Adding improved elements to GUI
This commit is contained in:
bmaltais
parent
469b15b579
commit
3834e5dbab
@ -1,4 +1,5 @@
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# v1: initial release
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# v2: add open and save folder icons
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import gradio as gr
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import json
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@ -9,6 +10,7 @@ import pathlib
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import shutil
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from glob import glob
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from os.path import join
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from easygui import fileopenbox, filesavebox, enterbox, diropenbox, msgbox
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def save_variables(
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@ -44,6 +46,103 @@ def save_variables(
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use_8bit_adam,
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xformers,
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):
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original_file_path = file_path
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if file_path == None or file_path == "":
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file_path = filesavebox(
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"Select the config file to save",
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default="finetune.json",
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filetypes="*.json",
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)
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if file_path == None:
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file_path = original_file_path # In case a file_path was provided and the user decide to cancel the open action
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return file_path
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# Return the values of the variables as a dictionary
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variables = {
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"pretrained_model_name_or_path": pretrained_model_name_or_path,
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"v2": v2,
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"v_parameterization": v_parameterization,
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"logging_dir": logging_dir,
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"train_data_dir": train_data_dir,
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"reg_data_dir": reg_data_dir,
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"output_dir": output_dir,
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"max_resolution": max_resolution,
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"learning_rate": learning_rate,
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"lr_scheduler": lr_scheduler,
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"lr_warmup": lr_warmup,
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"train_batch_size": train_batch_size,
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"epoch": epoch,
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"save_every_n_epochs": save_every_n_epochs,
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"mixed_precision": mixed_precision,
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"save_precision": save_precision,
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"seed": seed,
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"num_cpu_threads_per_process": num_cpu_threads_per_process,
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"convert_to_safetensors": convert_to_safetensors,
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"convert_to_ckpt": convert_to_ckpt,
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"cache_latent": cache_latent,
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"caption_extention": caption_extention,
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"use_safetensors": use_safetensors,
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"enable_bucket": enable_bucket,
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"gradient_checkpointing": gradient_checkpointing,
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"full_fp16": full_fp16,
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"no_token_padding": no_token_padding,
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"stop_text_encoder_training": stop_text_encoder_training,
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"use_8bit_adam": use_8bit_adam,
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"xformers": xformers,
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}
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# Save the data to the selected file
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with open(file_path, "w") as file:
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json.dump(variables, file)
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return file_path
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def save_as_variables(
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file_path,
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pretrained_model_name_or_path,
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v2,
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v_parameterization,
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logging_dir,
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train_data_dir,
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reg_data_dir,
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output_dir,
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max_resolution,
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learning_rate,
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lr_scheduler,
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lr_warmup,
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train_batch_size,
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epoch,
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save_every_n_epochs,
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mixed_precision,
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save_precision,
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seed,
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num_cpu_threads_per_process,
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convert_to_safetensors,
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convert_to_ckpt,
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cache_latent,
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caption_extention,
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use_safetensors,
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enable_bucket,
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gradient_checkpointing,
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full_fp16,
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no_token_padding,
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stop_text_encoder_training,
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use_8bit_adam,
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xformers,
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):
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original_file_path = file_path
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file_path = filesavebox(
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"Select the config file to save", default="finetune.json", filetypes="*.json"
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)
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if file_path == None:
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file_path = original_file_path # In case a file_path was provided and the user decide to cancel the open action
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return file_path
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# Return the values of the variables as a dictionary
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variables = {
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"pretrained_model_name_or_path": pretrained_model_name_or_path,
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@ -82,44 +181,88 @@ def save_variables(
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with open(file_path, "w") as file:
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json.dump(variables, file)
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return file_path
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def load_variables(file_path):
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# load variables from JSON file
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with open(file_path, "r") as f:
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my_data = json.load(f)
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def open_config_file(
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file_path,
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pretrained_model_name_or_path,
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v2,
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v_parameterization,
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logging_dir,
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train_data_dir,
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reg_data_dir,
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output_dir,
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max_resolution,
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learning_rate,
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lr_scheduler,
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lr_warmup,
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train_batch_size,
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epoch,
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save_every_n_epochs,
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mixed_precision,
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save_precision,
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seed,
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num_cpu_threads_per_process,
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convert_to_safetensors,
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convert_to_ckpt,
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cache_latent,
