diff --git a/comfy/ldm/ace/ace_step15.py b/comfy/ldm/ace/ace_step15.py
index 7fc7f1e8e..69338336d 100644
--- a/comfy/ldm/ace/ace_step15.py
+++ b/comfy/ldm/ace/ace_step15.py
@@ -738,7 +738,7 @@ class AttentionPooler(nn.Module):
     def forward(self, x):
         B, T, P, D = x.shape
         x = self.embed_tokens(x)
-        special = self.special_token.expand(B, T, 1, -1)
+        special = comfy.model_management.cast_to(self.special_token, device=x.device, dtype=x.dtype).expand(B, T, 1, -1)
         x = torch.cat([special, x], dim=2)
         x = x.view(B * T, P + 1, D)
 
@@ -789,7 +789,7 @@ class FSQ(nn.Module):
         self.register_buffer('implicit_codebook', implicit_codebook, persistent=False)
 
     def bound(self, z):
-        levels_minus_1 = (self._levels - 1).to(z.dtype)
+        levels_minus_1 = (comfy.model_management.cast_to(self._levels, device=z.device, dtype=z.dtype) - 1)
         scale = 2. / levels_minus_1
         bracket = (levels_minus_1 * (torch.tanh(z) + 1) / 2.) + 0.5
 
@@ -804,8 +804,8 @@ class FSQ(nn.Module):
         return codes_non_centered.float() * (2. / (self._levels.float() - 1)) - 1.
 
     def codes_to_indices(self, zhat):
-        zhat_normalized = (zhat + 1.) / (2. / (self._levels.to(zhat.dtype) - 1))
-        return (zhat_normalized * self._basis.to(zhat.dtype)).sum(dim=-1).round().to(torch.int32)
+        zhat_normalized = (zhat + 1.) / (2. / (comfy.model_management.cast_to(self._levels, device=zhat.device, dtype=zhat.dtype) - 1))
+        return (zhat_normalized * comfy.model_management.cast_to(self._basis, device=zhat.device, dtype=zhat.dtype)).sum(dim=-1).round().to(torch.int32)
 
     def forward(self, z):
         orig_dtype = z.dtype
@@ -887,7 +887,7 @@ class ResidualFSQ(nn.Module):
         x = self.project_in(x)
 
         if hasattr(self, 'soft_clamp_input_value'):
-            sc_val = self.soft_clamp_input_value.to(x.dtype)
+            sc_val = comfy.model_management.cast_to(self.soft_clamp_input_value, device=x.device, dtype=x.dtype)
             x = (x / sc_val).tanh() * sc_val
 
         quantized_out = torch.tensor(0., device=x.device, dtype=x.dtype)
@@ -895,7 +895,7 @@ class ResidualFSQ(nn.Module):
         all_indices = []
 
         for layer, scale in zip(self.layers, self.scales):
-            scale = scale.to(residual.dtype)
+            scale = comfy.model_management.cast_to(scale, device=x.device, dtype=x.dtype)
 
             quantized, indices = layer(residual / scale)
             quantized = quantized * scale