Allow short and long caption combinations like form the new captioning system. Merge the network into the model before inference and reextract when done. Doubles inference speed on locon models during inference. allow splitting a batch into individual components and run them through alone. Basicallt gradient accumulation with single batch size.

toolkit/data_transfer_object/data_loader.py

@@ -82,7 +82,7 @@ class DataLoaderBatchDTO:
             self.control_tensor: Union[torch.Tensor, None] = None
             self.mask_tensor: Union[torch.Tensor, None] = None
             self.unaugmented_tensor: Union[torch.Tensor, None] = None
-            self.sigmas: Union[torch.Tensor, None] = None # can be added elseware and passed along training code
+            self.sigmas: Union[torch.Tensor, None] = None  # can be added elseware and passed along training code
             if not is_latents_cached:
                 # only return a tensor if latents are not cached
                 self.tensor: torch.Tensor = torch.cat([x.tensor.unsqueeze(0) for x in self.file_items])
@@ -160,6 +160,19 @@ class DataLoaderBatchDTO:
             add_if_not_present=add_if_not_present
         ) for x in self.file_items]
 
+    def get_caption_short_list(
+            self,
+            trigger=None,
+            to_replace_list=None,
+            add_if_not_present=True
+    ):
+        return [x.get_caption(
+            trigger=trigger,
+            to_replace_list=to_replace_list,
+            add_if_not_present=add_if_not_present,
+            short_caption=False
+        ) for x in self.file_items]
+
     def cleanup(self):
         del self.latents
         del self.tensor