diff --git a/src/diffusers/models/attention_processor.py b/src/diffusers/models/attention_processor.py
index e2ece5cb3685..94c6d1387843 100755
--- a/src/diffusers/models/attention_processor.py
+++ b/src/diffusers/models/attention_processor.py
@@ -676,9 +676,16 @@ def get_attention_scores(
             key = key.float()
 
         if attention_mask is None:
-            baddbmm_input = torch.empty(
-                query.shape[0], query.shape[1], key.shape[1], dtype=query.dtype, device=query.device
-            )
+            if query.device.type == "mps":
+                # MPS' baddbmm does not short-circuit on beta=0, so an
+                # uninitialized input from torch.empty() can propagate NaN.
+                baddbmm_input = torch.zeros(
+                    query.shape[0], query.shape[1], key.shape[1], dtype=query.dtype, device=query.device
+                )
+            else:
+                baddbmm_input = torch.empty(
+                    query.shape[0], query.shape[1], key.shape[1], dtype=query.dtype, device=query.device
+                )
             beta = 0
         else:
             baddbmm_input = attention_mask
diff --git a/tests/models/test_attention_processor.py b/tests/models/test_attention_processor.py
index 8b45c2148504..e674a5d9ea01 100644
--- a/tests/models/test_attention_processor.py
+++ b/tests/models/test_attention_processor.py
@@ -133,3 +133,32 @@ def test_conversion_when_using_device_map(self):
 
         self.assertTrue(np.allclose(pre_conversion, conversion, atol=1e-3))
         self.assertTrue(np.allclose(conversion, after_conversion, atol=1e-3))
+
+
+class GetAttentionScoresMPSTests(unittest.TestCase):
+    @pytest.mark.skipif(torch_device != "mps", reason="test exercises an MPS-specific code path")
+    def test_no_nan_when_attention_mask_is_none_on_mps(self):
+        # Regression test: torch.empty() on MPS can return non-finite values,
+        # and MPS' baddbmm does not short-circuit on beta=0, so an unmasked
+        # call to get_attention_scores used to propagate NaN into the output.
+        torch.manual_seed(0)
+        heads, dim_head, seq_len = 4, 32, 256
+        attn = Attention(
+            query_dim=heads * dim_head,
+            heads=heads,
+            dim_head=dim_head,
+            bias=False,
+        ).to(torch_device, torch.float16)
+
+        for _ in range(20):
+            # Pollute the MPS allocator pool with non-finite values so that a
+            # subsequent torch.empty() is likely to return NaN-filled memory.
+            polluter = torch.full((heads, seq_len, seq_len), float("nan"), device=torch_device, dtype=torch.float16)
+            del polluter
+
+            query = torch.randn(1, seq_len, heads * dim_head, device=torch_device, dtype=torch.float16)
+            key = torch.randn(1, seq_len, heads * dim_head, device=torch_device, dtype=torch.float16)
+            scores = attn.get_attention_scores(
+                attn.head_to_batch_dim(query), attn.head_to_batch_dim(key), attention_mask=None
+            )
+            self.assertFalse(torch.isnan(scores).any().item(), "attention scores contain NaN on MPS")