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refactor and fix embedding registration

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This commit is contained in:
AI-Casanova
2024-02-13 11:00:33 -06:00
committed by Vladimir Mandic
parent 86e8791ed0
commit 08567b288f
1 changed files with 15 additions and 23 deletions
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modules/textual_inversion/textual_inversion.py
+15 -23
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@@ -153,7 +153,7 @@ class EmbeddingDatabase:
vec = shared.sd_model.cond_stage_model.encode_embedding_init_text(",", 1)
return vec.shape[1]
def load_diffusers_embedding(self, filename: Union[str, List[str]], path: Optional[Union[str, List[str]]] = None):
def load_diffusers_embedding(self, filename: Union[str, List[str]]):
_loaded_pre = len(self.word_embeddings)
embeddings_to_load = []
loaded_embeddings = {}
@@ -169,38 +169,32 @@ class EmbeddingDatabase:
elif clip_l and tokenizer:
model_type = 'SD'
else:
model_type = 'UNDEFINED'
return 0
filenames = [filename] if not isinstance(filename, list) else filename
exts = [".SAFETENSORS", '.BIN', '.PT', '.PNG', '.WEBP', '.JXL', '.AVIF'] # SDXL only uses safetensors
filename_paths = zip(filenames, len(filenames) * [path] if (isinstance(path, str) or path is None) else path)
unk_token_id = tokenizer.convert_tokens_to_ids(tokenizer.unk_token)
for filename, fullname in filename_paths:
debug(f'Embedding check: {filename}')
if fullname is None:
fullname = filename
filename = os.path.basename(fullname)
filenames = list(filename)
exts = [".SAFETENSORS", '.BIN', '.PT', '.PNG', '.WEBP', '.JXL', '.AVIF']
for filename in filenames:
# debug(f'Embedding check: {filename}')
fullname = filename
filename = os.path.basename(fullname)
fn, ext = os.path.splitext(filename)
name = os.path.basename(fn)
embedding = Embedding(vec=None, name=name, filename=fullname)
tokenizer_vocab = tokenizer.get_vocab()
try:
if ext.upper() not in exts:
raise ValueError(f'extension `{ext}` is invalid, expected one of: {exts}')
if name in tokenizer.get_vocab() or f"{name}_1" in tokenizer.get_vocab():
loaded_embeddings[name] = embedding
debug(f'Embedding already loaded: {name}')
if name in tokenizer_vocab:
raise ValueError(f'invalid embedding name (cannot exist in vocab.json)')
embeddings_to_load.append(embedding)
except Exception as e:
skipped_embeddings.append(embedding)
debug(f'Embedding check: {name} {e}')
debug(f'Embedding skipped: "{name}" {e}')
continue
embeddings_to_load = sorted(embeddings_to_load, key=lambda e: exts.index(os.path.splitext(e.filename)[1].upper()))
tokens_to_add = {}
tokenizer_vocab = tokenizer.get_vocab()
for embedding in embeddings_to_load:
try:
debug(f'Embedding load: {embedding.name} file={embedding.filename}')
if embedding.name in tokens_to_add or embedding.name in loaded_embeddings:
raise ValueError('duplicate token')
embeddings_dict = {}
@@ -236,7 +230,7 @@ class EmbeddingDatabase:
if not _tokens_to_add:
raise ValueError('no valid tokens to add')
tokens_to_add.update(_tokens_to_add)
loaded_embeddings[name] = embedding
loaded_embeddings[embedding.name] = embedding
except Exception as e:
debug(f"Embedding loading: {embedding.filename} {e}")
continue
@@ -248,11 +242,9 @@ class EmbeddingDatabase:
clip_g.resize_token_embeddings(len(tokenizer_2)) # type: ignore
for token, data in tokens_to_add.items():
token_id = tokenizer.convert_tokens_to_ids(token)
if token_id > unk_token_id:
clip_l.get_input_embeddings().weight.data[token_id] = data.clip_l
if model_type == 'SDXL':
clip_g.get_input_embeddings().weight.data[token_id] = data.clip_g # type: ignore
clip_l.get_input_embeddings().weight.data[token_id] = data.clip_l
if model_type == 'SDXL':
clip_g.get_input_embeddings().weight.data[token_id] = data.clip_g # type: ignore
for embedding in loaded_embeddings.values():
if not embedding:
continue