Properly handle and reshape masks when used on 3d latents.

comfy/sampler_helpers.py

@@ -1,14 +1,10 @@
 import torch
 import comfy.model_management
 import comfy.conds
+import comfy.utils
 
 def prepare_mask(noise_mask, shape, device):
-    """ensures noise mask is of proper dimensions"""
-    noise_mask = torch.nn.functional.interpolate(noise_mask.reshape((-1, 1, noise_mask.shape[-2], noise_mask.shape[-1])), size=(shape[2], shape[3]), mode="bilinear")
-    noise_mask = torch.cat([noise_mask] * shape[1], dim=1)
-    noise_mask = comfy.utils.repeat_to_batch_size(noise_mask, shape[0])
-    noise_mask = noise_mask.to(device)
-    return noise_mask
+    return comfy.utils.reshape_mask(noise_mask, shape).to(device)
 
 def get_models_from_cond(cond, model_type):
     models = []

comfy/utils.py

@@ -848,3 +848,24 @@ class ProgressBar:
 
     def update(self, value):
         self.update_absolute(self.current + value)
+
+def reshape_mask(input_mask, output_shape):
+    dims = len(output_shape) - 2
+
+    if dims == 1:
+        scale_mode = "linear"
+
+    if dims == 2:
+        mask = input_mask.reshape((-1, 1, input_mask.shape[-2], input_mask.shape[-1]))
+        scale_mode = "bilinear"
+
+    if dims == 3:
+        if len(input_mask.shape) < 5:
+            mask = input_mask.reshape((1, 1, -1, input_mask.shape[-2], input_mask.shape[-1]))
+        scale_mode = "trilinear"
+
+    mask = torch.nn.functional.interpolate(mask, size=output_shape[2:], mode=scale_mode)
+    if mask.shape[1] < output_shape[1]:
+        mask = mask.repeat((1, output_shape[1]) + (1,) * dims)[:,:output_shape[1]]
+    mask = comfy.utils.repeat_to_batch_size(mask, output_shape[0])
+    return mask