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sort out embeddings loading in dffusers

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Vladimir Mandic
2023-09-05 11:06:25 -04:00
parent 9058bfa250
commit a3033dc65f
13 changed files with 52 additions and 68 deletions
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modules/prompt_parser_diffusers.py
+5
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@@ -38,6 +38,8 @@ CLIP_SKIP_MAPPING = {
class DiffusersTextualInversionManager(BaseTextualInversionManager):
def __init__(self, pipe):
self.pipe = pipe
if hasattr(self.pipe, 'embedding_db'):
self.pipe.embedding_db.embeddings_used.clear()
#from https://github.com/huggingface/diffusers/blob/705c592ea98ba4e288d837b9cba2767623c78603/src/diffusers/loaders.py#L599
def maybe_convert_prompt(self, prompt: typing.Union[str, typing.List[str]], tokenizer = "PreTrainedTokenizer"):
@@ -52,12 +54,15 @@ class DiffusersTextualInversionManager(BaseTextualInversionManager):
unique_tokens = set(tokens)
for token in unique_tokens:
if token in tokenizer.added_tokens_encoder:
if hasattr(self.pipe, 'embedding_db'):
self.pipe.embedding_db.embeddings_used.append(token)
replacement = token
i = 1
while f"{token}_{i}" in tokenizer.added_tokens_encoder:
replacement += f" {token}_{i}"
i += 1
prompt = prompt.replace(token, replacement)
self.pipe.embedding_db.embeddings_used = list(set(self.pipe.embedding_db.embeddings_used))
return prompt
def expand_textual_inversion_token_ids_if_necessary(self, token_ids: typing.List[int]) -> typing.List[int]: