diff --git a/src/diffusers/models/transformers/transformer_skyreels_v2.py b/src/diffusers/models/transformers/transformer_skyreels_v2.py
index 358759164b9e..49a45c7c0525 100644
--- a/src/diffusers/models/transformers/transformer_skyreels_v2.py
+++ b/src/diffusers/models/transformers/transformer_skyreels_v2.py
@@ -40,6 +40,62 @@
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 
+class SkyReelsV2AdaLayerNorm(nn.Module):
+    r"""
+    Norm layer modified to incorporate timestep embeddings.
+
+    Parameters:
+        embedding_dim (`int`): The size of each embedding vector.
+        output_dim (`int`, *optional*):
+        norm_elementwise_affine (`bool`, defaults to `False):
+        norm_eps (`bool`, defaults to `False`):
+    """
+
+    def __init__(
+        self,
+        embedding_dim: int,
+        output_dim: Optional[int] = None,
+        norm_elementwise_affine: bool = False,
+        norm_eps: float = 1e-5,
+    ):
+        super().__init__()
+
+        output_dim = output_dim or embedding_dim * 2
+
+        self.linear = nn.Linear(embedding_dim, output_dim)
+        self.linear.weight.data[:embedding_dim, :] = torch.eye(embedding_dim)
+        self.linear.weight.data[embedding_dim:, :] = torch.eye(embedding_dim)
+        self.norm = FP32LayerNorm(embedding_dim, norm_eps, norm_elementwise_affine)
+
+    def forward(self, x: torch.Tensor, temb: torch.Tensor) -> torch.Tensor:
+        if temb.ndim == 2:
+            # If temb is 2D, we assume it has 1-D time embedding values for each batch.
+            # For models:
+            # - Skywork/SkyReels-V2-T2V-14B-540P-Diffusers
+            # - Skywork/SkyReels-V2-T2V-14B-720P-Diffusers
+            # - Skywork/SkyReels-V2-I2V-1.3B-540P-Diffusers
+            # - Skywork/SkyReels-V2-I2V-14B-540P-Diffusers
+            # - Skywork/SkyReels-V2-I2V-14B-720P-Diffusers
+            # 2D temb: (batch, embedding_dim)
+            temb = self.linear(temb.unsqueeze(1))  # (batch, 1, embedding_dim * 2)
+            shift, scale = temb.chunk(2, dim=2)
+        elif temb.ndim == 3:
+            # If temb is 3D, we assume it has 2-D time embedding values for each batch.
+            # Each time embedding tensor includes values for each latent frame; thus Diffusion Forcing.
+            # For models:
+            # - Skywork/SkyReels-V2-DF-1.3B-540P-Diffusers
+            # - Skywork/SkyReels-V2-DF-14B-540P-Diffusers
+            # - Skywork/SkyReels-V2-DF-14B-720P-Diffusers
+            # 3D temb: (batch, num_latent_frames * post_patch_height * post_patch_width, embedding_dim)
+            temb = self.linear(
+                temb
+            )  # (batch, num_latent_frames * post_patch_height * post_patch_width, embedding_dim * 2)
+            shift, scale = temb.chunk(2, dim=2)
+
+        x = self.norm(x) * (1 + scale) + shift
+        return x
+
+
 def _get_qkv_projections(
     attn: "SkyReelsV2Attention", hidden_states: torch.Tensor, encoder_hidden_states: torch.Tensor
 ):
@@ -559,9 +615,10 @@ class SkyReelsV2Transformer3DModel(
 
     _supports_gradient_checkpointing = True
     _skip_layerwise_casting_patterns = ["patch_embedding", "condition_embedder", "norm"]
-    _no_split_modules = ["SkyReelsV2TransformerBlock"]
+    _no_split_modules = ["SkyReelsV2TransformerBlock", "SkyReelsV2AdaLayerNorm"]
     _keep_in_fp32_modules = ["time_embedder", "scale_shift_table", "norm1", "norm2", "norm3"]
-    _keys_to_ignore_on_load_unexpected = ["norm_added_q"]
+    _keys_to_ignore_on_load_unexpected = ["norm_added_q", "scale_shift_table"]
+    _keys_to_ignore_on_load_missing = ["norm_out.linear.weight", "norm_out.linear.bias"]
     _repeated_blocks = ["SkyReelsV2TransformerBlock"]
 
     @register_to_config
@@ -617,9 +674,13 @@ def __init__(
         )
 
         # 4. Output norm & projection
-        self.norm_out = FP32LayerNorm(inner_dim, eps, elementwise_affine=False)
+        self.norm_out = SkyReelsV2AdaLayerNorm(
+            embedding_dim=inner_dim,
+            output_dim=2 * inner_dim,
+            norm_elementwise_affine=False,
+            norm_eps=eps,
+        )
         self.proj_out = nn.Linear(inner_dim, out_channels * math.prod(patch_size))
-        self.scale_shift_table = nn.Parameter(torch.randn(1, 2, inner_dim) / inner_dim**0.5)
 
         if inject_sample_info:
             self.fps_embedding = nn.Embedding(2, inner_dim)
@@ -732,34 +793,7 @@ def forward(
                     causal_mask,
                 )
 
-        if temb.dim() == 2:
-            # If temb is 2D, we assume it has time 1-D time embedding values for each batch.
-            # For models:
-            # - Skywork/SkyReels-V2-T2V-14B-540P-Diffusers
-            # - Skywork/SkyReels-V2-T2V-14B-720P-Diffusers
-            # - Skywork/SkyReels-V2-I2V-1.3B-540P-Diffusers
-            # - Skywork/SkyReels-V2-I2V-14B-540P-Diffusers
-            # - Skywork/SkyReels-V2-I2V-14B-720P-Diffusers
-            shift, scale = (self.scale_shift_table + temb.unsqueeze(1)).chunk(2, dim=1)
-        elif temb.dim() == 3:
-            # If temb is 3D, we assume it has 2-D time embedding values for each batch.
-            # Each time embedding tensor includes values for each latent frame; thus Diffusion Forcing.
-            # For models:
-            # - Skywork/SkyReels-V2-DF-1.3B-540P-Diffusers
-            # - Skywork/SkyReels-V2-DF-14B-540P-Diffusers
-            # - Skywork/SkyReels-V2-DF-14B-720P-Diffusers
-            shift, scale = (self.scale_shift_table.unsqueeze(2) + temb.unsqueeze(1)).chunk(2, dim=1)
-            shift, scale = shift.squeeze(1), scale.squeeze(1)
-
-        # Move the shift and scale tensors to the same device as hidden_states.
-        # When using multi-GPU inference via accelerate these will be on the
-        # first device rather than the last device, which hidden_states ends up
-        # on.
-        shift = shift.to(hidden_states.device)
-        scale = scale.to(hidden_states.device)
-
-        hidden_states = (self.norm_out(hidden_states.float()) * (1 + scale) + shift).type_as(hidden_states)
-
+        hidden_states = self.norm_out(hidden_states, temb=temb)
         hidden_states = self.proj_out(hidden_states)
 
         hidden_states = hidden_states.reshape(