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# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py
import numpy as np
import torch
import ldm_patched.modules.utils
from enum import Enum
def resize_mask(mask, shape):
return torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(shape[0], shape[1]), mode="bilinear").squeeze(1)
class PorterDuffMode(Enum):
ADD = 0
CLEAR = 1
DARKEN = 2
DST = 3
DST_ATOP = 4
DST_IN = 5
DST_OUT = 6
DST_OVER = 7
LIGHTEN = 8
MULTIPLY = 9
OVERLAY = 10
SCREEN = 11
SRC = 12
SRC_ATOP = 13
SRC_IN = 14
SRC_OUT = 15
SRC_OVER = 16
XOR = 17
def porter_duff_composite(src_image: torch.Tensor, src_alpha: torch.Tensor, dst_image: torch.Tensor, dst_alpha: torch.Tensor, mode: PorterDuffMode):
if mode == PorterDuffMode.ADD:
out_alpha = torch.clamp(src_alpha + dst_alpha, 0, 1)
out_image = torch.clamp(src_image + dst_image, 0, 1)
elif mode == PorterDuffMode.CLEAR:
out_alpha = torch.zeros_like(dst_alpha)
out_image = torch.zeros_like(dst_image)
elif mode == PorterDuffMode.DARKEN:
out_alpha = src_alpha + dst_alpha - src_alpha * dst_alpha
out_image = (1 - dst_alpha) * src_image + (1 - src_alpha) * dst_image + torch.min(src_image, dst_image)
elif mode == PorterDuffMode.DST:
out_alpha = dst_alpha
out_image = dst_image
elif mode == PorterDuffMode.DST_ATOP:
out_alpha = src_alpha
out_image = src_alpha * dst_image + (1 - dst_alpha) * src_image
elif mode == PorterDuffMode.DST_IN:
out_alpha = src_alpha * dst_alpha
out_image = dst_image * src_alpha
elif mode == PorterDuffMode.DST_OUT:
out_alpha = (1 - src_alpha) * dst_alpha
out_image = (1 - src_alpha) * dst_image
elif mode == PorterDuffMode.DST_OVER:
out_alpha = dst_alpha + (1 - dst_alpha) * src_alpha
out_image = dst_image + (1 - dst_alpha) * src_image
elif mode == PorterDuffMode.LIGHTEN:
out_alpha = src_alpha + dst_alpha - src_alpha * dst_alpha
out_image = (1 - dst_alpha) * src_image + (1 - src_alpha) * dst_image + torch.max(src_image, dst_image)
elif mode == PorterDuffMode.MULTIPLY:
out_alpha = src_alpha * dst_alpha
out_image = src_image * dst_image
elif mode == PorterDuffMode.OVERLAY:
out_alpha = src_alpha + dst_alpha - src_alpha * dst_alpha
out_image = torch.where(2 * dst_image < dst_alpha, 2 * src_image * dst_image,
src_alpha * dst_alpha - 2 * (dst_alpha - src_image) * (src_alpha - dst_image))
elif mode == PorterDuffMode.SCREEN:
out_alpha = src_alpha + dst_alpha - src_alpha * dst_alpha
out_image = src_image + dst_image - src_image * dst_image
elif mode == PorterDuffMode.SRC:
out_alpha = src_alpha
out_image = src_image
elif mode == PorterDuffMode.SRC_ATOP:
out_alpha = dst_alpha
out_image = dst_alpha * src_image + (1 - src_alpha) * dst_image
elif mode == PorterDuffMode.SRC_IN:
out_alpha = src_alpha * dst_alpha
out_image = src_image * dst_alpha
elif mode == PorterDuffMode.SRC_OUT:
out_alpha = (1 - dst_alpha) * src_alpha
out_image = (1 - dst_alpha) * src_image
elif mode == PorterDuffMode.SRC_OVER:
out_alpha = src_alpha + (1 - src_alpha) * dst_alpha
out_image = src_image + (1 - src_alpha) * dst_image
elif mode == PorterDuffMode.XOR:
out_alpha = (1 - dst_alpha) * src_alpha + (1 - src_alpha) * dst_alpha
out_image = (1 - dst_alpha) * src_image + (1 - src_alpha) * dst_image
else:
out_alpha = None
out_image = None
return out_image, out_alpha
class PorterDuffImageComposite:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"source": ("IMAGE",),
"source_alpha": ("MASK",),
