Spaces:
Running
on
Zero
Running
on
Zero
File size: 11,058 Bytes
0fd2f06 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 |
from typing import List
from einops import rearrange
import torch
import torch.nn as nn
from wan.modules.vae import AttentionBlock, CausalConv3d, RMS_norm, ResidualBlock, Upsample
class Resample(nn.Module):
def __init__(self, dim, mode):
assert mode in ('none', 'upsample2d', 'upsample3d', 'downsample2d',
'downsample3d')
super().__init__()
self.dim = dim
self.mode = mode
self.cache_t = 2
# layers
if mode == 'upsample2d':
self.resample = nn.Sequential(
Upsample(scale_factor=(2., 2.), mode='nearest'),
nn.Conv2d(dim, dim // 2, 3, padding=1))
elif mode == 'upsample3d':
self.resample = nn.Sequential(
Upsample(scale_factor=(2., 2.), mode='nearest'),
nn.Conv2d(dim, dim // 2, 3, padding=1))
self.time_conv = CausalConv3d(
dim, dim * 2, (3, 1, 1), padding=(1, 0, 0))
elif mode == 'downsample2d':
self.resample = nn.Sequential(
nn.ZeroPad2d((0, 1, 0, 1)),
nn.Conv2d(dim, dim, 3, stride=(2, 2)))
elif mode == 'downsample3d':
self.resample = nn.Sequential(
nn.ZeroPad2d((0, 1, 0, 1)),
nn.Conv2d(dim, dim, 3, stride=(2, 2)))
self.time_conv = CausalConv3d(
dim, dim, (3, 1, 1), stride=(2, 1, 1), padding=(0, 0, 0))
else:
self.resample = nn.Identity()
def forward(self, x, feat_cache=None, feat_idx=[0]):
b, c, t, h, w = x.size()
if self.mode == 'upsample3d':
if feat_cache is not None:
idx = feat_idx[0]
if feat_cache[idx] is None:
feat_cache[idx] = 'Rep'
feat_idx[0] += 1
else:
cache_x = x[:, :, -self.cache_t:, :, :].clone()
if cache_x.shape[2] < 2 and feat_cache[
idx] is not None and feat_cache[idx] != 'Rep':
# cache last frame of last two chunk
cache_x = torch.cat([
feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
cache_x.device), cache_x
],
dim=2)
if cache_x.shape[2] < 2 and feat_cache[
idx] is not None and feat_cache[idx] == 'Rep':
cache_x = torch.cat([
torch.zeros_like(cache_x).to(cache_x.device),
cache_x
],
dim=2)
if feat_cache[idx] == 'Rep':
x = self.time_conv(x)
else:
x = self.time_conv(x, feat_cache[idx])
feat_cache[idx] = cache_x
feat_idx[0] += 1
x = x.reshape(b, 2, c, t, h, w)
x = torch.stack((x[:, 0, :, :, :, :], x[:, 1, :, :, :, :]),
3)
x = x.reshape(b, c, t * 2, h, w)
t = x.shape[2]
x = rearrange(x, 'b c t h w -> (b t) c h w')
x = self.resample(x)
x = rearrange(x, '(b t) c h w -> b c t h w', t=t)
if self.mode == 'downsample3d':
if feat_cache is not None:
idx = feat_idx[0]
if feat_cache[idx] is None:
feat_cache[idx] = x.clone()
feat_idx[0] += 1
else:
cache_x = x[:, :, -1:, :, :].clone()
# if cache_x.shape[2] < 2 and feat_cache[idx] is not None and feat_cache[idx]!='Rep':
# # cache last frame of last two chunk
# cache_x = torch.cat([feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x], dim=2)
x = self.time_conv(
torch.cat([feat_cache[idx][:, :, -1:, :, :], x], 2))
feat_cache[idx] = cache_x
feat_idx[0] += 1
return x
def init_weight(self, conv):
conv_weight = conv.weight
nn.init.zeros_(conv_weight)
c1, c2, t, h, w = conv_weight.size()
one_matrix = torch.eye(c1, c2)
init_matrix = one_matrix
nn.init.zeros_(conv_weight)
# conv_weight.data[:,:,-1,1,1] = init_matrix * 0.5
conv_weight.data[:, :, 1, 0, 0] = init_matrix # * 0.5
conv.weight.data.copy_(conv_weight)
nn.init.zeros_(conv.bias.data)
def init_weight2(self, conv):
conv_weight = conv.weight.data
nn.init.zeros_(conv_weight)
c1, c2, t, h, w = conv_weight.size()
init_matrix = torch.eye(c1 // 2, c2)
# init_matrix = repeat(init_matrix, 'o ... -> (o 2) ...').permute(1,0,2).contiguous().reshape(c1,c2)
conv_weight[:c1 // 2, :, -1, 0, 0] = init_matrix
conv_weight[c1 // 2:, :, -1, 0, 0] = init_matrix
conv.weight.data.copy_(conv_weight)
nn.init.zeros_(conv.bias.data)
class VAEDecoderWrapper(nn.Module):
def __init__(self):
super().__init__()
self.decoder = VAEDecoder3d()
mean = [
-0.7571, -0.7089, -0.9113, 0.1075, -0.1745, 0.9653, -0.1517, 1.5508,
0.4134, -0.0715, 0.5517, -0.3632, -0.1922, -0.9497, 0.2503, -0.2921
]
std = [
2.8184, 1.4541, 2.3275, 2.6558, 1.2196, 1.7708, 2.6052, 2.0743,
3.2687, 2.1526, 2.8652, 1.5579, 1.6382, 1.1253, 2.8251, 1.9160
]
self.mean = torch.tensor(mean, dtype=torch.float32)
self.std = torch.tensor(std, dtype=torch.float32)
self.z_dim = 16
self.conv2 = CausalConv3d(self.z_dim, self.z_dim, 1)
def forward(
self,
z: torch.Tensor,
*feat_cache: List[torch.Tensor]
):
# from [batch_size, num_frames, num_channels, height, width]
# to [batch_size, num_channels, num_frames, height, width]
z = z.permute(0, 2, 1, 3, 4)
feat_cache = list(feat_cache)
print("Length of feat_cache: ", len(feat_cache))
device, dtype = z.device, z.dtype
scale = [self.mean.to(device=device, dtype=dtype),
1.0 / self.std.to(device=device, dtype=dtype)]
if isinstance(scale[0], torch.Tensor):
z = z / scale[1].view(1, self.z_dim, 1, 1, 1) + scale[0].view(
1, self.z_dim, 1, 1, 1)
else:
z = z / scale[1] + scale[0]
iter_ = z.shape[2]
x = self.conv2(z)
for i in range(iter_):
if i == 0:
out, feat_cache = self.decoder(
x[:, :, i:i + 1, :, :],
feat_cache=feat_cache)
else:
out_, feat_cache = self.decoder(
x[:, :, i:i + 1, :, :],
feat_cache=feat_cache)
out = torch.cat([out, out_], 2)
out = out.float().clamp_(-1, 1)
# from [batch_size, num_channels, num_frames, height, width]
# to [batch_size, num_frames, num_channels, height, width]
out = out.permute(0, 2, 1, 3, 4)
return out, feat_cache
class VAEDecoder3d(nn.Module):
def __init__(self,
dim=96,
z_dim=16,
dim_mult=[1, 2, 4, 4],
num_res_blocks=2,
attn_scales=[],
temperal_upsample=[True, True, False],
dropout=0.0):
super().__init__()
self.dim = dim
self.z_dim = z_dim
self.dim_mult = dim_mult
self.num_res_blocks = num_res_blocks
self.attn_scales = attn_scales
self.temperal_upsample = temperal_upsample
self.cache_t = 2
self.decoder_conv_num = 32
# dimensions
dims = [dim * u for u in [dim_mult[-1]] + dim_mult[::-1]]
scale = 1.0 / 2**(len(dim_mult) - 2)
# init block
self.conv1 = CausalConv3d(z_dim, dims[0], 3, padding=1)
# middle blocks
self.middle = nn.Sequential(
ResidualBlock(dims[0], dims[0], dropout), AttentionBlock(dims[0]),
ResidualBlock(dims[0], dims[0], dropout))
# upsample blocks
upsamples = []
for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])):
# residual (+attention) blocks
if i == 1 or i == 2 or i == 3:
in_dim = in_dim // 2
for _ in range(num_res_blocks + 1):
upsamples.append(ResidualBlock(in_dim, out_dim, dropout))
if scale in attn_scales:
upsamples.append(AttentionBlock(out_dim))
in_dim = out_dim
# upsample block
if i != len(dim_mult) - 1:
mode = 'upsample3d' if temperal_upsample[i] else 'upsample2d'
upsamples.append(Resample(out_dim, mode=mode))
scale *= 2.0
self.upsamples = nn.Sequential(*upsamples)
# output blocks
self.head = nn.Sequential(
RMS_norm(out_dim, images=False), nn.SiLU(),
CausalConv3d(out_dim, 3, 3, padding=1))
def forward(
self,
x: torch.Tensor,
feat_cache: List[torch.Tensor]
):
feat_idx = [0]
# conv1
idx = feat_idx[0]
cache_x = x[:, :, -self.cache_t:, :, :].clone()
if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
# cache last frame of last two chunk
cache_x = torch.cat([
feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
cache_x.device), cache_x
],
dim=2)
x = self.conv1(x, feat_cache[idx])
feat_cache[idx] = cache_x
feat_idx[0] += 1
# middle
for layer in self.middle:
if isinstance(layer, ResidualBlock) and feat_cache is not None:
x = layer(x, feat_cache, feat_idx)
else:
x = layer(x)
# upsamples
for layer in self.upsamples:
x = layer(x, feat_cache, feat_idx)
# head
for layer in self.head:
if isinstance(layer, CausalConv3d) and feat_cache is not None:
idx = feat_idx[0]
cache_x = x[:, :, -self.cache_t:, :, :].clone()
if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
# cache last frame of last two chunk
cache_x = torch.cat([
feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(
cache_x.device), cache_x
],
dim=2)
x = layer(x, feat_cache[idx])
feat_cache[idx] = cache_x
feat_idx[0] += 1
else:
x = layer(x)
return x, feat_cache
|