modules/textual_inversion/textual_inversion.py

import os
from collections import namedtuple
from contextlib import closing

import torch
import tqdm
import html
import datetime
import csv
import safetensors.torch

import numpy as np
from PIL import Image, PngImagePlugin
from torch.utils.tensorboard import SummaryWriter

from modules import shared, devices, sd_hijack, sd_models, images, sd_samplers, sd_hijack_checkpoint, errors, hashes
import modules.textual_inversion.dataset
from modules.textual_inversion.learn_schedule import LearnRateScheduler

from modules.textual_inversion.image_embedding import embedding_to_b64, embedding_from_b64, insert_image_data_embed, extract_image_data_embed, caption_image_overlay
from modules.textual_inversion.logging import save_settings_to_file


TextualInversionTemplate = namedtuple("TextualInversionTemplate", ["name", "path"])
textual_inversion_templates = {}


def list_textual_inversion_templates():
    textual_inversion_templates.clear()

    for root, _, fns in os.walk(shared.cmd_opts.textual_inversion_templates_dir):
        for fn in fns:
            path = os.path.join(root, fn)

            textual_inversion_templates[fn] = TextualInversionTemplate(fn, path)

    return textual_inversion_templates


class Embedding:
    def __init__(self, vec, name, step=None):
        self.vec = vec
        self.name = name
        self.step = step
        self.shape = None
        self.vectors = 0
        self.cached_checksum = None
        self.sd_checkpoint = None
        self.sd_checkpoint_name = None
        self.optimizer_state_dict = None
        self.filename = None
        self.hash = None
        self.shorthash = None

    def save(self, filename):
        embedding_data = {
            "string_to_token": {"*": 265},
            "string_to_param": {"*": self.vec},
            "name": self.name,
            "step": self.step,
            "sd_checkpoint": self.sd_checkpoint,
            "sd_checkpoint_name": self.sd_checkpoint_name,
        }

        torch.save(embedding_data, filename)

        if shared.opts.save_optimizer_state and self.optimizer_state_dict is not None:
            optimizer_saved_dict = {
                'hash': self.checksum(),
                'optimizer_state_dict': self.optimizer_state_dict,
            }
            torch.save(optimizer_saved_dict, f"{filename}.optim")

    def checksum(self):
        if self.cached_checksum is not None:
            return self.cached_checksum

        def const_hash(a):
            r = 0
            for v in a:
                r = (r * 281 ^ int(v) * 997) & 0xFFFFFFFF
            return r

        self.cached_checksum = f'{const_hash(self.vec.reshape(-1) * 100) & 0xffff:04x}'
        return self.cached_checksum

    def set_hash(self, v):
        self.hash = v
        self.shorthash = self.hash[0:12]


class DirWithTextualInversionEmbeddings:
    def __init__(self, path):
        self.path = path
        self.mtime = None

    def has_changed(self):
        if not os.path.isdir(self.path):
            return False

        mt = os.path.getmtime(self.path)
        if self.mtime is None or mt > self.mtime:
            return True

    def update(self):
        if not os.path.isdir(self.path):
            return

        self.mtime = os.path.getmtime(self.path)


class EmbeddingDatabase:
    def __init__(self):
        self.ids_lookup = {}
        self.word_embeddings = {}
        self.skipped_embeddings = {}
        self.expected_shape = -1
        self.embedding_dirs = {}
        self.previously_displayed_embeddings = ()

    def add_embedding_dir(self, path):
        self.embedding_dirs[path] = DirWithTextualInversionEmbeddings(path)

    def clear_embedding_dirs(self):
        self.embedding_dirs.clear()

    def register_embedding(self, embedding, model):
        return self.register_embedding_by_name(embedding, model, embedding.name)

    def register_embedding_by_name(self, embedding, model, name):
        ids = model.cond_stage_model.tokenize([name])[0]
        first_id = ids[0]
        if first_id not in self.ids_lookup:
            self.ids_lookup[first_id] = []
        if name in self.word_embeddings:
            # remove old one from the lookup list
            lookup = [x for x in self.ids_lookup[first_id] if x[1].name!=name]
        else:
            lookup = self.ids_lookup[first_id]
        if embedding is not None:
            lookup += [(ids, embedding)]
        self.ids_lookup[first_id] = sorted(lookup, key=lambda x: len(x[0]), reverse=True)
        if embedding is None:
            # unregister embedding with specified name
            if name in self.word_embeddings:
                del self.word_embeddings[name]
            if len(self.ids_lookup[first_id])==0:
                del self.ids_lookup[first_id]
            return None
        self.word_embeddings[name] = embedding
        return embedding

