easyanimate/models/autoencoder_magvit.py

# Copyright 2024 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Dict, Optional, Tuple, Union

import torch
import torch.nn as nn
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.loaders.single_file_model import FromOriginalModelMixin
from diffusers.models.autoencoders.vae import (DecoderOutput,
                                               DiagonalGaussianDistribution)
from diffusers.models.modeling_outputs import AutoencoderKLOutput
from diffusers.models.modeling_utils import ModelMixin
from diffusers.utils import logging
from diffusers.utils.accelerate_utils import apply_forward_hook

try:
    from diffusers.loaders import FromOriginalVAEMixin
except:
    from diffusers.loaders import FromOriginalModelMixin as FromOriginalVAEMixin

from diffusers.models.attention_processor import (
    ADDED_KV_ATTENTION_PROCESSORS, CROSS_ATTENTION_PROCESSORS, Attention,
    AttentionProcessor, AttnAddedKVProcessor, AttnProcessor)
from diffusers.models.autoencoders.vae import (DecoderOutput,
                                               DiagonalGaussianDistribution)
from diffusers.models.modeling_outputs import AutoencoderKLOutput
from diffusers.models.modeling_utils import ModelMixin
from diffusers.utils.accelerate_utils import apply_forward_hook
from torch import nn
from diffusers import AutoencoderKL

from ..vae.ldm.models.cogvideox_enc_dec import (CogVideoXCausalConv3d,
                                                CogVideoXDecoder3D,
                                                CogVideoXEncoder3D,
                                                CogVideoXSafeConv3d)
from ..vae.ldm.models.omnigen_enc_dec import CausalConv3d
from ..vae.ldm.models.omnigen_enc_dec import Decoder as omnigen_Mag_Decoder
from ..vae.ldm.models.omnigen_enc_dec import Encoder as omnigen_Mag_Encoder

logger = logging.get_logger(__name__)  # pylint: disable=invalid-name

def str_eval(item):
    if type(item) == str:
        return eval(item)
    else:
        return item

class AutoencoderKLMagvit(ModelMixin, ConfigMixin, FromOriginalVAEMixin):
    r"""
    A VAE model with KL loss for encoding images into latents and decoding latent representations into images.

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

    Parameters:
        in_channels (int, *optional*, defaults to 3): Number of channels in the input image.
        out_channels (int,  *optional*, defaults to 3): Number of channels in the output.
        down_block_types (`Tuple[str]`, *optional*, defaults to `("DownEncoderBlock2D",)`):
            Tuple of downsample block types.
        up_block_types (`Tuple[str]`, *optional*, defaults to `("UpDecoderBlock2D",)`):
            Tuple of upsample block types.
        block_out_channels (`Tuple[int]`, *optional*, defaults to `(64,)`):
            Tuple of block output channels.
        act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
        latent_channels (`int`, *optional*, defaults to 4): Number of channels in the latent space.
        sample_size (`int`, *optional*, defaults to `32`): Sample input size.
        scaling_factor (`float`, *optional*, defaults to 0.18215):
            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.
        force_upcast (`bool`, *optional*, default to `True`):
            If enabled it will force the VAE to run in float32 for high image resolution pipelines, such as SD-XL. VAE
            can be fine-tuned / trained to a lower range without loosing too much precision in which case
            `force_upcast` can be set to `False` - see: https://huggingface.co/madebyollin/sdxl-vae-fp16-fix
    """

