demo_utils/vae_block3.py

from typing import List
from einops import rearrange
import torch
import torch.nn as nn

from wan.modules.vae import (
    AttentionBlock,
    CausalConv3d,
    RMS_norm,
    ResidualBlock,
    Upsample,
)


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
        self.cache_t = 2

        # layers
        if mode == "upsample2d":
            self.resample = nn.Sequential(
                Upsample(scale_factor=(2.0, 2.0), mode="nearest"),
                nn.Conv2d(dim, dim // 2, 3, padding=1),
            )
        elif mode == "upsample3d":
            self.resample = nn.Sequential(
                Upsample(scale_factor=(2.0, 2.0), mode="nearest"),
                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[:, :, -self.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 VAEDecoderWrapper(nn.Module):
    def __init__(
        self,
    ):
        super().__init__()
        self.decoder = VAEDecoder3d()
        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, dtype=torch.float32)
        self.std = torch.tensor(std, dtype=torch.float32)
        self.z_dim = 16
        self.conv2 = CausalConv3d(self.z_dim, self.z_dim, 1)

    def forward(
        self,
        z: torch.Tensor,
        *feat_cache: List[torch.Tensor],
    ):
        # from [batch_size, num_frames, num_channels, height, width]
        # to [batch_size, num_channels, num_frames, height, width]
        z = z.permute(0, 2, 1, 3, 4)
        feat_cache = list(feat_cache)
        print("Length of feat_cache: ", len(feat_cache))

        device, dtype = z.device, z.dtype
        scale = [
            self.mean.to(device=device, dtype=dtype),
            1.0 / self.std.to(device=device, dtype=dtype),
        ]

        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_):
            if i == 0:
                out, feat_cache = self.decoder(x[:, :, i : i + 1, :, :], feat_cache=feat_cache)
            else:
                out_, feat_cache = self.decoder(x[:, :, i : i + 1, :, :], feat_cache=feat_cache)
                out = torch.cat([out, out_], 2)

        out = out.float().clamp_(-1, 1)
        # from [batch_size, num_channels, num_frames, height, width]
        # to [batch_size, num_frames, num_channels, height, width]
        out = out.permute(0, 2, 1, 3, 4)
        return out, feat_cache


class VAEDecoder3d(nn.Module):
    def __init__(
        self,
        dim=96,
        z_dim=16,
        dim_mult=[1, 2, 4, 4],
        num_res_blocks=2,
        attn_scales=[],
        temperal_upsample=[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_upsample = temperal_upsample
        self.cache_t = 2
        self.decoder_conv_num = 32

        # 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: torch.Tensor,
        feat_cache: List[torch.Tensor],
    ):
        feat_idx = [0]

        # conv1
        idx = feat_idx[0]
        cache_x = x[:, :, -self.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

        # 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:
            x = layer(x, feat_cache, feat_idx)

        # head
        for layer in self.head:
            if isinstance(layer, CausalConv3d) and feat_cache is not None:
                idx = feat_idx[0]
                cache_x = x[:, :, -self.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, feat_cache