AutoencoderDC

The 2D Autoencoder model used in SANA and introduced in DCAE by authors Junyu Chen*, Han Cai*, Junsong Chen, Enze Xie, Shang Yang, Haotian Tang, Muyang Li, Yao Lu, Song Han from MIT HAN Lab.

The abstract from the paper is:

We present Deep Compression Autoencoder (DC-AE), a new family of autoencoder models for accelerating high-resolution diffusion models. Existing autoencoder models have demonstrated impressive results at a moderate spatial compression ratio (e.g., 8x), but fail to maintain satisfactory reconstruction accuracy for high spatial compression ratios (e.g., 64x). We address this challenge by introducing two key techniques: (1) Residual Autoencoding, where we design our models to learn residuals based on the space-to-channel transformed features to alleviate the optimization difficulty of high spatial-compression autoencoders; (2) Decoupled High-Resolution Adaptation, an efficient decoupled three-phases training strategy for mitigating the generalization penalty of high spatial-compression autoencoders. With these designs, we improve the autoencoder's spatial compression ratio up to 128 while maintaining the reconstruction quality. Applying our DC-AE to latent diffusion models, we achieve significant speedup without accuracy drop. For example, on ImageNet 512x512, our DC-AE provides 19.1x inference speedup and 17.9x training speedup on H100 GPU for UViT-H while achieving a better FID, compared with the widely used SD-VAE-f8 autoencoder. Our code is available at this https URL.

The following DCAE models are released and supported in Diffusers.

Diffusers format	Original format
mit-han-lab/dc-ae-f32c32-sana-1.0-diffusers	mit-han-lab/dc-ae-f32c32-sana-1.0
mit-han-lab/dc-ae-f32c32-in-1.0-diffusers	mit-han-lab/dc-ae-f32c32-in-1.0
mit-han-lab/dc-ae-f32c32-mix-1.0-diffusers	mit-han-lab/dc-ae-f32c32-mix-1.0
mit-han-lab/dc-ae-f64c128-in-1.0-diffusers	mit-han-lab/dc-ae-f64c128-in-1.0
mit-han-lab/dc-ae-f64c128-mix-1.0-diffusers	mit-han-lab/dc-ae-f64c128-mix-1.0
mit-han-lab/dc-ae-f128c512-in-1.0-diffusers	mit-han-lab/dc-ae-f128c512-in-1.0
mit-han-lab/dc-ae-f128c512-mix-1.0-diffusers	mit-han-lab/dc-ae-f128c512-mix-1.0

This model was contributed by lawrence-cj.

Load a model in Diffusers format with ModelMixin.from_pretrained.

from mindone.diffusers import AutoencoderDC

ae = AutoencoderDC.from_pretrained("mit-han-lab/dc-ae-f32c32-sana-1.0-diffusers", mindspore_dtype=ms.float32)

Load a model in Diffusers via from_single_file

from mindone.difusers import AutoencoderDC

ckpt_path = "https://huggingface.co/mit-han-lab/dc-ae-f32c32-sana-1.0/blob/main/model.safetensors"
model = AutoencoderDC.from_single_file(ckpt_path)

The AutoencoderDC model has in and mix single file checkpoint variants that have matching checkpoint keys, but use different scaling factors. It is not possible for Diffusers to automatically infer the correct config file to use with the model based on just the checkpoint and will default to configuring the model using the mix variant config file. To override the automatically determined config, please use the config argument when using single file loading with in variant checkpoints.

from mindone.diffusers import AutoencoderDC

ckpt_path = "https://huggingface.co/mit-han-lab/dc-ae-f128c512-in-1.0/blob/main/model.safetensors"
model = AutoencoderDC.from_single_file(ckpt_path, config="mit-han-lab/dc-ae-f128c512-in-1.0-diffusers")

mindone.diffusers.AutoencoderDC

Bases: ModelMixin, ConfigMixin, FromOriginalModelMixin

An Autoencoder model introduced in DCAE and used in SANA.

