diff --git a/library/config_util.py b/library/config_util.py
index 834d6bfa..a2e07dc6 100644
--- a/library/config_util.py
+++ b/library/config_util.py
@@ -471,36 +471,49 @@ def generate_dataset_group_by_blueprint(dataset_group_blueprint: DatasetGroupBlu
     datasets: List[Union[DreamBoothDataset, FineTuningDataset, ControlNetDataset]] = []
 
     for dataset_blueprint in dataset_group_blueprint.datasets:
+        extra_dataset_params = {}
+
         if dataset_blueprint.is_controlnet:
             subset_klass = ControlNetSubset
             dataset_klass = ControlNetDataset
         elif dataset_blueprint.is_dreambooth:
             subset_klass = DreamBoothSubset
             dataset_klass = DreamBoothDataset
+            # DreamBooth datasets support splitting training and validation datasets
+            extra_dataset_params = {"is_training_dataset": True}
         else:
             subset_klass = FineTuningSubset
             dataset_klass = FineTuningDataset
 
         subsets = [subset_klass(**asdict(subset_blueprint.params)) for subset_blueprint in dataset_blueprint.subsets]
-        dataset = dataset_klass(subsets=subsets, is_training_dataset=True, **asdict(dataset_blueprint.params))
+        dataset = dataset_klass(subsets=subsets, **asdict(dataset_blueprint.params), **extra_dataset_params)
         datasets.append(dataset)
 
     val_datasets: List[Union[DreamBoothDataset, FineTuningDataset, ControlNetDataset]] = []
     for dataset_blueprint in dataset_group_blueprint.datasets:
-        if dataset_blueprint.params.validation_split <= 0.0:
+        if dataset_blueprint.params.validation_split < 0.0 or dataset_blueprint.params.validation_split > 1.0:
+            logging.warning(f"Dataset param `validation_split` ({dataset_blueprint.params.validation_split}) is not a valid number between 0.0 and 1.0, skipping validation split...")
             continue
+
+        # if the dataset isn't setting a validation split, there is no current validation dataset
+        if dataset_blueprint.params.validation_split == 0.0:
+            continue
+
+        extra_dataset_params = {}
         if dataset_blueprint.is_controlnet:
             subset_klass = ControlNetSubset
             dataset_klass = ControlNetDataset
         elif dataset_blueprint.is_dreambooth:
             subset_klass = DreamBoothSubset
             dataset_klass = DreamBoothDataset
+            # DreamBooth datasets support splitting training and validation datasets
+            extra_dataset_params = {"is_training_dataset": False}
         else:
             subset_klass = FineTuningSubset
             dataset_klass = FineTuningDataset
 
         subsets = [subset_klass(**asdict(subset_blueprint.params)) for subset_blueprint in dataset_blueprint.subsets]
-        dataset = dataset_klass(subsets=subsets, is_training_dataset=False, **asdict(dataset_blueprint.params))
+        dataset = dataset_klass(subsets=subsets, **asdict(dataset_blueprint.params), **extra_dataset_params)
         val_datasets.append(dataset)
 
     def print_info(_datasets, dataset_type: str):