diff --git a/comfy/ldm/wan/model.py b/comfy/ldm/wan/model.py
index c0efc455a..efbaecc70 100644
--- a/comfy/ldm/wan/model.py
+++ b/comfy/ldm/wan/model.py
@@ -178,8 +178,7 @@ class WanAttentionBlock(nn.Module):
                  window_size=(-1, -1),
                  qk_norm=True,
                  cross_attn_norm=False,
-                 eps=1e-6, operation_settings={},
-                 block_idx=None):
+                 eps=1e-6, operation_settings={}):
         super().__init__()
         self.dim = dim
         self.ffn_dim = ffn_dim
@@ -188,7 +187,6 @@ class WanAttentionBlock(nn.Module):
         self.qk_norm = qk_norm
         self.cross_attn_norm = cross_attn_norm
         self.eps = eps
-        self.block_idx = block_idx
 
         # layers
         self.norm1 = operation_settings.get("operations").LayerNorm(dim, eps, elementwise_affine=False, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
@@ -248,7 +246,7 @@ class WanAttentionBlock(nn.Module):
 
         if "cross_attn" in patches:
             for p in patches["cross_attn"]:
-                x = x + p({"x": x, "q": q, "k": k, "block_idx": self.block_idx, "transformer_options": transformer_options})
+                x = x + p({"x": x, "q": q, "k": k, "transformer_options": transformer_options})
 
         y = self.ffn(torch.addcmul(repeat_e(e[3], x), self.norm2(x), 1 + repeat_e(e[4], x)))
         x = torch.addcmul(x, y, repeat_e(e[5], x))
@@ -271,7 +269,6 @@ class VaceWanAttentionBlock(WanAttentionBlock):
     ):
         super().__init__(cross_attn_type, dim, ffn_dim, num_heads, window_size, qk_norm, cross_attn_norm, eps, operation_settings=operation_settings)
         self.block_id = block_id
-        self.block_idx = None
         if block_id == 0:
             self.before_proj = operation_settings.get("operations").Linear(self.dim, self.dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
         self.after_proj = operation_settings.get("operations").Linear(self.dim, self.dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
@@ -496,7 +493,7 @@ class WanModel(torch.nn.Module):
         cross_attn_type = 't2v_cross_attn' if model_type == 't2v' else 'i2v_cross_attn'
         self.blocks = nn.ModuleList([
             wan_attn_block_class(cross_attn_type, dim, ffn_dim, num_heads,
-                                 window_size, qk_norm, cross_attn_norm, eps, operation_settings=operation_settings, block_idx=i)
+                                 window_size, qk_norm, cross_attn_norm, eps, operation_settings=operation_settings)
             for i in range(num_layers)
         ])
 
@@ -579,6 +576,7 @@ class WanModel(torch.nn.Module):
         patches_replace = transformer_options.get("patches_replace", {})
         blocks_replace = patches_replace.get("dit", {})
         for i, block in enumerate(self.blocks):
+            transformer_options["block_idx"] = i
             if ("double_block", i) in blocks_replace:
                 def block_wrap(args):
                     out = {}
@@ -775,6 +773,7 @@ class VaceWanModel(WanModel):
         patches_replace = transformer_options.get("patches_replace", {})
         blocks_replace = patches_replace.get("dit", {})
         for i, block in enumerate(self.blocks):
+            transformer_options["block_idx"] = i
             if ("double_block", i) in blocks_replace:
                 def block_wrap(args):
                     out = {}