diff --git a/ppgan/models/base_model.py b/ppgan/models/base_model.py
index bc9ea3a6fac34721314b7c46cb2c3546273b08d6..467336b409fd197432a3a8ada93a8520d3e8a498 100644
--- a/ppgan/models/base_model.py
+++ b/ppgan/models/base_model.py
@@ -95,11 +95,6 @@ class BaseModel(ABC):
 
     def get_current_visuals(self):
         """Return visualization images."""
-        # visual_ret = OrderedDict()
-        # for name in self.visual_names:
-        #     if isinstance(name, str) and hasattr(self, name):
-        #         visual_ret[name] = getattr(self, name)
-        # return visual_ret
         return self.visual_items
 
     def get_current_losses(self):
@@ -117,6 +112,4 @@ class BaseModel(ABC):
         for net in nets:
             if net is not None:
                 for param in net.parameters():
-                    # print('trainable:', param.trainable)
                     param.trainable = requires_grad
-                    # param.stop_gradient = not requires_grad
diff --git a/ppgan/models/cycle_gan_model.py b/ppgan/models/cycle_gan_model.py
index 4fedec97e9f70d5b8c612ebcdbde1db66388f760..633c6534aaca6678ee34bd25d44aa100ca2c72c1 100644
--- a/ppgan/models/cycle_gan_model.py
+++ b/ppgan/models/cycle_gan_model.py
@@ -32,19 +32,6 @@ class CycleGANModel(BaseModel):
         """
         super(CycleGANModel, self).__init__(cfg)
 
-        # if identity loss is used, we also visualize idt_B=G_A(B) ad idt_A=G_A(B)
-        # if self.is_train and self.cfg.lambda_identity > 0.0:
-        #     visual_names_A.append('idt_B')
-        #     visual_names_B.append('idt_A')
-
-        # combine visualizations for A and B
-        # self.visual_names = visual_names_A + visual_names_B
-        # specify the models you want to save to the disk.
-        # if self.is_train:
-        #     self.model_names = ['G_A', 'G_B', 'D_A', 'D_B']
-        # else:  # during test time, only load Gs
-        #     self.model_names = ['G_A', 'G_B']
-
         # define networks (both Generators and discriminators)
         # The naming is different from those used in the paper.
         # Code (vs. paper): G_A (G), G_B (F), D_A (D_Y), D_B (D_X)
@@ -84,11 +71,6 @@ class CycleGANModel(BaseModel):
                 parameter_list=self.nets['netD_A'].parameters() +
                 self.nets['netD_B'].parameters())
 
-            # self.optimizers.append(self.optimizer_G)
-            # self.optimizers.append(self.optimizer_D)
-
-            # self.optimizer_names.extend(['optimizer_G', 'optimizer_D'])
-
     def set_input(self, input):
         """Unpack input data from the dataloader and perform necessary pre-processing steps.