---
tags:
- ControlNet
- Weight
---

# 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
```python
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)))

```