    def get_expected_shape(self):
        vec = shared.sd_model.cond_stage_model.encode_embedding_init_text(",", 1)
        return vec.shape[1]

    def load_from_file(self, path, filename):
        name, ext = os.path.splitext(filename)
        ext = ext.upper()

        if ext in ['.PNG', '.WEBP', '.JXL', '.AVIF']:
            _, second_ext = os.path.splitext(name)
            if second_ext.upper() == '.PREVIEW':
                return

            embed_image = Image.open(path)
            if hasattr(embed_image, 'text') and 'sd-ti-embedding' in embed_image.text:
                data = embedding_from_b64(embed_image.text['sd-ti-embedding'])
                name = data.get('name', name)
            else:
                data = extract_image_data_embed(embed_image)
                if data:
                    name = data.get('name', name)
                else:
                    # if data is None, means this is not an embeding, just a preview image
                    return
        elif ext in ['.BIN', '.PT']:
            data = torch.load(path, map_location="cpu")
        elif ext in ['.SAFETENSORS']:
            data = safetensors.torch.load_file(path, device="cpu")
        else:
            return


        # textual inversion embeddings
        if 'string_to_param' in data:
            param_dict = data['string_to_param']
            param_dict = getattr(param_dict, '_parameters', param_dict)  # fix for torch 1.12.1 loading saved file from torch 1.11
            assert len(param_dict) == 1, 'embedding file has multiple terms in it'
            emb = next(iter(param_dict.items()))[1]
            vec = emb.detach().to(devices.device, dtype=torch.float32)
            shape = vec.shape[-1]
            vectors = vec.shape[0]
        elif type(data) == dict and 'clip_g' in data and 'clip_l' in data:  # SDXL embedding
            vec = {k: v.detach().to(devices.device, dtype=torch.float32) for k, v in data.items()}
            shape = data['clip_g'].shape[-1] + data['clip_l'].shape[-1]
            vectors = data['clip_g'].shape[0]
        elif type(data) == dict and type(next(iter(data.values()))) == torch.Tensor: # diffuser concepts
            assert len(data.keys()) == 1, 'embedding file has multiple terms in it'

            emb = next(iter(data.values()))
            if len(emb.shape) == 1:
                emb = emb.unsqueeze(0)
            vec = emb.detach().to(devices.device, dtype=torch.float32)
            shape = vec.shape[-1]
            vectors = vec.shape[0]
        else:
            raise Exception(f"Couldn't identify {filename} as neither textual inversion embedding nor diffuser concept.")

        embedding = Embedding(vec, name)
        embedding.step = data.get('step', None)
        embedding.sd_checkpoint = data.get('sd_checkpoint', None)
        embedding.sd_checkpoint_name = data.get('sd_checkpoint_name', None)
        embedding.vectors = vectors
        embedding.shape = shape
        embedding.filename = path
        embedding.set_hash(hashes.sha256(embedding.filename, "textual_inversion/" + name) or '')

        if self.expected_shape == -1 or self.expected_shape == embedding.shape:
            self.register_embedding(embedding, shared.sd_model)
        else:
            self.skipped_embeddings[name] = embedding

    def load_from_dir(self, embdir):
        if not os.path.isdir(embdir.path):
            return

        for root, _, fns in os.walk(embdir.path, followlinks=True):
            for fn in fns:
                try:
                    fullfn = os.path.join(root, fn)

                    if os.stat(fullfn).st_size == 0:
                        continue

                    self.load_from_file(fullfn, fn)
                except Exception:
                    errors.report(f"Error loading embedding {fn}", exc_info=True)
                    continue

    def load_textual_inversion_embeddings(self, force_reload=False):
        if not force_reload:
            need_reload = False
            for embdir in self.embedding_dirs.values():
                if embdir.has_changed():
                    need_reload = True
                    break

            if not need_reload:
                return

        self.ids_lookup.clear()
        self.word_embeddings.clear()
        self.skipped_embeddings.clear()
        self.expected_shape = self.get_expected_shape()

        for embdir in self.embedding_dirs.values():
            self.load_from_dir(embdir)
            embdir.update()

