wan/modules/lightvae.py

# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.

import torch
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange

import os

import safetensors
from loguru import logger

__all__ = [
    "_video_vae",
]

CACHE_T = 2


def load_safetensors_from_path(in_path, remove_key=None, include_keys=None):
    include_keys = include_keys or []
    tensors = {}
    with safetensors.safe_open(in_path, framework="pt", device="cpu") as f:
        for key in f.keys():
            if include_keys:
                if any(inc_key in key for inc_key in include_keys):
                    tensors[key] = f.get_tensor(key)
            else:
                if not (remove_key and remove_key in key):
                    tensors[key] = f.get_tensor(key)
    return tensors


def load_safetensors_from_dir(in_dir, remove_key=None, include_keys=None):
    include_keys = include_keys or []
    tensors = {}
    safetensors_files = os.listdir(in_dir)
    safetensors_files = [f for f in safetensors_files if f.endswith(".safetensors")]
    for f in safetensors_files:
        tensors.update(load_safetensors_from_path(os.path.join(in_dir, f), remove_key, include_keys))
    return tensors


def load_safetensors(in_path, remove_key=None, include_keys=None):
    include_keys = include_keys or []
    if os.path.isdir(in_path):
        return load_safetensors_from_dir(in_path, remove_key, include_keys)
    elif os.path.isfile(in_path):
        return load_safetensors_from_path(in_path, remove_key, include_keys)
    else:
        raise ValueError(f"{in_path} does not exist")


def load_pt_safetensors(in_path, remove_key=None, include_keys=None):
    include_keys = include_keys or []
    ext = os.path.splitext(in_path)[-1]
    if ext in (".pt", ".pth", ".tar"):
        state_dict = torch.load(in_path, map_location="cpu", weights_only=True)
        keys_to_keep = []
        for key in state_dict.keys():
            if include_keys:
                if any(inc_key in key for inc_key in include_keys):
                    keys_to_keep.append(key)
            else:
                if not (remove_key and remove_key in key):
                    keys_to_keep.append(key)
        state_dict = {k: state_dict[k] for k in keys_to_keep}
    else:
        state_dict = load_safetensors(in_path, remove_key, include_keys)
    return state_dict


def load_weights(checkpoint_path, cpu_offload=False, remove_key=None, load_from_rank0=False, include_keys=None):
    if not dist.is_initialized() or not load_from_rank0:
        logger.info(f"Loading weights from {checkpoint_path}")
        cpu_weight_dict = load_pt_safetensors(checkpoint_path, remove_key, include_keys)
        return cpu_weight_dict

    is_weight_loader = False
    current_rank = dist.get_rank()
    if current_rank == 0:
        is_weight_loader = True

    cpu_weight_dict = {}
    if is_weight_loader:
        logger.info(f"Loading weights from {checkpoint_path}")
        cpu_weight_dict = load_pt_safetensors(checkpoint_path, remove_key)

    meta_dict = {}
    if is_weight_loader:
        for key, tensor in cpu_weight_dict.items():
            meta_dict[key] = {"shape": tensor.shape, "dtype": tensor.dtype}

    obj_list = [meta_dict] if is_weight_loader else [None]

    src_global_rank = 0
    dist.broadcast_object_list(obj_list, src=src_global_rank)
    synced_meta_dict = obj_list[0]

    if cpu_offload:
        target_device = "cpu"
        distributed_weight_dict = {key: torch.empty(meta["shape"], dtype=meta["dtype"], device=target_device) for key, meta in synced_meta_dict.items()}
        dist.barrier()
    else:
        target_device = torch.device(f"cuda:{current_rank}")
        distributed_weight_dict = {key: torch.empty(meta["shape"], dtype=meta["dtype"], device=target_device) for key, meta in synced_meta_dict.items()}
        dist.barrier(device_ids=[torch.cuda.current_device()])

    for key in sorted(synced_meta_dict.keys()):
        tensor_to_broadcast = distributed_weight_dict[key]
        if is_weight_loader:
            tensor_to_broadcast.copy_(cpu_weight_dict[key], non_blocking=True)

        if cpu_offload:
            if is_weight_loader:
                gpu_tensor = tensor_to_broadcast.cuda()
                dist.broadcast(gpu_tensor, src=src_global_rank)
                tensor_to_broadcast.copy_(gpu_tensor.cpu(), non_blocking=True)
                del gpu_tensor
                torch.cuda.empty_cache()
            else:
                gpu_tensor = torch.empty_like(tensor_to_broadcast, device="cuda")
                dist.broadcast(gpu_tensor, src=src_global_rank)
                tensor_to_broadcast.copy_(gpu_tensor.cpu(), non_blocking=True)
                del gpu_tensor
                torch.cuda.empty_cache()
        else:
            dist.broadcast(tensor_to_broadcast, src=src_global_rank)

    if is_weight_loader:
        del cpu_weight_dict

    if cpu_offload:
        torch.cuda.empty_cache()

    logger.info(f"Weights distributed across {dist.get_world_size()} devices on {target_device}")
    return distributed_weight_dict


class CausalConv3d(nn.Conv3d):
    """
    Causal 3d convolusion.
    """

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self._padding = (
            self.padding[2],
            self.padding[2],
            self.padding[1],
            self.padding[1],
            2 * self.padding[0],
            0,
        )
        self.padding = (0, 0, 0)

    def forward(self, x, cache_x=None):
        padding = list(self._padding)
        if cache_x is not None and self._padding[4] > 0:
            cache_x = cache_x.to(x.device)
            x = torch.cat([cache_x, x], dim=2)
            padding[4] -= cache_x.shape[2]
        x = F.pad(x, padding)

        return super().forward(x)


class RMS_norm(nn.Module):
    def __init__(self, dim, channel_first=True, images=True, bias=False):
        super().__init__()
        broadcastable_dims = (1, 1, 1) if not images else (1, 1)
        shape = (dim, *broadcastable_dims) if channel_first else (dim,)

        self.channel_first = channel_first
        self.scale = dim**0.5
        self.gamma = nn.Parameter(torch.ones(shape))
        self.bias = nn.Parameter(torch.zeros(shape)) if bias else 0.0

    def forward(self, x):
        return F.normalize(x, dim=(1 if self.channel_first else -1)) * self.scale * self.gamma + self.bias


class Upsample(nn.Upsample):
    def forward(self, x):
        """
        Fix bfloat16 support for nearest neighbor interpolation.
        """
        return super().forward(x)


class Resample(nn.Module):
    def __init__(self, dim, mode):
        assert mode in (
            "none",
            "upsample2d",
            "upsample3d",
            "downsample2d",
            "downsample3d",
        )
        super().__init__()
        self.dim = dim
        self.mode = mode

        # layers
        if mode == "upsample2d":
            self.resample = nn.Sequential(
                Upsample(scale_factor=(2.0, 2.0), mode="nearest-exact"),
                nn.Conv2d(dim, dim // 2, 3, padding=1),
            )
        elif mode == "upsample3d":
            self.resample = nn.Sequential(
                Upsample(scale_factor=(2.0, 2.0), mode="nearest-exact"),
                nn.Conv2d(dim, dim // 2, 3, padding=1),
            )
            self.time_conv = CausalConv3d(dim, dim * 2, (3, 1, 1), padding=(1, 0, 0))

        elif mode == "downsample2d":
            self.resample = nn.Sequential(nn.ZeroPad2d((0, 1, 0, 1)), nn.Conv2d(dim, dim, 3, stride=(2, 2)))
        elif mode == "downsample3d":
            self.resample = nn.Sequential(nn.ZeroPad2d((0, 1, 0, 1)), nn.Conv2d(dim, dim, 3, stride=(2, 2)))
            self.time_conv = CausalConv3d(dim, dim, (3, 1, 1), stride=(2, 1, 1), padding=(0, 0, 0))

