add warning for bucket_reso_steps with SDXL

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