mirror of
https://github.com/Wan-Video/Wan2.1.git
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308 lines
7.9 KiB
Python
308 lines
7.9 KiB
Python
# Modified from ``https://github.com/wyhsirius/LIA``
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# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
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import torch
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import torch.nn as nn
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from torch.nn import functional as F
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import math
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def custom_qr(input_tensor):
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original_dtype = input_tensor.dtype
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if original_dtype in [torch.bfloat16, torch.float16]:
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q, r = torch.linalg.qr(input_tensor.to(torch.float32))
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return q.to(original_dtype), r.to(original_dtype)
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return torch.linalg.qr(input_tensor)
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def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
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return F.leaky_relu(input + bias, negative_slope) * scale
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def upfirdn2d_native(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1):
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_, minor, in_h, in_w = input.shape
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kernel_h, kernel_w = kernel.shape
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out = input.view(-1, minor, in_h, 1, in_w, 1)
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out = F.pad(out, [0, up_x - 1, 0, 0, 0, up_y - 1, 0, 0])
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out = out.view(-1, minor, in_h * up_y, in_w * up_x)
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out = F.pad(out, [max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)])
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out = out[:, :, max(-pad_y0, 0): out.shape[2] - max(-pad_y1, 0),
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max(-pad_x0, 0): out.shape[3] - max(-pad_x1, 0), ]
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out = out.reshape([-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1])
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w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w)
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out = F.conv2d(out, w)
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out = out.reshape(-1, minor, in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1,
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in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1, )
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return out[:, :, ::down_y, ::down_x]
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def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)):
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return upfirdn2d_native(input, kernel, up, up, down, down, pad[0], pad[1], pad[0], pad[1])
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def make_kernel(k):
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k = torch.tensor(k, dtype=torch.float32)
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if k.ndim == 1:
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k = k[None, :] * k[:, None]
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k /= k.sum()
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return k
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class FusedLeakyReLU(nn.Module):
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def __init__(self, channel, negative_slope=0.2, scale=2 ** 0.5):
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super().__init__()
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self.bias = nn.Parameter(torch.zeros(1, channel, 1, 1))
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self.negative_slope = negative_slope
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self.scale = scale
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def forward(self, input):
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out = fused_leaky_relu(input, self.bias, self.negative_slope, self.scale)
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return out
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class Blur(nn.Module):
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def __init__(self, kernel, pad, upsample_factor=1):
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super().__init__()
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kernel = make_kernel(kernel)
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if upsample_factor > 1:
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kernel = kernel * (upsample_factor ** 2)
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self.register_buffer('kernel', kernel)
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self.pad = pad
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def forward(self, input):
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return upfirdn2d(input, self.kernel, pad=self.pad)
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class ScaledLeakyReLU(nn.Module):
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def __init__(self, negative_slope=0.2):
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super().__init__()
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self.negative_slope = negative_slope
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def forward(self, input):
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return F.leaky_relu(input, negative_slope=self.negative_slope)
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class EqualConv2d(nn.Module):
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def __init__(self, in_channel, out_channel, kernel_size, stride=1, padding=0, bias=True):
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super().__init__()
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self.weight = nn.Parameter(torch.randn(out_channel, in_channel, kernel_size, kernel_size))
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self.scale = 1 / math.sqrt(in_channel * kernel_size ** 2)
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self.stride = stride
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self.padding = padding
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if bias:
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self.bias = nn.Parameter(torch.zeros(out_channel))
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else:
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self.bias = None
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def forward(self, input):
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return F.conv2d(input, self.weight * self.scale, bias=self.bias, stride=self.stride, padding=self.padding)
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def __repr__(self):
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return (
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f'{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]},'
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f' {self.weight.shape[2]}, stride={self.stride}, padding={self.padding})'
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)
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class EqualLinear(nn.Module):
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def __init__(self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1, activation=None):
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super().__init__()
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self.weight = nn.Parameter(torch.randn(out_dim, in_dim).div_(lr_mul))
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if bias:
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self.bias = nn.Parameter(torch.zeros(out_dim).fill_(bias_init))
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else:
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self.bias = None
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self.activation = activation
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self.scale = (1 / math.sqrt(in_dim)) * lr_mul
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self.lr_mul = lr_mul
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def forward(self, input):
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if self.activation:
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out = F.linear(input, self.weight * self.scale)
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out = fused_leaky_relu(out, self.bias * self.lr_mul)
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else:
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out = F.linear(input, self.weight * self.scale, bias=self.bias * self.lr_mul)
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return out
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def __repr__(self):
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return (f'{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]})')
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class ConvLayer(nn.Sequential):
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def __init__(
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self,
