Weight Schedule Extend

Documentation

• Class name: WeightScheduleExtend
• Category: KJNodes
• Output node: False

The WeightScheduleExtend node is designed to extend, and convert if needed, different value lists/series. It supports various input types and can output the extended or converted values in the specified format, facilitating the manipulation and analysis of data within computational workflows.

Input types

Required

• input_values_i
  • unknown
  • Comfy dtype: FLOAT
  • Python dtype: unknown
• output_type
  • Specifies the desired output format of the extended or converted data, allowing for flexibility in how the results are utilized or further processed.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str

Output types

• float
  • Comfy dtype: FLOAT
  • The output is a float value or a collection of float values, depending on the operation performed and the output type specified.
  • Python dtype: float

Usage tips

• Infra type: CPU
• Common nodes: unknown

Source code

class WeightScheduleExtend:

    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "input_values_1": ("FLOAT", {"default": 0.0, "forceInput": True}),
                "input_values_2": ("FLOAT", {"default": 0.0, "forceInput": True}),
                "output_type": (
                [   
                    'match_input',
                    'list',
                    'pandas series',
                    'tensor',
                ],
                {
                "default": 'match_input'
                    }),
             },

        }
    RETURN_TYPES = ("FLOAT",)
    FUNCTION = "execute"
    CATEGORY = "KJNodes"
    DESCRIPTION = """
Extends, and converts if needed, different value lists/series  
"""

    def detect_input_type(self, input_values):
        import pandas as pd
        if isinstance(input_values, list):
            return 'list'
        elif isinstance(input_values, pd.Series):
            return 'pandas series'
        elif isinstance(input_values, torch.Tensor):
            return 'tensor'
        else:
            raise ValueError("Unsupported input type")

    def execute(self, input_values_1, input_values_2, output_type):
        import pandas as pd
        input_type_1 = self.detect_input_type(input_values_1)
        input_type_2 = self.detect_input_type(input_values_2)
        # Convert input_values_2 to the same format as input_values_1 if they do not match
        if not input_type_1 == input_type_2:
            print("Converting input_values_2 to the same format as input_values_1")
            if input_type_1 == 'pandas series':
                # Convert input_values_2 to a pandas Series
                float_values_2 = pd.Series(input_values_2)
            elif input_type_1 == 'tensor':
                # Convert input_values_2 to a tensor
                float_values_2 = torch.tensor(input_values_2, dtype=torch.float32)
        else:
            print("Input types match, no conversion needed")
            # If the types match, no conversion is needed
            float_values_2 = input_values_2

        float_values = input_values_1 + float_values_2

        if output_type == 'list':
            return float_values,
        elif output_type == 'pandas series':
            return pd.Series(float_values),
        elif output_type == 'tensor':
            if input_type_1 == 'pandas series':
                return torch.tensor(float_values.values, dtype=torch.float32),
            else:
                return torch.tensor(float_values, dtype=torch.float32),
        elif output_type == 'match_input':
            return float_values,
        else:
            raise ValueError(f"Unsupported output_type: {output_type}")