744 lines
20 KiB
Python
744 lines
20 KiB
Python
import math
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import random
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import functools
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import operator
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import torch
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from torch import nn
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from torch.nn import functional as F
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from torch.autograd import Function
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# Ops -> The rosinality repo uses native cuda kernels for fused LeakyReLUs and upsamplers. This version extracts the
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# "cpu" alternative code and uses that instead for compatibility reasons.
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class FusedLeakyReLU(nn.Module):
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def __init__(self, channel, bias=True, negative_slope=0.2, scale=2 ** 0.5):
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super().__init__()
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if bias:
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self.bias = nn.Parameter(torch.zeros(channel))
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else:
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self.bias = None
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self.negative_slope = negative_slope
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self.scale = scale
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def forward(self, input):
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return fused_leaky_relu(input, self.bias, self.negative_slope, self.scale)
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def fused_leaky_relu(input, bias=None, negative_slope=0.2, scale=2 ** 0.5):
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if bias is not None:
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rest_dim = [1] * (input.ndim - bias.ndim - 1)
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return (
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F.leaky_relu(
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input + bias.view(1, bias.shape[0], *rest_dim), negative_slope=0.2
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)
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* scale
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)
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else:
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return F.leaky_relu(input, negative_slope=0.2) * scale
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def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)):
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out = upfirdn2d_native(
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input, kernel, up, up, down, down, pad[0], pad[1], pad[0], pad[1]
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)
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return out
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def upfirdn2d_native(
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input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1
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):
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_, channel, in_h, in_w = input.shape
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input = input.reshape(-1, in_h, in_w, 1)
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_, in_h, in_w, minor = input.shape
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kernel_h, kernel_w = kernel.shape
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out = input.view(-1, in_h, 1, in_w, 1, minor)
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out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1])
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out = out.view(-1, in_h * up_y, in_w * up_x, minor)
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out = F.pad(
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out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)]
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)
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out = out[
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:,
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max(-pad_y0, 0) : out.shape[1] - max(-pad_y1, 0),
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max(-pad_x0, 0) : out.shape[2] - max(-pad_x1, 0),
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:,
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]
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out = out.permute(0, 3, 1, 2)
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out = out.reshape(
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[-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1]
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)
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w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w)
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out = F.conv2d(out, w)
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out = out.reshape(
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-1,
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minor,
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in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1,
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in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1,
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)
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out = out.permute(0, 2, 3, 1)
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out = out[:, ::down_y, ::down_x, :]
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out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1
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out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1
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return out.view(-1, channel, out_h, out_w)
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# /end Ops
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class PixelNorm(nn.Module):
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def __init__(self):
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super().__init__()
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def forward(self, input):
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return input * torch.rsqrt(torch.mean(input ** 2, dim=1, keepdim=True) + 1e-8)
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def make_kernel(k):
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k = torch.tensor(k, dtype=torch.float32)
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if k.ndim == 1:
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k = k[None, :] * k[:, None]
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k /= k.sum()
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return k
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class Upsample(nn.Module):
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def __init__(self, kernel, factor=2):
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super().__init__()
