diff --git a/packages/ltx-core/src/ltx_core/model/transformer/attention.py b/packages/ltx-core/src/ltx_core/model/transformer/attention.py
index 23e0447d..95fe5bda 100644
--- a/packages/ltx-core/src/ltx_core/model/transformer/attention.py
+++ b/packages/ltx-core/src/ltx_core/model/transformer/attention.py
@@ -18,6 +18,12 @@
 except ImportError:
     flash_attn_interface = None
 
+if flash_attn_interface is not None:
+    print("Using Flash Attention.")
+elif memory_efficient_attention is not None:
+    print("Using xformers.")
+else:
+    print("Neither Flash Attention nor xFormers xformers is available. Use PytorchAttention.")
 
 class AttentionCallable(Protocol):
     def __call__(
@@ -90,6 +96,7 @@ def __call__(
         out = out.reshape(b, -1, heads * dim_head)
         return out
 
+_pytorch_attention = PytorchAttention()
 
 class FlashAttention3(AttentionCallable):
     def __call__(
@@ -103,18 +110,15 @@ def __call__(
         if flash_attn_interface is None:
             raise RuntimeError("FlashAttention3 was selected but `FlashAttention3` is not installed.")
 
+        if mask is not None:
+            # FA3 does not support arbitrary attention masks — fall back to PyTorch SDPA
+            return _pytorch_attention(q, k, v, heads, mask)
+
         b, _, dim_head = q.shape
         dim_head //= heads
-
         q, k, v = (t.view(b, -1, heads, dim_head) for t in (q, k, v))
-
-        if mask is not None:
-            raise NotImplementedError("Mask is not supported for FlashAttention3")
-
         out = flash_attn_interface.flash_attn_func(q.to(v.dtype), k.to(v.dtype), v)
-        out = out.reshape(b, -1, heads * dim_head)
-        return out
-
+        return out.reshape(b, -1, heads * dim_head)
 
 class AttentionFunction(Enum):
     PYTORCH = "pytorch"
@@ -132,8 +136,13 @@ def to_callable(self) -> AttentionCallable:
         elif self is AttentionFunction.FLASH_ATTENTION_3:
             return FlashAttention3()
         else:
-            # Default behavior: XFormers if installed else - PyTorch
-            return XFormersAttention() if memory_efficient_attention is not None else PytorchAttention()
+            # Default behavior: FA3 > XFormers > PyTorch
+            if flash_attn_interface is not None:
+                return FlashAttention3()
+            elif memory_efficient_attention is not None:
+                return XFormersAttention()
+            else:
+                return PytorchAttention()
 
 
 class Attention(torch.nn.Module):