        else:
            self.resample = nn.Identity()

    def forward(self, x, feat_cache=None, feat_idx=[0]):
        b, c, t, h, w = x.size()
        if self.mode == "upsample3d":
            if feat_cache is not None:
                idx = feat_idx[0]
                if feat_cache[idx] is None:
                    feat_cache[idx] = "Rep"
                    feat_idx[0] += 1
                else:
                    cache_x = x[:, :, -CACHE_T:, :, :].clone()
                    if cache_x.shape[2] < 2 and feat_cache[idx] is not None and feat_cache[idx] != "Rep":
                        # cache last frame of last two chunk
                        cache_x = torch.cat(
                            [
                                feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device),
                                cache_x,
                            ],
                            dim=2,
                        )
                    if cache_x.shape[2] < 2 and feat_cache[idx] is not None and feat_cache[idx] == "Rep":
                        cache_x = torch.cat(
                            [torch.zeros_like(cache_x).to(cache_x.device), cache_x],
                            dim=2,
                        )
                    if feat_cache[idx] == "Rep":
                        x = self.time_conv(x)
                    else:
                        x = self.time_conv(x, feat_cache[idx])
                    feat_cache[idx] = cache_x
                    feat_idx[0] += 1

                    x = x.reshape(b, 2, c, t, h, w)
                    x = torch.stack((x[:, 0, :, :, :, :], x[:, 1, :, :, :, :]), 3)
                    x = x.reshape(b, c, t * 2, h, w)
        t = x.shape[2]
        x = rearrange(x, "b c t h w -> (b t) c h w")
        x = self.resample(x)
        x = rearrange(x, "(b t) c h w -> b c t h w", t=t)

        if self.mode == "downsample3d":
            if feat_cache is not None:
                idx = feat_idx[0]
                if feat_cache[idx] is None:
                    feat_cache[idx] = x.clone()
                    feat_idx[0] += 1
                else:
                    cache_x = x[:, :, -1:, :, :].clone()
                    # if cache_x.shape[2] < 2 and feat_cache[idx] is not None and feat_cache[idx]!='Rep':
                    #     # cache last frame of last two chunk
                    #     cache_x = torch.cat([feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x], dim=2)

                    x = self.time_conv(torch.cat([feat_cache[idx][:, :, -1:, :, :], x], 2))
                    feat_cache[idx] = cache_x
                    feat_idx[0] += 1
        return x

    def init_weight(self, conv):
        conv_weight = conv.weight
        nn.init.zeros_(conv_weight)
        c1, c2, t, h, w = conv_weight.size()
        one_matrix = torch.eye(c1, c2)
        init_matrix = one_matrix
        nn.init.zeros_(conv_weight)
        # conv_weight.data[:,:,-1,1,1] = init_matrix * 0.5
        conv_weight.data[:, :, 1, 0, 0] = init_matrix  # * 0.5
        conv.weight.data.copy_(conv_weight)
        nn.init.zeros_(conv.bias.data)

    def init_weight2(self, conv):
        conv_weight = conv.weight.data
        nn.init.zeros_(conv_weight)
        c1, c2, t, h, w = conv_weight.size()
        init_matrix = torch.eye(c1 // 2, c2)
        # init_matrix = repeat(init_matrix, 'o ... -> (o 2) ...').permute(1,0,2).contiguous().reshape(c1,c2)
        conv_weight[: c1 // 2, :, -1, 0, 0] = init_matrix
        conv_weight[c1 // 2 :, :, -1, 0, 0] = init_matrix
        conv.weight.data.copy_(conv_weight)
        nn.init.zeros_(conv.bias.data)


class ResidualBlock(nn.Module):
    def __init__(self, in_dim, out_dim, dropout=0.0):
        super().__init__()
        self.in_dim = in_dim
        self.out_dim = out_dim

