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

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import os
import sys
import traceback
import torch
import tqdm
import html
import datetime
import csv
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from PIL import Image, PngImagePlugin
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from modules import shared, devices, sd_hijack, processing, sd_models, images
import modules.textual_inversion.dataset
from modules.textual_inversion.learn_schedule import LearnRateScheduler
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from modules.textual_inversion.image_embedding import (embedding_to_b64, embedding_from_b64,
insert_image_data_embed, extract_image_data_embed,
caption_image_overlay)
class Embedding:
def __init__(self, vec, name, step=None):
self.vec = vec
self.name = name
self.step = step
self.cached_checksum = None
self.sd_checkpoint = None
self.sd_checkpoint_name = 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)
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 EmbeddingDatabase:
def __init__(self, embeddings_dir):
self.ids_lookup = {}
self.word_embeddings = {}
self.dir_mtime = None
self.embeddings_dir = embeddings_dir
def register_embedding(self, embedding, model):
self.word_embeddings[embedding.name] = embedding
ids = model.cond_stage_model.tokenizer([embedding.name], add_special_tokens=False)['input_ids'][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 load_textual_inversion_embeddings(self):
mt = os.path.getmtime(self.embeddings_dir)
if self.dir_mtime is not None and mt <= self.dir_mtime:
return
self.dir_mtime = mt
self.ids_lookup.clear()
self.word_embeddings.clear()
def process_file(path, filename):
name = os.path.splitext(filename)[0]
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data = []
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if os.path.splitext(filename.upper())[-1] in ['.PNG', '.WEBP', '.JXL', '.AVIF']:
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embed_image = Image.open(path)
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if hasattr(embed_image, 'text') and 'sd-ti-embedding' in embed_image.text:
data = embedding_from_b64(embed_image.text['sd-ti-embedding'])
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name = data.get('name', name)
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else:
data = extract_image_data_embed(embed_image)
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name = data.get('name', name)
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else:
data = torch.load(path, map_location="cpu")
# 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)
self.register_embedding(embedding, shared.sd_model)
for fn in os.listdir(self.embeddings_dir):
try:
fullfn = os.path.join(self.embeddings_dir, fn)
if os.stat(fullfn).st_size == 0:
continue
process_file(fullfn, fn)
except Exception:
print(f"Error loading emedding {fn}:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
continue
print(f"Loaded a total of {len(self.word_embeddings)} textual inversion embeddings.")
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print("Embeddings:", ', '.join(self.word_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
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def create_embedding(name, num_vectors_per_token, overwrite_old, init_text='*'):
cond_model = shared.sd_model.cond_stage_model
embedding_layer = cond_model.wrapped.transformer.text_model.embeddings
with devices.autocast():
cond_model([""]) # will send cond model to GPU if lowvram/medvram is active
ids = cond_model.tokenizer(init_text, max_length=num_vectors_per_token, return_tensors="pt", add_special_tokens=False)["input_ids"]
embedded = embedding_layer.token_embedding.wrapped(ids.to(devices.device)).squeeze(0)
vec = torch.zeros((num_vectors_per_token, embedded.shape[1]), device=devices.device)
for i in range(num_vectors_per_token):
vec[i] = embedded[i * int(embedded.shape[0]) // num_vectors_per_token]
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# 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")
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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
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if (step + 1) % 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 // epoch_len
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epoch_step = step % epoch_len
csv_writer.writerow({
"step": step + 1,
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"epoch": epoch,
"epoch_step": epoch_step + 1,
**values,
})
def validate_train_inputs(model_name, learn_rate, batch_size, data_root, template_file, 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 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_file, "Prompt template file is empty"
assert os.path.isfile(template_file), "Prompt template file 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(embedding_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
save_embedding_every = save_embedding_every or 0
create_image_every = create_image_every or 0
validate_train_inputs(embedding_name, learn_rate, batch_size, data_root, template_file, steps, save_embedding_every, create_image_every, log_directory, name="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')
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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
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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
cond_model = shared.sd_model.cond_stage_model
hijack = sd_hijack.model_hijack
embedding = hijack.embedding_db.word_embeddings[embedding_name]
checkpoint = sd_models.select_checkpoint()
ititial_step = embedding.step or 0
if ititial_step >= steps:
shared.state.textinfo = f"Model has already been trained beyond specified max steps"
