ControlNet Custom Weights 🛂🅐🅒🅝

Documentation

Class name: CustomControlNetWeights
Category: Adv-ControlNet 🛂🅐🅒🅝/weights/ControlNet
Output node: False

This node is designed for loading and applying custom control net weights to influence the behavior of an advanced control network. It allows for fine-tuning the control network's response by adjusting individual weight parameters and optionally flipping the weights to achieve desired effects.

Input types

Required

weight_i
- unknown
- Comfy dtype: FLOAT
- Python dtype: unknown
flip_weights
- Determines whether the control weights should be inverted, offering an alternative approach to modifying the control network's behavior.
- Comfy dtype: BOOLEAN
- Python dtype: bool

Output types

CN_WEIGHTS
- Comfy dtype: CONTROL_NET_WEIGHTS
- The adjusted control net weights, ready for application within the control network.
- Python dtype: list[float]
TK_SHORTCUT
- Comfy dtype: TIMESTEP_KEYFRAME
- A keyframe group representing the timestep adjustments made through the control weights.
- Python dtype: TimestepKeyframeGroup

Usage tips

Infra type: CPU
Common nodes: unknown

Source code

class CustomControlNetWeights:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "weight_00": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "weight_01": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "weight_02": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "weight_03": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "weight_04": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "weight_05": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "weight_06": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "weight_07": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "weight_08": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "weight_09": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "weight_10": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "weight_11": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "weight_12": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                "flip_weights": ("BOOLEAN", {"default": False}),
            }
        }
    
    RETURN_TYPES = ("CONTROL_NET_WEIGHTS", "TIMESTEP_KEYFRAME",)
    RETURN_NAMES = WEIGHTS_RETURN_NAMES
    FUNCTION = "load_weights"

    CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights/ControlNet"

    def load_weights(self, weight_00, weight_01, weight_02, weight_03, weight_04, weight_05, weight_06, 
                     weight_07, weight_08, weight_09, weight_10, weight_11, weight_12, flip_weights):
        weights = [weight_00, weight_01, weight_02, weight_03, weight_04, weight_05, weight_06, 
                   weight_07, weight_08, weight_09, weight_10, weight_11, weight_12]
        weights = ControlWeights.controlnet(weights, flip_weights=flip_weights)
        return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))

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