diff --git a/comfy/bg_removal_model.py b/comfy/bg_removal_model.py
index cb7c2ee53e50..7877afd7f1b5 100644
--- a/comfy/bg_removal_model.py
+++ b/comfy/bg_removal_model.py
@@ -47,7 +47,7 @@ def encode_image(self, image):
         out = self.model(pixel_values=pixel_values)
         out = torch.nn.functional.interpolate(out, size=(H, W), mode="bicubic", antialias=False)
 
-        mask = out.sigmoid()
+        mask = out.sigmoid().to(device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype())
         if mask.ndim == 3:
             mask = mask.unsqueeze(0)
         if mask.shape[1] != 1:
diff --git a/comfy_extras/nodes_compositing.py b/comfy_extras/nodes_compositing.py
index 5b44237348d8..720efc629f41 100644
--- a/comfy_extras/nodes_compositing.py
+++ b/comfy_extras/nodes_compositing.py
@@ -203,7 +203,7 @@ def define_schema(cls):
     @classmethod
     def execute(cls, image: torch.Tensor, alpha: torch.Tensor) -> io.NodeOutput:
         batch_size = max(len(image), len(alpha))
-        alpha = 1.0 - resize_mask(alpha, image.shape[1:])
+        alpha = 1.0 - resize_mask(alpha.to(image), image.shape[1:])
         alpha = comfy.utils.repeat_to_batch_size(alpha, batch_size)
         image = comfy.utils.repeat_to_batch_size(image, batch_size)
         return io.NodeOutput(torch.cat((image[..., :3], alpha.unsqueeze(-1)), dim=-1))
diff --git a/comfy_extras/nodes_lt.py b/comfy_extras/nodes_lt.py
index ab1359fdb543..a4c85db77927 100644
--- a/comfy_extras/nodes_lt.py
+++ b/comfy_extras/nodes_lt.py
@@ -106,12 +106,12 @@ def execute(cls, vae, image, latent, strength, bypass=False) -> io.NodeOutput:
         if bypass:
             return (latent,)
 
-        samples = latent["samples"]
+        samples = latent["samples"].clone()
         _, height_scale_factor, width_scale_factor = (
             vae.downscale_index_formula
         )
 
-        batch, _, latent_frames, latent_height, latent_width = samples.shape
+        _, _, _, latent_height, latent_width = samples.shape
         width = latent_width * width_scale_factor
         height = latent_height * height_scale_factor
 
@@ -124,11 +124,7 @@ def execute(cls, vae, image, latent, strength, bypass=False) -> io.NodeOutput:
 
         samples[:, :, :t.shape[2]] = t
 
-        conditioning_latent_frames_mask = torch.ones(
-            (batch, 1, latent_frames, 1, 1),
-            dtype=torch.float32,
-            device=samples.device,
-        )
+        conditioning_latent_frames_mask = get_noise_mask(latent)
         conditioning_latent_frames_mask[:, :, :t.shape[2]] = 1.0 - strength
 
         return io.NodeOutput({"samples": samples, "noise_mask": conditioning_latent_frames_mask})
@@ -236,7 +232,7 @@ def define_schema(cls):
     def encode(cls, vae, latent_width, latent_height, images, scale_factors):
         time_scale_factor, width_scale_factor, height_scale_factor = scale_factors
         images = images[:(images.shape[0] - 1) // time_scale_factor * time_scale_factor + 1]
-        pixels = comfy.utils.common_upscale(images.movedim(-1, 1), latent_width * width_scale_factor, latent_height * height_scale_factor, "bilinear", crop="disabled").movedim(1, -1)
+        pixels = comfy.utils.common_upscale(images.movedim(-1, 1), latent_width * width_scale_factor, latent_height * height_scale_factor, "bilinear", crop="center").movedim(1, -1)
         encode_pixels = pixels[:, :, :, :3]
         t = vae.encode(encode_pixels)
         return encode_pixels, t