diff --git a/comfy/ldm/wan/model_multitalk.py b/comfy/ldm/wan/model_multitalk.py
index 7e7dda7d5..a67fb5158 100644
--- a/comfy/ldm/wan/model_multitalk.py
+++ b/comfy/ldm/wan/model_multitalk.py
@@ -16,30 +16,30 @@ def calculate_x_ref_attn_map(visual_q, ref_k, ref_target_masks, split_num=8):
 
         x_ref_attnmap = torch.zeros(B, H, x_seqlens, device=visual_q.device, dtype=visual_q.dtype)
         chunk_size = min(max(x_seqlens // split_num, 1), x_seqlens)
-        
+
         for i in range(0, x_seqlens, chunk_size):
             end_i = min(i + chunk_size, x_seqlens)
-            
+
             attn_chunk = visual_q[:, :, i:end_i] @ ref_k.permute(0, 2, 3, 1)  # B, H, chunk, ref_seqlens
-            
+
             # Apply softmax
             attn_max = attn_chunk.max(dim=-1, keepdim=True).values
             attn_chunk = (attn_chunk - attn_max).exp()
             attn_sum = attn_chunk.sum(dim=-1, keepdim=True)
             attn_chunk = attn_chunk / (attn_sum + 1e-8)
-            
+
             # Apply mask and sum
             masked_attn = attn_chunk * ref_target_mask
             x_ref_attnmap[:, :, i:end_i] = masked_attn.sum(-1) / (ref_target_mask.sum() + 1e-8)
-            
+
             del attn_chunk, masked_attn
-        
+
         # Average across heads
         x_ref_attnmap = x_ref_attnmap.mean(dim=1)  # B, x_seqlens
         x_ref_attn_maps.append(x_ref_attnmap)
-    
+
     del visual_q, ref_k
-    
+
     return torch.cat(x_ref_attn_maps, dim=0)
 
 def get_attn_map_with_target(visual_q, ref_k, shape, ref_target_masks=None, split_num=2):