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import numpy as np
import scipy.ndimage
import torch
import comfy.utils
from nodes import MAX_RESOLUTION
def composite(destination, source, x, y, mask = None, multiplier = 8, resize_source = False):
source = source.to(destination.device)
if resize_source:
source = torch.nn.functional.interpolate(source, size=(destination.shape[2], destination.shape[3]), mode="bilinear")
source = comfy.utils.repeat_to_batch_size(source, destination.shape[0])
x = max(-source.shape[3] * multiplier, min(x, destination.shape[3] * multiplier))
y = max(-source.shape[2] * multiplier, min(y, destination.shape[2] * multiplier))
left, top = (x // multiplier, y // multiplier)
right, bottom = (left + source.shape[3], top + source.shape[2],)
if mask is None:
mask = torch.ones_like(source)
else:
mask = mask.to(destination.device, copy=True)
mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(source.shape[2], source.shape[3]), mode="bilinear")
mask = comfy.utils.repeat_to_batch_size(mask, source.shape[0])
# calculate the bounds of the source that will be overlapping the destination
# this prevents the source trying to overwrite latent pixels that are out of bounds
# of the destination
visible_width, visible_height = (destination.shape[3] - left + min(0, x), destination.shape[2] - top + min(0, y),)
mask = mask[:, :, :visible_height, :visible_width]
inverse_mask = torch.ones_like(mask) - mask
source_portion = mask * source[:, :, :visible_height, :visible_width]
destination_portion = inverse_mask * destination[:, :, top:bottom, left:right]
destination[:, :, top:bottom, left:right] = source_portion + destination_portion
return destination
class LatentCompositeMasked:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"destination": ("LATENT",),
"source": ("LATENT",),
"x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
"y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
"resize_source": ("BOOLEAN", {"default": False}),
},
"optional": {
"mask": ("MASK",),
}
}
RETURN_TYPES = ("LATENT",)
FUNCTION = "composite"
CATEGORY = "latent"
def composite(self, destination, source, x, y, resize_source, mask = None):
output = destination.copy()
destination = destination["samples"].clone()
source = source["samples"]
output["samples"] = composite(destination, source, x, y, mask, 8, resize_source)
return (output,)
class ImageCompositeMasked:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"destination": ("IMAGE",),
"source": ("IMAGE",),
"x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
"y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
"resize_source": ("BOOLEAN", {"default": False}),
},
"optional": {
"mask": ("MASK",),
}
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "composite"
CATEGORY = "image"
def composite(self, destination, source, x, y, resize_source, mask = None):
destination = destination.clone().movedim(-1, 1)
output = composite(destination, source.movedim(-1, 1), x, y, mask, 1, resize_source).movedim(1, -1)
return (output,)
class MaskToImage:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"mask": ("MASK",),
}
}
CATEGORY = "mask"
RETURN_TYPES = ("IMAGE",)
FUNCTION = "mask_to_image"
def mask_to_image(self, mask):
result = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])).movedim(1, -1).expand(-1, -1, -1, 3)
return (result,)
class ImageToMask:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"image": ("IMAGE",),
"channel": (["red", "green", "blue", "alpha"],),
}
}
CATEGORY = "mask"
RETURN_TYPES = ("MASK",)
FUNCTION = "image_to_mask"
def image_to_mask(self, image, channel):
channels = ["red", "green", "blue", "alpha"]
mask = image[:, :, :, channels.index(channel)]
return (mask,)
class ImageColorToMask:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"image": ("IMAGE",),
"color": ("INT", {"default": 0, "min": 0, "max": 0xFFFFFF, "step": 1, "display": "color"}),
}
}
CATEGORY = "mask"
RETURN_TYPES = ("MASK",)
FUNCTION = "image_to_mask"
def image_to_mask(self, image, color):
temp = (torch.clamp(image, 0, 1.0) * 255.0).round().to(torch.int)
temp = torch.bitwise_left_shift(temp[:,:,:,0], 16) + torch.bitwise_left_shift(temp[:,:,:,1], 8) + temp[:,:,:,2]
mask = torch.where(temp == color, 255, 0).float()
return (mask,)
class SolidMask:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"value": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
"width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
"height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
}
}
CATEGORY = "mask"
RETURN_TYPES = ("MASK",)
FUNCTION = "solid"
def solid(self, value, width, height):
out = torch.full((1, height, width), value, dtype=torch.float32, device="cpu")
return (out,)
class InvertMask:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"mask": ("MASK",),
}
}
CATEGORY = "mask"
RETURN_TYPES = ("MASK",)
FUNCTION = "invert"
def invert(self, mask):
out = 1.0 - mask
return (out,)
class CropMask:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"mask": ("MASK",),