return embedding, filename
scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
# 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)}..."
with torch.autocast("cuda"):
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, device=devices.device, template_file=template_file, batch_size=batch_size)
if unload:
shared.sd_model.first_stage_model.to(devices.cpu)
embedding.vec.requires_grad = True
optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate)
losses = torch.zeros((32,))
last_saved_file = "<none>"
last_saved_image = "<none>"
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forced_filename = "<none>"
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embedding_yet_to_be_embedded = False
pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
for i, entries in pbar:
embedding.step = i + ititial_step
scheduler.apply(optimizer, embedding.step)
if scheduler.finished:
break
if shared.state.interrupted:
break
with torch.autocast("cuda"):
c = cond_model([entry.cond_text for entry in entries])
x = torch.stack([entry.latent for entry in entries]).to(devices.device)
loss = shared.sd_model(x, c)[0]
del x
losses[embedding.step % losses.shape[0]] = loss.item()
optimizer.zero_grad()
loss.backward()
optimizer.step()
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steps_done = embedding.step + 1
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epoch_num = embedding.step // len(ds)
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epoch_step = embedding.step % len(ds)
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pbar.set_description(f"[Epoch {epoch_num}: {epoch_step+1}/{len(ds)}]loss: {losses.mean():.7f}")
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if embedding_dir is not None and steps_done % save_embedding_every == 0:
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# 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, checkpoint, embedding_name_every, last_saved_file, remove_cached_checksum=True)
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embedding_yet_to_be_embedded = True
write_loss(log_directory, "textual_inversion_loss.csv", embedding.step, len(ds), {
"loss": f"{losses.mean():.7f}",
"learn_rate": scheduler.learn_rate
})
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if images_dir is not None and steps_done % create_image_every == 0:
forced_filename = f'{embedding_name}-{steps_done}'
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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_index = preview_sampler_index
p.cfg_scale = preview_cfg_scale
p.seed = preview_seed
p.width = preview_width
p.height = preview_height
else:
p.prompt = entries[0].cond_text
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 unload:
shared.sd_model.first_stage_model.to(devices.cpu)
shared.state.current_image = image
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if save_image_with_stored_embedding and os.path.exists(last_saved_file) and embedding_yet_to_be_embedded:
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last_saved_image_chunks = os.path.join(images_embeds_dir, f'{embedding_name}-{steps_done}.png')
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info = PngImagePlugin.PngInfo()
data = torch.load(last_saved_file)
info.add_text("sd-ti-embedding", embedding_to_b64(data))
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title = "<{}>".format(data.get('name', '???'))
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try:
vectorSize = list(data['string_to_param'].values())[0].shape[0]
except Exception as e:
vectorSize = '?'
checkpoint = sd_models.select_checkpoint()
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footer_left = checkpoint.model_name
footer_mid = '[{}]'.format(checkpoint.hash)
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footer_right = '{}v {}s'.format(vectorSize, steps_done)
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captioned_image = caption_image_overlay(image, title, footer_left, footer_mid, footer_right)
captioned_image = insert_image_data_embed(captioned_image, data)
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captioned_image.save(last_saved_image_chunks, "PNG", pnginfo=info)
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embedding_yet_to_be_embedded = False
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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)
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last_saved_image += f", prompt: {preview_text}"
shared.state.job_no = embedding.step
shared.state.textinfo = f"""
<p>
Loss: {losses.mean():.7f}<br/>
Step: {embedding.step}<br/>
Last prompt: {html.escape(entries[0].cond_text)}<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, checkpoint, embedding_name, filename, remove_cached_checksum=True)
return embedding, filename
def save_embedding(embedding, 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.hash
embedding.sd_checkpoint_name = checkpoint.model_name
if remove_cached_checksum:
embedding.cached_checksum = None
embedding.name = embedding_name
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