diff --git a/src/diffusers/models/autoencoders/autoencoder_asym_kl.py b/src/diffusers/models/autoencoders/autoencoder_asym_kl.py
index 54b1fc677188..fa49fcfe79f8 100644
--- a/src/diffusers/models/autoencoders/autoencoder_asym_kl.py
+++ b/src/diffusers/models/autoencoders/autoencoder_asym_kl.py
@@ -20,10 +20,10 @@
 from ...utils.accelerate_utils import apply_forward_hook
 from ..modeling_outputs import AutoencoderKLOutput
 from ..modeling_utils import ModelMixin
-from .vae import DecoderOutput, DiagonalGaussianDistribution, Encoder, MaskConditionDecoder
+from .vae import AutoencoderMixin, DecoderOutput, DiagonalGaussianDistribution, Encoder, MaskConditionDecoder
 
 
-class AsymmetricAutoencoderKL(ModelMixin, ConfigMixin):
+class AsymmetricAutoencoderKL(ModelMixin, AutoencoderMixin, ConfigMixin):
     r"""
     Designing a Better Asymmetric VQGAN for StableDiffusion https://huggingface.co/papers/2306.04632 . A VAE model with
     KL loss for encoding images into latents and decoding latent representations into images.
@@ -107,9 +107,6 @@ def __init__(
         self.quant_conv = nn.Conv2d(2 * latent_channels, 2 * latent_channels, 1)
         self.post_quant_conv = nn.Conv2d(latent_channels, latent_channels, 1)
 
-        self.use_slicing = False
-        self.use_tiling = False
-
         self.register_to_config(block_out_channels=up_block_out_channels)
         self.register_to_config(force_upcast=False)
 
diff --git a/src/diffusers/models/autoencoders/autoencoder_dc.py b/src/diffusers/models/autoencoders/autoencoder_dc.py
index 783f22e97daf..724ec3bb760c 100644
--- a/src/diffusers/models/autoencoders/autoencoder_dc.py
+++ b/src/diffusers/models/autoencoders/autoencoder_dc.py
@@ -27,7 +27,7 @@
 from ..modeling_utils import ModelMixin
 from ..normalization import RMSNorm, get_normalization
 from ..transformers.sana_transformer import GLUMBConv
-from .vae import DecoderOutput, EncoderOutput
+from .vae import AutoencoderMixin, DecoderOutput, EncoderOutput
 
 
 class ResBlock(nn.Module):
@@ -378,7 +378,7 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
         return hidden_states
 
 
-class AutoencoderDC(ModelMixin, ConfigMixin, FromOriginalModelMixin):
+class AutoencoderDC(ModelMixin, AutoencoderMixin, ConfigMixin, FromOriginalModelMixin):
     r"""
     An Autoencoder model introduced in [DCAE](https://huggingface.co/papers/2410.10733) and used in
     [SANA](https://huggingface.co/papers/2410.10629).
@@ -536,27 +536,6 @@ def enable_tiling(
         self.tile_latent_min_height = self.tile_sample_min_height // self.spatial_compression_ratio
         self.tile_latent_min_width = self.tile_sample_min_width // self.spatial_compression_ratio
 
-    def disable_tiling(self) -> None:
-        r"""
-        Disable tiled AE decoding. If `enable_tiling` was previously enabled, this method will go back to computing
-        decoding in one step.
-        """
-        self.use_tiling = False
-
-    def enable_slicing(self) -> None:
-        r"""
-        Enable sliced AE decoding. When this option is enabled, the AE will split the input tensor in slices to compute
-        decoding in several steps. This is useful to save some memory and allow larger batch sizes.
-        """
-        self.use_slicing = True
-
-    def disable_slicing(self) -> None:
-        r"""
-        Disable sliced AE decoding. If `enable_slicing` was previously enabled, this method will go back to computing
-        decoding in one step.
-        """
-        self.use_slicing = False
-
     def _encode(self, x: torch.Tensor) -> torch.Tensor:
         batch_size, num_channels, height, width = x.shape
 
diff --git a/src/diffusers/models/autoencoders/autoencoder_kl.py b/src/diffusers/models/autoencoders/autoencoder_kl.py
index d823c2fb8b04..1a72aa3cfeb3 100644
--- a/src/diffusers/models/autoencoders/autoencoder_kl.py
+++ b/src/diffusers/models/autoencoders/autoencoder_kl.py
@@ -32,10 +32,10 @@
 )
 from ..modeling_outputs import AutoencoderKLOutput
 from ..modeling_utils import ModelMixin
-from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder
+from .vae import AutoencoderMixin, Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder
 
 
-class AutoencoderKL(ModelMixin, ConfigMixin, FromOriginalModelMixin, PeftAdapterMixin):
+class AutoencoderKL(ModelMixin, AutoencoderMixin, ConfigMixin, FromOriginalModelMixin, PeftAdapterMixin):
     r"""
     A VAE model with KL loss for encoding images into latents and decoding latent representations into images.
 
@@ -138,35 +138,6 @@ def __init__(
         self.tile_latent_min_size = int(sample_size / (2 ** (len(self.config.block_out_channels) - 1)))
         self.tile_overlap_factor = 0.25
 
-    def enable_tiling(self, use_tiling: bool = True):
-        r"""
-        Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
-        compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
-        processing larger images.
-        """
-        self.use_tiling = use_tiling
-
-    def disable_tiling(self):
-        r"""
-        Disable tiled VAE decoding. If `enable_tiling` was previously enabled, this method will go back to computing
-        decoding in one step.
-        """
-        self.enable_tiling(False)
-
-    def enable_slicing(self):
-        r"""
-        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
-        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
-        """
-        self.use_slicing = True
-
-    def disable_slicing(self):
-        r"""
-        Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing
-        decoding in one step.
-        """
-        self.use_slicing = False
-
     @property
     # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
     def attn_processors(self) -> Dict[str, AttentionProcessor]:
diff --git a/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py b/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py
index c24b8f42aca4..6756586460d3 100644
--- a/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py
+++ b/src/diffusers/models/autoencoders/autoencoder_kl_allegro.py
@@ -28,6 +28,7 @@
 from ..modeling_utils import ModelMixin
 from ..resnet import ResnetBlock2D
 from ..upsampling import Upsample2D
+from .vae import AutoencoderMixin
 
 
 class AllegroTemporalConvLayer(nn.Module):
@@ -673,7 +674,7 @@ def forward(self, sample: torch.Tensor) -> torch.Tensor:
         return sample
 
 
-class AutoencoderKLAllegro(ModelMixin, ConfigMixin):
+class AutoencoderKLAllegro(ModelMixin, AutoencoderMixin, ConfigMixin):
     r"""
     A VAE model with KL loss for encoding videos into latents and decoding latent representations into videos. Used in
     [Allegro](https://github.com/rhymes-ai/Allegro).
@@ -795,35 +796,6 @@ def __init__(
             sample_size - self.tile_overlap_w,
         )
 
-    def enable_tiling(self) -> None:
-        r"""
-        Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
-        compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
-        processing larger images.
-        """
-        self.use_tiling = True
-
-    def disable_tiling(self) -> None:
-        r"""
-        Disable tiled VAE decoding. If `enable_tiling` was previously enabled, this method will go back to computing
-        decoding in one step.
-        """
-        self.use_tiling = False
-
-    def enable_slicing(self) -> None:
-        r"""
-        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
-        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
-        """
-        self.use_slicing = True
-
-    def disable_slicing(self) -> None:
-        r"""
-        Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing
-        decoding in one step.
-        """
-        self.use_slicing = False
-
     def _encode(self, x: torch.Tensor) -> torch.Tensor:
         # TODO(aryan)
         # if self.use_tiling and (width > self.tile_sample_min_width or height > self.tile_sample_min_height):
diff --git a/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py b/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py
index e0e9436e8984..5096b725d0bb 100644
--- a/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py
+++ b/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py
@@ -29,7 +29,7 @@
 from ..modeling_outputs import AutoencoderKLOutput
 from ..modeling_utils import ModelMixin
 from ..upsampling import CogVideoXUpsample3D
-from .vae import DecoderOutput, DiagonalGaussianDistribution
+from .vae import AutoencoderMixin, DecoderOutput, DiagonalGaussianDistribution
 
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
@@ -955,7 +955,7 @@ def forward(
         return hidden_states, new_conv_cache
 
 
-class AutoencoderKLCogVideoX(ModelMixin, ConfigMixin, FromOriginalModelMixin):
+class AutoencoderKLCogVideoX(ModelMixin, AutoencoderMixin, ConfigMixin, FromOriginalModelMixin):
     r"""
     A VAE model with KL loss for encoding images into latents and decoding latent representations into images. Used in
     [CogVideoX](https://github.com/THUDM/CogVideo).
@@ -1124,27 +1124,6 @@ def enable_tiling(
         self.tile_overlap_factor_height = tile_overlap_factor_height or self.tile_overlap_factor_height
         self.tile_overlap_factor_width = tile_overlap_factor_width or self.tile_overlap_factor_width
 
-    def disable_tiling(self) -> None:
-        r"""
-        Disable tiled VAE decoding. If `enable_tiling` was previously enabled, this method will go back to computing
-        decoding in one step.
-        """
-        self.use_tiling = False
-
-    def enable_slicing(self) -> None:
-        r"""
-        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
-        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
-        """
-        self.use_slicing = True
-
-    def disable_slicing(self) -> None:
-        r"""
-        Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing
-        decoding in one step.
-        """
-        self.use_slicing = False
-
     def _encode(self, x: torch.Tensor) -> torch.Tensor:
         batch_size, num_channels, num_frames, height, width = x.shape
 
diff --git a/src/diffusers/models/autoencoders/autoencoder_kl_cosmos.py b/src/diffusers/models/autoencoders/autoencoder_kl_cosmos.py
index 500e316ebcf0..b17522d1c424 100644
--- a/src/diffusers/models/autoencoders/autoencoder_kl_cosmos.py
+++ b/src/diffusers/models/autoencoders/autoencoder_kl_cosmos.py
@@ -24,7 +24,7 @@
 from ...utils.accelerate_utils import apply_forward_hook
 from ..modeling_outputs import AutoencoderKLOutput
 from ..modeling_utils import ModelMixin
-from .vae import DecoderOutput, IdentityDistribution
+from .vae import AutoencoderMixin, DecoderOutput, IdentityDistribution
 
 
 logger = get_logger(__name__)
@@ -875,7 +875,7 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
         return hidden_states
 
 
-class AutoencoderKLCosmos(ModelMixin, ConfigMixin):
+class AutoencoderKLCosmos(ModelMixin, AutoencoderMixin, ConfigMixin):
     r"""
     Autoencoder used in [Cosmos](https://huggingface.co/papers/2501.03575).
 
@@ -1031,27 +1031,6 @@ def enable_tiling(
         self.tile_sample_stride_width = tile_sample_stride_width or self.tile_sample_stride_width
         self.tile_sample_stride_num_frames = tile_sample_stride_num_frames or self.tile_sample_stride_num_frames
 
-    def disable_tiling(self) -> None:
-        r"""
-        Disable tiled VAE decoding. If `enable_tiling` was previously enabled, this method will go back to computing
-        decoding in one step.
-        """
-        self.use_tiling = False
-
-    def enable_slicing(self) -> None:
-        r"""
-        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
-        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
-        """
-        self.use_slicing = True
-
-    def disable_slicing(self) -> None:
-        r"""
-        Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing
-        decoding in one step.
-        """
-        self.use_slicing = False
-
     def _encode(self, x: torch.Tensor) -> torch.Tensor:
         x = self.encoder(x)
         enc = self.quant_conv(x)
diff --git a/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py b/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py
index dc5e775f6769..88b9bb507ff6 100644
--- a/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py
+++ b/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py
@@ -26,7 +26,7 @@
 from ..attention_processor import Attention
 from ..modeling_outputs import AutoencoderKLOutput
 from ..modeling_utils import ModelMixin
-from .vae import DecoderOutput, DiagonalGaussianDistribution
+from .vae import AutoencoderMixin, DecoderOutput, DiagonalGaussianDistribution
 
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
@@ -624,7 +624,7 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
         return hidden_states
 
 
-class AutoencoderKLHunyuanVideo(ModelMixin, ConfigMixin):
+class AutoencoderKLHunyuanVideo(ModelMixin, AutoencoderMixin, ConfigMixin):
     r"""
     A VAE model with KL loss for encoding videos into latents and decoding latent representations into videos.
     Introduced in [HunyuanVideo](https://huggingface.co/papers/2412.03603).
@@ -763,27 +763,6 @@ def enable_tiling(
         self.tile_sample_stride_width = tile_sample_stride_width or self.tile_sample_stride_width
         self.tile_sample_stride_num_frames = tile_sample_stride_num_frames or self.tile_sample_stride_num_frames
 
-    def disable_tiling(self) -> None:
-        r"""
-        Disable tiled VAE decoding. If `enable_tiling` was previously enabled, this method will go back to computing
-        decoding in one step.
-        """
-        self.use_tiling = False
-
-    def enable_slicing(self) -> None:
-        r"""
-        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
-        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
-        """
-        self.use_slicing = True
-
-    def disable_slicing(self) -> None:
-        r"""
-        Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing
-        decoding in one step.
-        """
-        self.use_slicing = False
-
     def _encode(self, x: torch.Tensor) -> torch.Tensor:
         batch_size, num_channels, num_frames, height, width = x.shape
 
diff --git a/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py b/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py
index 51c600a4e915..47f2081b7e45 100644
--- a/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py
+++ b/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py
@@ -26,7 +26,7 @@
 from ..modeling_outputs import AutoencoderKLOutput
 from ..modeling_utils import ModelMixin
 from ..normalization import RMSNorm
-from .vae import DecoderOutput, DiagonalGaussianDistribution
+from .vae import AutoencoderMixin, DecoderOutput, DiagonalGaussianDistribution
 
 
 class LTXVideoCausalConv3d(nn.Module):
@@ -1034,7 +1034,7 @@ def forward(self, hidden_states: torch.Tensor, temb: Optional[torch.Tensor] = No
         return hidden_states
 
 
-class AutoencoderKLLTXVideo(ModelMixin, ConfigMixin, FromOriginalModelMixin):
+class AutoencoderKLLTXVideo(ModelMixin, AutoencoderMixin, ConfigMixin, FromOriginalModelMixin):
     r"""
     A VAE model with KL loss for encoding images into latents and decoding latent representations into images. Used in
     [LTX](https://huggingface.co/Lightricks/LTX-Video).
@@ -1219,27 +1219,6 @@ def enable_tiling(
         self.tile_sample_stride_width = tile_sample_stride_width or self.tile_sample_stride_width
         self.tile_sample_stride_num_frames = tile_sample_stride_num_frames or self.tile_sample_stride_num_frames
 
-    def disable_tiling(self) -> None:
-        r"""
-        Disable tiled VAE decoding. If `enable_tiling` was previously enabled, this method will go back to computing
-        decoding in one step.
-        """
-        self.use_tiling = False
-
-    def enable_slicing(self) -> None:
-        r"""
-        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
-        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
-        """
-        self.use_slicing = True
-
-    def disable_slicing(self) -> None:
-        r"""
-        Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing
-        decoding in one step.
-        """
-        self.use_slicing = False
-
     def _encode(self, x: torch.Tensor) -> torch.Tensor:
         batch_size, num_channels, num_frames, height, width = x.shape
 
diff --git a/src/diffusers/models/autoencoders/autoencoder_kl_magvit.py b/src/diffusers/models/autoencoders/autoencoder_kl_magvit.py
index 43294a901f02..97ca9d669264 100644
--- a/src/diffusers/models/autoencoders/autoencoder_kl_magvit.py
+++ b/src/diffusers/models/autoencoders/autoencoder_kl_magvit.py
@@ -26,7 +26,7 @@
 from ..activations import get_activation
 from ..modeling_outputs import AutoencoderKLOutput
 from ..modeling_utils import ModelMixin
-from .vae import DecoderOutput, DiagonalGaussianDistribution
+from .vae import AutoencoderMixin, DecoderOutput, DiagonalGaussianDistribution
 
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
@@ -663,7 +663,7 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
         return hidden_states
 
 
-class AutoencoderKLMagvit(ModelMixin, ConfigMixin):
+class AutoencoderKLMagvit(ModelMixin, AutoencoderMixin, ConfigMixin):
     r"""
     A VAE model with KL loss for encoding images into latents and decoding latent representations into images. This
     model is used in [EasyAnimate](https://huggingface.co/papers/2405.18991).
@@ -805,27 +805,6 @@ def enable_tiling(
         self.tile_sample_stride_width = tile_sample_stride_width or self.tile_sample_stride_width
         self.tile_sample_stride_num_frames = tile_sample_stride_num_frames or self.tile_sample_stride_num_frames
 
-    def disable_tiling(self) -> None:
-        r"""
-        Disable tiled VAE decoding. If `enable_tiling` was previously enabled, this method will go back to computing
-        decoding in one step.
-        """
-        self.use_tiling = False
-
-    def enable_slicing(self) -> None:
-        r"""
-        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
-        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
-        """
-        self.use_slicing = True
-
-    def disable_slicing(self) -> None:
-        r"""
-        Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing
-        decoding in one step.
-        """
-        self.use_slicing = False
-
     @apply_forward_hook
     def _encode(
         self, x: torch.Tensor, return_dict: bool = True
diff --git a/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py b/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py
index 404d2f6d860a..3ded9a0a5491 100644
--- a/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py
+++ b/src/diffusers/models/autoencoders/autoencoder_kl_mochi.py
@@ -27,7 +27,7 @@
 from ..modeling_outputs import AutoencoderKLOutput
 from ..modeling_utils import ModelMixin
 from .autoencoder_kl_cogvideox import CogVideoXCausalConv3d
-from .vae import DecoderOutput, DiagonalGaussianDistribution
+from .vae import AutoencoderMixin, DecoderOutput, DiagonalGaussianDistribution
 
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
@@ -657,7 +657,7 @@ def forward(
         return hidden_states, new_conv_cache
 
 
-class AutoencoderKLMochi(ModelMixin, ConfigMixin):
+class AutoencoderKLMochi(ModelMixin, AutoencoderMixin, ConfigMixin):
     r"""
     A VAE model with KL loss for encoding images into latents and decoding latent representations into images. Used in
     [Mochi 1 preview](https://github.com/genmoai/models).
@@ -818,27 +818,6 @@ def enable_tiling(
         self.tile_sample_stride_height = tile_sample_stride_height or self.tile_sample_stride_height
         self.tile_sample_stride_width = tile_sample_stride_width or self.tile_sample_stride_width
 
-    def disable_tiling(self) -> None:
-        r"""
-        Disable tiled VAE decoding. If `enable_tiling` was previously enabled, this method will go back to computing
-        decoding in one step.
-        """
-        self.use_tiling = False
-
-    def enable_slicing(self) -> None:
-        r"""
-        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
-        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
-        """
-        self.use_slicing = True
-
-    def disable_slicing(self) -> None:
-        r"""
-        Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing
-        decoding in one step.
-        """
-        self.use_slicing = False
-
     def _enable_framewise_encoding(self):
         r"""
         Enables the framewise VAE encoding implementation with past latent padding. By default, Diffusers uses the
diff --git a/src/diffusers/models/autoencoders/autoencoder_kl_qwenimage.py b/src/diffusers/models/autoencoders/autoencoder_kl_qwenimage.py
index 9872cf096875..14db6aeb61e3 100644
--- a/src/diffusers/models/autoencoders/autoencoder_kl_qwenimage.py
+++ b/src/diffusers/models/autoencoders/autoencoder_kl_qwenimage.py
@@ -31,7 +31,7 @@
 from ..activations import get_activation
 from ..modeling_outputs import AutoencoderKLOutput
 from ..modeling_utils import ModelMixin
-from .vae import DecoderOutput, DiagonalGaussianDistribution
+from .vae import AutoencoderMixin, DecoderOutput, DiagonalGaussianDistribution
 
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
@@ -663,7 +663,7 @@ def forward(self, x, feat_cache=None, feat_idx=[0]):
         return x
 
 
-class AutoencoderKLQwenImage(ModelMixin, ConfigMixin, FromOriginalModelMixin):
+class AutoencoderKLQwenImage(ModelMixin, AutoencoderMixin, ConfigMixin, FromOriginalModelMixin):
     r"""
     A VAE model with KL loss for encoding videos into latents and decoding latent representations into videos.
 
@@ -763,27 +763,6 @@ def enable_tiling(
         self.tile_sample_stride_height = tile_sample_stride_height or self.tile_sample_stride_height
         self.tile_sample_stride_width = tile_sample_stride_width or self.tile_sample_stride_width
 
-    def disable_tiling(self) -> None:
-        r"""
-        Disable tiled VAE decoding. If `enable_tiling` was previously enabled, this method will go back to computing
-        decoding in one step.
-        """
-        self.use_tiling = False
-
-    def enable_slicing(self) -> None:
-        r"""
-        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
-        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
-        """
-        self.use_slicing = True
-
-    def disable_slicing(self) -> None:
-        r"""
-        Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing
-        decoding in one step.
-        """
-        self.use_slicing = False
-
     def clear_cache(self):
         def _count_conv3d(model):
             count = 0
diff --git a/src/diffusers/models/autoencoders/autoencoder_kl_temporal_decoder.py b/src/diffusers/models/autoencoders/autoencoder_kl_temporal_decoder.py
index cf46e52564bf..ab76254d19e2 100644
--- a/src/diffusers/models/autoencoders/autoencoder_kl_temporal_decoder.py
+++ b/src/diffusers/models/autoencoders/autoencoder_kl_temporal_decoder.py
@@ -23,7 +23,7 @@
 from ..modeling_outputs import AutoencoderKLOutput
 from ..modeling_utils import ModelMixin
 from ..unets.unet_3d_blocks import MidBlockTemporalDecoder, UpBlockTemporalDecoder
-from .vae import DecoderOutput, DiagonalGaussianDistribution, Encoder
+from .vae import AutoencoderMixin, DecoderOutput, DiagonalGaussianDistribution, Encoder
 
 
 class TemporalDecoder(nn.Module):
@@ -135,7 +135,7 @@ def forward(
         return sample
 
 
-class AutoencoderKLTemporalDecoder(ModelMixin, ConfigMixin):
+class AutoencoderKLTemporalDecoder(ModelMixin, AutoencoderMixin, ConfigMixin):
     r"""
     A VAE model with KL loss for encoding images into latents and decoding latent representations into images.
 
diff --git a/src/diffusers/models/autoencoders/autoencoder_kl_wan.py b/src/diffusers/models/autoencoders/autoencoder_kl_wan.py
index f95c4cf37475..a3aaa8465092 100644
--- a/src/diffusers/models/autoencoders/autoencoder_kl_wan.py
+++ b/src/diffusers/models/autoencoders/autoencoder_kl_wan.py
@@ -25,7 +25,7 @@
 from ..activations import get_activation
 from ..modeling_outputs import AutoencoderKLOutput
 from ..modeling_utils import ModelMixin
-from .vae import DecoderOutput, DiagonalGaussianDistribution
+from .vae import AutoencoderMixin, DecoderOutput, DiagonalGaussianDistribution
 
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
@@ -951,7 +951,7 @@ def unpatchify(x, patch_size):
     return x
 
 
-class AutoencoderKLWan(ModelMixin, ConfigMixin, FromOriginalModelMixin):
+class AutoencoderKLWan(ModelMixin, AutoencoderMixin, ConfigMixin, FromOriginalModelMixin):
     r"""
     A VAE model with KL loss for encoding videos into latents and decoding latent representations into videos.
     Introduced in [Wan 2.1].
@@ -1110,27 +1110,6 @@ def enable_tiling(
         self.tile_sample_stride_height = tile_sample_stride_height or self.tile_sample_stride_height
         self.tile_sample_stride_width = tile_sample_stride_width or self.tile_sample_stride_width
 
-    def disable_tiling(self) -> None:
-        r"""
-        Disable tiled VAE decoding. If `enable_tiling` was previously enabled, this method will go back to computing
-        decoding in one step.
-        """
-        self.use_tiling = False
-
-    def enable_slicing(self) -> None:
-        r"""
-        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
-        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
-        """
-        self.use_slicing = True
-
-    def disable_slicing(self) -> None:
-        r"""
-        Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing
-        decoding in one step.
-        """
-        self.use_slicing = False
-
     def clear_cache(self):
         # Use cached conv counts for decoder and encoder to avoid re-iterating modules each call
         self._conv_num = self._cached_conv_counts["decoder"]
diff --git a/src/diffusers/models/autoencoders/autoencoder_oobleck.py b/src/diffusers/models/autoencoders/autoencoder_oobleck.py
index a10b616b4e25..d83264559209 100644
--- a/src/diffusers/models/autoencoders/autoencoder_oobleck.py
+++ b/src/diffusers/models/autoencoders/autoencoder_oobleck.py
@@ -25,6 +25,7 @@
 from ...utils.accelerate_utils import apply_forward_hook
 from ...utils.torch_utils import randn_tensor
 from ..modeling_utils import ModelMixin
+from .vae import AutoencoderMixin
 
 
 class Snake1d(nn.Module):
@@ -291,7 +292,7 @@ def forward(self, hidden_state):
         return hidden_state
 
 
-class AutoencoderOobleck(ModelMixin, ConfigMixin):
+class AutoencoderOobleck(ModelMixin, AutoencoderMixin, ConfigMixin):
     r"""
     An autoencoder for encoding waveforms into latents and decoding latent representations into waveforms. First
     introduced in Stable Audio.
@@ -356,20 +357,6 @@ def __init__(
 
         self.use_slicing = False
 
-    def enable_slicing(self):
-        r"""
-        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
-        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
-        """
-        self.use_slicing = True
-
-    def disable_slicing(self):
-        r"""
-        Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing
-        decoding in one step.
-        """
-        self.use_slicing = False
-
     @apply_forward_hook
     def encode(
         self, x: torch.Tensor, return_dict: bool = True
diff --git a/src/diffusers/models/autoencoders/autoencoder_tiny.py b/src/diffusers/models/autoencoders/autoencoder_tiny.py
index 3e2b28606e29..b9ac713d7392 100644
--- a/src/diffusers/models/autoencoders/autoencoder_tiny.py
+++ b/src/diffusers/models/autoencoders/autoencoder_tiny.py
@@ -22,7 +22,7 @@
 from ...utils import BaseOutput
 from ...utils.accelerate_utils import apply_forward_hook
 from ..modeling_utils import ModelMixin
-from .vae import DecoderOutput, DecoderTiny, EncoderTiny
+from .vae import AutoencoderMixin, DecoderOutput, DecoderTiny, EncoderTiny
 
 
 @dataclass
@@ -38,7 +38,7 @@ class AutoencoderTinyOutput(BaseOutput):
     latents: torch.Tensor
 
 
-class AutoencoderTiny(ModelMixin, ConfigMixin):
+class AutoencoderTiny(ModelMixin, AutoencoderMixin, ConfigMixin):
     r"""
     A tiny distilled VAE model for encoding images into latents and decoding latent representations into images.
 
@@ -162,35 +162,6 @@ def unscale_latents(self, x: torch.Tensor) -> torch.Tensor:
         """[0, 1] -> raw latents"""
         return x.sub(self.latent_shift).mul(2 * self.latent_magnitude)
 
-    def enable_slicing(self) -> None:
-        r"""
-        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
-        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
-        """
-        self.use_slicing = True
-
-    def disable_slicing(self) -> None:
-        r"""
-        Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing
-        decoding in one step.
-        """
-        self.use_slicing = False
-
-    def enable_tiling(self, use_tiling: bool = True) -> None:
-        r"""
-        Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
-        compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
-        processing larger images.
-        """
-        self.use_tiling = use_tiling
-
-    def disable_tiling(self) -> None:
-        r"""
-        Disable tiled VAE decoding. If `enable_tiling` was previously enabled, this method will go back to computing
-        decoding in one step.
-        """
-        self.enable_tiling(False)
-
     def _tiled_encode(self, x: torch.Tensor) -> torch.Tensor:
         r"""Encode a batch of images using a tiled encoder.
 
diff --git a/src/diffusers/models/autoencoders/consistency_decoder_vae.py b/src/diffusers/models/autoencoders/consistency_decoder_vae.py
index b3017a878092..0a6258fed37f 100644
--- a/src/diffusers/models/autoencoders/consistency_decoder_vae.py
+++ b/src/diffusers/models/autoencoders/consistency_decoder_vae.py
@@ -32,7 +32,7 @@
 )
 from ..modeling_utils import ModelMixin
 from ..unets.unet_2d import UNet2DModel
-from .vae import DecoderOutput, DiagonalGaussianDistribution, Encoder
+from .vae import AutoencoderMixin, DecoderOutput, DiagonalGaussianDistribution, Encoder
 
 
 @dataclass
@@ -49,7 +49,7 @@ class ConsistencyDecoderVAEOutput(BaseOutput):
     latent_dist: "DiagonalGaussianDistribution"
 
 
-class ConsistencyDecoderVAE(ModelMixin, ConfigMixin):
+class ConsistencyDecoderVAE(ModelMixin, AutoencoderMixin, ConfigMixin):
     r"""
     The consistency decoder used with DALL-E 3.
 
@@ -167,39 +167,6 @@ def __init__(
         self.tile_latent_min_size = int(sample_size / (2 ** (len(self.config.block_out_channels) - 1)))
         self.tile_overlap_factor = 0.25
 
-    # Copied from diffusers.models.autoencoders.autoencoder_kl.AutoencoderKL.enable_tiling
-    def enable_tiling(self, use_tiling: bool = True):
-        r"""
-        Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
-        compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
-        processing larger images.
-        """
-        self.use_tiling = use_tiling
-
-    # Copied from diffusers.models.autoencoders.autoencoder_kl.AutoencoderKL.disable_tiling
-    def disable_tiling(self):
-        r"""
-        Disable tiled VAE decoding. If `enable_tiling` was previously enabled, this method will go back to computing
-        decoding in one step.
-        """
-        self.enable_tiling(False)
-
-    # Copied from diffusers.models.autoencoders.autoencoder_kl.AutoencoderKL.enable_slicing
-    def enable_slicing(self):
-        r"""
-        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
-        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
-        """
-        self.use_slicing = True
-
-    # Copied from diffusers.models.autoencoders.autoencoder_kl.AutoencoderKL.disable_slicing
-    def disable_slicing(self):
-        r"""
-        Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing
-        decoding in one step.
-        """
-        self.use_slicing = False
-
     @property
     # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
     def attn_processors(self) -> Dict[str, AttentionProcessor]:
diff --git a/src/diffusers/models/autoencoders/vae.py b/src/diffusers/models/autoencoders/vae.py
index 1d74d4f472d7..7b171961254a 100644
--- a/src/diffusers/models/autoencoders/vae.py
+++ b/src/diffusers/models/autoencoders/vae.py
@@ -894,3 +894,38 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
 
         # scale image from [0, 1] to [-1, 1] to match diffusers convention
         return x.mul(2).sub(1)
+
+
+class AutoencoderMixin:
+    def enable_tiling(self):
+        r"""
+        Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to
+        compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow
+        processing larger images.
+        """
+        if not hasattr(self, "use_tiling"):
+            raise NotImplementedError(f"Tiling doesn't seem to be implemented for {self.__class__.__name__}.")
+        self.use_tiling = True
+
+    def disable_tiling(self):
+        r"""
+        Disable tiled VAE decoding. If `enable_tiling` was previously enabled, this method will go back to computing
+        decoding in one step.
+        """
+        self.use_tiling = False
+
+    def enable_slicing(self):
+        r"""
+        Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to
+        compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.
+        """
+        if not hasattr(self, "use_slicing"):
+            raise NotImplementedError(f"Slicing doesn't seem to be implemented for {self.__class__.__name__}.")
+        self.use_slicing = True
+
+    def disable_slicing(self):
+        r"""
+        Disable sliced VAE decoding. If `enable_slicing` was previously enabled, this method will go back to computing
+        decoding in one step.
+        """
+        self.use_slicing = False
diff --git a/src/diffusers/models/autoencoders/vq_model.py b/src/diffusers/models/autoencoders/vq_model.py
index c1094e62f7ec..82436473dfc6 100644
--- a/src/diffusers/models/autoencoders/vq_model.py
+++ b/src/diffusers/models/autoencoders/vq_model.py
@@ -22,6 +22,7 @@
 from ...utils.accelerate_utils import apply_forward_hook
 from ..autoencoders.vae import Decoder, DecoderOutput, Encoder, VectorQuantizer
 from ..modeling_utils import ModelMixin
+from .vae import AutoencoderMixin
 
 
 @dataclass
@@ -37,7 +38,7 @@ class VQEncoderOutput(BaseOutput):
     latents: torch.Tensor
 
 
-class VQModel(ModelMixin, ConfigMixin):
+class VQModel(ModelMixin, AutoencoderMixin, ConfigMixin):
     r"""
     A VQ-VAE model for decoding latent representations.
 
diff --git a/tests/models/autoencoders/testing_utils.py b/tests/models/autoencoders/testing_utils.py
index cf1f10a4a545..8ae362ac2e94 100644
--- a/tests/models/autoencoders/testing_utils.py
+++ b/tests/models/autoencoders/testing_utils.py
@@ -57,6 +57,9 @@ def test_enable_disable_tiling(self):
         torch.manual_seed(0)
         model = self.model_class(**init_dict).to(torch_device)
 
+        if not hasattr(model, "use_tiling"):
+            pytest.skip(f"Skipping test as {self.model_class.__name__} doesn't support tiling.")
+
         inputs_dict.update({"return_dict": False})
         _ = inputs_dict.pop("generator", None)
         accepts_generator = self._accepts_generator(model)
@@ -102,6 +105,8 @@ def test_enable_disable_slicing(self):
 
         torch.manual_seed(0)
         model = self.model_class(**init_dict).to(torch_device)
+        if not hasattr(model, "use_slicing"):
+            pytest.skip(f"Skipping test as {self.model_class.__name__} doesn't support tiling.")
 
         inputs_dict.update({"return_dict": False})
         _ = inputs_dict.pop("generator", None)