diff --git a/src/diffusers/models/autoencoders/autoencoder_kl_wan.py b/src/diffusers/models/autoencoders/autoencoder_kl_wan.py
index 49cefcd8a1..8ecde415dc 100644
--- a/src/diffusers/models/autoencoders/autoencoder_kl_wan.py
+++ b/src/diffusers/models/autoencoders/autoencoder_kl_wan.py
@@ -34,6 +34,104 @@ logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 CACHE_T = 2
 
 
+class AvgDown3D(nn.Module):
+
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        factor_t,
+        factor_s=1,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.factor_t = factor_t
+        self.factor_s = factor_s
+        self.factor = self.factor_t * self.factor_s * self.factor_s
+
+        assert in_channels * self.factor % out_channels == 0
+        self.group_size = in_channels * self.factor // out_channels
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        pad_t = (self.factor_t - x.shape[2] % self.factor_t) % self.factor_t
+        pad = (0, 0, 0, 0, pad_t, 0)
+        x = F.pad(x, pad)
+        B, C, T, H, W = x.shape
+        x = x.view(
+            B,
+            C,
+            T // self.factor_t,
+            self.factor_t,
+            H // self.factor_s,
+            self.factor_s,
+            W // self.factor_s,
+            self.factor_s,
+        )
+        x = x.permute(0, 1, 3, 5, 7, 2, 4, 6).contiguous()
+        x = x.view(
+            B,
+            C * self.factor,
+            T // self.factor_t,
+            H // self.factor_s,
+            W // self.factor_s,
+        )
+        x = x.view(
+            B,
+            self.out_channels,
+            self.group_size,
+            T // self.factor_t,
+            H // self.factor_s,
+            W // self.factor_s,
+        )
+        x = x.mean(dim=2)
+        return x
+
+
+class DupUp3D(nn.Module):
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        factor_t,
+        factor_s=1,
+    ):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+
+        self.factor_t = factor_t
+        self.factor_s = factor_s
+        self.factor = self.factor_t * self.factor_s * self.factor_s
+
+        assert out_channels * self.factor % in_channels == 0
+        self.repeats = out_channels * self.factor // in_channels
+
+    def forward(self, x: torch.Tensor, first_chunk=False) -> torch.Tensor:
+        x = x.repeat_interleave(self.repeats, dim=1)
+        x = x.view(
+            x.size(0),
+            self.out_channels,
+            self.factor_t,
+            self.factor_s,
+            self.factor_s,
+            x.size(2),
+            x.size(3),
+            x.size(4),
+        )
+        x = x.permute(0, 1, 5, 2, 6, 3, 7, 4).contiguous()
+        x = x.view(
+            x.size(0),
+            self.out_channels,
+            x.size(2) * self.factor_t,
+            x.size(4) * self.factor_s,
+            x.size(6) * self.factor_s,
+        )
+        if first_chunk:
+            x = x[:, :, self.factor_t - 1:, :, :]
+        return x
+
 class WanCausalConv3d(nn.Conv3d):
     r"""
     A custom 3D causal convolution layer with feature caching support.
@@ -134,19 +232,23 @@ class WanResample(nn.Module):
             - 'downsample3d': 3D downsampling with zero-padding, convolution, and causal 3D convolution.
     """
 
-    def __init__(self, dim: int, mode: str) -> None:
+    def __init__(self, dim: int, mode: str, upsample_out_dim: int = None) -> None:
         super().__init__()
         self.dim = dim
         self.mode = mode
+        
+        # default to dim //2
+        if upsample_out_dim is None:
+            upsample_out_dim = dim // 2
 
         # layers
         if mode == "upsample2d":
             self.resample = nn.Sequential(
-                WanUpsample(scale_factor=(2.0, 2.0), mode="nearest-exact"), nn.Conv2d(dim, dim // 2, 3, padding=1)
+                WanUpsample(scale_factor=(2.0, 2.0), mode="nearest-exact"), nn.Conv2d(dim, upsample_out_dim, 3, padding=1)
             )
         elif mode == "upsample3d":
             self.resample = nn.Sequential(
-                WanUpsample(scale_factor=(2.0, 2.0), mode="nearest-exact"), nn.Conv2d(dim, dim // 2, 3, padding=1)
+                WanUpsample(scale_factor=(2.0, 2.0), mode="nearest-exact"), nn.Conv2d(dim, upsample_out_dim, 3, padding=1)
             )
             self.time_conv = WanCausalConv3d(dim, dim * 2, (3, 1, 1), padding=(1, 0, 0))
 
@@ -363,6 +465,48 @@ class WanMidBlock(nn.Module):
         return x
 
 
+class WanResidualDownBlock(nn.Module):
+
+    def __init__(self,
+                 in_dim,
+                 out_dim,
+                 dropout,
+                 num_res_blocks,
+                 temperal_downsample=False,
+                 down_flag=False):
+        super().__init__()
+
+        # Shortcut path with downsample
+        self.avg_shortcut = AvgDown3D(
+            in_dim,
+            out_dim,
+            factor_t=2 if temperal_downsample else 1,
+            factor_s=2 if down_flag else 1,
+        )
+
+        # Main path with residual blocks and downsample
+        resnets = []
+        for _ in range(num_res_blocks):
+            resnets.append(WanResidualBlock(in_dim, out_dim, dropout))
+            in_dim = out_dim
+        self.resnets = nn.ModuleList(resnets)
+
+        # Add the final downsample block
+        if down_flag:
+            mode = "downsample3d" if temperal_downsample else "downsample2d"
+            self.downsampler = WanResample(out_dim, mode=mode)
+        else:
+            self.downsampler = None
+
+    def forward(self, x, feat_cache=None, feat_idx=[0]):
+        x_copy = x.clone()
+        for resnet in self.resnets:
+            x = resnet(x, feat_cache, feat_idx)
+        if self.downsampler is not None:
+            x = self.downsampler(x, feat_cache, feat_idx)
+
+        return x + self.avg_shortcut(x_copy)
+
 class WanEncoder3d(nn.Module):
     r"""
     A 3D encoder module.
@@ -380,6 +524,7 @@ class WanEncoder3d(nn.Module):
 
     def __init__(
         self,
+        in_channels: int = 3,   
         dim=128,
         z_dim=4,
         dim_mult=[1, 2, 4, 4],
@@ -388,6 +533,7 @@ class WanEncoder3d(nn.Module):
         temperal_downsample=[True, True, False],
         dropout=0.0,
         non_linearity: str = "silu",
+        is_residual: bool = False, # wan 2.2 vae use a residual downblock
     ):
         super().__init__()
         self.dim = dim
@@ -403,23 +549,35 @@ class WanEncoder3d(nn.Module):
         scale = 1.0
 
         # init block
-        self.conv_in = WanCausalConv3d(3, dims[0], 3, padding=1)
+        self.conv_in = WanCausalConv3d(in_channels, dims[0], 3, padding=1)
 
         # downsample blocks
         self.down_blocks = nn.ModuleList([])
         for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])):
             # residual (+attention) blocks
-            for _ in range(num_res_blocks):
-                self.down_blocks.append(WanResidualBlock(in_dim, out_dim, dropout))
-                if scale in attn_scales:
-                    self.down_blocks.append(WanAttentionBlock(out_dim))
-                in_dim = out_dim
+            if is_residual:
+                self.down_blocks.append(
+                    WanResidualDownBlock(
+                        in_dim, 
+                        out_dim, 
+                        dropout, 
+                        num_res_blocks, 
+                        temperal_downsample=temperal_downsample[i] if i != len(dim_mult) - 1 else False,
+                        down_flag=i != len(dim_mult) - 1,
+                        )
+                        )
+            else:
+                for _ in range(num_res_blocks):
+                    self.down_blocks.append(WanResidualBlock(in_dim, out_dim, dropout))
+                    if scale in attn_scales:
+                        self.down_blocks.append(WanAttentionBlock(out_dim))
+                    in_dim = out_dim
 
