add warning for bucket_reso_steps with SDXL

finetune/prepare_buckets_latents.py

@@ -52,6 +52,10 @@ def main(args):
     # assert args.bucket_reso_steps % 8 == 0, f"bucket_reso_steps must be divisible by 8 / bucket_reso_stepは8で割り切れる必要があります"
     if args.bucket_reso_steps % 8 > 0:
         print(f"resolution of buckets in training time is a multiple of 8 / 学習時の各bucketの解像度は8単位になります")
+    if args.bucket_reso_steps % 32 > 0:
+        print(
+            f"WARNING: bucket_reso_steps is not divisible by 32. It is not working with SDXL / bucket_reso_stepsが32で割り切れません。SDXLでは動作しません"
+        )
 
     train_data_dir_path = Path(args.train_data_dir)
     image_paths: List[str] = [str(p) for p in train_util.glob_images_pathlib(train_data_dir_path, args.recursive)]

library/train_util.py

@@ -800,6 +800,12 @@ class BaseDataset(torch.utils.data.Dataset):
         random.shuffle(self.buckets_indices)
         self.bucket_manager.shuffle()
 
+    def verify_bucket_reso_steps(self, min_steps: int):
+        assert self.bucket_reso_steps is None or self.bucket_reso_steps % min_steps == 0, (
+            f"bucket_reso_steps is {self.bucket_reso_steps}. it must be divisible by {min_steps}.\n"
+            + f"bucket_reso_stepsが{self.bucket_reso_steps}です。{min_steps}で割り切れる必要があります"
+        )
+
     def is_latent_cacheable(self):
         return all([not subset.color_aug and not subset.random_crop for subset in self.subsets])
 
@@ -1831,6 +1837,10 @@ class DatasetGroup(torch.utils.data.ConcatDataset):
         for dataset in self.datasets:
             dataset.set_caching_mode(caching_mode)
 
+    def verify_bucket_reso_steps(self, min_steps: int):
+        for dataset in self.datasets:
+            dataset.verify_bucket_reso_steps(min_steps)
+
     def is_latent_cacheable(self) -> bool:
         return all([dataset.is_latent_cacheable() for dataset in self.datasets])
 
@@ -2020,6 +2030,9 @@ class MinimalDataset(BaseDataset):
         self.is_reg = False
         self.image_dir = "dummy"  # for metadata
 
+    def verify_bucket_reso_steps(self, min_steps: int):
+        pass
+
     def is_latent_cacheable(self) -> bool:
         return False
 

sdxl_train.py

@@ -98,6 +98,8 @@ def train(args):
     ds_for_collater = train_dataset_group if args.max_data_loader_n_workers == 0 else None
     collater = train_util.collater_class(current_epoch, current_step, ds_for_collater)
 
+    train_dataset_group.verify_bucket_reso_steps(32)
+
     if args.debug_dataset:
         train_util.debug_dataset(train_dataset_group, True)
         return

sdxl_train_network.py

@@ -23,6 +23,8 @@ class SdxlNetworkTrainer(train_network.NetworkTrainer):
             args.network_train_unet_only or not args.cache_text_encoder_outputs
         ), "network for Text Encoder cannot be trained with caching Text Encoder outputs / Text Encoderの出力をキャッシュしながらText Encoderのネットワークを学習することはできません"
 
+        train_dataset_group.verify_bucket_reso_steps(32)
+
     def load_target_model(self, args, weight_dtype, accelerator):
         (
             load_stable_diffusion_format,

sdxl_train_textual_inversion.py

@@ -19,6 +19,8 @@ class SdxlTextualInversionTrainer(train_textual_inversion.TextualInversionTraine
         super().assert_extra_args(args, train_dataset_group)
         sdxl_train_util.verify_sdxl_training_args(args, supportTextEncoderCaching=False)
 
+        train_dataset_group.verify_bucket_reso_steps(32)
+
     def load_target_model(self, args, weight_dtype, accelerator):
         (
             load_stable_diffusion_format,