diff --git a/codes/models/steps/tecogan_losses.py b/codes/models/steps/tecogan_losses.py
index 63ea9325..7a09cfc1 100644
--- a/codes/models/steps/tecogan_losses.py
+++ b/codes/models/steps/tecogan_losses.py
@@ -189,26 +189,28 @@ def create_teco_discriminator_sextuplet(input_list, lr_imgs, scale, index, flow_
 
 
 def create_all_discriminator_sextuplets(input_list, lr_imgs, scale, total, flow_gen, resampler, margin):
-    # Combine everything and feed it into the flow network at once for better efficiency.
-    batch_sz = input_list.shape[0]
-    flux_doubles_forward = [torch.stack([input_list[:,i], input_list[:,i+1]], dim=2) for i in range(1, total+1)]
-    flux_doubles_backward = [torch.stack([input_list[:,i], input_list[:,i-1]], dim=2) for i in range(1, total+1)]
-    flows_forward = flow_gen(torch.cat(flux_doubles_forward, dim=0))
-    flows_backward = flow_gen(torch.cat(flux_doubles_backward, dim=0))
-    sexts = []
-    for i in range(total):
-        flow_forward = flows_forward[batch_sz*i:batch_sz*(i+1)]
-        flow_backward = flows_backward[batch_sz*i:batch_sz*(i+1)]
-        mid = input_list[:,i+1]
-        sext = torch.stack([input_list[:,i], mid, input_list[:,i+2],
-                          resampler(input_list[:,i], flow_backward),
-                          mid,
-                          resampler(input_list[:,i+2], flow_forward)], dim=1)
-        # Apply margin
-        b, f, c, h, w = sext.shape
-        sext = sext.view(b, 3*6, h, w)  # f*c = 6*3
-        sext = sext[:, :, margin:-margin, margin:-margin]
-        sexts.append(sext)
+    with autocast(enabled=False):
+        input_list = input_list.float()        
+        # Combine everything and feed it into the flow network at once for better efficiency.
+        batch_sz = input_list.shape[0]
+        flux_doubles_forward = [torch.stack([input_list[:,i], input_list[:,i+1]], dim=2) for i in range(1, total+1)]
+        flux_doubles_backward = [torch.stack([input_list[:,i], input_list[:,i-1]], dim=2) for i in range(1, total+1)]
+        flows_forward = flow_gen(torch.cat(flux_doubles_forward, dim=0))
+        flows_backward = flow_gen(torch.cat(flux_doubles_backward, dim=0))
+        sexts = []
+        for i in range(total):
+            flow_forward = flows_forward[batch_sz*i:batch_sz*(i+1)]
+            flow_backward = flows_backward[batch_sz*i:batch_sz*(i+1)]
+            mid = input_list[:,i+1]
+            sext = torch.stack([input_list[:,i], mid, input_list[:,i+2],
+                              resampler(input_list[:,i], flow_backward),
+                              mid,
+                              resampler(input_list[:,i+2], flow_forward)], dim=1)
+            # Apply margin
+            b, f, c, h, w = sext.shape
+            sext = sext.view(b, 3*6, h, w)  # f*c = 6*3
+            sext = sext[:, :, margin:-margin, margin:-margin]
+            sexts.append(sext)
     return torch.cat(sexts, dim=0)