change option position to Training setting

def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, shuffle_tags, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):

def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):

    # images allows training previews to have infotext. Importing it at the top causes a circular import problem.

    from modules import images

    # 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)}..."

    with torch.autocast("cuda"):

        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=hypernetwork_name, shuffle_tags=shuffle_tags, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size)

        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=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size)

    if unload:

        shared.sd_model.cond_stage_model.to(devices.cpu)

options_templates.update(options_section(('training', "Training"), {

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

    "shuffle_tags": OptionInfo(False, "Shuffleing tags by "," when create texts."),

    "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}),

class PersonalizedBase(Dataset):

    def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", shuffle_tags=True, model=None, device=None, template_file=None, include_cond=False, batch_size=1):

    def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None, include_cond=False, batch_size=1):

        re_word = re.compile(shared.opts.dataset_filename_word_regex) if len(shared.opts.dataset_filename_word_regex) > 0 else None

        self.placeholder_token = placeholder_token

        self.width = width

        self.height = height

        self.flip = transforms.RandomHorizontalFlip(p=flip_p)

        self.shuffle_tags = shuffle_tags

        self.dataset = []

    def create_text(self, filename_text):

        text = random.choice(self.lines)

        text = text.replace("[name]", self.placeholder_token)

        if self.tag_shuffle:

        if shared.opts.shuffle_tags:

            tags = filename_text.split(',')

            random.shuffle(tags)

            text = text.replace("[filewords]", ','.join(tags))

    if save_model_every or create_image_every:

        assert log_directory, "Log directory is empty"

def train_embedding(embedding_name, 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, shuffle_tags, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):

def train_embedding(embedding_name, 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):

    save_embedding_every = save_embedding_every or 0

    create_image_every = create_image_every or 0

    validate_train_inputs(embedding_name, learn_rate, batch_size, data_root, template_file, steps, save_embedding_every, create_image_every, log_directory, name="embedding")

    # 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)}..."

    with torch.autocast("cuda"):

        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, shuffle_tags=shuffle_tags, model=shared.sd_model, device=devices.device, template_file=template_file, batch_size=batch_size)

        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, device=devices.device, template_file=template_file, batch_size=batch_size)

    if unload:

        shared.sd_model.first_stage_model.to(devices.cpu)