LayerFilter: Sharp & Soft

Documentation

• Class name: LayerFilter: Sharp & Soft
• Category: 😺dzNodes/LayerFilter
• Output node: False

The 'Sharp & Soft' node applies varying degrees of sharpness or softness to images, allowing for detailed enhancement or smoothing effects based on the selected mode. It dynamically adjusts filter parameters to achieve the desired level of image clarity or softness, enhancing visual quality or creating a more subtle, softened appearance.

Input types

Required

• images
  • The input images to be processed. This parameter is crucial as it directly influences the outcome by serving as the base for the sharpness or softness enhancement.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• enhance
  • Specifies the degree of sharpness or softness to apply to the images, ranging from 'very sharp' to 'very soft'. This choice determines the intensity of the effect and directly impacts the visual characteristics of the output images.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str

Optional

Output types

• image
  • Comfy dtype: IMAGE
  • The processed images, enhanced with the specified level of sharpness or softness. This output reflects the visual modifications applied to the input images, showcasing the effect of the chosen enhancement mode.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class SharpAndSoft:

    def __init__(self):
        pass

    @classmethod
    def INPUT_TYPES(self):

        enhance_list = ['very sharp', 'sharp', 'soft', 'very soft', 'None']

        return {
            "required": {
                "images": ("IMAGE",),
                "enhance": (enhance_list, ),

            },
            "optional": {
            }
        }

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

    def sharp_and_soft(self, images, enhance, ):

        if enhance == 'very sharp':
            filter_radius = 1
            denoise = 0.6
            detail_mult = 2.8
        if enhance == 'sharp':
            filter_radius = 3
            denoise = 0.12
            detail_mult = 1.8
        if enhance == 'soft':
            filter_radius = 8
            denoise = 0.08
            detail_mult = 0.5
        if enhance == 'very soft':
            filter_radius = 15
            denoise = 0.06
            detail_mult = 0.01
        else:
            return (images,)

        d = int(filter_radius * 2) + 1
        s = 0.02
        n = denoise / 10
        dup = copy.deepcopy(images.cpu().numpy())

        for index, image in enumerate(dup):
            imgB = image
            if denoise > 0.0:
                imgB = cv2.bilateralFilter(image, d, n, d)
            imgG = np.clip(guidedFilter(image, image, d, s), 0.001, 1)
            details = (imgB / imgG - 1) * detail_mult + 1
            dup[index] = np.clip(details * imgG - imgB + image, 0, 1)

        log(f"{NODE_NAME} Processed {dup.shape[0]} image(s).", message_type='finish')
        return (torch.from_numpy(dup),)