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caption_extention,
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use_safetensors,
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enable_bucket,
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gradient_checkpointing,
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full_fp16,
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no_token_padding,
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stop_text_encoder_training,
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use_8bit_adam,
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xformers,
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):
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original_file_path = file_path
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file_path = get_file_path(file_path)
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if file_path != "" and file_path != None:
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print(file_path)
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# load variables from JSON file
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with open(file_path, "r") as f:
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my_data = json.load(f)
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else:
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file_path = original_file_path # In case a file_path was provided and the user decide to cancel the open action
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my_data = {}
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# Return the values of the variables as a dictionary
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return (
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my_data.get("pretrained_model_name_or_path", None),
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my_data.get("v2", None),
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my_data.get("v_parameterization", None),
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my_data.get("logging_dir", None),
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my_data.get("train_data_dir", None),
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my_data.get("reg_data_dir", None),
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my_data.get("output_dir", None),
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my_data.get("max_resolution", None),
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my_data.get("learning_rate", None),
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my_data.get("lr_scheduler", None),
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my_data.get("lr_warmup", None),
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my_data.get("train_batch_size", None),
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my_data.get("epoch", None),
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my_data.get("save_every_n_epochs", None),
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my_data.get("mixed_precision", None),
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my_data.get("save_precision", None),
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my_data.get("seed", None),
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my_data.get("num_cpu_threads_per_process", None),
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my_data.get("convert_to_safetensors", None),
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my_data.get("convert_to_ckpt", None),
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my_data.get("cache_latent", None),
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my_data.get("caption_extention", None),
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my_data.get("use_safetensors", None),
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my_data.get("enable_bucket", None),
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my_data.get("gradient_checkpointing", None),
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my_data.get("full_fp16", None),
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my_data.get("no_token_padding", None),
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my_data.get("stop_text_encoder_training", None),
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my_data.get("use_8bit_adam", None),
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my_data.get("xformers", None),
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file_path,
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my_data.get("pretrained_model_name_or_path", pretrained_model_name_or_path),
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my_data.get("v2", v2),
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my_data.get("v_parameterization", v_parameterization),
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my_data.get("logging_dir", logging_dir),
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my_data.get("train_data_dir", train_data_dir),
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my_data.get("reg_data_dir", reg_data_dir),
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my_data.get("output_dir", output_dir),
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my_data.get("max_resolution", max_resolution),
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my_data.get("learning_rate", learning_rate),
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my_data.get("lr_scheduler", lr_scheduler),
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my_data.get("lr_warmup", lr_warmup),
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my_data.get("train_batch_size", train_batch_size),
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my_data.get("epoch", epoch),
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my_data.get("save_every_n_epochs", save_every_n_epochs),
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my_data.get("mixed_precision", mixed_precision),
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my_data.get("save_precision", save_precision),
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my_data.get("seed", seed),
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my_data.get("num_cpu_threads_per_process", num_cpu_threads_per_process),
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my_data.get("convert_to_safetensors", convert_to_safetensors),
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my_data.get("convert_to_ckpt", convert_to_ckpt),
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my_data.get("cache_latent", cache_latent),
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my_data.get("caption_extention", caption_extention),
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my_data.get("use_safetensors", use_safetensors),
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my_data.get("enable_bucket", enable_bucket),
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my_data.get("gradient_checkpointing", gradient_checkpointing),
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my_data.get("full_fp16", full_fp16),
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my_data.get("no_token_padding", no_token_padding),
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my_data.get("stop_text_encoder_training", stop_text_encoder_training),
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my_data.get("use_8bit_adam", use_8bit_adam),
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my_data.get("xformers", xformers),
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)
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@ -356,28 +499,49 @@ def set_pretrained_model_name_or_path_input(value, v2, v_parameterization):
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return value, v2, v_parameterization
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def remove_doublequote(file_path):
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if file_path != None:
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file_path = file_path.replace('"', '')
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file_path = file_path.replace('"', "")
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return file_path
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interface = gr.Blocks()
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def get_file_path(file_path):
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file_path = fileopenbox(
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"Select the config file to load", default=file_path, filetypes="*.json"
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)
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return file_path
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def get_folder_path():
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folder_path = diropenbox("Select the directory to use")
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return folder_path
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css = ""
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if os.path.exists("./style.css"):
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with open(os.path.join("./style.css"), "r", encoding="utf8") as file:
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print("Load CSS...")
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css += file.read() + "\n"
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interface = gr.Blocks(css=css)
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with interface:
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gr.Markdown("Enter kohya finetuner parameter using this interface.")
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with gr.Accordion("Configuration File Load/Save", open=False):
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with gr.Row():
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config_file_name = gr.Textbox(label="Config file name")
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button_load_config = gr.Button("Load config")
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button_save_config = gr.Button("Save config")
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button_open_config = gr.Button("Open 📂", elem_id="open_folder")
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button_save_config = gr.Button("Save 💾", elem_id="open_folder")
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button_save_as_config = gr.Button("Save as... 💾", elem_id="open_folder")
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config_file_name = gr.Textbox(
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label="", placeholder="type config file path or use buttons..."