"destination": ("IMAGE",),
"destination_alpha": ("MASK",),
"mode": ([mode.name for mode in PorterDuffMode], {"default": PorterDuffMode.DST.name}),
},
}
RETURN_TYPES = ("IMAGE", "MASK")
FUNCTION = "composite"
CATEGORY = "mask/compositing"
def composite(self, source: torch.Tensor, source_alpha: torch.Tensor, destination: torch.Tensor, destination_alpha: torch.Tensor, mode):
batch_size = min(len(source), len(source_alpha), len(destination), len(destination_alpha))
out_images = []
out_alphas = []
for i in range(batch_size):
src_image = source[i]
dst_image = destination[i]
assert src_image.shape[2] == dst_image.shape[2] # inputs need to have same number of channels
src_alpha = source_alpha[i].unsqueeze(2)
dst_alpha = destination_alpha[i].unsqueeze(2)
if dst_alpha.shape[:2] != dst_image.shape[:2]:
upscale_input = dst_alpha.unsqueeze(0).permute(0, 3, 1, 2)
upscale_output = ldm_patched.modules.utils.common_upscale(upscale_input, dst_image.shape[1], dst_image.shape[0], upscale_method='bicubic', crop='center')
dst_alpha = upscale_output.permute(0, 2, 3, 1).squeeze(0)
if src_image.shape != dst_image.shape:
upscale_input = src_image.unsqueeze(0).permute(0, 3, 1, 2)
upscale_output = ldm_patched.modules.utils.common_upscale(upscale_input, dst_image.shape[1], dst_image.shape[0], upscale_method='bicubic', crop='center')
src_image = upscale_output.permute(0, 2, 3, 1).squeeze(0)
if src_alpha.shape != dst_alpha.shape:
upscale_input = src_alpha.unsqueeze(0).permute(0, 3, 1, 2)
upscale_output = ldm_patched.modules.utils.common_upscale(upscale_input, dst_alpha.shape[1], dst_alpha.shape[0], upscale_method='bicubic', crop='center')
src_alpha = upscale_output.permute(0, 2, 3, 1).squeeze(0)
out_image, out_alpha = porter_duff_composite(src_image, src_alpha, dst_image, dst_alpha, PorterDuffMode[mode])
out_images.append(out_image)
out_alphas.append(out_alpha.squeeze(2))
result = (torch.stack(out_images), torch.stack(out_alphas))
return result
class SplitImageWithAlpha:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"image": ("IMAGE",),
}
}
CATEGORY = "mask/compositing"
RETURN_TYPES = ("IMAGE", "MASK")
FUNCTION = "split_image_with_alpha"
def split_image_with_alpha(self, image: torch.Tensor):
out_images = [i[:,:,:3] for i in image]
out_alphas = [i[:,:,3] if i.shape[2] > 3 else torch.ones_like(i[:,:,0]) for i in image]
result = (torch.stack(out_images), 1.0 - torch.stack(out_alphas))
return result
class JoinImageWithAlpha:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"image": ("IMAGE",),
"alpha": ("MASK",),
}
}
CATEGORY = "mask/compositing"
RETURN_TYPES = ("IMAGE",)
FUNCTION = "join_image_with_alpha"
def join_image_with_alpha(self, image: torch.Tensor, alpha: torch.Tensor):
batch_size = min(len(image), len(alpha))
out_images = []
alpha = 1.0 - resize_mask(alpha, image.shape[1:])
for i in range(batch_size):
out_images.append(torch.cat((image[i][:,:,:3], alpha[i].unsqueeze(2)), dim=2))
result = (torch.stack(out_images),)
return result
NODE_CLASS_MAPPINGS = {
"PorterDuffImageComposite": PorterDuffImageComposite,
"SplitImageWithAlpha": SplitImageWithAlpha,
"JoinImageWithAlpha": JoinImageWithAlpha,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"PorterDuffImageComposite": "Porter-Duff Image Composite",
"SplitImageWithAlpha": "Split Image with Alpha",
"JoinImageWithAlpha": "Join Image with Alpha",
}
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