# This file includes code derived from:
# https://github.com/kandinskylab/kandinsky-5
# Copyright (c) 2025 Kandinsky Lab
# Licensed under the MIT License

import os
from math import sqrt, ceil
from typing import Optional, Tuple, Union, List
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.utils.accelerate_utils import apply_forward_hook
from diffusers.models.activations import get_activation
from diffusers.models.attention_processor import Attention
from diffusers.models.modeling_outputs import AutoencoderKLOutput
from diffusers.models.modeling_utils import ModelMixin
from diffusers.models.autoencoders.vae import (
    DecoderOutput,
    DiagonalGaussianDistribution,
)


def prepare_causal_attention_mask(f: int, s: int, dtype: torch.dtype, device: torch.device, b: int) -> torch.Tensor:
    return (
        torch.ones((f, f), dtype=dtype, device=device)
        .tril_()
        .log_()
        .repeat_interleave(s, dim=0)
        .repeat_interleave(s, dim=1)
        .unsqueeze(0)
        .expand(b, -1, -1)
        .contiguous()
    )


class HunyuanVideoCausalConv3d(nn.Module):
    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        kernel_size: Union[int, Tuple[int, int, int]] = 3,
        stride: Union[int, Tuple[int, int, int]] = 1,
        padding: Union[int, Tuple[int, int, int]] = 0,
        dilation: Union[int, Tuple[int, int, int]] = 1,
        bias: bool = True,
        pad_mode: str = "replicate",
    ) -> None:
        super().__init__()

        kernel_size = (kernel_size, kernel_size, kernel_size) if isinstance(kernel_size, int) else kernel_size

        self.pad_mode = pad_mode
        self.time_causal_padding = (
            kernel_size[0] // 2,
            kernel_size[0] // 2,
            kernel_size[1] // 2,
            kernel_size[1] // 2,
            kernel_size[2] - 1,
            0,
        )

        self.conv = nn.Conv3d(in_channels, out_channels, kernel_size, stride, padding, dilation, bias=bias)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        hidden_states = F.pad(hidden_states, self.time_causal_padding, mode=self.pad_mode)
        return self.conv(hidden_states)


class HunyuanVideoUpsampleCausal3D(nn.Module):
    def __init__(
        self,
        in_channels: int,
        out_channels: Optional[int] = None,
        kernel_size: int = 3,
        stride: int = 1,
        bias: bool = True,
        upsample_factor: Tuple[float, float, float] = (2, 2, 2),
    ) -> None:
        super().__init__()

        out_channels = out_channels or in_channels
        self.upsample_factor = upsample_factor

        self.conv = HunyuanVideoCausalConv3d(in_channels, out_channels, kernel_size, stride, bias=bias)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        num_frames = hidden_states.size(2)
        dtp = hidden_states.dtype
        first_frame, other_frames = hidden_states.split((1, num_frames - 1), dim=2)
        first_frame = (
            F.interpolate(
                first_frame.squeeze(2),
                scale_factor=self.upsample_factor[1:],
                mode="nearest",
            )
            .unsqueeze(2)
            .to(dtp)
        )  # force cast

        if num_frames > 1:
            other_frames = other_frames.contiguous()
            other_frames = F.interpolate(other_frames, scale_factor=self.upsample_factor, mode="nearest").to(dtp)  # force cast
            hidden_states = torch.cat((first_frame, other_frames), dim=2)
            del first_frame
            del other_frames
            torch.cuda.empty_cache()
        else:
            hidden_states = first_frame

        hidden_states = self.conv(hidden_states)
        return hidden_states


class HunyuanVideoDownsampleCausal3D(nn.Module):
    def __init__(
        self,
        channels: int,
        out_channels: Optional[int] = None,
        padding: int = 1,
        kernel_size: int = 3,
        bias: bool = True,
        stride=2,
    ) -> None:
        super().__init__()
        out_channels = out_channels or channels

        self.conv = HunyuanVideoCausalConv3d(channels, out_channels, kernel_size, stride, padding, bias=bias)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        hidden_states = self.conv(hidden_states)
        return hidden_states


