mirror of
https://github.com/Wan-Video/Wan2.1.git
synced 2025-11-04 14:16:57 +00:00
848 lines
31 KiB
Python
848 lines
31 KiB
Python
# 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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import torch.nn.functional as F
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from einops import rearrange
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__all__ = [
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'WanVAE',
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]
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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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self.padding[1], 2 * self.padding[0], 0)
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self.padding = (0, 0, 0)
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def forward(self, x, cache_x=None):
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padding = list(self._padding)
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if cache_x is not None and self._padding[4] > 0:
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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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try:
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out = super().forward(x)
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return out
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except RuntimeError as e:
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if "miopenStatus" in str(e):
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print("⚠️ MIOpen fallback: AMD gets upset when trying to work with large areas, and so CPU will be "
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"used for this decoding (which is very slow). Consider using tiled VAE Decoding.")
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x_cpu = x.float().cpu()
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weight_cpu = self.weight.float().cpu()
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bias_cpu = self.bias.float().cpu() if self.bias is not None else None
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print(f"[Fallback] x shape: {x_cpu.shape}, weight shape: {weight_cpu.shape}")
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out = F.conv3d(x_cpu, weight_cpu, bias_cpu,
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self.stride, (0, 0, 0), # avoid double padding here
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self.dilation, self.groups)
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out = out.to(x.device)
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if x.dtype in (torch.float16, torch.bfloat16):
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out = out.half()
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if x.dtype != out.dtype:
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out = out.to(x.dtype)
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return out
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raise
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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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dtype = x.dtype
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x = 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 = x.to(dtype)
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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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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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nn.Conv2d(dim, dim // 2, 3, padding=1))
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elif mode == 'upsample3d':
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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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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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nn.Conv2d(dim, dim, 3, stride=(2, 2)))
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elif mode == 'downsample3d':
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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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else:
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self.resample = nn.Identity()
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def forward(self, x, feat_cache=None, feat_idx=[0]):
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b, c, t, h, w = x.size()
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if self.mode == 'upsample3d':
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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] = 'Rep'
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feat_idx[0] += 1
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else:
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clone = True
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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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clone = False
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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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clone = False
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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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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 = 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 #.to("cpu") #x.clone() yyyy
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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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feat_cache[idx] = cache_x#.to("cpu") #yyyyy
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feat_idx[0] += 1
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return x
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def init_weight(self, conv):
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conv_weight = conv.weight
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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.copy_(conv_weight)
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nn.init.zeros_(conv.bias.data)
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def init_weight2(self, conv):
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conv_weight = conv.weight.data
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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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nn.init.zeros_(conv.bias.data)
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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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def forward(self, x, feat_cache=None, feat_idx=[0]):
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h = self.shortcut(x)
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dtype = x.dtype
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for layer in self.residual:
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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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x = layer(x, feat_cache[idx]).to(dtype)
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feat_cache[idx] = cache_x#.to("cpu")
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feat_idx[0] += 1
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else:
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x = layer(x).to(dtype)
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return x + h
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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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identity = x
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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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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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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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super().__init__()
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self.dim = dim
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self.z_dim = z_dim
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self.dim_mult = dim_mult
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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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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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def forward(self, x, feat_cache=None, feat_idx=[0]):
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dtype = x.dtype
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if 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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x = self.conv1(x, feat_cache[idx]).to(dtype)
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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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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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return x
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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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super().__init__()
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self.dim = dim
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self.z_dim = z_dim
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self.dim_mult = dim_mult
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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]),
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ResidualBlock(dims[0], dims[0], dropout))
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# upsample blocks
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upsamples = []
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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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if i == 1 or i == 2 or i == 3:
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in_dim = in_dim // 2
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for _ in range(num_res_blocks + 1):
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upsamples.append(ResidualBlock(in_dim, out_dim, dropout))
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if scale in attn_scales:
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upsamples.append(AttentionBlock(out_dim))
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in_dim = out_dim
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# upsample block
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if i != len(dim_mult) - 1:
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mode = 'upsample3d' if temperal_upsample[i] else 'upsample2d'
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upsamples.append(Resample(out_dim, mode=mode))
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scale *= 2.0
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self.upsamples = nn.Sequential(*upsamples)
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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, 3, 3, padding=1))
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def forward(self, x, feat_cache=None, feat_idx=[0]):
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## conv1
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if 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)
|
||
x = self.conv1(x, feat_cache[idx])
|
||
feat_cache[idx] = cache_x#.to("cpu")
|
||
del cache_x
|
||
feat_idx[0] += 1
|
||
else:
|
||
x = self.conv1(x)
|
||
cache_x = None
|
||
|
||
## 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#.to("cpu")
|
||
del 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):
|
||
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 = None, any_end_frame = False):
|
||
self.clear_cache()
|
||
## cache
|
||
t = x.shape[2]
|
||
if any_end_frame:
|
||
iter_ = 2 + (t - 2) // 4
|
||
else:
|
||
iter_ = 1 + (t - 1) // 4
|
||
## 对encode输入的x，按时间拆分为1、4、4、4....
|
||
out_list = []
|
||
for i in range(iter_):
|
||
self._enc_conv_idx = [0]
|
||
if i == 0:
|
||
out_list.append(self.encoder(
|
||
x[:, :, :1, :, :],
|
||
feat_cache=self._enc_feat_map,
|
||
feat_idx=self._enc_conv_idx))
|
||
elif any_end_frame and i== iter_ -1:
|
||
out_list.append(self.encoder(
|
||
x[:, :, -1:, :, :],
|
||
feat_cache= None,
|
||
feat_idx=self._enc_conv_idx))
|
||
else:
|
||
out_list.append(self.encoder(
|
||
x[:, :, 1 + 4 * (i - 1):1 + 4 * i, :, :],
|
||
feat_cache=self._enc_feat_map,
|
||
feat_idx=self._enc_conv_idx))
|
||
|
||
self.clear_cache()
|
||
out = torch.cat(out_list, 2)
|
||
out_list = None
|
||
|
||
mu, log_var = self.conv1(out).chunk(2, dim=1)
|
||
if scale != 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]
|
||
return mu
|
||
|
||
|
||
def decode(self, z, scale=None, any_end_frame = False):
|
||
self.clear_cache()
|
||
# z: [b,c,t,h,w]
|
||
if scale != 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)
|
||
out_list = []
|
||
for i in range(iter_):
|
||
self._conv_idx = [0]
|
||
if i == 0:
|
||
out_list.append(self.decoder(
|
||
x[:, :, i:i + 1, :, :],
|
||
feat_cache=self._feat_map,
|
||
feat_idx=self._conv_idx))
|
||
elif any_end_frame and i==iter_-1:
|
||
out_list.append(self.decoder(
|
||
x[:, :, -1:, :, :],
|
||
feat_cache=None ,
|
||
feat_idx=self._conv_idx))
|
||
else:
|
||
out_list.append(self.decoder(
|
||
x[:, :, i:i + 1, :, :],
|
||
feat_cache=self._feat_map,
|
||
feat_idx=self._conv_idx))
|
||
self.clear_cache()
|
||
out = torch.cat(out_list, 2)
|
||
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, any_end_frame= False):
|
||
tile_sample_min_size = tile_size
|
||
tile_latent_min_size = int(tile_sample_min_size / 8)
|
||
tile_overlap_factor = 0.25
|
||
|
||
# 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]
|
||
|
||
|
||
overlap_size = int(tile_latent_min_size * (1 - tile_overlap_factor)) #8 0.75
|
||
blend_extent = int(tile_sample_min_size * tile_overlap_factor) #256 0.25
|
||
row_limit = tile_sample_min_size - blend_extent
|
||
|
||
# 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, 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, any_end_frame= any_end_frame)
|
||
row.append(decoded)
|
||
rows.append(row)
|
||
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_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, any_end_frame = False) :
|
||
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
|
||
|
||
# Split video into tiles and encode them separately.
|
||
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, any_end_frame= any_end_frame)
|
||
row.append(tile)
|
||
rows.append(row)
|
||
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_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)
|
||
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
|
||
|
||
|
||
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.update(**kwargs)
|
||
|
||
# init model
|
||
with torch.device('meta'):
|
||
model = WanVAE_(**cfg)
|
||
|
||
from mmgp import offload
|
||
# load checkpoint
|
||
logging.info(f'loading {pretrained_path}')
|
||
# model.load_state_dict(
|
||
# torch.load(pretrained_path, map_location=device), assign=True)
|
||
# offload.load_model_data(model, pretrained_path.replace(".pth", "_bf16.safetensors"), writable_tensors= False)
|
||
offload.load_model_data(model, pretrained_path.replace(".pth", ".safetensors"), writable_tensors= False)
|
||
return model
|
||
|
||
|
||
class WanVAE:
|
||
|
||
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
|
||
]
|
||
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]
|
||
|
||
# init model
|
||
self.model = _video_vae(
|
||
pretrained_path=vae_pth,
|
||
z_dim=z_dim,
|
||
).to(dtype).eval() #.requires_grad_(False).to(device)
|
||
self.model._model_dtype = dtype
|
||
|
||
@staticmethod
|
||
def get_VAE_tile_size(vae_config, device_mem_capacity, mixed_precision):
|
||
# VAE Tiling
|
||
if vae_config == 0:
|
||
if mixed_precision:
|
||
device_mem_capacity = device_mem_capacity / 2
|
||
if device_mem_capacity >= 24000:
|
||
use_vae_config = 1
|
||
elif device_mem_capacity >= 8000:
|
||
use_vae_config = 2
|
||
else:
|
||
use_vae_config = 3
|
||
else:
|
||
use_vae_config = vae_config
|
||
|
||
if use_vae_config == 1:
|
||
VAE_tile_size = 0
|
||
elif use_vae_config == 2:
|
||
VAE_tile_size = 256
|
||
else:
|
||
VAE_tile_size = 128
|
||
|
||
return VAE_tile_size
|
||
|
||
def encode(self, videos, tile_size = 256, any_end_frame = False):
|
||
"""
|
||
videos: A list of videos each with shape [C, T, H, W].
|
||
"""
|
||
scale = [u.to(device = self.device) for u in self.scale]
|
||
if tile_size > 0:
|
||
return [ self.model.spatial_tiled_encode(u.to(self.dtype).unsqueeze(0), scale, tile_size, any_end_frame=any_end_frame).float().squeeze(0) for u in videos ]
|
||
else:
|
||
return [ self.model.encode(u.to(self.dtype).unsqueeze(0), scale, any_end_frame=any_end_frame).float().squeeze(0) for u in videos ]
|
||
|
||
|
||
def decode(self, zs, tile_size, any_end_frame = False):
|
||
scale = [u.to(device = self.device) for u in self.scale]
|
||
if tile_size > 0:
|
||
return [ self.model.spatial_tiled_decode(u.to(self.dtype).unsqueeze(0), scale, tile_size, any_end_frame=any_end_frame).clamp_(-1, 1).float().squeeze(0) for u in zs ]
|
||
else:
|
||
return [ self.model.decode(u.to(self.dtype).unsqueeze(0), scale, any_end_frame=any_end_frame).clamp_(-1, 1).float().squeeze(0) for u in zs ]
|