        # layers
        self.residual = nn.Sequential(
            RMS_norm(in_dim, images=False),
            nn.SiLU(),
            CausalConv3d(in_dim, out_dim, 3, padding=1),
            RMS_norm(out_dim, images=False),
            nn.SiLU(),
            nn.Dropout(dropout),
            CausalConv3d(out_dim, out_dim, 3, padding=1),
        )
        self.shortcut = CausalConv3d(in_dim, out_dim, 1) if in_dim != out_dim else nn.Identity()

    def forward(self, x, feat_cache=None, feat_idx=[0]):
        h = self.shortcut(x)
        for layer in self.residual:
            if isinstance(layer, CausalConv3d) and feat_cache is not None:
                idx = feat_idx[0]
                cache_x = x[:, :, -CACHE_T:, :, :].clone()
                if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
                    # cache last frame of last two chunk
                    cache_x = torch.cat(
                        [
                            feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device),
                            cache_x,
                        ],
                        dim=2,
                    )
                x = layer(x, feat_cache[idx])
                feat_cache[idx] = cache_x
                feat_idx[0] += 1
            else:
                x = layer(x)
        return x + h


class AttentionBlock(nn.Module):
    """
    Causal self-attention with a single head.
    """

    def __init__(self, dim):
        super().__init__()
        self.dim = dim

        # layers
        self.norm = RMS_norm(dim)
        self.to_qkv = nn.Conv2d(dim, dim * 3, 1)
        self.proj = nn.Conv2d(dim, dim, 1)

        # zero out the last layer params
        nn.init.zeros_(self.proj.weight)

    def forward(self, x):
        identity = x
        b, c, t, h, w = x.size()
        x = rearrange(x, "b c t h w -> (b t) c h w")
        x = self.norm(x)
        # compute query, key, value
        q, k, v = self.to_qkv(x).reshape(b * t, 1, c * 3, -1).permute(0, 1, 3, 2).contiguous().chunk(3, dim=-1)

        # apply attention
        x = F.scaled_dot_product_attention(
            q,
            k,
            v,
        )
        x = x.squeeze(1).permute(0, 2, 1).reshape(b * t, c, h, w)

        # output
        x = self.proj(x)
        x = rearrange(x, "(b t) c h w-> b c t h w", t=t)
        return x + identity


class Encoder3d(nn.Module):
    def __init__(self, dim=128, z_dim=4, dim_mult=[1, 2, 4, 4], num_res_blocks=2, attn_scales=[], temperal_downsample=[True, True, False], dropout=0.0, pruning_rate=0.0):
        super().__init__()
        self.dim = dim
        self.z_dim = z_dim
        self.dim_mult = dim_mult
        self.num_res_blocks = num_res_blocks
        self.attn_scales = attn_scales
        self.temperal_downsample = temperal_downsample

        # dimensions
        dims = [dim * u for u in [1] + dim_mult]
        dims = [int(d * (1 - pruning_rate)) for d in dims]
        scale = 1.0

        # init block
        self.conv1 = CausalConv3d(3, dims[0], 3, padding=1)

        # downsample blocks
        downsamples = []
        for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])):
            # residual (+attention) blocks
            for _ in range(num_res_blocks):
                downsamples.append(ResidualBlock(in_dim, out_dim, dropout))
                if scale in attn_scales:
                    downsamples.append(AttentionBlock(out_dim))
                in_dim = out_dim

            # downsample block
            if i != len(dim_mult) - 1:
                mode = "downsample3d" if temperal_downsample[i] else "downsample2d"
                downsamples.append(Resample(out_dim, mode=mode))
                scale /= 2.0
        self.downsamples = nn.Sequential(*downsamples)