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self.factor = factor
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kernel = make_kernel(kernel) * (factor ** 2)
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self.register_buffer("kernel", kernel)
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p = kernel.shape[0] - factor
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pad0 = (p + 1) // 2 + factor - 1
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pad1 = p // 2
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self.pad = (pad0, pad1)
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def forward(self, input):
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out = upfirdn2d(input, self.kernel, up=self.factor, down=1, pad=self.pad)
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return out
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class Downsample(nn.Module):
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def __init__(self, kernel, factor=2):
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super().__init__()
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self.factor = factor
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kernel = make_kernel(kernel)
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self.register_buffer("kernel", kernel)
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p = kernel.shape[0] - factor
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pad0 = (p + 1) // 2
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pad1 = p // 2
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self.pad = (pad0, pad1)
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def forward(self, input):
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out = upfirdn2d(input, self.kernel, up=1, down=self.factor, pad=self.pad)
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return out
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class Blur(nn.Module):
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def __init__(self, kernel, pad, upsample_factor=1):
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super().__init__()
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kernel = make_kernel(kernel)
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if upsample_factor > 1:
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kernel = kernel * (upsample_factor ** 2)
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self.register_buffer("kernel", kernel)
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self.pad = pad
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def forward(self, input):
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out = upfirdn2d(input, self.kernel, pad=self.pad)
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return out
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class EqualConv2d(nn.Module):
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def __init__(
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self, in_channel, out_channel, kernel_size, stride=1, padding=0, bias=True
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):
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super().__init__()
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self.weight = nn.Parameter(
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torch.randn(out_channel, in_channel, kernel_size, kernel_size)
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)
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self.scale = 1 / math.sqrt(in_channel * kernel_size ** 2)
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self.stride = stride
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self.padding = padding
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if bias:
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self.bias = nn.Parameter(torch.zeros(out_channel))
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else:
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self.bias = None
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def forward(self, input):
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out = F.conv2d(
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input,
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self.weight * self.scale,
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bias=self.bias,
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stride=self.stride,
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padding=self.padding,
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)
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return out
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def __repr__(self):
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return (
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f"{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]},"
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f" {self.weight.shape[2]}, stride={self.stride}, padding={self.padding})"
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)
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class EqualLinear(nn.Module):
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def __init__(
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self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1, activation=None
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):
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super().__init__()
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self.weight = nn.Parameter(torch.randn(out_dim, in_dim).div_(lr_mul))
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if bias:
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self.bias = nn.Parameter(torch.zeros(out_dim).fill_(bias_init))
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else:
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self.bias = None
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self.activation = activation
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self.scale = (1 / math.sqrt(in_dim)) * lr_mul
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self.lr_mul = lr_mul
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def forward(self, input):
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if self.activation:
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out = F.linear(input, self.weight * self.scale)
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out = fused_leaky_relu(out, self.bias * self.lr_mul)
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else:
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out = F.linear(
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input, self.weight * self.scale, bias=self.bias * self.lr_mul
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)
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return out
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def __repr__(self):
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return (
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f"{self.__class__.__name__}({self.weight.shape[1]}, {self.weight.shape[0]})"
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)
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class ModulatedConv2d(nn.Module):
