1310 lines
48 KiB
Python
1310 lines
48 KiB
Python
# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
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import torch
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import torch.distributed as dist
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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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from loguru import logger
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from .utils import load_weights
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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 = (
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self.padding[2],
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self.padding[2],
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self.padding[1],
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self.padding[1],
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2 * self.padding[0],
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0,
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)
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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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x = F.pad(x, padding)
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return super().forward(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 0.0
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def forward(self, x):
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return F.normalize(x, dim=(1 if self.channel_first else -1)) * self.scale * self.gamma + self.bias
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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)
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class Resample(nn.Module):
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def __init__(self, dim, mode):
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assert mode in (
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"none",
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"upsample2d",
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"upsample3d",
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"downsample2d",
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"downsample3d",
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)
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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=(2.0, 2.0), mode="nearest-exact"),
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nn.Conv2d(dim, dim // 2, 3, padding=1),
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)
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elif mode == "upsample3d":
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self.resample = nn.Sequential(
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Upsample(scale_factor=(2.0, 2.0), mode="nearest-exact"),
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nn.Conv2d(dim, dim // 2, 3, padding=1),
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)
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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(nn.ZeroPad2d((0, 1, 0, 1)), nn.Conv2d(dim, dim, 3, stride=(2, 2)))
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elif mode == "downsample3d":
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self.resample = nn.Sequential(nn.ZeroPad2d((0, 1, 0, 1)), nn.Conv2d(dim, dim, 3, stride=(2, 2)))
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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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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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cache_x = x[:, :, -CACHE_T:, :, :].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(
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[
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feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device),
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cache_x,
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],
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dim=2,
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)
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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_x = torch.cat(
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[torch.zeros_like(cache_x).to(cache_x.device), cache_x],
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dim=2,
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)
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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, :, :, :, :]), 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_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(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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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),
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nn.SiLU(),
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CausalConv3d(in_dim, out_dim, 3, padding=1),
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RMS_norm(out_dim, images=False),
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nn.SiLU(),
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nn.Dropout(dropout),
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CausalConv3d(out_dim, out_dim, 3, padding=1),
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)
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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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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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[
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feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device),
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cache_x,
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],
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dim=2,
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)
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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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else:
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x = layer(x)
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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, -1).permute(0, 1, 3, 2).contiguous().chunk(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, dim=128, z_dim=4, dim_mult=[1, 2, 4, 4], num_res_blocks=2, attn_scales=[], temperal_downsample=[True, True, False], dropout=0.0, pruning_rate=0.0):
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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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dims = [int(d * (1 - pruning_rate)) for d in dims]
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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[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),
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AttentionBlock(out_dim),
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ResidualBlock(out_dim, out_dim, dropout),
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)
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# output blocks
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self.head = nn.Sequential(
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RMS_norm(out_dim, images=False),
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nn.SiLU(),
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CausalConv3d(out_dim, z_dim, 3, padding=1),
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)
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def forward(self, x, feat_cache=None, feat_idx=[0]):
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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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[
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feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device),
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cache_x,
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],
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dim=2,
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)
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x = self.conv1(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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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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[
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feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device),
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cache_x,
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],
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dim=2,
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)
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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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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, dim=128, z_dim=4, dim_mult=[1, 2, 4, 4], num_res_blocks=2, attn_scales=[], temperal_upsample=[False, True, True], dropout=0.0, pruning_rate=0.0):
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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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dims = [int(d * (1 - pruning_rate)) for d in dims]
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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),
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AttentionBlock(dims[0]),
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ResidualBlock(dims[0], dims[0], dropout),
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)
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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),
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nn.SiLU(),
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CausalConv3d(out_dim, 3, 3, padding=1),
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)
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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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[
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feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device),
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cache_x,
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],
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dim=2,
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)
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x = self.conv1(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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else:
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x = self.conv1(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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## upsamples
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for layer in self.upsamples:
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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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## 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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[
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feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device),
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cache_x,
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],
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dim=2,
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)
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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
|
|
else:
|
|
x = layer(x)
|
|
return x
|
|
|
|
|
|
def count_conv3d(model):
|
|
count = 0
|
|
for m in model.modules():
|
|
if isinstance(m, CausalConv3d):
|
|
count += 1
|
|
return count
|
|
|
|
|
|
class WanVAE_(nn.Module):
|
|
def __init__(self, dim=128, z_dim=4, dim_mult=[1, 2, 4, 4], num_res_blocks=2, attn_scales=[], temperal_downsample=[True, True, False], dropout=0.0, pruning_rate=0.0):
|
|
super().__init__()
|
|
self.dim = dim
|
|
self.z_dim = z_dim
|
|
self.dim_mult = dim_mult
|
|
self.num_res_blocks = num_res_blocks
|
|
self.attn_scales = attn_scales
|
|
self.temperal_downsample = temperal_downsample
|
|
self.temperal_upsample = temperal_downsample[::-1]
|
|
self.spatial_compression_ratio = 2 ** len(self.temperal_downsample)
|
|
|
|
# The minimal tile height and width for spatial tiling to be used
|
|
self.tile_sample_min_height = 256
|
|
self.tile_sample_min_width = 256
|
|
|
|
# The minimal distance between two spatial tiles
|
|
self.tile_sample_stride_height = 192
|
|
self.tile_sample_stride_width = 192
|
|
# modules
|
|
self.encoder = Encoder3d(
|
|
dim,
|
|
z_dim * 2,
|
|
dim_mult,
|
|
num_res_blocks,
|
|
attn_scales,
|
|
self.temperal_downsample,
|
|
dropout,
|
|
pruning_rate,
|
|
)
|
|
self.conv1 = CausalConv3d(z_dim * 2, z_dim * 2, 1)
|
|
self.conv2 = CausalConv3d(z_dim, z_dim, 1)
|
|
self.decoder = Decoder3d(
|
|
dim,
|
|
z_dim,
|
|
dim_mult,
|
|
num_res_blocks,
|
|
attn_scales,
|
|
self.temperal_upsample,
|
|
dropout,
|
|
pruning_rate,
|
|
)
|
|
|
|
def forward(self, x):
|
|
mu, log_var = self.encode(x)
|
|
z = self.reparameterize(mu, log_var)
|
|
x_recon = self.decode(z)
|
|
return x_recon, mu, log_var
|
|
|
|
def blend_v(self, a, b, blend_extent):
|
|
blend_extent = min(a.shape[-2], b.shape[-2], blend_extent)
|
|
for y in range(blend_extent):
|
|
b[:, :, :, y, :] = a[:, :, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, :, y, :] * (y / blend_extent)
|
|
return b
|
|
|
|
def blend_h(self, a, b, blend_extent):
|
|
blend_extent = min(a.shape[-1], b.shape[-1], blend_extent)
|
|
for x in range(blend_extent):
|
|
b[:, :, :, :, x] = a[:, :, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, :, x] * (x / blend_extent)
|
|
return b
|
|
|
|
def tiled_encode(self, x, scale):
|
|
_, _, num_frames, height, width = x.shape
|
|
latent_height = height // self.spatial_compression_ratio
|
|
latent_width = width // self.spatial_compression_ratio
|
|
|
|
tile_latent_min_height = self.tile_sample_min_height // self.spatial_compression_ratio
|
|
tile_latent_min_width = self.tile_sample_min_width // self.spatial_compression_ratio
|
|
tile_latent_stride_height = self.tile_sample_stride_height // self.spatial_compression_ratio
|
|
tile_latent_stride_width = self.tile_sample_stride_width // self.spatial_compression_ratio
|
|
|
|
blend_height = tile_latent_min_height - tile_latent_stride_height
|
|
blend_width = tile_latent_min_width - tile_latent_stride_width
|
|
|
|
# Split x into overlapping tiles and encode them separately.
|
|
# The tiles have an overlap to avoid seams between tiles.
|
|
rows = []
|
|
for i in range(0, height, self.tile_sample_stride_height):
|
|
row = []
|
|
for j in range(0, width, self.tile_sample_stride_width):
|
|
self.clear_cache()
|
|
time = []
|
|
frame_range = 1 + (num_frames - 1) // 4
|
|
for k in range(frame_range):
|
|
self._enc_conv_idx = [0]
|
|
if k == 0:
|
|
tile = x[:, :, :1, i : i + self.tile_sample_min_height, j : j + self.tile_sample_min_width]
|
|
else:
|
|
tile = x[
|
|
:,
|
|
:,
|
|
1 + 4 * (k - 1) : 1 + 4 * k,
|
|
i : i + self.tile_sample_min_height,
|
|
j : j + self.tile_sample_min_width,
|
|
]
|
|
tile = self.encoder(tile, feat_cache=self._enc_feat_map, feat_idx=self._enc_conv_idx)
|
|
mu, log_var = self.conv1(tile).chunk(2, dim=1)
|
|
if isinstance(scale[0], torch.Tensor):
|
|
mu = (mu - scale[0].view(1, self.z_dim, 1, 1, 1)) * scale[1].view(1, self.z_dim, 1, 1, 1)
|
|
else:
|
|
mu = (mu - scale[0]) * scale[1]
|
|
|
|
time.append(mu)
|
|
|
|
row.append(torch.cat(time, dim=2))
|
|
rows.append(row)
|
|
self.clear_cache()
|
|
|
|
result_rows = []
|
|
for i, row in enumerate(rows):
|
|
result_row = []
|
|
for j, tile in enumerate(row):
|
|
# blend the above tile and the left tile
|
|
# to the current tile and add the current tile to the result row
|
|
if i > 0:
|
|
tile = self.blend_v(rows[i - 1][j], tile, blend_height)
|
|
if j > 0:
|
|
tile = self.blend_h(row[j - 1], tile, blend_width)
|
|
result_row.append(tile[:, :, :, :tile_latent_stride_height, :tile_latent_stride_width])
|
|
result_rows.append(torch.cat(result_row, dim=-1))
|
|
|
|
enc = torch.cat(result_rows, dim=3)[:, :, :, :latent_height, :latent_width]
|
|
return enc
|
|
|
|
def tiled_decode(self, z, scale):
|
|
if isinstance(scale[0], torch.Tensor):
|
|
z = z / scale[1].view(1, self.z_dim, 1, 1, 1) + scale[0].view(1, self.z_dim, 1, 1, 1)
|
|
else:
|
|
z = z / scale[1] + scale[0]
|
|
|
|
_, _, num_frames, height, width = z.shape
|
|
sample_height = height * self.spatial_compression_ratio
|
|
sample_width = width * self.spatial_compression_ratio
|
|
|
|
tile_latent_min_height = self.tile_sample_min_height // self.spatial_compression_ratio
|
|
tile_latent_min_width = self.tile_sample_min_width // self.spatial_compression_ratio
|
|
tile_latent_stride_height = self.tile_sample_stride_height // self.spatial_compression_ratio
|
|
tile_latent_stride_width = self.tile_sample_stride_width // self.spatial_compression_ratio
|
|
|
|
blend_height = self.tile_sample_min_height - self.tile_sample_stride_height
|
|
blend_width = self.tile_sample_min_width - self.tile_sample_stride_width
|
|
|
|
# Split z into overlapping tiles and decode them separately.
|
|
# The tiles have an overlap to avoid seams between tiles.
|
|
rows = []
|
|
for i in range(0, height, tile_latent_stride_height):
|
|
row = []
|
|
for j in range(0, width, tile_latent_stride_width):
|
|
self.clear_cache()
|
|
time = []
|
|
for k in range(num_frames):
|
|
self._conv_idx = [0]
|
|
tile = z[:, :, k : k + 1, i : i + tile_latent_min_height, j : j + tile_latent_min_width]
|
|
tile = self.conv2(tile)
|
|
decoded = self.decoder(tile, feat_cache=self._feat_map, feat_idx=self._conv_idx)
|
|
time.append(decoded)
|
|
row.append(torch.cat(time, dim=2))
|
|
rows.append(row)
|
|
self.clear_cache()
|
|
|
|
result_rows = []
|
|
for i, row in enumerate(rows):
|
|
result_row = []
|
|
for j, tile in enumerate(row):
|
|
# blend the above tile and the left tile
|
|
# to the current tile and add the current tile to the result row
|
|
if i > 0:
|
|
tile = self.blend_v(rows[i - 1][j], tile, blend_height)
|
|
if j > 0:
|
|
tile = self.blend_h(row[j - 1], tile, blend_width)
|
|
result_row.append(tile[:, :, :, : self.tile_sample_stride_height, : self.tile_sample_stride_width])
|
|
result_rows.append(torch.cat(result_row, dim=-1))
|
|
|
|
dec = torch.cat(result_rows, dim=3)[:, :, :, :sample_height, :sample_width]
|
|
|
|
return dec
|
|
|
|
def encode(self, x, scale, return_mu=False):
|
|
self.clear_cache()
|
|
## cache
|
|
t = x.shape[2]
|
|
iter_ = 1 + (t - 1) // 4
|
|
for i in range(iter_):
|
|
self._enc_conv_idx = [0]
|
|
if i == 0:
|
|
out = self.encoder(
|
|
x[:, :, :1, :, :],
|
|
feat_cache=self._enc_feat_map,
|
|
feat_idx=self._enc_conv_idx,
|
|
)
|
|
else:
|
|
out_ = self.encoder(
|
|
x[:, :, 1 + 4 * (i - 1) : 1 + 4 * i, :, :],
|
|
feat_cache=self._enc_feat_map,
|
|
feat_idx=self._enc_conv_idx,
|
|
)
|
|
out = torch.cat([out, out_], 2)
|
|
mu, log_var = self.conv1(out).chunk(2, dim=1)
|
|
if isinstance(scale[0], torch.Tensor):
|
|
mu = (mu - scale[0].view(1, self.z_dim, 1, 1, 1)) * scale[1].view(1, self.z_dim, 1, 1, 1)
|
|
else:
|
|
mu = (mu - scale[0]) * scale[1]
|
|
|
|
self.clear_cache()
|
|
if return_mu:
|
|
return mu, log_var
|
|
else:
|
|
return mu
|
|
|
|
def decode(self, z, scale):
|
|
self.clear_cache()
|
|
|
|
# z: [b,c,t,h,w]
|
|
if isinstance(scale[0], torch.Tensor):
|
|
z = z / scale[1].view(1, self.z_dim, 1, 1, 1) + scale[0].view(1, self.z_dim, 1, 1, 1)
|
|
else:
|
|
z = z / scale[1] + scale[0]
|
|
iter_ = z.shape[2]
|
|
x = self.conv2(z)
|
|
for i in range(iter_):
|
|
self._conv_idx = [0]
|
|
if i == 0:
|
|
out = self.decoder(
|
|
x[:, :, i : i + 1, :, :],
|
|
feat_cache=self._feat_map,
|
|
feat_idx=self._conv_idx,
|
|
)
|
|
else:
|
|
out_ = self.decoder(
|
|
x[:, :, i : i + 1, :, :],
|
|
feat_cache=self._feat_map,
|
|
feat_idx=self._conv_idx,
|
|
)
|
|
out = torch.cat([out, out_], 2)
|
|
|
|
self.clear_cache()
|
|
return out
|
|
|
|
def reparameterize(self, mu, log_var):
|
|
std = torch.exp(0.5 * log_var)
|
|
eps = torch.randn_like(std)
|
|
return eps * std + mu
|
|
|
|
def sample(self, imgs, deterministic=False, scale=[0, 1]):
|
|
mu, log_var = self.encode(imgs, scale, return_mu=True)
|
|
if deterministic:
|
|
return mu
|
|
std = torch.exp(0.5 * log_var.clamp(-30.0, 20.0))
|
|
return mu + std * torch.randn_like(std), mu, log_var
|
|
|
|
def clear_cache(self):
|
|
self._conv_num = count_conv3d(self.decoder)
|
|
self._conv_idx = [0]
|
|
self._feat_map = [None] * self._conv_num
|
|
# cache encode
|
|
self._enc_conv_num = count_conv3d(self.encoder)
