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Disable prompt weights for qwen. (#9438)

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comfyanonymous
2025-08-19 22:08:11 -07:00
committed by GitHub
parent 7cd2c4bd6a
commit 5a8f502db5
2 changed files with 6 additions and 3 deletions
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5
comfy/sd1_clip.py
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@@ -534,7 +534,10 @@ class SDTokenizer:
min_padding = tokenizer_options.get("{}_min_padding".format(self.embedding_key), self.min_padding)
text = escape_important(text)
parsed_weights = token_weights(text, 1.0)
if kwargs.get("disable_weights", False):
parsed_weights = [(text, 1.0)]
else:
parsed_weights = token_weights(text, 1.0)
# tokenize words
tokens = []

4
comfy/text_encoders/qwen_image.py
View File

@@ -15,7 +15,7 @@ class QwenImageTokenizer(sd1_clip.SD1Tokenizer):
def __init__(self, embedding_directory=None, tokenizer_data={}):
super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name="qwen25_7b", tokenizer=Qwen25_7BVLITokenizer)
self.llama_template = "<|im_start|>system\nDescribe the image by detailing the color, shape, size, texture, quantity, text, spatial relationships of the objects and background:<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n"
self.llama_template_images = "<|im_start|>system\nDescribe the key features of the input image \\(color, shape, size, texture, objects, background\\), then explain how the user's text instruction should alter or modify the image. Generate a new image that meets the user's requirements while maintaining consistency with the original input where appropriate.<|im_end|>\n<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>{}<|im_end|>\n<|im_start|>assistant\n"
self.llama_template_images = "<|im_start|>system\nDescribe the key features of the input image (color, shape, size, texture, objects, background), then explain how the user's text instruction should alter or modify the image. Generate a new image that meets the user's requirements while maintaining consistency with the original input where appropriate.<|im_end|>\n<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>{}<|im_end|>\n<|im_start|>assistant\n"
def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, images=[], **kwargs):
if llama_template is None:
@@ -25,7 +25,7 @@ class QwenImageTokenizer(sd1_clip.SD1Tokenizer):
llama_text = self.llama_template.format(text)
else:
llama_text = llama_template.format(text)
tokens = super().tokenize_with_weights(llama_text, return_word_ids=return_word_ids, **kwargs)
tokens = super().tokenize_with_weights(llama_text, return_word_ids=return_word_ids, disable_weights=True, **kwargs)
key_name = next(iter(tokens))
embed_count = 0
qwen_tokens = tokens[key_name]