        # middle blocks
        self.middle = nn.Sequential(
            ResidualBlock(out_dim, out_dim, dropout),
            AttentionBlock(out_dim),
            ResidualBlock(out_dim, out_dim, dropout),
        )

        # output blocks
        self.head = nn.Sequential(
            RMS_norm(out_dim, images=False),
            nn.SiLU(),
            CausalConv3d(out_dim, z_dim, 3, padding=1),
        )

    def forward(self, x, feat_cache=None, feat_idx=[0]):
        if feat_cache is not None:
            idx = feat_idx[0]
            cache_x = x[:, :, -CACHE_T:, :, :].clone()
            if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
                # cache last frame of last two chunk
                cache_x = torch.cat(
                    [
                        feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device),
                        cache_x,
                    ],
                    dim=2,
                )
            x = self.conv1(x, feat_cache[idx])
            feat_cache[idx] = cache_x
            feat_idx[0] += 1
        else:
            x = self.conv1(x)

        ## downsamples
        for layer in self.downsamples:
            if feat_cache is not None:
                x = layer(x, feat_cache, feat_idx)
            else:
                x = layer(x)

        ## middle
        for layer in self.middle:
            if isinstance(layer, ResidualBlock) and feat_cache is not None:
                x = layer(x, feat_cache, feat_idx)
            else:
                x = layer(x)

        ## head
        for layer in self.head:
            if isinstance(layer, CausalConv3d) and feat_cache is not None:
                idx = feat_idx[0]
                cache_x = x[:, :, -CACHE_T:, :, :].clone()
                if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
                    # cache last frame of last two chunk
                    cache_x = torch.cat(
                        [
                            feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device),
                            cache_x,
                        ],
                        dim=2,
                    )
                x = layer(x, feat_cache[idx])
                feat_cache[idx] = cache_x
                feat_idx[0] += 1
            else:
                x = layer(x)
        return x


class Decoder3d(nn.Module):
    def __init__(self, dim=128, z_dim=4, dim_mult=[1, 2, 4, 4], num_res_blocks=2, attn_scales=[], temperal_upsample=[False, True, True], dropout=0.0, pruning_rate=0.0):
        super().__init__()
        self.dim = dim
        self.z_dim = z_dim
        self.dim_mult = dim_mult
        self.num_res_blocks = num_res_blocks
        self.attn_scales = attn_scales
        self.temperal_upsample = temperal_upsample

        # dimensions
        dims = [dim * u for u in [dim_mult[-1]] + dim_mult[::-1]]
        dims = [int(d * (1 - pruning_rate)) for d in dims]

        scale = 1.0 / 2 ** (len(dim_mult) - 2)

        # init block
        self.conv1 = CausalConv3d(z_dim, dims[0], 3, padding=1)

        # middle blocks
        self.middle = nn.Sequential(
            ResidualBlock(dims[0], dims[0], dropout),
            AttentionBlock(dims[0]),
            ResidualBlock(dims[0], dims[0], dropout),
        )

        # upsample blocks
        upsamples = []
        for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])):
            # residual (+attention) blocks
            if i == 1 or i == 2 or i == 3:
                in_dim = in_dim // 2
            for _ in range(num_res_blocks + 1):
                upsamples.append(ResidualBlock(in_dim, out_dim, dropout))
                if scale in attn_scales:
                    upsamples.append(AttentionBlock(out_dim))
                in_dim = out_dim

            # upsample block
            if i != len(dim_mult) - 1:
                mode = "upsample3d" if temperal_upsample[i] else "upsample2d"
                upsamples.append(Resample(out_dim, mode=mode))
                scale *= 2.0
        self.upsamples = nn.Sequential(*upsamples)