    _supports_gradient_checkpointing = True

    @register_to_config
    def __init__(
        self,
        in_channels: int = 3,
        out_channels: int = 3,
        ch =  128,
        ch_mult = [ 1,2,4,4 ],
        block_out_channels = [128, 256, 512, 512],
        use_gc_blocks = None,
        down_block_types: tuple = None,
        up_block_types: tuple = None,
        mid_block_type: str = "MidBlock3D",
        mid_block_use_attention: bool = True,
        mid_block_attention_type: str = "3d",
        mid_block_num_attention_heads: int = 1,
        layers_per_block: int = 2,
        act_fn: str = "silu",
        num_attention_heads: int = 1,
        latent_channels: int = 4,
        norm_num_groups: int = 32,
        scaling_factor: float = 0.1825,
        force_upcast: float = True,
        slice_mag_vae=True,
        slice_compression_vae=False,
        cache_compression_vae=False,
        cache_mag_vae=False,
        use_tiling=False,
        use_tiling_encoder=False,
        use_tiling_decoder=False,
        mini_batch_encoder=9,
        mini_batch_decoder=3,
        upcast_vae=False,
        spatial_group_norm=False,
        tile_sample_min_size=384,
        tile_overlap_factor=0.25,
    ):
        super().__init__()
        down_block_types = str_eval(down_block_types)
        up_block_types = str_eval(up_block_types)
        self.encoder = omnigen_Mag_Encoder(
            in_channels=in_channels,
            out_channels=latent_channels,
            down_block_types=down_block_types,
            ch=ch,
            ch_mult=ch_mult,
            block_out_channels=block_out_channels,
            use_gc_blocks=use_gc_blocks,
            mid_block_type=mid_block_type,
            mid_block_use_attention=mid_block_use_attention,
            mid_block_attention_type=mid_block_attention_type,
            mid_block_num_attention_heads=mid_block_num_attention_heads,
            layers_per_block=layers_per_block,
            norm_num_groups=norm_num_groups,
            act_fn=act_fn,
            num_attention_heads=num_attention_heads,
            double_z=True,
            slice_mag_vae=slice_mag_vae,
            slice_compression_vae=slice_compression_vae,
            cache_compression_vae=cache_compression_vae,
            cache_mag_vae=cache_mag_vae,
            mini_batch_encoder=mini_batch_encoder,
            spatial_group_norm=spatial_group_norm,
        )

        self.decoder = omnigen_Mag_Decoder(
            in_channels=latent_channels,
            out_channels=out_channels,
            up_block_types=up_block_types,
            ch=ch,
            ch_mult=ch_mult,
            block_out_channels=block_out_channels,
            use_gc_blocks=use_gc_blocks,
            mid_block_type=mid_block_type,
            mid_block_use_attention=mid_block_use_attention,
            mid_block_attention_type=mid_block_attention_type,
            mid_block_num_attention_heads=mid_block_num_attention_heads,
            layers_per_block=layers_per_block,
            norm_num_groups=norm_num_groups,
            act_fn=act_fn,
            num_attention_heads=num_attention_heads,
            slice_mag_vae=slice_mag_vae,
            slice_compression_vae=slice_compression_vae,
            cache_compression_vae=cache_compression_vae,
            cache_mag_vae=cache_mag_vae,
            mini_batch_decoder=mini_batch_decoder,
            spatial_group_norm=spatial_group_norm,
        )

        self.quant_conv = nn.Conv3d(2 * latent_channels, 2 * latent_channels, kernel_size=1)
        self.post_quant_conv = nn.Conv3d(latent_channels, latent_channels, kernel_size=1)

        self.slice_mag_vae = slice_mag_vae
        self.slice_compression_vae = slice_compression_vae
        self.cache_compression_vae = cache_compression_vae
        self.cache_mag_vae = cache_mag_vae
        self.mini_batch_encoder = mini_batch_encoder
        self.mini_batch_decoder = mini_batch_decoder
        self.use_slicing = False
        self.use_tiling = use_tiling
        self.use_tiling_encoder = use_tiling_encoder
        self.use_tiling_decoder = use_tiling_decoder
        self.upcast_vae = upcast_vae
        self.tile_sample_min_size = tile_sample_min_size
        self.tile_overlap_factor = tile_overlap_factor
        self.tile_latent_min_size = int(self.tile_sample_min_size / (2 ** (len(ch_mult) - 1)))
        self.scaling_factor = scaling_factor

    def _set_gradient_checkpointing(self, module, value=False):
        if isinstance(module, (omnigen_Mag_Encoder, omnigen_Mag_Decoder)):
            module.gradient_checkpointing = value

    def _clear_conv_cache(self):
        for name, module in self.named_modules():
            if isinstance(module, CausalConv3d):
                module._clear_conv_cache()

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

        Args:
            x (`torch.FloatTensor`): 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.upcast_vae:
            x = x.float()
            self.encoder = self.encoder.float()
            self.quant_conv = self.quant_conv.float()
        if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
            x = self.tiled_encode(x, return_dict=return_dict)
            return x
        if self.use_tiling_encoder and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
            x = self.tiled_encode(x, return_dict=return_dict)
            return x

        if self.use_slicing and x.shape[0] > 1:
            encoded_slices = [self.encoder(x_slice) for x_slice in x.split(1)]
            h = torch.cat(encoded_slices)
        else:
            h = self.encoder(x)

        moments = self.quant_conv(h)
        posterior = DiagonalGaussianDistribution(moments)

        self._clear_conv_cache()
        if not return_dict:
            return (posterior,)

        return AutoencoderKLOutput(latent_dist=posterior)

    def _decode(self, z: torch.FloatTensor, return_dict: bool = True) -> Union[DecoderOutput, torch.FloatTensor]:
        if self.upcast_vae:
            z = z.float()
            self.decoder = self.decoder.float()
            self.post_quant_conv = self.post_quant_conv.float()
        if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
            return self.tiled_decode(z, return_dict=return_dict)
        if self.use_tiling_decoder and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
            return self.tiled_decode(z, return_dict=return_dict)

        z = self.post_quant_conv(z)
        dec = self.decoder(z)

        if not return_dict:
            return (dec,)

        return DecoderOutput(sample=dec)

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

        Args:
            z (`torch.FloatTensor`): 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).sample for z_slice in z.split(1)]
            decoded = torch.cat(decoded_slices)
        else:
            decoded = self._decode(z).sample

        self._clear_conv_cache()
        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 tiled_encode(self, x: torch.FloatTensor, return_dict: bool = True) -> AutoencoderKLOutput:
        overlap_size = int(self.tile_sample_min_size * (1 - self.tile_overlap_factor))
        blend_extent = int(self.tile_latent_min_size * self.tile_overlap_factor)
        row_limit = self.tile_latent_min_size - blend_extent

        # Split the image into 512x512 tiles and encode them separately.
        rows = []
        for i in range(0, x.shape[3], overlap_size):
            row = []
            for j in range(0, x.shape[4], overlap_size):
                tile = x[
                    :,
                    :,
                    :,
                    i : i + self.tile_sample_min_size,
                    j : j + self.tile_sample_min_size,
                ]
                tile = self.encoder(tile)
                tile = self.quant_conv(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_extent)
                if j > 0:
                    tile = self.blend_h(row[j - 1], tile, blend_extent)
                result_row.append(tile[:, :, :, :row_limit, :row_limit])
            result_rows.append(torch.cat(result_row, dim=4))

        moments = torch.cat(result_rows, dim=3)
        posterior = DiagonalGaussianDistribution(moments)

        if not return_dict:
            return (posterior,)

        return AutoencoderKLOutput(latent_dist=posterior)

    def tiled_decode(self, z: torch.FloatTensor, return_dict: bool = True) -> Union[DecoderOutput, torch.FloatTensor]:
        overlap_size = int(self.tile_latent_min_size * (1 - self.tile_overlap_factor))
        blend_extent = int(self.tile_sample_min_size * self.tile_overlap_factor)
        row_limit = self.tile_sample_min_size - blend_extent

        # 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, z.shape[3], overlap_size):
            row = []
            for j in range(0, z.shape[4], overlap_size):
                tile = z[
                    :,
                    :,
                    :,
                    i : i + self.tile_latent_min_size,
                    j : j + self.tile_latent_min_size,
                ]
                tile = self.post_quant_conv(tile)
                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_extent)
                if j > 0:
                    tile = self.blend_h(row[j - 1], tile, blend_extent)
                result_row.append(tile[:, :, :, :row_limit, :row_limit])
            result_rows.append(torch.cat(result_row, dim=4))

        dec = torch.cat(result_rows, dim=3)
    
        # Handle the lower right corner tile separately
        lower_right_original = z[
            :,
            :,
            :,
            -self.tile_latent_min_size:,
            -self.tile_latent_min_size:
        ]
        quantized_lower_right = self.decoder(self.post_quant_conv(lower_right_original))

        # Combine
        H, W = quantized_lower_right.size(-2), quantized_lower_right.size(-1)
        x_weights = torch.linspace(0, 1, W).unsqueeze(0).repeat(H, 1)
        y_weights = torch.linspace(0, 1, H).unsqueeze(1).repeat(1, W)
        weights = torch.min(x_weights, y_weights)

        if len(dec.size()) == 4:
            weights = weights.unsqueeze(0).unsqueeze(0)
        elif len(dec.size()) == 5:
            weights = weights.unsqueeze(0).unsqueeze(0).unsqueeze(0)

        weights = weights.to(dec.device)
        quantized_area = dec[:, :, :, -H:, -W:]
        combined = weights * quantized_lower_right + (1 - weights) * quantized_area

        dec[:, :, :, -H:, -W:] = combined

        if not return_dict:
            return (dec,)

        return DecoderOutput(sample=dec)

    def forward(
        self,
        sample: torch.FloatTensor,
        sample_posterior: bool = False,
        return_dict: bool = True,
        generator: Optional[torch.Generator] = None,
    ) -> Union[DecoderOutput, torch.FloatTensor]:
        r"""
        Args:
            sample (`torch.FloatTensor`): 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).latent_dist
        if sample_posterior:
            z = posterior.sample(generator=generator)
        else:
            z = posterior.mode()
        dec = self.decode(z).sample

        if not return_dict:
            return (dec,)

        return DecoderOutput(sample=dec)

    @classmethod
    def from_pretrained(cls, pretrained_model_path, subfolder=None, **vae_additional_kwargs):
        import json
        import os
        if subfolder is not None:
            pretrained_model_path = os.path.join(pretrained_model_path, subfolder)

        config_file = os.path.join(pretrained_model_path, 'config.json')
        if not os.path.isfile(config_file):
            raise RuntimeError(f"{config_file} does not exist")
        with open(config_file, "r") as f:
            config = json.load(f)

        model = cls.from_config(config, **vae_additional_kwargs)
        from diffusers.utils import WEIGHTS_NAME
        model_file = os.path.join(pretrained_model_path, WEIGHTS_NAME)
        model_file_safetensors = model_file.replace(".bin", ".safetensors")
        if os.path.exists(model_file_safetensors):
            from safetensors.torch import load_file, safe_open
            state_dict = load_file(model_file_safetensors)
        else:
            if not os.path.isfile(model_file):
                raise RuntimeError(f"{model_file} does not exist")
            state_dict = torch.load(model_file, map_location="cpu")
        m, u = model.load_state_dict(state_dict, strict=False)
        print(f"### missing keys: {len(m)}; \n### unexpected keys: {len(u)};")
        print(m, u)
        return model


# Modified from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/autoencoders/autoencoder_kl_cogvideox.py
# Copyright 2024 The CogVideoX team, Tsinghua University & ZhipuAI and The HuggingFace Team.
# All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


class AutoencoderKLCogVideoX(ModelMixin, 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).

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

    Parameters:
        in_channels (int, *optional*, defaults to 3): Number of channels in the input image.
        out_channels (int,  *optional*, defaults to 3): Number of channels in the output.
        down_block_types (`Tuple[str]`, *optional*, defaults to `("DownEncoderBlock2D",)`):
            Tuple of downsample block types.
        up_block_types (`Tuple[str]`, *optional*, defaults to `("UpDecoderBlock2D",)`):
            Tuple of upsample block types.
        block_out_channels (`Tuple[int]`, *optional*, defaults to `(64,)`):
            Tuple of block output channels.
        act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
        sample_size (`int`, *optional*, defaults to `32`): Sample input size.
        scaling_factor (`float`, *optional*, defaults to `1.15258426`):
            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.
        force_upcast (`bool`, *optional*, default to `True`):
            If enabled it will force the VAE to run in float32 for high image resolution pipelines, such as SD-XL. VAE
            can be fine-tuned / trained to a lower range without loosing too much precision in which case
            `force_upcast` can be set to `False` - see: https://huggingface.co/madebyollin/sdxl-vae-fp16-fix
    """

    _supports_gradient_checkpointing = True
    _no_split_modules = ["CogVideoXResnetBlock3D"]

    @register_to_config
    def __init__(
        self,
        in_channels: int = 3,
        out_channels: int = 3,
        down_block_types: Tuple[str] = (
            "CogVideoXDownBlock3D",
            "CogVideoXDownBlock3D",
            "CogVideoXDownBlock3D",
            "CogVideoXDownBlock3D",
        ),
        up_block_types: Tuple[str] = (
            "CogVideoXUpBlock3D",
            "CogVideoXUpBlock3D",
            "CogVideoXUpBlock3D",
            "CogVideoXUpBlock3D",
        ),
        block_out_channels: Tuple[int] = (128, 256, 256, 512),
        latent_channels: int = 16,
        layers_per_block: int = 3,
        act_fn: str = "silu",
        norm_eps: float = 1e-6,
        norm_num_groups: int = 32,
        temporal_compression_ratio: float = 4,
        sample_height: int = 480,
        sample_width: int = 720,
        scaling_factor: float = 1.15258426,
        shift_factor: Optional[float] = None,
        latents_mean: Optional[Tuple[float]] = None,
        latents_std: Optional[Tuple[float]] = None,
        force_upcast: float = True,
        use_quant_conv: bool = False,
        use_post_quant_conv: bool = False,
        slice_mag_vae=False,
        slice_compression_vae=False,
        cache_compression_vae=False,
        cache_mag_vae=True,
        use_tiling=False,
        mini_batch_encoder=4,
        mini_batch_decoder=1,
    ):
        super().__init__()

        self.encoder = CogVideoXEncoder3D(
            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,
            act_fn=act_fn,
            norm_eps=norm_eps,
            norm_num_groups=norm_num_groups,
            temporal_compression_ratio=temporal_compression_ratio,
        )
        self.decoder = CogVideoXDecoder3D(
            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,
            act_fn=act_fn,
            norm_eps=norm_eps,
            norm_num_groups=norm_num_groups,
            temporal_compression_ratio=temporal_compression_ratio,
        )
        self.quant_conv = CogVideoXSafeConv3d(2 * out_channels, 2 * out_channels, 1) if use_quant_conv else None
        self.post_quant_conv = CogVideoXSafeConv3d(out_channels, out_channels, 1) if use_post_quant_conv else None

        self.use_slicing = False
        self.use_tiling = use_tiling

        # Can be increased to decode more latent frames at once, but comes at a reasonable memory cost and it is not
        # recommended because the temporal parts of the VAE, here, are tricky to understand.
        # If you decode X latent frames together, the number of output frames is:
        #     (X + (2 conv cache) + (2 time upscale_1) + (4 time upscale_2) - (2 causal conv downscale)) => X + 6 frames
        #
        # Example with num_latent_frames_batch_size = 2:
        #     - 12 latent frames: (0, 1), (2, 3), (4, 5), (6, 7), (8, 9), (10, 11) are processed together
        #         => (12 // 2 frame slices) * ((2 num_latent_frames_batch_size) + (2 conv cache) + (2 time upscale_1) + (4 time upscale_2) - (2 causal conv downscale))
        #         => 6 * 8 = 48 frames
        #     - 13 latent frames: (0, 1, 2) (special case), (3, 4), (5, 6), (7, 8), (9, 10), (11, 12) are processed together
        #         => (1 frame slice) * ((3 num_latent_frames_batch_size) + (2 conv cache) + (2 time upscale_1) + (4 time upscale_2) - (2 causal conv downscale)) +
        #            ((13 - 3) // 2) * ((2 num_latent_frames_batch_size) + (2 conv cache) + (2 time upscale_1) + (4 time upscale_2) - (2 causal conv downscale))
        #         => 1 * 9 + 5 * 8 = 49 frames
        # It has been implemented this way so as to not have "magic values" in the code base that would be hard to explain. Note that
        # setting it to anything other than 2 would give poor results because the VAE hasn't been trained to be adaptive with different
        # number of temporal frames.
        self.num_latent_frames_batch_size = 2

