diff --git a/checkpoints/ROMA_UNSB_001/loss_log.txt b/checkpoints/ROMA_UNSB_001/loss_log.txt
index 2bc6f9f..d27cb3a 100644
--- a/checkpoints/ROMA_UNSB_001/loss_log.txt
+++ b/checkpoints/ROMA_UNSB_001/loss_log.txt
@@ -44,3 +44,5 @@
 ================ Training Loss (Sun Feb 23 22:29:52 2025) ================
 ================ Training Loss (Sun Feb 23 22:30:40 2025) ================
 ================ Training Loss (Sun Feb 23 22:33:48 2025) ================
+================ Training Loss (Sun Feb 23 22:39:16 2025) ================
+================ Training Loss (Sun Feb 23 22:39:48 2025) ================
diff --git a/models/__pycache__/roma_unsb_model.cpython-39.pyc b/models/__pycache__/roma_unsb_model.cpython-39.pyc
index e819f44..bd981a8 100644
Binary files a/models/__pycache__/roma_unsb_model.cpython-39.pyc and b/models/__pycache__/roma_unsb_model.cpython-39.pyc differ
diff --git a/models/roma_unsb_model.py b/models/roma_unsb_model.py
index ec2fe8d..2c275a7 100644
--- a/models/roma_unsb_model.py
+++ b/models/roma_unsb_model.py
@@ -284,10 +284,10 @@ class RomaUnsbModel(BaseModel):
             for nce_layer in self.nce_layers:
                 self.criterionNCE.append(PatchNCELoss(opt).to(self.device))
             self.criterionIdt = torch.nn.L1Loss().to(self.device)
-            self.optimizer_G1 = torch.optim.Adam(self.netG.parameters(), lr=opt.lr, betas=(opt.beta1, opt.beta2))
-            self.optimizer_D1 = torch.optim.Adam(self.netD.parameters(), lr=opt.lr, betas=(opt.beta1, opt.beta2))
-            self.optimizer_E1 = torch.optim.Adam(self.netE.parameters(), lr=opt.lr, betas=(opt.beta1, opt.beta2))
-            self.optimizers = [self.optimizer_G1, self.optimizer_D1, self.optimizer_E1]
+            self.optimizer_G = torch.optim.Adam(self.netG.parameters(), lr=opt.lr, betas=(opt.beta1, opt.beta2))
+            self.optimizer_D = torch.optim.Adam(self.netD.parameters(), lr=opt.lr, betas=(opt.beta1, opt.beta2))
+            self.optimizer_E = torch.optim.Adam(self.netE.parameters(), lr=opt.lr, betas=(opt.beta1, opt.beta2))
+            self.optimizers = [self.optimizer_G, self.optimizer_D, self.optimizer_E]
             
             self.cao = ContentAwareOptimization(opt.lambda_inc, opt.eta_ratio) #损失函数
             self.ctn = ContentAwareTemporalNorm()  #生成的伪光流
@@ -483,28 +483,28 @@ class RomaUnsbModel(BaseModel):
             # [[1,576,768],[1,576,768],[1,576,768]]
             #  [3,576,768]
             
-            # 生成图像的梯度
-            fake_gradient = torch.autograd.grad(self.mutil_fake_B0_tokens.sum(), self.mutil_fake_B0_tokens, create_graph=True)[0]
+            ## 生成图像的梯度
+            #fake_gradient = torch.autograd.grad(self.mutil_fake_B0_tokens.sum(), self.mutil_fake_B0_tokens, create_graph=True)[0]
+            #
+            ## 梯度图
+            #self.weight_fake = self.cao.generate_weight_map(fake_gradient)
+            #
+            ## 生成图像的CTN光流图
+            #self.f_content = self.ctn(self.weight_fake)
+            #
+            ## 变换后的图片
+            #self.warped_real_A_noisy2 = warp(self.real_A_noisy, self.f_content)
+            #self.warped_fake_B0       = warp(self.fake_B0,self.f_content)
+            #
+            ## 经过第二次生成器
+            #self.warped_fake_B0_2 = self.netG(self.warped_real_A_noisy2, self.time, z_in)   
             
-            # 梯度图
-            self.weight_fake = self.cao.generate_weight_map(fake_gradient)
-            
-            # 生成图像的CTN光流图
-            self.f_content = self.ctn(self.weight_fake)
-            
-            # 变换后的图片
-            self.warped_real_A_noisy2 = warp(self.real_A_noisy, self.f_content)
-            self.warped_fake_B0       = warp(self.fake_B0,self.f_content)
-            
-            # 经过第二次生成器
-            self.warped_fake_B0_2 = self.netG(self.warped_real_A_noisy2, self.time, z_in)   
-            
-            warped_fake_B0_2=self.warped_fake_B0_2
-            warped_fake_B0=self.warped_fake_B0
-            self.warped_fake_B0_2_resize = self.resize(warped_fake_B0_2)
-            self.warped_fake_B0_resize = self.resize(warped_fake_B0)
-            self.mutil_warped_fake_B0_tokens = self.netPreViT(self.warped_fake_B0_resize, self.atten_layers, get_tokens=True)
-            self.mutil_fake_B0_2_tokens = self.netPreViT(self.warped_fake_B0_2_resize, self.atten_layers, get_tokens=True)
+            #warped_fake_B0_2=self.warped_fake_B0_2
+            #warped_fake_B0=self.warped_fake_B0
+            #self.warped_fake_B0_2_resize = self.resize(warped_fake_B0_2)
+            #self.warped_fake_B0_resize = self.resize(warped_fake_B0)
+            #self.mutil_warped_fake_B0_tokens = self.netPreViT(self.warped_fake_B0_resize, self.atten_layers, get_tokens=True)
+            #self.mutil_fake_B0_2_tokens = self.netPreViT(self.warped_fake_B0_2_resize, self.atten_layers, get_tokens=True)
 
             
     def compute_D_loss(self):
@@ -563,9 +563,10 @@ class RomaUnsbModel(BaseModel):
         else:
             loss_global = 0.0
         
-        if self.opt.lambda_ctn > 0.0:
-            wapped_fake_B = warp(self.fake_B, self.f_content)  # use updated self.f_content
-            self.l2_loss = F.mse_loss(self.fake_B_2, wapped_fake_B)  # complete the loss calculation
+        self.l2_loss = 0.0
+        #if self.opt.lambda_ctn > 0.0:
+        #    wapped_fake_B = warp(self.fake_B, self.f_content)  # use updated self.f_content
+        #    self.l2_loss = F.mse_loss(self.fake_B_2, wapped_fake_B)  # complete the loss calculation
         
         self.loss_G = self.loss_G_GAN + self.opt.lambda_SB * self.loss_SB + self.opt.lambda_ctn * self.l2_loss + loss_global * self.opt.lambda_global
         return self.loss_G