Method __init__

models/float/encoder.py:140–176

(
			self,
			in_channel,
			out_channel,
			kernel_size,
			downsample=False,
			blur_kernel=[1, 3, 3, 1],
			bias=True,
			activate=True,
	)

class ConvLayer(nn.Sequential):
	def __init__(
			self,
			in_channel,
			out_channel,
			kernel_size,
			downsample=False,
			blur_kernel=[1, 3, 3, 1],
			bias=True,
			activate=True,
	):
		layers = []

		if downsample:
			factor = 2
			p = (len(blur_kernel) - factor) + (kernel_size - 1)
			pad0 = (p + 1) // 2
			pad1 = p // 2

			layers.append(Blur(blur_kernel, pad=(pad0, pad1)))

			stride = 2
			self.padding = 0

		else:
			stride = 1
			self.padding = kernel_size // 2

		layers.append(EqualConv2d(in_channel, out_channel, kernel_size, padding=self.padding, stride=stride,
								  bias=bias and not activate))

		if activate:
			if bias:
				layers.append(FusedLeakyReLU(out_channel))
			else:
				layers.append(ScaledLeakyReLU(0.2))

		super().__init__(*layers)


class ResBlock(nn.Module):