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def __init__(
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self,
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in_channel,
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out_channel,
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kernel_size,
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style_dim,
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demodulate=True,
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upsample=False,
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downsample=False,
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blur_kernel=[1, 3, 3, 1],
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):
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super().__init__()
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self.eps = 1e-8
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self.kernel_size = kernel_size
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self.in_channel = in_channel
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self.out_channel = out_channel
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self.upsample = upsample
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self.downsample = downsample
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if upsample:
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factor = 2
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p = (len(blur_kernel) - factor) - (kernel_size - 1)
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pad0 = (p + 1) // 2 + factor - 1
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pad1 = p // 2 + 1
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self.blur = Blur(blur_kernel, pad=(pad0, pad1), upsample_factor=factor)
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if downsample:
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factor = 2
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p = (len(blur_kernel) - factor) + (kernel_size - 1)
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pad0 = (p + 1) // 2
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pad1 = p // 2
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self.blur = Blur(blur_kernel, pad=(pad0, pad1))
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fan_in = in_channel * kernel_size ** 2
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self.scale = 1 / math.sqrt(fan_in)
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self.padding = kernel_size // 2
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self.weight = nn.Parameter(
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torch.randn(1, out_channel, in_channel, kernel_size, kernel_size)
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)
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self.modulation = EqualLinear(style_dim, in_channel, bias_init=1)
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self.demodulate = demodulate
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def __repr__(self):
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return (
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f"{self.__class__.__name__}({self.in_channel}, {self.out_channel}, {self.kernel_size}, "
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f"upsample={self.upsample}, downsample={self.downsample})"
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)
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def forward(self, input, style):
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batch, in_channel, height, width = input.shape
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style = self.modulation(style).view(batch, 1, in_channel, 1, 1)
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weight = self.scale * self.weight * style
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if self.demodulate:
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demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + 1e-8)
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weight = weight * demod.view(batch, self.out_channel, 1, 1, 1)
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weight = weight.view(
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batch * self.out_channel, in_channel, self.kernel_size, self.kernel_size
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)
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if self.upsample:
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input = input.view(1, batch * in_channel, height, width)
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weight = weight.view(
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batch, self.out_channel, in_channel, self.kernel_size, self.kernel_size
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)
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weight = weight.transpose(1, 2).reshape(
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batch * in_channel, self.out_channel, self.kernel_size, self.kernel_size
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)
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out = F.conv_transpose2d(input, weight, padding=0, stride=2, groups=batch)
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_, _, height, width = out.shape
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out = out.view(batch, self.out_channel, height, width)
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out = self.blur(out)
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elif self.downsample:
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input = self.blur(input)
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_, _, height, width = input.shape
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input = input.view(1, batch * in_channel, height, width)
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out = F.conv2d(input, weight, padding=0, stride=2, groups=batch)
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_, _, height, width = out.shape
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out = out.view(batch, self.out_channel, height, width)
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else:
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input = input.view(1, batch * in_channel, height, width)
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out = F.conv2d(input, weight, padding=self.padding, groups=batch)
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_, _, height, width = out.shape
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out = out.view(batch, self.out_channel, height, width)
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return out
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class NoiseInjection(nn.Module):
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def __init__(self):
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super().__init__()
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self.weight = nn.Parameter(torch.zeros(1))
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def forward(self, image, noise=None):
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if noise is None:
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batch, _, height, width = image.shape
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noise = image.new_empty(batch, 1, height, width).normal_()
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return image + self.weight * noise
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class ConstantInput(nn.Module):
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def __init__(self, channel, size=4):
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super().__init__()
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self.input = nn.Parameter(torch.randn(1, channel, size, size))
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def forward(self, input):
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batch = input.shape[0]
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out = self.input.repeat(batch, 1, 1, 1)
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return out
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class StyledConv(nn.Module):
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def __init__(
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self,
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in_channel,
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out_channel,
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kernel_size,
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style_dim,
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upsample=False,
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blur_kernel=[1, 3, 3, 1],
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demodulate=True,
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):
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super().__init__()
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self.conv = ModulatedConv2d(
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in_channel,
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out_channel,
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kernel_size,
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style_dim,
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upsample=upsample,
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blur_kernel=blur_kernel,
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demodulate=demodulate,
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)
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self.noise = NoiseInjection()
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# self.bias = nn.Parameter(torch.zeros(1, out_channel, 1, 1))
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# self.activate = ScaledLeakyReLU(0.2)
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self.activate = FusedLeakyReLU(out_channel)
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def forward(self, input, style, noise=None):
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out = self.conv(input, style)
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out = self.noise(out, noise=noise)
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# out = out + self.bias
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out = self.activate(out)
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return out
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class ToRGB(nn.Module):
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def __init__(self, in_channel, style_dim, upsample=True, blur_kernel=[1, 3, 3, 1]):
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super().__init__()
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if upsample:
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self.upsample = Upsample(blur_kernel)
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self.conv = ModulatedConv2d(in_channel, 3, 1, style_dim, demodulate=False)
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self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1))
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def forward(self, input, style, skip=None):
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out = self.conv(input, style)
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out = out + self.bias
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if skip is not None:
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skip = self.upsample(skip)
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out = out + skip
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return out
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class Generator(nn.Module):
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def __init__(
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self,
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size,
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style_dim,
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n_mlp,
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channel_multiplier=2,
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blur_kernel=[1, 3, 3, 1],
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lr_mlp=0.01,
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):
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super().__init__()
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self.size = size
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self.style_dim = style_dim
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layers = [PixelNorm()]
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for i in range(n_mlp):
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layers.append(
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EqualLinear(
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style_dim, style_dim, lr_mul=lr_mlp, activation="fused_lrelu"
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)
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)
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self.style = nn.Sequential(*layers)
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self.channels = {
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4: 512,
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8: 512,
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16: 512,
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32: 512,
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64: 256 * channel_multiplier,
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128: 128 * channel_multiplier,
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256: 64 * channel_multiplier,
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512: 32 * channel_multiplier,
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1024: 16 * channel_multiplier,
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}
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self.input = ConstantInput(self.channels[4])
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self.conv1 = StyledConv(
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self.channels[4], self.channels[4], 3, style_dim, blur_kernel=blur_kernel
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)
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self.to_rgb1 = ToRGB(self.channels[4], style_dim, upsample=False)
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self.log_size = int(math.log(size, 2))
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self.num_layers = (self.log_size - 2) * 2 + 1
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self.convs = nn.ModuleList()
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self.upsamples = nn.ModuleList()
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self.to_rgbs = nn.ModuleList()
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self.noises = nn.Module()
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in_channel = self.channels[4]
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for layer_idx in range(self.num_layers):
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res = (layer_idx + 5) // 2
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shape = [1, 1, 2 ** res, 2 ** res]
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self.noises.register_buffer(f"noise_{layer_idx}", torch.randn(*shape))
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for i in range(3, self.log_size + 1):
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out_channel = self.channels[2 ** i]
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self.convs.append(
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StyledConv(
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in_channel,
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out_channel,
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3,
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style_dim,
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upsample=True,
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blur_kernel=blur_kernel,
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)
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)
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self.convs.append(
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StyledConv(
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out_channel, out_channel, 3, style_dim, blur_kernel=blur_kernel
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)
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)
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self.to_rgbs.append(ToRGB(out_channel, style_dim))
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in_channel = out_channel
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self.n_latent = self.log_size * 2 - 2
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def make_noise(self):
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device = self.input.input.device
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noises = [torch.randn(1, 1, 2 ** 2, 2 ** 2, device=device)]
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for i in range(3, self.log_size + 1):
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for _ in range(2):
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noises.append(torch.randn(1, 1, 2 ** i, 2 ** i, device=device))
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return noises
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def mean_latent(self, n_latent):
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latent_in = torch.randn(
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n_latent, self.style_dim, device=self.input.input.device
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)
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latent = self.style(latent_in).mean(0, keepdim=True)
|
|
|
|
return latent
|
|
|
|
def get_latent(self, input):
|
|
return self.style(input)
|
|
|
|
def forward(
|
|
self,
|
|
styles,
|
|
return_latents=False,
|
|
inject_index=None,
|
|
truncation=1,
|
|
truncation_latent=None,
|
|
input_is_latent=False,
|
|
noise=None,
|
|
randomize_noise=True,
|
|
):
|
|
if not input_is_latent:
|
|
styles = [self.style(s) for s in styles]
|
|
|
|
if noise is None:
|
|
if randomize_noise:
|
|
noise = [None] * self.num_layers
|
|
else:
|
|
noise = [
|
|
getattr(self.noises, f"noise_{i}") for i in range(self.num_layers)
|
|
]
|
|
|
|
if truncation < 1:
|
|
style_t = []
|
|
|
|
for style in styles:
|
|
style_t.append(
|
|
truncation_latent + truncation * (style - truncation_latent)
|
|
)
|
|
|
|
styles = style_t
|
|
|
|
if len(styles) < 2:
|
|
inject_index = self.n_latent
|
|
|
|
if styles[0].ndim < 3:
|
|
latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
|
|
|
|
else:
|
|
latent = styles[0]
|
|
|
|
else:
|
|
if inject_index is None:
|
|
inject_index = random.randint(1, self.n_latent - 1)
|
|
|
|
latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
|
|
latent2 = styles[1].unsqueeze(1).repeat(1, self.n_latent - inject_index, 1)
|
|
|
|
latent = torch.cat([latent, latent2], 1)
|
|
|
|
out = self.input(latent)
|
|
out = self.conv1(out, latent[:, 0], noise=noise[0])
|
|
|
|
skip = self.to_rgb1(out, latent[:, 1])
|
|
|
|
i = 1
|
|
for conv1, conv2, noise1, noise2, to_rgb in zip(
|
|
self.convs[::2], self.convs[1::2], noise[1::2], noise[2::2], self.to_rgbs
|
|
):
|
|
out = conv1(out, latent[:, i], noise=noise1)
|
|
out = conv2(out, latent[:, i + 1], noise=noise2)
|
|
skip = to_rgb(out, latent[:, i + 2], skip)
|
|
|
|
i += 2
|
|
|
|
image = skip
|
|
|
|
if return_latents:
|
|
return image, latent
|
|
|
|
else:
|
|
return image, None
|
|
|
|
|
|
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:
|
|
layers.append(FusedLeakyReLU(out_channel, bias=bias))
|
|
|
|
super().__init__(*layers)
|
|
|
|
|
|
class ResBlock(nn.Module):
|
|
def __init__(self, in_channel, out_channel, blur_kernel=[1, 3, 3, 1]):
|
|
super().__init__()
|
|
|
|
self.conv1 = ConvLayer(in_channel, in_channel, 3)
|
|
self.conv2 = ConvLayer(in_channel, out_channel, 3, downsample=True)
|
|
|
|
self.skip = ConvLayer(
|
|
in_channel, out_channel, 1, downsample=True, activate=False, bias=False
|
|
)
|
|
|
|
def forward(self, input):
|
|
out = self.conv1(input)
|
|
out = self.conv2(out)
|
|
|
|
skip = self.skip(input)
|
|
out = (out + skip) / math.sqrt(2)
|
|
|
|
return out
|
|
|
|
|
|
class Discriminator(nn.Module):
|
|
def __init__(self, size, channel_multiplier=2, blur_kernel=[1, 3, 3, 1]):
|
|
super().__init__()
|
|
|
|
channels = {
|
|
4: 512,
|
|
8: 512,
|
|
16: 512,
|
|
32: 512,
|
|
64: 256 * channel_multiplier,
|
|
128: 128 * channel_multiplier,
|
|
256: 64 * channel_multiplier,
|
|
512: 32 * channel_multiplier,
|
|
1024: 16 * channel_multiplier,
|
|
}
|
|
|
|
convs = [ConvLayer(3, channels[size], 1)]
|
|
|
|
log_size = int(math.log(size, 2))
|
|
|
|
in_channel = channels[size]
|
|
|
|
for i in range(log_size, 2, -1):
|
|
out_channel = channels[2 ** (i - 1)]
|
|
|
|
convs.append(ResBlock(in_channel, out_channel, blur_kernel))
|
|
|
|
in_channel = out_channel
|
|
|
|
self.convs = nn.Sequential(*convs)
|
|
|
|
self.stddev_group = 4
|
|
self.stddev_feat = 1
|
|
|
|
self.final_conv = ConvLayer(in_channel + 1, channels[4], 3)
|
|
self.final_linear = nn.Sequential(
|
|
EqualLinear(channels[4] * 4 * 4, channels[4], activation="fused_lrelu"),
|
|
EqualLinear(channels[4], 1),
|
|
)
|
|
|
|
def forward(self, input):
|
|
out = self.convs(input)
|
|
|
|
batch, channel, height, width = out.shape
|
|
group = min(batch, self.stddev_group)
|
|
stddev = out.view(
|
|
group, -1, self.stddev_feat, channel // self.stddev_feat, height, width
|
|
)
|
|
stddev = torch.sqrt(stddev.var(0, unbiased=False) + 1e-8)
|
|
stddev = stddev.mean([2, 3, 4], keepdims=True).squeeze(2)
|
|
stddev = stddev.repeat(group, 1, height, width)
|
|
out = torch.cat([out, stddev], 1)
|
|
|
|
out = self.final_conv(out)
|
|
|
|
out = out.view(batch, -1)
|
|
out = self.final_linear(out)
|
|
|
|
return out
|