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Disable prompt weights on newbie te. (#11434)

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comfyanonymous 2025-12-19 21:19:47 -08:00 committed by GitHub
parent 0aa7fa464e
commit 3ab9748903
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2 changed files with 5 additions and 3 deletions
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6
comfy/sd1_clip.py
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@ -466,7 +466,7 @@ def load_embed(embedding_name, embedding_directory, embedding_size, embed_key=No
return embed_out
class SDTokenizer:
def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True, embedding_directory=None, embedding_size=768, embedding_key='clip_l', tokenizer_class=CLIPTokenizer, has_start_token=True, has_end_token=True, pad_to_max_length=True, min_length=None, pad_token=None, end_token=None, min_padding=None, pad_left=False, tokenizer_data={}, tokenizer_args={}):
def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True, embedding_directory=None, embedding_size=768, embedding_key='clip_l', tokenizer_class=CLIPTokenizer, has_start_token=True, has_end_token=True, pad_to_max_length=True, min_length=None, pad_token=None, end_token=None, min_padding=None, pad_left=False, disable_weights=False, tokenizer_data={}, tokenizer_args={}):
if tokenizer_path is None:
tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_tokenizer")
self.tokenizer = tokenizer_class.from_pretrained(tokenizer_path, **tokenizer_args)
@ -513,6 +513,8 @@ class SDTokenizer:
self.embedding_size = embedding_size
self.embedding_key = embedding_key
self.disable_weights = disable_weights
def _try_get_embedding(self, embedding_name:str):
'''
Takes a potential embedding name and tries to retrieve it.
@ -547,7 +549,7 @@ class SDTokenizer:
min_padding = tokenizer_options.get("{}_min_padding".format(self.embedding_key), self.min_padding)
text = escape_important(text)
if kwargs.get("disable_weights", False):
if kwargs.get("disable_weights", self.disable_weights):
parsed_weights = [(text, 1.0)]
else:
parsed_weights = token_weights(text, 1.0)

2
comfy/text_encoders/lumina2.py
View File

@ -14,7 +14,7 @@ class Gemma2BTokenizer(sd1_clip.SDTokenizer):
class Gemma3_4BTokenizer(sd1_clip.SDTokenizer):
def __init__(self, embedding_directory=None, tokenizer_data={}):
tokenizer = tokenizer_data.get("spiece_model", None)
super().__init__(tokenizer, pad_with_end=False, embedding_size=2560, embedding_key='gemma3_4b', tokenizer_class=SPieceTokenizer, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, tokenizer_args={"add_bos": True, "add_eos": False}, tokenizer_data=tokenizer_data)
super().__init__(tokenizer, pad_with_end=False, embedding_size=2560, embedding_key='gemma3_4b', tokenizer_class=SPieceTokenizer, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, tokenizer_args={"add_bos": True, "add_eos": False}, disable_weights=True, tokenizer_data=tokenizer_data)
def state_dict(self):
return {"spiece_model": self.tokenizer.serialize_model()}