        # re-sort word_embeddings because load_from_dir may not load in alphabetic order.
        # using a temporary copy so we don't reinitialize self.word_embeddings in case other objects have a reference to it.
        sorted_word_embeddings = {e.name: e for e in sorted(self.word_embeddings.values(), key=lambda e: e.name.lower())}
        self.word_embeddings.clear()
        self.word_embeddings.update(sorted_word_embeddings)

        displayed_embeddings = (tuple(self.word_embeddings.keys()), tuple(self.skipped_embeddings.keys()))
        if shared.opts.textual_inversion_print_at_load and self.previously_displayed_embeddings != displayed_embeddings:
            self.previously_displayed_embeddings = displayed_embeddings
            print(f"Textual inversion embeddings loaded({len(self.word_embeddings)}): {', '.join(self.word_embeddings.keys())}")
            if self.skipped_embeddings:
                print(f"Textual inversion embeddings skipped({len(self.skipped_embeddings)}): {', '.join(self.skipped_embeddings.keys())}")

    def find_embedding_at_position(self, tokens, offset):
        token = tokens[offset]
        possible_matches = self.ids_lookup.get(token, None)

        if possible_matches is None:
            return None, None

        for ids, embedding in possible_matches:
            if tokens[offset:offset + len(ids)] == ids:
                return embedding, len(ids)

        return None, None


def create_embedding(name, num_vectors_per_token, overwrite_old, init_text='*'):
    cond_model = shared.sd_model.cond_stage_model

    with devices.autocast():
        cond_model([""])  # will send cond model to GPU if lowvram/medvram is active

    #cond_model expects at least some text, so we provide '*' as backup.
    embedded = cond_model.encode_embedding_init_text(init_text or '*', num_vectors_per_token)
    vec = torch.zeros((num_vectors_per_token, embedded.shape[1]), device=devices.device)

    #Only copy if we provided an init_text, otherwise keep vectors as zeros
    if init_text:
        for i in range(num_vectors_per_token):
            vec[i] = embedded[i * int(embedded.shape[0]) // num_vectors_per_token]

    # Remove illegal characters from name.
    name = "".join( x for x in name if (x.isalnum() or x in "._- "))
    fn = os.path.join(shared.cmd_opts.embeddings_dir, f"{name}.pt")
    if not overwrite_old:
        assert not os.path.exists(fn), f"file {fn} already exists"

    embedding = Embedding(vec, name)
    embedding.step = 0
    embedding.save(fn)

    return fn


def write_loss(log_directory, filename, step, epoch_len, values):
    if shared.opts.training_write_csv_every == 0:
        return

    if step % shared.opts.training_write_csv_every != 0:
        return
    write_csv_header = False if os.path.exists(os.path.join(log_directory, filename)) else True

    with open(os.path.join(log_directory, filename), "a+", newline='') as fout:
        csv_writer = csv.DictWriter(fout, fieldnames=["step", "epoch", "epoch_step", *(values.keys())])

        if write_csv_header:
            csv_writer.writeheader()

        epoch = (step - 1) // epoch_len
        epoch_step = (step - 1) % epoch_len

        csv_writer.writerow({
            "step": step,
            "epoch": epoch,
            "epoch_step": epoch_step,
            **values,
        })

def tensorboard_setup(log_directory):
    os.makedirs(os.path.join(log_directory, "tensorboard"), exist_ok=True)
    return SummaryWriter(
            log_dir=os.path.join(log_directory, "tensorboard"),
            flush_secs=shared.opts.training_tensorboard_flush_every)

def tensorboard_add(tensorboard_writer, loss, global_step, step, learn_rate, epoch_num):
    tensorboard_add_scaler(tensorboard_writer, "Loss/train", loss, global_step)
    tensorboard_add_scaler(tensorboard_writer, f"Loss/train/epoch-{epoch_num}", loss, step)
    tensorboard_add_scaler(tensorboard_writer, "Learn rate/train", learn_rate, global_step)
    tensorboard_add_scaler(tensorboard_writer, f"Learn rate/train/epoch-{epoch_num}", learn_rate, step)

