diff --git a/core/imagelib/color_transfer.py b/core/imagelib/color_transfer.py
index 175920b..0c1760e 100644
--- a/core/imagelib/color_transfer.py
+++ b/core/imagelib/color_transfer.py
@@ -366,8 +366,8 @@ def color_hist_match(src_im, tar_im, hist_match_threshold=255):
     return matched
 
 def color_transfer_mix(img_src,img_trg):
-    img_src = (img_src*255.0).astype(np.uint8)
-    img_trg = (img_trg*255.0).astype(np.uint8)
+    img_src = np.clip(img_src*255.0, 0, 255).astype(np.uint8)
+    img_trg = np.clip(img_trg*255.0, 0, 255).astype(np.uint8)
 
     img_src_lab = cv2.cvtColor(img_src, cv2.COLOR_BGR2LAB)
     img_trg_lab = cv2.cvtColor(img_trg, cv2.COLOR_BGR2LAB)
diff --git a/merger/MergeMasked.py b/merger/MergeMasked.py
index 928d234..1f113dd 100644
--- a/merger/MergeMasked.py
+++ b/merger/MergeMasked.py
@@ -248,8 +248,8 @@ def MergeMaskedFace (predictor_func, predictor_input_shape, cfg, frame_info, img
 
             if 'seamless' in cfg.mode and cfg.color_transfer_mode != 0:
                 if cfg.color_transfer_mode == 1:
-                    out_face_bgr = imagelib.reinhard_color_transfer ( (out_face_bgr*prd_face_mask_area_a*255).astype(np.uint8),
-                                                                      (dst_face_bgr*prd_face_mask_area_a*255).astype(np.uint8) )
+                    out_face_bgr = imagelib.reinhard_color_transfer ( np.clip(out_face_bgr*prd_face_mask_area_a*255, 0, 255).astype(np.uint8),
+                                                                      np.clip(dst_face_bgr*prd_face_mask_area_a*255, 0, 255).astype(np.uint8) )
                     out_face_bgr = np.clip( out_face_bgr.astype(np.float32) / 255.0, 0.0, 1.0)
                 elif cfg.color_transfer_mode == 2: #lct
                     out_face_bgr = imagelib.linear_color_transfer (out_face_bgr, dst_face_bgr)
diff --git a/models/Model_SAEHD/Model.py b/models/Model_SAEHD/Model.py
index d4c402f..1b279d0 100644
--- a/models/Model_SAEHD/Model.py
+++ b/models/Model_SAEHD/Model.py
@@ -33,8 +33,8 @@ class SAEHDModel(ModelBase):
         default_archi              = self.options['archi']              = self.load_or_def_option('archi', 'df')
         default_ae_dims            = self.options['ae_dims']            = self.load_or_def_option('ae_dims', 256)
         default_e_dims             = self.options['e_dims']             = self.load_or_def_option('e_dims', 64)
-        self.options['d_dims'] = None
-        self.options['d_mask_dims'] = None
+        default_d_dims             = self.options['d_dims']             = self.options.get('d_dims', None)
+        default_d_mask_dims        = self.options['d_mask_dims']        = self.options.get('d_mask_dims', None)
         default_learn_mask         = self.options['learn_mask']         = self.load_or_def_option('learn_mask', True)
         default_eyes_prio          = self.options['eyes_prio']          = self.load_or_def_option('eyes_prio', False)
         default_lr_dropout         = self.options['lr_dropout']         = self.load_or_def_option('lr_dropout', False)
@@ -65,6 +65,7 @@ class SAEHDModel(ModelBase):
         default_d_dims             = 48 if self.options['archi'] == 'dfhd' else 64
         default_d_dims             = self.options['d_dims']             = self.load_or_def_option('d_dims', default_d_dims)
 
+
         default_d_mask_dims        = default_d_dims // 3
         default_d_mask_dims        += default_d_mask_dims % 2
         default_d_mask_dims        = self.options['d_mask_dims']        = self.load_or_def_option('d_mask_dims', default_d_mask_dims)