Files
kadirnar e21711a049 Refactor computer vision and NLP pipelines to simplify model selection and enhance functionality
- Updated model selection in image-to-image, mask generation, object detection, video classification, zero-shot image classification, zero-shot object detection, document question answering, feature extraction, image-to-text, image-text-to-text, visual question answering, conversational, fill mask, question answering, sentence similarity, table question answering, zero-shot classification, text classification, text generation, and token classification pipelines to use a default string instead of a model list.
- Removed unused keypoint detection, summarization, text2text generation, and translation pipelines.
- Added utility nodes for type conversion: IntToString, StringToInt, FloatToString, and StringToFloat.
- Updated all pipeline calls to include trust_remote_code=True for enhanced security.
- Improved requirements by adding timm and opencv-python for better functionality.
2026-02-08 18:26:10 +00:00

86 lines
1.8 KiB
Python

class IntToString:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"value": ("INT", {"default": 0}),
},
}
RETURN_TYPES = ("STRING",)
RETURN_NAMES = ("text",)
FUNCTION = "convert"
CATEGORY = "Transformers/Utility"
def convert(self, value):
return (str(value),)
class StringToInt:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"text": ("STRING", {"default": "0"}),
},
}
RETURN_TYPES = ("INT",)
RETURN_NAMES = ("value",)
FUNCTION = "convert"
CATEGORY = "Transformers/Utility"
def convert(self, text):
return (int(text.strip()),)
class FloatToString:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"value": ("FLOAT", {"default": 0.0}),
},
}
RETURN_TYPES = ("STRING",)
RETURN_NAMES = ("text",)
FUNCTION = "convert"
CATEGORY = "Transformers/Utility"
def convert(self, value):
return (str(value),)
class StringToFloat:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"text": ("STRING", {"default": "0.0"}),
},
}
RETURN_TYPES = ("FLOAT",)
RETURN_NAMES = ("value",)
FUNCTION = "convert"
CATEGORY = "Transformers/Utility"
def convert(self, text):
return (float(text.strip()),)
NODE_CLASS_MAPPINGS = {
"IntToString": IntToString,
"StringToInt": StringToInt,
"FloatToString": FloatToString,
"StringToFloat": StringToFloat,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"IntToString": "Int to String",
"StringToInt": "String to Int",
"FloatToString": "Float to String",
"StringToFloat": "String to Float",
}