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

PARAMETER	DESCRIPTION
in_channels	The number of input channels in samples. TYPE: `int`, defaults to `3` DEFAULT: 3
latent_channels	The number of channels in the latent space representation. TYPE: `int`, defaults to `32` DEFAULT: 32
encoder_block_types	The type(s) of block to use in the encoder. TYPE: `Union[str, Tuple[str]]`, defaults to `"ResBlock"` DEFAULT: 'ResBlock'
decoder_block_types	The type(s) of block to use in the decoder. TYPE: `Union[str, Tuple[str]]`, defaults to `"ResBlock"` DEFAULT: 'ResBlock'
encoder_block_out_channels	The number of output channels for each block in the encoder. TYPE: `Tuple[int, ...]`, defaults to `(128, 256, 512, 512, 1024, 1024)` DEFAULT: (128, 256, 512, 512, 1024, 1024)
decoder_block_out_channels	The number of output channels for each block in the decoder. TYPE: `Tuple[int, ...]`, defaults to `(128, 256, 512, 512, 1024, 1024)` DEFAULT: (128, 256, 512, 512, 1024, 1024)
encoder_layers_per_block	The number of layers per block in the encoder. TYPE: `Tuple[int]`, defaults to `(2, 2, 2, 3, 3, 3)` DEFAULT: (2, 2, 2, 3, 3, 3)
decoder_layers_per_block	The number of layers per block in the decoder. TYPE: `Tuple[int]`, defaults to `(3, 3, 3, 3, 3, 3)` DEFAULT: (3, 3, 3, 3, 3, 3)
encoder_qkv_multiscales	Multi-scale configurations for the encoder's QKV (query-key-value) transformations. TYPE: `Tuple[Tuple[int, ...], ...]`, defaults to `((), (), (), (5,), (5,), (5,))` DEFAULT: ((), (), (), (5), (5), (5))
decoder_qkv_multiscales	Multi-scale configurations for the decoder's QKV (query-key-value) transformations. TYPE: `Tuple[Tuple[int, ...], ...]`, defaults to `((), (), (), (5,), (5,), (5,))` DEFAULT: ((), (), (), (5), (5), (5))
upsample_block_type	The type of block to use for upsampling in the decoder. TYPE: `str`, defaults to `"pixel_shuffle"` DEFAULT: 'pixel_shuffle'
downsample_block_type	The type of block to use for downsampling in the encoder. TYPE: `str`, defaults to `"pixel_unshuffle"` DEFAULT: 'pixel_unshuffle'
decoder_norm_types	The normalization type(s) to use in the decoder. TYPE: `Union[str, Tuple[str]]`, defaults to `"rms_norm"` DEFAULT: 'rms_norm'
decoder_act_fns	The activation function(s) to use in the decoder. TYPE: `Union[str, Tuple[str]]`, defaults to `"silu"` DEFAULT: 'silu'
scaling_factor	The multiplicative inverse of the root mean square of the latent features. This is used to scale the latent space to have unit variance when training the diffusion model. The latents are scaled with the formula z = z * scaling_factor before being passed to the diffusion model. When decoding, the latents are scaled back to the original scale with the formula: z = 1 / scaling_factor * z. TYPE: `float`, defaults to `1.0` DEFAULT: 1.0

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

class AutoencoderDC(ModelMixin, ConfigMixin, FromOriginalModelMixin):
    r"""
    An Autoencoder model introduced in [DCAE](https://arxiv.org/abs/2410.10733) and used in
    [SANA](https://arxiv.org/abs/2410.10629).

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

    Args:
        in_channels (`int`, defaults to `3`):
            The number of input channels in samples.
        latent_channels (`int`, defaults to `32`):
            The number of channels in the latent space representation.
        encoder_block_types (`Union[str, Tuple[str]]`, defaults to `"ResBlock"`):
            The type(s) of block to use in the encoder.
        decoder_block_types (`Union[str, Tuple[str]]`, defaults to `"ResBlock"`):
            The type(s) of block to use in the decoder.
        encoder_block_out_channels (`Tuple[int, ...]`, defaults to `(128, 256, 512, 512, 1024, 1024)`):
            The number of output channels for each block in the encoder.
        decoder_block_out_channels (`Tuple[int, ...]`, defaults to `(128, 256, 512, 512, 1024, 1024)`):
            The number of output channels for each block in the decoder.
        encoder_layers_per_block (`Tuple[int]`, defaults to `(2, 2, 2, 3, 3, 3)`):
            The number of layers per block in the encoder.
        decoder_layers_per_block (`Tuple[int]`, defaults to `(3, 3, 3, 3, 3, 3)`):
            The number of layers per block in the decoder.
        encoder_qkv_multiscales (`Tuple[Tuple[int, ...], ...]`, defaults to `((), (), (), (5,), (5,), (5,))`):
            Multi-scale configurations for the encoder's QKV (query-key-value) transformations.
        decoder_qkv_multiscales (`Tuple[Tuple[int, ...], ...]`, defaults to `((), (), (), (5,), (5,), (5,))`):
            Multi-scale configurations for the decoder's QKV (query-key-value) transformations.
        upsample_block_type (`str`, defaults to `"pixel_shuffle"`):
            The type of block to use for upsampling in the decoder.
        downsample_block_type (`str`, defaults to `"pixel_unshuffle"`):
            The type of block to use for downsampling in the encoder.
        decoder_norm_types (`Union[str, Tuple[str]]`, defaults to `"rms_norm"`):
            The normalization type(s) to use in the decoder.
        decoder_act_fns (`Union[str, Tuple[str]]`, defaults to `"silu"`):
            The activation function(s) to use in the decoder.
        scaling_factor (`float`, defaults to `1.0`):
            The multiplicative inverse of the root mean square of the latent features. This is used to scale the latent
            space to have unit variance when training the diffusion model. The latents are scaled with the formula `z =
            z * scaling_factor` before being passed to the diffusion model. When decoding, the latents are scaled back
            to the original scale with the formula: `z = 1 / scaling_factor * z`.
    """