class HunyuanVideoResnetBlockCausal3D(nn.Module):
    def __init__(
        self,
        in_channels: int,
        out_channels: Optional[int] = None,
        dropout: float = 0.0,
        groups: int = 32,
        eps: float = 1e-6,
        non_linearity: str = "swish",
    ) -> None:
        super().__init__()
        out_channels = out_channels or in_channels

        self.nonlinearity = get_activation(non_linearity)

        self.norm1 = nn.GroupNorm(groups, in_channels, eps=eps, affine=True)
        self.conv1 = HunyuanVideoCausalConv3d(in_channels, out_channels, 3, 1, 0)

        self.norm2 = nn.GroupNorm(groups, out_channels, eps=eps, affine=True)
        self.dropout = nn.Dropout(dropout)
        self.conv2 = HunyuanVideoCausalConv3d(out_channels, out_channels, 3, 1, 0)

        self.conv_shortcut = None
        if in_channels != out_channels:
            self.conv_shortcut = HunyuanVideoCausalConv3d(in_channels, out_channels, 1, 1, 0)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        dtp = hidden_states.dtype
        hidden_states = hidden_states.contiguous()
        residual = hidden_states

        hidden_states = self.norm1(hidden_states).to(dtp)  # force cast
        hidden_states = self.nonlinearity(hidden_states)
        hidden_states = self.conv1(hidden_states)

        hidden_states = self.norm2(hidden_states).to(dtp)  # force cast
        hidden_states = self.nonlinearity(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.conv2(hidden_states)

        if self.conv_shortcut is not None:
            residual = self.conv_shortcut(residual)

        hidden_states = hidden_states + residual
        return hidden_states


class HunyuanVideoMidBlock3D(nn.Module):
    def __init__(
        self,
        in_channels: int,
        dropout: float = 0.0,
        num_layers: int = 1,
        resnet_eps: float = 1e-6,
        resnet_act_fn: str = "swish",
        resnet_groups: int = 32,
        add_attention: bool = True,
        attention_head_dim: int = 1,
    ) -> None:
        super().__init__()
        resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)
        self.add_attention = add_attention

        # There is always at least one resnet
        resnets = [
            HunyuanVideoResnetBlockCausal3D(
                in_channels=in_channels,
                out_channels=in_channels,
                eps=resnet_eps,
                groups=resnet_groups,
                dropout=dropout,
                non_linearity=resnet_act_fn,
            )
        ]
        attentions = []

        for _ in range(num_layers):
            if self.add_attention:
                attentions.append(
                    Attention(
                        in_channels,
                        heads=in_channels // attention_head_dim,
                        dim_head=attention_head_dim,
                        eps=resnet_eps,
                        norm_num_groups=resnet_groups,
                        residual_connection=True,
                        bias=True,
                        upcast_softmax=True,
                        _from_deprecated_attn_block=True,
                    )
                )
            else:
                attentions.append(None)

            resnets.append(
                HunyuanVideoResnetBlockCausal3D(
                    in_channels=in_channels,
                    out_channels=in_channels,
                    eps=resnet_eps,
                    groups=resnet_groups,
                    dropout=dropout,
                    non_linearity=resnet_act_fn,
                )
            )

        self.attentions = nn.ModuleList(attentions)
        self.resnets = nn.ModuleList(resnets)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        hidden_states = self.resnets[0](hidden_states)

        for attn, resnet in zip(self.attentions, self.resnets[1:]):
            if attn is not None:
                batch_size, _, num_frames, height, width = hidden_states.shape
                hidden_states = hidden_states.permute(0, 2, 3, 4, 1).flatten(1, 3)
                mask = prepare_causal_attention_mask(
                    num_frames,
                    height * width,
                    hidden_states.dtype,
                    hidden_states.device,
                    batch_size,
                )
                hidden_states = attn(hidden_states, attention_mask=mask)
                hidden_states = hidden_states.unflatten(1, (num_frames, height, width)).permute(0, 4, 1, 2, 3)

            hidden_states = resnet(hidden_states)