|
|
self._enc_conv_idx = [0]
|
|
self._enc_feat_map = [None] * self._enc_conv_num
|
|
|
|
def encode_video(self, x, scale=[0, 1]):
|
|
assert x.ndim == 5 # NTCHW
|
|
assert x.shape[2] % 3 == 0
|
|
x = x.transpose(1, 2)
|
|
y = x.mul(2).sub_(1)
|
|
y, mu, log_var = self.sample(y, scale=scale)
|
|
return y.transpose(1, 2).to(x), mu, log_var
|
|
|
|
def decode_video(self, x, scale=[0, 1]):
|
|
assert x.ndim == 5 # NTCHW
|
|
assert x.shape[2] % self.z_dim == 0
|
|
x = x.transpose(1, 2)
|
|
# B, C, T, H, W
|
|
y = x
|
|
y = self.decode(y, scale).clamp_(-1, 1)
|
|
y = y.mul_(0.5).add_(0.5).clamp_(0, 1) # NCTHW
|
|
return y.transpose(1, 2).to(x)
|
|
|
|
|
|
def _video_vae(pretrained_path=None, z_dim=None, device="cpu", cpu_offload=False, dtype=torch.float, load_from_rank0=False, pruning_rate=0.0, **kwargs):
|
|
"""
|
|
Autoencoder3d adapted from Stable Diffusion 1.x, 2.x and XL.
|
|
"""
|
|
# params
|
|
cfg = dict(
|
|
dim=96,
|
|
z_dim=z_dim,
|
|
dim_mult=[1, 2, 4, 4],
|
|
num_res_blocks=2,
|
|
attn_scales=[],
|
|
temperal_downsample=[False, True, True],
|
|
dropout=0.0,
|
|
pruning_rate=pruning_rate,
|
|
)
|
|
cfg.update(**kwargs)
|
|
|
|
# init model
|
|
with torch.device("meta"):
|
|
model = WanVAE_(**cfg)
|
|
|
|
# load checkpoint
|
|
weights_dict = load_weights(pretrained_path, cpu_offload=cpu_offload, load_from_rank0=load_from_rank0)
|
|
for k in weights_dict.keys():
|
|
if weights_dict[k].dtype != dtype:
|
|
weights_dict[k] = weights_dict[k].to(dtype)
|
|
model.load_state_dict(weights_dict, assign=True)
|
|
|
|
return model
|
|
|
|
|
|
class WanVAE:
|
|
def __init__(
|
|
self,
|
|
z_dim=16,
|
|
vae_path="cache/vae_step_411000.pth",
|
|
dtype=torch.float,
|
|
device="cuda",
|
|
parallel=False,
|
|
use_tiling=False,
|
|
cpu_offload=False,
|
|
use_2d_split=True,
|
|
load_from_rank0=False,
|
|
use_lightvae=False,
|
|
):
|
|
self.dtype = dtype
|
|
self.device = device
|
|
self.parallel = parallel
|
|
self.use_tiling = use_tiling
|
|
self.cpu_offload = cpu_offload
|
|
self.use_2d_split = use_2d_split
|
|
if use_lightvae:
|
|
pruning_rate = 0.75 # 0.75
|
|
else:
|
|
pruning_rate = 0.0
|
|
|
|
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.inv_std = 1.0 / torch.tensor(std, dtype=dtype, device=device)
|
|
self.scale = [self.mean, self.inv_std]
|
|
|
|
# (height, width, world_size) -> (world_size_h, world_size_w)
|
|
self.grid_table = {
|
|
# world_size = 2
|
|
(60, 104, 2): (1, 2),
|
|
(68, 120, 2): (1, 2),
|
|
(90, 160, 2): (1, 2),
|
|
(60, 60, 2): (1, 2),
|
|
(72, 72, 2): (1, 2),
|
|
(88, 88, 2): (1, 2),
|
|
(120, 120, 2): (1, 2),
|
|
(104, 60, 2): (2, 1),
|
|
(120, 68, 2): (2, 1),
|
|
(160, 90, 2): (2, 1),
|
|
# world_size = 4
|
|
(60, 104, 4): (2, 2),
|
|
(68, 120, 4): (2, 2),
|
|
(90, 160, 4): (2, 2),
|
|
(60, 60, 4): (2, 2),
|
|
(72, 72, 4): (2, 2),
|
|
(88, 88, 4): (2, 2),
|
|
(120, 120, 4): (2, 2),
|
|
(104, 60, 4): (2, 2),
|
|
(120, 68, 4): (2, 2),
|
|
(160, 90, 4): (2, 2),
|
|
# world_size = 8
|
|
(60, 104, 8): (2, 4),
|
|
(68, 120, 8): (2, 4),
|
|
(90, 160, 8): (2, 4),
|
|
(60, 60, 8): (2, 4),
|
|
(72, 72, 8): (2, 4),
|
|
(88, 88, 8): (2, 4),
|
|
(120, 120, 8): (2, 4),
|
|
(104, 60, 8): (4, 2),
|
|
(120, 68, 8): (4, 2),
|
|
(160, 90, 8): (4, 2),
|
|
}
|
|
|
|
# init model
|
|
self.model = (
|
|
_video_vae(pretrained_path=vae_path, z_dim=z_dim, cpu_offload=cpu_offload, dtype=dtype, load_from_rank0=load_from_rank0, pruning_rate=pruning_rate)
|
|
.eval()
|
|
.requires_grad_(False)
|
|
.to(device)
|
|
.to(dtype)
|
|
)
|
|
|
|
def _calculate_2d_grid(self, latent_height, latent_width, world_size):
|
|
if (latent_height, latent_width, world_size) in self.grid_table:
|