    _supports_gradient_checkpointing = False

    @register_to_config
    def __init__(
        self,
        in_channels: int = 3,
        latent_channels: int = 32,
        attention_head_dim: int = 32,
        encoder_block_types: Union[str, Tuple[str]] = "ResBlock",
        decoder_block_types: Union[str, Tuple[str]] = "ResBlock",
        encoder_block_out_channels: Tuple[int, ...] = (128, 256, 512, 512, 1024, 1024),
        decoder_block_out_channels: Tuple[int, ...] = (128, 256, 512, 512, 1024, 1024),
        encoder_layers_per_block: Tuple[int] = (2, 2, 2, 3, 3, 3),
        decoder_layers_per_block: Tuple[int] = (3, 3, 3, 3, 3, 3),
        encoder_qkv_multiscales: Tuple[Tuple[int, ...], ...] = ((), (), (), (5,), (5,), (5,)),
        decoder_qkv_multiscales: Tuple[Tuple[int, ...], ...] = ((), (), (), (5,), (5,), (5,)),
        upsample_block_type: str = "pixel_shuffle",
        downsample_block_type: str = "pixel_unshuffle",
        decoder_norm_types: Union[str, Tuple[str]] = "rms_norm",
        decoder_act_fns: Union[str, Tuple[str]] = "silu",
        scaling_factor: float = 1.0,
    ) -> None:
        super().__init__()

        self.encoder = Encoder(
            in_channels=in_channels,
            latent_channels=latent_channels,
            attention_head_dim=attention_head_dim,
            block_type=encoder_block_types,
            block_out_channels=encoder_block_out_channels,
            layers_per_block=encoder_layers_per_block,
            qkv_multiscales=encoder_qkv_multiscales,
            downsample_block_type=downsample_block_type,
        )
        self.decoder = Decoder(
            in_channels=in_channels,
            latent_channels=latent_channels,
            attention_head_dim=attention_head_dim,
            block_type=decoder_block_types,
            block_out_channels=decoder_block_out_channels,
            layers_per_block=decoder_layers_per_block,
            qkv_multiscales=decoder_qkv_multiscales,
            norm_type=decoder_norm_types,
            act_fn=decoder_act_fns,
            upsample_block_type=upsample_block_type,
        )

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

        # 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

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

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

        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 enable_tiling(
        self,
        tile_sample_min_height: Optional[int] = None,
        tile_sample_min_width: Optional[int] = None,
        tile_sample_stride_height: Optional[float] = None,
        tile_sample_stride_width: Optional[float] = None,
    ) -> None:
        r"""
        Enable tiled AE decoding. When this option is enabled, the AE 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.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_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_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: ms.Tensor) -> ms.Tensor:
        batch_size, num_channels, height, width = x.shape

        if self.use_tiling and (width > self.tile_sample_min_width or height > self.tile_sample_min_height):
            return self.tiled_encode(x, return_dict=False)[0]

        encoded = self.encoder(x)

        return encoded

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

        Args:
            x (`ms.Tensor`): Input batch of images.
            return_dict (`bool`, defaults to `False`):
                Whether to return a [`~models.vae.EncoderOutput`] instead of a plain tuple.

