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resolved issue with black and white images in nodes_mask.py #4110

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resolved issue with black and white images in nodes_mask.py #4110

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12 changes: 12 additions & 0 deletions comfy_extras/nodes_mask.py
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Expand Up @@ -5,7 +5,19 @@

from nodes import MAX_RESOLUTION

def tensor_to_rgb(img):
# convert from bw to rgb : cwh -> bchw
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I don't think this is correct. I'm fairly certain that masks in ComfyUI are BHW and images are BHWC. Your logic here works out when the batch size is 1, but will break if there is a batch of more than one mask.

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@guill guill Jul 29, 2024 •
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Ah, I see -- .movedim(-1, 1) is called before calling this function for the RGB case -- so it would be BCHW. Still, I think you want to insert the channel dimension rather than the batch dimension.

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@shawnington shawnington Jul 29, 2024 •
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Ah, I see -- .movedim(-1, 1) is called before calling this function for the RGB case -- so it would be BCHW. Still, I think you want to insert the channel dimension rather than the batch dimension.

Yeah, I am not sure why it wants BCHW When the LoadImage node outputs BHWC, but the composite nodes in nodes_mask.py definitely require this format to perform their operations, and it is also the format it returns.

The batch dimension is needed because it accesses channel dimension via index position, C is expected at index[1] in the composite node so HWC would also fail without Batch being added, but batch is only added if there is no batch currently, otherwise the channel is repeated at index[1]

Im guessing all of this is a result of there not being an enforced format for what an image is through the pipeline.

So forcing conformance to either BCHW or BHWC would fix a lot of confusion with things like this.

if img.shape[1] != 3:
if len(img.shape) == 3:
img = img.unsqueeze(0)
img = img.permute(0, 1, 3, 2).repeat(1, 3, 1, 1)

return img

def composite(destination, source, x, y, mask = None, multiplier = 8, resize_source = False):
destination = tensor_to_rgb(destination)
source = tensor_to_rgb(source)

source = source.to(destination.device)
if resize_source:
source = torch.nn.functional.interpolate(source, size=(destination.shape[2], destination.shape[3]), mode="bilinear")
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