LayerFilter: WaterColor

Documentation

• Class name: LayerFilter: WaterColor
• Category: 😺dzNodes/LayerFilter
• Output node: False

The WaterColor node applies a watercolor effect to images, transforming their appearance with customizable line density and opacity levels to simulate the look of watercolor paintings.

Input types

Required

• image
  • The input image to which the watercolor effect will be applied. This parameter is crucial for defining the base image that will undergo the transformation.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• line_density
  • Controls the density of the lines in the watercolor effect, affecting the overall texture and detail of the resulting image.
  • Comfy dtype: INT
  • Python dtype: int
• opacity
  • Determines the opacity of the watercolor effect, allowing for fine-tuning of the visual impact on the original image.
  • Comfy dtype: INT
  • Python dtype: int

Optional

Output types

• image
  • Comfy dtype: IMAGE
  • The output image with the watercolor effect applied, showcasing the transformation from the original input.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class WaterColor:

    def __init__(self):
        pass

    @classmethod
    def INPUT_TYPES(self):

        return {
            "required": {
                "image": ("IMAGE", ),
                "line_density": ("INT", {"default": 50, "min": 1, "max": 100, "step": 1}),  # 透明度
                "opacity": ("INT", {"default": 100, "min": 0, "max": 100, "step": 1}),  # 透明度
            },
            "optional": {
            }
        }

    RETURN_TYPES = ("IMAGE",)
    RETURN_NAMES = ("image",)
    FUNCTION = 'water_color'
    CATEGORY = '😺dzNodes/LayerFilter'

    def water_color(self, image, line_density, opacity
                  ):

        ret_images = []

        for i in image:
            i = torch.unsqueeze(i, 0)
            _canvas = tensor2pil(i).convert('RGB')
            _image = image_watercolor(_canvas, level=101-line_density)
            ret_image = chop_image(_canvas, _image, 'normal', opacity)

            ret_images.append(pil2tensor(ret_image))

        log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
        return (torch.cat(ret_images, dim=0),)