CavinHuang-comfyui-nodes-docs/docs/ConcatConditioningsWithMultiplier __Inspire.md

Documentation

• Class name: ConcatConditioningsWithMultiplier __Inspire
• Category: InspirePack/__for_testing
• Output node: False

该节点旨在将多个条件向量进行连接，每个向量可能会通过相应的乘数进行修改，以增强或调整它们在生成模型输出中的影响。它允许在组合条件向量之前动态调整条件强度，从而实现对条件过程的精细控制。

Input types

Required

• conditioning1
  • 作为基础的主要条件向量，其他条件向量将与之连接。它为进一步的条件修改提供了基础。
  • Comfy dtype: CONDITIONING
  • Python dtype: List[Tuple[torch.Tensor, Any]]

Optional

• multiplier1
  • 用于调整主要条件向量强度的乘数，允许在与其他条件向量连接之前调整其影响力。
  • Comfy dtype: FLOAT
  • Python dtype: float

Output types

• conditioning
  • 连接后的条件向量，每个组成部分的强度都经过相应乘数的调整，可直接用于模型的条件设置。
  • Comfy dtype: CONDITIONING
  • Python dtype: List[Tuple[torch.Tensor, Any]]

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class ConcatConditioningsWithMultiplier:
    @classmethod
    def INPUT_TYPES(s):
        flex_inputs = {}

        stack = inspect.stack()
        if stack[1].function == 'get_input_data':
            # bypass validation
            for x in range(0, 100):
                flex_inputs[f"multiplier{x}"] = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01})
        else:
            flex_inputs["multiplier1"] = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01})

        return {
                "required": {"conditioning1": ("CONDITIONING",), },
                "optional": flex_inputs
                }

    RETURN_TYPES = ("CONDITIONING",)
    FUNCTION = "doit"

    CATEGORY = "InspirePack/__for_testing"

    def doit(self, **kwargs):
        if "ConditioningMultiplier_PoP" in nodes.NODE_CLASS_MAPPINGS:
            obj = nodes.NODE_CLASS_MAPPINGS["ConditioningMultiplier_PoP"]()
        else:
            utils.try_install_custom_node('https://github.com/picturesonpictures/comfy_PoP',
                                          "To use 'ConcatConditioningsWithMultiplier' node, 'comfy_PoP' extension is required.")
            raise Exception("'comfy_PoP' node isn't installed.")

        conditioning_to = kwargs['conditioning1']
        conditioning_to = obj.multiply_conditioning_strength(conditioning=conditioning_to, multiplier=float(kwargs['multiplier1']))[0]

        out = None
        for k, conditioning_from in kwargs.items():
            if k == 'conditioning1' or not k.startswith('conditioning'):
                continue

            out = []
            if len(conditioning_from) > 1:
                print(f"Warning: ConcatConditioningsWithMultiplier {k} contains more than 1 cond, only the first one will actually be applied to conditioning1.")

            mkey = 'multiplier'+k[12:]
            multiplier = float(kwargs[mkey])
            conditioning_from = obj.multiply_conditioning_strength(conditioning=conditioning_from, multiplier=multiplier)[0]
            cond_from = conditioning_from[0][0]

            for i in range(len(conditioning_to)):
                t1 = conditioning_to[i][0]
                tw = torch.cat((t1, cond_from), 1)
                n = [tw, conditioning_to[i][1].copy()]
                out.append(n)

            conditioning_to = out

        if out is None:
            return (kwargs['conditioning1'], )
        else:
            return (out,)