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Remove optimization that caused border.
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8ddd081b09
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@ -85,11 +85,10 @@ class Downsample(nn.Module):
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stride=2,
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stride=2,
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padding=0)
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padding=0)
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def forward(self, x, already_padded=False):
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def forward(self, x):
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if self.with_conv:
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if self.with_conv:
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if not already_padded:
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pad = (0,1,0,1)
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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 = torch.nn.functional.pad(x, pad, mode="constant", value=0)
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x = self.conv(x)
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x = self.conv(x)
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else:
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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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x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
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@ -606,9 +605,6 @@ class Encoder(nn.Module):
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def forward(self, x):
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def forward(self, x):
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# timestep embedding
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# timestep embedding
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temb = None
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temb = None
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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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already_padded = True
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# downsampling
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# downsampling
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h = self.conv_in(x)
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h = self.conv_in(x)
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for i_level in range(self.num_resolutions):
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for i_level in range(self.num_resolutions):
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@ -617,8 +613,7 @@ class Encoder(nn.Module):
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if len(self.down[i_level].attn) > 0:
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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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h = self.down[i_level].attn[i_block](h)
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if i_level != self.num_resolutions-1:
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if i_level != self.num_resolutions-1:
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h = self.down[i_level].downsample(h, already_padded)
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h = self.down[i_level].downsample(h)
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already_padded = False
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# middle
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# middle
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h = self.mid.block_1(h, temb)
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h = self.mid.block_1(h, temb)
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