-            # downsample block
-            if i != len(dim_mult) - 1:
-                mode = "downsample3d" if temperal_downsample[i] else "downsample2d"
-                self.down_blocks.append(WanResample(out_dim, mode=mode))
-                scale /= 2.0
+                # downsample block
+                if i != len(dim_mult) - 1:
+                    mode = "downsample3d" if temperal_downsample[i] else "downsample2d"
+                    self.down_blocks.append(WanResample(out_dim, mode=mode))
+                    scale /= 2.0
 
         # middle blocks
         self.mid_block = WanMidBlock(out_dim, dropout, non_linearity, num_layers=1)
@@ -469,6 +627,92 @@ class WanEncoder3d(nn.Module):
             x = self.conv_out(x)
         return x
 
+class WanResidualUpBlock(nn.Module):
+    """
+    A block that handles upsampling for the WanVAE decoder.
+
+    Args:
+        in_dim (int): Input dimension
+        out_dim (int): Output dimension
+        num_res_blocks (int): Number of residual blocks
+        dropout (float): Dropout rate
+        temperal_upsample (bool): Whether to upsample on temporal dimension
+        up_flag (bool): Whether to upsample or not
+        non_linearity (str): Type of non-linearity to use
+    """
+
+    def __init__(
+        self,
+        in_dim: int,
+        out_dim: int,
+        num_res_blocks: int,
+        dropout: float = 0.0,
+        temperal_upsample: bool = False,
+        up_flag: bool = False,
+        non_linearity: str = "silu",
+    ):
+        super().__init__()
+        self.in_dim = in_dim
+        self.out_dim = out_dim
+
+        if up_flag:
+            self.avg_shortcut = DupUp3D(
+                in_dim,
+                out_dim,
+                factor_t=2 if temperal_upsample else 1,
+                factor_s=2,
+            )
+        else:
+            self.avg_shortcut = None
+
+        # create residual blocks
+        resnets = []
+        current_dim = in_dim
+        for _ in range(num_res_blocks + 1):
+            resnets.append(WanResidualBlock(current_dim, out_dim, dropout, non_linearity))
+            current_dim = out_dim
+
+        self.resnets = nn.ModuleList(resnets)
+
+        # Add upsampling layer if needed
+        if up_flag:
+            upsample_mode = "upsample3d" if temperal_upsample else "upsample2d"
+            self.upsampler = WanResample(out_dim, mode=upsample_mode, upsample_out_dim=out_dim)
+        else:
+            self.upsampler = None
+
+        self.gradient_checkpointing = False
+
+    def forward(self, x, feat_cache=None, feat_idx=[0], first_chunk=False):
+        """
+        Forward pass through the upsampling block.
+
+        Args:
+            x (torch.Tensor): Input tensor
+            feat_cache (list, optional): Feature cache for causal convolutions
+            feat_idx (list, optional): Feature index for cache management
+
+        Returns:
+            torch.Tensor: Output tensor
+        """
+        x_copy = x.clone()
+
+        for resnet in self.resnets:
+            if feat_cache is not None:
+                x = resnet(x, feat_cache, feat_idx)
+            else:
+                x = resnet(x)
+
+        if self.upsampler is not None:
+            if feat_cache is not None:
+                x = self.upsampler(x, feat_cache, feat_idx)
+            else:
+                x = self.upsampler(x)
+        
+        if self.avg_shortcut is not None:
+            x = x + self.avg_shortcut(x_copy, first_chunk=first_chunk)
+        
+        return x
 
 class WanUpBlock(nn.Module):
     """
@@ -513,7 +757,7 @@ class WanUpBlock(nn.Module):
 
         self.gradient_checkpointing = False
 
-    def forward(self, x, feat_cache=None, feat_idx=[0]):
+    def forward(self, x, feat_cache=None, feat_idx=[0], first_chunk=None):
         """
         Forward pass through the upsampling block.
 
