Adding improved elements to GUI
This commit is contained in:
bmaltais
parent
469b15b579
commit
3834e5dbab
@ -1,4 +1,5 @@
|
||||
# v1: initial release
|
||||
# v2: add open and save folder icons
|
||||
|
||||
import gradio as gr
|
||||
import json
|
||||
@ -9,6 +10,7 @@ import pathlib
|
||||
import shutil
|
||||
from glob import glob
|
||||
from os.path import join
|
||||
from easygui import fileopenbox, filesavebox, enterbox, diropenbox, msgbox
|
||||
|
||||
|
||||
def save_variables(
|
||||
@ -44,6 +46,19 @@ def save_variables(
|
||||
use_8bit_adam,
|
||||
xformers,
|
||||
):
|
||||
original_file_path = file_path
|
||||
|
||||
if file_path == None or file_path == "":
|
||||
file_path = filesavebox(
|
||||
"Select the config file to save",
|
||||
default="finetune.json",
|
||||
filetypes="*.json",
|
||||
)
|
||||
|
||||
if file_path == None:
|
||||
file_path = original_file_path # In case a file_path was provided and the user decide to cancel the open action
|
||||
return file_path
|
||||
|
||||
# Return the values of the variables as a dictionary
|
||||
variables = {
|
||||
"pretrained_model_name_or_path": pretrained_model_name_or_path,
|
||||
@ -82,44 +97,172 @@ def save_variables(
|
||||
with open(file_path, "w") as file:
|
||||
json.dump(variables, file)
|
||||
|
||||
return file_path
|
||||
|
||||
def load_variables(file_path):
|
||||
# load variables from JSON file
|
||||
with open(file_path, "r") as f:
|
||||
my_data = json.load(f)
|
||||
|
||||
def save_as_variables(
|
||||
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,
|
||||
convert_to_safetensors,
|
||||
convert_to_ckpt,
|
||||
cache_latent,
|
||||
caption_extention,
|
||||
use_safetensors,
|
||||
enable_bucket,
|
||||
gradient_checkpointing,
|
||||
full_fp16,
|
||||
no_token_padding,
|
||||
stop_text_encoder_training,
|
||||
use_8bit_adam,
|
||||
xformers,
|
||||
):
|
||||
original_file_path = file_path
|
||||
|
||||
file_path = filesavebox(
|
||||
"Select the config file to save", default="finetune.json", filetypes="*.json"
|
||||
)
|
||||
|
||||
if file_path == None:
|
||||
file_path = original_file_path # In case a file_path was provided and the user decide to cancel the open action
|
||||
return file_path
|
||||
|
||||
# Return the values of the variables as a dictionary
|
||||
variables = {
|
||||
"pretrained_model_name_or_path": pretrained_model_name_or_path,
|
||||
"v2": v2,
|
||||
"v_parameterization": v_parameterization,
|
||||
"logging_dir": logging_dir,
|
||||
"train_data_dir": train_data_dir,
|
||||
"reg_data_dir": reg_data_dir,
|
||||
"output_dir": output_dir,
|
||||
"max_resolution": max_resolution,
|
||||
"learning_rate": learning_rate,
|
||||
"lr_scheduler": lr_scheduler,
|
||||
"lr_warmup": lr_warmup,
|
||||
"train_batch_size": train_batch_size,
|
||||
"epoch": epoch,
|
||||
"save_every_n_epochs": save_every_n_epochs,
|
||||
"mixed_precision": mixed_precision,
|
||||
"save_precision": save_precision,
|
||||
"seed": seed,
|
||||
"num_cpu_threads_per_process": num_cpu_threads_per_process,
|
||||
"convert_to_safetensors": convert_to_safetensors,
|
||||
"convert_to_ckpt": convert_to_ckpt,
|
||||
"cache_latent": cache_latent,
|
||||
"caption_extention": caption_extention,
|
||||
"use_safetensors": use_safetensors,
|
||||
"enable_bucket": enable_bucket,
|
||||
"gradient_checkpointing": gradient_checkpointing,
|
||||
"full_fp16": full_fp16,
|
||||
"no_token_padding": no_token_padding,
|
||||
"stop_text_encoder_training": stop_text_encoder_training,
|
||||
"use_8bit_adam": use_8bit_adam,
|
||||
"xformers": xformers,
|
||||
}
|
||||
|
||||
# Save the data to the selected file
|
||||
with open(file_path, "w") as file:
|
||||
json.dump(variables, file)
|
||||
|
||||
return file_path
|
||||
|
||||
|
||||
def open_config_file(
|
||||
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,
|
||||
convert_to_safetensors,
|
||||
convert_to_ckpt,
|
||||
cache_latent,
|
||||
caption_extention,
|
||||
use_safetensors,
|
||||
enable_bucket,
|
||||
gradient_checkpointing,
|
||||
full_fp16,
|
||||
no_token_padding,
|
||||
stop_text_encoder_training,
|
||||
use_8bit_adam,
|
||||
xformers,
|
||||
):
|
||||
|
||||
original_file_path = file_path
|
||||
file_path = get_file_path(file_path)
|
||||
|
||||
if file_path != "" and file_path != None:
