XY Input: Denoise¶
Documentation¶
- Class name:
XY Input: Denoise
- Category:
Efficiency Nodes/XY Inputs
- Output node:
False
This node is designed to generate a set of floating-point values representing denoising levels for a batch of data. It abstracts the process of calculating denoise values across a specified range, facilitating the visualization or analysis of denoising effects in a computational efficiency context.
Input types¶
Required¶
batch_count
- Specifies the number of denoise values to generate, allowing for control over the granularity of the denoising analysis.
- Comfy dtype:
INT
- Python dtype:
int
first_denoise
- Defines the starting point of the denoise range, enabling customization of the denoising process's lower bound.
- Comfy dtype:
FLOAT
- Python dtype:
float
last_denoise
- Sets the endpoint of the denoise range, allowing for the adjustment of the upper limit in the denoising analysis.
- Comfy dtype:
FLOAT
- Python dtype:
float
Output types¶
X or Y
- Comfy dtype:
XY
- Outputs a tuple containing the type of values ('Denoise') and the generated floating-point denoise values, suitable for plotting or further analysis.
- Python dtype:
Tuple[str, List[float]]
- Comfy dtype:
Usage tips¶
- Infra type:
CPU
- Common nodes: unknown
Source code¶
class TSC_XYplot_Denoise:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"batch_count": ("INT", {"default": XYPLOT_DEF, "min": 0, "max": XYPLOT_LIM}),
"first_denoise": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"last_denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
}
}
RETURN_TYPES = ("XY",)
RETURN_NAMES = ("X or Y",)
FUNCTION = "xy_value"
CATEGORY = "Efficiency Nodes/XY Inputs"
def xy_value(self, batch_count, first_denoise, last_denoise):
xy_type = "Denoise"
xy_value = generate_floats(batch_count, first_denoise, last_denoise)
return ((xy_type, xy_value),) if xy_value else (None,)