diff --git a/flashvsr_arch/models/wan_video_dit.py b/flashvsr_arch/models/wan_video_dit.py
index 6bdb9d0..3b71b47 100644
--- a/flashvsr_arch/models/wan_video_dit.py
+++ b/flashvsr_arch/models/wan_video_dit.py
@@ -218,27 +218,37 @@ def generate_draft_block_mask_refined(batch_size, nheads, seqlen,
 # ----------------------------
 # Attention kernels
 # ----------------------------
+def _sdpa_fallback(q, k, v, num_heads):
+    """PyTorch scaled dot-product attention (always available)."""
+    q = rearrange(q, "b s (n d) -> b n s d", n=num_heads)
+    k = rearrange(k, "b s (n d) -> b n s d", n=num_heads)
+    v = rearrange(v, "b s (n d) -> b n s d", n=num_heads)
+    x = F.scaled_dot_product_attention(q, k, v)
+    return rearrange(x, "b n s d -> b s (n d)", n=num_heads)
+
+
 def flash_attention(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, num_heads: int, compatibility_mode=False, attention_mask=None, return_KV=False, enable_sageattention=True):
+    global SPARSE_SAGE_AVAILABLE, SAGE_ATTN_AVAILABLE, FLASH_ATTN_2_AVAILABLE, FLASH_ATTN_3_AVAILABLE
+
     if attention_mask is not None and enable_sageattention and SPARSE_SAGE_AVAILABLE:
-        seqlen = q.shape[1]
-        seqlen_kv = k.shape[1]
-        q = rearrange(q, "b s (n d) -> b n s d", n=num_heads)
-        k = rearrange(k, "b s (n d) -> b n s d", n=num_heads)
-        v = rearrange(v, "b s (n d) -> b n s d", n=num_heads)
-        base_blockmask = attention_mask
-        x = sparse_sageattn(
-            q, k, v,
-            mask_id=base_blockmask.to(torch.int8),
-            is_causal=False,
-            tensor_layout="HND"
-        )
-        x = rearrange(x, "b n s d -> b s (n d)", n=num_heads)
+        try:
+            q = rearrange(q, "b s (n d) -> b n s d", n=num_heads)
+            k = rearrange(k, "b s (n d) -> b n s d", n=num_heads)
+            v = rearrange(v, "b s (n d) -> b n s d", n=num_heads)
+            base_blockmask = attention_mask
+            x = sparse_sageattn(
+                q, k, v,
+                mask_id=base_blockmask.to(torch.int8),
+                is_causal=False,
+                tensor_layout="HND"
+            )
+            x = rearrange(x, "b n s d -> b s (n d)", n=num_heads)
+        except Exception:
+            SPARSE_SAGE_AVAILABLE = False
+            print("[FlashVSR] sparse_sageattn failed (unsupported GPU?), falling back to SDPA")
+            x = _sdpa_fallback(q, k, v, num_heads)
     elif compatibility_mode:
-        q = rearrange(q, "b s (n d) -> b n s d", n=num_heads)
-        k = rearrange(k, "b s (n d) -> b n s d", n=num_heads)
-        v = rearrange(v, "b s (n d) -> b n s d", n=num_heads)
-        x = F.scaled_dot_product_attention(q, k, v)
-        x = rearrange(x, "b n s d -> b s (n d)", n=num_heads)
+        x = _sdpa_fallback(q, k, v, num_heads)
     elif FLASH_ATTN_3_AVAILABLE:
         q = rearrange(q, "b s (n d) -> b s n d", n=num_heads)
         k = rearrange(k, "b s (n d) -> b s n d", n=num_heads)
@@ -254,17 +264,18 @@ def flash_attention(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, num_heads
         x = flash_attn.flash_attn_func(q, k, v)
         x = rearrange(x, "b s n d -> b s (n d)", n=num_heads)
     elif SAGE_ATTN_AVAILABLE:
-        q = rearrange(q, "b s (n d) -> b n s d", n=num_heads)
-        k = rearrange(k, "b s (n d) -> b n s d", n=num_heads)
-        v = rearrange(v, "b s (n d) -> b n s d", n=num_heads)
-        x = sageattn(q, k, v)
-        x = rearrange(x, "b n s d -> b s (n d)", n=num_heads)
+        try:
+            q = rearrange(q, "b s (n d) -> b n s d", n=num_heads)
+            k = rearrange(k, "b s (n d) -> b n s d", n=num_heads)
+            v = rearrange(v, "b s (n d) -> b n s d", n=num_heads)
+            x = sageattn(q, k, v)
+            x = rearrange(x, "b n s d -> b s (n d)", n=num_heads)
+        except Exception:
+            SAGE_ATTN_AVAILABLE = False
+            print("[FlashVSR] sageattn failed (unsupported GPU?), falling back to SDPA")
+            x = _sdpa_fallback(q, k, v, num_heads)
     else:
-        q = rearrange(q, "b s (n d) -> b n s d", n=num_heads)
-        k = rearrange(k, "b s (n d) -> b n s d", n=num_heads)
-        v = rearrange(v, "b s (n d) -> b n s d", n=num_heads)
-        x = F.scaled_dot_product_attention(q, k, v)
-        x = rearrange(x, "b n s d -> b s (n d)", n=num_heads)
+        x = _sdpa_fallback(q, k, v, num_heads)
     return x