Added arg to disable tag shuffling

4 files changed, 37 insertions, 18 deletions

diff --git a/data/csv.py b/data/csv.py
index ed8e93d..9ad7dd6 100644
--- a/data/csv.py
+++ b/data/csv.py
@@ -122,6 +122,7 @@ class VlpnDataModule():
         bucket_max_pixels: Optional[int] = None,
         progressive_buckets: bool = False,
         dropout: float = 0,
+        shuffle: bool = False,
         interpolation: str = "bicubic",
         template_key: str = "template",
         valid_set_size: Optional[int] = None,
@@ -150,6 +151,7 @@ class VlpnDataModule():
         self.bucket_max_pixels = bucket_max_pixels
         self.progressive_buckets = progressive_buckets
         self.dropout = dropout
+        self.shuffle = shuffle
         self.template_key = template_key
         self.interpolation = interpolation
         self.valid_set_size = valid_set_size
@@ -240,7 +242,7 @@ class VlpnDataModule():
             bucket_step_size=self.bucket_step_size, bucket_max_pixels=self.bucket_max_pixels,
             batch_size=self.batch_size, generator=generator,
             size=self.size, interpolation=self.interpolation,
-            num_class_images=self.num_class_images, dropout=self.dropout, shuffle=True,
+            num_class_images=self.num_class_images, dropout=self.dropout, shuffle=self.shuffle,
         )
 
         val_dataset = VlpnDataset(
diff --git a/train_dreambooth.py b/train_dreambooth.py
index 1a1f516..48a513c 100644
--- a/train_dreambooth.py
+++ b/train_dreambooth.py
@@ -133,6 +133,12 @@ def parse_args():
         help="Tag dropout probability.",
     )
     parser.add_argument(
+        "--tag_shuffle",
+        type="store_true",
+        default=True,
+        help="Shuffle tags.",
+    )
+    parser.add_argument(
         "--vector_dropout",
         type=int,
         default=0,
@@ -398,7 +404,7 @@ def parse_args():
     parser.add_argument(
         "--sample_steps",
         type=int,
-        default=15,
+        default=20,
         help="Number of steps for sample generation. Higher values will result in more detailed samples, but longer runtimes.",
     )
     parser.add_argument(
@@ -768,6 +774,7 @@ def main():
         bucket_step_size=args.bucket_step_size,
         bucket_max_pixels=args.bucket_max_pixels,
         dropout=args.tag_dropout,
+        shuffle=args.tag_shuffle,
         template_key=args.train_data_template,
         valid_set_size=args.valid_set_size,
         valid_set_repeat=args.valid_set_repeat,
diff --git a/train_ti.py b/train_ti.py
index df8d443..35be74c 100644
--- a/train_ti.py
+++ b/train_ti.py
@@ -169,6 +169,11 @@ def parse_args():
         help="Tag dropout probability.",
     )
     parser.add_argument(
+        "--tag_shuffle",
+        type="store_true",
+        help="Shuffle tags.",
+    )
+    parser.add_argument(
         "--vector_dropout",
         type=int,
         default=0,
@@ -395,7 +400,7 @@ def parse_args():
     parser.add_argument(
         "--sample_steps",
         type=int,
-        default=15,
+        default=20,
         help="Number of steps for sample generation. Higher values will result in more detailed samples, but longer runtimes.",
     )
     parser.add_argument(
@@ -745,6 +750,7 @@ def main():
         bucket_step_size=args.bucket_step_size,
         bucket_max_pixels=args.bucket_max_pixels,
         dropout=args.tag_dropout,
+        shuffle=args.tag_shuffle,
         template_key=args.train_data_template,
         valid_set_size=args.valid_set_size,
         valid_set_repeat=args.valid_set_repeat,
@@ -860,6 +866,12 @@ def main():
         finally:
             pass
 
+    def on_clip():
+        accelerator.clip_grad_norm_(
+            text_encoder.text_model.embeddings.temp_token_embedding.parameters(),
+            args.max_grad_norm
+        )
+
     loop = partial(
         run_model,
         vae,
@@ -894,8 +906,9 @@ def main():
             loop,
             on_train=on_train,
             on_eval=on_eval,
+            on_clip=on_clip,
         )
-        lr_finder.run(num_epochs=200, end_lr=1e3)
+        lr_finder.run(num_epochs=100, end_lr=1e3)
 
         plt.savefig(basepath.joinpath("lr.png"), dpi=300)
         plt.close()
@@ -975,10 +988,7 @@ def main():
                         accelerator.backward(loss)
 
                         if accelerator.sync_gradients:
-                            accelerator.clip_grad_norm_(
-                                text_encoder.text_model.embeddings.temp_token_embedding.parameters(),
-                                args.max_grad_norm
-                            )
+                            on_clip()
 
                         optimizer.step()
                         if not accelerator.optimizer_step_was_skipped:
diff --git a/training/lr.py b/training/lr.py
index 68e0f72..dfb1743 100644
--- a/training/lr.py
+++ b/training/lr.py
@@ -48,7 +48,7 @@ class LRFinder():
         skip_start: int = 10,
         skip_end: int = 5,
         num_epochs: int = 100,
-        num_train_batches: int = 1,
+        num_train_batches: int = math.inf,
         num_val_batches: int = math.inf,
         smooth_f: float = 0.05,
     ):
@@ -156,6 +156,15 @@ class LRFinder():
             # self.model.load_state_dict(self.model_state)
             # self.optimizer.load_state_dict(self.optimizer_state)
 
+        if skip_end == 0:
+            lrs = lrs[skip_start:]
+            losses = losses[skip_start:]
+            accs = accs[skip_start:]
+        else:
+            lrs = lrs[skip_start:-skip_end]
+            losses = losses[skip_start:-skip_end]
+            accs = accs[skip_start:-skip_end]
+
         fig, ax_loss = plt.subplots()
         ax_acc = ax_loss.twinx()
 
@@ -171,15 +180,6 @@ class LRFinder():
         print("LR suggestion: steepest gradient")
         min_grad_idx = None
 
-        if skip_end == 0:
-            lrs = lrs[skip_start:]
-            losses = losses[skip_start:]
-            accs = accs[skip_start:]
-        else:
-            lrs = lrs[skip_start:-skip_end]
-            losses = losses[skip_start:-skip_end]
-            accs = accs[skip_start:-skip_end]
-
         try:
             min_grad_idx = np.gradient(np.array(losses)).argmin()
         except ValueError: