Add support for audo frame count so datasets can have varrying length videos. Varous ltx 2.3 VAE optimizations such as removing tiling articacts, and doing frame split encoding to reduce vram on encoding/decoding.

toolkit/data_transfer_object/data_loader.py

@@ -54,7 +54,9 @@ class FileItemDTO(
     def __init__(self, *args, **kwargs):
         self.path = kwargs.get("path", "")
         self.dataset_config: "DatasetConfig" = kwargs.get("dataset_config", None)
-        self.is_video = self.dataset_config.num_frames > 1
+        self.is_video = self.dataset_config.num_frames > 1 or self.dataset_config.auto_frame_count
+        self.num_frames = self.dataset_config.num_frames
+        self.temporal_compression = kwargs.get("temporal_compression", 8)
         size_database = kwargs.get("size_database", {})
         dataset_root = kwargs.get("dataset_root", None)
         self.encode_control_in_text_embeddings = kwargs.get(
@@ -197,6 +199,8 @@ class DataLoaderBatchDTO:
             # just for holding noise and preds during training
             self.audio_target: Union[torch.Tensor, None] = None
             self.audio_pred: Union[torch.Tensor, None] = None
+            
+            self.num_frames: int = self.file_items[0].num_frames
 
             if not is_latents_cached:
                 # only return a tensor if latents are not cached