Add support for Tensorboard for training embeddings

    "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}),

    "training_write_csv_every": OptionInfo(500, "Save an csv containing the loss to log directory every N steps, 0 to disable"),

    "training_enable_tensorboard": OptionInfo(False, "Enable tensorboard logging."),

    "training_tensorboard_save_images": OptionInfo(False, "Save generated images within tensorboard."),

    "training_tensorboard_flush_every": OptionInfo(120, "How often, in seconds, to flush the pending tensorboard events and summaries to disk."),

}))

options_templates.update(options_section(('sd', "Stable Diffusion"), {

import html

import datetime

import csv

import numpy as np

import torchvision.transforms

from PIL import Image, PngImagePlugin

from torch.utils.tensorboard import SummaryWriter

from modules import shared, devices, sd_hijack, processing, sd_models

import modules.textual_inversion.dataset

from modules.textual_inversion.learn_schedule import LearnRateScheduler

            **values,

        })

def tensorboard_add_scaler(tensorboard_writer, tag, value, step):

    if shared.opts.training_enable_tensorboard:

        tensorboard_writer.add_scalar(tag=tag, 

            scalar_value=value, global_step=step)

def tensorboard_add_image(tensorboard_writer, tag, pil_image, step):

    if shared.opts.training_enable_tensorboard:

        # Convert a pil image to a torch tensor

        img_tensor = torch.as_tensor(np.array(pil_image, copy=True))

        img_tensor = img_tensor.view(pil_image.size[1], pil_image.size[0], len(pil_image.getbands()))

        img_tensor = img_tensor.permute((2, 0, 1))

        tensorboard_writer.add_image(tag, img_tensor, global_step=step)

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):

    assert embedding_name, 'embedding not selected'

    scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)

    optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate)

    if shared.opts.training_enable_tensorboard:

        os.makedirs(os.path.join(log_directory, "tensorboard"), exist_ok=True)

        tensorboard_writer = SummaryWriter(

                log_dir=os.path.join(log_directory, "tensorboard"),

                flush_secs=shared.opts.training_tensorboard_flush_every)

    pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)

    for i, entries in pbar:

        embedding.step = i + ititial_step

            losses[embedding.step % losses.shape[0]] = loss.item()

            optimizer.zero_grad()

            loss.backward()

            optimizer.step()

            embedding.save(last_saved_file)

            embedding_yet_to_be_embedded = True

        if shared.opts.training_enable_tensorboard:

            tensorboard_add_scaler(tensorboard_writer, "Loss/train", losses.mean(), embedding.step)

            tensorboard_add_scaler(tensorboard_writer, f"Loss/train/epoch-{epoch_num}", losses.mean(), epoch_step)

            tensorboard_add_scaler(tensorboard_writer, "Learn rate/train", scheduler.learn_rate, embedding.step)

            tensorboard_add_scaler(tensorboard_writer, f"Learn rate/train/epoch-{epoch_num}", scheduler.learn_rate, epoch_step)

        write_loss(log_directory, "textual_inversion_loss.csv", embedding.step, len(ds), {

            "loss": f"{losses.mean():.7f}",

            "learn_rate": scheduler.learn_rate

                embedding_yet_to_be_embedded = False

            image.save(last_saved_image)

            tensorboard_add_image(tensorboard_writer, f"Validation at epoch {epoch_num}", image, embedding.step)

            last_saved_image += f", prompt: {preview_text}"