def tensorboard_add_scaler(tensorboard_writer, tag, value, step):
    tensorboard_writer.add_scalar(tag=tag,
        scalar_value=value, global_step=step)

def tensorboard_add_image(tensorboard_writer, tag, pil_image, step):
    # 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 validate_train_inputs(model_name, learn_rate, batch_size, gradient_step, data_root, template_file, template_filename, 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"
    assert isinstance(batch_size, int), "Batch size must be integer"
    assert batch_size > 0, "Batch size must be positive"
    assert isinstance(gradient_step, int), "Gradient accumulation step must be integer"
    assert gradient_step > 0, "Gradient accumulation step must be positive"
    assert data_root, "Dataset directory is empty"
    assert os.path.isdir(data_root), "Dataset directory doesn't exist"
    assert os.listdir(data_root), "Dataset directory is empty"
    assert template_filename, "Prompt template file not selected"
    assert template_file, f"Prompt template file {template_filename} not found"
    assert os.path.isfile(template_file.path), f"Prompt template file {template_filename} doesn't exist"
    assert steps, "Max steps is empty or 0"
    assert isinstance(steps, int), "Max steps must be integer"
    assert steps > 0, "Max steps must be positive"
    assert isinstance(save_model_every, int), "Save {name} must be integer"
    assert save_model_every >= 0, "Save {name} must be positive or 0"
    assert isinstance(create_image_every, int), "Create image must be integer"
    assert create_image_every >= 0, "Create image must be positive or 0"
    if save_model_every or create_image_every:
        assert log_directory, "Log directory is empty"


def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, varsize, steps, clip_grad_mode, clip_grad_value, shuffle_tags, tag_drop_out, latent_sampling_method, use_weight, create_image_every, save_embedding_every, template_filename, 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):
    from modules import processing

    save_embedding_every = save_embedding_every or 0
    create_image_every = create_image_every or 0
    template_file = textual_inversion_templates.get(template_filename, None)
    validate_train_inputs(embedding_name, learn_rate, batch_size, gradient_step, data_root, template_file, template_filename, steps, save_embedding_every, create_image_every, log_directory, name="embedding")
    template_file = template_file.path

    shared.state.job = "train-embedding"
    shared.state.textinfo = "Initializing textual inversion training..."
    shared.state.job_count = steps

    filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')

    log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%m-%d"), embedding_name)
    unload = shared.opts.unload_models_when_training

    if save_embedding_every > 0:
        embedding_dir = os.path.join(log_directory, "embeddings")
        os.makedirs(embedding_dir, exist_ok=True)
    else:
        embedding_dir = None

    if create_image_every > 0:
        images_dir = os.path.join(log_directory, "images")
        os.makedirs(images_dir, exist_ok=True)
    else:
        images_dir = None

    if create_image_every > 0 and save_image_with_stored_embedding:
        images_embeds_dir = os.path.join(log_directory, "image_embeddings")
        os.makedirs(images_embeds_dir, exist_ok=True)
    else:
        images_embeds_dir = None

    hijack = sd_hijack.model_hijack

    embedding = hijack.embedding_db.word_embeddings[embedding_name]
    checkpoint = sd_models.select_checkpoint()

    initial_step = embedding.step 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)
    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, 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

    if shared.opts.training_enable_tensorboard:
        tensorboard_writer = tensorboard_setup(log_directory)

    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, varsize=varsize, use_weight=use_weight)

    if shared.opts.save_training_settings_to_txt:
        save_settings_to_file(log_directory, {**dict(model_name=checkpoint.model_name, model_hash=checkpoint.shorthash, num_of_dataset_images=len(ds), num_vectors_per_token=len(embedding.vec)), **locals()})