        Returns:
                The latent representations of the encoded videos. If `return_dict` is True, a
                [`~models.vae.EncoderOutput`] 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)]
            encoded = mint.cat(encoded_slices)
        else:
            encoded = self._encode(x)

        if not return_dict:
            return (encoded,)
        return EncoderOutput(latent=encoded)

    def _decode(self, z: ms.Tensor) -> ms.Tensor:
        batch_size, num_channels, height, width = z.shape

        if self.use_tiling and (width > self.tile_latent_min_width or height > self.tile_latent_min_height):
            return self.tiled_decode(z, return_dict=False)[0]

        decoded = self.decoder(z)

        return decoded

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

        Args:
            z (`ms.Tensor`): Input batch of latent vectors.
            return_dict (`bool`, defaults to `False`):
                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.size(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)

        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[2], b.shape[2], 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[3], b.shape[3], 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) -> ms.Tensor:
        batch_size, num_channels, 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 x into overlapping tiles and encode them separately.
        # The tiles have an overlap to avoid seams between tiles.
        rows = []
        for i in range(0, x.shape[2], self.tile_sample_stride_height):
            row = []
            for j in range(0, x.shape[3], self.tile_sample_stride_width):
                tile = x[:, :, i : i + self.tile_sample_min_height, j : j + self.tile_sample_min_width]
                if (
                    tile.shape[2] % self.spatial_compression_ratio != 0
                    or tile.shape[3] % self.spatial_compression_ratio != 0
                ):
                    pad_h = (self.spatial_compression_ratio - tile.shape[2]) % self.spatial_compression_ratio
                    pad_w = (self.spatial_compression_ratio - tile.shape[3]) % self.spatial_compression_ratio
                    tile = F.pad(tile, (0, pad_w, 0, pad_h))
                tile = self.encoder(tile)
                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[:, :, :tile_latent_stride_height, :tile_latent_stride_width])
            result_rows.append(mint.cat(result_row, dim=3))

        encoded = mint.cat(result_rows, dim=2)[:, :, :latent_height, :latent_width]

        if not return_dict:
            return (encoded,)
        return EncoderOutput(latent=encoded)

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

        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 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]
                decoded = self.decoder(tile)
                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=3))

        decoded = mint.cat(result_rows, dim=2)

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

    def construct(self, sample: ms.Tensor, return_dict: bool = False) -> ms.Tensor:
        encoded = self.encode(sample, return_dict=False)[0]
        decoded = self.decode(encoded, return_dict=False)[0]
        if not return_dict:
            return (decoded,)
        return DecoderOutput(sample=decoded)

mindone.diffusers.AutoencoderDC.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`, defaults to `False` DEFAULT: False

RETURNS	DESCRIPTION
Union[DecoderOutput, Tuple[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_dc.py

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

    Args:
        z (`ms.Tensor`): Input batch of latent vectors.
        return_dict (`bool`, defaults to `False`):
            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.size(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)

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

mindone.diffusers.AutoencoderDC.disable_slicing()

Disable sliced AE 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_dc.py

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

mindone.diffusers.AutoencoderDC.disable_tiling()

Disable tiled AE 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_dc.py

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

mindone.diffusers.AutoencoderDC.enable_slicing()

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.

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

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

mindone.diffusers.AutoencoderDC.enable_tiling(tile_sample_min_height=None, tile_sample_min_width=None, tile_sample_stride_height=None, tile_sample_stride_width=None)

Enable tiled AE decoding. When this option is enabled, the AE 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_dc.py

def enable_tiling(
    self,
    tile_sample_min_height: Optional[int] = None,
    tile_sample_min_width: Optional[int] = None,
    tile_sample_stride_height: Optional[float] = None,
    tile_sample_stride_width: Optional[float] = None,
) -> None:
    r"""
    Enable tiled AE decoding. When this option is enabled, the AE 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.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_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_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

mindone.diffusers.AutoencoderDC.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.vae.EncoderOutput] instead of a plain tuple. TYPE: `bool`, defaults to `False` DEFAULT: False

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

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

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

    Args:
        x (`ms.Tensor`): Input batch of images.
        return_dict (`bool`, defaults to `False`):
            Whether to return a [`~models.vae.EncoderOutput`] instead of a plain tuple.

    Returns:
            The latent representations of the encoded videos. If `return_dict` is True, a
            [`~models.vae.EncoderOutput`] 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)]
        encoded = mint.cat(encoded_slices)
    else:
        encoded = self._encode(x)

    if not return_dict:
        return (encoded,)
    return EncoderOutput(latent=encoded)

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