        # We make the minimum height and width of sample for tiling half that of the generally supported
        self.tile_sample_min_height = sample_height // 2
        self.tile_sample_min_width = sample_width // 2
        self.tile_latent_min_height = int(
            self.tile_sample_min_height / (2 ** (len(self.config.block_out_channels) - 1))
        )
        self.tile_latent_min_width = int(self.tile_sample_min_width / (2 ** (len(self.config.block_out_channels) - 1)))

        # These are experimental overlap factors that were chosen based on experimentation and seem to work best for
        # 720x480 (WxH) resolution. The above resolution is the strongly recommended generation resolution in CogVideoX
        # and so the tiling implementation has only been tested on those specific resolutions.
        self.tile_overlap_factor_height = 1 / 6
        self.tile_overlap_factor_width = 1 / 5

    def _set_gradient_checkpointing(self, module, value=False):
        if isinstance(module, (CogVideoXEncoder3D, CogVideoXDecoder3D)):
            module.gradient_checkpointing = value

    def _clear_fake_context_parallel_cache(self):
        for name, module in self.named_modules():
            if isinstance(module, CogVideoXCausalConv3d):
                logger.debug(f"Clearing fake Context Parallel cache for layer: {name}")
                module._clear_fake_context_parallel_cache()

    def enable_tiling(
        self,
        tile_sample_min_height: Optional[int] = None,
        tile_sample_min_width: Optional[int] = None,
        tile_overlap_factor_height: Optional[float] = None,
        tile_overlap_factor_width: 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_overlap_factor_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. Must be between 0 and 1. Setting a higher
                value might cause more tiles to be processed leading to slow down of the decoding process.
            tile_overlap_factor_width (`int`, *optional*):
                The minimum amount of overlap between two consecutive horizontal tiles. This is to ensure that there
                are no tiling artifacts produced across the width dimension. Must be between 0 and 1. Setting a higher
                value might cause more tiles to be processed leading to slow down of the decoding process.
        """
        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_latent_min_height = int(
            self.tile_sample_min_height / (2 ** (len(self.config.block_out_channels) - 1))
        )
        self.tile_latent_min_width = int(self.tile_sample_min_width / (2 ** (len(self.config.block_out_channels) - 1)))
        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

    @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 images. If `return_dict` is True, a
                [`~models.autoencoder_kl.AutoencoderKLOutput`] is returned, otherwise a plain `tuple` is returned.
        """
        batch_size, num_channels, num_frames, height, width = x.shape
        if num_frames == 1:
            h = self.encoder(x)
            if self.quant_conv is not None:
                h = self.quant_conv(h)
            posterior = DiagonalGaussianDistribution(h)
        else:
            frame_batch_size = 4
            h = []
            for i in range(num_frames // frame_batch_size):
                remaining_frames = num_frames % frame_batch_size
                start_frame = frame_batch_size * i + (0 if i == 0 else remaining_frames)
                end_frame = frame_batch_size * (i + 1) + remaining_frames
                z_intermediate = x[:, :, start_frame:end_frame]
                z_intermediate = self.encoder(z_intermediate)
                if self.quant_conv is not None:
                    z_intermediate = self.quant_conv(z_intermediate)
                h.append(z_intermediate)
            self._clear_fake_context_parallel_cache()
            h = torch.cat(h, dim=2)
            posterior = DiagonalGaussianDistribution(h)
        self._clear_fake_context_parallel_cache()
        if not return_dict:
            return (posterior,)
        return AutoencoderKLOutput(latent_dist=posterior)

    def _decode(self, z: torch.Tensor, return_dict: bool = True) -> Union[DecoderOutput, torch.Tensor]:
        batch_size, num_channels, num_frames, 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=return_dict)

