mirror of
https://github.com/Wan-Video/Wan2.1.git
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VAE Tiling Support - credits to deepbeepmeep's WanGP
This commit is contained in:
pftq
GitHub
parent
a0de59e928
commit
09ba994635
@ -1,6 +1,6 @@
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# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
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import logging
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from mmgp import offload
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import torch
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import torch.cuda.amp as amp
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import torch.nn as nn
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@ -15,10 +15,6 @@ CACHE_T = 2
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class CausalConv3d(nn.Conv3d):
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"""
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Causal 3d convolusion.
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"""
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self._padding = (self.padding[2], self.padding[2], self.padding[1],
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@ -31,48 +27,38 @@ class CausalConv3d(nn.Conv3d):
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cache_x = cache_x.to(x.device)
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x = torch.cat([cache_x, x], dim=2)
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padding[4] -= cache_x.shape[2]
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cache_x = None
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x = F.pad(x, padding)
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return super().forward(x)
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x = super().forward(x)
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return x
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class RMS_norm(nn.Module):
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def __init__(self, dim, channel_first=True, images=True, bias=False):
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super().__init__()
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broadcastable_dims = (1, 1, 1) if not images else (1, 1)
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shape = (dim, *broadcastable_dims) if channel_first else (dim,)
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self.channel_first = channel_first
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self.scale = dim**0.5
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self.gamma = nn.Parameter(torch.ones(shape))
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self.bias = nn.Parameter(torch.zeros(shape)) if bias else 0.
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def forward(self, x):
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return F.normalize(
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x, dim=(1 if self.channel_first else
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-1)) * self.scale * self.gamma + self.bias
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x = F.normalize(x, dim=(1 if self.channel_first else -1)) * self.scale * self.gamma + self.bias
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return x
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class Upsample(nn.Upsample):
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def forward(self, x):
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"""
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Fix bfloat16 support for nearest neighbor interpolation.
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"""
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return super().forward(x.float()).type_as(x)
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class Resample(nn.Module):
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def __init__(self, dim, mode):
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assert mode in ('none', 'upsample2d', 'upsample3d', 'downsample2d',
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'downsample3d')
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assert mode in ('none', 'upsample2d', 'upsample3d', 'downsample2d', 'downsample3d')
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super().__init__()
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self.dim = dim
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self.mode = mode
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# layers
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if mode == 'upsample2d':
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self.resample = nn.Sequential(
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Upsample(scale_factor=(2., 2.), mode='nearest-exact'),
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@ -81,9 +67,7 @@ class Resample(nn.Module):
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self.resample = nn.Sequential(
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Upsample(scale_factor=(2., 2.), mode='nearest-exact'),
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nn.Conv2d(dim, dim // 2, 3, padding=1))
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self.time_conv = CausalConv3d(
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dim, dim * 2, (3, 1, 1), padding=(1, 0, 0))
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self.time_conv = CausalConv3d(dim, dim * 2, (3, 1, 1), padding=(1, 0, 0))
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elif mode == 'downsample2d':
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self.resample = nn.Sequential(
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nn.ZeroPad2d((0, 1, 0, 1)),
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@ -92,9 +76,7 @@ class Resample(nn.Module):
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self.resample = nn.Sequential(
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nn.ZeroPad2d((0, 1, 0, 1)),
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nn.Conv2d(dim, dim, 3, stride=(2, 2)))
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self.time_conv = CausalConv3d(
