diff --git a/vllm_omni/diffusion/models/qwen_image/qwen_image_transformer.py b/vllm_omni/diffusion/models/qwen_image/qwen_image_transformer.py
index e61b59c56..1070f1369 100644
--- a/vllm_omni/diffusion/models/qwen_image/qwen_image_transformer.py
+++ b/vllm_omni/diffusion/models/qwen_image/qwen_image_transformer.py
@@ -1137,24 +1137,44 @@ class QwenImageTransformer2DModel(CachedTransformer):
         # Check for SP auto_pad: create attention mask dynamically if padding was applied
         # In Ulysses mode, attention is computed on the FULL sequence (after All-to-All)
         hidden_states_mask = None  # default
-        if self.parallel_config is not None and self.parallel_config.sequence_parallel_size > 1:
-            ctx = get_forward_context()
-            if ctx.sp_original_seq_len is not None and ctx.sp_padding_size > 0:
-                # Create mask for the full (padded) sequence
-                # valid positions = True, padding positions = False
-                batch_size = hidden_states.shape[0]
-                padded_seq_len = ctx.sp_original_seq_len + ctx.sp_padding_size
-                hidden_states_mask = torch.ones(
-                    batch_size,
-                    padded_seq_len,
-                    dtype=torch.bool,
-                    device=hidden_states.device,
-                )
-                hidden_states_mask[:, ctx.sp_original_seq_len :] = False
+        ctx = get_forward_context()
+        if (
+            self.parallel_config is not None
+            and self.parallel_config.sequence_parallel_size > 1
+            and self.parallel_config.mask_sp_padding
+            and ctx.sp_original_seq_len is not None
+            and ctx.sp_padding_size > 0
+        ):
+            # Create mask for the full (padded) sequence
+            # valid positions = True, padding positions = False
+            batch_size = hidden_states.shape[0]
+            padded_seq_len = ctx.sp_original_seq_len + ctx.sp_padding_size
+            hidden_states_mask = torch.ones(
+                batch_size,
+                padded_seq_len,
+                dtype=torch.bool,
+                device=hidden_states.device,
+            )
+            hidden_states_mask[:, ctx.sp_original_seq_len :] = False
+            if hidden_states_mask.all():
+                hidden_states_mask = None
+        elif (
+            self.parallel_config is not None
+            and self.parallel_config.sequence_parallel_size > 1
+            and not self.parallel_config.mask_sp_padding
+            and ctx.sp_original_seq_len is not None
+            and ctx.sp_padding_size > 0
+        ):
+            logger.warning_once(
+                "SP auto-padding applied %d token(s) (seq_len=%d, ulysses_degree=%d). "
+                "Padding tokens are not masked from attention (mask_sp_padding=False), "
+                "which avoids the varlen attention path but may produce minor numerical differences. "
+                "Set parallel_config.mask_sp_padding=True to restore strict masking.",
+                ctx.sp_padding_size,
+                ctx.sp_original_seq_len,
+                self.parallel_config.sequence_parallel_size,
+            )
 
-        # if mask is all true, set it to None
-        if hidden_states_mask is not None and hidden_states_mask.all():
-            hidden_states_mask = None
         if encoder_hidden_states_mask is not None and encoder_hidden_states_mask.all():
             encoder_hidden_states_mask = None