diff --git a/enhance.py b/enhance.py
index 4f23967..ecd8afc 100755
--- a/enhance.py
+++ b/enhance.py
@@ -368,23 +368,26 @@ class Model(object):
             name = list(self.network.keys())[list(self.network.values()).index(l)]
             yield (name, l)
 
+    def get_filename(self):
+        filename = 'ne%ix-%s-%s-%s.pkl.bz2' % (args.zoom, args.type, args.model, __version__)
+        return os.path.join(os.path.dirname(__file__), filename)
+
     def save_generator(self):
         def cast(p): return p.get_value().astype(np.float16)
         params = {k: [cast(p) for p in l.get_params()] for (k, l) in self.list_generator_layers()}
         config = {k: getattr(args, k) for k in ['generator_blocks', 'generator_residual', 'generator_filters'] + \
                                                ['generator_upscale', 'generator_downscale']}
-        filename = 'ne%ix-%s-%s-%s.pkl.bz2' % (args.zoom, args.type, args.model, __version__)
-        pickle.dump((config, params), bz2.open(filename, 'wb'))
-        print('  - Saved model as `{}` after training.'.format(filename))
+        
+        pickle.dump((config, params), bz2.open(self.get_filename(), 'wb'))
+        print('  - Saved model as `{}` after training.'.format(self.get_filename()))
 
     def load_model(self):
-        filename = 'ne%ix-%s-%s-%s.pkl.bz2' % (args.zoom, args.type, args.model, __version__)
-        if not os.path.exists(filename):
+        if not os.path.exists(self.get_filename()):
             if args.train: return {}, {}
             error("Model file with pre-trained convolution layers not found. Download it here...",
-                  "https://github.com/alexjc/neural-enhance/releases/download/v%s/%s"%(__version__, filename))
-        print('  - Loaded file `{}` with trained model.'.format(filename))
-        return pickle.load(bz2.open(filename, 'rb'))
+                  "https://github.com/alexjc/neural-enhance/releases/download/v%s/%s"%(__version__, self.get_filename()))
+        print('  - Loaded file `{}` with trained model.'.format(self.get_filename()))
+        return pickle.load(bz2.open(self.get_filename(), 'rb'))
 
     def load_generator(self, params):
         if len(params) == 0: return