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)
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config_file_name.change(
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remove_doublequote,
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inputs=[config_file_name],
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outputs=[
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config_file_name
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]
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remove_doublequote, inputs=[config_file_name], outputs=[config_file_name]
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)
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with gr.Tab("Source model"):
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# Define the input elements
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@ -406,9 +570,7 @@ with interface:
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pretrained_model_name_or_path_input.change(
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remove_doublequote,
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inputs=[pretrained_model_name_or_path_input],
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outputs=[
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pretrained_model_name_or_path_input
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]
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outputs=[pretrained_model_name_or_path_input],
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)
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model_list.change(
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set_pretrained_model_name_or_path_input,
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@ -419,53 +581,49 @@ with interface:
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v_parameterization_input,
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],
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)
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with gr.Tab("Directories"):
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with gr.Row():
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train_data_dir_input = gr.Textbox(
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label="Image folder",
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placeholder="Directory where the training folders containing the images are located",
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)
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train_data_dir_input_folder = gr.Button("📂", elem_id="open_folder_small")
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train_data_dir_input_folder.click(get_folder_path, outputs=train_data_dir_input)
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reg_data_dir_input = gr.Textbox(
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label="Regularisation folder",
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placeholder="(Optional) Directory where where the regularization folders containing the images are located",
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)
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reg_data_dir_input_folder = gr.Button("📂", elem_id="open_folder_small")
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reg_data_dir_input_folder.click(get_folder_path, outputs=reg_data_dir_input)
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with gr.Row():
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output_dir_input = gr.Textbox(
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label="Output directory",
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placeholder="Directory to output trained model",
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)
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output_dir_input_folder = gr.Button("📂", elem_id="open_folder_small")
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output_dir_input_folder.click(get_folder_path, outputs=output_dir_input)
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logging_dir_input = gr.Textbox(
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label="Logging directory",
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placeholder="Optional: enable logging and output TensorBoard log to this directory",
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)
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logging_dir_input_folder = gr.Button("📂", elem_id="open_folder_small")
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logging_dir_input_folder.click(get_folder_path, outputs=logging_dir_input)
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train_data_dir_input.change(
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remove_doublequote,
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inputs=[train_data_dir_input],
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outputs=[
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train_data_dir_input
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]
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outputs=[train_data_dir_input],
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)
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reg_data_dir_input.change(
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remove_doublequote,
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inputs=[reg_data_dir_input],
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outputs=[
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reg_data_dir_input
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]
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outputs=[reg_data_dir_input],
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)
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output_dir_input.change(
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remove_doublequote,
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inputs=[output_dir_input],
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outputs=[
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output_dir_input
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]
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remove_doublequote, inputs=[output_dir_input], outputs=[output_dir_input]
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)
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logging_dir_input.change(
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remove_doublequote,
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inputs=[logging_dir_input],
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outputs=[
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logging_dir_input
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]
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remove_doublequote, inputs=[logging_dir_input], outputs=[logging_dir_input]
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)
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with gr.Tab("Training parameters"):
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with gr.Row():
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@ -523,7 +681,11 @@ with interface:
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label="Caption Extension",
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placeholder="(Optional) Extension for caption files. default: .caption",
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)
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stop_text_encoder_training_input = gr.Slider(minimum=0, maximum=100, value=0, step=1,
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stop_text_encoder_training_input = gr.Slider(
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minimum=0,
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maximum=100,
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value=0,
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step=1,
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label="Stop text encoder training",
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)
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with gr.Row():
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@ -551,10 +713,43 @@ with interface:
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button_run = gr.Button("Run")
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button_load_config.click(
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load_variables,
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inputs=[config_file_name],
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button_open_config.click(
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open_config_file,
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inputs=[
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config_file_name,
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pretrained_model_name_or_path_input,
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v2_input,
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v_parameterization_input,
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logging_dir_input,
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train_data_dir_input,
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reg_data_dir_input,
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output_dir_input,
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max_resolution_input,
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learning_rate_input,
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lr_scheduler_input,
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lr_warmup_input,
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train_batch_size_input,
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epoch_input,
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save_every_n_epochs_input,
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mixed_precision_input,
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save_precision_input,
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seed_input,
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num_cpu_threads_per_process_input,
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convert_to_safetensors_input,
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convert_to_ckpt_input,
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cache_latent_input,
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caption_extention_input,
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use_safetensors_input,
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enable_bucket_input,
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gradient_checkpointing_input,
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full_fp16_input,
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no_token_padding_input,
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stop_text_encoder_training_input,
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use_8bit_adam_input,
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xformers_input,
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],
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outputs=[
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config_file_name,
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pretrained_model_name_or_path_input,
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v2_input,
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v_parameterization_input,
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@ -623,7 +818,47 @@ with interface:
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use_8bit_adam_input,
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xformers_input,
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],
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outputs=[config_file_name],
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)
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button_save_as_config.click(
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save_as_variables,
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inputs=[
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config_file_name,
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pretrained_model_name_or_path_input,
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v2_input,
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v_parameterization_input,
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logging_dir_input,
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train_data_dir_input,
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reg_data_dir_input,
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output_dir_input,
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max_resolution_input,
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learning_rate_input,
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lr_scheduler_input,
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lr_warmup_input,
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train_batch_size_input,
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epoch_input,
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save_every_n_epochs_input,
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mixed_precision_input,
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save_precision_input,
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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=[
|
||||
|
||||
@ -10,4 +10,5 @@ bitsandbytes==0.35.0
|
||||
tensorboard
|
||||
safetensors==0.2.6
|
||||
gradio
|
||||
altair
|
||||
altair
|
||||
easygui
|
||||
Loading…
Reference in New Issue
Block a user