From ad9128d1131f2ae298cee56a2393486806f23c73 Mon Sep 17 00:00:00 2001
From: Volpeon <git@volpeon.ink>
Date: Mon, 2 Jan 2023 15:56:44 +0100
Subject: Update

---
 train_dreambooth.py | 13 ++++++-------
 train_ti.py         | 12 ++++++------
 2 files changed, 12 insertions(+), 13 deletions(-)

diff --git a/train_dreambooth.py b/train_dreambooth.py
index 92f9b96..c0fe328 100644
--- a/train_dreambooth.py
+++ b/train_dreambooth.py
@@ -941,9 +941,6 @@ def main():
         seed=args.seed
     )
 
-    if accelerator.is_main_process:
-        checkpointer.save_samples(0, args.sample_steps)
-
     local_progress_bar = tqdm(
         range(num_update_steps_per_epoch + num_val_steps_per_epoch),
         disable=not accelerator.is_local_main_process,
@@ -960,6 +957,10 @@ def main():
 
     try:
         for epoch in range(num_epochs):
+            if accelerator.is_main_process:
+                if epoch % args.sample_frequency == 0:
+                    checkpointer.save_samples(global_step, args.sample_steps)
+
             local_progress_bar.set_description(f"Epoch {epoch + 1} / {num_epochs}")
             local_progress_bar.reset()
 
@@ -1064,19 +1065,17 @@ def main():
                         f"Global step {global_step}: Validation accuracy reached new maximum: {max_acc_val:.2e} -> {avg_acc_val.avg.item():.2e}")
                     max_acc_val = avg_acc_val.avg.item()
 
-                if (epoch + 1) % args.sample_frequency == 0:
-                    checkpointer.save_samples(global_step, args.sample_steps)
-
         # Create the pipeline using using the trained modules and save it.
         if accelerator.is_main_process:
             print("Finished! Saving final checkpoint and resume state.")
+            checkpointer.save_samples(0, args.sample_steps)
             checkpointer.save_model()
-
             accelerator.end_training()
 
     except KeyboardInterrupt:
         if accelerator.is_main_process:
             print("Interrupted, saving checkpoint and resume state...")
+            checkpointer.save_samples(0, args.sample_steps)
             checkpointer.save_model()
             accelerator.end_training()
         quit()
diff --git a/train_ti.py b/train_ti.py
index 775b918..f1dbed1 100644
--- a/train_ti.py
+++ b/train_ti.py
@@ -889,9 +889,6 @@ def main():
         seed=args.seed
     )
 
-    if accelerator.is_main_process:
-        checkpointer.save_samples(global_step_offset, args.sample_steps)
-
     local_progress_bar = tqdm(
         range(num_update_steps_per_epoch + num_val_steps_per_epoch),
         disable=not accelerator.is_local_main_process,
@@ -908,6 +905,10 @@ def main():
 
     try:
         for epoch in range(num_epochs):
+            if accelerator.is_main_process:
+                if epoch % args.sample_frequency == 0:
+                    checkpointer.save_samples(global_step + global_step_offset, args.sample_steps)
+
             local_progress_bar.set_description(f"Epoch {epoch + 1} / {num_epochs}")
             local_progress_bar.reset()
 
@@ -1001,13 +1002,11 @@ def main():
                         "global_step": global_step + global_step_offset
                     })
 
-                if (epoch + 1) % args.sample_frequency == 0:
-                    checkpointer.save_samples(global_step + global_step_offset, args.sample_steps)
-
         # Create the pipeline using using the trained modules and save it.
         if accelerator.is_main_process:
             print("Finished! Saving final checkpoint and resume state.")
             checkpointer.checkpoint(global_step + global_step_offset, "end")
+            checkpointer.save_samples(global_step_offset, args.sample_steps)
             save_args(basepath, args, {
                 "global_step": global_step + global_step_offset
             })
@@ -1017,6 +1016,7 @@ def main():
         if accelerator.is_main_process:
             print("Interrupted, saving checkpoint and resume state...")
             checkpointer.checkpoint(global_step + global_step_offset, "end")
+            checkpointer.save_samples(global_step_offset, args.sample_steps)
             save_args(basepath, args, {
                 "global_step": global_step + global_step_offset
             })
-- 
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