Color Pallete

Documentation

• Class name: ColorPreprocessor
• Category: ControlNet Preprocessors/T2IAdapter-only
• Output node: False

The ColorPreprocessor node is designed to process images by detecting and annotating colors within them, utilizing a specialized color detection model. This preprocessing step is crucial for tasks that require color analysis or modification before further processing.

Input types

Required

• image
  • The input image to be processed for color detection and annotation.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor

Optional

• resolution
  • Specifies the resolution to which the input image should be resized before processing. This parameter influences the precision and performance of color detection.
  • Comfy dtype: INT
  • Python dtype: int

Output types

• image
  • Comfy dtype: IMAGE
  • The processed image with color detection and annotation applied.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: CPU
• Common nodes: unknown

Source code

class Color_Preprocessor:
    @classmethod
    def INPUT_TYPES(s):
        return create_node_input_types()

    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "execute"

    CATEGORY = "ControlNet Preprocessors/T2IAdapter-only"

    def execute(self, image, resolution=512, **kwargs):
        from controlnet_aux.color import ColorDetector

        return (common_annotator_call(ColorDetector(), image, resolution=resolution), )