        # output blocks
        self.head = nn.Sequential(
            RMS_norm(out_dim, images=False),
            nn.SiLU(),
            CausalConv3d(out_dim, 3, 3, padding=1),
        )

    def forward(self, x, feat_cache=None, feat_idx=[0]):
        ## conv1
        if feat_cache is not None:
            idx = feat_idx[0]
            cache_x = x[:, :, -CACHE_T:, :, :].clone()
            if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
                # cache last frame of last two chunk
                cache_x = torch.cat(
                    [
                        feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device),
                        cache_x,
                    ],
                    dim=2,
                )
            x = self.conv1(x, feat_cache[idx])
            feat_cache[idx] = cache_x
            feat_idx[0] += 1
        else:
            x = self.conv1(x)

        ## middle
        for layer in self.middle:
            if isinstance(layer, ResidualBlock) and feat_cache is not None:
                x = layer(x, feat_cache, feat_idx)
            else:
                x = layer(x)

        ## upsamples
        for layer in self.upsamples:
            if feat_cache is not None:
                x = layer(x, feat_cache, feat_idx)
            else:
                x = layer(x)

        ## head
        for layer in self.head:
            if isinstance(layer, CausalConv3d) and feat_cache is not None:
                idx = feat_idx[0]
                cache_x = x[:, :, -CACHE_T:, :, :].clone()
                if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
                    # cache last frame of last two chunk
                    cache_x = torch.cat(
                        [
                            feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device),
                            cache_x,
                        ],
                        dim=2,
                    )
                x = layer(x, feat_cache[idx])
                feat_cache[idx] = cache_x
                feat_idx[0] += 1
            else:
                x = layer(x)
        return x


def count_conv3d(model):
    count = 0
    for m in model.modules():
        if isinstance(m, CausalConv3d):
            count += 1
    return count


class WanVAE_(nn.Module):
    def __init__(self, dim=128, z_dim=4, dim_mult=[1, 2, 4, 4], num_res_blocks=2, attn_scales=[], temperal_downsample=[True, True, False], dropout=0.0, pruning_rate=0.0):
        super().__init__()
        self.dim = dim
        self.z_dim = z_dim
        self.dim_mult = dim_mult
        self.num_res_blocks = num_res_blocks
        self.attn_scales = attn_scales
        self.temperal_downsample = temperal_downsample
        self.temperal_upsample = temperal_downsample[::-1]
        self.spatial_compression_ratio = 2 ** len(self.temperal_downsample)

        # The minimal tile height and width for spatial tiling to be used
        self.tile_sample_min_height = 256
        self.tile_sample_min_width = 256

        # The minimal distance between two spatial tiles
        self.tile_sample_stride_height = 192
        self.tile_sample_stride_width = 192
        # modules
        self.encoder = Encoder3d(
            dim,
            z_dim * 2,
            dim_mult,
            num_res_blocks,
            attn_scales,
            self.temperal_downsample,
            dropout,
            pruning_rate,
        )
        self.conv1 = CausalConv3d(z_dim * 2, z_dim * 2, 1)
        self.conv2 = CausalConv3d(z_dim, z_dim, 1)
        self.decoder = Decoder3d(
            dim,
            z_dim,
            dim_mult,
            num_res_blocks,
            attn_scales,
            self.temperal_upsample,
            dropout,
            pruning_rate,
        )

    def forward(self, x):
        mu, log_var = self.encode(x)
        z = self.reparameterize(mu, log_var)
        x_recon = self.decode(z)
        return x_recon, mu, log_var

    def blend_v(self, a, b, blend_extent):
        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, b, blend_extent):
        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 tiled_encode(self, x, scale):
        _, _, 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):
                self.clear_cache()
                time = []
                frame_range = 1 + (num_frames - 1) // 4
                for k in range(frame_range):
                    self._enc_conv_idx = [0]
                    if k == 0:
                        tile = x[:, :, :1, i : i + self.tile_sample_min_height, j : j + self.tile_sample_min_width]
                    else:
                        tile = x[
                            :,
                            :,
                            1 + 4 * (k - 1) : 1 + 4 * k,
                            i : i + self.tile_sample_min_height,
                            j : j + self.tile_sample_min_width,
                        ]
                    tile = self.encoder(tile, feat_cache=self._enc_feat_map, feat_idx=self._enc_conv_idx)
                    mu, log_var = self.conv1(tile).chunk(2, dim=1)
                    if isinstance(scale[0], torch.Tensor):
                        mu = (mu - scale[0].view(1, self.z_dim, 1, 1, 1)) * scale[1].view(1, self.z_dim, 1, 1, 1)
                    else:
                        mu = (mu - scale[0]) * scale[1]

