diff --git a/map2map/data/fields.py b/map2map/data/fields.py
index 08cde15..10e05b6 100644
--- a/map2map/data/fields.py
+++ b/map2map/data/fields.py
@@ -172,9 +172,10 @@ def crop(fields, start, crop, pad, scale_factor=1):
             x = F.interpolate(x, scale_factor=scale_factor, mode='trilinear')
             x = x.numpy().squeeze(0)
 
-            # remove buffer
+            # remove buffer and excess padding
             for d, (N, (p0, p1)) in enumerate(zip(crop, pad)):
-                begin, end = scale_factor, N + p0 + p1 - scale_factor
+                begin = scale_factor + (scale_factor - 1) * p0
+                end =  scale_factor * (N + p0 + 1) + p1
                 x = x.take(range(begin, end), axis=1 + d)
 
         new_fields.append(x)
diff --git a/map2map/train.py b/map2map/train.py
index 2775911..eb98e7d 100644
--- a/map2map/train.py
+++ b/map2map/train.py
@@ -140,6 +140,7 @@ def gpu_worker(local_rank, args):
     if args.load_state:
         state = torch.load(args.load_state, map_location=args.device)
         args.start_epoch = state['epoch']
+        args.adv_delay += args.start_epoch
         model.module.load_state_dict(state['model'])
         optimizer.load_state_dict(state['optimizer'])
         scheduler.load_state_dict(state['scheduler'])
@@ -150,6 +151,8 @@ def gpu_worker(local_rank, args):
         torch.set_rng_state(state['rng'].cpu())  # move rng state back
         if args.rank == 0:
             min_loss = state['min_loss']
+            if 'adv_model' not in state and args.adv:
+                min_loss = None  # restarting with adversary wipes the record
             print('checkpoint at epoch {} loaded from {}'.format(
                 state['epoch'], args.load_state))
         del state