    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)

    if unload:
        shared.parallel_processing_allowed = False
        shared.sd_model.first_stage_model.to(devices.cpu)

    embedding.vec.requires_grad = True
    optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate, weight_decay=0.0)
    if shared.opts.save_optimizer_state:
        optimizer_state_dict = None
        if os.path.exists(f"{filename}.optim"):
            optimizer_saved_dict = torch.load(f"{filename}.optim", map_location='cpu')
            if embedding.checksum() == optimizer_saved_dict.get('hash', None):
                optimizer_state_dict = optimizer_saved_dict.get('optimizer_state_dict', None)

        if optimizer_state_dict is not None:
            optimizer.load_state_dict(optimizer_state_dict)
            print("Loaded existing optimizer from checkpoint")
        else:
            print("No saved optimizer exists in checkpoint")

    scaler = torch.cuda.amp.GradScaler()

    batch_size = ds.batch_size
    gradient_step = ds.gradient_step
    # n steps = batch_size * gradient_step * n image processed
    steps_per_epoch = len(ds) // batch_size // gradient_step
    max_steps_per_epoch = len(ds) // batch_size - (len(ds) // batch_size) % gradient_step
    loss_step = 0
    _loss_step = 0 #internal

    last_saved_file = "<none>"
    last_saved_image = "<none>"
    forced_filename = "<none>"
    embedding_yet_to_be_embedded = False

    is_training_inpainting_model = shared.sd_model.model.conditioning_key in {'hybrid', 'concat'}
    img_c = None

    pbar = tqdm.tqdm(total=steps - initial_step)
    try:
        sd_hijack_checkpoint.add()

        for _ in range((steps-initial_step) * gradient_step):
            if scheduler.finished:
                break
            if shared.state.interrupted:
                break
            for j, batch in enumerate(dl):
                # works as a drop_last=True for gradient accumulation
                if j == max_steps_per_epoch:
                    break
                scheduler.apply(optimizer, embedding.step)
                if scheduler.finished:
                    break
                if shared.state.interrupted:
                    break

                if clip_grad:
                    clip_grad_sched.step(embedding.step)

                with devices.autocast():
                    x = batch.latent_sample.to(devices.device, non_blocking=pin_memory)
                    if use_weight:
                        w = batch.weight.to(devices.device, non_blocking=pin_memory)
                    c = shared.sd_model.cond_stage_model(batch.cond_text)

                    if is_training_inpainting_model:
                        if img_c is None:
                            img_c = processing.txt2img_image_conditioning(shared.sd_model, c, training_width, training_height)

                        cond = {"c_concat": [img_c], "c_crossattn": [c]}
                    else:
                        cond = c

                    if use_weight:
                        loss = shared.sd_model.weighted_forward(x, cond, w)[0] / gradient_step
                        del w
                    else:
                        loss = shared.sd_model.forward(x, cond)[0] / gradient_step
                    del x

                    _loss_step += loss.item()
                scaler.scale(loss).backward()

                # go back until we reach gradient accumulation steps
                if (j + 1) % gradient_step != 0:
                    continue

                if clip_grad:
                    clip_grad(embedding.vec, clip_grad_sched.learn_rate)

                scaler.step(optimizer)
                scaler.update()
                embedding.step += 1
                pbar.update()
                optimizer.zero_grad(set_to_none=True)
                loss_step = _loss_step
                _loss_step = 0

                steps_done = embedding.step + 1

                epoch_num = embedding.step // steps_per_epoch
                epoch_step = embedding.step % steps_per_epoch

                description = f"Training textual inversion [Epoch {epoch_num}: {epoch_step+1}/{steps_per_epoch}] loss: {loss_step:.7f}"
                pbar.set_description(description)
                if embedding_dir is not None and steps_done % save_embedding_every == 0:
                    # Before saving, change name to match current checkpoint.
                    embedding_name_every = f'{embedding_name}-{steps_done}'
                    last_saved_file = os.path.join(embedding_dir, f'{embedding_name_every}.pt')
                    save_embedding(embedding, optimizer, checkpoint, embedding_name_every, last_saved_file, remove_cached_checksum=True)
                    embedding_yet_to_be_embedded = True

                write_loss(log_directory, "textual_inversion_loss.csv", embedding.step, steps_per_epoch, {
                    "loss": f"{loss_step:.7f}",
                    "learn_rate": scheduler.learn_rate
                })

                if images_dir is not None and steps_done % create_image_every == 0:
                    forced_filename = f'{embedding_name}-{steps_done}'
                    last_saved_image = os.path.join(images_dir, forced_filename)

