LayerStyle: InnerGlow V2¶
Documentation¶
- Class name:
LayerStyle: InnerGlow V2 - Category:
😺dzNodes/LayerStyle - Output node:
False
The InnerGlow V2 node is designed to apply an inner glow effect to images, enhancing their visual appeal by adding a soft light from within. This node focuses on creating a more vibrant and dynamic look for graphical elements by simulating the effect of light emanating from the inside out.
Input types¶
Required¶
background_image- The base image over which the inner glow effect will be applied, serving as the backdrop for the effect.
- Comfy dtype:
IMAGE - Python dtype:
torch.Tensor
layer_image- The image layer to which the inner glow effect will be applied, directly influencing the appearance of the glow.
- Comfy dtype:
IMAGE - Python dtype:
torch.Tensor
invert_mask- A boolean flag to invert the mask applied to the layer image, affecting the areas where the glow effect is visible.
- Comfy dtype:
BOOLEAN - Python dtype:
bool
blend_mode- Specifies the blending mode used to combine the glow effect with the layer image, impacting the overall look of the effect.
- Comfy dtype:
COMBO[STRING] - Python dtype:
str
opacity- Controls the opacity level of the inner glow effect, allowing for fine-tuning of its visibility.
- Comfy dtype:
INT - Python dtype:
int
brightness- Adjusts the brightness of the inner glow effect, influencing its intensity and spread.
- Comfy dtype:
INT - Python dtype:
int
glow_range- Defines the range of the glow effect, determining how far the glow extends from the layer image.
- Comfy dtype:
INT - Python dtype:
int
blur- Sets the blur amount for the glow effect, contributing to its softness and diffusion.
- Comfy dtype:
INT - Python dtype:
int
light_color- The color at the center of the glow effect, setting the tone for the light source.
- Comfy dtype:
STRING - Python dtype:
str
glow_color- The color at the outer edge of the glow effect, defining the peripheral color transition.
- Comfy dtype:
STRING - Python dtype:
str
Optional¶
layer_mask- An optional mask that can be applied to the layer image, dictating the specific areas where the glow effect is applied.
- Comfy dtype:
MASK - Python dtype:
torch.Tensor
Output types¶
image- Comfy dtype:
IMAGE - The resulting image after applying the inner glow effect, showcasing the enhanced visual appeal with a soft, emanating light.
- Python dtype:
torch.Tensor
- Comfy dtype:
Usage tips¶
- Infra type:
GPU - Common nodes: unknown
Source code¶
class InnerGlowV2:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(self):
modes = copy.copy(BLEND_MODES)
chop_mode_list = ["screen", "linear dodge(add)", "color dodge", "lighten", "dodge", "hard light", "linear light"]
for i in chop_mode_list:
modes.pop(i)
chop_mode_list.extend(list(modes.keys()))
return {
"required": {
"background_image": ("IMAGE", ), #
"layer_image": ("IMAGE",), #
"invert_mask": ("BOOLEAN", {"default": True}), # 反转mask
"blend_mode": (chop_mode_list,), # 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 = 'inner_glow_v2'
CATEGORY = '😺dzNodes/LayerStyle'
def inner_glow_v2(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))
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')
blur_factor = blur / 20.0
grow = glow_range
inner_mask = _mask
for x in range(brightness):
blur = int(grow * blur_factor)
_color = step_color(glow_color, light_color, brightness, x)
glow_mask = expand_mask(image2mask(inner_mask), -grow, blur) #扩张，模糊
# 合成glow
color_image = Image.new("RGB", _layer.size, color=_color)
alpha = tensor2pil(mask_invert(glow_mask)).convert('L')
_glow = chop_image_v2(_layer, color_image, blend_mode, int(step_value(1, opacity, brightness, x)))
_layer.paste(_glow, mask=alpha)
grow = grow - int(glow_range/brightness)
# 合成layer
_layer.paste(_canvas, mask=ImageChops.invert(_mask))
ret_images.append(pil2tensor(_layer))
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
return (torch.cat(ret_images, dim=0),)