update
This commit is contained in:
Patrick von Platen
parent
1468f754e0
commit
14bd3567b0
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@ -1,6 +1,5 @@
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from abc import abstractmethod
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import functools
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import numpy as np
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import torch
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import torch.nn as nn
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@ -160,211 +159,7 @@ class Downsample(nn.Module):
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# return self.conv(x)
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# RESNETS
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# unet_score_estimation.py
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class ResnetBlockBigGANppNew(nn.Module):
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def __init__(
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self,
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act,
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in_ch,
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out_ch=None,
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temb_dim=None,
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up=False,
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down=False,
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dropout=0.1,
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fir_kernel=(1, 3, 3, 1),
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skip_rescale=True,
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init_scale=0.0,
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overwrite=True,
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):
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super().__init__()
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out_ch = out_ch if out_ch else in_ch
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self.GroupNorm_0 = nn.GroupNorm(num_groups=min(in_ch // 4, 32), num_channels=in_ch, eps=1e-6)
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self.up = up
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self.down = down
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self.fir_kernel = fir_kernel
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self.Conv_0 = conv2d(in_ch, out_ch, kernel_size=3, padding=1)
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if temb_dim is not None:
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self.Dense_0 = nn.Linear(temb_dim, out_ch)
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self.Dense_0.weight.data = variance_scaling()(self.Dense_0.weight.shape)
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nn.init.zeros_(self.Dense_0.bias)
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self.GroupNorm_1 = nn.GroupNorm(num_groups=min(out_ch // 4, 32), num_channels=out_ch, eps=1e-6)
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self.Dropout_0 = nn.Dropout(dropout)
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self.Conv_1 = conv2d(out_ch, out_ch, init_scale=init_scale, kernel_size=3, padding=1)
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if in_ch != out_ch or up or down:
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# 1x1 convolution with DDPM initialization.
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self.Conv_2 = conv2d(in_ch, out_ch, kernel_size=1, padding=0)
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self.skip_rescale = skip_rescale
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self.act = act
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self.in_ch = in_ch
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self.out_ch = out_ch
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self.is_overwritten = False
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self.overwrite = overwrite
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if overwrite:
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self.output_scale_factor = np.sqrt(2.0)
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self.in_channels = in_channels = in_ch
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self.out_channels = out_channels = out_ch
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groups = min(in_ch // 4, 32)
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out_groups = min(out_ch // 4, 32)
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eps = 1e-6
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self.pre_norm = True
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temb_channels = temb_dim
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non_linearity = "silu"
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self.time_embedding_norm = time_embedding_norm = "default"
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if self.pre_norm:
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self.norm1 = Normalize(in_channels, num_groups=groups, eps=eps)
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else:
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self.norm1 = Normalize(out_channels, num_groups=groups, eps=eps)
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self.conv1 = torch.nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
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if time_embedding_norm == "default":
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self.temb_proj = torch.nn.Linear(temb_channels, out_channels)
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elif time_embedding_norm == "scale_shift":
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self.temb_proj = torch.nn.Linear(temb_channels, 2 * out_channels)
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self.norm2 = Normalize(out_channels, num_groups=out_groups, eps=eps)
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self.dropout = torch.nn.Dropout(dropout)
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self.conv2 = torch.nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
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if non_linearity == "swish":
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self.nonlinearity = nonlinearity
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elif non_linearity == "mish":
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self.nonlinearity = Mish()
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elif non_linearity == "silu":
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self.nonlinearity = nn.SiLU()
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if up:
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self.h_upd = Upsample(in_channels, use_conv=False, dims=2)
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self.x_upd = Upsample(in_channels, use_conv=False, dims=2)
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elif down:
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self.h_upd = Downsample(in_channels, use_conv=False, dims=2, padding=1, name="op")
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self.x_upd = Downsample(in_channels, use_conv=False, dims=2, padding=1, name="op")
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if self.in_channels != self.out_channels or self.up or self.down:
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self.nin_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
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def set_weights(self):
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self.conv1.weight.data = self.Conv_0.weight.data
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self.conv1.bias.data = self.Conv_0.bias.data
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self.norm1.weight.data = self.GroupNorm_0.weight.data
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self.norm1.bias.data = self.GroupNorm_0.bias.data
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self.conv2.weight.data = self.Conv_1.weight.data
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self.conv2.bias.data = self.Conv_1.bias.data
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self.norm2.weight.data = self.GroupNorm_1.weight.data
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self.norm2.bias.data = self.GroupNorm_1.bias.data
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self.temb_proj.weight.data = self.Dense_0.weight.data
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self.temb_proj.bias.data = self.Dense_0.bias.data
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if self.in_channels != self.out_channels or self.up or self.down:
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self.nin_shortcut.weight.data = self.Conv_2.weight.data
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self.nin_shortcut.bias.data = self.Conv_2.bias.data
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def forward(self, x, temb=None):
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if self.overwrite and not self.is_overwritten:
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self.set_weights()
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self.is_overwritten = True
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orig_x = x
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h = self.act(self.GroupNorm_0(x))
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if self.up:
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h = upsample_2d(h, self.fir_kernel, factor=2)
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x = upsample_2d(x, self.fir_kernel, factor=2)
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elif self.down:
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h = downsample_2d(h, self.fir_kernel, factor=2)
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x = downsample_2d(x, self.fir_kernel, factor=2)
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h = self.Conv_0(h)
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# Add bias to each feature map conditioned on the time embedding
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if temb is not None:
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h += self.Dense_0(self.act(temb))[:, :, None, None]
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h = self.act(self.GroupNorm_1(h))
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h = self.Dropout_0(h)
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h = self.Conv_1(h)
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if self.in_ch != self.out_ch or self.up or self.down:
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x = self.Conv_2(x)
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if not self.skip_rescale:
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raise ValueError("Is this branch run?!")
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# import ipdb; ipdb.set_trace()
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result = x + h
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else:
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result = (x + h) / np.sqrt(2.0)
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result_2 = self.forward_2(orig_x, temb)
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return result_2
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def forward_2(self, x, temb, mask=1.0):
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h = x
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h = h * mask
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if self.pre_norm:
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h = self.norm1(h)
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h = self.nonlinearity(h)
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# if self.up or self.down:
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# x = self.x_upd(x)
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# h = self.h_upd(h)
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if self.up:
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h = upsample_2d(h, self.fir_kernel, factor=2)
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x = upsample_2d(x, self.fir_kernel, factor=2)
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elif self.down:
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h = downsample_2d(h, self.fir_kernel, factor=2)
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x = downsample_2d(x, self.fir_kernel, factor=2)
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h = self.conv1(h)
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if not self.pre_norm:
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h = self.norm1(h)
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h = self.nonlinearity(h)
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h = h * mask
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temb = self.temb_proj(self.nonlinearity(temb))[:, :, None, None]
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if self.time_embedding_norm == "scale_shift":
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scale, shift = torch.chunk(temb, 2, dim=1)
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h = self.norm2(h)
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h = h + h * scale + shift
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h = self.nonlinearity(h)
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elif self.time_embedding_norm == "default":
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h = h + temb
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h = h * mask
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if self.pre_norm:
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h = self.norm2(h)
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h = self.nonlinearity(h)
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else:
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raise ValueError("Nananan nanana - don't go here!")
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h = self.dropout(h)
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h = self.conv2(h)
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if not self.pre_norm:
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h = self.norm2(h)
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h = self.nonlinearity(h)
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h = h * mask
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x = x * mask
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# if self.in_channels != self.out_channels:
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if self.in_channels != self.out_channels or self.up or self.down:
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x = self.nin_shortcut(x)
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result = x + h
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return result / self.output_scale_factor
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# unet.py, unet_grad_tts.py, unet_ldm.py, unet_glide.py
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# unet.py, unet_grad_tts.py, unet_ldm.py, unet_glide.py, unet_score_vde.py
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class ResnetBlock(nn.Module):
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def __init__(
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self,
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@ -464,7 +464,7 @@ class NCSNpp(ModelMixin, ConfigMixin):
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groups_out=min(out_ch // 4, 32),
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overwrite_for_score_vde=True,
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up=True,
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kernel="fir",
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kernel="fir", # TODO(Patrick) - it seems like both fir and non-fir kernels are fine
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use_nin_shortcut=True,
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)
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)
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