diff --git a/codes/data/__init__.py b/codes/data/__init__.py
index f3fff285..8bb498dd 100644
--- a/codes/data/__init__.py
+++ b/codes/data/__init__.py
@@ -38,6 +38,8 @@ def create_dataset(dataset_opt):
         from data.Downsample_dataset import DownsampleDataset as D
     elif mode == 'fullimage':
         from data.full_image_dataset import FullImageDataset as D
+    elif mode == 'single_image_extensible':
+        from data.single_image_dataset import SingleImageDataset as D
     elif mode == 'combined':
         from data.combined_dataset import CombinedDataset as D
     else:
diff --git a/codes/data/chunk_with_reference.py b/codes/data/chunk_with_reference.py
index 40f83302..8704b327 100644
--- a/codes/data/chunk_with_reference.py
+++ b/codes/data/chunk_with_reference.py
@@ -6,13 +6,16 @@ import numpy as np
 # Iterable that reads all the images in a directory that contains a reference image, tile images and center coordinates.
 class ChunkWithReference:
     def __init__(self, opt, path):
-        self.opt = opt
+        self.reload(opt)
         self.path = path.path
-        self.ref = None  # This is loaded on the fly.
-        self.cache_ref = opt['cache_ref'] if 'cache_ref' in opt.keys() else True
         self.tiles, _ = util.get_image_paths('img', path)
         self.centers = None
 
+    def reload(self, opt):
+        self.opt = opt
+        self.ref = None  # This is loaded on the fly.
+        self.cache_ref = opt['cache_ref'] if 'cache_ref' in opt.keys() else False
+
     def __getitem__(self, item):
         # Load centers on the fly and always cache.
         if self.centers is None:
@@ -20,10 +23,9 @@ class ChunkWithReference:
         if self.cache_ref:
             if self.ref is None:
                 self.ref = util.read_img(None, osp.join(self.path, "ref.jpg"), rgb=True)
-                self.centers = torch.load(osp.join(self.path, "centers.pt"))
             ref = self.ref
         else:
-            self.ref = util.read_img(None, osp.join(self.path, "ref.jpg"), rgb=True)
+            ref = util.read_img(None, osp.join(self.path, "ref.jpg"), rgb=True)
         tile = util.read_img(None, self.tiles[item], rgb=True)
         tile_id = int(osp.splitext(osp.basename(self.tiles[item]))[0])
         center, tile_width = self.centers[tile_id]
diff --git a/codes/data/image_corruptor.py b/codes/data/image_corruptor.py
index 196114f1..935d85dc 100644
--- a/codes/data/image_corruptor.py
+++ b/codes/data/image_corruptor.py
@@ -10,10 +10,16 @@ from io import BytesIO
 class ImageCorruptor:
     def __init__(self, opt):
         self.num_corrupts = opt['num_corrupts_per_image'] if 'num_corrupts_per_image' in opt.keys() else 2
+        if self.num_corrupts == 0:
+            return
         self.fixed_corruptions = opt['fixed_corruptions']
         self.random_corruptions = opt['random_corruptions']
+        self.blur_scale = opt['corruption_blur_scale'] if 'corruption_blur_scale' in opt.keys() else 1
 
     def corrupt_images(self, imgs):
+        if self.num_corrupts == 0:
+            return imgs
+
         augmentations = random.choices(self.random_corruptions, k=self.num_corrupts)
         # Source of entropy, which should be used across all images.
         rand_int_f = random.randint(1, 999999)
@@ -38,11 +44,11 @@ class ImageCorruptor:
             img = img / 255
         elif 'gaussian_blur' in aug:
             # Gaussian Blur
-            kernel = 2 * (rand_int % 3) + 1
+            kernel = 2 * self.blur_scale * (rand_int % 3) + 1
             img = cv2.GaussianBlur(img, (kernel, kernel), 3)
         elif 'motion_blur' in aug:
             # Motion blur
-            intensity = 2 * (rand_int % 3) + 1
+            intensity = 2 * self.blur_scale * (rand_int % 3) + 1
             angle = (rand_int // 3) % 360
             k = np.zeros((intensity, intensity), dtype=np.float32)
             k[(intensity - 1) // 2, :] = np.ones(intensity, dtype=np.float32)
@@ -52,7 +58,7 @@ class ImageCorruptor:
             img = cv2.filter2D(img, -1, k)
         elif 'smooth_blur' in aug:
             # Smooth blur
-            kernel = 2 * (rand_int % 3) + 1
+            kernel = 2 * self.blur_scale * (rand_int % 3) + 1
             img = cv2.blur(img, ksize=(kernel, kernel))
         elif 'block_noise' in aug:
             # Large distortion blocks in part of an img, such as is used to mask out a face.
diff --git a/codes/data/single_image_dataset.py b/codes/data/single_image_dataset.py
index 6354d3a5..cdea7f4a 100644
--- a/codes/data/single_image_dataset.py
+++ b/codes/data/single_image_dataset.py
@@ -14,6 +14,7 @@ import torchvision.transforms.functional as F
 class SingleImageDataset(data.Dataset):
 
