diff --git a/codes/data/image_folder_dataset.py b/codes/data/image_folder_dataset.py
index fe334eab..7637d864 100644
--- a/codes/data/image_folder_dataset.py
+++ b/codes/data/image_folder_dataset.py
@@ -12,6 +12,7 @@ from data import util
 # Builds a dataset created from a simple folder containing a list of training/test/validation images.
 from data.image_corruptor import ImageCorruptor
 from data.image_label_parser import VsNetImageLabeler
+from utils.util import opt_get
 
 
 class ImageFolderDataset:
@@ -25,8 +26,8 @@ class ImageFolderDataset:
         self.corrupt_before_downsize = opt['corrupt_before_downsize'] if 'corrupt_before_downsize' in opt.keys() else False
         self.fetch_alt_image = opt['fetch_alt_image']  # If specified, this dataset will attempt to find a second image
                                                        # from the same video source. Search for 'fetch_alt_image' for more info.
-        self.skip_lq = opt['skip_lq']
-        self.disable_flip = opt['disable_flip']
+        self.skip_lq = opt_get(opt, ['skip_lq'], False)
+        self.disable_flip = opt_get(opt, ['disable_flip'], False)
         assert (self.target_hq_size // self.scale) % self.multiple == 0  # If we dont throw here, we get some really obscure errors.
         if not isinstance(self.paths, list):
             self.paths = [self.paths]
@@ -119,16 +120,12 @@ class ImageFolderDataset:
 
         hs = self.resize_hq([hq])
         if not self.skip_lq:
-            ls = self.synthesize_lq(hs)
+            for_lq = [hs[0]]
 
         # Convert to torch tensor
         hq = torch.from_numpy(np.ascontiguousarray(np.transpose(hs[0], (2, 0, 1)))).float()
-        if not self.skip_lq:
-            lq = torch.from_numpy(np.ascontiguousarray(np.transpose(ls[0], (2, 0, 1)))).float()
 
         out_dict = {'hq': hq, 'LQ_path': self.image_paths[item], 'HQ_path': self.image_paths[item]}
-        if not self.skip_lq:
-            out_dict['lq'] = lq
 
         if self.fetch_alt_image:
             # This works by assuming a specific filename structure as would produced by ffmpeg. ex:
@@ -151,22 +148,28 @@ class ImageFolderDataset:
                     # the file rather than searching the path list. Let the exception handler below do its work.
                     next_img = self.image_paths[item].replace(str(imnumber), str(imnumber+1))
                     alt_hq = util.read_img(None, next_img, rgb=True)
-                    alt_hq = self.resize_hq([alt_hq])
-                    alt_hq = torch.from_numpy(np.ascontiguousarray(np.transpose(alt_hq[0], (2, 0, 1)))).float()
+                    alt_hs = self.resize_hq([alt_hq])
+                    alt_hq = torch.from_numpy(np.ascontiguousarray(np.transpose(alt_hs[0], (2, 0, 1)))).float()
                     if not self.skip_lq:
-                        alt_lq = self.synthesize_lq(alt_hq)
-                        alt_lq = torch.from_numpy(np.ascontiguousarray(np.transpose(alt_lq[0], (2, 0, 1)))).float()
+                        for_lq.append(alt_hs[0])
                 except:
                     alt_hq = hq
                     if not self.skip_lq:
-                        alt_lq = lq
+                        for_lq.append(hs[0])
             else:
                 alt_hq = hq
                 if not self.skip_lq:
-                    alt_lq = lq
+                    for_lq.append(hs[0])
             out_dict['alt_hq'] = alt_hq
-            if not self.skip_lq:
-                out_dict['alt_lq'] = alt_lq
+
+        if not self.skip_lq:
+            lqs = self.synthesize_lq(for_lq)
+            ls = lqs[0]
+            out_dict['lq'] = torch.from_numpy(np.ascontiguousarray(np.transpose(ls, (2, 0, 1)))).float()
+            if len(lqs) > 1:
+                alt_lq = lqs[1]
+                out_dict['alt_lq'] = torch.from_numpy(np.ascontiguousarray(np.transpose(alt_lq, (2, 0, 1)))).float()
+
 
         if self.labeler:
             base_file = self.image_paths[item].replace(self.paths[0], "")
@@ -190,7 +193,7 @@ if __name__ == '__main__':
         'fixed_corruptions': ['jpeg-broad', 'gaussian_blur'],
         'random_corruptions': ['noise-5', 'none'],
         'num_corrupts_per_image': 1,
-        'corrupt_before_downsize': True,
+        'corrupt_before_downsize': False,
         'fetch_alt_image': True,
         #'labeler': {
         #    'type': 'patch_labels',
@@ -203,11 +206,11 @@ if __name__ == '__main__':
     os.makedirs("debug", exist_ok=True)
     for i in range(0, len(ds)):
         o = ds[random.randint(0, len(ds)-1)]
-        hq = o['hq']
+        hq = o['lq']
         #masked = (o['labels_mask'] * .5 + .5) * hq
         import torchvision
-        torchvision.utils.save_image(hq.unsqueeze(0), "debug/%i_hq.png" % (i,))
-        torchvision.utils.save_image(o['alt_hq'].unsqueeze(0), "debug/%i_hq_alt.png" % (i,))
+        torchvision.utils.save_image(hq.unsqueeze(0), "debug/%i_lq.png" % (i,))
+        torchvision.utils.save_image(o['alt_lq'].unsqueeze(0), "debug/%i_lq_alt.png" % (i,))
         #if len(o['labels'].unique()) > 1:
         #    randlbl = np.random.choice(o['labels'].unique()[1:])
         #    moremask = hq * ((1*(o['labels'] == randlbl))*.5+.5)