fix control net

library/train_util.py

@@ -1816,6 +1816,7 @@ class ControlNetDataset(BaseDataset):
     def __init__(
         self,
         subsets: Sequence[ControlNetSubset],
+        is_train: bool,
         batch_size: int,
         tokenizer,
         max_token_length,
@@ -1826,6 +1827,8 @@ class ControlNetDataset(BaseDataset):
         max_bucket_reso: int,
         bucket_reso_steps: int,
         bucket_no_upscale: bool,
+        validation_split: float,
+        validation_seed: Optional[int],        
         debug_dataset: float,
     ) -> None:
         super().__init__(tokenizer, max_token_length, resolution, network_multiplier, debug_dataset)
@@ -1860,6 +1863,7 @@ class ControlNetDataset(BaseDataset):
 
         self.dreambooth_dataset_delegate = DreamBoothDataset(
             db_subsets,
+            is_train,
             batch_size,
             tokenizer,
             max_token_length,
@@ -1871,6 +1875,8 @@ class ControlNetDataset(BaseDataset):
             bucket_reso_steps,
             bucket_no_upscale,
             1.0,
+            validation_split,
+            validation_seed,
             debug_dataset,
         )
 
@@ -1878,7 +1884,10 @@ class ControlNetDataset(BaseDataset):
         self.image_data = self.dreambooth_dataset_delegate.image_data
         self.batch_size = batch_size
         self.num_train_images = self.dreambooth_dataset_delegate.num_train_images
-        self.num_reg_images = self.dreambooth_dataset_delegate.num_reg_images
+        self.num_reg_images = self.dreambooth_dataset_delegate.num_reg_images        
+        self.is_train = is_train
+        self.validation_split = validation_split
+        self.validation_seed = validation_seed 
 
         # assert all conditioning data exists
         missing_imgs = []
@@ -1911,8 +1920,8 @@ class ControlNetDataset(BaseDataset):
                 [cond_img_path for cond_img_path in conditioning_img_paths if cond_img_path not in cond_imgs_with_img]
             )
 
-        assert len(missing_imgs) == 0, f"missing conditioning data for {len(missing_imgs)} images: {missing_imgs}"
-        assert len(extra_imgs) == 0, f"extra conditioning data for {len(extra_imgs)} images: {extra_imgs}"
+        #assert len(missing_imgs) == 0, f"missing conditioning data for {len(missing_imgs)} images: {missing_imgs}"
+        #assert len(extra_imgs) == 0, f"extra conditioning data for {len(extra_imgs)} images: {extra_imgs}"
 
         self.conditioning_image_transforms = IMAGE_TRANSFORMS