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Merge ed0c0d1c26b8a935a8625e5b198f4f27b173e264 into c8d2117f02bcad6d8316ffd8273bdc27adf83b44

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Denys Smirnov 2025-09-26 00:12:39 +08:00 committed by GitHub
commit 2701e9df15
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1 changed files with 17 additions and 1 deletions
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18
comfy_extras/nodes_compositing.py
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@ -119,10 +119,24 @@ class PorterDuffImageComposite:
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))
batch_size = min(len(source), len(destination))
if batch_size == 1:
if len(source) != 1:
batch_size = len(source)
elif len(destination) != 1:
batch_size = len(destination)
out_images = []
out_alphas = []
if batch_size != 1:
if len(source) == 1:
source = source.repeat(batch_size, 1, 1, 1)
if len(destination) == 1:
destination = destination.repeat(batch_size, 1, 1, 1)
if len(source_alpha) == 1:
source_alpha = source_alpha.repeat(batch_size, 1, 1)
if len(destination_alpha) == 1:
destination_alpha = destination_alpha.repeat(batch_size, 1, 1)
for i in range(batch_size):
src_image = source[i]
dst_image = destination[i]
@ -192,6 +206,8 @@ class JoinImageWithAlpha:
batch_size = min(len(image), len(alpha))
out_images = []
if len(alpha) == 1 and batch_size != 1:
alpha = alpha.repeat(batch_size, 1, 1, 1)
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))