diff --git a/map2map/models/style.py b/map2map/models/style.py
new file mode 100644
index 0000000..2bdaf74
--- /dev/null
+++ b/map2map/models/style.py
@@ -0,0 +1,163 @@
+import math
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class PixelNorm(nn.Module):
+    """Pixelwise normalization after conv layers.
+
+    See ProGAN, StyleGAN.
+    """
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, x, eps=1e-8):
+        return x * torch.rsqrt(x.pow(2).mean(dim=1, keepdim=True) + eps)
+
+
+class LinearElr(nn.Module):
+    """Linear layer with equalized learning rate.
+
+    See ProGAN, StyleGAN, and 1706.05350
+
+    Useful at all if not for regularization(1706.05350)?
+    """
+    def __init__(self, in_size, out_size, bias=True, act=None):
+        super().__init__()
+
+        self.weight = nn.Parameter(torch.randn(out_size, in_size))
+        self.wnorm = 1 / math.sqrt(in_size)
+
+        if bias:
+            self.bias = nn.Parameter(torch.zeros(out_size))
+        else:
+            self.register_parameter('bias', None)
+
+        self.act = act
+
+    def forward(self, x):
+        x = F.linear(x, self.weight * self.wnorm, bias=self.bias)
+
+        if self.act:
+            x = F.leaky_relu(x, negative_slope=0.2)
+
+        return x
+
+
+class ConvElr3d(nn.Module):
+    """Conv3d layer with equalized learning rate.
+
+    See ProGAN, StyleGAN, and 1706.05350
+
+    Useful at all if not for regularization(1706.05350)?
+    """
+    def __init__(self, in_chan, out_chan, kernel_size,
+                 stride=1, padding=0, bias=True):
+        super().__init__()
+
+        self.weight = nn.Parameter(
+            torch.randn(out_chan, in_chan, *(kernel_size,) * 3),
+        )
+        fan_in = in_chan * kernel_size ** 3
+        self.wnorm = 1 / math.sqrt(fan_in)
+
+        if bias:
+            self.bias = nn.Parameter(torch.zeros(out_chan))
+        else:
+            self.register_parameter('bias', None)
+
+        self.stride = stride
+        self.padding = padding
+
+    def forward(self, x):
+        x = F.conv2d(
+            x,
+            self.weight * self.wnorm,
+            bias=self.bias,
+            stride=self.stride,
+            padding=self.padding,
+        )
+
+        return x
+
+
+class ConvMod3d(nn.Module):
+    """Convolution layer with modulation and demodulation, from StyleGAN2.
+
+    Weight and bias initialization from `torch.nn._ConvNd.reset_parameters()`.
+    """
+    def __init__(self, style_size, in_chan, out_chan, kernel_size=3, stride=1,
+                 bias=True, resample=None):
+        super().__init__()
+
+        self.style_weight = nn.Parameter(torch.empty(in_chan, style_size))
+        nn.init.kaiming_uniform_(self.style_weight, a=math.sqrt(5),
+                                 mode='fan_in', nonlinearity='leaky_relu')
+        self.style_bias = nn.Parameter(torch.ones(in_chan))  # NOTE: init to 1
+
+        if resample is None:
+            K3 = (kernel_size,) * 3
+            self.weight = nn.Parameter(torch.empty(out_chan, in_chan, *K3))
+            self.stride = stride
+            self.conv = F.conv3d
+        elif resample == 'U':
+            K3 = (2,) * 3
+            # NOTE not clear to me why convtranspose have channels swapped
+            self.weight = nn.Parameter(torch.empty(in_chan, out_chan, *K3))
+            self.stride = 2
+            self.conv = F.conv_transpose3d
+        elif resample == 'D':
+            K3 = (2,) * 3
+            self.weight = nn.Parameter(torch.empty(out_chan, in_chan, *K3))
+            self.stride = 2
+            self.conv = F.conv3d
+        else:
+            raise ValueError('resample type {} not supported'.format(resample))
+        self.resample = resample
+
+        nn.init.kaiming_uniform_(
+            self.weight, a=math.sqrt(5),
+            mode='fan_in',  # effectively 'fan_out' for 'D'
+            nonlinearity='leaky_relu',
+        )
+
+        if bias:
+            self.bias = nn.Parameter(torch.zeros(out_chan))
+            fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight)
+            bound = 1 / math.sqrt(fan_in)
+            nn.init.uniform_(self.bias, -bound, bound)
+        else:
+            self.register_parameter('bias', None)
+
+    def forward(self, x, s, eps=1e-8):
+        N, Cin, *DHWin = x.shape
+        C0, C1, *K3 = self.weight.shape
+        if self.resample == 'U':
+            Cin, Cout = C0, C1
+        else:
+            Cout, Cin = C0, C1
+
+        s = F.linear(s, self.style_weight, bias=self.style_bias)
+
+        # modulation
+        if self.resample == 'U':
+            s = s.reshape(N, Cin, 1, 1, 1, 1)
+        else:
+            s = s.reshape(N, 1, Cin, 1, 1, 1)
+        w = self.weight * s
+
+        # demodulation
+        if self.resample == 'U':
+            fan_in_dim = (1, 3, 4, 5)
+        else:
+            fan_in_dim = (2, 3, 4, 5)
+        w = w * torch.rsqrt(w.pow(2).sum(dim=fan_in_dim, keepdim=True) + eps)
+
+        w = w.reshape(N * C0, C1, *K3)
+        x = x.reshape(1, N * Cin, *DHWin)
+        x = self.conv(x, w, bias=self.bias, stride=self.stride, groups=N)
+        _, _, *DHWout = x.shape
+        x = x.reshape(N, Cout, *DHWout)
+
+        return x