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dim, dim, (3, 1, 1), stride=(2, 1, 1), padding=(0, 0, 0))
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self.time_conv = CausalConv3d(dim, dim, (3, 1, 1), stride=(2, 1, 1), padding=(0, 0, 0))
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else:
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self.resample = nn.Identity()
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@ -107,54 +89,38 @@ class Resample(nn.Module):
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feat_cache[idx] = 'Rep'
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feat_idx[0] += 1
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else:
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cache_x = x[:, :, -CACHE_T:, :, :].clone()
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if cache_x.shape[2] < 2 and feat_cache[
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idx] is not None and feat_cache[idx] != 'Rep':
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# cache last frame of last two chunk
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cache_x = torch.cat([
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feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
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cache_x.device), cache_x
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],
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dim=2)
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if cache_x.shape[2] < 2 and feat_cache[
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idx] is not None and feat_cache[idx] == 'Rep':
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cache_x = torch.cat([
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torch.zeros_like(cache_x).to(cache_x.device),
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cache_x
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],
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dim=2)
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clone = True
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cache_x = x[:, :, -CACHE_T:, :, :]
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if cache_x.shape[2] < 2 and feat_cache[idx] is not None and feat_cache[idx] != 'Rep':
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clone = False
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cache_x = torch.cat([feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x], dim=2)
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if cache_x.shape[2] < 2 and feat_cache[idx] is not None and feat_cache[idx] == 'Rep':
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clone = False
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cache_x = torch.cat([torch.zeros_like(cache_x).to(cache_x.device), cache_x], dim=2)
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if clone:
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cache_x = cache_x.clone()
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if feat_cache[idx] == 'Rep':
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x = self.time_conv(x)
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else:
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x = self.time_conv(x, feat_cache[idx])
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feat_cache[idx] = cache_x
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feat_idx[0] += 1
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x = x.reshape(b, 2, c, t, h, w)
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x = torch.stack((x[:, 0, :, :, :, :], x[:, 1, :, :, :, :]),
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3)
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x = torch.stack((x[:, 0, :, :, :, :], x[:, 1, :, :, :, :]), 3)
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x = x.reshape(b, c, t * 2, h, w)
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t = x.shape[2]
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x = rearrange(x, 'b c t h w -> (b t) c h w')
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x = self.resample(x)
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x = rearrange(x, '(b t) c h w -> b c t h w', t=t)
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if self.mode == 'downsample3d':
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if feat_cache is not None:
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idx = feat_idx[0]
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if feat_cache[idx] is None:
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feat_cache[idx] = x.clone()
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feat_cache[idx] = x
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feat_idx[0] += 1
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else:
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cache_x = x[:, :, -1:, :, :].clone()
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# if cache_x.shape[2] < 2 and feat_cache[idx] is not None and feat_cache[idx]!='Rep':
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# # cache last frame of last two chunk
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# cache_x = torch.cat([feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x], dim=2)
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x = self.time_conv(
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torch.cat([feat_cache[idx][:, :, -1:, :, :], x], 2))
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x = self.time_conv(torch.cat([feat_cache[idx][:, :, -1:, :, :], x], 2))
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feat_cache[idx] = cache_x
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feat_idx[0] += 1
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return x
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@ -164,10 +130,8 @@ class Resample(nn.Module):
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nn.init.zeros_(conv_weight)
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c1, c2, t, h, w = conv_weight.size()
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one_matrix = torch.eye(c1, c2)
