Skip to content

Make LatentCompositeMasked work with basic video latents. #10023

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 2 commits into from
Sep 25, 2025
Merged

Make LatentCompositeMasked work with basic video latents. #10023

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
0925313
Make LatentCompositeMasked work with basic video latents.
comfyanonymous Sep 25, 2025
aab7a9b
Remove print.
comfyanonymous Sep 25, 2025
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
21 changes: 12 additions & 9 deletions comfy_extras/nodes_mask.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -12,35 +12,38 @@
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:
Expand Down
Loading