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Prevent empty negative prompt

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Really doesn't work otherwise
This commit is contained in:
kijai 2025-11-17 16:29:47 +02:00 committed by comfyanonymous
parent eaef7b764e
commit 521cc1d38d
2 changed files with 5 additions and 1 deletions
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2
comfy/text_encoders/hunyuan_video.py
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@ -79,6 +79,8 @@ class HunyuanVideo15Tokenizer(HunyuanImageTokenizer):
super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)
self.llama_template = "<|im_start|>system\nYou are a helpful assistant. Describe the video by detailing the following aspects:\n1. The main content and theme of the video.\n2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects.\n3. Actions, events, behaviors temporal relationships, physical movement changes of the objects.\n4. background environment, light, style and atmosphere.\n5. camera angles, movements, and transitions used in the video.<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n"
def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs):
return super().tokenize_with_weights(text, return_word_ids, prevent_empty_text=True, **kwargs)
class HunyuanVideoClipModel(torch.nn.Module):
def __init__(self, dtype_llama=None, device="cpu", dtype=None, model_options={}):

4
comfy/text_encoders/qwen_image.py
View File

@ -17,12 +17,14 @@ class QwenImageTokenizer(sd1_clip.SD1Tokenizer):
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"
def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, images=[], **kwargs):
def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, images=[], prevent_empty_text=False, **kwargs):
skip_template = False
if text.startswith('<|im_start|>'):
skip_template = True
if text.startswith('<|start_header_id|>'):
skip_template = True
if prevent_empty_text and text == '':
text = ' '
if skip_template:
llama_text = text