        return hidden_states


class HunyuanVideoDownBlock3D(nn.Module):
    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        dropout: float = 0.0,
        num_layers: int = 1,
        resnet_eps: float = 1e-6,
        resnet_act_fn: str = "swish",
        resnet_groups: int = 32,
        add_downsample: bool = True,
        downsample_stride: int = 2,
        downsample_padding: int = 1,
    ) -> None:
        super().__init__()
        resnets = []

        for i in range(num_layers):
            in_channels = in_channels if i == 0 else out_channels
            resnets.append(
                HunyuanVideoResnetBlockCausal3D(
                    in_channels=in_channels,
                    out_channels=out_channels,
                    eps=resnet_eps,
                    groups=resnet_groups,
                    dropout=dropout,
                    non_linearity=resnet_act_fn,
                )
            )

        self.resnets = nn.ModuleList(resnets)

        if add_downsample:
            self.downsamplers = nn.ModuleList(
                [
                    HunyuanVideoDownsampleCausal3D(
                        out_channels,
                        out_channels=out_channels,
                        padding=downsample_padding,
                        stride=downsample_stride,
                    )
                ]
            )
        else:
            self.downsamplers = None

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        for resnet in self.resnets:
            hidden_states = resnet(hidden_states)

        if self.downsamplers is not None:
            for downsampler in self.downsamplers:
                hidden_states = downsampler(hidden_states)

        return hidden_states


class HunyuanVideoUpBlock3D(nn.Module):
    def __init__(
        self,
        in_channels: int,
        out_channels: int,
        dropout: float = 0.0,
        num_layers: int = 1,
        resnet_eps: float = 1e-6,
        resnet_act_fn: str = "swish",
        resnet_groups: int = 32,
        add_upsample: bool = True,
        upsample_scale_factor: Tuple[int, int, int] = (2, 2, 2),
    ) -> None:
        super().__init__()
        resnets = []

        for i in range(num_layers):
            input_channels = in_channels if i == 0 else out_channels

            resnets.append(
                HunyuanVideoResnetBlockCausal3D(
                    in_channels=input_channels,
                    out_channels=out_channels,
                    eps=resnet_eps,
                    groups=resnet_groups,
                    dropout=dropout,
                    non_linearity=resnet_act_fn,
                )
            )

        self.resnets = nn.ModuleList(resnets)

        if add_upsample:
            self.upsamplers = nn.ModuleList(
                [
                    HunyuanVideoUpsampleCausal3D(
                        out_channels,
                        out_channels=out_channels,
                        upsample_factor=upsample_scale_factor,
                    )
                ]
            )
        else:
            self.upsamplers = None

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        for resnet in self.resnets:
            hidden_states = resnet(hidden_states)

        if self.upsamplers is not None:
            for upsampler in self.upsamplers:
                hidden_states = upsampler(hidden_states)

        return hidden_states


class HunyuanVideoEncoder3D(nn.Module):
    r"""
    Causal encoder for 3D video-like data introduced
    in [Hunyuan Video](https://huggingface.co/papers/2412.03603).
    """

    def __init__(
        self,
        in_channels: int = 3,
        out_channels: int = 3,
        down_block_types: Tuple[str, ...] = (
            "HunyuanVideoDownBlock3D",
            "HunyuanVideoDownBlock3D",
            "HunyuanVideoDownBlock3D",
            "HunyuanVideoDownBlock3D",
        ),
        block_out_channels: Tuple[int, ...] = (128, 256, 512, 512),
        layers_per_block: int = 2,
        norm_num_groups: int = 32,
        act_fn: str = "silu",
        double_z: bool = True,
        mid_block_add_attention=True,
        temporal_compression_ratio: int = 4,
        spatial_compression_ratio: int = 8,
    ) -> None:
        super().__init__()

        self.conv_in = HunyuanVideoCausalConv3d(in_channels, block_out_channels[0], kernel_size=3, stride=1)
        self.mid_block = None
        self.down_blocks = nn.ModuleList([])

        output_channel = block_out_channels[0]
        for i, down_block_type in enumerate(down_block_types):
            if down_block_type != "HunyuanVideoDownBlock3D":
                raise ValueError(f"Unsupported down_block_type: {down_block_type}")

            input_channel = output_channel
            output_channel = block_out_channels[i]
            is_final_block = i == len(block_out_channels) - 1
            num_spatial_downsample_layers = int(np.log2(spatial_compression_ratio))
            num_time_downsample_layers = int(np.log2(temporal_compression_ratio))

            if temporal_compression_ratio == 4:
                add_spatial_downsample = bool(i < num_spatial_downsample_layers)
                add_time_downsample = bool(i >= (len(block_out_channels) - 1 - num_time_downsample_layers) and not is_final_block)
            elif temporal_compression_ratio == 8:
                add_spatial_downsample = bool(i < num_spatial_downsample_layers)
                add_time_downsample = bool(i < num_time_downsample_layers)
            else:
                raise ValueError(f"Unsupported time_compression_ratio: {temporal_compression_ratio}")

            downsample_stride_HW = (2, 2) if add_spatial_downsample else (1, 1)
            downsample_stride_T = (2,) if add_time_downsample else (1,)
            downsample_stride = tuple(downsample_stride_T + downsample_stride_HW)

            down_block = HunyuanVideoDownBlock3D(
                num_layers=layers_per_block,
                in_channels=input_channel,
                out_channels=output_channel,
                add_downsample=bool(add_spatial_downsample or add_time_downsample),
                resnet_eps=1e-6,
                resnet_act_fn=act_fn,
                resnet_groups=norm_num_groups,
                downsample_stride=downsample_stride,
                downsample_padding=0,
            )

            self.down_blocks.append(down_block)

        self.mid_block = HunyuanVideoMidBlock3D(
            in_channels=block_out_channels[-1],
            resnet_eps=1e-6,
            resnet_act_fn=act_fn,
            attention_head_dim=block_out_channels[-1],
            resnet_groups=norm_num_groups,
            add_attention=mid_block_add_attention,
        )

        self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[-1], num_groups=norm_num_groups, eps=1e-6)
        self.conv_act = nn.SiLU()

        conv_out_channels = 2 * out_channels if double_z else out_channels
        self.conv_out = HunyuanVideoCausalConv3d(block_out_channels[-1], conv_out_channels, kernel_size=3)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        hidden_states = self.conv_in(hidden_states)

        for down_block in self.down_blocks:
            hidden_states = down_block(hidden_states)

        hidden_states = self.mid_block(hidden_states)

        hidden_states = self.conv_norm_out(hidden_states)
        hidden_states = self.conv_act(hidden_states)
        hidden_states = self.conv_out(hidden_states)

        return hidden_states


class HunyuanVideoDecoder3D(nn.Module):
    r"""
    Causal decoder for 3D video-like data introduced
    in [Hunyuan Video](https://huggingface.co/papers/2412.03603).
    """

    def __init__(
        self,
        in_channels: int = 3,
        out_channels: int = 3,
        up_block_types: Tuple[str, ...] = (
            "HunyuanVideoUpBlock3D",
            "HunyuanVideoUpBlock3D",
            "HunyuanVideoUpBlock3D",
            "HunyuanVideoUpBlock3D",
        ),
        block_out_channels: Tuple[int, ...] = (128, 256, 512, 512),
        layers_per_block: int = 2,
        norm_num_groups: int = 32,
        act_fn: str = "silu",
        mid_block_add_attention=True,
        time_compression_ratio: int = 4,
        spatial_compression_ratio: int = 8,
    ):
        super().__init__()
        self.layers_per_block = layers_per_block

        self.conv_in = HunyuanVideoCausalConv3d(in_channels, block_out_channels[-1], kernel_size=3, stride=1)
        self.up_blocks = nn.ModuleList([])

        # mid
        self.mid_block = HunyuanVideoMidBlock3D(
            in_channels=block_out_channels[-1],
            resnet_eps=1e-6,
            resnet_act_fn=act_fn,
            attention_head_dim=block_out_channels[-1],
            resnet_groups=norm_num_groups,
            add_attention=mid_block_add_attention,
        )

        # up
        reversed_block_out_channels = list(reversed(block_out_channels))
        output_channel = reversed_block_out_channels[0]
        for i, up_block_type in enumerate(up_block_types):
            if up_block_type != "HunyuanVideoUpBlock3D":
                raise ValueError(f"Unsupported up_block_type: {up_block_type}")

            prev_output_channel = output_channel
            output_channel = reversed_block_out_channels[i]
            is_final_block = i == len(block_out_channels) - 1
            num_spatial_upsample_layers = int(np.log2(spatial_compression_ratio))
            num_time_upsample_layers = int(np.log2(time_compression_ratio))

            if time_compression_ratio == 4:
                add_spatial_upsample = bool(i < num_spatial_upsample_layers)
                add_time_upsample = bool(i >= len(block_out_channels) - 1 - num_time_upsample_layers and not is_final_block)
            else:
                raise ValueError(f"Unsupported time_compression_ratio: {time_compression_ratio}")

            upsample_scale_factor_HW = (2, 2) if add_spatial_upsample else (1, 1)
            upsample_scale_factor_T = (2,) if add_time_upsample else (1,)
            upsample_scale_factor = tuple(upsample_scale_factor_T + upsample_scale_factor_HW)

            up_block = HunyuanVideoUpBlock3D(
                num_layers=self.layers_per_block + 1,
                in_channels=prev_output_channel,
                out_channels=output_channel,
                add_upsample=bool(add_spatial_upsample or add_time_upsample),
                upsample_scale_factor=upsample_scale_factor,
                resnet_eps=1e-6,
                resnet_act_fn=act_fn,
                resnet_groups=norm_num_groups,
            )

            self.up_blocks.append(up_block)
            prev_output_channel = output_channel

        # out
        self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=1e-6)
        self.conv_act = nn.SiLU()
        self.conv_out = HunyuanVideoCausalConv3d(block_out_channels[0], out_channels, kernel_size=3)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        dtp = hidden_states.dtype
        hidden_states = self.conv_in(hidden_states)

        hidden_states = self.mid_block(hidden_states)

        for up_block in self.up_blocks:
            hidden_states = up_block(hidden_states)

        hidden_states = self.conv_norm_out(hidden_states)
        hidden_states = self.conv_act(hidden_states).to(dtp)  # force cast
        hidden_states = self.conv_out(hidden_states)

        return hidden_states


class AutoencoderKLHunyuanVideo(ModelMixin, ConfigMixin):
    r"""
    A VAE model with KL loss for encoding videos into latents
    and decoding latent representations into videos.
    Introduced in [HunyuanVideo](https://huggingface.co/papers/2412.03603).

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

    @register_to_config
    def __init__(
        self,
        in_channels: int = 3,
        out_channels: int = 3,
        latent_channels: int = 16,
        down_block_types: Tuple[str, ...] = (
            "HunyuanVideoDownBlock3D",
            "HunyuanVideoDownBlock3D",
            "HunyuanVideoDownBlock3D",
            "HunyuanVideoDownBlock3D",
        ),
        up_block_types: Tuple[str, ...] = (
            "HunyuanVideoUpBlock3D",
            "HunyuanVideoUpBlock3D",
            "HunyuanVideoUpBlock3D",
            "HunyuanVideoUpBlock3D",
        ),
        block_out_channels: Tuple[int] = (128, 256, 512, 512),
        layers_per_block: int = 2,
        act_fn: str = "silu",
        norm_num_groups: int = 32,
        scaling_factor: float = 0.476986,
        spatial_compression_ratio: int = 8,
        temporal_compression_ratio: int = 4,
        mid_block_add_attention: bool = True,
    ) -> None:
        super().__init__()

        self.time_compression_ratio = temporal_compression_ratio

        self.encoder = HunyuanVideoEncoder3D(
            in_channels=in_channels,
            out_channels=latent_channels,
            down_block_types=down_block_types,
            block_out_channels=block_out_channels,
            layers_per_block=layers_per_block,
            norm_num_groups=norm_num_groups,
            act_fn=act_fn,
            double_z=True,
            mid_block_add_attention=mid_block_add_attention,
            temporal_compression_ratio=temporal_compression_ratio,
            spatial_compression_ratio=spatial_compression_ratio,
        )

        self.decoder = HunyuanVideoDecoder3D(
            in_channels=latent_channels,
            out_channels=out_channels,
            up_block_types=up_block_types,
            block_out_channels=block_out_channels,
            layers_per_block=layers_per_block,
            norm_num_groups=norm_num_groups,
            act_fn=act_fn,
            time_compression_ratio=temporal_compression_ratio,
            spatial_compression_ratio=spatial_compression_ratio,
            mid_block_add_attention=mid_block_add_attention,
        )

