diff --git a/map2map/data/fields.py b/map2map/data/fields.py
index 4ef0c9e..65a9867 100644
--- a/map2map/data/fields.py
+++ b/map2map/data/fields.py
@@ -56,6 +56,10 @@ class FieldDataset(Dataset):
 
         tgt_file_lists = [sorted(glob(p)) for p in tgt_patterns]
         self.tgt_files = list(zip(* tgt_file_lists))
+        
+        # self.style_files = self.style_files[:1]
+        # self.in_files = self.in_files[:1]
+        # self.tgt_files = self.tgt_files[:1]
 
         if len(self.style_files) != len(self.in_files) != len(self.tgt_files):
             raise ValueError('number of style, input, and target files do not match')
@@ -64,7 +68,7 @@ class FieldDataset(Dataset):
         if self.nfile == 0:
             raise FileNotFoundError('file not found for {}'.format(in_patterns))
 
-        self.style_size = np.loadtxt(self.style_files[0]).shape[0]
+        self.style_size = np.loadtxt(self.style_files[0], ndmin=1).shape[0]
         self.in_chan = [np.load(f, mmap_mode='r').shape[0]
                         for f in self.in_files[0]]
         self.tgt_chan = [np.load(f, mmap_mode='r').shape[0]
@@ -155,7 +159,7 @@ class FieldDataset(Dataset):
     def __getitem__(self, idx):
         ifile, icrop = divmod(idx, self.ncrop)
 
-        style = np.loadtxt(self.style_files[ifile])
+        style = np.loadtxt(self.style_files[ifile], ndmin=1)
         in_fields = [np.load(f) for f in self.in_files[ifile]]
         tgt_fields = [np.load(f) for f in self.tgt_files[ifile]]
 
@@ -188,13 +192,16 @@ class FieldDataset(Dataset):
         tgt_fields = [torch.from_numpy(f).to(torch.float32) for f in tgt_fields]
 
         if self.in_norms is not None:
-            for norm, x in zip(self.in_norms, in_fields):
+            in_norms = np.atleast_1d(self.in_norms[ifile])
+            for norm, x in zip(in_norms, in_fields):
                 norm = import_attr(norm, norms, callback_at=self.callback_at)
                 norm(x, **self.kwargs)
         if self.tgt_norms is not None:
-            for norm, x in zip(self.tgt_norms, tgt_fields):
-                norm = import_attr(norm, norms, callback_at=self.callback_at)
-                norm(x, **self.kwargs)
+            tgt_norms = np.atleast_1d(self.tgt_norms[ifile])
+            for norm, x in zip(tgt_norms, tgt_fields):
+                # norm = import_attr(norm, norms, callback_at=self.callback_at)
+                # norm(x, **self.kwargs)
+                x /= norm
 
         if self.augment:
             flip_axes = flip(in_fields, None, self.ndim)
diff --git a/map2map/models/style.py b/map2map/models/style.py
index 2c1c80c..24a8349 100644
--- a/map2map/models/style.py
+++ b/map2map/models/style.py
@@ -95,7 +95,6 @@ class ConvStyled3d(nn.Module):
         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))
@@ -156,7 +155,7 @@ class ConvStyled3d(nn.Module):
 
         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)
+        x = self.conv(x, w, bias=self.bias.repeat(N), stride=self.stride, groups=N)
         _, _, *DHWout = x.shape
         x = x.reshape(N, Cout, *DHWout)
 
diff --git a/map2map/train.py b/map2map/train.py
index d9690d8..db80b16 100644
--- a/map2map/train.py
+++ b/map2map/train.py
@@ -292,6 +292,7 @@ def train(epoch, loader, model, criterion,
         logger.add_scalar('loss/epoch/train', epoch_loss[0],
                           global_step=epoch+1)
 
+        skip_chan = 0
         fig = plt_slices(
             input[-1], output[-1, skip_chan:], target[-1, skip_chan:],
             output[-1, skip_chan:] - target[-1, skip_chan:],
@@ -351,6 +352,7 @@ def validate(epoch, loader, model, criterion, logger, device, args):
     if rank == 0:
         logger.add_scalar('loss/epoch/val', epoch_loss[0],
                           global_step=epoch+1)
+        skip_chan = 0
 
         fig = plt_slices(
             input[-1], output[-1, skip_chan:], target[-1, skip_chan:],
diff --git a/map2map/utils/figures.py b/map2map/utils/figures.py
index 316bbf6..c6ae4a8 100644
--- a/map2map/utils/figures.py
+++ b/map2map/utils/figures.py
@@ -146,7 +146,7 @@ def plt_power(*fields, dis=None, label=None, **kwargs):
 
         ks, Ps = [], []
         for field in fields:
-            k, P, _ = power(field)
+            k, P, _ = power.power(field)
             ks.append(k)
             Ps.append(P)