|
best_h, best_w = self.grid_table[(latent_height, latent_width, world_size)]
|
|
# logger.info(f"Vae using cached 2D grid: {best_h}x{best_w} grid for {latent_height}x{latent_width} latent")
|
|
return best_h, best_w
|
|
|
|
best_h, best_w = 1, world_size
|
|
min_aspect_diff = float("inf")
|
|
|
|
for h in range(1, world_size + 1):
|
|
if world_size % h == 0:
|
|
w = world_size // h
|
|
if latent_height % h == 0 and latent_width % w == 0:
|
|
# Calculate how close this grid is to square
|
|
aspect_diff = abs((latent_height / h) - (latent_width / w))
|
|
if aspect_diff < min_aspect_diff:
|
|
min_aspect_diff = aspect_diff
|
|
best_h, best_w = h, w
|
|
# logger.info(f"Vae using 2D grid & Update cache: {best_h}x{best_w} grid for {latent_height}x{latent_width} latent")
|
|
self.grid_table[(latent_height, latent_width, world_size)] = (best_h, best_w)
|
|
return best_h, best_w
|
|
|
|
def current_device(self):
|
|
return next(self.model.parameters()).device
|
|
|
|
def to_cpu(self):
|
|
self.model.encoder = self.model.encoder.to("cpu")
|
|
self.model.decoder = self.model.decoder.to("cpu")
|
|
self.model = self.model.to("cpu")
|
|
self.mean = self.mean.cpu()
|
|
self.inv_std = self.inv_std.cpu()
|
|
self.scale = [self.mean, self.inv_std]
|
|
|
|
def to_cuda(self):
|
|
self.model.encoder = self.model.encoder.to("cuda")
|
|
self.model.decoder = self.model.decoder.to("cuda")
|
|
self.model = self.model.to("cuda")
|
|
self.mean = self.mean.cuda()
|
|
self.inv_std = self.inv_std.cuda()
|
|
self.scale = [self.mean, self.inv_std]
|
|
|
|
def encode_dist(self, video, world_size, cur_rank, split_dim):
|
|
spatial_ratio = 8
|
|
|
|
if split_dim == 3:
|
|
total_latent_len = video.shape[3] // spatial_ratio
|
|
elif split_dim == 4:
|
|
total_latent_len = video.shape[4] // spatial_ratio
|
|
else:
|
|
raise ValueError(f"Unsupported split_dim: {split_dim}")
|
|
|
|
splited_chunk_len = total_latent_len // world_size
|
|
padding_size = 1
|
|
|
|
video_chunk_len = splited_chunk_len * spatial_ratio
|
|
video_padding_len = padding_size * spatial_ratio
|
|
|
|
if cur_rank == 0:
|
|
if split_dim == 3:
|
|
video_chunk = video[:, :, :, : video_chunk_len + 2 * video_padding_len, :].contiguous()
|
|
elif split_dim == 4:
|
|
video_chunk = video[:, :, :, :, : video_chunk_len + 2 * video_padding_len].contiguous()
|
|
elif cur_rank == world_size - 1:
|
|
if split_dim == 3:
|
|
video_chunk = video[:, :, :, -(video_chunk_len + 2 * video_padding_len) :, :].contiguous()
|
|
elif split_dim == 4:
|
|
video_chunk = video[:, :, :, :, -(video_chunk_len + 2 * video_padding_len) :].contiguous()
|
|
else:
|
|
start_idx = cur_rank * video_chunk_len - video_padding_len
|
|
end_idx = (cur_rank + 1) * video_chunk_len + video_padding_len
|
|
if split_dim == 3:
|
|
video_chunk = video[:, :, :, start_idx:end_idx, :].contiguous()
|
|
elif split_dim == 4:
|
|
video_chunk = video[:, :, :, :, start_idx:end_idx].contiguous()
|
|
|
|
if self.use_tiling:
|
|
encoded_chunk = self.model.tiled_encode(video_chunk, self.scale)
|
|
else:
|
|
encoded_chunk = self.model.encode(video_chunk, self.scale)
|
|
|
|
if cur_rank == 0:
|
|
if split_dim == 3:
|
|
encoded_chunk = encoded_chunk[:, :, :, :splited_chunk_len, :].contiguous()
|
|
elif split_dim == 4:
|
|
encoded_chunk = encoded_chunk[:, :, :, :, :splited_chunk_len].contiguous()
|
|
elif cur_rank == world_size - 1:
|
|
if split_dim == 3:
|
|
encoded_chunk = encoded_chunk[:, :, :, -splited_chunk_len:, :].contiguous()
|
|
elif split_dim == 4:
|
|
encoded_chunk = encoded_chunk[:, :, :, :, -splited_chunk_len:].contiguous()
|
|
else:
|
|
if split_dim == 3:
|
|
encoded_chunk = encoded_chunk[:, :, :, padding_size:-padding_size, :].contiguous()