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init_matrix = one_matrix
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nn.init.zeros_(conv_weight)
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#conv_weight.data[:,:,-1,1,1] = init_matrix * 0.5
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conv_weight.data[:, :, 1, 0, 0] = init_matrix #* 0.5
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conv_weight.data[:, :, 1, 0, 0] = one_matrix
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conv.weight.data.copy_(conv_weight)
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nn.init.zeros_(conv.bias.data)
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@ -176,7 +140,6 @@ class Resample(nn.Module):
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nn.init.zeros_(conv_weight)
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c1, c2, t, h, w = conv_weight.size()
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init_matrix = torch.eye(c1 // 2, c2)
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#init_matrix = repeat(init_matrix, 'o ... -> (o 2) ...').permute(1,0,2).contiguous().reshape(c1,c2)
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conv_weight[:c1 // 2, :, -1, 0, 0] = init_matrix
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conv_weight[c1 // 2:, :, -1, 0, 0] = init_matrix
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conv.weight.data.copy_(conv_weight)
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@ -184,20 +147,16 @@ class Resample(nn.Module):
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class ResidualBlock(nn.Module):
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def __init__(self, in_dim, out_dim, dropout=0.0):
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super().__init__()
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self.in_dim = in_dim
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self.out_dim = out_dim
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# layers
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self.residual = nn.Sequential(
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RMS_norm(in_dim, images=False), nn.SiLU(),
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CausalConv3d(in_dim, out_dim, 3, padding=1),
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RMS_norm(out_dim, images=False), nn.SiLU(), nn.Dropout(dropout),
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CausalConv3d(out_dim, out_dim, 3, padding=1))
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self.shortcut = CausalConv3d(in_dim, out_dim, 1) \
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if in_dim != out_dim else nn.Identity()
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self.shortcut = CausalConv3d(in_dim, out_dim, 1) if in_dim != out_dim else nn.Identity()
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def forward(self, x, feat_cache=None, feat_idx=[0]):
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h = self.shortcut(x)
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@ -206,12 +165,7 @@ class ResidualBlock(nn.Module):
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idx = feat_idx[0]
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cache_x = x[:, :, -CACHE_T:, :, :].clone()
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if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
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# cache last frame of last two chunk
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cache_x = torch.cat([
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feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
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cache_x.device), cache_x
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],
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dim=2)
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cache_x = torch.cat([feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(x.device), cache_x], dim=2)
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x = layer(x, feat_cache[idx])
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feat_cache[idx] = cache_x
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feat_idx[0] += 1
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@ -221,20 +175,12 @@ class ResidualBlock(nn.Module):
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class AttentionBlock(nn.Module):
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"""
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Causal self-attention with a single head.
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"""
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def __init__(self, dim):
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super().__init__()
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self.dim = dim
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# layers
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self.norm = RMS_norm(dim)
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self.to_qkv = nn.Conv2d(dim, dim * 3, 1)
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self.proj = nn.Conv2d(dim, dim, 1)
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# zero out the last layer params
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nn.init.zeros_(self.proj.weight)
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def forward(self, x):
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@ -242,36 +188,17 @@ class AttentionBlock(nn.Module):
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b, c, t, h, w = x.size()
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x = rearrange(x, 'b c t h w -> (b t) c h w')
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x = self.norm(x)
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# compute query, key, value
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q, k, v = self.to_qkv(x).reshape(b * t, 1, c * 3,
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-1).permute(0, 1, 3,
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2).contiguous().chunk(
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3, dim=-1)
