diff --git a/QEfficient/diffusers/models/modeling_utils.py b/QEfficient/diffusers/models/modeling_utils.py
index 59727be2de..799acbffdc 100644
--- a/QEfficient/diffusers/models/modeling_utils.py
+++ b/QEfficient/diffusers/models/modeling_utils.py
@@ -42,6 +42,7 @@ def apply_head_blocking(
     v: torch.FloatTensor,
     head_block_size: int,
     attention_mask: Optional[torch.FloatTensor] = None,
+    is_cross_attention: bool = False,
 ) -> torch.FloatTensor:
     """
     Forward pass with head-only blocking (default mode).
@@ -67,8 +68,7 @@ def apply_head_blocking(
     num_head_blocks = math.ceil(NH / head_block_size)
 
     # Optimization: Handle small sequences with standard attention
-    BS, NH, K_CL, DH = k.shape
-    if K_CL <= 512:
+    if is_cross_attention:
         scores = torch.matmul(q, k.transpose(-2, -1)) * scale_factor
         if attention_mask is not None:
             scores = torch.where(attention_mask, scores, torch.tensor(-1e4, dtype=scores.dtype, device=scores.device))
@@ -110,6 +110,7 @@ def apply_kv_blocking(
     head_block_size: int,
     num_kv_blocks: int,
     attention_mask: Optional[torch.FloatTensor] = None,
+    is_cross_attention: bool = False,
 ) -> torch.FloatTensor:
     """
     Forward pass with Key-Value blocking and head blocking.
@@ -137,8 +138,7 @@ def apply_kv_blocking(
     block_positions = [(i * CL) // num_kv_blocks for i in range(num_kv_blocks)]
 
     # Handle small sequences with standard attention
-    BS, NH, K_CL, DH = k.shape
-    if K_CL <= 512:
+    if is_cross_attention:
         scores = torch.matmul(q, k.transpose(-2, -1)) * scale_factor
         if attention_mask is not None:
             scores = torch.where(attention_mask, scores, torch.tensor(-1e4, dtype=scores.dtype, device=scores.device))
@@ -214,6 +214,7 @@ def apply_q_blocking(
     head_block_size: int,
     num_q_blocks: int,
     attention_mask: Optional[torch.FloatTensor] = None,
+    is_cross_attention: bool = False,
 ) -> torch.FloatTensor:
     """
     Forward pass with Query blocking and head blocking.
@@ -241,8 +242,7 @@ def apply_q_blocking(
     q_block_positions = [(i * CL) // num_q_blocks for i in range(num_q_blocks)]
 
     # Handle small sequences with standard attention
-    BS, NH, K_CL, DH = k.shape
-    if K_CL <= 512:
+    if is_cross_attention:
         scores = torch.matmul(q, k.transpose(-2, -1)) * scale_factor
         if attention_mask is not None:
             scores = torch.where(attention_mask, scores, torch.tensor(-1e4, dtype=scores.dtype, device=scores.device))
@@ -298,6 +298,7 @@ def apply_qkv_blocking(
     num_kv_blocks: int,
     num_q_blocks: int,
     attention_mask: Optional[torch.FloatTensor] = None,
+    is_cross_attention: bool = False,
 ) -> torch.FloatTensor:
     """
     Forward pass with combined Query, Key, Value blocking and head blocking.
@@ -328,8 +329,7 @@ def apply_qkv_blocking(
     q_block_positions = [(i * CL) // num_q_blocks for i in range(num_q_blocks)]
 
     # Optimization: Use standard attention for small sequences
-    BS, NH, K_CL, DH = k.shape
-    if K_CL <= 512:
+    if is_cross_attention:
         scores = torch.matmul(q, k.transpose(-2, -1)) * scale_factor
         if attention_mask is not None:
             scores = torch.where(attention_mask, scores, torch.tensor(-1e4, dtype=scores.dtype, device=scores.device))
@@ -429,6 +429,7 @@ def compute_blocked_attention(
     num_q_blocks: int,
     blocking_mode: str = "default",
     attention_mask: Optional[torch.FloatTensor] = None,
+    is_cross_attention: bool = False,
 ) -> torch.FloatTensor:
     """
     Main dispatcher function for different attention blocking strategies.
@@ -447,10 +448,12 @@ def compute_blocked_attention(
         torch.FloatTensor: Attention output of shape (BS, NH, CL, DH)
     """
     if blocking_mode == "kv":
-        return apply_kv_blocking(q, k, v, head_block_size, num_kv_blocks, attention_mask)
+        return apply_kv_blocking(q, k, v, head_block_size, num_kv_blocks, attention_mask, is_cross_attention)
     elif blocking_mode == "q":
-        return apply_q_blocking(q, k, v, head_block_size, num_q_blocks, attention_mask)
+        return apply_q_blocking(q, k, v, head_block_size, num_q_blocks, attention_mask, is_cross_attention)
     elif blocking_mode == "qkv":
-        return apply_qkv_blocking(q, k, v, head_block_size, num_kv_blocks, num_q_blocks, attention_mask)
+        return apply_qkv_blocking(
+            q, k, v, head_block_size, num_kv_blocks, num_q_blocks, attention_mask, is_cross_attention
+        )
     else:  # default
-        return apply_head_blocking(q, k, v, head_block_size, attention_mask)
+        return apply_head_blocking(q, k, v, head_block_size, attention_mask, is_cross_attention)
diff --git a/QEfficient/diffusers/models/transformers/transformer_wan.py b/QEfficient/diffusers/models/transformers/transformer_wan.py
index 9200997d71..930c5e90a3 100644
--- a/QEfficient/diffusers/models/transformers/transformer_wan.py
+++ b/QEfficient/diffusers/models/transformers/transformer_wan.py
@@ -79,6 +79,10 @@ def __call__(
         Returns:
             torch.Tensor: Processed hidden states after attention
         """
+        is_cross_attention = False
+        # encoder_hidden_states is only passed for cross-attention
+        if encoder_hidden_states is not None:
+            is_cross_attention = True
         # Project inputs to query, key, value
         query, key, value = _get_qkv_projections(attn, hidden_states, encoder_hidden_states)
 
@@ -126,6 +130,7 @@ def apply_rotary_emb(
             num_q_blocks,
             blocking_mode=blocking_mode,
             attention_mask=attention_mask,
+            is_cross_attention=is_cross_attention,
         )
 
         # Reshape back to original format