diff --git a/comfy/ldm/lightricks/model.py b/comfy/ldm/lightricks/model.py
index bfbc08357..d3ba8ad2e 100644
--- a/comfy/ldm/lightricks/model.py
+++ b/comfy/ldm/lightricks/model.py
@@ -1252,24 +1252,21 @@ class LTXVModel(LTXBaseModel):
             dtype: Target dtype.
 
         Returns:
-            (1, 1, total_tokens, total_tokens) additive bias mask.
+            (1, 1, 1, total_tokens) additive bias mask. Broadcasts across queries
+            inside attention, dropping the persistent allocation from O(T²) to O(T).
             0.0 = full attention, negative = attenuated, finfo.min = effectively fully masked.
         """
         finfo = torch.finfo(dtype)
-        mask = torch.zeros((1, 1, total_tokens, total_tokens), device=device, dtype=dtype)
+        mask = torch.zeros((1, 1, 1, total_tokens), device=device, dtype=dtype)
         tracked_end = guide_start + tracked_count
 
-        # Convert weights to log-space bias
         w = tracked_weights.to(device=device, dtype=dtype)  # (1, tracked_count)
         log_w = torch.full_like(w, finfo.min)
         positive_mask = w > 0
         if positive_mask.any():
             log_w[positive_mask] = torch.log(w[positive_mask].clamp(min=finfo.tiny))
 
-        # noisy → tracked guides: each noisy row gets the same per-guide weight
-        mask[:, :, :guide_start, guide_start:tracked_end] = log_w.view(1, 1, 1, -1)
-        # tracked guides → noisy: each guide row broadcasts its weight across noisy cols
-        mask[:, :, guide_start:tracked_end, :guide_start] = log_w.view(1, 1, -1, 1)
+        mask[:, :, :, guide_start:tracked_end] = log_w.view(1, 1, 1, -1)
 
         return mask