XY Inputs: Controlnet //EasyUse¶
Documentation¶
- Class name:
easy XYInputs: ControlNet - Category:
EasyUse/XY Inputs - Output node:
False
This node is designed to facilitate the application of control net parameters over a range of values, enabling dynamic adjustments to image processing based on control net strength and positioning. It aims to provide a versatile tool for experimenting with different control net configurations, enhancing the ability to fine-tune image generation or modification tasks.
Input types¶
Required¶
control_net_name- Specifies the name of the control net to be applied. This parameter is crucial for identifying which control net configuration to use during the operation.
- Comfy dtype:
COMBO[STRING] - Python dtype:
str
image- The image to which the control net adjustments will be applied. This parameter is central to the node's functionality, serving as the base for subsequent control net manipulations.
- Comfy dtype:
IMAGE - Python dtype:
torch.Tensor
target_parameter- Determines the specific control net parameter to vary, such as strength or positioning, guiding the node's processing and output generation.
- Comfy dtype:
COMBO[STRING] - Python dtype:
str
batch_count- Defines the number of variations to generate, allowing for a range of control net adjustments to be explored in a single operation.
- Comfy dtype:
INT - Python dtype:
int
first_strength- Sets the starting strength value for the control net, marking the beginning of the range for strength adjustments.
- Comfy dtype:
FLOAT - Python dtype:
float
last_strength- Sets the ending strength value for the control net, marking the end of the range for strength adjustments.
- Comfy dtype:
FLOAT - Python dtype:
float
first_start_percent- Specifies the starting percentage for the control net's application, defining the initial point of effect along the image's axis.
- Comfy dtype:
FLOAT - Python dtype:
float
last_start_percent- Specifies the ending percentage for the control net's application, defining the final point of effect along the image's axis.
- Comfy dtype:
FLOAT - Python dtype:
float
first_end_percent- Determines the starting end percentage for the control net's effect, establishing the initial boundary of the control net's influence.
- Comfy dtype:
FLOAT - Python dtype:
float
last_end_percent- Determines the ending end percentage for the control net's effect, establishing the final boundary of the control net's influence.
- Comfy dtype:
FLOAT - Python dtype:
float
strength- The strength of the control net effect to be applied, influencing the intensity of the control net's impact on the image.
- Comfy dtype:
FLOAT - Python dtype:
float
start_percent- The starting percentage of the control net's application, indicating the initial point of the control net's effect on the image.
- Comfy dtype:
FLOAT - Python dtype:
float
end_percent- The ending percentage of the control net's application, indicating the final point of the control net's effect on the image.
- Comfy dtype:
FLOAT - Python dtype:
float
Output types¶
X or Y- Comfy dtype:
X_Y - Outputs a range of values or configurations based on the specified control net parameters, facilitating exploration and fine-tuning of image processing tasks.
- Python dtype:
List[Tuple[str, torch.Tensor, float, float, float]]
- Comfy dtype:
Usage tips¶
- Infra type:
CPU - Common nodes: unknown
Source code¶
class XYplot_Control_Net:
parameters = ["strength", "start_percent", "end_percent"]
@classmethod
def INPUT_TYPES(cls):
def get_file_list(filenames):
return [file for file in filenames if file != "put_models_here.txt" and "lllite" not in file]
return {
"required": {
"control_net_name": (get_file_list(folder_paths.get_filename_list("controlnet")),),
"image": ("IMAGE",),
"target_parameter": (cls.parameters,),
"batch_count": ("INT", {"default": 3, "min": 1, "max": 30}),
"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}),
},
}
RETURN_TYPES = ("X_Y",)
RETURN_NAMES = ("X or Y",)
FUNCTION = "xy_value"
CATEGORY = "EasyUse/XY Inputs"
def xy_value(self, control_net_name, 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):
axis, = None,
values = []
if target_parameter == "strength":
axis = "advanced: ControlNetStrength"
values.append([(control_net_name, image, first_strength, start_percent, end_percent)])
strength_increment = (last_strength - first_strength) / (batch_count - 1) if batch_count > 1 else 0
for i in range(1, batch_count - 1):
values.append([(control_net_name, image, first_strength + i * strength_increment, start_percent,
end_percent)])
if batch_count > 1:
values.append([(control_net_name, image, last_strength, start_percent, end_percent)])
elif target_parameter == "start_percent":
axis = "advanced: ControlNetStart%"
percent_increment = (last_start_percent - first_start_percent) / (batch_count - 1) if batch_count > 1 else 0
values.append([(control_net_name, image, strength, first_start_percent, end_percent)])
for i in range(1, batch_count - 1):
values.append([(control_net_name, image, strength, first_start_percent + i * percent_increment,
end_percent)])
# Always add the last start_percent if batch_count is more than 1.
if batch_count > 1:
values.append((control_net_name, image, strength, last_start_percent, end_percent))
elif target_parameter == "end_percent":
axis = "advanced: ControlNetEnd%"
percent_increment = (last_end_percent - first_end_percent) / (batch_count - 1) if batch_count > 1 else 0
values.append([(control_net_name, image, image, strength, start_percent, first_end_percent)])
for i in range(1, batch_count - 1):
values.append([(control_net_name, image, strength, start_percent,
first_end_percent + i * percent_increment)])
if batch_count > 1:
values.append([(control_net_name, image, strength, start_percent, last_end_percent)])
return ({"axis": axis, "values": values},)