP to listen on for connections to Gradio',
)
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'
)
parser.add_argument(
'--share', action='store_true', help='Share the gradio UI'
)
parser.add_argument(
'--headless', action='store_true', help='Is the server headless'