diff --git a/codes/models/steps/losses.py b/codes/models/steps/losses.py
index 7b161a4d..b99d5f73 100644
--- a/codes/models/steps/losses.py
+++ b/codes/models/steps/losses.py
@@ -76,9 +76,11 @@ class GeneratorGanLoss(ConfigurableLoss):
 
     def forward(self, net, state):
         netD = self.env['discriminators'][self.opt['discriminator']]
-        if self.opt['gan_type'] in ['gan', 'pixgan', 'pixgan_fea', 'crossgan']:
+        if self.opt['gan_type'] in ['gan', 'pixgan', 'pixgan_fea', 'crossgan', 'crossgan_lrref']:
             if self.opt['gan_type'] == 'crossgan':
                 pred_g_fake = netD(state[self.opt['fake']], state['lq_fullsize_ref'])
+            elif self.opt['gan_type'] == 'crossgan_lrref':
+                pred_g_fake = netD(state[self.opt['fake']], state['lq'])
             else:
                 pred_g_fake = netD(state[self.opt['fake']])
             return self.criterion(pred_g_fake, True)
@@ -106,16 +108,22 @@ class DiscriminatorGanLoss(ConfigurableLoss):
             mismatched_lq = torch.roll(state['lq_fullsize_ref'], shifts=1, dims=0)
             d_mismatch_real = net(state[self.opt['real']], mismatched_lq)
             d_mismatch_fake = net(state[self.opt['fake']].detach(), mismatched_lq)
+        elif self.opt['gan_type'] == 'crossgan_lrref':
+            d_real = net(state[self.opt['real']], state['lq'])
+            d_fake = net(state[self.opt['fake']].detach(), state['lq'])
+            mismatched_lq = torch.roll(state['lq'], shifts=1, dims=0)
+            d_mismatch_real = net(state[self.opt['real']], mismatched_lq)
+            d_mismatch_fake = net(state[self.opt['fake']].detach(), mismatched_lq)
         else:
             d_real = net(state[self.opt['real']])
             d_fake = net(state[self.opt['fake']].detach())
         self.metrics.append(("d_fake", torch.mean(d_fake)))
 
-        if self.opt['gan_type'] in ['gan', 'pixgan', 'crossgan']:
+        if self.opt['gan_type'] in ['gan', 'pixgan', 'crossgan', 'crossgan_lrref']:
             l_real = self.criterion(d_real, True)
             l_fake = self.criterion(d_fake, False)
             l_total = l_real + l_fake
-            if self.opt['gan_type'] == 'crossgan':
+            if 'crossgan' in self.opt['gan_type']:
                 l_mreal = self.criterion(d_mismatch_real, False)
                 l_mfake = self.criterion(d_mismatch_fake, False)
                 l_total += l_mreal + l_mfake