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in_channel,
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out_channel,
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kernel_size,
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downsample=False,
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blur_kernel=[1, 3, 3, 1],
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bias=True,
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activate=True,
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):
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layers = []
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if downsample:
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factor = 2
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p = (len(blur_kernel) - factor) + (kernel_size - 1)
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pad0 = (p + 1) // 2
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pad1 = p // 2
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layers.append(Blur(blur_kernel, pad=(pad0, pad1)))
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stride = 2
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self.padding = 0
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else:
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stride = 1
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self.padding = kernel_size // 2
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layers.append(EqualConv2d(in_channel, out_channel, kernel_size, padding=self.padding, stride=stride,
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bias=bias and not activate))
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if activate:
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if bias:
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layers.append(FusedLeakyReLU(out_channel))
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else:
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layers.append(ScaledLeakyReLU(0.2))
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super().__init__(*layers)
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class ResBlock(nn.Module):
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def __init__(self, in_channel, out_channel, blur_kernel=[1, 3, 3, 1]):
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super().__init__()
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self.conv1 = ConvLayer(in_channel, in_channel, 3)
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self.conv2 = ConvLayer(in_channel, out_channel, 3, downsample=True)
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self.skip = ConvLayer(in_channel, out_channel, 1, downsample=True, activate=False, bias=False)
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def forward(self, input):
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out = self.conv1(input)
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out = self.conv2(out)
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skip = self.skip(input)
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out = (out + skip) / math.sqrt(2)
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return out
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class EncoderApp(nn.Module):
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def __init__(self, size, w_dim=512):
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super(EncoderApp, self).__init__()
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channels = {
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4: 512,
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8: 512,
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16: 512,
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32: 512,
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64: 256,
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128: 128,
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256: 64,
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512: 32,
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1024: 16
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}
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self.w_dim = w_dim
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log_size = int(math.log(size, 2))
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self.convs = nn.ModuleList()
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self.convs.append(ConvLayer(3, channels[size], 1))
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in_channel = channels[size]
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for i in range(log_size, 2, -1):
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out_channel = channels[2 ** (i - 1)]
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self.convs.append(ResBlock(in_channel, out_channel))
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in_channel = out_channel
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self.convs.append(EqualConv2d(in_channel, self.w_dim, 4, padding=0, bias=False))
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def forward(self, x):
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res = []
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h = x
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for conv in self.convs:
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h = conv(h)
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res.append(h)
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return res[-1].squeeze(-1).squeeze(-1), res[::-1][2:]
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class Encoder(nn.Module):
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def __init__(self, size, dim=512, dim_motion=20):
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super(Encoder, self).__init__()
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# appearance netmork
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self.net_app = EncoderApp(size, dim)
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# motion network
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fc = [EqualLinear(dim, dim)]
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for i in range(3):
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fc.append(EqualLinear(dim, dim))
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fc.append(EqualLinear(dim, dim_motion))
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self.fc = nn.Sequential(*fc)
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def enc_app(self, x):
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h_source = self.net_app(x)
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return h_source
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def enc_motion(self, x):
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h, _ = self.net_app(x)
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h_motion = self.fc(h)
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return h_motion
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class Direction(nn.Module):
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def __init__(self, motion_dim):
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super(Direction, self).__init__()
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self.weight = nn.Parameter(torch.randn(512, motion_dim))
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def forward(self, input):
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weight = self.weight + 1e-8
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Q, R = custom_qr(weight)
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if input is None:
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return Q
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else:
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input_diag = torch.diag_embed(input) # alpha, diagonal matrix
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out = torch.matmul(input_diag, Q.T)
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out = torch.sum(out, dim=1)
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return out
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class Synthesis(nn.Module):
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def __init__(self, motion_dim):
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super(Synthesis, self).__init__()
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self.direction = Direction(motion_dim)
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class Generator(nn.Module):
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def __init__(self, size, style_dim=512, motion_dim=20):
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super().__init__()
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self.enc = Encoder(size, style_dim, motion_dim)
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self.dec = Synthesis(motion_dim)
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def get_motion(self, img):
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#motion_feat = self.enc.enc_motion(img)
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# motion_feat = torch.utils.checkpoint.checkpoint((self.enc.enc_motion), img, use_reentrant=True)
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with torch.cuda.amp.autocast(dtype=torch.float32):
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motion_feat = self.enc.enc_motion(img)
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motion = self.dec.direction(motion_feat)
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return motion |