diff --git a/longcat_video/modules/attention.py b/longcat_video/modules/attention.py
index bb5630f..9b9f3cc 100644
--- a/longcat_video/modules/attention.py
+++ b/longcat_video/modules/attention.py
@@ -2,6 +2,7 @@ from typing import List, Optional

 import torch
 import torch.nn as nn
+import torch.nn.functional as F

 from einops import rearrange

@@ -100,7 +101,8 @@ class Attention(nn.Module):
             x = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=None,)
             x = rearrange(x, "B M H K -> B H M K")
         else:
-            raise RuntimeError("Unsupported attention operations.")
+            # Keep a dependency-free path for systems without optional kernels.
+            x = F.scaled_dot_product_attention(q, k, v, scale=self.scale)

         return x

@@ -245,8 +247,22 @@ class MultiHeadCrossAttention(nn.Module):
             attn_bias = xformers.ops.fmha.attn_bias.BlockDiagonalMask.from_seqlens([N] * B, kv_seqlen)
             x = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=attn_bias)
         else:
-            raise RuntimeError("Unsupported attention operations.")
-
+            # Preserve the variable-length block boundaries without materializing
+            # a dense attention mask.
+            blocks = []
+            offset = 0
+            for batch_index, key_count in enumerate(kv_seqlen):
+                query = q[0][batch_index * N:(batch_index + 1) * N]
+                key = k[0][offset:offset + key_count]
+                value = v[0][offset:offset + key_count]
+                output = F.scaled_dot_product_attention(
+                    query.transpose(0, 1),
+                    key.transpose(0, 1),
+                    value.transpose(0, 1),
+                )
+                blocks.append(output.transpose(0, 1))
+                offset += key_count
+            x = torch.cat(blocks, dim=0)

         x = x.view(B, -1, C)
         x = self.proj(x)
diff --git a/longcat_video/modules/avatar/attention.py b/longcat_video/modules/avatar/attention.py
index a169a7a..df9a469 100644
--- a/longcat_video/modules/avatar/attention.py
+++ b/longcat_video/modules/avatar/attention.py
@@ -2,6 +2,7 @@ from typing import List, Optional

 import torch
 import torch.nn as nn
+import torch.nn.functional as F

 from einops import rearrange

@@ -111,7 +112,8 @@ class Attention(nn.Module):
             x = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=None,)
             x = rearrange(x, "B M H K -> B H M K")
         else:
-            raise RuntimeError("Unsupported attention operations.")
+            # Keep a dependency-free path for systems without optional kernels.
+            x = F.scaled_dot_product_attention(q, k, v, scale=self.scale)

         return x

@@ -429,2 +431,5 @@ class SingleStreamAttention(nn.Module):
+        else:
+            # This branch uses the native PyTorch kernel when optional kernels are off.
+            x = F.scaled_dot_product_attention(q, encoder_k, encoder_v, scale=self.scale)

         # linear transform