diff --git a/models/Model_SAEHD/Model.py b/models/Model_SAEHD/Model.py
index 820bba0..4dcecd9 100644
--- a/models/Model_SAEHD/Model.py
+++ b/models/Model_SAEHD/Model.py
@@ -30,7 +30,7 @@ class SAEHDModel(ModelBase):
         min_res = 64
         max_res = 640
     
-        default_usefp16            = self.options['use_fp16']           = self.load_or_def_option('use_fp16', False)
+        #default_usefp16            = self.options['use_fp16']           = self.load_or_def_option('use_fp16', False)
         default_resolution         = self.options['resolution']         = self.load_or_def_option('resolution', 128)
         default_face_type          = self.options['face_type']          = self.load_or_def_option('face_type', 'f')
         default_models_opt_on_gpu  = self.options['models_opt_on_gpu']  = self.load_or_def_option('models_opt_on_gpu', True)
@@ -69,7 +69,7 @@ class SAEHDModel(ModelBase):
             self.ask_random_src_flip()
             self.ask_random_dst_flip()
             self.ask_batch_size(suggest_batch_size)
-            self.options['use_fp16'] = io.input_bool ("Use fp16", default_usefp16, help_message='Increases training/inference speed, reduces model size. Model may crash. Enable it after 1-5k iters.')
+            #self.options['use_fp16'] = io.input_bool ("Use fp16", default_usefp16, help_message='Increases training/inference speed, reduces model size. Model may crash. Enable it after 1-5k iters.')
             
         if self.is_first_run():
             resolution = io.input_int("Resolution", default_resolution, add_info="64-640", help_message="More resolution requires more VRAM and time to train. Value will be adjusted to multiple of 16 and 32 for -d archi.")
@@ -219,7 +219,8 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
             self.set_iter(0)
 
         adabelief = self.options['adabelief']
-
+        use_fp16 = False#self.options['use_fp16']
+        
         self.gan_power = gan_power = 0.0 if self.pretrain else self.options['gan_power']
         random_warp = False if self.pretrain else self.options['random_warp']
         random_src_flip = self.random_src_flip if not self.pretrain else True
@@ -262,7 +263,7 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
             self.target_dstm_em = tf.placeholder (nn.floatx, mask_shape, name='target_dstm_em')
 
         # Initializing model classes
-        model_archi = nn.DeepFakeArchi(resolution, use_fp16=self.options['use_fp16'], opts=archi_opts)
+        model_archi = nn.DeepFakeArchi(resolution, use_fp16=use_fp16, opts=archi_opts)
 
         with tf.device (models_opt_device):
             if 'df' in archi_type:
@@ -303,7 +304,7 @@ Examples: df, liae, df-d, df-ud, liae-ud, ...
 
             if self.is_training:
                 if gan_power != 0:
-                    self.D_src = nn.UNetPatchDiscriminator(patch_size=self.options['gan_patch_size'], in_ch=input_ch, base_ch=self.options['gan_dims'], use_fp16=self.options['use_fp16'], name="D_src")
+                    self.D_src = nn.UNetPatchDiscriminator(patch_size=self.options['gan_patch_size'], in_ch=input_ch, base_ch=self.options['gan_dims'], name="D_src")
                     self.model_filename_list += [ [self.D_src, 'GAN.npy'] ]
 
                 # Initialize optimizers