model/vae.py

from einops import rearrange, repeat

import torch
import torch.nn as nn
import torch.nn.functional as F
from tqdm import tqdm

CACHE_T = 2


def check_is_instance(model, module_class):
    if isinstance(model, module_class):
        return True
    if hasattr(model, "module") and isinstance(model.module, module_class):
        return True
    return False


def block_causal_mask(x, block_size):
    # params
    b, n, s, _, device = *x.size(), x.device
    assert s % block_size == 0
    num_blocks = s // block_size

    # build mask
    mask = torch.zeros(b, n, s, s, dtype=torch.bool, device=device)
    for i in range(num_blocks):
        mask[:, :,
             i * block_size:(i + 1) * block_size, :(i + 1) * block_size] = 1
    return mask


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.

    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.float()).type_as(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., 2.), mode='nearest-exact'),
                nn.Conv2d(dim, dim // 2, 3, padding=1))
        elif mode == 'upsample3d':
            self.resample = nn.Sequential(
                Upsample(scale_factor=(2., 2.), 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()
                    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, 0, 0] = init_matrix
        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)
        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 check_is_instance(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,
            #attn_mask=block_causal_mask(q, block_size=h * w)
        )
        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):
        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]
        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 check_is_instance(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 check_is_instance(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):
        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]]
        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 check_is_instance(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 check_is_instance(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 check_is_instance(m, CausalConv3d):
            count += 1
    return count


class VideoVAE_(nn.Module):

    def __init__(self,
                 dim=96,
                 z_dim=16,
                 dim_mult=[1, 2, 4, 4],
                 num_res_blocks=2,
                 attn_scales=[],
                 temperal_downsample=[False, True, True],
                 dropout=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]

        # modules
        self.encoder = Encoder3d(dim, z_dim * 2, dim_mult, num_res_blocks,
                                 attn_scales, self.temperal_downsample, dropout)
        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)

    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 encode(self, x, scale):
        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):
            scale = [s.to(dtype=mu.dtype, device=mu.device) for s in scale]
            mu = (mu - scale[0].view(1, self.z_dim, 1, 1, 1)) * scale[1].view(
                1, self.z_dim, 1, 1, 1)
        else:
            scale = scale.to(dtype=mu.dtype, device=mu.device)
            mu = (mu - scale[0]) * scale[1]
        return mu

    def decode(self, z, scale):
        self.clear_cache()
        # z: [b,c,t,h,w]
        if isinstance(scale[0], torch.Tensor):
            scale = [s.to(dtype=z.dtype, device=z.device) for s in scale]
            z = z / scale[1].view(1, self.z_dim, 1, 1, 1) + scale[0].view(
                1, self.z_dim, 1, 1, 1)
        else:
            scale = scale.to(dtype=z.dtype, device=z.device)
            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) # may add tensor offload
        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):
        mu, log_var = self.encode(imgs)
        if deterministic:
            return mu
        std = torch.exp(0.5 * log_var.clamp(-30.0, 20.0))
        return mu + std * torch.randn_like(std)

    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


class WanVideoVAE(nn.Module):

    def __init__(self, z_dim=16):
        super().__init__()

        mean = [
            -0.7571, -0.7089, -0.9113, 0.1075, -0.1745, 0.9653, -0.1517, 1.5508,
            0.4134, -0.0715, 0.5517, -0.3632, -0.1922, -0.9497, 0.2503, -0.2921
        ]
        std = [
            2.8184, 1.4541, 2.3275, 2.6558, 1.2196, 1.7708, 2.6052, 2.0743,
            3.2687, 2.1526, 2.8652, 1.5579, 1.6382, 1.1253, 2.8251, 1.9160
        ]
        self.mean = torch.tensor(mean)
        self.std = torch.tensor(std)
        self.scale = [self.mean, 1.0 / self.std]

        # init model
        self.model = VideoVAE_(z_dim=z_dim).eval().requires_grad_(False)
        self.upsampling_factor = 8


    def build_1d_mask(self, length, left_bound, right_bound, border_width):
        x = torch.ones((length,))
        if not left_bound:
            x[:border_width] = (torch.arange(border_width) + 1) / border_width
        if not right_bound:
            x[-border_width:] = torch.flip((torch.arange(border_width) + 1) / border_width, dims=(0,))
        return x


    def build_mask(self, data, is_bound, border_width):
        _, _, _, H, W = data.shape
        h = self.build_1d_mask(H, is_bound[0], is_bound[1], border_width[0])
        w = self.build_1d_mask(W, is_bound[2], is_bound[3], border_width[1])

        h = repeat(h, "H -> H W", H=H, W=W)
        w = repeat(w, "W -> H W", H=H, W=W)

        mask = torch.stack([h, w]).min(dim=0).values
        mask = rearrange(mask, "H W -> 1 1 1 H W")
        return mask


    def tiled_decode(self, hidden_states, device, tile_size, tile_stride):
        _, _, T, H, W = hidden_states.shape
        size_h, size_w = tile_size
        stride_h, stride_w = tile_stride