@@ -564,6 +808,8 @@ class WanDecoder3d(nn.Module):
         temperal_upsample=[False, True, True],
         dropout=0.0,
         non_linearity: str = "silu",
+        out_channels: int = 3,
+        is_residual: bool = False,
     ):
         super().__init__()
         self.dim = dim
@@ -577,7 +823,6 @@ class WanDecoder3d(nn.Module):
 
         # dimensions
         dims = [dim * u for u in [dim_mult[-1]] + dim_mult[::-1]]
-        scale = 1.0 / 2 ** (len(dim_mult) - 2)
 
         # init block
         self.conv_in = WanCausalConv3d(z_dim, dims[0], 3, padding=1)
@@ -589,36 +834,47 @@ class WanDecoder3d(nn.Module):
         self.up_blocks = nn.ModuleList([])
         for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])):
             # residual (+attention) blocks
-            if i > 0:
+            if i > 0 and not is_residual:
+                # wan vae 2.1
                 in_dim = in_dim // 2
 
-            # Determine if we need upsampling
+            # determine if we need upsampling
+            up_flag = i != len(dim_mult) - 1
+            # determine upsampling mode, if not upsampling, set to None
             upsample_mode = None
-            if i != len(dim_mult) - 1:
-                upsample_mode = "upsample3d" if temperal_upsample[i] else "upsample2d"
-
+            if up_flag and temperal_upsample[i]:
+                upsample_mode = "upsample3d"
+            elif up_flag:
+                upsample_mode = "upsample2d"
             # Create and add the upsampling block
-            up_block = WanUpBlock(
-                in_dim=in_dim,
-                out_dim=out_dim,
-                num_res_blocks=num_res_blocks,
-                dropout=dropout,
-                upsample_mode=upsample_mode,
-                non_linearity=non_linearity,
-            )
+            if is_residual:
+                up_block = WanResidualUpBlock(
+                    in_dim=in_dim,
+                    out_dim=out_dim,
+                    num_res_blocks=num_res_blocks,
+                    dropout=dropout,
+                    temperal_upsample=temperal_upsample[i] if up_flag else False,
+                    up_flag= up_flag,
+                    non_linearity=non_linearity,
+                )
+            else:
+                up_block = WanUpBlock(
+                    in_dim=in_dim,
+                    out_dim=out_dim,
+                    num_res_blocks=num_res_blocks,
+                    dropout=dropout,
+                    upsample_mode=upsample_mode,
+                    non_linearity=non_linearity,
+                )
             self.up_blocks.append(up_block)
 
-            # Update scale for next iteration
-            if upsample_mode is not None:
-                scale *= 2.0
-
         # output blocks
         self.norm_out = WanRMS_norm(out_dim, images=False)
-        self.conv_out = WanCausalConv3d(out_dim, 3, 3, padding=1)
+        self.conv_out = WanCausalConv3d(out_dim, out_channels, 3, padding=1)
 
         self.gradient_checkpointing = False
 
-    def forward(self, x, feat_cache=None, feat_idx=[0]):
+    def forward(self, x, feat_cache=None, feat_idx=[0], first_chunk=False):
         ## conv1
         if feat_cache is not None:
             idx = feat_idx[0]
@@ -637,7 +893,7 @@ class WanDecoder3d(nn.Module):
 
         ## upsamples
         for up_block in self.up_blocks:
-            x = up_block(x, feat_cache, feat_idx)
+            x = up_block(x, feat_cache, feat_idx, first_chunk = first_chunk)
 
