🔧 Mask Blur¶
Documentation¶
- Class name:
MaskBlur+
- Category:
essentials
- Output node:
False
The MaskBlur node applies a Gaussian blur to a given mask, allowing for the softening of edges and the creation of a smoother mask. This operation is particularly useful in graphics and image processing tasks where the harshness of a binary mask needs to be mitigated.
Input types¶
Required¶
mask
- The mask to be blurred. This input is crucial for defining the area within the image where the blur effect will be applied.
- Comfy dtype:
MASK
- Python dtype:
torch.Tensor
amount
- Specifies the intensity of the blur effect. A higher value results in a more pronounced blur, affecting the mask's smoothness and the transition between masked and unmasked areas.
- Comfy dtype:
FLOAT
- Python dtype:
float
Output types¶
mask
- Comfy dtype:
MASK
- The output is a blurred version of the input mask, with softened edges and transitions, suitable for further image processing or visualization tasks.
- Python dtype:
torch.Tensor
- Comfy dtype:
Usage tips¶
- Infra type:
GPU
- Common nodes:
- IPAdapterApply
- MaskToImage
- Masks Subtract
Source code¶
class MaskBlur:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"mask": ("MASK",),
"amount": ("FLOAT", { "default": 6.0, "min": 0, "step": 0.5, }),
}
}
RETURN_TYPES = ("MASK",)
FUNCTION = "execute"
CATEGORY = "essentials"
def execute(self, mask, amount):
size = int(6 * amount +1)
if size % 2 == 0:
size+= 1
if mask.dim() == 2:
mask = mask.unsqueeze(0)
blurred = mask.unsqueeze(1)
blurred = T.GaussianBlur(size, amount)(blurred)
blurred = blurred.squeeze(1)
return(blurred,)