From eb6a92abda5893c975437026cdaf0ce0bfefe2a4 Mon Sep 17 00:00:00 2001
From: Volpeon <git@volpeon.ink>
Date: Mon, 10 Apr 2023 10:34:12 +0200
Subject: Update

---
 train_ti.py | 68 +++++++++++++++++++++++++++++++++++++------------------------
 1 file changed, 41 insertions(+), 27 deletions(-)

(limited to 'train_ti.py')

diff --git a/train_ti.py b/train_ti.py
index ebac302..eb08bda 100644
--- a/train_ti.py
+++ b/train_ti.py
@@ -151,6 +151,11 @@ def parse_args():
         default=None,
         help="The embeddings directory where Textual Inversion embeddings are stored.",
     )
+    parser.add_argument(
+        "--train_dir_embeddings",
+        action="store_true",
+        help="Train embeddings loaded from embeddings directory.",
+    )
     parser.add_argument(
         "--collection",
         type=str,
@@ -403,6 +408,12 @@ def parse_args():
         action='store_true',
         help="If checkpoints are saved on maximum accuracy",
     )
+    parser.add_argument(
+        "--sample_num",
+        type=int,
+        default=None,
+        help="How often to save a checkpoint and sample image (in number of samples)",
+    )
     parser.add_argument(
         "--sample_frequency",
         type=int,
@@ -669,9 +680,14 @@ def main():
             raise ValueError("--embeddings_dir must point to an existing directory")
 
         added_tokens, added_ids = load_embeddings_from_dir(tokenizer, embeddings, embeddings_dir)
-        embeddings.persist()
         print(f"Added {len(added_tokens)} tokens from embeddings dir: {list(zip(added_tokens, added_ids))}")
 
+        if args.train_dir_embeddings:
+            args.placeholder_tokens = added_tokens
+            print("Training embeddings from embeddings dir")
+        else:
+            embeddings.persist()
+
     if args.scale_lr:
         args.learning_rate = (
             args.learning_rate * args.gradient_accumulation_steps *
@@ -852,28 +868,8 @@ def main():
                 args.num_train_steps / len(datamodule.train_dataset)
             ) * args.gradient_accumulation_steps
             sample_frequency = math.ceil(num_train_epochs * (sample_frequency / args.num_train_steps))
-
-        optimizer = create_optimizer(
-            text_encoder.text_model.embeddings.token_override_embedding.parameters(),
-            lr=args.learning_rate,
-        )
-
-        lr_scheduler = get_scheduler(
-            args.lr_scheduler,
-            optimizer=optimizer,
-            num_training_steps_per_epoch=len(datamodule.train_dataloader),
-            gradient_accumulation_steps=args.gradient_accumulation_steps,
-            min_lr=args.lr_min_lr,
-            warmup_func=args.lr_warmup_func,
-            annealing_func=args.lr_annealing_func,
-            warmup_exp=args.lr_warmup_exp,
-            annealing_exp=args.lr_annealing_exp,
-            cycles=args.lr_cycles,
-            end_lr=1e3,
-            train_epochs=num_train_epochs,
-            warmup_epochs=args.lr_warmup_epochs,
-            mid_point=args.lr_mid_point,
-        )
+        if args.sample_num is not None:
+            sample_frequency = math.ceil(num_train_epochs / args.sample_num)
 
         training_iter = 0
 
@@ -888,6 +884,28 @@ def main():
             print(f"------------ TI cycle {training_iter} ------------")
             print("")
 
+            optimizer = create_optimizer(
+                text_encoder.text_model.embeddings.token_override_embedding.parameters(),
+                lr=args.learning_rate,
+            )
+
+            lr_scheduler = get_scheduler(
+                args.lr_scheduler,
+                optimizer=optimizer,
+                num_training_steps_per_epoch=len(datamodule.train_dataloader),
+                gradient_accumulation_steps=args.gradient_accumulation_steps,
+                min_lr=args.lr_min_lr,
+                warmup_func=args.lr_warmup_func,
+                annealing_func=args.lr_annealing_func,
+                warmup_exp=args.lr_warmup_exp,
+                annealing_exp=args.lr_annealing_exp,
+                cycles=args.lr_cycles,
+                end_lr=1e3,
+                train_epochs=num_train_epochs,
+                warmup_epochs=args.lr_warmup_epochs,
+                mid_point=args.lr_mid_point,
+            )
+
             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"
@@ -908,10 +926,6 @@ def main():
                 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)
     else:
-- 
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