CavinHuang-comfyui-nodes-docs/nodes-docs/en-US/Comfy/docs/ControlNetApply.md

tags

tags
ControlNet

Apply ControlNet

Documentation

• Class name: ControlNetApply
• Category: conditioning
• Output node: False

This node applies a control network to a given image and conditioning, adjusting the image's attributes based on the control network's parameters and a specified strength. It enables dynamic modification of image characteristics through control hints, facilitating targeted adjustments without altering the original conditioning structure.

Input types

Required

• conditioning
  • The conditioning data to be modified by the control network. It serves as the basis for the control network's adjustments, influencing the final output.
  • Comfy dtype: CONDITIONING
  • Python dtype: List[Tuple[Any, Dict[str, Any]]]
• control_net
  • The control network to be applied. It defines the specific adjustments to be made to the image, based on its trained parameters.
  • Comfy dtype: CONTROL_NET
  • Python dtype: ControlNet
• image
  • The image to which the control network's adjustments will be applied. It provides the visual context for the control network's operations.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• strength
  • A scalar value determining the intensity of the control network's adjustments. It allows for fine-tuning the impact of the control network on the image.
  • Comfy dtype: FLOAT
  • Python dtype: float

Output types

• conditioning
  • Comfy dtype: CONDITIONING
  • The modified conditioning data, reflecting the adjustments made by the control network.
  • Python dtype: List[Tuple[Any, Dict[str, Any]]]

Usage tips

• Infra type: GPU
• Common nodes:
  • KSampler
  • Reroute
  • ConditioningSetArea
  • UltimateSDUpscale
  • [KSampler Adv. (Efficient)](../../efficiency-nodes-comfyui/Nodes/KSampler Adv. (Efficient).md)
  • [ttN pipeKSampler](../../ComfyUI_tinyterraNodes/Nodes/ttN pipeKSampler.md)
  • YDetailer
  • ToBasicPipe
  • ToDetailerPipe
  • ControlNetApply

Source code

class ControlNetApply:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {"conditioning": ("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_controlnet"

    CATEGORY = "conditioning"

    def apply_controlnet(self, conditioning, control_net, image, strength):
        if strength == 0:
            return (conditioning, )

        c = []
        control_hint = image.movedim(-1,1)
        for t in conditioning:
            n = [t[0], t[1].copy()]
            c_net = control_net.copy().set_cond_hint(control_hint, strength)
            if 'control' in t[1]:
                c_net.set_previous_controlnet(t[1]['control'])
            n[1]['control'] = c_net
            n[1]['control_apply_to_uncond'] = True
            c.append(n)
        return (c, )