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Configuration error
| import os | |
| import sys | |
| import traceback | |
| import inspect | |
| from collections import namedtuple | |
| 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, processing, sd_models, images, sd_samplers, sd_hijack_checkpoint | |
| 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, dirs, 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 | |
| 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, 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 | |
| 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): | |
| self.word_embeddings[embedding.name] = embedding | |
| ids = model.cond_stage_model.tokenize([embedding.name])[0] | |
| first_id = ids[0] | |
| if first_id not in self.ids_lookup: | |
| self.ids_lookup[first_id] = [] | |
| self.ids_lookup[first_id] = sorted(self.ids_lookup[first_id] + [(ids, embedding)], key=lambda x: len(x[0]), reverse=True) | |
| 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'] | |
| if hasattr(param_dict, '_parameters'): | |
| param_dict = getattr(param_dict, '_parameters') # 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] | |
| # diffuser concepts | |
| elif type(data) == dict and type(next(iter(data.values()))) == torch.Tensor: | |
| 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) | |
| else: | |
| raise Exception(f"Couldn't identify {filename} as neither textual inversion embedding nor diffuser concept.") | |
| vec = emb.detach().to(devices.device, dtype=torch.float32) | |
| 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 = vec.shape[0] | |
| embedding.shape = vec.shape[-1] | |
| embedding.filename = path | |
| 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, dirs, 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: | |
| print(f"Error loading embedding {fn}:", file=sys.stderr) | |
| print(traceback.format_exc(), file=sys.stderr) | |
| continue | |
| def load_textual_inversion_embeddings(self, force_reload=False): | |
| if not force_reload: | |
| need_reload = False | |
| for path, embdir in self.embedding_dirs.items(): | |
| 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 path, embdir in self.embedding_dirs.items(): | |
| self.load_from_dir(embdir) | |
| embdir.update() | |
| displayed_embeddings = (tuple(self.word_embeddings.keys()), tuple(self.skipped_embeddings.keys())) | |
| if 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 len(self.skipped_embeddings) > 0: | |
| 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): | |
| 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(filename + '.optim'): | |
| optimizer_saved_dict = torch.load(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 i 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 | |
| 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 = "<{}>".format(data.get('name', '???')) | |
| try: | |
| vectorSize = list(data['string_to_param'].values())[0].shape[0] | |
| except Exception as e: | |
| vectorSize = '?' | |
| checkpoint = sd_models.select_checkpoint() | |
| footer_left = checkpoint.model_name | |
| footer_mid = '[{}]'.format(checkpoint.shorthash) | |
| footer_right = '{}v {}s'.format(vectorSize, steps_done) | |
| 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: | |
| print(traceback.format_exc(), file=sys.stderr) | |
| pass | |
| 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 | |