         ## head
         x = self.norm_out(x)
@@ -656,6 +912,44 @@ class WanDecoder3d(nn.Module):
         return x
 
 
+# YiYi TODO: refactor this
+from einops import rearrange
+
+def patchify(x, patch_size):
+    if patch_size == 1:
+        return x
+    if x.dim() == 4:
+        x = rearrange(
+            x, "b c (h q) (w r) -> b (c r q) h w", q=patch_size, r=patch_size)
+    elif x.dim() == 5:
+        x = rearrange(
+            x,
+            "b c f (h q) (w r) -> b (c r q) f h w",
+            q=patch_size,
+            r=patch_size,
+        )
+    else:
+        raise ValueError(f"Invalid input shape: {x.shape}")
+
+    return x
+
+
+def unpatchify(x, patch_size):
+    if patch_size == 1:
+        return x
+
+    if x.dim() == 4:
+        x = rearrange(
+            x, "b (c r q) h w -> b c (h q) (w r)", q=patch_size, r=patch_size)
+    elif x.dim() == 5:
+        x = rearrange(
+            x,
+            "b (c r q) f h w -> b c f (h q) (w r)",
+            q=patch_size,
+            r=patch_size,
+        )
+    return x
+
 class AutoencoderKLWan(ModelMixin, ConfigMixin, FromOriginalModelMixin):
     r"""
     A VAE model with KL loss for encoding videos into latents and decoding latent representations into videos.
@@ -671,6 +965,7 @@ class AutoencoderKLWan(ModelMixin, ConfigMixin, FromOriginalModelMixin):
     def __init__(
         self,
         base_dim: int = 96,
+        decoder_base_dim: Optional[int] = None,
         z_dim: int = 16,
         dim_mult: Tuple[int] = [1, 2, 4, 4],
         num_res_blocks: int = 2,
@@ -713,6 +1008,10 @@ class AutoencoderKLWan(ModelMixin, ConfigMixin, FromOriginalModelMixin):
             2.8251,
             1.9160,
         ],
+        is_residual: bool = False,
+        in_channels: int = 3,
+        out_channels: int = 3,
+        patch_size: Optional[int] = None,
     ) -> None:
         super().__init__()
 
@@ -720,14 +1019,17 @@ class AutoencoderKLWan(ModelMixin, ConfigMixin, FromOriginalModelMixin):
         self.temperal_downsample = temperal_downsample
         self.temperal_upsample = temperal_downsample[::-1]
 
+        if decoder_base_dim is None:
+            decoder_base_dim = base_dim
+
         self.encoder = WanEncoder3d(
-            base_dim, z_dim * 2, dim_mult, num_res_blocks, attn_scales, self.temperal_downsample, dropout
+            in_channels=in_channels, dim=base_dim, z_dim=z_dim * 2, dim_mult=dim_mult, num_res_blocks=num_res_blocks, attn_scales=attn_scales, temperal_downsample=temperal_downsample, dropout=dropout, is_residual=is_residual
         )
         self.quant_conv = WanCausalConv3d(z_dim * 2, z_dim * 2, 1)
         self.post_quant_conv = WanCausalConv3d(z_dim, z_dim, 1)
 
         self.decoder = WanDecoder3d(
-            base_dim, z_dim, dim_mult, num_res_blocks, attn_scales, self.temperal_upsample, dropout
+            dim=decoder_base_dim, z_dim=z_dim, dim_mult=dim_mult, num_res_blocks=num_res_blocks, attn_scales=attn_scales, temperal_upsample=self.temperal_upsample, dropout=dropout, out_channels=out_channels, is_residual=is_residual
         )
 
         self.spatial_compression_ratio = 2 ** len(self.temperal_downsample)
@@ -827,6 +1129,8 @@ class AutoencoderKLWan(ModelMixin, ConfigMixin, FromOriginalModelMixin):
             return self.tiled_encode(x)
 
         self.clear_cache()
+        if self.config.patch_size is not None:
+            x = patchify(x, patch_size=self.config.patch_size)
         iter_ = 1 + (num_frame - 1) // 4
         for i in range(iter_):
             self._enc_conv_idx = [0]
@@ -884,12 +1188,14 @@ class AutoencoderKLWan(ModelMixin, ConfigMixin, FromOriginalModelMixin):
         for i in range(num_frame):
             self._conv_idx = [0]
             if i == 0:
-                out = self.decoder(x[:, :, i : i + 1, :, :], feat_cache=self._feat_map, feat_idx=self._conv_idx)
+                out = self.decoder(x[:, :, i : i + 1, :, :], feat_cache=self._feat_map, feat_idx=self._conv_idx, first_chunk=True)
             else:
                 out_ = self.decoder(x[:, :, i : i + 1, :, :], feat_cache=self._feat_map, feat_idx=self._conv_idx)
                 out = torch.cat([out, out_], 2)
 