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# apply attention
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x = F.scaled_dot_product_attention(
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q,
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k,
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v,
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)
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q, k, v = self.to_qkv(x).reshape(b * t, 1, c * 3, -1).permute(0, 1, 3, 2).contiguous().chunk(3, dim=-1)
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x = F.scaled_dot_product_attention(q, k, v)
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x = x.squeeze(1).permute(0, 2, 1).reshape(b * t, c, h, w)
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# output
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x = self.proj(x)
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x = rearrange(x, '(b t) c h w-> b c t h w', t=t)
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x = rearrange(x, '(b t) c h w -> b c t h w', t=t)
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return x + identity
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class Encoder3d(nn.Module):
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def __init__(self,
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dim=128,
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z_dim=4,
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dim_mult=[1, 2, 4, 4],
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num_res_blocks=2,
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attn_scales=[],
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temperal_downsample=[True, True, False],
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dropout=0.0):
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def __init__(self, dim=128, z_dim=4, dim_mult=[1, 2, 4, 4], num_res_blocks=2, attn_scales=[],
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temperal_downsample=[True, True, False], dropout=0.0):
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super().__init__()
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self.dim = dim
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self.z_dim = z_dim
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@ -279,38 +206,24 @@ class Encoder3d(nn.Module):
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self.num_res_blocks = num_res_blocks
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self.attn_scales = attn_scales
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self.temperal_downsample = temperal_downsample
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# dimensions
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dims = [dim * u for u in [1] + dim_mult]
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scale = 1.0
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# init block
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self.conv1 = CausalConv3d(3, dims[0], 3, padding=1)
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# downsample blocks
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downsamples = []
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for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])):
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# residual (+attention) blocks
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for _ in range(num_res_blocks):
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downsamples.append(ResidualBlock(in_dim, out_dim, dropout))
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if scale in attn_scales:
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downsamples.append(AttentionBlock(out_dim))
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in_dim = out_dim
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# downsample block
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if i != len(dim_mult) - 1:
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mode = 'downsample3d' if temperal_downsample[
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i] else 'downsample2d'
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mode = 'downsample3d' if temperal_downsample[i] else 'downsample2d'
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downsamples.append(Resample(out_dim, mode=mode))
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scale /= 2.0
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self.downsamples = nn.Sequential(*downsamples)
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# middle blocks
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self.middle = nn.Sequential(
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ResidualBlock(out_dim, out_dim, dropout), AttentionBlock(out_dim),
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ResidualBlock(out_dim, out_dim, dropout))
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# output blocks
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self.head = nn.Sequential(
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RMS_norm(out_dim, images=False), nn.SiLU(),
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CausalConv3d(out_dim, z_dim, 3, padding=1))
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@ -320,46 +233,32 @@ class Encoder3d(nn.Module):
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idx = feat_idx[0]
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cache_x = x[:, :, -CACHE_T:, :, :].clone()
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if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
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# cache last frame of last two chunk
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cache_x = torch.cat([
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feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
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cache_x.device), cache_x
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],
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dim=2)
