1 files changed, 8 insertions, 4 deletions

diff --git a/training/functional.py b/training/functional.py
index 6ae35a0..e7cc20f 100644
--- a/training/functional.py
+++ b/training/functional.py
@@ -624,24 +624,24 @@ def train_loop(
                 accelerator.log(logs, step=global_step)
 
                 if accelerator.is_main_process:
-                    if avg_acc_val.avg > best_acc_val and milestone_checkpoints:
+                    if avg_acc_val.max > best_acc_val and milestone_checkpoints:
                         local_progress_bar.clear()
                         global_progress_bar.clear()
 
                         accelerator.print(
                             f"Global step {global_step}: Validation accuracy reached new maximum: {best_acc_val:.2e} -> {avg_acc_val.avg:.2e}")
                         on_checkpoint(global_step, "milestone")
-                        best_acc_val = avg_acc_val.avg
+                        best_acc_val = avg_acc_val.max
             else:
                 if accelerator.is_main_process:
-                    if avg_acc.avg > best_acc and milestone_checkpoints:
+                    if avg_acc.max > best_acc and milestone_checkpoints:
                         local_progress_bar.clear()
                         global_progress_bar.clear()
 
                         accelerator.print(
                             f"Global step {global_step}: Training accuracy reached new maximum: {best_acc:.2e} -> {avg_acc.avg:.2e}")
                         on_checkpoint(global_step, "milestone")
-                        best_acc = avg_acc.avg
+                        best_acc = avg_acc.max
 
         # Create the pipeline using using the trained modules and save it.
         if accelerator.is_main_process:
@@ -699,6 +699,10 @@ def train(
     vae.requires_grad_(False)
     vae.eval()
 
+    unet = torch.compile(unet)
+    text_encoder = torch.compile(text_encoder)
+    vae = torch.compile(vae)
+
     callbacks = strategy.callbacks(
         accelerator=accelerator,
         unet=unet,