|
|
elif split_dim == 4:
|
|
encoded_chunk = encoded_chunk[:, :, :, :, padding_size:-padding_size].contiguous()
|
|
|
|
full_encoded = [torch.empty_like(encoded_chunk) for _ in range(world_size)]
|
|
dist.all_gather(full_encoded, encoded_chunk)
|
|
|
|
torch.cuda.synchronize()
|
|
|
|
encoded = torch.cat(full_encoded, dim=split_dim)
|
|
|
|
return encoded.squeeze(0)
|
|
|
|
def encode_dist_2d(self, video, world_size_h, world_size_w, cur_rank_h, cur_rank_w):
|
|
spatial_ratio = 8
|
|
|
|
# Calculate chunk sizes for both dimensions
|
|
total_latent_h = video.shape[3] // spatial_ratio
|
|
total_latent_w = video.shape[4] // spatial_ratio
|
|
|
|
chunk_h = total_latent_h // world_size_h
|
|
chunk_w = total_latent_w // world_size_w
|
|
|
|
padding_size = 1
|
|
video_chunk_h = chunk_h * spatial_ratio
|
|
video_chunk_w = chunk_w * spatial_ratio
|
|
video_padding_h = padding_size * spatial_ratio
|
|
video_padding_w = padding_size * spatial_ratio
|
|
|
|
# Calculate H dimension slice
|
|
if cur_rank_h == 0:
|
|
h_start = 0
|
|
h_end = video_chunk_h + 2 * video_padding_h
|
|
elif cur_rank_h == world_size_h - 1:
|
|
h_start = video.shape[3] - (video_chunk_h + 2 * video_padding_h)
|
|
h_end = video.shape[3]
|
|
else:
|
|
h_start = cur_rank_h * video_chunk_h - video_padding_h
|
|
h_end = (cur_rank_h + 1) * video_chunk_h + video_padding_h
|
|
|
|
# Calculate W dimension slice
|
|
if cur_rank_w == 0:
|
|
w_start = 0
|
|
w_end = video_chunk_w + 2 * video_padding_w
|
|
elif cur_rank_w == world_size_w - 1:
|
|
w_start = video.shape[4] - (video_chunk_w + 2 * video_padding_w)
|
|
w_end = video.shape[4]
|
|
else:
|
|
w_start = cur_rank_w * video_chunk_w - video_padding_w
|
|
w_end = (cur_rank_w + 1) * video_chunk_w + video_padding_w
|
|
|
|
# Extract the video chunk for this process
|
|
video_chunk = video[:, :, :, h_start:h_end, w_start:w_end].contiguous()
|
|
|
|
# Encode the chunk
|
|
if self.use_tiling:
|
|
encoded_chunk = self.model.tiled_encode(video_chunk, self.scale)
|
|
else:
|
|
encoded_chunk = self.model.encode(video_chunk, self.scale)
|
|
|
|
# Remove padding from encoded chunk
|
|
if cur_rank_h == 0:
|
|
encoded_h_start = 0
|
|
encoded_h_end = chunk_h
|
|
elif cur_rank_h == world_size_h - 1:
|
|
encoded_h_start = encoded_chunk.shape[3] - chunk_h
|
|
encoded_h_end = encoded_chunk.shape[3]
|
|
else:
|
|
encoded_h_start = padding_size
|
|
encoded_h_end = encoded_chunk.shape[3] - padding_size
|
|
|
|
if cur_rank_w == 0:
|
|
encoded_w_start = 0
|
|
encoded_w_end = chunk_w
|
|
elif cur_rank_w == world_size_w - 1:
|
|
encoded_w_start = encoded_chunk.shape[4] - chunk_w
|
|
encoded_w_end = encoded_chunk.shape[4]
|
|
else:
|
|
encoded_w_start = padding_size
|
|
encoded_w_end = encoded_chunk.shape[4] - padding_size
|
|
|
|
encoded_chunk = encoded_chunk[:, :, :, encoded_h_start:encoded_h_end, encoded_w_start:encoded_w_end].contiguous()
|
|
|
|
# Gather all chunks
|
|
total_processes = world_size_h * world_size_w
|
|
full_encoded = [torch.empty_like(encoded_chunk) for _ in range(total_processes)]
|
|
|
|
dist.all_gather(full_encoded, encoded_chunk)
|
|
|
|
torch.cuda.synchronize()
|
|
|
|
# Reconstruct the full encoded tensor
|
|
encoded_rows = []
|
|
for h_idx in range(world_size_h):
|
|
encoded_cols = []
|
|
for w_idx in range(world_size_w):
|
|
process_idx = h_idx * world_size_w + w_idx
|
|
encoded_cols.append(full_encoded[process_idx])
|
|
encoded_rows.append(torch.cat(encoded_cols, dim=4))
|
|
|
|
encoded = torch.cat(encoded_rows, dim=3)
|
|
|
|
return encoded.squeeze(0)
|
|
|
|
def encode(self, video):
|
|
"""
|
|
video: one video with shape [1, C, T, H, W].