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cache_x = torch.cat([feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(x.device), cache_x], dim=2)
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x = self.conv1(x, feat_cache[idx])
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feat_cache[idx] = cache_x
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del cache_x
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feat_idx[0] += 1
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else:
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x = self.conv1(x)
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## downsamples
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for layer in self.downsamples:
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if feat_cache is not None:
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x = layer(x, feat_cache, feat_idx)
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else:
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x = layer(x)
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## middle
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for layer in self.middle:
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if isinstance(layer, ResidualBlock) and feat_cache is not None:
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x = layer(x, feat_cache, feat_idx)
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else:
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x = layer(x)
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## head
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for layer in self.head:
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if isinstance(layer, CausalConv3d) and feat_cache is not None:
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idx = feat_idx[0]
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cache_x = x[:, :, -CACHE_T:, :, :].clone()
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if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
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# cache last frame of last two chunk
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cache_x = torch.cat([
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feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
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cache_x.device), cache_x
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],
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dim=2)
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cache_x = torch.cat([feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(x.device), cache_x], dim=2)
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x = layer(x, feat_cache[idx])
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feat_cache[idx] = cache_x
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del cache_x
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feat_idx[0] += 1
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else:
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x = layer(x)
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@ -367,15 +266,8 @@ class Encoder3d(nn.Module):
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class Decoder3d(nn.Module):
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def __init__(self,
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dim=128,
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z_dim=4,
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dim_mult=[1, 2, 4, 4],
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num_res_blocks=2,
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attn_scales=[],
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temperal_upsample=[False, True, True],
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dropout=0.0):
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def __init__(self, dim=128, z_dim=4, dim_mult=[1, 2, 4, 4], num_res_blocks=2, attn_scales=[],
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temperal_upsample=[False, True, True], dropout=0.0):
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super().__init__()
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self.dim = dim
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self.z_dim = z_dim
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@ -383,23 +275,14 @@ class Decoder3d(nn.Module):
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self.num_res_blocks = num_res_blocks
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self.attn_scales = attn_scales
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self.temperal_upsample = temperal_upsample
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# dimensions
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dims = [dim * u for u in [dim_mult[-1]] + dim_mult[::-1]]
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scale = 1.0 / 2**(len(dim_mult) - 2)
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# init block
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self.conv1 = CausalConv3d(z_dim, dims[0], 3, padding=1)
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# middle blocks
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self.middle = nn.Sequential(
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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):
|
||||
@ -407,65 +290,46 @@ class Decoder3d(nn.Module):
|
||||
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)
|
||||
cache_x = torch.cat([feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(x.device), cache_x], dim=2)
|
||||
x = self.conv1(x, feat_cache[idx])
|
||||
feat_cache[idx] = cache_x
|
||||
del 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)
|
||||
cache_x = torch.cat([feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(x.device), cache_x], dim=2)
|
||||
x = layer(x, feat_cache[idx])
|
||||
feat_cache[idx] = cache_x
|
||||
del cache_x
|
||||
feat_idx[0] += 1
|
||||
else:
|
||||
x = layer(x)
|
||||
@ -481,15 +345,8 @@ def count_conv3d(model):
|
||||
|
||||
|
||||
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):
|
||||
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
|
||||
@ -498,14 +355,10 @@ class WanVAE_(nn.Module):
|
||||
