Add Validation loss for LoRA training

library/config_util.py

@@ -73,6 +73,8 @@ class BaseSubsetParams:
     token_warmup_min: int = 1
     token_warmup_step: float = 0
     custom_attributes: Optional[Dict[str, Any]] = None
+    validation_seed: int = 0
+    validation_split: float = 0.0
 
 
 @dataclass
@@ -102,6 +104,8 @@ class BaseDatasetParams:
     resolution: Optional[Tuple[int, int]] = None
     network_multiplier: float = 1.0
     debug_dataset: bool = False
+    validation_seed: Optional[int] = None
+    validation_split: float = 0.0
 
 
 @dataclass
@@ -478,9 +482,27 @@ def generate_dataset_group_by_blueprint(dataset_group_blueprint: DatasetGroupBlu
             dataset_klass = FineTuningDataset
 
         subsets = [subset_klass(**asdict(subset_blueprint.params)) for subset_blueprint in dataset_blueprint.subsets]
-        dataset = dataset_klass(subsets=subsets, **asdict(dataset_blueprint.params))
+        dataset = dataset_klass(subsets=subsets, is_train=True, **asdict(dataset_blueprint.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:
+            continue
+        if dataset_blueprint.is_controlnet:
+            subset_klass = ControlNetSubset
+            dataset_klass = ControlNetDataset
+        elif dataset_blueprint.is_dreambooth:
+            subset_klass = DreamBoothSubset
+            dataset_klass = DreamBoothDataset
+        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_train=False, **asdict(dataset_blueprint.params))
+        val_datasets.append(dataset)
+
     # print info
     info = ""
     for i, dataset in enumerate(datasets):
@@ -566,6 +588,50 @@ def generate_dataset_group_by_blueprint(dataset_group_blueprint: DatasetGroupBlu
 
     logger.info(f"{info}")
 
+    if len(val_datasets) > 0:
+        info = ""
+
+        for i, dataset in enumerate(val_datasets):
+            info += dedent(
+                f"""\
+      [Validation Dataset {i}]
+        batch_size: {dataset.batch_size}
+        resolution: {(dataset.width, dataset.height)}
+        enable_bucket: {dataset.enable_bucket}
+        network_multiplier: {dataset.network_multiplier}
+    """
+        )
+
+            if dataset.enable_bucket:
+                info += indent(
+                    dedent(
+                        f"""\
+        min_bucket_reso: {dataset.min_bucket_reso}
+        max_bucket_reso: {dataset.max_bucket_reso}
+        bucket_reso_steps: {dataset.bucket_reso_steps}
+        bucket_no_upscale: {dataset.bucket_no_upscale}
+      \n"""
+                ),
+                "  ",
+            )
+            else:
+                info += "\n"
+
+            for j, subset in enumerate(dataset.subsets):
+                info += indent(
+                    dedent(
+                       f"""\
+        [Subset {j} of Validation Dataset {i}]
+          image_dir: "{subset.image_dir}"
+          image_count: {subset.img_count}
+          num_repeats: {subset.num_repeats}
+      """
+                ),
+                "  ",
+            )
+
+        logger.info(f"{info}")
+
     # make buckets first because it determines the length of dataset
     # and set the same seed for all datasets
     seed = random.randint(0, 2**31)  # actual seed is seed + epoch_no
@@ -574,7 +640,15 @@ def generate_dataset_group_by_blueprint(dataset_group_blueprint: DatasetGroupBlu
         dataset.make_buckets()
         dataset.set_seed(seed)
 
-    return DatasetGroup(datasets)
+    for i, dataset in enumerate(val_datasets):
+        logger.info(f"[Validation Dataset {i}]")
+        dataset.make_buckets()
+        dataset.set_seed(seed)
+
+    return (
+        DatasetGroup(datasets),
+        DatasetGroup(val_datasets) if val_datasets else None
+    )
 
 
 def generate_dreambooth_subsets_config_by_subdirs(train_data_dir: Optional[str] = None, reg_data_dir: Optional[str] = None):