|
|
"""
|
|
if self.cpu_offload:
|
|
self.to_cuda()
|
|
|
|
if self.parallel:
|
|
world_size = dist.get_world_size()
|
|
cur_rank = dist.get_rank()
|
|
height, width = video.shape[3], video.shape[4]
|
|
|
|
if self.use_2d_split:
|
|
world_size_h, world_size_w = self._calculate_2d_grid(height // 8, width // 8, world_size)
|
|
cur_rank_h = cur_rank // world_size_w
|
|
cur_rank_w = cur_rank % world_size_w
|
|
out = self.encode_dist_2d(video, world_size_h, world_size_w, cur_rank_h, cur_rank_w)
|
|
else:
|
|
# Original 1D splitting logic
|
|
if width % world_size == 0:
|
|
out = self.encode_dist(video, world_size, cur_rank, split_dim=4)
|
|
elif height % world_size == 0:
|
|
out = self.encode_dist(video, world_size, cur_rank, split_dim=3)
|
|
else:
|
|
logger.info("Fall back to naive encode mode")
|
|
if self.use_tiling:
|
|
out = self.model.tiled_encode(video, self.scale).squeeze(0)
|
|
else:
|
|
out = self.model.encode(video, self.scale).squeeze(0)
|
|
else:
|
|
if self.use_tiling:
|
|
out = self.model.tiled_encode(video, self.scale).squeeze(0)
|
|
else:
|
|
out = self.model.encode(video, self.scale).squeeze(0)
|
|
|
|
if self.cpu_offload:
|
|
self.to_cpu()
|
|
return out
|
|
|
|
def decode_dist(self, zs, world_size, cur_rank, split_dim):
|
|
splited_total_len = zs.shape[split_dim]
|
|
splited_chunk_len = splited_total_len // world_size
|
|
padding_size = 1
|
|
|
|
if cur_rank == 0:
|
|
if split_dim == 2:
|
|
zs = zs[:, :, : splited_chunk_len + 2 * padding_size, :].contiguous()
|
|
elif split_dim == 3:
|
|
zs = zs[:, :, :, : splited_chunk_len + 2 * padding_size].contiguous()
|
|
elif cur_rank == world_size - 1:
|
|
if split_dim == 2:
|
|
zs = zs[:, :, -(splited_chunk_len + 2 * padding_size) :, :].contiguous()
|
|
elif split_dim == 3:
|
|
zs = zs[:, :, :, -(splited_chunk_len + 2 * padding_size) :].contiguous()
|
|
else:
|
|
if split_dim == 2:
|
|
zs = zs[:, :, cur_rank * splited_chunk_len - padding_size : (cur_rank + 1) * splited_chunk_len + padding_size, :].contiguous()
|
|
elif split_dim == 3:
|
|
zs = zs[:, :, :, cur_rank * splited_chunk_len - padding_size : (cur_rank + 1) * splited_chunk_len + padding_size].contiguous()
|
|
|
|
decode_func = self.model.tiled_decode if self.use_tiling else self.model.decode
|
|
images = decode_func(zs.unsqueeze(0), self.scale).clamp_(-1, 1)
|
|
|
|
if cur_rank == 0:
|
|
if split_dim == 2:
|
|
images = images[:, :, :, : splited_chunk_len * 8, :].contiguous()
|
|
elif split_dim == 3:
|
|
images = images[:, :, :, :, : splited_chunk_len * 8].contiguous()
|
|
elif cur_rank == world_size - 1:
|
|
if split_dim == 2:
|
|
images = images[:, :, :, -splited_chunk_len * 8 :, :].contiguous()
|
|
elif split_dim == 3:
|
|
images = images[:, :, :, :, -splited_chunk_len * 8 :].contiguous()
|
|
else:
|
|
if split_dim == 2:
|
|
images = images[:, :, :, 8 * padding_size : -8 * padding_size, :].contiguous()
|
|
elif split_dim == 3:
|
|
images = images[:, :, :, :, 8 * padding_size : -8 * padding_size].contiguous()
|
|
|
|
full_images = [torch.empty_like(images) for _ in range(world_size)]
|
|
dist.all_gather(full_images, images)
|
|
|
|
torch.cuda.synchronize()
|
|
|
|
images = torch.cat(full_images, dim=split_dim + 1)
|
|
|
|
return images
|
|
|
|
def decode_dist_2d(self, zs, world_size_h, world_size_w, cur_rank_h, cur_rank_w):
|
|
total_h = zs.shape[2]
|
|
total_w = zs.shape[3]
|
|
|
|
chunk_h = total_h // world_size_h
|
|
chunk_w = total_w // world_size_w
|
|
|
|
padding_size = 1
|
|
|
|
# Calculate H dimension slice
|
|
if cur_rank_h == 0:
|
|
h_start = 0
|
|
h_end = chunk_h + 2 * padding_size
|
|
elif cur_rank_h == world_size_h - 1:
|
|
h_start = total_h - (chunk_h + 2 * padding_size)
|
|
h_end = total_h
|
|
else:
|
|
h_start = cur_rank_h * chunk_h - padding_size
|
|
h_end = (cur_rank_h + 1) * chunk_h + padding_size
|
|
|
|
# Calculate W dimension slice
|
|
if cur_rank_w == 0:
|
|
w_start = 0
|
|
w_end = chunk_w + 2 * padding_size
|
|
elif cur_rank_w == world_size_w - 1:
|
|
w_start = total_w - (chunk_w + 2 * padding_size)
|
|
w_end = total_w
|
|
else:
|
|
w_start = cur_rank_w * chunk_w - padding_size
|
|
w_end = (cur_rank_w + 1) * chunk_w + padding_size
|
|
|
|
# Extract the latent chunk for this process
|
|
zs_chunk = zs[:, :, h_start:h_end, w_start:w_end].contiguous()
|
|
|
|
# Decode the chunk
|
|
decode_func = self.model.tiled_decode if self.use_tiling else self.model.decode
|
|
images_chunk = decode_func(zs_chunk.unsqueeze(0), self.scale).clamp_(-1, 1)
|
|
|
|
# Remove padding from decoded chunk
|
|
spatial_ratio = 8
|
|
if cur_rank_h == 0:
|
|
decoded_h_start = 0
|
|
decoded_h_end = chunk_h * spatial_ratio
|
|
elif cur_rank_h == world_size_h - 1:
|
|
decoded_h_start = images_chunk.shape[3] - chunk_h * spatial_ratio
|
|
decoded_h_end = images_chunk.shape[3]
|
|
else:
|
|
decoded_h_start = padding_size * spatial_ratio
|
|
decoded_h_end = images_chunk.shape[3] - padding_size * spatial_ratio
|
|
|
|
if cur_rank_w == 0:
|
|
decoded_w_start = 0
|
|
decoded_w_end = chunk_w * spatial_ratio
|
|
elif cur_rank_w == world_size_w - 1:
|
|
decoded_w_start = images_chunk.shape[4] - chunk_w * spatial_ratio
|
|
decoded_w_end = images_chunk.shape[4]
|
|
else:
|
|
decoded_w_start = padding_size * spatial_ratio
|
|
decoded_w_end = images_chunk.shape[4] - padding_size * spatial_ratio
|
|
|
|
images_chunk = images_chunk[:, :, :, decoded_h_start:decoded_h_end, decoded_w_start:decoded_w_end].contiguous()
|
|
|
|
# Gather all chunks
|
|
total_processes = world_size_h * world_size_w
|
|
full_images = [torch.empty_like(images_chunk) for _ in range(total_processes)]
|
|
|
|
dist.all_gather(full_images, images_chunk)
|
|
|
|
torch.cuda.synchronize()
|
|
|
|
# Reconstruct the full image tensor
|
|
image_rows = []
|
|
for h_idx in range(world_size_h):
|
|
image_cols = []
|
|
for w_idx in range(world_size_w):
|
|
process_idx = h_idx * world_size_w + w_idx
|
|
image_cols.append(full_images[process_idx])
|
|
image_rows.append(torch.cat(image_cols, dim=4))
|
|
|
|
images = torch.cat(image_rows, dim=3)
|
|
|
|
return images
|
|
|
|
def decode(self, zs):
|
|
if self.cpu_offload:
|
|
self.to_cuda()
|
|
|
|
if self.parallel:
|
|
world_size = dist.get_world_size()
|
|
cur_rank = dist.get_rank()
|
|
latent_height, latent_width = zs.shape[2], zs.shape[3]
|
|
|
|
if self.use_2d_split:
|
|
world_size_h, world_size_w = self._calculate_2d_grid(latent_height, latent_width, world_size)
|
|
cur_rank_h = cur_rank // world_size_w
|
|
cur_rank_w = cur_rank % world_size_w
|
|
images = self.decode_dist_2d(zs, world_size_h, world_size_w, cur_rank_h, cur_rank_w)
|
|
else:
|
|
# Original 1D splitting logic
|
|
if latent_width % world_size == 0:
|
|
images = self.decode_dist(zs, world_size, cur_rank, split_dim=3)
|
|
elif latent_height % world_size == 0:
|
|
images = self.decode_dist(zs, world_size, cur_rank, split_dim=2)
|
|
else:
|
|
logger.info("Fall back to naive decode mode")
|
|
images = self.model.decode(zs.unsqueeze(0), self.scale).clamp_(-1, 1)
|
|
else:
|
|
decode_func = self.model.tiled_decode if self.use_tiling else self.model.decode
|
|
images = decode_func(zs.unsqueeze(0), self.scale).clamp_(-1, 1)
|
|
|
|
if self.cpu_offload:
|
|
images = images.cpu()
|
|
self.to_cpu()
|
|
|
|
return images
|
|
|
|
def encode_video(self, vid):
|
|
return self.model.encode_video(vid)
|
|
|
|
def decode_video(self, vid_enc):
|
|
return self.model.decode_video(vid_enc)
|