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import os
import safetensors.torch
import torch
from modules import devices, errors, hashes, shared
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):
raise NotImplementedError("Training is not supported...")
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:
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:
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):
devices.torch_npu_set_device()
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 (".BIN", ".PT"):
data = torch.load(path, map_location="cpu")
elif ext in (".SAFETENSORS",):
data = safetensors.torch.load_file(path, device="cpu")
else:
if ext in (".PNG", ".WEBP", ".JXL", ".AVIF"):
second_ext = os.path.splitext(name)[1]
if second_ext.upper() != ".PREVIEW":
raise NotImplementedError("Image-Embedding is not supported...")
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)
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_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 LookupError(f"Couldn't identify {filename}...")
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, "/".join(["textual_inversion", name])) or "")
return embedding
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