        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.spatial_compression_ratio = spatial_compression_ratio
        self.temporal_compression_ratio = temporal_compression_ratio

        self.use_slicing = False

        self.use_tiling = True

        self.use_framewise_encoding = True
        self.use_framewise_decoding = True

        self.tile_sample_min_height = 256
        self.tile_sample_min_width = 256
        self.tile_sample_min_num_frames = 16

        self.tile_sample_stride_height = 192
        self.tile_sample_stride_width = 192
        self.tile_sample_stride_num_frames = 12

        self.tile_size = None

    def _encode(self, x: torch.Tensor) -> torch.Tensor:
        _, _, num_frames, height, width = x.shape

        if self.use_framewise_decoding and num_frames > (self.tile_sample_min_num_frames + 1):
            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)

        x = self.encoder(x)
        enc = self.quant_conv(x)
        return enc

    @apply_forward_hook
    def encode(
        self, x: torch.Tensor, opt_tiling: bool = True, return_dict: bool = True
    ) -> Union[AutoencoderKLOutput, Tuple[DiagonalGaussianDistribution]]:
        r"""
        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 opt_tiling:
            tile_size, tile_stride = self.get_enc_optimal_tiling(x.shape)
        else:
            b, _, f, h, w = x.shape
            tile_size, tile_stride = (b, f, h, w), (f, h, w)
        if tile_size != self.tile_size:
            self.tile_size = tile_size
            self.apply_tiling(tile_size, tile_stride)

        h = self._encode(x)

        posterior = DiagonalGaussianDistribution(h)

        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]:
        _, _, 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_min_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 + 1):
            return self._temporal_tiled_decode(z, 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, 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.Tensor, return_dict: bool = True) -> Union[DecoderOutput, torch.Tensor]:
        r"""
        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.
        """
        tile_size, tile_stride = self.get_dec_optimal_tiling(z.shape)
        if tile_size != self.tile_size:
            self.tile_size = tile_size
            self.apply_tiling(tile_size, tile_stride)

        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[-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: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor:
        blend_extent = min(a.shape[-1], b.shape[-1], 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) -> AutoencoderKLOutput:
        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.
        """
        _, _, _, 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

        rows = []
        for i in range(0, height - self.tile_sample_min_height + 1, self.tile_sample_stride_height):
            row = []
            for j in range(0, width - self.tile_sample_min_width + 1, self.tile_sample_stride_width):
                tile = x[
                    :,
                    :,
                    :,
                    i : i + self.tile_sample_min_height,
                    j : j + self.tile_sample_min_width,
                ]
                tile = self.encoder(tile).clone()
                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):
                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)
                height_lim = tile_latent_min_height if i == len(rows) - 1 else tile_latent_stride_height
                width_lim = tile_latent_min_width if j == len(row) - 1 else tile_latent_stride_width
                result_row.append(tile[:, :, :, :height_lim, :width_lim])
            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, 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.
        """

        _, _, _, 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

        rows = []
        for i in range(0, height - tile_latent_min_height + 1, tile_latent_stride_height):
            row = []
            for j in range(0, width - tile_latent_min_width + 1, tile_latent_stride_width):
                tile = z[
                    :,
                    :,
                    :,
                    i : i + tile_latent_min_height,
                    j : j + tile_latent_min_width,
                ]
                tile = self.post_quant_conv(tile)
                decoded = self.decoder(tile).clone()
                row.append(decoded)
            rows.append(row)

        result_rows = []
        for i, row in enumerate(rows):
            result_row = []
            for j, tile in enumerate(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)
                height_lim = self.tile_sample_min_height if i == len(rows) - 1 else self.tile_sample_stride_height
                width_lim = self.tile_sample_min_width if j == len(row) - 1 else self.tile_sample_stride_width
                result_row.append(tile[:, :, :, :height_lim, :width_lim])
            result_rows.append(torch.cat(result_row, dim=-1))