        if num_frames == 1:
            dec = []
            z_intermediate = z
            if self.post_quant_conv is not None:
                z_intermediate = self.post_quant_conv(z_intermediate)
            z_intermediate = self.decoder(z_intermediate)
            dec.append(z_intermediate)
        else:
            frame_batch_size = self.num_latent_frames_batch_size
            dec = []
            for i in range(num_frames // frame_batch_size):
                remaining_frames = num_frames % frame_batch_size
                start_frame = frame_batch_size * i + (0 if i == 0 else remaining_frames)
                end_frame = frame_batch_size * (i + 1) + remaining_frames
                z_intermediate = z[:, :, start_frame:end_frame]
                if self.post_quant_conv is not None:
                    z_intermediate = self.post_quant_conv(z_intermediate)
                z_intermediate = self.decoder(z_intermediate)
                dec.append(z_intermediate)

        self._clear_fake_context_parallel_cache()
        dec = torch.cat(dec, dim=2)

        if not return_dict:
            return (dec,)

        return DecoderOutput(sample=dec)

    @apply_forward_hook
    def decode(self, z: torch.Tensor, 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:
            decoded_slices = [self._decode(z_slice).sample for z_slice in z.split(1)]
            decoded = torch.cat(decoded_slices)
        else:
            decoded = self._decode(z).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 tiled_decode(self, z: 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.
        """
        # Rough memory assessment:
        #   - In CogVideoX-2B, there are a total of 24 CausalConv3d layers.
        #   - The biggest intermediate dimensions are: [1, 128, 9, 480, 720].
        #   - Assume fp16 (2 bytes per value).
        # Memory required: 1 * 128 * 9 * 480 * 720 * 24 * 2 / 1024**3 = 17.8 GB
        #
        # Memory assessment when using tiling:
        #   - Assume everything as above but now HxW is 240x360 by tiling in half
        # Memory required: 1 * 128 * 9 * 240 * 360 * 24 * 2 / 1024**3 = 4.5 GB

        batch_size, num_channels, num_frames, height, width = z.shape

        overlap_height = int(self.tile_latent_min_height * (1 - self.tile_overlap_factor_height))
        overlap_width = int(self.tile_latent_min_width * (1 - self.tile_overlap_factor_width))
        blend_extent_height = int(self.tile_sample_min_height * self.tile_overlap_factor_height)
        blend_extent_width = int(self.tile_sample_min_width * self.tile_overlap_factor_width)
        row_limit_height = self.tile_sample_min_height - blend_extent_height
        row_limit_width = self.tile_sample_min_width - blend_extent_width
        frame_batch_size = self.num_latent_frames_batch_size

        # 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, overlap_height):
            row = []
            for j in range(0, width, overlap_width):
                time = []
                for k in range(num_frames // frame_batch_size):
                    remaining_frames = num_frames % frame_batch_size
                    start_frame = frame_batch_size * k + (0 if k == 0 else remaining_frames)
                    end_frame = frame_batch_size * (k + 1) + remaining_frames
                    tile = z[
                        :,
                        :,
                        start_frame:end_frame,
                        i : i + self.tile_latent_min_height,
                        j : j + self.tile_latent_min_width,
                    ]
                    if self.post_quant_conv is not None:
                        tile = self.post_quant_conv(tile)
                    tile = self.decoder(tile)
                    time.append(tile)
                self._clear_fake_context_parallel_cache()
                row.append(torch.cat(time, dim=2))
            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_extent_height)
                if j > 0:
                    tile = self.blend_h(row[j - 1], tile, blend_extent_width)
                result_row.append(tile[:, :, :, :row_limit_height, :row_limit_width])
            result_rows.append(torch.cat(result_row, dim=4))

        dec = torch.cat(result_rows, dim=3)

        if not return_dict:
            return (dec,)

        return DecoderOutput(sample=dec)

    def forward(
        self,
        sample: torch.Tensor,
        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)
        if not return_dict:
            return (dec,)
        return dec