From 6a49074dce78615bce54777fb2be3bfd0dd8f780 Mon Sep 17 00:00:00 2001
From: Volpeon <git@volpeon.ink>
Date: Fri, 14 Oct 2022 20:03:01 +0200
Subject: Removed aesthetic gradients; training improvements

---
 dreambooth_plus.py | 59 +++++++++++++++++++++++++++++++++++++++++++++---------
 1 file changed, 50 insertions(+), 9 deletions(-)

(limited to 'dreambooth_plus.py')

diff --git a/dreambooth_plus.py b/dreambooth_plus.py
index 7996bc2..b5ec2fc 100644
--- a/dreambooth_plus.py
+++ b/dreambooth_plus.py
@@ -58,6 +58,12 @@ def parse_args():
     parser.add_argument(
         "--placeholder_token",
         type=str,
+        default="<*>",
+        help="A token to use as a placeholder for the concept.",
+    )
+    parser.add_argument(
+        "--class_identifier",
+        type=str,
         default=None,
         help="A token to use as a placeholder for the concept.",
     )
@@ -71,7 +77,7 @@ def parse_args():
         "--num_class_images",
         type=int,
         default=400,
-        help="How many class images to generate per training image."
+        help="How many class images to generate."
     )
     parser.add_argument(
         "--repeats",
@@ -112,7 +118,7 @@ def parse_args():
     parser.add_argument(
         "--max_train_steps",
         type=int,
-        default=1600,
+        default=2300,
         help="Total number of training steps to perform.  If provided, overrides num_train_epochs.",
     )
     parser.add_argument(
@@ -135,7 +141,7 @@ def parse_args():
     parser.add_argument(
         "--learning_rate_text",
         type=float,
-        default=5e-4,
+        default=5e-6,
         help="Initial learning rate (after the potential warmup period) to use.",
     )
     parser.add_argument(
@@ -221,6 +227,12 @@ def parse_args():
             "and an Nvidia Ampere GPU."
         ),
     )
+    parser.add_argument(
+        "--checkpoint_frequency",
+        type=int,
+        default=500,
+        help="How often to save a checkpoint and sample image",
+    )
     parser.add_argument(
         "--sample_frequency",
         type=int,
@@ -352,7 +364,26 @@ class Checkpointer:
         self.sample_batch_size = sample_batch_size
 
     @torch.no_grad()
-    def checkpoint(self):
+    def checkpoint(self, step, postfix):
+        print("Saving checkpoint for step %d..." % step)
+
+        checkpoints_path = self.output_dir.joinpath("checkpoints")
+        checkpoints_path.mkdir(parents=True, exist_ok=True)
+
+        unwrapped = self.accelerator.unwrap_model(self.text_encoder)
+
+        # Save a checkpoint
+        learned_embeds = unwrapped.get_input_embeddings().weight[self.placeholder_token_id]
+        learned_embeds_dict = {self.placeholder_token: learned_embeds.detach().cpu()}
+
+        filename = f"%s_%d_%s.bin" % (slugify(self.placeholder_token), step, postfix)
+        torch.save(learned_embeds_dict, checkpoints_path.joinpath(filename))
+
+        del unwrapped
+        del learned_embeds
+
+    @torch.no_grad()
+    def save_model(self):
         print("Saving model...")
 
         unwrapped_unet = self.accelerator.unwrap_model(
@@ -612,7 +643,7 @@ def main():
         batch_size=args.train_batch_size,
         tokenizer=tokenizer,
         instance_identifier=args.placeholder_token,
-        class_identifier=args.initializer_token,
+        class_identifier=args.class_identifier,
         class_subdir="cls",
         num_class_images=args.num_class_images,
         size=args.resolution,
@@ -648,7 +679,7 @@ def main():
             with torch.inference_mode():
                 for batch in batched_data:
                     image_name = [p.class_image_path for p in batch]
-                    prompt = [p.prompt.format(args.initializer_token) for p in batch]
+                    prompt = [p.prompt.format(args.class_identifier) for p in batch]
                     nprompt = [p.nprompt for p in batch]
 
                     images = pipeline(
@@ -842,6 +873,12 @@ def main():
                     if global_step % args.sample_frequency == 0:
                         sample_checkpoint = True
 
+                    if global_step % args.checkpoint_frequency == 0 and global_step > 0 and accelerator.is_main_process:
+                        local_progress_bar.clear()
+                        global_progress_bar.clear()
+
+                        checkpointer.checkpoint(global_step + global_step_offset, "training")
+
                 logs = {
                     "train/loss": loss,
                     "lr/unet": lr_scheduler.get_last_lr()[0],
@@ -903,6 +940,9 @@ def main():
             global_progress_bar.clear()
 
             if min_val_loss > val_loss:
+                accelerator.print(
+                    f"Global step {global_step}: Validation loss reached new minimum: {min_val_loss:.2e} -> {val_loss:.2e}")
+                checkpointer.checkpoint(global_step + global_step_offset, "milestone")
                 min_val_loss = val_loss
 
             if sample_checkpoint and accelerator.is_main_process:
@@ -913,14 +953,15 @@ def main():
         # 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()
-
+            checkpointer.checkpoint(global_step + global_step_offset, "end")
+            checkpointer.save_model()
             accelerator.end_training()
 
     except KeyboardInterrupt:
         if accelerator.is_main_process:
             print("Interrupted, saving checkpoint and resume state...")
-            checkpointer.checkpoint()
+            checkpointer.checkpoint(global_step + global_step_offset, "end")
+            checkpointer.save_model()
             accelerator.end_training()
         quit()
 
-- 
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