stable-diffusion-webui/modules/textual_inversion/textual_inversion.py
2022-10-10 17:05:12 +03:00

280 lines
9.8 KiB
Python

import os
import sys
import traceback
import torch
import tqdm
import html
import datetime
from modules import shared, devices, sd_hijack, processing, sd_models
import modules.textual_inversion.dataset
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]
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('hash', 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.")
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, init_text='*'):
cond_model = shared.sd_model.cond_stage_model
embedding_layer = cond_model.wrapped.transformer.text_model.embeddings
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]
fn = os.path.join(shared.cmd_opts.embeddings_dir, f"{name}.pt")
assert not os.path.exists(fn), f"file {fn} already exists"
embedding = Embedding(vec, name)
embedding.step = 0
embedding.save(fn)
return fn
def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, num_repeats, create_image_every, save_embedding_every, template_file):
assert embedding_name, 'embedding not selected'
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)
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
cond_model = shared.sd_model.cond_stage_model
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=num_repeats, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file)
hijack = sd_hijack.model_hijack
embedding = hijack.embedding_db.word_embeddings[embedding_name]
embedding.vec.requires_grad = True
optimizer = torch.optim.AdamW([embedding.vec], lr=learn_rate)
losses = torch.zeros((32,))
last_saved_file = "<none>"
last_saved_image = "<none>"
ititial_step = embedding.step or 0
if ititial_step > steps:
return embedding, filename
tr_img_len = len([os.path.join(data_root, file_path) for file_path in os.listdir(data_root)])
epoch_len = (tr_img_len * num_repeats) + tr_img_len
pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
for i, (x, text) in pbar:
embedding.step = i + ititial_step
if embedding.step > steps:
break
if shared.state.interrupted:
break
with torch.autocast("cuda"):
c = cond_model([text])
x = x.to(devices.device)
loss = shared.sd_model(x.unsqueeze(0), c)[0]
del x
losses[embedding.step % losses.shape[0]] = loss.item()
optimizer.zero_grad()
loss.backward()
optimizer.step()
epoch_num = embedding.step // epoch_len
epoch_step = embedding.step - (epoch_num * epoch_len) + 1
pbar.set_description(f"[Epoch {epoch_num}: {epoch_step}/{epoch_len}]loss: {losses.mean():.7f}")
if embedding.step > 0 and embedding_dir is not None and embedding.step % save_embedding_every == 0:
last_saved_file = os.path.join(embedding_dir, f'{embedding_name}-{embedding.step}.pt')
embedding.save(last_saved_file)
if embedding.step > 0 and images_dir is not None and embedding.step % create_image_every == 0:
last_saved_image = os.path.join(images_dir, f'{embedding_name}-{embedding.step}.png')
p = processing.StableDiffusionProcessingTxt2Img(
sd_model=shared.sd_model,
prompt=text,
steps=20,
height=training_height,
width=training_width,
do_not_save_grid=True,
do_not_save_samples=True,
)
processed = processing.process_images(p)
image = processed.images[0]
shared.state.current_image = image
image.save(last_saved_image)
last_saved_image += f", prompt: {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(text)}<br/>
Last saved embedding: {html.escape(last_saved_file)}<br/>
Last saved image: {html.escape(last_saved_image)}<br/>
</p>
"""
checkpoint = sd_models.select_checkpoint()
embedding.sd_checkpoint = checkpoint.hash
embedding.sd_checkpoint_name = checkpoint.model_name
embedding.cached_checksum = None
embedding.save(filename)
return embedding, filename