108 lines
3.7 KiB
Markdown
108 lines
3.7 KiB
Markdown
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# Documentation
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- Class name: WeightScheduleExtend
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- Category: KJNodes
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- Output node: False
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WeightScheduleExtend节点旨在扩展和转换不同类型的数值列表或序列。它支持多种输入类型，并能够以指定格式输出扩展或转换后的值，从而在计算工作流中便于数据的操作和分析。
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# Input types
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## Required
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- input_values_i
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- 这是节点的主要输入，但其具体格式和内容未知。它可能是一个需要被扩展或转换的数值列表或序列。
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- Comfy dtype: FLOAT
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- Python dtype: unknown
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- output_type
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- 指定扩展或转换后的数据的期望输出格式。这为结果的使用或进一步处理提供了灵活性。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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# Output types
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- float
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- 输出可以是单个浮点值或浮点值集合，具体取决于所执行的操作和指定的输出类型。
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- Comfy dtype: FLOAT
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- Python dtype: float
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## Usage tips
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- Infra type: `CPU`
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- Common nodes: unknown
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## Source code
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```python
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class WeightScheduleExtend:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"input_values_1": ("FLOAT", {"default": 0.0, "forceInput": True}),
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"input_values_2": ("FLOAT", {"default": 0.0, "forceInput": True}),
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"output_type": (
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[
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'match_input',
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'list',
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'pandas series',
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'tensor',
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],
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{
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"default": 'match_input'
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}),
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},
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}
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RETURN_TYPES = ("FLOAT",)
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FUNCTION = "execute"
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CATEGORY = "KJNodes"
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DESCRIPTION = """
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Extends, and converts if needed, different value lists/series
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"""
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def detect_input_type(self, input_values):
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import pandas as pd
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if isinstance(input_values, list):
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return 'list'
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elif isinstance(input_values, pd.Series):
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return 'pandas series'
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elif isinstance(input_values, torch.Tensor):
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return 'tensor'
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else:
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raise ValueError("Unsupported input type")
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def execute(self, input_values_1, input_values_2, output_type):
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import pandas as pd
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input_type_1 = self.detect_input_type(input_values_1)
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input_type_2 = self.detect_input_type(input_values_2)
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# Convert input_values_2 to the same format as input_values_1 if they do not match
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if not input_type_1 == input_type_2:
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print("Converting input_values_2 to the same format as input_values_1")
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if input_type_1 == 'pandas series':
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# Convert input_values_2 to a pandas Series
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float_values_2 = pd.Series(input_values_2)
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elif input_type_1 == 'tensor':
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# Convert input_values_2 to a tensor
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float_values_2 = torch.tensor(input_values_2, dtype=torch.float32)
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else:
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print("Input types match, no conversion needed")
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# If the types match, no conversion is needed
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float_values_2 = input_values_2
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float_values = input_values_1 + float_values_2
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if output_type == 'list':
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return float_values,
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elif output_type == 'pandas series':
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return pd.Series(float_values),
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elif output_type == 'tensor':
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if input_type_1 == 'pandas series':
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return torch.tensor(float_values.values, dtype=torch.float32),
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else:
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return torch.tensor(float_values, dtype=torch.float32),
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elif output_type == 'match_input':
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return float_values,
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else:
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raise ValueError(f"Unsupported output_type: {output_type}")
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```
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