diff --git a/library/lumina_models.py b/library/lumina_models.py
index d12a9922..7881726e 100644
--- a/library/lumina_models.py
+++ b/library/lumina_models.py
@@ -558,7 +558,7 @@ class JointAttention(nn.Module):
                 f"Could not load flash attention. Please install flash_attn. / フラッシュアテンションを読み込めませんでした。flash_attn をインストールしてください。 / {e}"
             )
 
-@torch.compiler.disable
+@torch.compiler.disable(reason="complex ops inside")
 def apply_rope(
     x_in: torch.Tensor,
     freqs_cis: torch.Tensor,
@@ -634,14 +634,10 @@ class FeedForward(nn.Module):
             bias=False,
         )
         nn.init.xavier_uniform_(self.w3.weight)
-
-    # @torch.compile
-    def _forward_silu_gating(self, x1, x3):
-        return F.silu(x1) * x3
     
     @torch.compile(disable=disable_selective_torch_compile)
     def forward(self, x):
-        return self.w2(self._forward_silu_gating(self.w1(x), self.w3(x)))
+        return self.w2(F.silu(self.w1(x)*self.w3(x))) 
 
 
 class JointTransformerBlock(GradientCheckpointMixin):
@@ -820,6 +816,7 @@ class RopeEmbedder:
         self.axes_lens = axes_lens
         self.freqs_cis = NextDiT.precompute_freqs_cis(self.axes_dims, self.axes_lens, theta=self.theta)
 
+    @torch.compiler.disable(reason="complex ops inside")
     def __call__(self, ids: torch.Tensor):
         device = ids.device
         self.freqs_cis = [freqs_cis.to(ids.device) for freqs_cis in self.freqs_cis]
@@ -1232,6 +1229,7 @@ class NextDiT(nn.Module):
         return output
 
     @staticmethod
+    @torch.compiler.disable(reason="complex ops inside")
     def precompute_freqs_cis(
         dim: List[int],
         end: List[int],