LayerStyle: InnerShadow V2¶
Documentation¶
- Class name:
LayerStyle: InnerShadow V2 - Category:
😺dzNodes/LayerStyle - Output node:
False
The InnerShadow V2 node is designed to apply an inner shadow effect to a given layer image, enhancing visual depth and dimensionality. It processes images by applying a shadow inside the layer boundaries based on specified parameters such as shadow color, distance, blur, and opacity, ensuring a more dynamic and visually appealing result.
Input types¶
Required¶
background_image- The background image over which the layer image will be placed. It serves as the canvas for the inner shadow effect, ensuring that the shadow blends seamlessly with the background.
- Comfy dtype:
IMAGE - Python dtype:
torch.Tensor
layer_image- The layer image to which the inner shadow effect will be applied. This image is modified to include the shadow, creating a sense of depth within the layer itself.
- Comfy dtype:
IMAGE - Python dtype:
torch.Tensor
invert_mask- A boolean flag indicating whether the mask should be inverted. This affects how the shadow effect is applied relative to the masked areas.
- Comfy dtype:
BOOLEAN - Python dtype:
bool
blend_mode- The blending mode used to combine the shadow with the layer, affecting how the shadow merges with the layer's colors and textures.
- Comfy dtype:
COMBO[STRING] - Python dtype:
str
opacity- The opacity of the inner shadow, controlling the transparency of the shadow effect for subtler or more pronounced visual impact.
- Comfy dtype:
INT - Python dtype:
float
distance_x- The horizontal displacement of the shadow within the layer, allowing for the customization of the shadow's position relative to the layer.
- Comfy dtype:
INT - Python dtype:
int
distance_y- The vertical displacement of the shadow within the layer, enabling the adjustment of the shadow's vertical position for a more tailored effect.
- Comfy dtype:
INT - Python dtype:
int
grow- The amount by which the shadow's size is increased beyond the original layer boundaries, contributing to a more pronounced shadow effect.
- Comfy dtype:
INT - Python dtype:
int
blur- The level of blur applied to the shadow, determining the softness of the shadow edges for a more natural and integrated appearance.
- Comfy dtype:
INT - Python dtype:
int
shadow_color- The color of the inner shadow, allowing for the customization of the shadow to match or contrast with the layer and background for desired visual effects.
- Comfy dtype:
STRING - Python dtype:
str
Optional¶
layer_mask- An optional mask that defines the area of the layer image where the inner shadow effect should be applied, providing additional control over the effect's application.
- Comfy dtype:
MASK - Python dtype:
torch.Tensor
Output types¶
image- Comfy dtype:
IMAGE - The resulting image after applying the inner shadow effect, showcasing the enhanced visual depth and dimensionality of the layer.
- Python dtype:
torch.Tensor
- Comfy dtype:
Usage tips¶
- Infra type:
GPU - Common nodes: unknown
Source code¶
class InnerShadowV2:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(self):
return {
"required": {
"background_image": ("IMAGE", ), #
"layer_image": ("IMAGE",), #
"invert_mask": ("BOOLEAN", {"default": True}), # 反转mask
"blend_mode": (chop_mode_v2,), # 混合模式
"opacity": ("INT", {"default": 50, "min": 0, "max": 100, "step": 1}), # 透明度
"distance_x": ("INT", {"default": 5, "min": -9999, "max": 9999, "step": 1}), # x_偏移
"distance_y": ("INT", {"default": 5, "min": -9999, "max": 9999, "step": 1}), # y_偏移
"grow": ("INT", {"default": 2, "min": -9999, "max": 9999, "step": 1}), # 扩张
"blur": ("INT", {"default": 15, "min": 0, "max": 100, "step": 1}), # 模糊
"shadow_color": ("STRING", {"default": "#000000"}), # 背景颜色
},
"optional": {
"layer_mask": ("MASK",), #
}
}
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("image",)
FUNCTION = 'inner_shadow_v2'
CATEGORY = '😺dzNodes/LayerStyle'
def inner_shadow_v2(self, background_image, layer_image,
invert_mask, blend_mode, opacity, distance_x, distance_y,
grow, blur, shadow_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))
distance_x = -distance_x
distance_y = -distance_y
shadow_color = Image.new("RGB", tensor2pil(l_images[0]).size, color=shadow_color)
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')
if distance_x != 0 or distance_y != 0:
__mask = shift_image(_mask, distance_x, distance_y) # 位移
shadow_mask = expand_mask(image2mask(__mask), grow, blur) #扩张，模糊
# 合成阴影
alpha = tensor2pil(shadow_mask).convert('L')
_shadow = chop_image_v2(_layer, shadow_color, blend_mode, opacity)
_layer.paste(_shadow, mask=ImageChops.invert(alpha))
# 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),)