diff --git a/checkpoints/ROMA_UNSB_001/loss_log.txt b/checkpoints/ROMA_UNSB_001/loss_log.txt
index e2b8c6e..741a618 100644
--- a/checkpoints/ROMA_UNSB_001/loss_log.txt
+++ b/checkpoints/ROMA_UNSB_001/loss_log.txt
@@ -33,3 +33,4 @@
 ================ Training Loss (Sun Feb 23 18:59:52 2025) ================
 ================ Training Loss (Sun Feb 23 19:03:05 2025) ================
 ================ Training Loss (Sun Feb 23 19:03:57 2025) ================
+================ Training Loss (Sun Feb 23 21:11:47 2025) ================
diff --git a/models/__pycache__/roma_unsb_model.cpython-39.pyc b/models/__pycache__/roma_unsb_model.cpython-39.pyc
index cb9f345..0c9cd4b 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 45b220e..05cfaa7 100644
--- a/models/roma_unsb_model.py
+++ b/models/roma_unsb_model.py
@@ -527,26 +527,17 @@ class RomaUnsbModel(BaseModel):
                     setattr(self, "fake_"+str(t+1), Xt_1)
                     
         if self.opt.phase == 'train':
-            print(f'real_B0.shape = {real_B0.shape}  fake_B0.shape = {self.fake_B0.shape}')
-            print(f"self.real_B0.requires_grad: {real_B0.requires_grad}")
-            # 真实图像的梯度
-            real_gradient = torch.autograd.grad(real_B0.sum(), real_B0, create_graph=True)[0]
             # 生成图像的梯度
             fake_gradient = torch.autograd.grad(self.fake_B0.sum(), self.fake_B0, create_graph=True)[0]
             # 梯度图
-            self.weight_real, self.weight_fake = self.cao.generate_weight_map(fake_gradient)
-            
+            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)
-            
-            # 把前面生成后的图片再加上noisy_map
-            self.fake_B_2 = self.fake_B + self.noisy_map
-            
             # 变换后的图片
-            wapped_fake_B = warp(self.fake_B, self.f_content)
-            
+            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.fake_B_2 = self.netG(wapped_fake_B, self.time, z_in)
+            self.warped_fake_B0_2 = self.netG(self.warped_real_A_noisy2, self.time, z_in)   
             
     def compute_D_loss(self):
         """计算判别器的 GAN 损失"""