6 files changed, 91 insertions, 76 deletions

diff --git a/data/csv.py b/data/csv.py
index 002fdd2..968af8d 100644
--- a/data/csv.py
+++ b/data/csv.py
@@ -269,18 +269,22 @@ class VlpnDataModule():
 
         num_images = len(items)
 
-        valid_set_size = self.valid_set_size if self.valid_set_size is not None else num_images // 10
-        valid_set_size = max(valid_set_size, 1)
-        train_set_size = num_images - valid_set_size
+        valid_set_size = min(self.valid_set_size, num_images) if self.valid_set_size is not None else num_images // 10
+        train_set_size = max(num_images - valid_set_size, 1)
+        valid_set_size = num_images - train_set_size
 
         generator = torch.Generator(device="cpu")
         if self.seed is not None:
             generator = generator.manual_seed(self.seed)
 
-        data_train, data_val = random_split(items, [train_set_size, valid_set_size], generator=generator)
+        collate_fn_ = partial(collate_fn, self.dtype, self.tokenizer, self.num_class_images != 0)
+
+        if valid_set_size == 0:
+            data_train, data_val = items, []
+        else:
+            data_train, data_val = random_split(items, [train_set_size, valid_set_size], generator=generator)
 
         self.data_train = self.pad_items(data_train, self.num_class_images)
-        self.data_val = self.pad_items(data_val)
 
         train_dataset = VlpnDataset(
             self.data_train, self.tokenizer,
@@ -291,26 +295,29 @@ class VlpnDataModule():
             num_class_images=self.num_class_images, dropout=self.dropout, shuffle=self.shuffle,
         )
 
-        val_dataset = VlpnDataset(
-            self.data_val, self.tokenizer,
-            num_buckets=self.num_buckets, progressive_buckets=True,
-            bucket_step_size=self.bucket_step_size, bucket_max_pixels=self.bucket_max_pixels,
-            repeat=self.valid_set_repeat,
-            batch_size=self.batch_size, generator=generator,
-            size=self.size, interpolation=self.interpolation,
-        )
-
-        collate_fn_ = partial(collate_fn, self.dtype, self.tokenizer, self.num_class_images != 0)
-
         self.train_dataloader = DataLoader(
             train_dataset,
             batch_size=None, pin_memory=True, collate_fn=collate_fn_
         )
 
-        self.val_dataloader = DataLoader(
-            val_dataset,
-            batch_size=None, pin_memory=True, collate_fn=collate_fn_
-        )
+        if valid_set_size != 0:
+            self.data_val = self.pad_items(data_val)
+
+            val_dataset = VlpnDataset(
+                self.data_val, self.tokenizer,
+                num_buckets=self.num_buckets, progressive_buckets=True,
+                bucket_step_size=self.bucket_step_size, bucket_max_pixels=self.bucket_max_pixels,
+                repeat=self.valid_set_repeat,
+                batch_size=self.batch_size, generator=generator,
+                size=self.size, interpolation=self.interpolation,
+            )
+
+            self.val_dataloader = DataLoader(
+                val_dataset,
+                batch_size=None, pin_memory=True, collate_fn=collate_fn_
+            )
+        else:
+            self.val_dataloader = None
 
 
 class VlpnDataset(IterableDataset):
diff --git a/train_dreambooth.py b/train_dreambooth.py
index 05777d0..4e41f77 100644
--- a/train_dreambooth.py
+++ b/train_dreambooth.py
@@ -564,7 +564,7 @@ def main():
             embeddings=embeddings,
             placeholder_tokens=[placeholder_token],
             initializer_tokens=[initializer_token],
-            num_vectors=num_vectors
+            num_vectors=[num_vectors]
         )
 
         datamodule = VlpnDataModule(
@@ -579,7 +579,7 @@ def main():
             valid_set_size=args.valid_set_size,
             valid_set_repeat=args.valid_set_repeat,
             seed=args.seed,
-            filter=partial(keyword_filter, placeholder_token, args.collection, args.exclude_collections),
+            filter=partial(keyword_filter, [placeholder_token], args.collection, args.exclude_collections),
             dtype=weight_dtype
         )
         datamodule.setup()
@@ -654,7 +654,7 @@ def main():
         valid_set_size=args.valid_set_size,
         valid_set_repeat=args.valid_set_repeat,
         seed=args.seed,
-        filter=partial(keyword_filter, args.placeholder_tokens, args.collection, args.exclude_collections),
+        filter=partial(keyword_filter, None, args.collection, args.exclude_collections),
         dtype=weight_dtype
     )
     datamodule.setup()
diff --git a/train_ti.py b/train_ti.py
index 48a2333..a894ee7 100644
--- a/train_ti.py
+++ b/train_ti.py
@@ -582,9 +582,6 @@ def main():
     )
     datamodule.setup()
 
-    train_dataloader = datamodule.train_dataloader
-    val_dataloader = datamodule.val_dataloader
-
     if args.num_class_images != 0:
         generate_class_images(
             accelerator,
@@ -623,7 +620,7 @@ def main():
     lr_scheduler = get_scheduler(
         args.lr_scheduler,
         optimizer=optimizer,
-        num_training_steps_per_epoch=len(train_dataloader),
+        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,
@@ -637,8 +634,8 @@ def main():
 
     trainer(
         project="textual_inversion",
-        train_dataloader=train_dataloader,
-        val_dataloader=val_dataloader,
+        train_dataloader=datamodule.train_dataloader,
+        val_dataloader=datamodule.val_dataloader,
         optimizer=optimizer,
         lr_scheduler=lr_scheduler,
         num_train_epochs=args.num_train_epochs,
diff --git a/training/functional.py b/training/functional.py
index 1b6162b..c6b4dc3 100644
--- a/training/functional.py
+++ b/training/functional.py
@@ -73,7 +73,7 @@ def save_samples(
     vae: AutoencoderKL,
     sample_scheduler: DPMSolverMultistepScheduler,
     train_dataloader: DataLoader,
-    val_dataloader: DataLoader,
+    val_dataloader: Optional[DataLoader],
     dtype: torch.dtype,
     output_dir: Path,
     seed: int,
@@ -111,11 +111,13 @@ def save_samples(
 
     generator = torch.Generator(device=accelerator.device).manual_seed(seed)
 
-    for pool, data, gen in [
-        ("stable", val_dataloader, generator),
-        ("val", val_dataloader, None),
-        ("train", train_dataloader, None)
-    ]:
+    datasets: list[tuple[str, DataLoader, Optional[torch.Generator]]] = [("train", train_dataloader, None)]
+
+    if val_dataloader is not None:
+        datasets.append(("stable", val_dataloader, generator))
+        datasets.append(("val", val_dataloader, None))
+
+    for pool, data, gen in datasets:
         all_samples = []
         file_path = samples_path.joinpath(pool, f"step_{step}.jpg")
         file_path.parent.mkdir(parents=True, exist_ok=True)
@@ -328,7 +330,7 @@ def train_loop(
     optimizer: torch.optim.Optimizer,
     lr_scheduler: torch.optim.lr_scheduler._LRScheduler,
     train_dataloader: DataLoader,
-    val_dataloader: DataLoader,
+    val_dataloader: Optional[DataLoader],
     loss_step: Union[Callable[[int, Any], Tuple[Any, Any, int]], Callable[[int, Any, bool], Tuple[Any, Any, int]]],
     sample_frequency: int = 10,
     checkpoint_frequency: int = 50,
@@ -337,7 +339,7 @@ def train_loop(
     callbacks: TrainingCallbacks = TrainingCallbacks(),
 ):
     num_training_steps_per_epoch = math.ceil(len(train_dataloader) / accelerator.gradient_accumulation_steps)
-    num_val_steps_per_epoch = len(val_dataloader)
+    num_val_steps_per_epoch = len(val_dataloader) if val_dataloader is not None else 0
 
     num_training_steps = num_training_steps_per_epoch * num_epochs
     num_val_steps = num_val_steps_per_epoch * num_epochs
@@ -350,6 +352,7 @@ def train_loop(
     avg_loss_val = AverageMeter()
     avg_acc_val = AverageMeter()
 
+    max_acc = 0.0
     max_acc_val = 0.0
 
     local_progress_bar = tqdm(
@@ -432,49 +435,57 @@ def train_loop(
 
             accelerator.wait_for_everyone()
 
-            model.eval()
-
-            cur_loss_val = AverageMeter()
-            cur_acc_val = AverageMeter()
-
-            with torch.inference_mode(), on_eval():
-                for step, batch in enumerate(val_dataloader):
-                    loss, acc, bsz = loss_step(step, batch, True)
-
-                    loss = loss.detach_()
-                    acc = acc.detach_()
-
-                    cur_loss_val.update(loss, bsz)
-                    cur_acc_val.update(acc, bsz)
+            if val_dataloader is not None:
+                model.eval()
 
-                    avg_loss_val.update(loss, bsz)
-                    avg_acc_val.update(acc, bsz)
+                cur_loss_val = AverageMeter()
+                cur_acc_val = AverageMeter()
 