        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:
        _, _, 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):
        for i in range(
            0,
            num_frames - self.tile_sample_min_num_frames + 1,
            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).clone()
                tile = self.quant_conv(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)
                t_lim = tile_latent_min_num_frames if i == len(row) - 1 else tile_latent_stride_num_frames
                result_row.append(tile[:, :, :t_lim, :, :])
            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, return_dict: bool = True) -> Union[DecoderOutput, torch.Tensor]:
        _, _, num_frames, _, _ = 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_min_num_frames + 1,
            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, return_dict=True).sample
            else:
                tile = self.post_quant_conv(tile)
                decoded = self.decoder(tile).clone()
            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)
                t_lim = self.tile_sample_min_num_frames if i == len(row) - 1 else self.tile_sample_stride_num_frames
                result_row.append(tile[:, :, :t_lim, :, :])
            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,
        sample_posterior: bool = False,
        return_dict: bool = True,
        generator: Optional[torch.Generator] = None,
    ) -> Union[DecoderOutput, torch.Tensor]:
        r"""
        Args:
            sample (`torch.Tensor`): Input sample.
            sample_posterior (`bool`, *optional*, defaults to `False`):
                Whether to sample from the posterior.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`DecoderOutput`] instead of a plain tuple.
        """
        x = sample
        posterior = self.encode(x).latent_dist
        if sample_posterior:
            z = posterior.sample(generator=generator)
        else:
            z = posterior.mode()
        dec = self.decode(z, return_dict=return_dict)
        return dec

    def apply_tiling(self, tile: Tuple[int, int, int, int], stride: Tuple[int, int, int]):
        """Applies tiling."""
        _, ft, ht, wt = tile
        fs, hs, ws = stride

        self.use_tiling = True
        self.tile_sample_min_num_frames = ft - 1
        self.tile_sample_stride_num_frames = fs
        self.tile_sample_min_height = ht
        self.tile_sample_min_width = wt
        self.tile_sample_stride_height = hs
        self.tile_sample_stride_width = ws

    def get_enc_optimal_tiling(self, shape: List[int]) -> Tuple[Tuple[int, int, int, int], Tuple[int, int, int]]:
        """Returns optimal tiling for given shape."""
        h, w = shape[3:]

        free_mem = torch.cuda.mem_get_info()[0]
        max_area = free_mem / 256 / 17 / 8
        num_vals = 256 * 17 * (h + 32) * (w + 32)

        if h * w < max_area and num_vals < 2**31:
            return (1, 17, h, w), (8, h, w)

        def factorize(n, k):
            a = sqrt(n / k)
            b = sqrt(n * k)
            return ceil(a), ceil(b)

        k = max(h / w, w / h)
        N = max(ceil(h * w / max_area), ceil(num_vals / 2**31))
        a, b = factorize(N, k)
        if h >= w:
            wn, hn = a, b
        else:
            wn, hn = b, a

        if wn > 1:
            wt = ceil(w / wn / 8) * 8 + 16
            ws = wt - 32
        else:
            wt = w
            ws = w
        if hn > 1:
            ht = ceil(h / hn / 8) * 8 + 16
            hs = ht - 32
        else:
            ht = h
            hs = h
        return (1, 17, ht, wt), (8, hs, ws)

    def get_dec_optimal_tiling(self, shape: List[int]) -> Tuple[Tuple[int, int, int, int], Tuple[int, int, int]]:
        """Returns optimal tiling for given shape."""
        b, _, f, h, w = shape
        enc_inp_shape = [b, 3, 4 * (f - 1) + 1, 8 * h, 8 * w]
        return self.get_enc_optimal_tiling(enc_inp_shape)


def build_vae(conf, vae_dtype=torch.float16):
    if conf.name == "hunyuan":
        # Check if checkpoint_path is a direct file (safetensors or pt)
        if os.path.isfile(conf.checkpoint_path):
            # Load VAE from direct file using the hunyuan model loader
            from musubi_tuner.hunyuan_model.vae import load_vae as load_vae_hunyuan