         out = torch.clamp(out, min=-1.0, max=1.0)
+        if self.config.patch_size is not None:
+            out = unpatchify(out, patch_size=self.config.patch_size)
         self.clear_cache()
         if not return_dict:
             return (out,)
diff --git a/src/diffusers/pipelines/wan/pipeline_wan.py b/src/diffusers/pipelines/wan/pipeline_wan.py
index d14dac91f1..748b20c112 100644
--- a/src/diffusers/pipelines/wan/pipeline_wan.py
+++ b/src/diffusers/pipelines/wan/pipeline_wan.py
@@ -112,10 +112,21 @@ class WanPipeline(DiffusionPipeline, WanLoraLoaderMixin):
             A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
         vae ([`AutoencoderKLWan`]):
             Variational Auto-Encoder (VAE) Model to encode and decode videos to and from latent representations.
+        transformer_2 ([`WanTransformer3DModel`], *optional*):
+            Conditional Transformer to denoise the input latents during the low-noise stage.
+            If provided, enables two-stage denoising where `transformer` handles high-noise stages
+            and `transformer_2` handles low-noise stages. If not provided, only `transformer` is used.
+        boundary_ratio (`float`, *optional*, defaults to `None`):
+            Ratio of total timesteps to use as the boundary for switching between transformers in two-stage denoising.
+            The actual boundary timestep is calculated as `boundary_ratio * num_train_timesteps`.
+            When provided, `transformer` handles timesteps >= boundary_timestep and `transformer_2` handles timesteps < boundary_timestep.
+            If `None`, only `transformer` is used for the entire denoising process.
     """
 
-    model_cpu_offload_seq = "text_encoder->transformer->vae"
+    model_cpu_offload_seq = "text_encoder->transformer->transformer_2->vae"
     _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
+    _optional_components = ["transformer_2"]
+
 
     def __init__(
         self,
@@ -124,6 +135,8 @@ class WanPipeline(DiffusionPipeline, WanLoraLoaderMixin):
         transformer: WanTransformer3DModel,
         vae: AutoencoderKLWan,
         scheduler: FlowMatchEulerDiscreteScheduler,
+        transformer_2: Optional[WanTransformer3DModel] = None,
+        boundary_ratio: Optional[float] = None,
     ):
         super().__init__()
 
@@ -133,8 +146,9 @@ class WanPipeline(DiffusionPipeline, WanLoraLoaderMixin):
             tokenizer=tokenizer,
             transformer=transformer,
             scheduler=scheduler,
+            transformer_2=transformer_2,
         )
-
+        self.register_to_config(boundary_ratio=boundary_ratio)
         self.vae_scale_factor_temporal = 2 ** sum(self.vae.temperal_downsample) if getattr(self, "vae", None) else 4
         self.vae_scale_factor_spatial = 2 ** len(self.vae.temperal_downsample) if getattr(self, "vae", None) else 8
         self.video_processor = VideoProcessor(vae_scale_factor=self.vae_scale_factor_spatial)
@@ -270,6 +284,7 @@ class WanPipeline(DiffusionPipeline, WanLoraLoaderMixin):
         prompt_embeds=None,
         negative_prompt_embeds=None,
         callback_on_step_end_tensor_inputs=None,
+        guidance_scale_2=None,
     ):
         if height % 16 != 0 or width % 16 != 0:
             raise ValueError(f"`height` and `width` have to be divisible by 16 but are {height} and {width}.")
@@ -301,6 +316,9 @@ class WanPipeline(DiffusionPipeline, WanLoraLoaderMixin):
             not isinstance(negative_prompt, str) and not isinstance(negative_prompt, list)
         ):
             raise ValueError(f"`negative_prompt` has to be of type `str` or `list` but is {type(negative_prompt)}")
+        
+        if self.config.boundary_ratio is None and guidance_scale_2 is not None:
+            raise ValueError("`guidance_scale_2` is only supported when the pipeline's `boundary_ratio` is not None.")
 