|
||||
print(file_path)
|
||||
# load variables from JSON file
|
||||
with open(file_path, "r") as f:
|
||||
my_data = json.load(f)
|
||||
else:
|
||||
file_path = original_file_path # In case a file_path was provided and the user decide to cancel the open action
|
||||
my_data = {}
|
||||
|
||||
# Return the values of the variables as a dictionary
|
||||
return (
|
||||
my_data.get("pretrained_model_name_or_path", None),
|
||||
my_data.get("v2", None),
|
||||
my_data.get("v_parameterization", None),
|
||||
my_data.get("logging_dir", None),
|
||||
my_data.get("train_data_dir", None),
|
||||
my_data.get("reg_data_dir", None),
|
||||
my_data.get("output_dir", None),
|
||||
my_data.get("max_resolution", None),
|
||||
my_data.get("learning_rate", None),
|
||||
my_data.get("lr_scheduler", None),
|
||||
my_data.get("lr_warmup", None),
|
||||
my_data.get("train_batch_size", None),
|
||||
my_data.get("epoch", None),
|
||||
my_data.get("save_every_n_epochs", None),
|
||||
my_data.get("mixed_precision", None),
|
||||
my_data.get("save_precision", None),
|
||||
my_data.get("seed", None),
|
||||
my_data.get("num_cpu_threads_per_process", None),
|
||||
my_data.get("convert_to_safetensors", None),
|
||||
my_data.get("convert_to_ckpt", None),
|
||||
my_data.get("cache_latent", None),
|
||||
my_data.get("caption_extention", None),
|
||||
my_data.get("use_safetensors", None),
|
||||
my_data.get("enable_bucket", None),
|
||||
my_data.get("gradient_checkpointing", None),
|
||||
my_data.get("full_fp16", None),
|
||||
my_data.get("no_token_padding", None),
|
||||
my_data.get("stop_text_encoder_training", None),
|
||||
my_data.get("use_8bit_adam", None),
|
||||
my_data.get("xformers", None),
|
||||
file_path,
|
||||
my_data.get("pretrained_model_name_or_path", pretrained_model_name_or_path),
|
||||
my_data.get("v2", v2),
|
||||
my_data.get("v_parameterization", v_parameterization),
|
||||
my_data.get("logging_dir", logging_dir),
|
||||
my_data.get("train_data_dir", train_data_dir),
|
||||
my_data.get("reg_data_dir", reg_data_dir),
|
||||
my_data.get("output_dir", output_dir),
|
||||
my_data.get("max_resolution", max_resolution),
|
||||
my_data.get("learning_rate", learning_rate),
|
||||
my_data.get("lr_scheduler", lr_scheduler),
|
||||
my_data.get("lr_warmup", lr_warmup),
|
||||
my_data.get("train_batch_size", train_batch_size),
|
||||
my_data.get("epoch", epoch),
|
||||
my_data.get("save_every_n_epochs", save_every_n_epochs),
|
||||
my_data.get("mixed_precision", mixed_precision),
|
||||
my_data.get("save_precision", save_precision),
|
||||
my_data.get("seed", seed),
|
||||
my_data.get("num_cpu_threads_per_process", num_cpu_threads_per_process),
|
||||
my_data.get("convert_to_safetensors", convert_to_safetensors),
|
||||
my_data.get("convert_to_ckpt", convert_to_ckpt),
|
||||
my_data.get("cache_latent", cache_latent),
|
||||
my_data.get("caption_extention", caption_extention),
|
||||
my_data.get("use_safetensors", use_safetensors),
|
||||
my_data.get("enable_bucket", enable_bucket),
|
||||
my_data.get("gradient_checkpointing", gradient_checkpointing),
|
||||
my_data.get("full_fp16", full_fp16),
|
||||
my_data.get("no_token_padding", no_token_padding),
|
||||
my_data.get("stop_text_encoder_training", stop_text_encoder_training),
|
||||
my_data.get("use_8bit_adam", use_8bit_adam),
|
||||
my_data.get("xformers", xformers),
|
||||
)
|
||||
|
||||
|
||||
@ -356,28 +499,49 @@ def set_pretrained_model_name_or_path_input(value, v2, v_parameterization):
|
||||
|
||||
return value, v2, v_parameterization
|
||||
|
||||
|
||||
def remove_doublequote(file_path):
|
||||
if file_path != None:
|
||||
file_path = file_path.replace('"', '')
|
||||
file_path = file_path.replace('"', "")
|
||||
|
||||
return file_path
|
||||
|
||||
|
||||
interface = gr.Blocks()
|
||||
def get_file_path(file_path):
|
||||
file_path = fileopenbox(
|
||||
"Select the config file to load", default=file_path, filetypes="*.json"
|
||||
)
|
||||
|
||||
return file_path
|
||||
|
||||
|
||||
def get_folder_path():
|
||||
folder_path = diropenbox("Select the directory to use")
|
||||
|
||||
return folder_path
|
||||
|
||||
|
||||
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:
|
||||
gr.Markdown("Enter kohya finetuner parameter using this interface.")
