class AutoencoderKLLTXVideo(ModelMixin, 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).

    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`):
            Number of input channels.
        out_channels (`int`, defaults to `3`):
            Number of output channels.
        latent_channels (`int`, defaults to `128`):
            Number of latent channels.
        block_out_channels (`Tuple[int, ...]`, defaults to `(128, 256, 512, 512)`):
            The number of output channels for each block.
        spatio_temporal_scaling (`Tuple[bool, ...], defaults to `(True, True, True, False)`:
            Whether a block should contain spatio-temporal downscaling or not.
        layers_per_block (`Tuple[int, ...]`, defaults to `(4, 3, 3, 3, 4)`):
            The number of layers per block.
        patch_size (`int`, defaults to `4`):
            The size of spatial patches.
        patch_size_t (`int`, defaults to `1`):
            The size of temporal patches.
        resnet_norm_eps (`float`, defaults to `1e-6`):
            Epsilon value for ResNet normalization layers.
        scaling_factor (`float`, *optional*, defaults to `1.0`):
            The component-wise standard deviation of the trained latent space computed using the first batch of the
            training set. 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`. For more details, refer to sections 4.3.2 and D.1 of the [High-Resolution Image
            Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752) paper.
        encoder_causal (`bool`, defaults to `True`):
            Whether the encoder should behave causally (future frames depend only on past frames) or not.
        decoder_causal (`bool`, defaults to `False`):
            Whether the decoder should behave causally (future frames depend only on past frames) or not.
    """

    _supports_gradient_checkpointing = True

    @register_to_config
    def __init__(
        self,
        in_channels: int = 3,
        out_channels: int = 3,
        latent_channels: int = 128,
        block_out_channels: Tuple[int, ...] = (128, 256, 512, 512),
        decoder_block_out_channels: Tuple[int, ...] = (128, 256, 512, 512),
        layers_per_block: Tuple[int, ...] = (4, 3, 3, 3, 4),
        decoder_layers_per_block: Tuple[int, ...] = (4, 3, 3, 3, 4),
        spatio_temporal_scaling: Tuple[bool, ...] = (True, True, True, False),
        decoder_spatio_temporal_scaling: Tuple[bool, ...] = (True, True, True, False),
        decoder_inject_noise: Tuple[bool, ...] = (False, False, False, False, False),
        upsample_residual: Tuple[bool, ...] = (False, False, False, False),
        upsample_factor: Tuple[int, ...] = (1, 1, 1, 1),
        timestep_conditioning: bool = False,
        patch_size: int = 4,
        patch_size_t: int = 1,
        resnet_norm_eps: float = 1e-6,
        scaling_factor: float = 1.0,
        encoder_causal: bool = True,
        decoder_causal: bool = False,
    ) -> None:
        super().__init__()

        self.encoder = LTXVideoEncoder3d(
            in_channels=in_channels,
            out_channels=latent_channels,
            block_out_channels=block_out_channels,
            spatio_temporal_scaling=spatio_temporal_scaling,
            layers_per_block=layers_per_block,
            patch_size=patch_size,
            patch_size_t=patch_size_t,
            resnet_norm_eps=resnet_norm_eps,
            is_causal=encoder_causal,
        )
        self.decoder = LTXVideoDecoder3d(
            in_channels=latent_channels,
            out_channels=out_channels,
            block_out_channels=decoder_block_out_channels,
            spatio_temporal_scaling=decoder_spatio_temporal_scaling,
            layers_per_block=decoder_layers_per_block,
            patch_size=patch_size,
            patch_size_t=patch_size_t,
            resnet_norm_eps=resnet_norm_eps,
            is_causal=decoder_causal,
            timestep_conditioning=timestep_conditioning,
            inject_noise=decoder_inject_noise,
            upsample_residual=upsample_residual,
            upsample_factor=upsample_factor,
        )

        latents_mean = torch.zeros((latent_channels,), requires_grad=False)
        latents_std = torch.ones((latent_channels,), requires_grad=False)
        self.register_buffer("latents_mean", latents_mean, persistent=True)
        self.register_buffer("latents_std", latents_std, persistent=True)

        self.spatial_compression_ratio = patch_size * 2 ** sum(spatio_temporal_scaling)
        self.temporal_compression_ratio = patch_size_t * 2 ** sum(spatio_temporal_scaling)

        # 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_sizes`), the memory requirement can be lowered.
        self.use_framewise_encoding = False
        self.use_framewise_decoding = False

        # This can be configured based on the amount of GPU memory available.
        # `16` for sample frames and `2` for latent frames are sensible defaults for consumer GPUs.
        # Setting it to higher values results in higher memory usage.
        self.num_sample_frames_batch_size = 16
        self.num_latent_frames_batch_size = 2

        # 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 = 16

        # 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 _set_gradient_checkpointing(self, module, value=False):
        if isinstance(module, (LTXVideoEncoder3d, LTXVideoDecoder3d)):
            module.gradient_checkpointing = value

    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.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: torch.Tensor) -> torch.Tensor:
        batch_size, num_channels, num_frames, height, width = x.shape

        if self.use_framewise_decoding and num_frames > self.tile_sample_min_num_frames:
            return self._temporal_tiled_encode(x)

        if self.use_tiling and (width > self.tile_sample_min_width or height > self.tile_sample_min_height):
            return self.tiled_encode(x)

        enc = self.encoder(x)

        return enc

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

        Args:
            x (`torch.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 = torch.cat(encoded_slices)
        else:
            h = self._encode(x)
        posterior = DiagonalGaussianDistribution(h)

        if not return_dict:
            return (posterior,)
        return AutoencoderKLOutput(latent_dist=posterior)

    def _decode(
        self, z: torch.Tensor, temb: Optional[torch.Tensor] = None, return_dict: bool = True
    ) -> Union[DecoderOutput, torch.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
        tile_latent_min_num_frames = self.tile_sample_min_num_frames // self.temporal_compression_ratio

        if self.use_framewise_decoding and num_frames > tile_latent_min_num_frames:
            return self._temporal_tiled_decode(z, temb, return_dict=return_dict)

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

        dec = self.decoder(z, temb)

        if not return_dict:
            return (dec,)

        return DecoderOutput(sample=dec)

    @apply_forward_hook
    def decode(
        self, z: torch.Tensor, temb: Optional[torch.Tensor] = None, return_dict: bool = True
    ) -> Union[DecoderOutput, torch.Tensor]:
        """
        Decode a batch of images.

        Args:
            z (`torch.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:
            if temb is not None:
                decoded_slices = [
                    self._decode(z_slice, t_slice).sample for z_slice, t_slice in (z.split(1), temb.split(1))
                ]
            else:
                decoded_slices = [self._decode(z_slice).sample for z_slice in z.split(1)]
            decoded = torch.cat(decoded_slices)
        else:
            decoded = self._decode(z, temb).sample

        if not return_dict:
            return (decoded,)

        return DecoderOutput(sample=decoded)

    def blend_v(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.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: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.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 blend_t(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.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: torch.Tensor) -> torch.Tensor:
        r"""Encode a batch of images using a tiled encoder.

        Args:
            x (`torch.Tensor`): Input batch of videos.

        Returns:
            `torch.Tensor`:
                The latent representation of the encoded videos.
        """
        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 x into overlapping tiles and encode them separately.
        # The tiles have an overlap to avoid seams between tiles.
        rows = []
        for i in range(0, height, self.tile_sample_stride_height):
            row = []
            for j in range(0, width, self.tile_sample_stride_width):
                time = self.encoder(
                    x[:, :, :, i : i + self.tile_sample_min_height, j : j + self.tile_sample_min_width]
                )

                row.append(time)
            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(torch.cat(result_row, dim=4))

        enc = torch.cat(result_rows, dim=3)[:, :, :, :latent_height, :latent_width]
        return enc

    def tiled_decode(
        self, z: torch.Tensor, temb: Optional[torch.Tensor], return_dict: bool = True
    ) -> Union[DecoderOutput, torch.Tensor]:
        r"""
        Decode a batch of images using a tiled decoder.

        Args:
            z (`torch.Tensor`): Input batch of latent vectors.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not 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.
        """

        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 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):
                time = self.decoder(z[:, :, :, i : i + tile_latent_min_height, j : j + tile_latent_min_width], temb)

                row.append(time)
            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(torch.cat(result_row, dim=4))

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

        if not return_dict:
            return (dec,)

        return DecoderOutput(sample=dec)

    def _temporal_tiled_encode(self, x: torch.Tensor) -> AutoencoderKLOutput:
        batch_size, num_channels, num_frames, height, width = x.shape
        latent_num_frames = (num_frames - 1) // self.temporal_compression_ratio + 1

        tile_latent_min_num_frames = self.tile_sample_min_num_frames // self.temporal_compression_ratio
        tile_latent_stride_num_frames = self.tile_sample_stride_num_frames // self.temporal_compression_ratio
        blend_num_frames = tile_latent_min_num_frames - tile_latent_stride_num_frames

        row = []
        for i in range(0, num_frames, self.tile_sample_stride_num_frames):
            tile = x[:, :, i : i + self.tile_sample_min_num_frames + 1, :, :]
            if self.use_tiling and (height > self.tile_sample_min_height or width > self.tile_sample_min_width):
                tile = self.tiled_encode(tile)
            else:
                tile = self.encoder(tile)
            if i > 0:
                tile = tile[:, :, 1:, :, :]
            row.append(tile)

        result_row = []
        for i, tile in enumerate(row):
            if i > 0:
                tile = self.blend_t(row[i - 1], tile, blend_num_frames)
                result_row.append(tile[:, :, :tile_latent_stride_num_frames, :, :])
            else:
                result_row.append(tile[:, :, : tile_latent_stride_num_frames + 1, :, :])

        enc = torch.cat(result_row, dim=2)[:, :, :latent_num_frames]
        return enc

    def _temporal_tiled_decode(
        self, z: torch.Tensor, temb: Optional[torch.Tensor], return_dict: bool = True
    ) -> Union[DecoderOutput, torch.Tensor]:
        batch_size, num_channels, num_frames, height, width = z.shape
        num_sample_frames = (num_frames - 1) * self.temporal_compression_ratio + 1

        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_min_num_frames = self.tile_sample_min_num_frames // self.temporal_compression_ratio
        tile_latent_stride_num_frames = self.tile_sample_stride_num_frames // self.temporal_compression_ratio
        blend_num_frames = self.tile_sample_min_num_frames - self.tile_sample_stride_num_frames

        row = []
        for i in range(0, num_frames, tile_latent_stride_num_frames):
            tile = z[:, :, i : i + tile_latent_min_num_frames + 1, :, :]
            if self.use_tiling and (tile.shape[-1] > tile_latent_min_width or tile.shape[-2] > tile_latent_min_height):
                decoded = self.tiled_decode(tile, temb, return_dict=True).sample
            else:
                decoded = self.decoder(tile, temb)
            if i > 0:
                decoded = decoded[:, :, :-1, :, :]
            row.append(decoded)

        result_row = []
        for i, tile in enumerate(row):
            if i > 0:
                tile = self.blend_t(row[i - 1], tile, blend_num_frames)
                tile = tile[:, :, : self.tile_sample_stride_num_frames, :, :]
                result_row.append(tile)
            else:
                result_row.append(tile[:, :, : self.tile_sample_stride_num_frames + 1, :, :])

        dec = torch.cat(result_row, dim=2)[:, :, :num_sample_frames]

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

    def forward(
        self,
        sample: torch.Tensor,
        temb: Optional[torch.Tensor] = None,
        sample_posterior: bool = False,
        return_dict: bool = True,
        generator: Optional[torch.Generator] = None,
    ) -> Union[torch.Tensor, torch.Tensor]:
        x = sample
        posterior = self.encode(x).latent_dist
        if sample_posterior:
            z = posterior.sample(generator=generator)
        else:
            z = posterior.mode()
        dec = self.decode(z, temb)
        if not return_dict:
            return (dec.sample,)
        return dec
/Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/autoencoders/autoencoder_kl_ltx.py