                    time.append(mu)

                row.append(torch.cat(time, dim=2))
            rows.append(row)
        self.clear_cache()

        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=-1))

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

    def tiled_decode(self, z, scale):
        if isinstance(scale[0], torch.Tensor):
            z = z / scale[1].view(1, self.z_dim, 1, 1, 1) + scale[0].view(1, self.z_dim, 1, 1, 1)
        else:
            z = z / scale[1] + scale[0]

        _, _, 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):
                self.clear_cache()
                time = []
                for k in range(num_frames):
                    self._conv_idx = [0]
                    tile = z[:, :, k : k + 1, i : i + tile_latent_min_height, j : j + tile_latent_min_width]
                    tile = self.conv2(tile)
                    decoded = self.decoder(tile, feat_cache=self._feat_map, feat_idx=self._conv_idx)
                    time.append(decoded)
                row.append(torch.cat(time, dim=2))
            rows.append(row)
        self.clear_cache()

        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=-1))

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

        return dec

    def encode(self, x, scale, return_mu=False):
        self.clear_cache()
        ## cache
        t = x.shape[2]
        iter_ = 1 + (t - 1) // 4
        for i in range(iter_):
            self._enc_conv_idx = [0]
            if i == 0:
                out = self.encoder(
                    x[:, :, :1, :, :],
                    feat_cache=self._enc_feat_map,
                    feat_idx=self._enc_conv_idx,
                )
            else:
                out_ = self.encoder(
                    x[:, :, 1 + 4 * (i - 1) : 1 + 4 * i, :, :],
                    feat_cache=self._enc_feat_map,
                    feat_idx=self._enc_conv_idx,
                )
                out = torch.cat([out, out_], 2)
        mu, log_var = self.conv1(out).chunk(2, dim=1)
        if isinstance(scale[0], torch.Tensor):
            mu = (mu - scale[0].view(1, self.z_dim, 1, 1, 1)) * scale[1].view(1, self.z_dim, 1, 1, 1)
        else:
            mu = (mu - scale[0]) * scale[1]

        self.clear_cache()
        if return_mu:
            return mu, log_var
        else:
            return mu

    def decode(self, z, scale):
        self.clear_cache()

        # z: [b,c,t,h,w]
        if isinstance(scale[0], torch.Tensor):
            z = z / scale[1].view(1, self.z_dim, 1, 1, 1) + scale[0].view(1, self.z_dim, 1, 1, 1)
        else:
            z = z / scale[1] + scale[0]
        iter_ = z.shape[2]
        x = self.conv2(z)
        for i in range(iter_):
            self._conv_idx = [0]
            if i == 0:
                out = self.decoder(
                    x[:, :, i : i + 1, :, :],
                    feat_cache=self._feat_map,
                    feat_idx=self._conv_idx,
                )
            else:
                out_ = self.decoder(
                    x[:, :, i : i + 1, :, :],
                    feat_cache=self._feat_map,
                    feat_idx=self._conv_idx,
                )
                out = torch.cat([out, out_], 2)

        self.clear_cache()
        return out

    def decode_stream(self, z, scale):
        self.clear_cache()

