diff --git a/src/diffusers/models/transformers/transformer_z_image.py b/src/diffusers/models/transformers/transformer_z_image.py
index 5983c34ab640..085e9000d7bd 100644
--- a/src/diffusers/models/transformers/transformer_z_image.py
+++ b/src/diffusers/models/transformers/transformer_z_image.py
@@ -789,9 +789,12 @@ def _prepare_sequence(
         freqs_cis = pad_sequence(freqs_cis, batch_first=True, padding_value=0.0)[:, : feats.shape[1]]
 
         # Attention mask
-        attn_mask = torch.zeros((bsz, max_seqlen), dtype=torch.bool, device=device)
-        for i, seq_len in enumerate(item_seqlens):
-            attn_mask[i, :seq_len] = 1
+        if all(seq == max_seqlen for seq in item_seqlens):
+            attn_mask = None
+        else:
+            attn_mask = torch.zeros((bsz, max_seqlen), dtype=torch.bool, device=device)
+            for i, seq_len in enumerate(item_seqlens):
+                attn_mask[i, :seq_len] = 1
 
         # Noise mask
         noise_mask_tensor = None
@@ -872,9 +875,12 @@ def _build_unified_sequence(
         unified_freqs = pad_sequence(unified_freqs, batch_first=True, padding_value=0.0)
 
         # Attention mask
-        attn_mask = torch.zeros((bsz, max_seqlen), dtype=torch.bool, device=device)
-        for i, seq_len in enumerate(unified_seqlens):
-            attn_mask[i, :seq_len] = 1
+        if all(seq == max_seqlen for seq in unified_seqlens):
+            attn_mask = None
+        else:
+            attn_mask = torch.zeros((bsz, max_seqlen), dtype=torch.bool, device=device)
+            for i, seq_len in enumerate(unified_seqlens):
+                attn_mask[i, :seq_len] = 1
 
         # Noise mask
         noise_mask_tensor = None