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matching mask dimensions of (W, H)

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Sammy Franklin 2023-09-27 22:02:24 -07:00
parent bf3fc2f1b7
commit fb458878d8
1 changed files with 7 additions and 7 deletions
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14
comfy_extras/nodes_mask.py
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@ -237,24 +237,24 @@ class MaskComposite:
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]))
right, bottom = (min(left + source.shape[-2], destination.shape[-2]), min(top + source.shape[-1], destination.shape[-1]))
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
output[:, left:right, top:bottom] = destination_portion * source_portion
elif operation == "add":
output[:, top:bottom, left:right] = destination_portion + source_portion
output[:, left:right, top:bottom] = destination_portion + source_portion
elif operation == "subtract":
output[:, top:bottom, left:right] = destination_portion - source_portion
output[:, left:right, top:bottom] = destination_portion - source_portion
elif operation == "and":
output[:, top:bottom, left:right] = torch.bitwise_and(destination_portion.round().bool(), source_portion.round().bool()).float()
output[:, left:right, top:bottom] = 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()
output[:, left:right, top:bottom] = 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[:, left:right, top:bottom] = torch.bitwise_xor(destination_portion.round().bool(), source_portion.round().bool()).float()
output = torch.clamp(output, 0.0, 1.0)