        # z: [b,c,t,h,w]
        if isinstance(scale[0], torch.Tensor):
            z = z / scale[1].view(1, self.z_dim, 1, 1, 1) + scale[0].view(1, self.z_dim, 1, 1, 1)
        else:
            z = z / scale[1] + scale[0]
        iter_ = z.shape[2]
        x = self.conv2(z)
        for i in range(iter_):
            self._conv_idx = [0]
            out = self.decoder(
                x[:, :, i : i + 1, :, :],
                feat_cache=self._feat_map,
                feat_idx=self._conv_idx,
            )
            yield out

    def cached_decode(self, z, scale):
        # z: [b,c,t,h,w]
        if isinstance(scale[0], torch.Tensor):
            z = z / scale[1].view(1, self.z_dim, 1, 1, 1) + scale[0].view(1, self.z_dim, 1, 1, 1)
        else:
            z = z / scale[1] + scale[0]
        iter_ = z.shape[2]
        x = self.conv2(z)
        for i in range(iter_):
            self._conv_idx = [0]
            if i == 0:
                out = self.decoder(x[:, :, i : i + 1, :, :], feat_cache=self._feat_map, feat_idx=self._conv_idx)
            else:
                out_ = self.decoder(x[:, :, i : i + 1, :, :], feat_cache=self._feat_map, feat_idx=self._conv_idx)
                out = torch.cat([out, out_], 2)
        return out

    def reparameterize(self, mu, log_var):
        std = torch.exp(0.5 * log_var)
        eps = torch.randn_like(std)
        return eps * std + mu

    def sample(self, imgs, deterministic=False, scale=[0, 1]):
        mu, log_var = self.encode(imgs, scale, return_mu=True)
        if deterministic:
            return mu
        std = torch.exp(0.5 * log_var.clamp(-30.0, 20.0))
        return mu + std * torch.randn_like(std), mu, log_var

    def clear_cache(self):
        self._conv_num = count_conv3d(self.decoder)
        self._conv_idx = [0]
        self._feat_map = [None] * self._conv_num
        # cache encode
        self._enc_conv_num = count_conv3d(self.encoder)
        self._enc_conv_idx = [0]
        self._enc_feat_map = [None] * self._enc_conv_num

    def encode_video(self, x, scale=[0, 1]):
        assert x.ndim == 5  # NTCHW
        assert x.shape[2] % 3 == 0
        x = x.transpose(1, 2)
        y = x.mul(2).sub_(1)
        y, mu, log_var = self.sample(y, scale=scale)
        return y.transpose(1, 2).to(x), mu, log_var

    def decode_video(self, x, scale=[0, 1]):
        assert x.ndim == 5  # NTCHW
        assert x.shape[2] % self.z_dim == 0
        x = x.transpose(1, 2)
        # B, C, T, H, W
        y = x
        y = self.decode(y, scale).clamp_(-1, 1)
        y = y.mul_(0.5).add_(0.5).clamp_(0, 1)  # NCTHW
        return y.transpose(1, 2).to(x)


def _video_vae(pretrained_path=None, z_dim=None, device="cpu", cpu_offload=False, dtype=torch.float, load_from_rank0=False, pruning_rate=0.0, **kwargs):
    """
    Autoencoder3d adapted from Stable Diffusion 1.x, 2.x and XL.
    """
    # params
    cfg = dict(
        dim=96,
        z_dim=z_dim,
        dim_mult=[1, 2, 4, 4],
        num_res_blocks=2,
        attn_scales=[],
        temperal_downsample=[False, True, True],
        dropout=0.0,
        pruning_rate=pruning_rate,
    )
    cfg.update(**kwargs)

    # init model
    with torch.device("meta"):
        model = WanVAE_(**cfg)

    # load checkpoint
    weights_dict = load_weights(pretrained_path, cpu_offload=cpu_offload, load_from_rank0=load_from_rank0)
    for k in weights_dict.keys():
        if weights_dict[k].dtype != dtype:
            weights_dict[k] = weights_dict[k].to(dtype)
    model.load_state_dict(weights_dict, assign=True)

    return model