     def prepare_latents(
         self,
@@ -369,6 +387,7 @@ class WanPipeline(DiffusionPipeline, WanLoraLoaderMixin):
         num_frames: int = 81,
         num_inference_steps: int = 50,
         guidance_scale: float = 5.0,
+        guidance_scale_2: Optional[float] = None,
         num_videos_per_prompt: Optional[int] = 1,
         generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
         latents: Optional[torch.Tensor] = None,
@@ -407,6 +426,9 @@ class WanPipeline(DiffusionPipeline, WanLoraLoaderMixin):
                 of [Imagen Paper](https://huggingface.co/papers/2205.11487). Guidance scale is enabled by setting
                 `guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked to
                 the text `prompt`, usually at the expense of lower image quality.
+            guidance_scale_2 (`float`, *optional*, defaults to `None`):
+                Guidance scale for the low-noise stage transformer (`transformer_2`). If `None` and the pipeline's `boundary_ratio` is not None,
+                uses the same value as `guidance_scale`. Only used when `transformer_2` and the pipeline's `boundary_ratio` are not None.
             num_videos_per_prompt (`int`, *optional*, defaults to 1):
                 The number of images to generate per prompt.
             generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
@@ -461,6 +483,7 @@ class WanPipeline(DiffusionPipeline, WanLoraLoaderMixin):
             prompt_embeds,
             negative_prompt_embeds,
             callback_on_step_end_tensor_inputs,
+            guidance_scale_2,
         )
 
         if num_frames % self.vae_scale_factor_temporal != 1:
@@ -470,7 +493,11 @@ class WanPipeline(DiffusionPipeline, WanLoraLoaderMixin):
             num_frames = num_frames // self.vae_scale_factor_temporal * self.vae_scale_factor_temporal + 1
         num_frames = max(num_frames, 1)
 
+        if self.config.boundary_ratio is not None and guidance_scale_2 is None:
+            guidance_scale_2 = guidance_scale
+
         self._guidance_scale = guidance_scale
+        self._guidance_scale_2 = guidance_scale_2
         self._attention_kwargs = attention_kwargs
         self._current_timestep = None
         self._interrupt = False
@@ -524,34 +551,49 @@ class WanPipeline(DiffusionPipeline, WanLoraLoaderMixin):
         num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
         self._num_timesteps = len(timesteps)
 
+        if self.config.boundary_ratio is not None:
+            boundary_timestep = self.config.boundary_ratio * self.scheduler.config.num_train_timesteps
+        else:
+            boundary_timestep = None
+
         with self.progress_bar(total=num_inference_steps) as progress_bar:
             for i, t in enumerate(timesteps):
                 if self.interrupt:
                     continue
 
                 self._current_timestep = t
+
+                if boundary_timestep is None or t >= boundary_timestep:
+                    # wan2.1 or high-noise stage in wan2.2
+                    current_model = self.transformer
+                    current_guidance_scale = guidance_scale
+                else:
+                    # low-noise stage in wan2.2
+                    current_model = self.transformer_2
+                    current_guidance_scale = guidance_scale_2
+
                 latent_model_input = latents.to(transformer_dtype)
                 timestep = t.expand(latents.shape[0])
 
-                with self.transformer.cache_context("cond"):
-                    noise_pred = self.transformer(
+                #with current_model.cache_context("cond"):
+                noise_pred = current_model(
+                    hidden_states=latent_model_input,
+                    timestep=timestep,
+                    encoder_hidden_states=prompt_embeds,
+                    attention_kwargs=attention_kwargs,
+                    return_dict=False,
+                )[0]
+
+                if self.do_classifier_free_guidance:
+                    #with current_model.cache_context("uncond"):
+                    noise_uncond = current_model(
                         hidden_states=latent_model_input,
                         timestep=timestep,
-                        encoder_hidden_states=prompt_embeds,
+                        encoder_hidden_states=negative_prompt_embeds,
                         attention_kwargs=attention_kwargs,
                         return_dict=False,
                     )[0]
-
-                if self.do_classifier_free_guidance:
-                    with self.transformer.cache_context("uncond"):
-                        noise_uncond = self.transformer(
-                            hidden_states=latent_model_input,
-                            timestep=timestep,
-                            encoder_hidden_states=negative_prompt_embeds,
-                            attention_kwargs=attention_kwargs,
-                            return_dict=False,
-                        )[0]
-                    noise_pred = noise_uncond + guidance_scale * (noise_pred - noise_uncond)
+                    noise_pred = noise_uncond + current_guidance_scale * (noise_pred - noise_uncond)
 
                 # compute the previous noisy sample x_t -> x_t-1
                 latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]