Support batches of masks in mask composite nodes.

comfy_extras/nodes_mask.py

 import numpy as np
 from scipy.ndimage import grey_dilation
 import torch
+import comfy.utils
 
 from nodes import MAX_RESOLUTION
 
@@ -8,6 +9,8 @@ def composite(destination, source, x, y, mask = None, multiplier = 8, resize_sou
     if resize_source:
         source = torch.nn.functional.interpolate(source, size=(destination.shape[2], destination.shape[3]), 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))
 
@@ -18,8 +21,8 @@ def composite(destination, source, x, y, mask = None, multiplier = 8, resize_sou
         mask = torch.ones_like(source)
     else:
         mask = mask.clone()
-        mask = torch.nn.functional.interpolate(mask[None, None], size=(source.shape[2], source.shape[3]), mode="bilinear")
-        mask = mask.repeat((source.shape[0], source.shape[1], 1, 1))
+        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 = 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
@@ -122,7 +125,7 @@ class ImageToMask:
 
     def image_to_mask(self, image, channel):
         channels = ["red", "green", "blue"]
-        mask = image[0, :, :, channels.index(channel)]
+        mask = image[:, :, :, channels.index(channel)]
         return (mask,)
 
 class ImageColorToMask: