AutoencoderKLMagvit

The 3D variational autoencoder (VAE) model with KL loss used in EasyAnimate was introduced by Alibaba PAI.

The model can be loaded with the following code snippet.

from mindone.diffusers import AutoencoderKLMagvit

vae = AutoencoderKLMagvit.from_pretrained("alibaba-pai/EasyAnimateV5.1-12b-zh", subfolder="vae", mindspore_dtype=ms.float16).to("cuda")

mindone.diffusers.AutoencoderKLMagvit

Bases: ModelMixin, ConfigMixin

A VAE model with KL loss for encoding images into latents and decoding latent representations into images. This model is used in EasyAnimate.

This model inherits from [ModelMixin]. Check the superclass documentation for it's generic methods implemented for all models (such as downloading or saving).

Source code in mindone/diffusers/models/autoencoders/autoencoder_kl_magvit.py

class AutoencoderKLMagvit(ModelMixin, 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://arxiv.org/abs/2405.18991).

    This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
    for all models (such as downloading or saving).
    """

    _supports_gradient_checkpointing = True

    @register_to_config
    def __init__(
        self,
        in_channels: int = 3,
        latent_channels: int = 16,
        out_channels: int = 3,
        block_out_channels: Tuple[int, ...] = [128, 256, 512, 512],
        down_block_types: Tuple[str, ...] = [
            "SpatialDownBlock3D",
            "SpatialTemporalDownBlock3D",
            "SpatialTemporalDownBlock3D",
            "SpatialTemporalDownBlock3D",
        ],
        up_block_types: Tuple[str, ...] = [
            "SpatialUpBlock3D",
            "SpatialTemporalUpBlock3D",
            "SpatialTemporalUpBlock3D",
            "SpatialTemporalUpBlock3D",
        ],
        layers_per_block: int = 2,
        act_fn: str = "silu",
        norm_num_groups: int = 32,
        scaling_factor: float = 0.7125,
        spatial_group_norm: bool = True,
    ):
        super().__init__()

        # Initialize the encoder
        self.encoder = EasyAnimateEncoder(
            in_channels=in_channels,
            out_channels=latent_channels,
            down_block_types=down_block_types,
            block_out_channels=block_out_channels,
            layers_per_block=layers_per_block,
            norm_num_groups=norm_num_groups,
            act_fn=act_fn,
            double_z=True,
            spatial_group_norm=spatial_group_norm,
        )

        # Initialize the decoder
        self.decoder = EasyAnimateDecoder(
            in_channels=latent_channels,
            out_channels=out_channels,
            up_block_types=up_block_types,
            block_out_channels=block_out_channels,
            layers_per_block=layers_per_block,
            norm_num_groups=norm_num_groups,
            act_fn=act_fn,
            spatial_group_norm=spatial_group_norm,
        )

        # Initialize convolution layers for quantization and post-quantization
        self.quant_conv = mint.nn.Conv3d(2 * latent_channels, 2 * latent_channels, kernel_size=1)
        self.post_quant_conv = mint.nn.Conv3d(latent_channels, latent_channels, kernel_size=1)

        self.diag_gauss_dist = DiagonalGaussianDistribution()

        self.spatial_compression_ratio = 2 ** (len(block_out_channels) - 1)
        self.temporal_compression_ratio = 2 ** (len(block_out_channels) - 2)

        # When decoding a batch of video latents at a time, one can save memory by slicing across the batch dimension
        # to perform decoding of a single video latent at a time.
        self.use_slicing = False

        # When decoding spatially large video latents, the memory requirement is very high. By breaking the video latent
        # frames spatially into smaller tiles and performing multiple forward passes for decoding, and then blending the
        # intermediate tiles together, the memory requirement can be lowered.
        self.use_tiling = False

        # When decoding temporally long video latents, the memory requirement is very high. By decoding latent frames
        # at a fixed frame batch size (based on `self.num_latent_frames_batch_size`), the memory requirement can be lowered.
        self.use_framewise_encoding = False
        self.use_framewise_decoding = False

        # Assign mini-batch sizes for encoder and decoder
        self.num_sample_frames_batch_size = 4
        self.num_latent_frames_batch_size = 1

        # The minimal tile height and width for spatial tiling to be used
        self.tile_sample_min_height = 512
        self.tile_sample_min_width = 512
        self.tile_sample_min_num_frames = 4

        # The minimal distance between two spatial tiles
        self.tile_sample_stride_height = 448
        self.tile_sample_stride_width = 448
        self.tile_sample_stride_num_frames = 8

    def _clear_conv_cache(self):
        # Clear cache for convolutional layers if needed
        for name, module in self.name_cells().items():
            if isinstance(module, EasyAnimateCausalConv3d):
                module._clear_conv_cache()
            if isinstance(module, EasyAnimateUpsampler3D):
                module._clear_conv_cache()

    def enable_tiling(
        self,
        tile_sample_min_height: Optional[int] = None,
        tile_sample_min_width: Optional[int] = None,
        tile_sample_min_num_frames: Optional[int] = None,
        tile_sample_stride_height: Optional[float] = None,
        tile_sample_stride_width: Optional[float] = None,
        tile_sample_stride_num_frames: Optional[float] = None,
    ) -> 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.

        Args:
            tile_sample_min_height (`int`, *optional*):
                The minimum height required for a sample to be separated into tiles across the height dimension.
            tile_sample_min_width (`int`, *optional*):
                The minimum width required for a sample to be separated into tiles across the width dimension.
            tile_sample_stride_height (`int`, *optional*):
                The minimum amount of overlap between two consecutive vertical tiles. This is to ensure that there are
                no tiling artifacts produced across the height dimension.
            tile_sample_stride_width (`int`, *optional*):
                The stride between two consecutive horizontal tiles. This is to ensure that there are no tiling
                artifacts produced across the width dimension.
        """
        self.use_tiling = True
        self.use_framewise_decoding = True
        self.use_framewise_encoding = True
        self.tile_sample_min_height = tile_sample_min_height or self.tile_sample_min_height
        self.tile_sample_min_width = tile_sample_min_width or self.tile_sample_min_width
        self.tile_sample_min_num_frames = tile_sample_min_num_frames or self.tile_sample_min_num_frames
        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
        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: ms.Tensor, return_dict: bool = False
    ) -> Union[AutoencoderKLOutput, Tuple[DiagonalGaussianDistribution]]:
        """
        Encode a batch of images into latents.

        Args:
            x (`ms.Tensor`): Input batch of images.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether to return a [`~models.autoencoder_kl.AutoencoderKLOutput`] instead of a plain tuple.

        Returns:
                The latent representations of the encoded images. If `return_dict` is True, a
                [`~models.autoencoder_kl.AutoencoderKLOutput`] is returned, otherwise a plain `tuple` is returned.
        """
        if self.use_tiling and (x.shape[-1] > self.tile_sample_min_height or x.shape[-2] > self.tile_sample_min_width):
            return self.tiled_encode(x, return_dict=return_dict)

        first_frames = self.encoder(x[:, :, :1, :, :])
        h = [first_frames]
        for i in range(1, x.shape[2], self.num_sample_frames_batch_size):
            next_frames = self.encoder(x[:, :, i : i + self.num_sample_frames_batch_size, :, :])
            h.append(next_frames)
        h = mint.cat(h, dim=2)
        moments = self.quant_conv(h)

        self._clear_conv_cache()
        return moments

    def encode(
        self, x: ms.Tensor, return_dict: bool = False
    ) -> Union[AutoencoderKLOutput, Tuple[DiagonalGaussianDistribution]]:
        """
        Encode a batch of images into latents.

        Args:
            x (`ms.Tensor`): Input batch of images.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether to return a [`~models.autoencoder_kl.AutoencoderKLOutput`] instead of a plain tuple.

        Returns:
                The latent representations of the encoded videos. If `return_dict` is True, a
                [`~models.autoencoder_kl.AutoencoderKLOutput`] is returned, otherwise a plain `tuple` is returned.
        """
        if self.use_slicing and x.shape[0] > 1:
            encoded_slices = [self._encode(x_slice) for x_slice in x.split(1)]
            h = mint.cat(encoded_slices)
        else:
            h = self._encode(x)

        # we cannot use class in graph mode, even for jit_class or subclass of Tensor. :-(
        # posterior = DiagonalGaussianDistribution(h)

        if not return_dict:
            return (h,)
        # return AutoencoderKLOutput(latent_dist=h)
        return AutoencoderKLOutput(latent=h)

    def _decode(self, z: ms.Tensor, return_dict: bool = False) -> Union[DecoderOutput, ms.Tensor]:
        batch_size, num_channels, num_frames, height, width = z.shape
        tile_latent_min_height = self.tile_sample_min_height // self.spatial_compression_ratio
        tile_latent_min_width = self.tile_sample_stride_width // self.spatial_compression_ratio

        if self.use_tiling and (z.shape[-1] > tile_latent_min_height or z.shape[-2] > tile_latent_min_width):
            return self.tiled_decode(z, return_dict=return_dict)

        z = self.post_quant_conv(z)

        # Process the first frame and save the result
        first_frames = self.decoder(z[:, :, :1, :, :])
        # Initialize the list to store the processed frames, starting with the first frame
        dec = [first_frames]
        # Process the remaining frames, with the number of frames processed at a time determined by mini_batch_decoder
        for i in range(1, z.shape[2], self.num_latent_frames_batch_size):
            next_frames = self.decoder(z[:, :, i : i + self.num_latent_frames_batch_size, :, :])
            dec.append(next_frames)
        # Concatenate all processed frames along the channel dimension
        dec = mint.cat(dec, dim=2)

        if not return_dict:
            return (dec,)

        return DecoderOutput(sample=dec)

    def decode(self, z: ms.Tensor, return_dict: bool = False) -> Union[DecoderOutput, ms.Tensor]:
        """
        Decode a batch of images.

        Args:
            z (`ms.Tensor`): Input batch of latent vectors.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether to return a [`~models.vae.DecoderOutput`] instead of a plain tuple.

        Returns:
            [`~models.vae.DecoderOutput`] or `tuple`:
                If return_dict is True, a [`~models.vae.DecoderOutput`] is returned, otherwise a plain `tuple` is
                returned.
        """
        if self.use_slicing and z.shape[0] > 1:
            decoded_slices = [self._decode(z_slice)[0] for z_slice in z.split(1)]
            decoded = mint.cat(decoded_slices)
        else:
            decoded = self._decode(z)[0]

        self._clear_conv_cache()
        if not return_dict:
            return (decoded,)
        return DecoderOutput(sample=decoded)

    def blend_v(self, a: ms.Tensor, b: ms.Tensor, blend_extent: int) -> ms.Tensor:
        blend_extent = min(a.shape[3], b.shape[3], blend_extent)
        for y in range(blend_extent):
            b[:, :, :, y, :] = a[:, :, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, :, y, :] * (
                y / blend_extent
            )
        return b

    def blend_h(self, a: ms.Tensor, b: ms.Tensor, blend_extent: int) -> ms.Tensor:
        blend_extent = min(a.shape[4], b.shape[4], blend_extent)
        for x in range(blend_extent):
            b[:, :, :, :, x] = a[:, :, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, :, x] * (
                x / blend_extent
            )
        return b

    def tiled_encode(self, x: ms.Tensor, return_dict: bool = False) -> AutoencoderKLOutput:
        batch_size, num_channels, num_frames, height, width = x.shape
        latent_height = height // self.spatial_compression_ratio
        latent_width = width // self.spatial_compression_ratio

        tile_latent_min_height = self.tile_sample_min_height // self.spatial_compression_ratio
        tile_latent_min_width = self.tile_sample_min_width // self.spatial_compression_ratio
        tile_latent_stride_height = self.tile_sample_stride_height // self.spatial_compression_ratio
        tile_latent_stride_width = self.tile_sample_stride_width // self.spatial_compression_ratio

        blend_height = tile_latent_min_height - tile_latent_stride_height
        blend_width = tile_latent_min_width - tile_latent_stride_width

        # Split the image into 512x512 tiles and encode them separately.
        rows = []
        for i in range(0, height, self.tile_sample_stride_height):
            row = []
            for j in range(0, width, self.tile_sample_stride_width):
                tile = x[
                    :,
                    :,
                    :,
                    i : i + self.tile_sample_min_height,
                    j : j + self.tile_sample_min_width,
                ]

                first_frames = self.encoder(tile[:, :, 0:1, :, :])
                tile_h = [first_frames]
                for k in range(1, num_frames, self.num_sample_frames_batch_size):
                    next_frames = self.encoder(tile[:, :, k : k + self.num_sample_frames_batch_size, :, :])
                    tile_h.append(next_frames)
                tile = mint.cat(tile_h, dim=2)
                tile = self.quant_conv(tile)
                self._clear_conv_cache()
                row.append(tile)
            rows.append(row)
        result_rows = []
        for i, row in enumerate(rows):
            result_row = []
            for j, tile in enumerate(row):
                # blend the above tile and the left tile
                # to the current tile and add the current tile to the result row
                if i > 0:
                    tile = self.blend_v(rows[i - 1][j], tile, blend_height)
                if j > 0:
                    tile = self.blend_h(row[j - 1], tile, blend_width)
                result_row.append(tile[:, :, :, :latent_height, :latent_width])
            result_rows.append(mint.cat(result_row, dim=4))

        moments = mint.cat(result_rows, dim=3)[:, :, :, :latent_height, :latent_width]
        return moments

    def tiled_decode(self, z: ms.Tensor, return_dict: bool = False) -> Union[DecoderOutput, ms.Tensor]:
        batch_size, num_channels, num_frames, height, width = z.shape
        sample_height = height * self.spatial_compression_ratio
        sample_width = width * self.spatial_compression_ratio

        tile_latent_min_height = self.tile_sample_min_height // self.spatial_compression_ratio
        tile_latent_min_width = self.tile_sample_min_width // self.spatial_compression_ratio
        tile_latent_stride_height = self.tile_sample_stride_height // self.spatial_compression_ratio
        tile_latent_stride_width = self.tile_sample_stride_width // self.spatial_compression_ratio

        blend_height = self.tile_sample_min_height - self.tile_sample_stride_height
        blend_width = self.tile_sample_min_width - self.tile_sample_stride_width

        # Split z into overlapping 64x64 tiles and decode them separately.
        # The tiles have an overlap to avoid seams between tiles.
        rows = []
        for i in range(0, height, tile_latent_stride_height):
            row = []
            for j in range(0, width, tile_latent_stride_width):
                tile = z[
                    :,
                    :,
                    :,
                    i : i + tile_latent_min_height,
                    j : j + tile_latent_min_width,
                ]
                tile = self.post_quant_conv(tile)

                # Process the first frame and save the result
                first_frames = self.decoder(tile[:, :, :1, :, :])
                # Initialize the list to store the processed frames, starting with the first frame
                tile_dec = [first_frames]
                # Process the remaining frames, with the number of frames processed at a time determined by mini_batch_decoder
                for k in range(1, num_frames, self.num_latent_frames_batch_size):
                    next_frames = self.decoder(tile[:, :, k : k + self.num_latent_frames_batch_size, :, :])
                    tile_dec.append(next_frames)
                # Concatenate all processed frames along the channel dimension
                decoded = mint.cat(tile_dec, dim=2)
                self._clear_conv_cache()
                row.append(decoded)
            rows.append(row)
        result_rows = []
        for i, row in enumerate(rows):
            result_row = []
            for j, tile in enumerate(row):
                # blend the above tile and the left tile
                # to the current tile and add the current tile to the result row
                if i > 0:
                    tile = self.blend_v(rows[i - 1][j], tile, blend_height)
                if j > 0:
                    tile = self.blend_h(row[j - 1], tile, blend_width)
                result_row.append(tile[:, :, :, : self.tile_sample_stride_height, : self.tile_sample_stride_width])
            result_rows.append(mint.cat(result_row, dim=4))

        dec = mint.cat(result_rows, dim=3)[:, :, :, :sample_height, :sample_width]

        if not return_dict:
            return (dec,)

        return DecoderOutput(sample=dec)

    def construct(
        self,
        sample: ms.Tensor,
        sample_posterior: bool = False,
        return_dict: bool = False,
        generator: Optional[np.random.Generator] = None,
    ) -> Union[DecoderOutput, ms.Tensor]:
        r"""
        Args:
            sample (`ms.Tensor`): Input sample.
            sample_posterior (`bool`, *optional*, defaults to `False`):
                Whether to sample from the posterior.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`DecoderOutput`] instead of a plain tuple.
        """
        x = sample
        posterior = self.encode(x)[0]
        if sample_posterior:
            z = self.diag_gauss_dist.sample(posterior, generator=generator)
        else:
            z = self.diag_gauss_dist.mode(posterior)
        dec = self.decode(z)[0]

        if not return_dict:
            return (dec,)

        return DecoderOutput(sample=dec)

mindone.diffusers.AutoencoderKLMagvit.construct(sample, sample_posterior=False, return_dict=False, generator=None)

PARAMETER	DESCRIPTION
sample	Input sample. TYPE: `ms.Tensor`
sample_posterior	Whether to sample from the posterior. TYPE: `bool`, *optional*, defaults to `False` DEFAULT: False
return_dict	Whether or not to return a [DecoderOutput] instead of a plain tuple. TYPE: `bool`, *optional*, defaults to `True` DEFAULT: False

Source code in mindone/diffusers/models/autoencoders/autoencoder_kl_magvit.py

def construct(
    self,
    sample: ms.Tensor,
    sample_posterior: bool = False,
    return_dict: bool = False,
    generator: Optional[np.random.Generator] = None,
) -> Union[DecoderOutput, ms.Tensor]:
    r"""
    Args:
        sample (`ms.Tensor`): Input sample.
        sample_posterior (`bool`, *optional*, defaults to `False`):
            Whether to sample from the posterior.
        return_dict (`bool`, *optional*, defaults to `True`):
            Whether or not to return a [`DecoderOutput`] instead of a plain tuple.
    """
    x = sample
    posterior = self.encode(x)[0]
    if sample_posterior:
        z = self.diag_gauss_dist.sample(posterior, generator=generator)
    else:
        z = self.diag_gauss_dist.mode(posterior)
    dec = self.decode(z)[0]

    if not return_dict:
        return (dec,)

    return DecoderOutput(sample=dec)

mindone.diffusers.AutoencoderKLMagvit.decode(z, return_dict=False)

Decode a batch of images.

PARAMETER	DESCRIPTION
z	Input batch of latent vectors. TYPE: `ms.Tensor`
return_dict	Whether to return a [~models.vae.DecoderOutput] instead of a plain tuple. TYPE: `bool`, *optional*, defaults to `True` DEFAULT: False

RETURNS	DESCRIPTION
Union[DecoderOutput, Tensor]	[~models.vae.DecoderOutput] or tuple: If return_dict is True, a [~models.vae.DecoderOutput] is returned, otherwise a plain tuple is returned.

Source code in mindone/diffusers/models/autoencoders/autoencoder_kl_magvit.py

def decode(self, z: ms.Tensor, return_dict: bool = False) -> Union[DecoderOutput, ms.Tensor]:
    """
    Decode a batch of images.

    Args:
        z (`ms.Tensor`): Input batch of latent vectors.
        return_dict (`bool`, *optional*, defaults to `True`):
            Whether to return a [`~models.vae.DecoderOutput`] instead of a plain tuple.

    Returns:
        [`~models.vae.DecoderOutput`] or `tuple`:
            If return_dict is True, a [`~models.vae.DecoderOutput`] is returned, otherwise a plain `tuple` is
            returned.
    """
    if self.use_slicing and z.shape[0] > 1:
        decoded_slices = [self._decode(z_slice)[0] for z_slice in z.split(1)]
        decoded = mint.cat(decoded_slices)
    else:
        decoded = self._decode(z)[0]

    self._clear_conv_cache()
    if not return_dict:
        return (decoded,)
    return DecoderOutput(sample=decoded)

mindone.diffusers.AutoencoderKLMagvit.disable_slicing()

Disable sliced VAE decoding. If enable_slicing was previously enabled, this method will go back to computing decoding in one step.

Source code in mindone/diffusers/models/autoencoders/autoencoder_kl_magvit.py

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

mindone.diffusers.AutoencoderKLMagvit.disable_tiling()

Disable tiled VAE decoding. If enable_tiling was previously enabled, this method will go back to computing decoding in one step.

Source code in mindone/diffusers/models/autoencoders/autoencoder_kl_magvit.py

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

mindone.diffusers.AutoencoderKLMagvit.enable_slicing()

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.

Source code in mindone/diffusers/models/autoencoders/autoencoder_kl_magvit.py

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

mindone.diffusers.AutoencoderKLMagvit.enable_tiling(tile_sample_min_height=None, tile_sample_min_width=None, tile_sample_min_num_frames=None, tile_sample_stride_height=None, tile_sample_stride_width=None, tile_sample_stride_num_frames=None)

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.

PARAMETER	DESCRIPTION
tile_sample_min_height	The minimum height required for a sample to be separated into tiles across the height dimension. TYPE: `int`, *optional* DEFAULT: None
tile_sample_min_width	The minimum width required for a sample to be separated into tiles across the width dimension. TYPE: `int`, *optional* DEFAULT: None
tile_sample_stride_height	The minimum amount of overlap between two consecutive vertical tiles. This is to ensure that there are no tiling artifacts produced across the height dimension. TYPE: `int`, *optional* DEFAULT: None
tile_sample_stride_width	The stride between two consecutive horizontal tiles. This is to ensure that there are no tiling artifacts produced across the width dimension. TYPE: `int`, *optional* DEFAULT: None

Source code in mindone/diffusers/models/autoencoders/autoencoder_kl_magvit.py

def enable_tiling(
    self,
    tile_sample_min_height: Optional[int] = None,
    tile_sample_min_width: Optional[int] = None,
    tile_sample_min_num_frames: Optional[int] = None,
    tile_sample_stride_height: Optional[float] = None,
    tile_sample_stride_width: Optional[float] = None,
    tile_sample_stride_num_frames: Optional[float] = None,
) -> 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.

    Args:
        tile_sample_min_height (`int`, *optional*):
            The minimum height required for a sample to be separated into tiles across the height dimension.
        tile_sample_min_width (`int`, *optional*):
            The minimum width required for a sample to be separated into tiles across the width dimension.
        tile_sample_stride_height (`int`, *optional*):
            The minimum amount of overlap between two consecutive vertical tiles. This is to ensure that there are
            no tiling artifacts produced across the height dimension.
        tile_sample_stride_width (`int`, *optional*):
            The stride between two consecutive horizontal tiles. This is to ensure that there are no tiling
            artifacts produced across the width dimension.
    """
    self.use_tiling = True
    self.use_framewise_decoding = True
    self.use_framewise_encoding = True
    self.tile_sample_min_height = tile_sample_min_height or self.tile_sample_min_height
    self.tile_sample_min_width = tile_sample_min_width or self.tile_sample_min_width
    self.tile_sample_min_num_frames = tile_sample_min_num_frames or self.tile_sample_min_num_frames
    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
    self.tile_sample_stride_num_frames = tile_sample_stride_num_frames or self.tile_sample_stride_num_frames

mindone.diffusers.AutoencoderKLMagvit.encode(x, return_dict=False)

Encode a batch of images into latents.

PARAMETER	DESCRIPTION
x	Input batch of images. TYPE: `ms.Tensor`
return_dict	Whether to return a [~models.autoencoder_kl.AutoencoderKLOutput] instead of a plain tuple. TYPE: `bool`, *optional*, defaults to `True` DEFAULT: False

RETURNS	DESCRIPTION
Union[AutoencoderKLOutput, Tuple[DiagonalGaussianDistribution]]	The latent representations of the encoded videos. If return_dict is True, a
Union[AutoencoderKLOutput, Tuple[DiagonalGaussianDistribution]]	[~models.autoencoder_kl.AutoencoderKLOutput] is returned, otherwise a plain tuple is returned.

Source code in mindone/diffusers/models/autoencoders/autoencoder_kl_magvit.py

def encode(
    self, x: ms.Tensor, return_dict: bool = False
) -> Union[AutoencoderKLOutput, Tuple[DiagonalGaussianDistribution]]:
    """
    Encode a batch of images into latents.

    Args:
        x (`ms.Tensor`): Input batch of images.
        return_dict (`bool`, *optional*, defaults to `True`):
            Whether to return a [`~models.autoencoder_kl.AutoencoderKLOutput`] instead of a plain tuple.

    Returns:
            The latent representations of the encoded videos. If `return_dict` is True, a
            [`~models.autoencoder_kl.AutoencoderKLOutput`] is returned, otherwise a plain `tuple` is returned.
    """
    if self.use_slicing and x.shape[0] > 1:
        encoded_slices = [self._encode(x_slice) for x_slice in x.split(1)]
        h = mint.cat(encoded_slices)
    else:
        h = self._encode(x)

    # we cannot use class in graph mode, even for jit_class or subclass of Tensor. :-(
    # posterior = DiagonalGaussianDistribution(h)

    if not return_dict:
        return (h,)
    # return AutoencoderKLOutput(latent_dist=h)
    return AutoencoderKLOutput(latent=h)

mindone.diffusers.models.autoencoders.autoencoder_kl.AutoencoderKLOutput dataclass

Bases: BaseOutput

Output of AutoencoderKL encoding method.

PARAMETER	DESCRIPTION
latent	Encoded outputs of Encoder represented as the mean and logvar of DiagonalGaussianDistribution. DiagonalGaussianDistribution allows for sampling latents from the distribution. TYPE: `ms.Tensor`

Source code in mindone/diffusers/models/modeling_outputs.py

@dataclass
class AutoencoderKLOutput(BaseOutput):
    """
    Output of AutoencoderKL encoding method.

    Args:
        latent (`ms.Tensor`):
            Encoded outputs of `Encoder` represented as the mean and logvar of `DiagonalGaussianDistribution`.
            `DiagonalGaussianDistribution` allows for sampling latents from the distribution.
    """

    latent: ms.Tensor

mindone.diffusers.models.autoencoders.vae.DecoderOutput dataclass

Bases: BaseOutput

Output of decoding method.

PARAMETER	DESCRIPTION
sample	The decoded output sample from the last layer of the model. TYPE: `ms.Tensor` of shape `(batch_size, num_channels, height, width)`

Source code in mindone/diffusers/models/autoencoders/vae.py

@dataclass
class DecoderOutput(BaseOutput):
    r"""
    Output of decoding method.

    Args:
        sample (`ms.Tensor` of shape `(batch_size, num_channels, height, width)`):
            The decoded output sample from the last layer of the model.
    """

    sample: ms.Tensor
    commit_loss: Optional[ms.Tensor] = None