        # Split tasks
        tasks = []
        for h in range(0, H, stride_h):
            if (h-stride_h >= 0 and h-stride_h+size_h >= H): continue
            for w in range(0, W, stride_w):
                if (w-stride_w >= 0 and w-stride_w+size_w >= W): continue
                h_, w_ = h + size_h, w + size_w
                tasks.append((h, h_, w, w_))

        data_device = "cpu"
        computation_device = device

        out_T = T * 4 - 3
        weight = torch.zeros((1, 1, out_T, H * self.upsampling_factor, W * self.upsampling_factor), dtype=hidden_states.dtype, device=data_device)
        values = torch.zeros((1, 3, out_T, H * self.upsampling_factor, W * self.upsampling_factor), dtype=hidden_states.dtype, device=data_device)

        for h, h_, w, w_ in tqdm(tasks, desc="VAE decoding"):
            hidden_states_batch = hidden_states[:, :, :, h:h_, w:w_].to(computation_device)
            hidden_states_batch = self.model.decode(hidden_states_batch, self.scale).to(data_device)

            mask = self.build_mask(
                hidden_states_batch,
                is_bound=(h==0, h_>=H, w==0, w_>=W),
                border_width=((size_h - stride_h) * self.upsampling_factor, (size_w - stride_w) * self.upsampling_factor)
            ).to(dtype=hidden_states.dtype, device=data_device)

            target_h = h * self.upsampling_factor
            target_w = w * self.upsampling_factor
            values[
                :,
                :,
                :,
                target_h:target_h + hidden_states_batch.shape[3],
                target_w:target_w + hidden_states_batch.shape[4],
            ] += hidden_states_batch * mask
            weight[
                :,
                :,
                :,
                target_h: target_h + hidden_states_batch.shape[3],
                target_w: target_w + hidden_states_batch.shape[4],
            ] += mask
        values = values / weight
        values = values.float().clamp_(-1, 1)
        return values


    def tiled_encode(self, video, device, tile_size, tile_stride):
        _, _, T, H, W = video.shape
        size_h, size_w = tile_size
        stride_h, stride_w = tile_stride

        # Split tasks
        tasks = []
        for h in range(0, H, stride_h):
            if (h-stride_h >= 0 and h-stride_h+size_h >= H): continue
            for w in range(0, W, stride_w):
                if (w-stride_w >= 0 and w-stride_w+size_w >= W): continue
                h_, w_ = h + size_h, w + size_w
                tasks.append((h, h_, w, w_))

        data_device = "cpu"
        computation_device = device

        out_T = (T + 3) // 4
        weight = torch.zeros((1, 1, out_T, H // self.upsampling_factor, W // self.upsampling_factor), dtype=video.dtype, device=data_device)
        values = torch.zeros((1, 16, out_T, H // self.upsampling_factor, W // self.upsampling_factor), dtype=video.dtype, device=data_device)

        for h, h_, w, w_ in tqdm(tasks, desc="VAE encoding"):
            hidden_states_batch = video[:, :, :, h:h_, w:w_].to(computation_device)
            hidden_states_batch = self.model.encode(hidden_states_batch, self.scale).to(data_device)

            mask = self.build_mask(
                hidden_states_batch,
                is_bound=(h==0, h_>=H, w==0, w_>=W),
                border_width=((size_h - stride_h) // self.upsampling_factor, (size_w - stride_w) // self.upsampling_factor)
            ).to(dtype=video.dtype, device=data_device)

            target_h = h // self.upsampling_factor
            target_w = w // self.upsampling_factor
            values[
                :,
                :,
                :,
                target_h:target_h + hidden_states_batch.shape[3],
                target_w:target_w + hidden_states_batch.shape[4],
            ] += hidden_states_batch * mask
            weight[
                :,
                :,
                :,
                target_h: target_h + hidden_states_batch.shape[3],
                target_w: target_w + hidden_states_batch.shape[4],
            ] += mask
        values = values / weight
        values = values.float()
        return values


    def single_encode(self, video, device):
        video = video.to(device)
        x = self.model.encode(video, self.scale)
        return x.float()


    def single_decode(self, hidden_state, device):
        hidden_state = hidden_state.to(device)
        video = self.model.decode(hidden_state, self.scale)
        return video.float().clamp_(-1, 1)


    def encode(self, videos, device, tiled=False, tile_size=(34, 34), tile_stride=(18, 16)):

        videos = [video.to("cpu") for video in videos]
        hidden_states = []
        for video in videos:
            video = video.unsqueeze(0)
            if tiled:
                tile_size = (tile_size[0] * 8, tile_size[1] * 8)
                tile_stride = (tile_stride[0] * 8, tile_stride[1] * 8)
                hidden_state = self.tiled_encode(video, device, tile_size, tile_stride)
            else:
                hidden_state = self.single_encode(video, device)
            hidden_state = hidden_state.squeeze(0)
            hidden_states.append(hidden_state)
        hidden_states = torch.stack(hidden_states)
        return hidden_states


    def decode(self, hidden_states, device, tiled=False, tile_size=(34, 34), tile_stride=(18, 16)):
        hidden_states = [hidden_state.to("cpu") for hidden_state in hidden_states]
        videos = []
        for hidden_state in hidden_states:
            hidden_state = hidden_state.unsqueeze(0)
            if tiled:
                video = self.tiled_decode(hidden_state, device, tile_size, tile_stride)
            else:
                video = self.single_decode(hidden_state, device)
            video = video.squeeze(0)
            videos.append(video)
        videos = torch.stack(videos)
        return videos


    @staticmethod
    def state_dict_converter():
        return WanVideoVAEStateDictConverter()


class WanVideoVAEStateDictConverter:

    def __init__(self):
        pass

    def from_civitai(self, state_dict):
        state_dict_ = {}
        if 'model_state' in state_dict:
            state_dict = state_dict['model_state']
        for name in state_dict:
            state_dict_['model.' + name] = state_dict[name]
        return state_dict_