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Make LatentCompositeMasked work with basic video latents. (#10023)

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comfyanonymous 2025-09-25 14:20:13 -07:00 committed by GitHub
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1 changed files with 12 additions and 9 deletions
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comfy_extras/nodes_mask.py
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@ -12,35 +12,38 @@ 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 = torch.nn.functional.interpolate(source, size=(destination.shape[-2], destination.shape[-1]), 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))
x = max(-source.shape[-1] * multiplier, min(x, destination.shape[-1] * 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],)
right, bottom = (left + source.shape[-1], 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 = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(source.shape[-2], source.shape[-1]), 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),)
visible_width, visible_height = (destination.shape[-1] - left + min(0, x), destination.shape[-2] - top + min(0, y),)
mask = mask[:, :, :visible_height, :visible_width]
if mask.ndim < source.ndim:
mask = mask.unsqueeze(1)
inverse_mask = torch.ones_like(mask) - mask
source_portion = mask * source[:, :, :visible_height, :visible_width]
destination_portion = inverse_mask * destination[:, :, top:bottom, left:right]
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
destination[..., top:bottom, left:right] = source_portion + destination_portion
return destination
class LatentCompositeMasked: