diff --git a/codes/models/SRGAN_model.py b/codes/models/SRGAN_model.py
index 6de9664c..427b027c 100644
--- a/codes/models/SRGAN_model.py
+++ b/codes/models/SRGAN_model.py
@@ -246,7 +246,6 @@ class SRGANModel(BaseModel):
             else:
                 gen_img = fake_GenOut
                 self.fake_GenOut.append(fake_GenOut.detach())
-            var_ref_skips.append(var_ref[0].detach())
 
             l_g_total = 0
             if step % self.D_update_ratio == 0 and step > self.D_init_iters:
@@ -308,7 +307,7 @@ class SRGANModel(BaseModel):
             noise = torch.randn_like(var_ref[0]) * noise_theta
             noise.to(self.device)
             self.optimizer_D.zero_grad()
-            for var_L, var_H, var_ref, pix in zip(self.var_L, self.var_H, var_ref_skips, self.pix):
+            for var_L, var_H, var_ref, pix in zip(self.var_L, self.var_H, self.var_ref, self.pix):
                 # Re-compute generator outputs (post-update).
                 with torch.no_grad():
                     fake_H = self.netG(var_L)
@@ -320,9 +319,8 @@ class SRGANModel(BaseModel):
                         _t = time()
 
                 # Apply noise to the inputs to slow discriminator convergence.
-                var_ref = (var_ref[0] + noise,)
+                var_ref = (var_ref + noise,)
                 fake_H = (fake_H[0] + noise,) + fake_H[1:]
-                self.fake_H.append(fake_H[0].detach())
                 if self.opt['train']['gan_type'] == 'gan':
                     # need to forward and backward separately, since batch norm statistics differ
                     # real
@@ -351,9 +349,9 @@ class SRGANModel(BaseModel):
                     if random.random() > .25:
 
                         # Make the swap across fake_H and var_ref
-                        SWAP_MAX_DIM = var_ref[0].shape[2] // (2 * PIXDISC_MAX_REDUCTION) - 1
+                        SWAP_MAX_DIM = var_ref[0].shape[2] // (2 * PIXDISC_MAX_REDUCTION)
                         assert SWAP_MAX_DIM > 0
-                        swap_x, swap_y = random.randint(0, SWAP_MAX_DIM+1) * PIXDISC_MAX_REDUCTION, random.randint(0, SWAP_MAX_DIM+1) * PIXDISC_MAX_REDUCTION
+                        swap_x, swap_y = random.randint(0, SWAP_MAX_DIM) * PIXDISC_MAX_REDUCTION, random.randint(0, SWAP_MAX_DIM) * PIXDISC_MAX_REDUCTION
                         swap_w, swap_h = random.randint(1, SWAP_MAX_DIM) * PIXDISC_MAX_REDUCTION, random.randint(1, SWAP_MAX_DIM) * PIXDISC_MAX_REDUCTION
                         t = fake_H[0][:, :, swap_x:(swap_x+swap_w), swap_y:(swap_y+swap_h)].clone()
                         fake_H[0][:, :, swap_x:(swap_x+swap_w), swap_y:(swap_y+swap_h)] = var_ref[0][:, :, swap_x:(swap_x+swap_w), swap_y:(swap_y+swap_h)]
@@ -406,8 +404,13 @@ class SRGANModel(BaseModel):
                     if _profile:
                         print("Disc forward/backward 2 (RAGAN) %f" % (time() - _t,))
                         _t = time()
+
+                # Append var_ref here, so that we can inspect the alterations the disc made if pixgan
+                var_ref_skips.append(var_ref[0].detach())
+                self.fake_H.append(fake_H[0].detach())
             self.optimizer_D.step()
 
+
             if _profile:
                 print("Disc step %f" % (time() - _t,))
                 _t = time()
@@ -432,8 +435,8 @@ class SRGANModel(BaseModel):
                 utils.save_image(self.pix[i].cpu(), os.path.join(sample_save_path, "pix", "%05i_%02i.png" % (step, i)))
                 if multi_gen:
                     utils.save_image(self.fake_GenOut[i][0].cpu(), os.path.join(sample_save_path, "gen", "%05i_%02i.png" % (step, i)))
-                    utils.save_image(var_ref_skips[i].cpu(), os.path.join(sample_save_path, "ref", "%05i_%02i.png" % (step, i)))
                     if step % self.D_update_ratio == 0 and step > self.D_init_iters:
+                        utils.save_image(var_ref_skips[i].cpu(), os.path.join(sample_save_path, "ref", "%05i_%02i.png" % (step, i)))
                         utils.save_image(self.fake_H[i], os.path.join(sample_save_path, "disc_fake", "%05i_%02i.png" % (step, i)))
                 else:
                     utils.save_image(self.fake_GenOut[i].cpu(), os.path.join(sample_save_path, "gen", "%05i_%02i.png" % (step, i)))