From ba9fd1a10746d85d2502c8a79ac49db63d346b04 Mon Sep 17 00:00:00 2001
From: Volpeon <git@volpeon.ink>
Date: Sun, 9 Apr 2023 11:29:31 +0200
Subject: Update

---
 train_ti.py | 66 +++++++++++++++++++++++++++++++++++++++----------------------
 1 file changed, 42 insertions(+), 24 deletions(-)

(limited to 'train_ti.py')

diff --git a/train_ti.py b/train_ti.py
index daf8bc5..2d51800 100644
--- a/train_ti.py
+++ b/train_ti.py
@@ -457,6 +457,12 @@ def parse_args():
         default=1.0,
         help="The weight of prior preservation loss."
     )
+    parser.add_argument(
+        "--emb_dropout",
+        type=float,
+        default=0,
+        help="Embedding dropout probability.",
+    )
     parser.add_argument(
         "--use_emb_decay",
         action="store_true",
@@ -624,7 +630,7 @@ def main():
     save_args(output_dir, args)
 
     tokenizer, text_encoder, vae, unet, noise_scheduler, sample_scheduler, embeddings = get_models(
-        args.pretrained_model_name_or_path)
+        args.pretrained_model_name_or_path, args.emb_dropout)
 
     tokenizer.set_use_vector_shuffle(args.vector_shuffle)
     tokenizer.set_dropout(args.vector_dropout)
@@ -755,8 +761,6 @@ def main():
     else:
         raise ValueError(f"Unknown --optimizer \"{args.optimizer}\"")
 
-    checkpoint_output_dir = output_dir / "checkpoints"
-
     trainer = partial(
         train,
         accelerator=accelerator,
@@ -777,7 +781,6 @@ def main():
         global_step_offset=global_step_offset,
         offset_noise_strength=args.offset_noise_strength,
         # --
-        checkpoint_output_dir=checkpoint_output_dir,
         use_emb_decay=args.use_emb_decay,
         emb_decay_target=args.emb_decay_target,
         emb_decay=args.emb_decay,
@@ -793,11 +796,6 @@ def main():
     )
 
     def run(i: int, placeholder_tokens: list[str], initializer_tokens: list[str], num_vectors: Union[int, list[int]], data_template: str):
-        if len(placeholder_tokens) == 1:
-            sample_output_dir = output_dir / f"samples_{placeholder_tokens[0]}"
-        else:
-            sample_output_dir = output_dir / "samples"
-
         placeholder_token_ids, initializer_token_ids = add_placeholder_tokens(
             tokenizer=tokenizer,
             embeddings=embeddings,
@@ -809,7 +807,11 @@ def main():
 
         stats = list(zip(placeholder_tokens, placeholder_token_ids, initializer_tokens, initializer_token_ids))
 
-        print(f"{i + 1}: {stats}")
+        print("")
+        print(f"============ TI batch {i + 1} ============")
+        print("")
+        print(stats)
+        print("")
 
         filter_tokens = [token for token in args.filter_tokens if token in placeholder_tokens]
 
@@ -868,20 +870,36 @@ def main():
             mid_point=args.lr_mid_point,
         )
 
-        trainer(
-            project="textual_inversion",
-            train_dataloader=datamodule.train_dataloader,
-            val_dataloader=datamodule.val_dataloader,
-            optimizer=optimizer,
-            lr_scheduler=lr_scheduler,
-            num_train_epochs=num_train_epochs,
-            # --
-            group_labels=["emb"],
-            sample_output_dir=sample_output_dir,
-            sample_frequency=sample_frequency,
-            placeholder_tokens=placeholder_tokens,
-            placeholder_token_ids=placeholder_token_ids,
-        )
+        continue_training = True
+        training_iter = 1
+
+        while continue_training:
+            print(f"------------ TI cycle {training_iter} ------------")
+            print("")
+
+            project = f"{placeholder_tokens[0]}_{training_iter}" if len(placeholder_tokens) == 1 else f"{training_iter}"
+            sample_output_dir = output_dir / project / "samples"
+            checkpoint_output_dir = output_dir / project / "checkpoints"
+
+            trainer(
+                project=project,
+                train_dataloader=datamodule.train_dataloader,
+                val_dataloader=datamodule.val_dataloader,
+                optimizer=optimizer,
+                lr_scheduler=lr_scheduler,
+                num_train_epochs=num_train_epochs,
+                # --
+                group_labels=["emb"],
+                checkpoint_output_dir=checkpoint_output_dir,
+                sample_output_dir=sample_output_dir,
+                sample_frequency=sample_frequency,
+                placeholder_tokens=placeholder_tokens,
+                placeholder_token_ids=placeholder_token_ids,
+            )
+
+            response = input("Run another cycle? [y/n] ")
+            continue_training = response.lower().strip() != "n"
+            training_iter += 1
 
     if not args.sequential:
         run(0, args.placeholder_tokens, args.initializer_tokens, args.num_vectors, args.train_data_template)
-- 
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