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]
- Comfy dtype:
TK_SHORTCUT
- Comfy dtype:
TIMESTEP_KEYFRAME
- A keyframe group representing the timestep adjustments made through the control weights.
- Python dtype:
TimestepKeyframeGroup
- Comfy dtype:
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)))