add _native_npu_attention support mask shape like [B,1,1,S]

src/diffusers/models/attention_dispatch.py

@@ -1521,17 +1521,16 @@ def _maybe_modify_attn_mask_npu(query: torch.Tensor, key: torch.Tensor, attn_mas
     if attn_mask is not None and torch.all(attn_mask != 0):
         attn_mask = None
 
-    # Reshape Attention Mask: [batch_size, seq_len_k] -> [batch_size, 1, sqe_len_q, seq_len_k]
+    # Reshape Attention Mask: [batch_size, seq_len_k] or [batch_size, 1, 1, seq_len_k] -> [batch_size, 1, sqe_len_q, seq_len_k]
     # https://www.hiascend.com/document/detail/zh/Pytorch/730/apiref/torchnpuCustomsapi/docs/context/torch_npu-npu_fusion_attention.md
-    if (
-        attn_mask is not None
-        and attn_mask.ndim == 2
-        and attn_mask.shape[0] == query.shape[0]
-        and attn_mask.shape[1] == key.shape[1]
-    ):
-        B, Sq, Skv = attn_mask.shape[0], query.shape[1], key.shape[1]
+    if attn_mask is not None:
+        if attn_mask.ndim == 2 and attn_mask.shape[0] == query.shape[0] and attn_mask.shape[1] == key.shape[1]:
+            batch_size, seq_len_q, seq_len_kv = attn_mask.shape[0], query.shape[1], key.shape[1]
+            attn_mask = attn_mask.unsqueeze(1).expand(batch_size, seq_len_q, seq_len_kv).unsqueeze(1).contiguous()
+        elif attn_mask.ndim == 4 and attn_mask.shape[1:3] == (1, 1):
+            attn_mask = attn_mask.expand(-1, -1, query.shape[1], -1).contiguous()
+
         attn_mask = ~attn_mask.to(torch.bool)
-        attn_mask = attn_mask.unsqueeze(1).expand(B, Sq, Skv).unsqueeze(1).contiguous()
 
     return attn_mask