419 lines
16 KiB
Python
419 lines
16 KiB
Python
import math
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import torch
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import torch.nn as nn
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import numpy as np
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import torch.nn.functional as F
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def nonlinearity(x):
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return x*torch.sigmoid(x)
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class SpatialNorm(nn.Module):
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def __init__(
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self, f_channels, zq_channels=None, norm_layer=nn.GroupNorm, freeze_norm_layer=False, add_conv=False, **norm_layer_params
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):
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super().__init__()
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self.norm_layer = norm_layer(num_channels=f_channels, **norm_layer_params)
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if zq_channels is not None:
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if freeze_norm_layer:
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for p in self.norm_layer.parameters:
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p.requires_grad = False
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self.add_conv = add_conv
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if self.add_conv:
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self.conv = nn.Conv2d(zq_channels, zq_channels, kernel_size=3, stride=1, padding=1)
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self.conv_y = nn.Conv2d(zq_channels, f_channels, kernel_size=1, stride=1, padding=0)
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self.conv_b = nn.Conv2d(zq_channels, f_channels, kernel_size=1, stride=1, padding=0)
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def forward(self, f, zq=None):
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norm_f = self.norm_layer(f)
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if zq is not None:
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f_size = f.shape[-2:]
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zq = torch.nn.functional.interpolate(zq.float(), size=f_size, mode="nearest").to(zq.dtype)
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if self.add_conv:
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zq = self.conv(zq)
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norm_f = norm_f * self.conv_y(zq) + self.conv_b(zq)
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return norm_f
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def Normalize(in_channels, zq_ch=None, add_conv=None):
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return SpatialNorm(
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in_channels, zq_ch, norm_layer=nn.GroupNorm,
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freeze_norm_layer=False, add_conv=add_conv, num_groups=32, eps=1e-6, affine=True
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)
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class Upsample(nn.Module):
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def __init__(self, in_channels, with_conv):
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super().__init__()
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self.with_conv = with_conv
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if self.with_conv:
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self.conv = torch.nn.Conv2d(in_channels,
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in_channels,
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kernel_size=3,
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stride=1,
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padding=1)
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def forward(self, x):
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x = torch.nn.functional.interpolate(x.float(), scale_factor=2.0, mode="nearest").to(x.dtype)
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if self.with_conv:
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x = self.conv(x)
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return x
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class Downsample(nn.Module):
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def __init__(self, in_channels, with_conv):
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super().__init__()
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self.with_conv = with_conv
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if self.with_conv:
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self.conv = torch.nn.Conv2d(in_channels,
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in_channels,
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kernel_size=3,
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stride=2,
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padding=0)
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def forward(self, x):
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if self.with_conv:
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pad = (0,1,0,1)
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x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
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x = self.conv(x)
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else:
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x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
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return x
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class ResnetBlock(nn.Module):
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def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False,
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dropout, temb_channels=512, zq_ch=None, add_conv=False):
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super().__init__()
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self.in_channels = in_channels
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out_channels = in_channels if out_channels is None else out_channels
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self.out_channels = out_channels
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self.use_conv_shortcut = conv_shortcut
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self.norm1 = Normalize(in_channels, zq_ch, add_conv=add_conv)
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self.conv1 = torch.nn.Conv2d(in_channels,
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out_channels,
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kernel_size=3,
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stride=1,
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padding=1)
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if temb_channels > 0:
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self.temb_proj = torch.nn.Linear(temb_channels,
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out_channels)
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self.norm2 = Normalize(out_channels, zq_ch, add_conv=add_conv)
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self.dropout = torch.nn.Dropout(dropout)
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self.conv2 = torch.nn.Conv2d(out_channels,
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out_channels,
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kernel_size=3,
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stride=1,
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padding=1)
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if self.in_channels != self.out_channels:
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if self.use_conv_shortcut:
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self.conv_shortcut = torch.nn.Conv2d(in_channels,
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out_channels,
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kernel_size=3,
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stride=1,
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padding=1)
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else:
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self.nin_shortcut = torch.nn.Conv2d(in_channels,
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out_channels,
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kernel_size=1,
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stride=1,
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padding=0)
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def forward(self, x, temb, zq=None):
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h = x
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h = self.norm1(h, zq)
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h = nonlinearity(h)
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h = self.conv1(h)
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if temb is not None:
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h = h + self.temb_proj(nonlinearity(temb))[:,:,None,None]
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h = self.norm2(h, zq)
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h = nonlinearity(h)
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h = self.dropout(h)
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h = self.conv2(h)
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if self.in_channels != self.out_channels:
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if self.use_conv_shortcut:
