Method __init__

ldm/modules/diffusionmodules/model.py:704–735

(self, in_channels, out_channels, *args, **kwargs)

class SimpleDecoder(nn.Module):
    def __init__(self, in_channels, out_channels, *args, **kwargs):
        super().__init__()
        self.model = nn.ModuleList(
            [
                nn.Conv2d(in_channels, in_channels, 1),
                ResnetBlock(
                    in_channels=in_channels,
                    out_channels=2 * in_channels,
                    temb_channels=0,
                    dropout=0.0,
                ),
                ResnetBlock(
                    in_channels=2 * in_channels,
                    out_channels=4 * in_channels,
                    temb_channels=0,
                    dropout=0.0,
                ),
                ResnetBlock(
                    in_channels=4 * in_channels,
                    out_channels=2 * in_channels,
                    temb_channels=0,
                    dropout=0.0,
                ),
                nn.Conv2d(2 * in_channels, in_channels, 1),
                Upsample(in_channels, with_conv=True),
            ]
        )
        # end
        self.norm_out = Normalize(in_channels)
        self.conv_out = torch.nn.Conv2d(
            in_channels, out_channels, kernel_size=3, stride=1, padding=1
        )

    def forward(self, x):
        for i, layer in enumerate(self.model):