CavinHuang-comfyui-nodes-docs/docs/ControlNetHadamard.md

Documentation

• Class name: ControlNetHadamard
• Category: Bmad/conditioning
• Output node: False

ControlNetHadamard节点应用控制网络到一组图像和条件上，根据控制网络的输出和指定的强度调整条件。它旨在实现动态图像调节，允许根据控制网络的逻辑修改图像属性或风格。

Input types

Required

• conds
  • 要应用于每个图像的条件，这些条件会被控制网络修改以达到预期效果。
  • Comfy dtype: CONDITIONING
  • Python dtype: List[Tuple[torch.Tensor, Dict[str, Any]]]
• control_net
  • 用于基于图像和指定强度修改条件的控制网络。
  • Comfy dtype: CONTROL_NET
  • Python dtype: torch.nn.Module
• image
  • 应用条件的图像，作为控制网络修改的基础。
  • Comfy dtype: IMAGE
  • Python dtype: List[torch.Tensor]
• strength
  • 决定控制网络对条件影响的强度，允许进行精细调整。
  • Comfy dtype: FLOAT
  • Python dtype: float

Output types

• conditioning
  • 经过控制网络处理后的修改条件，反映了应用的变化。
  • Comfy dtype: CONDITIONING
  • Python dtype: List[Tuple[torch.Tensor, Dict[str, Any]]]

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class ControlNetHadamard(nodes.ControlNetApply):
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {"conds": ("CONDITIONING",),
                             "control_net": ("CONTROL_NET",),
                             "image": ("IMAGE",),
                             "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
                             }}

    RETURN_TYPES = ("CONDITIONING",)
    FUNCTION = "apply"
    CATEGORY = "Bmad/conditioning"
    INPUT_IS_LIST = True
    OUTPUT_IS_LIST = (True,)

    def apply(self, conds, control_net, images, strength):
        control_net = control_net[0]
        strength = strength[0]

        if len(images) != len(conds):
            raise "lists sizes do not match"  # maybe relax check and allow for fewer conds than images?

        print(len(images))
        print(len(images[0]))
        print(len(conds))
        new_conds = []
        for i in range(len(images)):
            new_conds.append(super().apply_controlnet(conds[i], control_net, images[i], strength)[0])
        return (new_conds,)