diff --git a/comfy/ldm/lightricks/vae/causal_video_autoencoder.py b/comfy/ldm/lightricks/vae/causal_video_autoencoder.py
index 4d43feb2..e0344dee 100644
--- a/comfy/ldm/lightricks/vae/causal_video_autoencoder.py
+++ b/comfy/ldm/lightricks/vae/causal_video_autoencoder.py
@@ -378,7 +378,7 @@ class Decoder(nn.Module):
             assert (
                 timestep is not None
             ), "should pass timestep with timestep_conditioning=True"
-            scaled_timestep = timestep * self.timestep_scale_multiplier
+            scaled_timestep = timestep * self.timestep_scale_multiplier.to(dtype=sample.dtype, device=sample.device)
 
         for up_block in self.up_blocks:
             if self.timestep_conditioning and isinstance(up_block, UNetMidBlock3D):
@@ -403,7 +403,7 @@ class Decoder(nn.Module):
             )
             ada_values = self.last_scale_shift_table[
                 None, ..., None, None, None
-            ] + embedded_timestep.reshape(
+            ].to(device=sample.device, dtype=sample.dtype) + embedded_timestep.reshape(
                 batch_size,
                 2,
                 -1,
@@ -697,7 +697,7 @@ class ResnetBlock3D(nn.Module):
             ), "should pass timestep with timestep_conditioning=True"
             ada_values = self.scale_shift_table[
                 None, ..., None, None, None
-            ] + timestep.reshape(
+            ].to(device=hidden_states.device, dtype=hidden_states.dtype) + timestep.reshape(
                 batch_size,
                 4,
                 -1,
@@ -715,7 +715,7 @@ class ResnetBlock3D(nn.Module):
 
         if self.inject_noise:
             hidden_states = self._feed_spatial_noise(
-                hidden_states, self.per_channel_scale1
+                hidden_states, self.per_channel_scale1.to(device=hidden_states.device, dtype=hidden_states.dtype)
             )
 
         hidden_states = self.norm2(hidden_states)
@@ -731,7 +731,7 @@ class ResnetBlock3D(nn.Module):
 
         if self.inject_noise:
             hidden_states = self._feed_spatial_noise(
-                hidden_states, self.per_channel_scale2
+                hidden_states, self.per_channel_scale2.to(device=hidden_states.device, dtype=hidden_states.dtype)
             )
 
         input_tensor = self.norm3(input_tensor)