Diff Output Preserv loss for SDXL

library/train_util.py

@@ -396,6 +396,7 @@ class BaseSubset:
         caption_suffix: Optional[str],
         token_warmup_min: int,
         token_warmup_step: Union[float, int],
+        custom_attributes: Optional[Dict[str, Any]] = None,
     ) -> None:
         self.image_dir = image_dir
         self.alpha_mask = alpha_mask if alpha_mask is not None else False
@@ -419,6 +420,8 @@ class BaseSubset:
         self.token_warmup_min = token_warmup_min  # step=0におけるタグの数
         self.token_warmup_step = token_warmup_step  # N（N<1ならN*max_train_steps）ステップ目でタグの数が最大になる
 
+        self.custom_attributes = custom_attributes if custom_attributes is not None else {}
+
         self.img_count = 0
 
 
@@ -449,6 +452,7 @@ class DreamBoothSubset(BaseSubset):
         caption_suffix,
         token_warmup_min,
         token_warmup_step,
+        custom_attributes: Optional[Dict[str, Any]] = None,
     ) -> None:
         assert image_dir is not None, "image_dir must be specified / image_dirは指定が必須です"
 
@@ -473,6 +477,7 @@ class DreamBoothSubset(BaseSubset):
             caption_suffix,
             token_warmup_min,
             token_warmup_step,
+            custom_attributes=custom_attributes,
         )
 
         self.is_reg = is_reg
@@ -512,6 +517,7 @@ class FineTuningSubset(BaseSubset):
         caption_suffix,
         token_warmup_min,
         token_warmup_step,
+        custom_attributes: Optional[Dict[str, Any]] = None,
     ) -> None:
         assert metadata_file is not None, "metadata_file must be specified / metadata_fileは指定が必須です"
 
@@ -536,6 +542,7 @@ class FineTuningSubset(BaseSubset):
             caption_suffix,
             token_warmup_min,
             token_warmup_step,
+            custom_attributes=custom_attributes,
         )
 
         self.metadata_file = metadata_file
@@ -1474,11 +1481,14 @@ class BaseDataset(torch.utils.data.Dataset):
         target_sizes_hw = []
         flippeds = []  # 変数名が微妙
         text_encoder_outputs_list = []
+        custom_attributes = []
 
         for image_key in bucket[image_index : image_index + bucket_batch_size]:
             image_info = self.image_data[image_key]
             subset = self.image_to_subset[image_key]
 
+            custom_attributes.append(subset.custom_attributes)
+
             # in case of fine tuning, is_reg is always False
             loss_weights.append(self.prior_loss_weight if image_info.is_reg else 1.0)
 
@@ -1646,7 +1656,9 @@ class BaseDataset(torch.utils.data.Dataset):
                 return None
             return [torch.stack([converter(x[i]) for x in tensors_list]) for i in range(len(tensors_list[0]))]
 
+        # set example
         example = {}
+        example["custom_attributes"] = custom_attributes  # may be list of empty dict
         example["loss_weights"] = torch.FloatTensor(loss_weights)
         example["text_encoder_outputs_list"] = none_or_stack_elements(text_encoder_outputs_list, torch.FloatTensor)
         example["input_ids_list"] = none_or_stack_elements(input_ids_list, lambda x: x)
@@ -2630,7 +2642,9 @@ def debug_dataset(train_dataset, show_input_ids=False):
                     f'{ik}, size: {train_dataset.image_data[ik].image_size}, loss weight: {lw}, caption: "{cap}", original size: {orgsz}, crop top left: {crptl}, target size: {trgsz}, flipped: {flpdz}'
                 )
                 if "network_multipliers" in example:
-                    print(f"network multiplier: {example['network_multipliers'][j]}")
+                    logger.info(f"network multiplier: {example['network_multipliers'][j]}")
+                if "custom_attributes" in example:
+                    logger.info(f"custom attributes: {example['custom_attributes'][j]}")
 
                 # if show_input_ids:
                 #     logger.info(f"input ids: {iid}")
@@ -4091,6 +4105,7 @@ def enable_high_vram(args: argparse.Namespace):
         global HIGH_VRAM
         HIGH_VRAM = True
 
+
 def verify_training_args(args: argparse.Namespace):
     r"""
     Verify training arguments. Also reflect highvram option to global variable