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

                    p = processing.StableDiffusionProcessingTxt2Img(
                        sd_model=shared.sd_model,
                        do_not_save_grid=True,
                        do_not_save_samples=True,
                        do_not_reload_embeddings=True,
                    )

                    if preview_from_txt2img:
                        p.prompt = preview_prompt
                        p.negative_prompt = preview_negative_prompt
                        p.steps = preview_steps
                        p.sampler_name = sd_samplers.samplers[preview_sampler_index].name
                        p.cfg_scale = preview_cfg_scale
                        p.seed = preview_seed
                        p.width = preview_width
                        p.height = preview_height
                    else:
                        p.prompt = batch.cond_text[0]
                        p.steps = 20
                        p.width = training_width
                        p.height = training_height

                    preview_text = p.prompt

                    with closing(p):
                        processed = processing.process_images(p)
                        image = processed.images[0] if len(processed.images) > 0 else None

                    if unload:
                        shared.sd_model.first_stage_model.to(devices.cpu)

                    if image is not None:
                        shared.state.assign_current_image(image)

                        last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)
                        last_saved_image += f", prompt: {preview_text}"

                        if shared.opts.training_enable_tensorboard and shared.opts.training_tensorboard_save_images:
                            tensorboard_add_image(tensorboard_writer, f"Validation at epoch {epoch_num}", image, embedding.step)

                    if save_image_with_stored_embedding and os.path.exists(last_saved_file) and embedding_yet_to_be_embedded:

                        last_saved_image_chunks = os.path.join(images_embeds_dir, f'{embedding_name}-{steps_done}.png')

                        info = PngImagePlugin.PngInfo()
                        data = torch.load(last_saved_file)
                        info.add_text("sd-ti-embedding", embedding_to_b64(data))

                        title = f"<{data.get('name', '???')}>"

                        try:
                            vectorSize = list(data['string_to_param'].values())[0].shape[0]
                        except Exception:
                            vectorSize = '?'

                        checkpoint = sd_models.select_checkpoint()
                        footer_left = checkpoint.model_name
                        footer_mid = f'[{checkpoint.shorthash}]'
                        footer_right = f'{vectorSize}v {steps_done}s'

                        captioned_image = caption_image_overlay(image, title, footer_left, footer_mid, footer_right)
                        captioned_image = insert_image_data_embed(captioned_image, data)

                        captioned_image.save(last_saved_image_chunks, "PNG", pnginfo=info)
                        embedding_yet_to_be_embedded = False

                    last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)
                    last_saved_image += f", prompt: {preview_text}"

                shared.state.job_no = embedding.step

                shared.state.textinfo = f"""
<p>
Loss: {loss_step:.7f}<br/>
Step: {steps_done}<br/>
Last prompt: {html.escape(batch.cond_text[0])}<br/>
Last saved embedding: {html.escape(last_saved_file)}<br/>
Last saved image: {html.escape(last_saved_image)}<br/>
</p>
"""
        filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')
        save_embedding(embedding, optimizer, checkpoint, embedding_name, filename, remove_cached_checksum=True)
    except Exception:
        errors.report("Error training embedding", exc_info=True)
    finally:
        pbar.leave = False
        pbar.close()
        shared.sd_model.first_stage_model.to(devices.device)
        shared.parallel_processing_allowed = old_parallel_processing_allowed
        sd_hijack_checkpoint.remove()

    return embedding, filename


def save_embedding(embedding, optimizer, checkpoint, embedding_name, filename, remove_cached_checksum=True):
    old_embedding_name = embedding.name
    old_sd_checkpoint = embedding.sd_checkpoint if hasattr(embedding, "sd_checkpoint") else None
    old_sd_checkpoint_name = embedding.sd_checkpoint_name if hasattr(embedding, "sd_checkpoint_name") else None
    old_cached_checksum = embedding.cached_checksum if hasattr(embedding, "cached_checksum") else None
    try:
        embedding.sd_checkpoint = checkpoint.shorthash
        embedding.sd_checkpoint_name = checkpoint.model_name
        if remove_cached_checksum:
            embedding.cached_checksum = None
        embedding.name = embedding_name
        embedding.optimizer_state_dict = optimizer.state_dict()
        embedding.save(filename)
    except:
        embedding.sd_checkpoint = old_sd_checkpoint
        embedding.sd_checkpoint_name = old_sd_checkpoint_name
        embedding.name = old_embedding_name
        embedding.cached_checksum = old_cached_checksum
        raise