Add option to save ti settings to file.

    "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."),

    "pin_memory": OptionInfo(False, "Turn on pin_memory for DataLoader. Makes training slightly faster but can increase memory usage."),

    "save_optimizer_state": OptionInfo(False, "Saves Optimizer state as separate *.optim file. Training of embedding or HN can be resumed with the matching optim file."),

    "save_train_settings_to_txt": OptionInfo(False, "Save textual inversion and hypernet settings to a text file when training starts."),

    "dataset_filename_word_regex": OptionInfo("", "Filename word regex"),

    "dataset_filename_join_string": OptionInfo(" ", "Filename join string"),

    "training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),

import os

import sys

import traceback

import inspect

import torch

import tqdm

            **values,

        })

def save_settings_to_file(initial_step, num_of_dataset_images, embedding_name, vectors_per_token, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):

    checkpoint = sd_models.select_checkpoint()

    model_name = checkpoint.model_name

    model_hash = '[{}]'.format(checkpoint.hash)

    # Get a list of the argument names.

    arg_names = inspect.getfullargspec(save_settings_to_file).args

    # Create a list of the argument names to include in the settings string.

    names = arg_names[:16]  # Include all arguments up until the preview-related ones.

    if preview_from_txt2img:

        names.extend(arg_names[16:])  # Include all remaining arguments if `preview_from_txt2img` is True.

    # Build the settings string.

    settings_str = "datetime : " + datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S") + "\n"

    for name in names:

        value = locals()[name]

        settings_str += f"{name}: {value}\n"

    with open(os.path.join(log_directory, 'settings.txt'), "a+") as fout:

        fout.write(settings_str + "\n\n")

def validate_train_inputs(model_name, learn_rate, batch_size, gradient_step, data_root, template_file, steps, save_model_every, create_image_every, log_directory, name="embedding"):

    assert model_name, f"{name} not selected"

    assert learn_rate, "Learning rate is empty or 0"

    if initial_step >= steps:

        shared.state.textinfo = "Model has already been trained beyond specified max steps"

        return embedding, filename

    scheduler = LearnRateScheduler(learn_rate, steps, initial_step)

    scheduler = LearnRateScheduler(learn_rate, steps, initial_step)

    clip_grad = torch.nn.utils.clip_grad_value_ if clip_grad_mode == "value" else \

        torch.nn.utils.clip_grad_norm_ if clip_grad_mode == "norm" else \

        None

    if clip_grad:

        clip_grad_sched = LearnRateScheduler(clip_grad_value, steps, ititial_step, verbose=False)

        clip_grad_sched = LearnRateScheduler(clip_grad_value, steps, initial_step, verbose=False)

    # dataset loading may take a while, so input validations and early returns should be done before this

    shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."

    old_parallel_processing_allowed = shared.parallel_processing_allowed

    pin_memory = shared.opts.pin_memory

    ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method)

    if shared.opts.save_train_settings_to_txt:

            save_settings_to_file(initial_step , len(ds) , embedding_name, len(embedding.vec) , learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height)

    latent_sampling_method = ds.latent_sampling_method

    dl = modules.textual_inversion.dataset.PersonalizedDataLoader(ds, latent_sampling_method=latent_sampling_method, batch_size=ds.batch_size, pin_memory=pin_memory)