diff --git a/python/sglang/multimodal_gen/runtime/models/dits/sana.py b/python/sglang/multimodal_gen/runtime/models/dits/sana.py
index d33ba49ba..868676264 100644
--- a/python/sglang/multimodal_gen/runtime/models/dits/sana.py
+++ b/python/sglang/multimodal_gen/runtime/models/dits/sana.py
@@ -89,7 +89,7 @@ class GLUMBConv(nn.Module):
 class SanaLinearAttention(nn.Module):
     """Linear attention with O(N*D^2) complexity instead of O(N^2*D)."""
 
-    def __init__(self, query_dim, num_heads, head_dim, qk_norm_dim, bias=False):
+    def __init__(self, query_dim, num_heads, head_dim, bias=False):
         super().__init__()
         inner_dim = num_heads * head_dim
         self.num_heads = num_heads
@@ -101,8 +101,6 @@ class SanaLinearAttention(nn.Module):
         self.to_out = nn.ModuleList(
             [nn.Linear(inner_dim, query_dim, bias=True), nn.Identity()]
         )
-        self.norm_q = RMSNorm(qk_norm_dim)
-        self.norm_k = RMSNorm(qk_norm_dim)
 
     def forward(self, hidden_states):
         B, S, _ = hidden_states.shape
@@ -111,9 +109,6 @@ class SanaLinearAttention(nn.Module):
         key = self.to_k(hidden_states)
         value = self.to_v(hidden_states)
 
-        query = self.norm_q(query)
-        key = self.norm_k(key)
-
         query = query.view(B, S, self.num_heads, self.head_dim).transpose(1, 2)
         key = key.view(B, S, self.num_heads, self.head_dim).transpose(1, 2)
         value = value.view(B, S, self.num_heads, self.head_dim).transpose(1, 2)
@@ -146,9 +141,6 @@ class SanaCrossAttention(nn.Module):
             [nn.Linear(inner_dim, query_dim, bias=True), nn.Identity()]
         )
 
-        self.norm_q = RMSNorm(inner_dim)
-        self.norm_k = RMSNorm(inner_dim)
-
     def forward(
         self, hidden_states, encoder_hidden_states, encoder_attention_mask=None
     ):
@@ -159,9 +151,6 @@ class SanaCrossAttention(nn.Module):
         key = self.to_k(encoder_hidden_states)
         value = self.to_v(encoder_hidden_states)
 
-        query = self.norm_q(query)
-        key = self.norm_k(key)
-
         query = query.view(B, S, self.num_heads, self.head_dim).transpose(1, 2)
         key = key.view(B, T, self.num_heads, self.head_dim).transpose(1, 2)
         value = value.view(B, T, self.num_heads, self.head_dim).transpose(1, 2)
@@ -201,7 +190,6 @@ class SanaTransformerBlock(nn.Module):
             query_dim=dim,
             num_heads=num_attention_heads,
             head_dim=attention_head_dim,
-            qk_norm_dim=num_attention_heads * attention_head_dim,
             bias=attention_bias,
         )