LayerStyle: GradientOverlay¶
Documentation¶
- Class name:
LayerStyle: GradientOverlay - Category:
😺dzNodes/LayerStyle - Output node:
False
The GradientOverlay node applies a gradient overlay effect to images, allowing for the blending of layer images with background images using specified gradients, blend modes, and opacity settings. It provides a way to enhance visual aesthetics or achieve specific design goals by modifying the appearance of images.
Input types¶
Required¶
background_image- The background image over which the gradient overlay will be applied. It serves as the base layer for the overlay effect.
- Comfy dtype:
IMAGE - Python dtype:
torch.Tensor
layer_image- The layer image to which the gradient overlay effect will be applied. This image is blended with the background image according to the specified gradient and blend mode.
- Comfy dtype:
IMAGE - Python dtype:
torch.Tensor
invert_mask- A boolean flag to indicate whether the mask should be inverted. This affects which parts of the layer image the gradient overlay will be applied to.
- Comfy dtype:
BOOLEAN - Python dtype:
bool
blend_mode- The blend mode used to combine the layer image with the background image under the gradient overlay effect.
- Comfy dtype:
COMBO[STRING] - Python dtype:
str
opacity- The opacity level of the gradient overlay, determining how transparent the overlay appears over the background image.
- Comfy dtype:
INT - Python dtype:
int
start_color- The starting color of the gradient overlay. It defines the initial color of the gradient effect applied to the layer image.
- Comfy dtype:
STRING - Python dtype:
str
start_alpha- The starting opacity of the gradient overlay, affecting the transparency of the start color.
- Comfy dtype:
INT - Python dtype:
int
end_color- The ending color of the gradient overlay. It specifies the final color in the gradient transition applied to the layer image.
- Comfy dtype:
STRING - Python dtype:
str
end_alpha- The ending opacity of the gradient overlay, affecting the transparency of the end color.
- Comfy dtype:
INT - Python dtype:
int
angle- The angle at which the gradient is applied, affecting the direction of the color transition on the layer image.
- Comfy dtype:
INT - Python dtype:
int
Optional¶
layer_mask- An optional mask that can be used to limit the area of the layer image affected by the gradient overlay. If provided, it specifies the exact areas where the gradient effect should be applied.
- Comfy dtype:
MASK - Python dtype:
torch.Tensor
Output types¶
image- Comfy dtype:
IMAGE - The resulting image after applying the gradient overlay effect, combining the background and layer images with the specified gradient, blend mode, and opacity settings.
- Python dtype:
torch.Tensor
- Comfy dtype:
Usage tips¶
- Infra type:
GPU - Common nodes: unknown
Source code¶
class GradientOverlay:
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": 100, "min": 0, "max": 100, "step": 1}), # 透明度
"start_color": ("STRING", {"default": "#FFBF30"}), # 渐变开始颜色
"start_alpha": ("INT", {"default": 255, "min": 0, "max": 255, "step": 1}),
"end_color": ("STRING", {"default": "#FE0000"}), # 渐变结束颜色
"end_alpha": ("INT", {"default": 255, "min": 0, "max": 255, "step": 1}),
"angle": ("INT", {"default": 0, "min": -180, "max": 180, "step": 1}), # 渐变角度
},
"optional": {
"layer_mask": ("MASK",), #
}
}
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("image",)
FUNCTION = 'gradient_overlay'
CATEGORY = '😺dzNodes/LayerStyle'
def gradient_overlay(self, background_image, layer_image,
invert_mask, blend_mode, opacity,
start_color, start_alpha, end_color, end_alpha, angle,
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))
width, height = tensor2pil(l_images[0]).size
_gradient = gradient(start_color, end_color, width, height, float(angle))
start_color = RGB_to_Hex((start_alpha, start_alpha, start_alpha))
end_color = RGB_to_Hex((end_alpha, end_alpha, end_alpha))
comp_alpha = gradient(start_color, end_color, width, height, float(angle))
comp_alpha = ImageChops.invert(comp_alpha).convert('L')
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')
# 合成layer
_comp = chop_image(_layer, _gradient, blend_mode, opacity)
if start_alpha < 255 or end_alpha < 255:
_comp.paste(_layer, comp_alpha)
_canvas.paste(_comp, 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),)