            vae, _, _, _ = load_vae_hunyuan(vae_dtype=vae_dtype, vae_path=conf.checkpoint_path)
        else:
            # Load from directory with subfolder
            vae = AutoencoderKLHunyuanVideo.from_pretrained(conf.checkpoint_path, subfolder="vae", torch_dtype=vae_dtype)
        # Kandinsky-specific safety: patch causal attention mask to avoid huge (F*HW)^2 allocations at runtime.
        try:
            import musubi_tuner.modules.unet_causal_3d_blocks as _ucb

            if not hasattr(_ucb, "_orig_prepare_causal_attention_mask"):
                _ucb._orig_prepare_causal_attention_mask = _ucb.prepare_causal_attention_mask

            def _safe_causal_mask(n_frame, n_hw, dtype, device, batch_size=None):
                seq_len = n_frame * n_hw
                max_tokens = 4096
                if seq_len > max_tokens:
                    base = torch.ones((n_frame, n_frame), dtype=dtype, device=device).tril_().log_()
                    if batch_size is not None:
                        base = base.unsqueeze(0).expand(batch_size, -1, -1).contiguous()
                    return base
                return _ucb._orig_prepare_causal_attention_mask(n_frame, n_hw, dtype, device, batch_size)

            _ucb.prepare_causal_attention_mask = _safe_causal_mask
        except Exception:
            pass
        # Kandinsky-specific safety: force tiling to trigger earlier for large videos/images without touching core modules.
        try:
            if hasattr(vae, "enable_tiling"):
                vae.enable_tiling(True)
            if hasattr(vae, "tile_sample_min_size"):
                vae.tile_sample_min_size = min(getattr(vae, "tile_sample_min_size", 256), 192)
            if hasattr(vae, "tile_latent_min_size"):
                vae.tile_latent_min_size = min(getattr(vae, "tile_latent_min_size", 32), 24)
            if hasattr(vae, "tile_sample_min_tsize"):
                vae.tile_sample_min_tsize = min(getattr(vae, "tile_sample_min_tsize", 64), 24)
            if hasattr(vae, "tile_latent_min_tsize"):
                vae.tile_latent_min_tsize = min(getattr(vae, "tile_latent_min_tsize", 16), 6)
        except Exception:
            pass
        return vae
    elif conf.name == "flux":
        from diffusers.models import AutoencoderKL

        # Check if checkpoint_path is a direct file (safetensors or pt)
        if os.path.isfile(conf.checkpoint_path):
            # Load VAE from direct file path

            # Use Flux VAE config
            config = {
                "_class_name": "AutoencoderKL",
                "_diffusers_version": "0.32.1",
                "act_fn": "silu",
                "block_out_channels": [128, 256, 512, 512],
                "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D", "DownEncoderBlock2D", "DownEncoderBlock2D"],
                "force_upcast": True,
                "in_channels": 3,
                "latent_channels": 16,
                "layers_per_block": 2,
                "norm_num_groups": 32,
                "out_channels": 3,
                "sample_size": 1024,
                "scaling_factor": 0.3611,
                "shift_factor": 0.1159,
                "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
                "use_post_quant_conv": False,
                "use_quant_conv": False,
            }

            vae = AutoencoderKL.from_config(config)

            # Load weights
            if conf.checkpoint_path.endswith(".safetensors"):
                from safetensors.torch import load_file

                state_dict = load_file(conf.checkpoint_path)
            else:
                state_dict = torch.load(conf.checkpoint_path, map_location="cpu", weights_only=True)
                if "state_dict" in state_dict:
                    state_dict = state_dict["state_dict"]

            # Handle potential prefix in keys
            if any(k.startswith("vae.") for k in state_dict.keys()):
                state_dict = {k.replace("vae.", ""): v for k, v in state_dict.items() if k.startswith("vae.")}

            vae.load_state_dict(state_dict)
            vae = vae.to(torch.bfloat16)
        else:
            # Load from directory with subfolder
            vae = AutoencoderKL.from_pretrained(conf.checkpoint_path, subfolder="flux/vae", torch_dtype=torch.bfloat16)

        return vae
    else:
        assert False, f"unknown vae name {conf.name}"