| 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, 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
|
|
|
|
|
| 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:
|
|
|
| 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
|
|
|
|
|
| if 'string_to_param' in data:
|
| param_dict = data['string_to_param']
|
| if hasattr(param_dict, '_parameters'):
|
| param_dict = getattr(param_dict, '_parameters')
|
| assert len(param_dict) == 1, 'embedding file has multiple terms in it'
|
| emb = next(iter(param_dict.items()))[1]
|
|
|
| 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()
|
|
|
|
|
|
|
| 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 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([""])
|
|
|
|
|
| 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)
|
|
|
|
|
| if init_text:
|
| for i in range(num_vectors_per_token):
|
| vec[i] = embedded[i * int(embedded.shape[0]) // num_vectors_per_token]
|
|
|
|
|
| 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):
|
|
|
| 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)
|
|
|
| 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
|
|
|
| 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
|
|
|
| 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):
|
|
|
| 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()
|
|
|
|
|
| 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:
|
|
|
| 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 = f"<{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 = 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:
|
| 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
|
|
|
