diff --git a/run_generator.py b/run_generator.py
index 2f154143..31dc0062 100755
--- a/run_generator.py
+++ b/run_generator.py
@@ -11,12 +11,15 @@
 import dnnlib.tflib as tflib
 import re
 import sys
+import time
+from tqdm import tqdm
 
 import pretrained_networks
 
 #----------------------------------------------------------------------------
 
-def generate_images(network_pkl, seeds, truncation_psi):
+def generate_images(network_pkl, seeds, truncation_psi, minibatch_size=8):
+    mb = minibatch_size
     print('Loading networks from "%s"...' % network_pkl)
     _G, _D, Gs = pretrained_networks.load_networks(network_pkl)
     noise_vars = [var for name, var in Gs.components.synthesis.vars.items() if name.startswith('noise')]
@@ -24,16 +27,28 @@ def generate_images(network_pkl, seeds, truncation_psi):
     Gs_kwargs = dnnlib.EasyDict()
     Gs_kwargs.output_transform = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True)
     Gs_kwargs.randomize_noise = False
+    Gs_kwargs.minibatch_size = mb
     if truncation_psi is not None:
         Gs_kwargs.truncation_psi = truncation_psi
-
-    for seed_idx, seed in enumerate(seeds):
-        print('Generating image for seed %d (%d/%d) ...' % (seed, seed_idx, len(seeds)))
-        rnd = np.random.RandomState(seed)
-        z = rnd.randn(1, *Gs.input_shape[1:]) # [minibatch, component]
+    rnd = np.random.RandomState(seeds[0])
+    z = rnd.randn(len(seeds), *Gs.input_shape[1:]) # [minibatch, component]
+
+    tstart = time.time()
+    for i in tqdm(range(len(seeds)//mb + 1)):
+        zs = z[i*mb:min((i+1)*mb, len(seeds))]
+        print("\n")
+        print('Generating image for seeds %d to %d (%d/%d) ...' % (seeds[0]+i*mb, min(seeds[0]+(i+1) * mb, seeds[0] + len(seeds)), i, len(seeds)//mb + 1))
+        t0 = time.time()
         tflib.set_vars({var: rnd.randn(*var.shape.as_list()) for var in noise_vars}) # [height, width]
-        images = Gs.run(z, None, **Gs_kwargs) # [minibatch, height, width, channel]
-        PIL.Image.fromarray(images[0], 'RGB').save(dnnlib.make_run_dir_path('seed%04d.png' % seed))
+        images = Gs.run(zs, None, **Gs_kwargs) # [minibatch, height, width, channel]
+        for j in range(len(images)):
+            seed = seeds[0]+i*mb+j
+            PIL.Image.fromarray(images[j], 'RGB').save(dnnlib.make_run_dir_path('seed%05d.png' % seed))
+        elapsed = time.time() - t0
+        print('{:.2f} images per second (in {:.3f}sec)'.format(len(images) / elapsed, elapsed))
+
+    elapsed = time.time() - tstart
+    print('{:.2f} images per second (in {:.3f}sec)'.format(len(seeds) / elapsed, elapsed))
 
 #----------------------------------------------------------------------------
 
@@ -93,7 +108,7 @@ def _parse_num_range(s):
     range_re = re.compile(r'^(\d+)-(\d+)$')
     m = range_re.match(s)
     if m:
-        return range(int(m.group(1)), int(m.group(2))+1)
+        return list(range(int(m.group(1)), int(m.group(2))+1))
     vals = s.split(',')
     return [int(x) for x in vals]
 
@@ -131,6 +146,7 @@ def main():
     parser_generate_images.add_argument('--network', help='Network pickle filename', dest='network_pkl', required=True)
     parser_generate_images.add_argument('--seeds', type=_parse_num_range, help='List of random seeds', required=True)
     parser_generate_images.add_argument('--truncation-psi', type=float, help='Truncation psi (default: %(default)s)', default=0.5)
+    parser_generate_images.add_argument('--minibatch_size', type=int, help='Minibatch size (default: %(default)s)', default=8)
     parser_generate_images.add_argument('--result-dir', help='Root directory for run results (default: %(default)s)', default='results', metavar='DIR')
 
     parser_style_mixing_example = subparsers.add_parser('style-mixing-example', help='Generate style mixing video')