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x = self.conv_shortcut(x)
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else:
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x = self.nin_shortcut(x)
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return x+h
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class AttnBlock(nn.Module):
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def __init__(self, in_channels, zq_ch=None, add_conv=False):
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super().__init__()
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self.in_channels = in_channels
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self.norm = Normalize(in_channels, zq_ch, add_conv=add_conv)
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self.q = torch.nn.Conv2d(in_channels,
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in_channels,
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kernel_size=1,
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stride=1,
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padding=0)
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self.k = torch.nn.Conv2d(in_channels,
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in_channels,
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kernel_size=1,
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stride=1,
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padding=0)
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self.v = torch.nn.Conv2d(in_channels,
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in_channels,
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kernel_size=1,
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stride=1,
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padding=0)
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self.proj_out = torch.nn.Conv2d(in_channels,
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in_channels,
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kernel_size=1,
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stride=1,
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padding=0)
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def forward(self, x, zq=None):
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h_ = x
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h_ = self.norm(h_, zq)
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q = self.q(h_)
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k = self.k(h_)
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v = self.v(h_)
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# compute attention
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b,c,h,w = q.shape
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q = q.reshape(b,c,h*w)
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q = q.permute(0,2,1) # b,hw,c
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k = k.reshape(b,c,h*w) # b,c,hw
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w_ = torch.bmm(q,k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
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w_ = w_ * (int(c)**(-0.5))
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w_ = torch.nn.functional.softmax(w_, dim=2)
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# attend to values
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v = v.reshape(b,c,h*w)
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w_ = w_.permute(0,2,1) # b,hw,hw (first hw of k, second of q)
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h_ = torch.bmm(v,w_) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
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h_ = h_.reshape(b,c,h,w)
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h_ = self.proj_out(h_)
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return x+h_
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class Encoder(nn.Module):
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def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
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attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
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resolution, z_channels, double_z=True, **ignore_kwargs):
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super().__init__()
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self.ch = ch
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self.temb_ch = 0
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self.num_resolutions = len(ch_mult)
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self.num_res_blocks = num_res_blocks
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self.resolution = resolution
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self.in_channels = in_channels
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# downsampling
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self.conv_in = torch.nn.Conv2d(in_channels,
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self.ch,
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kernel_size=3,
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stride=1,
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padding=1)
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curr_res = resolution
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in_ch_mult = (1,)+tuple(ch_mult)
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self.down = nn.ModuleList()
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for i_level in range(self.num_resolutions):
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block = nn.ModuleList()
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attn = nn.ModuleList()
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block_in = ch*in_ch_mult[i_level]
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block_out = ch*ch_mult[i_level]
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for i_block in range(self.num_res_blocks):
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block.append(ResnetBlock(in_channels=block_in,
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out_channels=block_out,
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temb_channels=self.temb_ch,
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dropout=dropout))
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block_in = block_out
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if curr_res in attn_resolutions:
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attn.append(AttnBlock(block_in))
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down = nn.Module()
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down.block = block
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down.attn = attn
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if i_level != self.num_resolutions-1:
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down.downsample = Downsample(block_in, resamp_with_conv)
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curr_res = curr_res // 2
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self.down.append(down)
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# middle
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self.mid = nn.Module()
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self.mid.block_1 = ResnetBlock(in_channels=block_in,
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out_channels=block_in,
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temb_channels=self.temb_ch,
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dropout=dropout)
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self.mid.attn_1 = AttnBlock(block_in)
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self.mid.block_2 = ResnetBlock(in_channels=block_in,
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out_channels=block_in,
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temb_channels=self.temb_ch,
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dropout=dropout)
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# end
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self.norm_out = Normalize(block_in)
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self.conv_out = torch.nn.Conv2d(block_in,
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2*z_channels if double_z else z_channels,
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kernel_size=3,
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stride=1,
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padding=1)
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def forward(self, x):
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temb = None
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# downsampling
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hs = [self.conv_in(x)]
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for i_level in range(self.num_resolutions):
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for i_block in range(self.num_res_blocks):
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h = self.down[i_level].block[i_block](hs[-1], temb)
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if len(self.down[i_level].attn) > 0:
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h = self.down[i_level].attn[i_block](h)
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hs.append(h)
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if i_level != self.num_resolutions-1:
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hs.append(self.down[i_level].downsample(hs[-1]))
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# middle
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h = hs[-1]
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h = self.mid.block_1(h, temb)
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h = self.mid.attn_1(h)
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h = self.mid.block_2(h, temb)
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# end
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h = self.norm_out(h)
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h = nonlinearity(h)
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h = self.conv_out(h)
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return h
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class Decoder(nn.Module):
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def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
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attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
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resolution, z_channels, give_pre_end=False, zq_ch=None, add_conv=False, **ignorekwargs):
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super().__init__()
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self.ch = ch
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self.temb_ch = 0
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self.num_resolutions = len(ch_mult)
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self.num_res_blocks = num_res_blocks
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self.resolution = resolution
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self.in_channels = in_channels
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self.give_pre_end = give_pre_end
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# compute in_ch_mult, block_in and curr_res at lowest res
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in_ch_mult = (1,)+tuple(ch_mult)
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block_in = ch*ch_mult[self.num_resolutions-1]
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curr_res = resolution // 2**(self.num_resolutions-1)
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self.z_shape = (1,z_channels,curr_res,curr_res)
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# z to block_in
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self.conv_in = torch.nn.Conv2d(z_channels,
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block_in,
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kernel_size=3,
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stride=1,
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padding=1)
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# middle
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self.mid = nn.Module()
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self.mid.block_1 = ResnetBlock(in_channels=block_in,
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out_channels=block_in,
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temb_channels=self.temb_ch,
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dropout=dropout,
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zq_ch=zq_ch,
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add_conv=add_conv)
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self.mid.attn_1 = AttnBlock(block_in, zq_ch, add_conv=add_conv)
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self.mid.block_2 = ResnetBlock(in_channels=block_in,
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out_channels=block_in,
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temb_channels=self.temb_ch,
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dropout=dropout,
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zq_ch=zq_ch,
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add_conv=add_conv)
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# upsampling
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self.up = nn.ModuleList()
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for i_level in reversed(range(self.num_resolutions)):
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block = nn.ModuleList()
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attn = nn.ModuleList()
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block_out = ch*ch_mult[i_level]
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for i_block in range(self.num_res_blocks+1):
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block.append(ResnetBlock(in_channels=block_in,
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out_channels=block_out,
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temb_channels=self.temb_ch,
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dropout=dropout,
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zq_ch=zq_ch,
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add_conv=add_conv))
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block_in = block_out
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if curr_res in attn_resolutions:
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attn.append(AttnBlock(block_in, zq_ch, add_conv=add_conv))
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up = nn.Module()
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up.block = block
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up.attn = attn
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if i_level != 0:
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up.upsample = Upsample(block_in, resamp_with_conv)
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curr_res = curr_res * 2
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self.up.insert(0, up) # prepend to get consistent order
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# end
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self.norm_out = Normalize(block_in, zq_ch, add_conv=add_conv)
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self.conv_out = torch.nn.Conv2d(block_in,
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out_ch,
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kernel_size=3,
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stride=1,
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padding=1)
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def forward(self, z, zq):
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#assert z.shape[1:] == self.z_shape[1:]
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self.last_z_shape = z.shape
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# timestep embedding
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temb = None
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# z to block_in
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h = self.conv_in(z)
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# middle
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h = self.mid.block_1(h, temb, zq)
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h = self.mid.attn_1(h, zq)
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h = self.mid.block_2(h, temb, zq)
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# upsampling
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for i_level in reversed(range(self.num_resolutions)):
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for i_block in range(self.num_res_blocks+1):
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h = self.up[i_level].block[i_block](h, temb, zq)
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if len(self.up[i_level].attn) > 0:
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h = self.up[i_level].attn[i_block](h, zq)
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if i_level != 0:
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h = self.up[i_level].upsample(h)
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# end
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if self.give_pre_end:
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return h
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h = self.norm_out(h, zq)
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h = nonlinearity(h)
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h = self.conv_out(h)
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return h
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class MoVQ(nn.Module):
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def __init__(self, generator_params):
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super().__init__()
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z_channels = generator_params["z_channels"]
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self.encoder = Encoder(**generator_params)
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self.quant_conv = torch.nn.Conv2d(z_channels, z_channels, 1)
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self.post_quant_conv = torch.nn.Conv2d(z_channels, z_channels, 1)
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self.decoder = Decoder(zq_ch=z_channels, **generator_params)
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@torch.no_grad()
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def encode(self, x):
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h = self.encoder(x)
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h = self.quant_conv(h)
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return h
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@torch.no_grad()
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def decode(self, quant):
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decoder_input = self.post_quant_conv(quant)
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decoded = self.decoder(decoder_input, quant)
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return decoded
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