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.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)
|
||||
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)
|
||||
@ -513,60 +366,120 @@ class WanVAE_(nn.Module):
|
||||
x_recon = self.decode(z)
|
||||
return x_recon, mu, log_var
|
||||
|
||||
def encode(self, x, scale):
|
||||
def encode(self, x, scale=None):
|
||||
self.clear_cache()
|
||||
## cache
|
||||
t = x.shape[2]
|
||||
iter_ = 1 + (t - 1) // 4
|
||||
## 对encode输入的x，按时间拆分为1、4、4、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)
|
||||
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_ = 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]
|
||||
if scale is not None:
|
||||
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()
|
||||
return mu
|
||||
|
||||
def decode(self, z, scale):
|
||||
def decode(self, z, scale=None):
|
||||
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]
|
||||
if scale is not None:
|
||||
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)
|
||||
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_ = 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 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 spatial_tiled_decode(self, z, scale, tile_size):
|
||||
tile_sample_min_size = tile_size
|
||||
tile_latent_min_size = int(tile_sample_min_size / 8)
|
||||
tile_overlap_factor = 0.25
|
||||
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]
|
||||
overlap_size = int(tile_latent_min_size * (1 - tile_overlap_factor))
|
||||
blend_extent = int(tile_sample_min_size * tile_overlap_factor)
|
||||
row_limit = tile_sample_min_size - blend_extent
|
||||
rows = []
|
||||
for i in range(0, z.shape[-2], overlap_size):
|
||||
row = []
|
||||
for j in range(0, z.shape[-1], overlap_size):
|
||||
tile = z[:, :, :, i: i + tile_latent_min_size, j: j + tile_latent_min_size]
|
||||
decoded = self.decode(tile)
|
||||
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_extent)
|
||||
if j > 0:
|
||||
tile = self.blend_h(row[j - 1], tile, blend_extent)
|
||||
result_row.append(tile[:, :, :, :row_limit, :row_limit])
|
||||
result_rows.append(torch.cat(result_row, dim=-1))
|
||||
return torch.cat(result_rows, dim=-2)
|
||||
|
||||
def spatial_tiled_encode(self, x, scale, tile_size):
|
||||
tile_sample_min_size = tile_size
|
||||
tile_latent_min_size = int(tile_sample_min_size / 8)
|
||||
tile_overlap_factor = 0.25
|
||||
overlap_size = int(tile_sample_min_size * (1 - tile_overlap_factor))
|
||||
blend_extent = int(tile_latent_min_size * tile_overlap_factor)
|
||||
row_limit = tile_latent_min_size - blend_extent
|
||||
rows = []
|
||||
for i in range(0, x.shape[-2], overlap_size):
|
||||
row = []
|
||||
for j in range(0, x.shape[-1], overlap_size):
|
||||
tile = x[:, :, :, i: i + tile_sample_min_size, j: j + tile_sample_min_size]
|
||||
tile = self.encode(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_extent)
|
||||
if j > 0:
|
||||
tile = self.blend_h(row[j - 1], tile, blend_extent)
|
||||
result_row.append(tile[:, :, :, :row_limit, :row_limit])
|
||||
result_rows.append(torch.cat(result_row, dim=-1))
|
||||
mu = torch.cat(result_rows, dim=-2)
|
||||
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]
|
||||
return mu
|
||||
|
||||
def reparameterize(self, mu, log_var):
|
||||
std = torch.exp(0.5 * log_var)
|
||||
eps = torch.randn_like(std)
|
||||
@ -583,81 +496,43 @@ class WanVAE_(nn.Module):
|
||||
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 _video_vae(pretrained_path=None, z_dim=None, device='cpu', **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)
|
||||
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)
|
||||
cfg.update(**kwargs)
|
||||
|
||||
# init model
|
||||
with torch.device('meta'):
|
||||
model = WanVAE_(**cfg)
|
||||
|
||||
# load checkpoint
|
||||
model = WanVAE_(**cfg)
|
||||
logging.info(f'loading {pretrained_path}')
|
||||
model.load_state_dict(
|
||||
torch.load(pretrained_path, map_location=device), assign=True)
|
||||
|
||||
model.load_state_dict(torch.load(pretrained_path, map_location=device), assign=True)
|
||||
return model
|
||||
|
||||
|
||||
class WanVAE:
|
||||
|
||||
def __init__(self,
|
||||
z_dim=16,
|
||||
vae_pth='cache/vae_step_411000.pth',
|
||||
dtype=torch.float,
|
||||
device="cuda"):
|
||||
def __init__(self, z_dim=16, vae_pth='cache/vae_step_411000.pth', dtype=torch.float, device="cuda"):
|
||||
self.dtype = dtype
|
||||
self.device = device
|
||||
|
||||
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
|
||||
]
|
||||
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=dtype, device=device)
|
||||
self.std = torch.tensor(std, dtype=dtype, device=device)
|
||||
self.scale = [self.mean, 1.0 / self.std]
|
||||
self.model = _video_vae(pretrained_path=vae_pth, z_dim=z_dim, device=device)
|
||||
self.model = self.model.eval().requires_grad_(False).to(device)
|
||||
|
||||
# init model
|
||||
self.model = _video_vae(
|
||||
pretrained_path=vae_pth,
|
||||
z_dim=z_dim,
|
||||
).eval().requires_grad_(False).to(device)
|
||||
def encode(self, videos, tile_size=256):
|
||||
if tile_size > 0:
|
||||
return [self.model.spatial_tiled_encode(u.unsqueeze(0), self.scale, tile_size).float().squeeze(0) for u in videos]
|
||||
else:
|
||||
return [self.model.encode(u.unsqueeze(0), self.scale).float().squeeze(0) for u in videos]
|
||||
|
||||
def encode(self, videos):
|
||||
"""
|
||||
videos: A list of videos each with shape [C, T, H, W].
|
||||
"""
|
||||
with amp.autocast(dtype=self.dtype):
|
||||
return [
|
||||
self.model.encode(u.unsqueeze(0), self.scale).float().squeeze(0)
|
||||
for u in videos
|
||||
]
|
||||
|
||||
def decode(self, zs):
|
||||
with amp.autocast(dtype=self.dtype):
|
||||
return [
|
||||
self.model.decode(u.unsqueeze(0),
|
||||
self.scale).float().clamp_(-1, 1).squeeze(0)
|
||||
for u in zs
|
||||
]
|
||||
def decode(self, zs, tile_size):
|
||||
if tile_size > 0:
|
||||
return [self.model.spatial_tiled_decode(u.unsqueeze(0), self.scale, tile_size).float().clamp_(-1, 1).squeeze(0) for u in zs]
|
||||
else:
|
||||
return [self.model.decode(u.unsqueeze(0), self.scale).float().clamp_(-1, 1).squeeze(0) for u in zs]
|
||||
|
||||
Loading…
Reference in New Issue
Block a user