stable-diffusion-webui/modules/textual_inversion/textual_inversion.py

701 lines
31 KiB
Python

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
embedding = create_embedding_from_data(data, name, filename=filename, filepath=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, _, 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 create_embedding_from_data(data, name, filename='unknown embedding file', filepath=None):
if 'string_to_param' in data: # textual inversion embeddings
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
if filepath:
embedding.filename = filepath
embedding.set_hash(hashes.sha256(filepath, "textual_inversion/" + name) or '')
return embedding
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_name, 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_map[preview_sampler_name.lower()]
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