diff --git a/pix2pix.py b/pix2pix.py
index d766cb78d..ae36bfe3d 100644
--- a/pix2pix.py
+++ b/pix2pix.py
@@ -39,6 +39,8 @@
 parser.add_argument("--flip", dest="flip", action="store_true", help="flip images horizontally")
 parser.add_argument("--no_flip", dest="flip", action="store_false", help="don't flip images horizontally")
 parser.set_defaults(flip=True)
+parser.add_argument("--png16bits", dest="png16bits", action="store_true", help="use png 16 bits images encoder and decoders")
+parser.set_defaults(png16bits=False)
 parser.add_argument("--lr", type=float, default=0.0002, help="initial learning rate for adam")
 parser.add_argument("--beta1", type=float, default=0.5, help="momentum term of adam")
 parser.add_argument("--l1_weight", type=float, default=100.0, help="weight on L1 term for generator gradient")
@@ -258,7 +260,10 @@ def get_name(path):
         path_queue = tf.train.string_input_producer(input_paths, shuffle=a.mode == "train")
         reader = tf.WholeFileReader()
         paths, contents = reader.read(path_queue)
-        raw_input = decode(contents)
+        if a.png16bits:
+            raw_input = decode(contents,dtype=tf.uint16)
+        else:
+            raw_input = decode(contents)
         raw_input = tf.image.convert_image_dtype(raw_input, dtype=tf.float32)
 
         assertion = tf.assert_equal(tf.shape(raw_input)[2], 3, message="image does not have 3 channels")
@@ -586,7 +591,11 @@ def main():
         with tf.variable_scope("generator"):
             batch_output = deprocess(create_generator(preprocess(batch_input), 3))
 
-        output_image = tf.image.convert_image_dtype(batch_output, dtype=tf.uint8)[0]
+        if a.png16bits:
+            output_image = tf.image.convert_image_dtype(batch_output, dtype=tf.uint16, saturate=True)[0]
+        else:
+            output_image = tf.image.convert_image_dtype(batch_output, dtype=tf.uint8)[0]
+
         if a.output_filetype == "png":
             output_data = tf.image.encode_png(output_image)
         elif a.output_filetype == "jpeg":
@@ -656,7 +665,10 @@ def convert(image):
             size = [CROP_SIZE, int(round(CROP_SIZE * a.aspect_ratio))]
             image = tf.image.resize_images(image, size=size, method=tf.image.ResizeMethod.BICUBIC)
 
-        return tf.image.convert_image_dtype(image, dtype=tf.uint8, saturate=True)
+        if a.png16bits:
+            return tf.image.convert_image_dtype(image, dtype=tf.uint16, saturate=True)
+        else:
+            return tf.image.convert_image_dtype(image, dtype=tf.uint8, saturate=True)
 
     # reverse any processing on images so they can be written to disk or displayed to user
     with tf.name_scope("convert_inputs"):
@@ -677,14 +689,25 @@ def convert(image):
         }
 
     # summaries
-    with tf.name_scope("inputs_summary"):
-        tf.summary.image("inputs", converted_inputs)
+    if not a.png16bits:
+        with tf.name_scope("inputs_summary"):
+            tf.summary.image("inputs", converted_inputs)
 
-    with tf.name_scope("targets_summary"):
-        tf.summary.image("targets", converted_targets)
+        with tf.name_scope("targets_summary"):
+            tf.summary.image("targets", converted_targets)
 
-    with tf.name_scope("outputs_summary"):
-        tf.summary.image("outputs", converted_outputs)
+        with tf.name_scope("outputs_summary"):
+            tf.summary.image("outputs", converted_outputs)
+
+    else:
+        with tf.name_scope("inputs_summary"):
+            tf.summary.image("inputs", tf.image.convert_image_dtype(converted_inputs,dtype=tf.uint8))
+
+        with tf.name_scope("targets_summary"):
+            tf.summary.image("targets", tf.image.convert_image_dtype(converted_targets,dtype=tf.uint8))
+
+        with tf.name_scope("outputs_summary"):
+            tf.summary.image("outputs", tf.image.convert_image_dtype(converted_outputs,dtype=tf.uint8))
 
     with tf.name_scope("predict_real_summary"):
         tf.summary.image("predict_real", tf.image.convert_image_dtype(model.predict_real, dtype=tf.uint8))
@@ -692,6 +715,7 @@ def convert(image):
     with tf.name_scope("predict_fake_summary"):
         tf.summary.image("predict_fake", tf.image.convert_image_dtype(model.predict_fake, dtype=tf.uint8))
 
+
     tf.summary.scalar("discriminator_loss", model.discrim_loss)
     tf.summary.scalar("generator_loss_GAN", model.gen_loss_GAN)
     tf.summary.scalar("generator_loss_L1", model.gen_loss_L1)