Wan VAE move scaling to pipeline

src/diffusers/models/autoencoders/autoencoder_kl_wan.py

@@ -715,11 +715,6 @@ def __init__(
     ) -> None:
         super().__init__()
 
-        # Store normalization parameters as tensors
-        self.mean = torch.tensor(latents_mean)
-        self.std = torch.tensor(latents_std)
-        self.scale = torch.stack([self.mean, 1.0 / self.std])  # Shape: [2, C]
-
         self.z_dim = z_dim
         self.temperal_downsample = temperal_downsample
         self.temperal_upsample = temperal_downsample[::-1]
@@ -751,7 +746,6 @@ def _count_conv3d(model):
         self._enc_feat_map = [None] * self._enc_conv_num
 
     def _encode(self, x: torch.Tensor) -> torch.Tensor:
-        scale = self.scale.type_as(x)
         self.clear_cache()
         ## cache
         t = x.shape[2]
@@ -770,8 +764,6 @@ def _encode(self, x: torch.Tensor) -> torch.Tensor:
 
         enc = self.quant_conv(out)
         mu, logvar = enc[:, : self.z_dim, :, :, :], enc[:, self.z_dim :, :, :, :]
-        mu = (mu - scale[0].view(1, self.z_dim, 1, 1, 1)) * scale[1].view(1, self.z_dim, 1, 1, 1)
-        logvar = (logvar - scale[0].view(1, self.z_dim, 1, 1, 1)) * scale[1].view(1, self.z_dim, 1, 1, 1)
         enc = torch.cat([mu, logvar], dim=1)
         self.clear_cache()
         return enc
@@ -798,10 +790,8 @@ def encode(
             return (posterior,)
         return AutoencoderKLOutput(latent_dist=posterior)
 
-    def _decode(self, z: torch.Tensor, scale, return_dict: bool = True) -> Union[DecoderOutput, torch.Tensor]:
+    def _decode(self, z: torch.Tensor, return_dict: bool = True) -> Union[DecoderOutput, torch.Tensor]:
         self.clear_cache()
-        # z: [b,c,t,h,w]
-        z = z / scale[1].view(1, self.z_dim, 1, 1, 1) + scale[0].view(1, self.z_dim, 1, 1, 1)
 
         iter_ = z.shape[2]
         x = self.post_quant_conv(z)
@@ -835,8 +825,7 @@ def decode(self, z: torch.Tensor, return_dict: bool = True) -> Union[DecoderOutp
                 If return_dict is True, a [`~models.vae.DecoderOutput`] is returned, otherwise a plain `tuple` is
                 returned.
         """
-        scale = self.scale.type_as(z)
-        decoded = self._decode(z, scale).sample
+        decoded = self._decode(z).sample
         if not return_dict:
             return (decoded,)
 

src/diffusers/pipelines/wan/pipeline_wan.py

@@ -563,6 +563,15 @@ def __call__(
 
         if not output_type == "latent":
             latents = latents.to(self.vae.dtype)
+            latents_mean = (
+                torch.tensor(self.vae.config.latents_mean)
+                .view(1, self.vae.config.z_dim, 1, 1, 1)
+                .to(latents.device, latents.dtype)
+            )
+            latents_std = 1.0 / torch.tensor(self.vae.config.latents_std).view(1, self.vae.config.z_dim, 1, 1, 1).to(
+                latents.device, latents.dtype
+            )
+            latents = latents / latents_std + latents_mean
             video = self.vae.decode(latents, return_dict=False)[0]
             video = self.video_processor.postprocess_video(video, output_type=output_type)
         else:

src/diffusers/pipelines/wan/pipeline_wan_i2v.py

@@ -392,6 +392,17 @@ def prepare_latents(
             latent_condition = retrieve_latents(self.vae.encode(video_condition), generator)
             latent_condition = latent_condition.repeat(batch_size, 1, 1, 1, 1)
 
+        latents_mean = (
+            torch.tensor(self.vae.config.latents_mean)
+            .view(1, self.vae.config.z_dim, 1, 1, 1)
+            .to(latents.device, latents.dtype)
+        )
+        latents_std = 1.0 / torch.tensor(self.vae.config.latents_std).view(1, self.vae.config.z_dim, 1, 1, 1).to(
+            latents.device, latents.dtype
+        )
+
+        latent_condition = (latent_condition - latents_mean) * latents_std
+
         mask_lat_size = torch.ones(batch_size, 1, num_frames, latent_height, latent_width)
         mask_lat_size[:, :, list(range(1, num_frames))] = 0
         first_frame_mask = mask_lat_size[:, :, 0:1]
@@ -654,6 +665,15 @@ def __call__(
 
         if not output_type == "latent":
             latents = latents.to(self.vae.dtype)
+            latents_mean = (
+                torch.tensor(self.vae.config.latents_mean)
+                .view(1, self.vae.config.z_dim, 1, 1, 1)
+                .to(latents.device, latents.dtype)
+            )
+            latents_std = 1.0 / torch.tensor(self.vae.config.latents_std).view(1, self.vae.config.z_dim, 1, 1, 1).to(
+                latents.device, latents.dtype
+            )
+            latents = latents / latents_std + latents_mean
             video = self.vae.decode(latents, return_dict=False)[0]
             video = self.video_processor.postprocess_video(video, output_type=output_type)
         else: