diff --git a/enhance.py b/enhance.py
index 2662ebb..882fa68 100755
--- a/enhance.py
+++ b/enhance.py
@@ -14,7 +14,7 @@
 # without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
 #
 
-__version__ = '0.1'
+__version__ = '0.2'
 
 import io
 import os
@@ -159,10 +159,10 @@ class DataLoader(threading.Thread):
         filename = os.path.join(self.cwd, f)
         try:
             orig = PIL.Image.open(filename).convert('RGB')
-            if all(s > args.batch_shape * 2 for s in orig.size): 
-                orig = orig.resize((orig.size[0]//2, orig.size[1]//2), resample=PIL.Image.LANCZOS)
-            if any(s < args.batch_shape * 2 for s in orig.size):
-                raise ValueError('Image is too small for training with size {}'.format(img.shape))
+            # if all(s > args.batch_shape * 2 for s in orig.size): 
+            #     orig = orig.resize((orig.size[0]//2, orig.size[1]//2), resample=PIL.Image.LANCZOS)
+            if any(s < args.batch_shape for s in orig.size):
+                raise ValueError('Image is too small for training with size {}'.format(orig.size))
         except Exception as e:
             warn('Could not load `{}` as image.'.format(filename),
                  '  - Try fixing or removing the file before next run.')
@@ -468,9 +468,9 @@ class NeuralEnhancer(object):
     def show_progress(self, orign, scald, repro):
         os.makedirs('valid', exist_ok=True)
         for i in range(args.batch_size):
-            self.imsave('valid/%03i_origin.png' % i, orign[i])
-            self.imsave('valid/%03i_pixels.png' % i, scald[i])
-            self.imsave('valid/%03i_reprod.png' % i, repro[i])
+            self.imsave('valid/%s_%03i_origin.png' % (args.model, i), orign[i])
+            self.imsave('valid/%s_%03i_pixels.png' % (args.model, i), scald[i])
+            self.imsave('valid/%s_%03i_reprod.png' % (args.model, i), repro[i])
 
     def decay_learning_rate(self):
         l_r, t_cur = args.learning_rate, 0
@@ -510,7 +510,7 @@ class NeuralEnhancer(object):
                 stats /= args.epoch_size
                 totals, labels = [sum(total)] + list(total), ['total', 'prcpt', 'smthn', 'advrs']
                 gen_info = ['{}{}{}={:4.2e}'.format(ansi.WHITE_B, k, ansi.ENDC, v) for k, v in zip(labels, totals)]
-                print('\rEpoch #{} at {:4.1f}s, lr={:4.2e}{}'.format(epoch+1, time.time()-start, l_r, ' '*(args.epoch_size-60)))
+                print('\rEpoch #{} at {:4.1f}s, lr={:4.2e}{}'.format(epoch+1, time.time()-start, l_r, ' '*(args.epoch_size-35)))
                 print('  - generator {}'.format(' '.join(gen_info)))
 
                 real, fake = stats[:args.batch_size], stats[args.batch_size:]