|
||||
with gr.Accordion("Configuration File Load/Save", open=False):
|
||||
with gr.Row():
|
||||
config_file_name = gr.Textbox(label="Config file name")
|
||||
button_load_config = gr.Button("Load config")
|
||||
button_save_config = gr.Button("Save config")
|
||||
button_open_config = gr.Button("Open 📂", elem_id="open_folder")
|
||||
button_save_config = gr.Button("Save 💾", elem_id="open_folder")
|
||||
button_save_as_config = gr.Button("Save as... 💾", elem_id="open_folder")
|
||||
config_file_name = gr.Textbox(
|
||||
label="", placeholder="type config file path or use buttons..."
|
||||
)
|
||||
config_file_name.change(
|
||||
remove_doublequote,
|
||||
inputs=[config_file_name],
|
||||
outputs=[
|
||||
config_file_name
|
||||
]
|
||||
remove_doublequote, inputs=[config_file_name], outputs=[config_file_name]
|
||||
)
|
||||
with gr.Tab("Source model"):
|
||||
# Define the input elements
|
||||
@ -406,9 +570,7 @@ with interface:
|
||||
pretrained_model_name_or_path_input.change(
|
||||
remove_doublequote,
|
||||
inputs=[pretrained_model_name_or_path_input],
|
||||
outputs=[
|
||||
pretrained_model_name_or_path_input
|
||||
]
|
||||
outputs=[pretrained_model_name_or_path_input],
|
||||
)
|
||||
model_list.change(
|
||||
set_pretrained_model_name_or_path_input,
|
||||
@ -426,46 +588,42 @@ with interface:
|
||||
label="Image folder",
|
||||
placeholder="Directory 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_input)
|
||||
reg_data_dir_input = gr.Textbox(
|
||||
label="Regularisation folder",
|
||||
placeholder="(Optional) Directory 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_input)
|
||||
with gr.Row():
|
||||
output_dir_input = gr.Textbox(
|
||||
label="Output directory",
|
||||
placeholder="Directory 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_input)
|
||||
logging_dir_input = gr.Textbox(
|
||||
label="Logging directory",
|
||||
placeholder="Optional: enable logging and output TensorBoard log to this directory",
|
||||
)
|
||||
logging_dir_input_folder = gr.Button("📂", elem_id="open_folder_small")
|
||||
logging_dir_input_folder.click(get_folder_path, outputs=logging_dir_input)
|
||||
train_data_dir_input.change(
|
||||
remove_doublequote,
|
||||
inputs=[train_data_dir_input],
|
||||
outputs=[
|
||||
train_data_dir_input
|
||||
]
|
||||
outputs=[train_data_dir_input],
|
||||
)
|
||||
reg_data_dir_input.change(
|
||||
remove_doublequote,
|
||||
inputs=[reg_data_dir_input],
|
||||
outputs=[
|
||||
reg_data_dir_input
|
||||
]
|
||||
outputs=[reg_data_dir_input],
|
||||
)
|
||||
output_dir_input.change(
|
||||
remove_doublequote,
|
||||
inputs=[output_dir_input],
|
||||
outputs=[
|
||||
output_dir_input
|
||||
]
|
||||
remove_doublequote, inputs=[output_dir_input], outputs=[output_dir_input]
|
||||
)
|
||||
logging_dir_input.change(
|
||||
remove_doublequote,
|
||||
inputs=[logging_dir_input],
|
||||
outputs=[
|
||||
logging_dir_input
|
||||
]
|
||||
remove_doublequote, inputs=[logging_dir_input], outputs=[logging_dir_input]
|
||||
)
|
||||
with gr.Tab("Training parameters"):
|
||||
with gr.Row():
|
||||
@ -523,7 +681,11 @@ with interface:
|
||||
