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LayerStyle: OuterGlow

Documentation

  • Class name: LayerStyle: OuterGlow
  • Category: 😺dzNodes/LayerStyle
  • Output node: False

The OuterGlow node applies an outer glow effect to images, enhancing their visual appeal by adding a soft, diffused light around their borders. This node is designed to stylize images by simulating the effect of a light source illuminating the outer edges, creating a distinctive glow that can be customized to fit various aesthetic requirements.

Input types

Required

  • background_image
    • The 'background_image' input specifies the base image to which the outer glow effect will be applied. It serves as the foundational layer for the effect.
    • Comfy dtype: IMAGE
    • Python dtype: IMAGE
  • layer_image
    • The 'layer_image' input represents the image layer that will receive the outer glow effect. This layer is superimposed on the background image to create the glow effect.
    • Comfy dtype: IMAGE
    • Python dtype: IMAGE
  • invert_mask
    • The 'invert_mask' input determines whether the mask applied to the layer image should be inverted, affecting how the glow effect is masked around the image.
    • Comfy dtype: BOOLEAN
    • Python dtype: BOOLEAN
  • blend_mode
    • The 'blend_mode' input specifies the blending mode used to combine the layer image with the outer glow, influencing the overall appearance of the effect.
    • Comfy dtype: COMBO[STRING]
    • Python dtype: STRING
  • opacity
    • The 'opacity' input controls the transparency of the outer glow effect, allowing for fine-tuning of its visibility against the background image.
    • Comfy dtype: INT
    • Python dtype: INT
  • brightness
    • The 'brightness' input adjusts the intensity of the glow effect, affecting how brightly the glow appears around the layer image.
    • Comfy dtype: INT
    • Python dtype: INT
  • glow_range
    • The 'glow_range' input defines the spread of the outer glow effect, determining how far the glow extends from the edges of the layer image.
    • Comfy dtype: INT
    • Python dtype: INT
  • blur
    • The 'blur' input sets the level of blur applied to the outer glow, softening the edges of the glow for a more diffused appearance.
    • Comfy dtype: INT
    • Python dtype: INT
  • light_color
    • The 'light_color' input specifies the color at the center of the glow effect, contributing to the overall color scheme of the glow.
    • Comfy dtype: STRING
    • Python dtype: STRING
  • glow_color
    • The 'glow_color' input determines the color of the outer edges of the glow, allowing for a gradient effect from the center to the outskirts of the glow.
    • Comfy dtype: STRING
    • Python dtype: STRING

Optional

  • layer_mask
    • The 'layer_mask' input allows for an additional masking layer to be applied to the layer image, providing further control over how the glow effect is applied and integrated with the image.
    • Comfy dtype: MASK
    • Python dtype: MASK

Output types

  • image
    • Comfy dtype: IMAGE
    • The 'image' output type represents the final image with the applied outer glow effect, showcasing the enhanced visual appeal with a soft, diffused light around its borders.
    • Python dtype: IMAGE

Usage tips

  • Infra type: GPU
  • Common nodes: unknown

Source code

class OuterGlow:

    def __init__(self):
        pass

    @classmethod
    def INPUT_TYPES(self):
        chop_mode = ['screen', 'add', 'lighter', 'normal', 'multply', 'subtract', 'difference', 'darker',
                     'color_burn', 'color_dodge', 'linear_burn', 'linear_dodge', 'overlay',
                     'soft_light', 'hard_light', 'vivid_light', 'pin_light', 'linear_light', 'hard_mix']
        return {
            "required": {
                "background_image": ("IMAGE", ),  #
                "layer_image": ("IMAGE",),  #
                "invert_mask": ("BOOLEAN", {"default": True}),  # 反转mask
                "blend_mode": (chop_mode,),  # 混合模式
                "opacity": ("INT", {"default": 100, "min": 0, "max": 100, "step": 1}),  # 透明度
                "brightness": ("INT", {"default": 5, "min": 2, "max": 20, "step": 1}),  # 迭代
                "glow_range": ("INT", {"default": 48, "min": -9999, "max": 9999, "step": 1}),  # 扩张
                "blur": ("INT", {"default": 25, "min": 0, "max": 9999, "step": 1}),  # 扩张
                "light_color": ("STRING", {"default": "#FFBF30"}),  # 光源中心颜色
                "glow_color": ("STRING", {"default": "#FE0000"}),  # 辉光外围颜色
            },
            "optional": {
                "layer_mask": ("MASK",),  #
            }
        }

    RETURN_TYPES = ("IMAGE",)
    RETURN_NAMES = ("image",)
    FUNCTION = 'outer_glow'
    CATEGORY = '😺dzNodes/LayerStyle'

    def outer_glow(self, background_image, layer_image,
                  invert_mask, blend_mode, opacity,
                  brightness, glow_range, blur, light_color, glow_color,
                  layer_mask=None
                  ):

        b_images = []
        l_images = []
        l_masks = []
        ret_images = []

        for b in background_image:
            b_images.append(torch.unsqueeze(b, 0))
        for l in layer_image:
            l_images.append(torch.unsqueeze(l, 0))
            m = tensor2pil(l)
            if m.mode == 'RGBA':
                l_masks.append(m.split()[-1])
        if layer_mask is not None:
            if layer_mask.dim() == 2:
                layer_mask = torch.unsqueeze(layer_mask, 0)
            l_masks = []
            for m in layer_mask:
                if invert_mask:
                    m = 1 - m
                l_masks.append(tensor2pil(torch.unsqueeze(m, 0)).convert('L'))
        if len(l_masks) == 0:
            log(f"Error: {NODE_NAME} skipped, because the available mask is not found.", message_type='error')
            return (background_image,)
        max_batch = max(len(b_images), len(l_images), len(l_masks))
        blur_factor = blur / 20.0
        for i in range(max_batch):
            background_image = b_images[i] if i < len(b_images) else b_images[-1]
            layer_image = l_images[i] if i < len(l_images) else l_images[-1]
            _mask = l_masks[i] if i < len(l_masks) else l_masks[-1]
            # preprocess
            _canvas = tensor2pil(background_image).convert('RGB')
            _layer = tensor2pil(layer_image).convert('RGB')
            if _mask.size != _layer.size:
                _mask = Image.new('L', _layer.size, 'white')
                log(f"Warning: {NODE_NAME} mask mismatch, dropped!", message_type='warning')
            grow = glow_range
            for x in range(brightness):
                blur = int(grow * blur_factor)
                _color = step_color(glow_color, light_color, brightness, x)
                glow_mask = expand_mask(image2mask(_mask), grow, blur)  #扩张，模糊
                # 合成glow
                color_image = Image.new("RGB", _layer.size, color=_color)
                alpha = tensor2pil(glow_mask).convert('L')
                _glow = chop_image(_canvas, color_image, blend_mode, int(step_value(1, opacity, brightness, x)))
                _canvas.paste(_glow.convert('RGB'), mask=alpha)
                grow = grow - int(glow_range/brightness)
            # 合成layer
            _canvas.paste(_layer, mask=_mask)

            ret_images.append(pil2tensor(_canvas))

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