Fix regularization images with validation

Adding metadata recording for validation arguments
Add comments about the validation split for clarity of intention

library/train_util.py

@@ -146,7 +146,12 @@ IMAGE_TRANSFORMS = transforms.Compose(
 TEXT_ENCODER_OUTPUTS_CACHE_SUFFIX = "_te_outputs.npz"
 TEXT_ENCODER_OUTPUTS_CACHE_SUFFIX_SD3 = "_sd3_te.npz"
 
-def split_train_val(paths: List[str], is_training_dataset: bool, validation_split: float, validation_seed: int) -> List[str]:
+def split_train_val(
+    paths: List[str], 
+    is_training_dataset: bool, 
+    validation_split: float, 
+    validation_seed: int | None
+) -> List[str]:
     """
     Split the dataset into train and validation
 
@@ -1830,6 +1835,9 @@ class BaseDataset(torch.utils.data.Dataset):
 class DreamBoothDataset(BaseDataset):
     IMAGE_INFO_CACHE_FILE = "metadata_cache.json"
 
+    # The is_training_dataset defines the type of dataset, training or validation 
+    # if is_training_dataset is True -> training dataset
+    # if is_training_dataset is False -> validation dataset
     def __init__(
         self,
         subsets: Sequence[DreamBoothSubset],
@@ -1965,8 +1973,29 @@ class DreamBoothDataset(BaseDataset):
                             size_set_count += 1
                     logger.info(f"set image size from cache files: {size_set_count}/{len(img_paths)}")
 
+            # We want to create a training and validation split. This should be improved in the future
+            # to allow a clearer distinction between training and validation. This can be seen as a 
+            # short-term solution to limit what is necessary to implement validation datasets
+            # 
+            # We split the dataset for the subset based on if we are doing a validation split
+            # The self.is_training_dataset defines the type of dataset, training or validation 
+            # if self.is_training_dataset is True -> training dataset
+            # if self.is_training_dataset is False -> validation dataset
             if self.validation_split > 0.0:
-                img_paths = split_train_val(img_paths, self.is_training_dataset, self.validation_split, self.validation_seed)
+                # For regularization images we do not want to split this dataset. 
+                if subset.is_reg is True:
+                    # Skip any validation dataset for regularization images
+                    if self.is_training_dataset is False:
+                        img_paths = []
+                    # Otherwise the img_paths remain as original img_paths and no split 
+                    # required for training images dataset of regularization images
+                else:
+                    img_paths = split_train_val(
+                        img_paths, 
+                        self.is_training_dataset, 
+                        self.validation_split, 
+                        self.validation_seed
+                    )
 
             logger.info(f"found directory {subset.image_dir} contains {len(img_paths)} image files")
 

train_network.py

@@ -898,6 +898,7 @@ class NetworkTrainer:
 
         accelerator.print("running training / 学習開始")
         accelerator.print(f"  num train images * repeats / 学習画像の数×繰り返し回数: {train_dataset_group.num_train_images}")
+        accelerator.print(f"  num validation images * repeats / 学習画像の数×繰り返し回数: {val_dataset_group.num_train_images if val_dataset_group is not None else 0}")
         accelerator.print(f"  num reg images / 正則化画像の数: {train_dataset_group.num_reg_images}")
         accelerator.print(f"  num batches per epoch / 1epochのバッチ数: {len(train_dataloader)}")
         accelerator.print(f"  num epochs / epoch数: {num_train_epochs}")
@@ -917,6 +918,7 @@ class NetworkTrainer:
             "ss_text_encoder_lr": text_encoder_lr,
             "ss_unet_lr": args.unet_lr,
             "ss_num_train_images": train_dataset_group.num_train_images,
+            "ss_num_validation_images": val_dataset_group.num_train_images if val_dataset_group is not None else 0,
             "ss_num_reg_images": train_dataset_group.num_reg_images,
             "ss_num_batches_per_epoch": len(train_dataloader),
             "ss_num_epochs": num_train_epochs,
@@ -964,6 +966,11 @@ class NetworkTrainer:
             "ss_huber_c": args.huber_c,
             "ss_fp8_base": bool(args.fp8_base),
             "ss_fp8_base_unet": bool(args.fp8_base_unet),
+            "ss_validation_seed": args.validation_seed, 
+            "ss_validation_split": args.validation_split, 
+            "ss_max_validation_steps": args.max_validation_steps, 
+            "ss_validate_every_n_epochs": args.validate_every_n_epochs, 
+            "ss_validate_every_n_steps": args.validate_every_n_steps, 
         }
 
         self.update_metadata(metadata, args)  # architecture specific metadata