LayerStyle: InnerShadow¶
Documentation¶
- Class name:
LayerStyle: InnerShadow - Category:
😺dzNodes/LayerStyle - Output node:
False
The InnerShadow node applies an inner shadow effect to a layer within an image, enhancing depth and visual interest by simulating shadows within the boundaries of the layer. It allows for customization of shadow properties such as direction, blur, and color, catering to various stylistic needs.
Input types¶
Required¶
background_image- Serves as the canvas for the inner shadow effect, providing context and depth to the layer.
- Comfy dtype:
IMAGE - Python dtype:
torch.Tensor
layer_image- The target layer image for the inner shadow effect, enhancing its visual appearance by simulating shadows within its boundaries.
- Comfy dtype:
IMAGE - Python dtype:
torch.Tensor
invert_mask- Determines whether the layer mask should be inverted, affecting the area where the inner shadow effect is applied.
- Comfy dtype:
BOOLEAN - Python dtype:
bool
blend_mode- Determines how the shadow's color interacts with the layer through the blending mode, influencing the final visual effect.
- Comfy dtype:
COMBO[STRING] - Python dtype:
str
opacity- Controls the shadow's visibility and impact on the layer image by adjusting its opacity level.
- Comfy dtype:
INT - Python dtype:
int
distance_x- The horizontal displacement of the shadow effect, affecting the shadow's direction and perceived depth within the layer.
- Comfy dtype:
INT - Python dtype:
int
distance_y- The vertical displacement of the shadow effect, similar to distance_x, it influences the shadow's direction and depth perception.
- Comfy dtype:
INT - Python dtype:
int
grow- Adjusts the shadow's spread by increasing its size, affecting its softness and the overall depth effect.
- Comfy dtype:
INT - Python dtype:
int
blur- The blur radius applied to the shadow, softening its edges and contributing to the realism of the effect.
- Comfy dtype:
INT - Python dtype:
int
shadow_color- Allows customization of the shadow's color to complement or contrast with the layer image.
- Comfy dtype:
STRING - Python dtype:
str
Optional¶
layer_mask- Defines the specific regions of the layer image where the inner shadow effect should be applied, ensuring precision in the effect's placement.
- 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 layer with the shadow.
- Python dtype:
torch.Tensor
- Comfy dtype:
Usage tips¶
- Infra type:
GPU - Common nodes: unknown
Source code¶
class InnerShadow:
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,), # 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'
CATEGORY = '😺dzNodes/LayerStyle'
def inner_shadow(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(_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),)