Pad dataset if len(items) < batch_size

2 files changed, 23 insertions, 20 deletions

diff --git a/data/csv.py b/data/csv.py
index 968af8d..dec66d7 100644
--- a/data/csv.py
+++ b/data/csv.py
@@ -267,6 +267,9 @@ class VlpnDataModule():
         items = self.prepare_items(template, expansions, items)
         items = self.filter_items(items)
 
+        if (len(items) < self.batch_size):
+            items = (items * self.batch_size)[:self.batch_size]
+
         num_images = len(items)
 
         valid_set_size = min(self.valid_set_size, num_images) if self.valid_set_size is not None else num_images // 10
diff --git a/train_dreambooth.py b/train_dreambooth.py
index 4e41f77..a9fbbbd 100644
--- a/train_dreambooth.py
+++ b/train_dreambooth.py
@@ -87,12 +87,6 @@ def parse_args():
         help="Exclude all items with a listed collection.",
     )
     parser.add_argument(
-        "--train_text_encoder",
-        action="store_true",
-        default=True,
-        help="Whether to train the whole text encoder."
-    )
-    parser.add_argument(
         "--train_text_encoder_epochs",
         default=999999,
         help="Number of epochs the text encoder will be trained."
@@ -194,6 +188,11 @@ def parse_args():
         default=100
     )
     parser.add_argument(
+        "--ti_num_train_epochs",
+        type=int,
+        default=10
+    )
+    parser.add_argument(
         "--max_train_steps",
         type=int,
         default=None,
@@ -222,6 +221,12 @@ def parse_args():
         help="Initial learning rate (after the potential warmup period) to use.",
     )
     parser.add_argument(
+        "--ti_learning_rate",
+        type=float,
+        default=1e-2,
+        help="Initial learning rate (after the potential warmup period) to use.",
+    )
+    parser.add_argument(
         "--scale_lr",
         action="store_true",
         help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
@@ -541,15 +546,14 @@ def main():
         noise_scheduler=noise_scheduler,
         dtype=weight_dtype,
         seed=args.seed,
+        with_prior_preservation=args.num_class_images != 0,
+        prior_loss_weight=args.prior_loss_weight,
     )
 
     # Initial TI
 
     print("Phase 1: Textual Inversion")
 
-    ti_lr = 1e-1
-    ti_train_epochs = 10
-
     cur_dir = output_dir.joinpath("1-ti")
     cur_dir.mkdir(parents=True, exist_ok=True)
 
@@ -576,7 +580,7 @@ def main():
             size=args.resolution,
             shuffle=not args.no_tag_shuffle,
             template_key=args.train_data_template,
-            valid_set_size=args.valid_set_size,
+            valid_set_size=1,
             valid_set_repeat=args.valid_set_repeat,
             seed=args.seed,
             filter=partial(keyword_filter, [placeholder_token], args.collection, args.exclude_collections),
@@ -586,7 +590,7 @@ def main():
 
         optimizer = optimizer_class(
             text_encoder.text_model.embeddings.temp_token_embedding.parameters(),
-            lr=ti_lr,
+            lr=args.ti_learning_rate,
             weight_decay=0.0,
         )
 
@@ -595,8 +599,8 @@ def main():
             optimizer=optimizer,
             num_training_steps_per_epoch=len(datamodule.train_dataloader),
             gradient_accumulation_steps=args.gradient_accumulation_steps,
-            train_epochs=ti_train_epochs,
-            warmup_epochs=ti_train_epochs // 4,
+            train_epochs=args.ti_num_train_epochs,
+            warmup_epochs=args.ti_num_train_epochs // 4,
         )
 
         trainer(
@@ -606,18 +610,16 @@ def main():
             val_dataloader=datamodule.val_dataloader,
             optimizer=optimizer,
             lr_scheduler=lr_scheduler,
-            num_train_epochs=ti_train_epochs,
-            sample_frequency=1,
+            num_train_epochs=args.ti_num_train_epochs,
+            sample_frequency=2,
             checkpoint_frequency=9999999,
-            with_prior_preservation=args.num_class_images != 0,
-            prior_loss_weight=args.prior_loss_weight,
             # --
             tokenizer=tokenizer,
             sample_scheduler=sample_scheduler,
             output_dir=cur_subdir,
             placeholder_tokens=[placeholder_token],
             placeholder_token_ids=placeholder_token_ids,
-            learning_rate=ti_lr,
+            learning_rate=args.ti_learning_rate,
             gradient_checkpointing=args.gradient_checkpointing,
             use_emb_decay=True,
             sample_batch_size=args.sample_batch_size,
@@ -699,8 +701,6 @@ def main():
         lr_scheduler=lr_scheduler,
         num_train_epochs=args.num_train_epochs,
         sample_frequency=args.sample_frequency,
-        with_prior_preservation=args.num_class_images != 0,
-        prior_loss_weight=args.prior_loss_weight,
         # --
         tokenizer=tokenizer,
         sample_scheduler=sample_scheduler,