XY Input: Control Net

Documentation

• Class name: XY Input: Control Net
• Category: Efficiency Nodes/XY Inputs
• Output node: False

The node is designed to generate and manipulate XY input data for control networks, facilitating the exploration of different control parameters (such as strength, start percent, and end percent) across batches of images. It dynamically adjusts control net parameters to optimize image generation, supporting complex conditioning scenarios and enabling detailed analysis and visualization of control net effects.

Input types

Required

• control_net
  • The control network configuration to be applied or manipulated across the image batch, central to determining the influence on the generated images.
  • Comfy dtype: CONTROL_NET
  • Python dtype: object
• image
  • The image or batch of images to which the control net parameters are applied, serving as the basis for the control net's effect analysis.
  • Comfy dtype: IMAGE
  • Python dtype: object
• target_parameter
  • The specific control net parameter to be varied across the batch, such as strength, start percent, or end percent, guiding the generation process.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• batch_count
  • Specifies the number of variations to generate for the given control parameter, enabling a detailed exploration of its impact.
  • Comfy dtype: INT
  • Python dtype: int
• first_strength
  • The initial strength value for the control net parameter variation.
  • Comfy dtype: FLOAT
  • Python dtype: float
• last_strength
  • The final strength value for the control net parameter variation, allowing for a range of adjustments.
  • Comfy dtype: FLOAT
  • Python dtype: float
• first_start_percent
  • The initial start percent value for the control net parameter variation.
  • Comfy dtype: FLOAT
  • Python dtype: float
• last_start_percent
  • The final start percent value for the control net parameter variation.
  • Comfy dtype: FLOAT
  • Python dtype: float
• first_end_percent
  • The initial end percent value for the control net parameter variation.
  • Comfy dtype: FLOAT
  • Python dtype: float
• last_end_percent
  • The final end percent value for the control net parameter variation.
  • Comfy dtype: FLOAT
  • Python dtype: float
• strength
  • The strength level applied to the control net, affecting the intensity of its influence.
  • Comfy dtype: FLOAT
  • Python dtype: float
• start_percent
  • The starting percentage of the control net's effect, determining its initial influence.
  • Comfy dtype: FLOAT
  • Python dtype: float
• end_percent
  • The ending percentage of the control net's effect, determining its final influence.
  • Comfy dtype: FLOAT
  • Python dtype: float

Optional

• cnet_stack
  • An optional stack of additional control net configurations, allowing for layered or sequential application of multiple control nets.
  • Comfy dtype: CONTROL_NET_STACK
  • Python dtype: object

Output types

• X or Y
  • Comfy dtype: XY
  • The generated values for either the X or Y axis, based on the control net parameters, structured for integration into visualization or analysis tools.
  • Python dtype: tuple

Usage tips

• Infra type: CPU
• Common nodes: unknown

Source code

class TSC_XYplot_Control_Net(TSC_XYplot_Control_Net_Strength, TSC_XYplot_Control_Net_Start, TSC_XYplot_Control_Net_End):
    parameters = ["strength", "start_percent", "end_percent"]
    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "control_net": ("CONTROL_NET",),
                "image": ("IMAGE",),
                "target_parameter": (cls.parameters,),
                "batch_count": ("INT", {"default": XYPLOT_DEF, "min": 0, "max": XYPLOT_LIM}),
                "first_strength": ("FLOAT", {"default": 0.0, "min": 0.00, "max": 10.0, "step": 0.01}),
                "last_strength": ("FLOAT", {"default": 1.0, "min": 0.00, "max": 10.0, "step": 0.01}),
                "first_start_percent": ("FLOAT", {"default": 0.0, "min": 0.00, "max": 1.0, "step": 0.01}),
                "last_start_percent": ("FLOAT", {"default": 1.0, "min": 0.00, "max": 1.0, "step": 0.01}),
                "first_end_percent": ("FLOAT", {"default": 0.0, "min": 0.00, "max": 1.0, "step": 0.01}),
                "last_end_percent": ("FLOAT", {"default": 1.0, "min": 0.00, "max": 1.0, "step": 0.01}),
                "strength": ("FLOAT", {"default": 1.0, "min": 0.00, "max": 10.0, "step": 0.01}),
                "start_percent": ("FLOAT", {"default": 0.0, "min": 0.00, "max": 1.0, "step": 0.01}),
                "end_percent": ("FLOAT", {"default": 1.0, "min": 0.00, "max": 1.0, "step": 0.01}),
            },
            "optional": {"cnet_stack": ("CONTROL_NET_STACK",)},
        }

    RETURN_TYPES = ("XY",)
    RETURN_NAMES = ("X or Y",)
    FUNCTION = "xy_value"
    CATEGORY = "Efficiency Nodes/XY Inputs"

    def xy_value(self, control_net, image, target_parameter, batch_count, first_strength, last_strength, first_start_percent,
                 last_start_percent, first_end_percent, last_end_percent, strength, start_percent, end_percent, cnet_stack=None):

        if target_parameter == "strength":
            return TSC_XYplot_Control_Net_Strength.xy_value(self, control_net, image, batch_count, first_strength,
                                                            last_strength, start_percent, end_percent, cnet_stack=cnet_stack)
        elif target_parameter == "start_percent":
            return TSC_XYplot_Control_Net_Start.xy_value(self, control_net, image, batch_count, first_start_percent,
                                                         last_start_percent, strength, end_percent, cnet_stack=cnet_stack)
        elif target_parameter == "end_percent":
            return TSC_XYplot_Control_Net_End.xy_value(self, control_net, image, batch_count, first_end_percent,
                                                       last_end_percent, strength, start_percent, cnet_stack=cnet_stack)