     def __init__(self, opt):
+        self.opt = opt
         self.corruptor = ImageCorruptor(opt)
         self.target_hq_size = opt['target_size'] if 'target_size' in opt.keys() else None
         self.multiple = opt['force_multiple'] if 'force_multiple' in opt.keys() else 1
@@ -33,6 +34,9 @@ class SingleImageDataset(data.Dataset):
             cache_path = os.path.join(path, 'cache.pth')
             if os.path.exists(cache_path):
                 chunks = torch.load(cache_path)
+                # Update the options.
+                for c in chunks:
+                    c.reload(opt)
             else:
                 chunks = [ChunkWithReference(opt, d) for d in os.scandir(path) if d.is_dir()]
                 torch.save(chunks, cache_path)
@@ -101,7 +105,7 @@ class SingleImageDataset(data.Dataset):
         lq_ref = torch.cat([lq_ref, lq_mask], dim=0)
 
         return {'LQ': lq, 'GT': hq, 'gt_fullsize_ref': hq_ref, 'lq_fullsize_ref': lq_ref,
-             'lq_center': lq_center, 'gt_center': hq_center,
+             'lq_center': torch.tensor(lq_center, dtype=torch.long), 'gt_center': torch.tensor(hq_center, dtype=torch.long),
              'LQ_path': path, 'GT_path': path}
 
     def __len__(self):
diff --git a/codes/data_scripts/use_discriminator_as_filter.py b/codes/data_scripts/use_discriminator_as_filter.py
index 6615dcdf..89df1344 100644
--- a/codes/data_scripts/use_discriminator_as_filter.py
+++ b/codes/data_scripts/use_discriminator_as_filter.py
@@ -60,16 +60,20 @@ if __name__ == "__main__":
         util.mkdir(dataset_dir)
 
         tq = tqdm(test_loader)
+        removed = 0
         for data in tq:
             model.feed_data(data, need_GT=True)
             model.test()
             results = model.eval_state['discriminator_out'][0]
+            print(torch.mean(results), torch.max(results), torch.min(results))
             for i in range(results.shape[0]):
-                imname = osp.basename(data['GT_path'][i])
-                if results[i] < 1:
-                    torchvision.utils.save_image(data['GT'][i], osp.join(bin_path, imname))
-                else:
-                    torchvision.utils.save_image(data['GT'][i], osp.join(good_path, imname))
+                if results[i] < .8:
+                    os.remove(data['GT_path'][i])
+                    removed += 1
+                #imname = osp.basename(data['GT_path'][i])
+                #if results[i] > .8:
+                #    torchvision.utils.save_image(data['GT'][i], osp.join(good_path, imname))
+                #else:
+                #    torchvision.utils.save_image(data['GT'][i], osp.join(bin_path, imname))
 
-        # log
-        logger.info('# Validation # Fea: {:.4e}'.format(fea_loss / len(test_loader)))
\ No newline at end of file
+        print("Removed %i/%i images" % (removed, len(test_set)))
\ No newline at end of file
diff --git a/codes/options/options.py b/codes/options/options.py
index 28afe856..297426f9 100644
--- a/codes/options/options.py
+++ b/codes/options/options.py
@@ -43,9 +43,12 @@ def parse(opt_path, is_train=True):
                 dataset['mode'] = dataset['mode'].replace('_mc', '')
 
     # path
-    for key, path in opt['path'].items():
-        if path and key in opt['path'] and key != 'strict_load':
-            opt['path'][key] = osp.expanduser(path)
+    if 'path' in opt.keys():
+        for key, path in opt['path'].items():
+            if path and key in opt['path'] and key != 'strict_load':
+                opt['path'][key] = osp.expanduser(path)
+    else:
+        opt['path'] = {}
     opt['path']['root'] = osp.abspath(osp.join(__file__, osp.pardir, osp.pardir, osp.pardir))
     if is_train:
         experiments_root = osp.join(opt['path']['root'], 'experiments', opt['name'])
diff --git a/codes/test.py b/codes/test.py
index bf1061ef..f025290b 100644
--- a/codes/test.py
+++ b/codes/test.py
@@ -86,7 +86,6 @@ def forward_pass(model, output_dir, alteration_suffix=''):
 if __name__ == "__main__":
     #### options
     torch.backends.cudnn.benchmark = True
-    want_just_images = True
     srg_analyze = False
     parser = argparse.ArgumentParser()
     parser.add_argument('-opt', type=str, help='Path to options YAML file.', default='../options/srgan_compute_feature.yml')
diff --git a/codes/train.py b/codes/train.py
index f18cb224..53c0c4f2 100644
--- a/codes/train.py
+++ b/codes/train.py
@@ -32,9 +32,8 @@ def init_dist(backend='nccl', **kwargs):
 def main():
     #### options
     parser = argparse.ArgumentParser()
-    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_imgset_ssgr1.yml')
-    parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none',
-                        help='job launcher')
+    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_blur_discriminator.yml')
+    parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
     parser.add_argument('--local_rank', type=int, default=0)
     args = parser.parse_args()
     opt = option.parse(args.opt, is_train=True)
@@ -83,7 +82,7 @@ def main():
         if resume_state is None:
             util.mkdir_and_rename(
                 opt['path']['experiments_root'])  # rename experiment folder if exists
-            util.mkdirs((path for key, path in opt['path'].items() if not key == 'experiments_root'
+            util.mkdirs((path for key, path in opt['path'].items() if not key == 'experiments_root' and path is not None
                          and 'pretrain_model' not in key and 'resume' not in key))
 
         # config loggers. Before it, the log will not work