@@ -28,7 +28,7 @@
 from ..embeddings import CogView3CombinedTimestepSizeEmbeddings
 from ..modeling_outputs import Transformer2DModelOutput
 from ..modeling_utils import ModelMixin
-from ..normalization import AdaLayerNormContinuous
+from ..normalization import LayerNorm, RMSNorm
 
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
@@ -584,6 +584,38 @@ def forward(self, hidden_states: torch.Tensor) -> Tuple[torch.Tensor, torch.Tens
         return (freqs.cos(), freqs.sin())
 
 
+class CogView4AdaLayerNormContinuous(nn.Module):
+    """
+    CogView4-only final AdaLN: LN(x) -> Linear(cond) -> chunk -> affine. Matches Megatron: **no activation** before the
+    Linear on conditioning embedding.
+    """
+
+    def __init__(
+        self,
+        embedding_dim: int,
+        conditioning_embedding_dim: int,
+        elementwise_affine: bool = True,
+        eps: float = 1e-5,
+        bias: bool = True,
+        norm_type: str = "layer_norm",
+    ):
+        super().__init__()
+        self.linear = nn.Linear(conditioning_embedding_dim, embedding_dim * 2, bias=bias)
+        if norm_type == "layer_norm":
+            self.norm = LayerNorm(embedding_dim, eps, elementwise_affine, bias)
+        elif norm_type == "rms_norm":
+            self.norm = RMSNorm(embedding_dim, eps, elementwise_affine)
+        else:
+            raise ValueError(f"unknown norm_type {norm_type}")
+
+    def forward(self, x: torch.Tensor, conditioning_embedding: torch.Tensor) -> torch.Tensor:
+        # *** NO SiLU here ***
+        emb = self.linear(conditioning_embedding.to(x.dtype))
+        scale, shift = torch.chunk(emb, 2, dim=1)
+        x = self.norm(x) * (1 + scale)[:, None, :] + shift[:, None, :]
+        return x
+
+
 class CogView4Transformer2DModel(ModelMixin, ConfigMixin, PeftAdapterMixin, CacheMixin):
     r"""
     Args:
@@ -666,7 +698,7 @@ def __init__(
         )
 
         # 4. Output projection
-        self.norm_out = AdaLayerNormContinuous(inner_dim, time_embed_dim, elementwise_affine=False)
+        self.norm_out = CogView4AdaLayerNormContinuous(inner_dim, time_embed_dim, elementwise_affine=False)
         self.proj_out = nn.Linear(inner_dim, patch_size * patch_size * out_channels, bias=True)
 
         self.gradient_checkpointing = False