"x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
"y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
"width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
"height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
}
}
CATEGORY = "mask"
RETURN_TYPES = ("MASK",)
FUNCTION = "crop"
def crop(self, mask, x, y, width, height):
mask = mask.reshape((-1, mask.shape[-2], mask.shape[-1]))
out = mask[:, y:y + height, x:x + width]
return (out,)
class MaskComposite:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"destination": ("MASK",),
"source": ("MASK",),
"x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
"y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
"operation": (["multiply", "add", "subtract", "and", "or", "xor"],),
}
}
CATEGORY = "mask"
RETURN_TYPES = ("MASK",)
FUNCTION = "combine"
def combine(self, destination, source, x, y, operation):
output = destination.reshape((-1, destination.shape[-2], destination.shape[-1])).clone()
source = source.reshape((-1, source.shape[-2], source.shape[-1]))
left, top = (x, y,)
right, bottom = (min(left + source.shape[-1], destination.shape[-1]), min(top + source.shape[-2], destination.shape[-2]))
visible_width, visible_height = (right - left, bottom - top,)
source_portion = source[:, :visible_height, :visible_width]
destination_portion = destination[:, top:bottom, left:right]
if operation == "multiply":
output[:, top:bottom, left:right] = destination_portion * source_portion
elif operation == "add":
output[:, top:bottom, left:right] = destination_portion + source_portion
elif operation == "subtract":
output[:, top:bottom, left:right] = destination_portion - source_portion
elif operation == "and":
output[:, top:bottom, left:right] = torch.bitwise_and(destination_portion.round().bool(), source_portion.round().bool()).float()
elif operation == "or":
output[:, top:bottom, left:right] = torch.bitwise_or(destination_portion.round().bool(), source_portion.round().bool()).float()
elif operation == "xor":
output[:, top:bottom, left:right] = torch.bitwise_xor(destination_portion.round().bool(), source_portion.round().bool()).float()
output = torch.clamp(output, 0.0, 1.0)
return (output,)
class FeatherMask:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"mask": ("MASK",),
"left": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
"top": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
"right": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
"bottom": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
}
}
CATEGORY = "mask"
RETURN_TYPES = ("MASK",)
FUNCTION = "feather"
def feather(self, mask, left, top, right, bottom):
output = mask.reshape((-1, mask.shape[-2], mask.shape[-1])).clone()
left = min(left, output.shape[-1])
right = min(right, output.shape[-1])
top = min(top, output.shape[-2])
bottom = min(bottom, output.shape[-2])
for x in range(left):
feather_rate = (x + 1.0) / left
output[:, :, x] *= feather_rate
for x in range(right):
feather_rate = (x + 1) / right
output[:, :, -x] *= feather_rate
for y in range(top):
feather_rate = (y + 1) / top
output[:, y, :] *= feather_rate
for y in range(bottom):
feather_rate = (y + 1) / bottom
output[:, -y, :] *= feather_rate
return (output,)
class GrowMask:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"mask": ("MASK",),
"expand": ("INT", {"default": 0, "min": -MAX_RESOLUTION, "max": MAX_RESOLUTION, "step": 1}),
"tapered_corners": ("BOOLEAN", {"default": True}),
},
}
CATEGORY = "mask"
RETURN_TYPES = ("MASK",)
FUNCTION = "expand_mask"
def expand_mask(self, mask, expand, tapered_corners):
c = 0 if tapered_corners else 1
kernel = np.array([[c, 1, c],
[1, 1, 1],
[c, 1, c]])
mask = mask.reshape((-1, mask.shape[-2], mask.shape[-1]))
out = []
for m in mask:
output = m.numpy()
for _ in range(abs(expand)):
if expand < 0:
output = scipy.ndimage.grey_erosion(output, footprint=kernel)
else:
output = scipy.ndimage.grey_dilation(output, footprint=kernel)
output = torch.from_numpy(output)
out.append(output)
return (torch.stack(out, dim=0),)
class ThresholdMask:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"mask": ("MASK",),
"value": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
}
}
CATEGORY = "mask"
RETURN_TYPES = ("MASK",)
FUNCTION = "image_to_mask"
def image_to_mask(self, mask, value):
mask = (mask > value).float()
return (mask,)
NODE_CLASS_MAPPINGS = {
"LatentCompositeMasked": LatentCompositeMasked,
"ImageCompositeMasked": ImageCompositeMasked,
"MaskToImage": MaskToImage,
"ImageToMask": ImageToMask,
"ImageColorToMask": ImageColorToMask,
"SolidMask": SolidMask,
"InvertMask": InvertMask,
"CropMask": CropMask,
"MaskComposite": MaskComposite,
"FeatherMask": FeatherMask,
"GrowMask": GrowMask,
"ThresholdMask": ThresholdMask,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"ImageToMask": "Convert Image to Mask",
"MaskToImage": "Convert Mask to Image",
}
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