label="Caption Extension",
|
||||
placeholder="(Optional) Extension for caption files. default: .caption",
|
||||
)
|
||||
stop_text_encoder_training_input = gr.Slider(minimum=0, maximum=100, value=0, step=1,
|
||||
stop_text_encoder_training_input = gr.Slider(
|
||||
minimum=0,
|
||||
maximum=100,
|
||||
value=0,
|
||||
step=1,
|
||||
label="Stop text encoder training",
|
||||
)
|
||||
with gr.Row():
|
||||
@ -551,10 +713,43 @@ with interface:
|
||||
|
||||
button_run = gr.Button("Run")
|
||||
|
||||
button_load_config.click(
|
||||
load_variables,
|
||||
inputs=[config_file_name],
|
||||
button_open_config.click(
|
||||
open_config_file,
|
||||
inputs=[
|
||||
config_file_name,
|
||||
pretrained_model_name_or_path_input,
|
||||
v2_input,
|
||||
v_parameterization_input,
|
||||
logging_dir_input,
|
||||
train_data_dir_input,
|
||||
reg_data_dir_input,
|
||||
output_dir_input,
|
||||
max_resolution_input,
|
||||
learning_rate_input,
|
||||
lr_scheduler_input,
|
||||
lr_warmup_input,
|
||||
train_batch_size_input,
|
||||
epoch_input,
|
||||
save_every_n_epochs_input,
|
||||
mixed_precision_input,
|
||||
save_precision_input,
|
||||
seed_input,
|
||||
num_cpu_threads_per_process_input,
|
||||
convert_to_safetensors_input,
|
||||
convert_to_ckpt_input,
|
||||
cache_latent_input,
|
||||
caption_extention_input,
|
||||
use_safetensors_input,
|
||||
enable_bucket_input,
|
||||
gradient_checkpointing_input,
|
||||
full_fp16_input,
|
||||
no_token_padding_input,
|
||||
stop_text_encoder_training_input,
|
||||
use_8bit_adam_input,
|
||||
xformers_input,
|
||||
],
|
||||
outputs=[
|
||||
config_file_name,
|
||||
pretrained_model_name_or_path_input,
|
||||
v2_input,
|
||||
v_parameterization_input,
|
||||
@ -623,7 +818,47 @@ with interface:
|
||||
use_8bit_adam_input,
|
||||
xformers_input,
|
||||
],
|
||||
outputs=[config_file_name],
|
||||
)
|
||||
|
||||
button_save_as_config.click(
|
||||
save_as_variables,
|
||||
inputs=[
|
||||
config_file_name,
|
||||
pretrained_model_name_or_path_input,
|
||||
v2_input,
|
||||
v_parameterization_input,
|
||||
logging_dir_input,
|
||||
train_data_dir_input,
|
||||
reg_data_dir_input,
|
||||
output_dir_input,
|
||||
max_resolution_input,
|
||||
learning_rate_input,
|
||||
lr_scheduler_input,
|
||||
lr_warmup_input,
|
||||
train_batch_size_input,
|
||||
epoch_input,
|
||||
save_every_n_epochs_input,
|
||||
mixed_precision_input,
|
||||
save_precision_input,
|
||||
seed_input,
|
||||
num_cpu_threads_per_process_input,
|
||||
convert_to_safetensors_input,
|
||||
convert_to_ckpt_input,
|
||||
cache_latent_input,
|
||||
caption_extention_input,
|
||||
use_safetensors_input,
|
||||
enable_bucket_input,
|
||||
gradient_checkpointing_input,
|
||||
full_fp16_input,
|
||||
no_token_padding_input,
|
||||
stop_text_encoder_training_input,
|
||||
use_8bit_adam_input,
|
||||
xformers_input,
|
||||
],
|
||||
outputs=[config_file_name],
|
||||
)
|
||||
|
||||
button_run.click(
|
||||
train_model,
|
||||
inputs=[
|
||||
|
||||
@ -11,3 +11,4 @@ tensorboard
|
||||
safetensors==0.2.6
|
||||
gradio
|
||||
altair
|
||||
easygui
|
||||
Loading…
Reference in New Issue
Block a user