ControlNetHadamard

Documentation

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

This node applies a control network to a set of images and conditions, modulating the latter based on the control network's output and a specified strength. It's designed to integrate control network effects into image conditioning, allowing for dynamic adjustments to the conditioning process.

Input types

Required

• conds
  • The conditions to be modulated by the control network, representing the contextual or semantic guidance for image generation.
  • Comfy dtype: CONDITIONING
  • Python dtype: List[Dict[str, Any]]
• control_net
  • The control network to be applied, determining the nature and extent of modulation on the conditions.
  • Comfy dtype: CONTROL_NET
  • Python dtype: torch.nn.Module
• image
  • The images to which the control network adjustments are applied, serving as the basis for condition modulation.
  • Comfy dtype: IMAGE
  • Python dtype: List[torch.Tensor]
• strength
  • A scalar value determining the intensity of the control network's effect on the conditions, allowing for fine-tuned control over the modulation.
  • Comfy dtype: FLOAT
  • Python dtype: float

Output types

• conditioning
  • Comfy dtype: CONDITIONING
  • The modulated conditions, adjusted by the control network to reflect the desired changes.
  • Python dtype: List[Dict[str, Any]]

Usage tips

• Infra type: CPU
• Common nodes: unknown

Source code

class ControlNetHadamard(nodes.ControlNetApply):
    @classmethod
    def INPUT_TYPES(cls):
        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 = conditioning_category_path
    INPUT_IS_LIST = True
    OUTPUT_IS_LIST = (True,)

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

        assert len(images) == len(conds), "lists sizes do not match"

        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,)