-                    local_progress_bar.update(1)
-                    global_progress_bar.update(1)
+                with torch.inference_mode(), on_eval():
+                    for step, batch in enumerate(val_dataloader):
+                        loss, acc, bsz = loss_step(step, batch, True)
 
-                    logs = {
-                        "val/loss": avg_loss_val.avg.item(),
-                        "val/acc": avg_acc_val.avg.item(),
-                        "val/cur_loss": loss.item(),
-                        "val/cur_acc": acc.item(),
-                    }
-                    local_progress_bar.set_postfix(**logs)
+                        loss = loss.detach_()
+                        acc = acc.detach_()
 
-            logs["val/cur_loss"] = cur_loss_val.avg.item()
-            logs["val/cur_acc"] = cur_acc_val.avg.item()
+                        cur_loss_val.update(loss, bsz)
+                        cur_acc_val.update(acc, bsz)
 
-            accelerator.log(logs, step=global_step)
+                        avg_loss_val.update(loss, bsz)
+                        avg_acc_val.update(acc, bsz)
 
-            local_progress_bar.clear()
-            global_progress_bar.clear()
+                        local_progress_bar.update(1)
+                        global_progress_bar.update(1)
 
-            if accelerator.is_main_process:
-                if avg_acc_val.avg.item() > max_acc_val:
-                    accelerator.print(
-                        f"Global step {global_step}: Validation accuracy reached new maximum: {max_acc_val:.2e} -> {avg_acc_val.avg.item():.2e}")
-                    on_checkpoint(global_step + global_step_offset, "milestone")
-                    max_acc_val = avg_acc_val.avg.item()
+                        logs = {
+                            "val/loss": avg_loss_val.avg.item(),
+                            "val/acc": avg_acc_val.avg.item(),
+                            "val/cur_loss": loss.item(),
+                            "val/cur_acc": acc.item(),
+                        }
+                        local_progress_bar.set_postfix(**logs)
+
+                logs["val/cur_loss"] = cur_loss_val.avg.item()
+                logs["val/cur_acc"] = cur_acc_val.avg.item()
+
+                accelerator.log(logs, step=global_step)
+
+                local_progress_bar.clear()
+                global_progress_bar.clear()
+
+                if accelerator.is_main_process:
+                    if avg_acc_val.avg.item() > max_acc_val:
+                        accelerator.print(
+                            f"Global step {global_step}: Validation accuracy reached new maximum: {max_acc_val:.2e} -> {avg_acc_val.avg.item():.2e}")
+                        on_checkpoint(global_step + global_step_offset, "milestone")
+                        max_acc_val = avg_acc_val.avg.item()
+            else:
+                if accelerator.is_main_process:
+                    if avg_acc.avg.item() > max_acc:
+                        accelerator.print(
+                            f"Global step {global_step}: Training accuracy reached new maximum: {max_acc:.2e} -> {avg_acc.avg.item():.2e}")
+                        on_checkpoint(global_step + global_step_offset, "milestone")
+                        max_acc = avg_acc.avg.item()
 
         # Create the pipeline using using the trained modules and save it.
         if accelerator.is_main_process:
@@ -499,7 +510,7 @@ def train(
     seed: int,
     project: str,
     train_dataloader: DataLoader,
-    val_dataloader: DataLoader,
+    val_dataloader: Optional[DataLoader],
     optimizer: torch.optim.Optimizer,
     lr_scheduler: torch.optim.lr_scheduler._LRScheduler,
     callbacks_fn: Callable[..., TrainingCallbacks],
diff --git a/training/strategy/dreambooth.py b/training/strategy/dreambooth.py
index 6e7ebe2..aeaa828 100644
--- a/training/strategy/dreambooth.py
+++ b/training/strategy/dreambooth.py
@@ -26,7 +26,7 @@ def dreambooth_strategy(
     vae: AutoencoderKL,
     sample_scheduler: DPMSolverMultistepScheduler,
     train_dataloader: DataLoader,
-    val_dataloader: DataLoader,
+    val_dataloader: Optional[DataLoader],
     output_dir: Path,
     seed: int,
     train_text_encoder_epochs: int,
diff --git a/training/strategy/ti.py b/training/strategy/ti.py
index 753dce0..568f9eb 100644
--- a/training/strategy/ti.py
+++ b/training/strategy/ti.py
@@ -26,7 +26,7 @@ def textual_inversion_strategy(
     vae: AutoencoderKL,
     sample_scheduler: DPMSolverMultistepScheduler,
     train_dataloader: DataLoader,
-    val_dataloader: DataLoader,
+    val_dataloader: Optional[DataLoader],
     output_dir: Path,
     seed: int,
     placeholder_tokens: list[str],