[Inference.Core] Color Pallete

Documentation

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

The Color Preprocessor node is designed to analyze and process images to detect and adjust their color palette. It utilizes a color detection algorithm to enhance or modify the image's color properties based on the specified resolution.

Input types

Required

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

Optional

• resolution
  • Specifies the resolution at which the color detection and adjustment should be performed, affecting the precision and quality of the output.
  • Comfy dtype: INT
  • Python dtype: int

Output types

• image
  • Comfy dtype: IMAGE
  • The processed image with adjusted color properties, based on the color detection algorithm.
  • 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), )