diff --git a/k_diffusion/models/image_transformer_v2.py b/k_diffusion/models/image_transformer_v2.py
index 54be0696..303c91e0 100644
--- a/k_diffusion/models/image_transformer_v2.py
+++ b/k_diffusion/models/image_transformer_v2.py
@@ -407,18 +407,28 @@ def forward(self, x, pos, cond):
         skip = x
         x = self.norm(x, cond)
         qkv = self.qkv_proj(x)
-        q, k, v = rearrange(qkv, "n h w (t nh e) -> t n nh h w e", t=3, e=self.d_head)
-        q, k = scale_for_cosine_sim(q, k, self.scale[:, None, None, None], 1e-6)
-        theta = self.pos_emb(pos).movedim(-2, -4)
-        q = apply_rotary_emb_(q, theta)
-        k = apply_rotary_emb_(k, theta)
         if natten is None:
             raise ModuleNotFoundError("natten is required for neighborhood attention")
-        flops.op(flops.op_natten, q.shape, k.shape, v.shape, self.kernel_size)
-        qk = natten.functional.natten2dqk(q, k, self.kernel_size, 1)
-        a = torch.softmax(qk, dim=-1).to(v.dtype)
-        x = natten.functional.natten2dav(a, v, self.kernel_size, 1)
-        x = rearrange(x, "n nh h w e -> n h w (nh e)")
+        if natten.has_fused_na():
+            q, k, v = rearrange(qkv, "n h w (t nh e) -> t n h w nh e", t=3, e=self.d_head)
+            q, k = scale_for_cosine_sim(q, k, self.scale[:, None], 1e-6)
+            theta = self.pos_emb(pos)
+            q = apply_rotary_emb_(q, theta)
+            k = apply_rotary_emb_(k, theta)
+            flops.op(flops.op_natten, q.shape, k.shape, v.shape, self.kernel_size)
+            x = natten.functional.na2d(q, k, v, self.kernel_size, scale=1.0)
+            x = rearrange(x, "n h w nh e -> n h w (nh e)")
+        else:
+            q, k, v = rearrange(qkv, "n h w (t nh e) -> t n nh h w e", t=3, e=self.d_head)
+            q, k = scale_for_cosine_sim(q, k, self.scale[:, None, None, None], 1e-6)
+            theta = self.pos_emb(pos).movedim(-2, -4)
+            q = apply_rotary_emb_(q, theta)
+            k = apply_rotary_emb_(k, theta)
+            flops.op(flops.op_natten, q.shape, k.shape, v.shape, self.kernel_size)
+            qk = natten.functional.na2d_qk(q, k, self.kernel_size)
+            a = torch.softmax(qk, dim=-1).to(v.dtype)
+            x = natten.functional.na2d_av(a, v, self.kernel_size)
+            x = rearrange(x, "n nh h w e -> n h w (nh e)")
         x = self.dropout(x)
         x = self.out_proj(x)
         return x + skip