saving more changes related to args.

.gitignore

@@ -163,7 +163,7 @@ cython_debug/
 #  and can be added to the global gitignore or merged into this file.  For a more nuclear
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
 #.idea/
-
+ltx-video/samples
 # manually added
 wandb/
 *.txt

finetrainers/args.py

@@ -556,7 +556,7 @@ def _add_dataset_arguments(parser: argparse.ArgumentParser) -> None:
     parser.add_argument(
         "--pose_column",
         type=str,
-        default="text",
+        default=None,
         help="The column of the dataset containing the instance prompt for each video. Or, the name of the file in `--data_root` folder containing the line-separated instance prompts.",
     )
     parser.add_argument(

finetrainers/dataset.py

@@ -46,11 +46,11 @@ class ImageOrVideoDataset(Dataset):
         data_root: str,
         caption_column: str,
         video_column: str,
-        pose_column: str,
         resolution_buckets: List[Tuple[int, int, int]],
         dataset_file: Optional[str] = None,
         id_token: Optional[str] = None,
         remove_llm_prefixes: bool = False,
+        pose_column: str = None,
     ) -> None:
         super().__init__()
 
@@ -178,8 +178,20 @@ class ImageOrVideoDataset(Dataset):
 
         prompt_path = self.data_root.joinpath(self.caption_column)
         video_path = self.data_root.joinpath(self.video_column)
-        pose_path = self.data_root.joinpath(self.pose_column)
+
-    
+        if self.pose_column != None:
+            pose_path = self.data_root.joinpath(self.pose_column)
+            pose_conditioning = True
+
+            if not pose_path.exists() or not pose_path.is_file() or not self.pose_column:
+                pose_conditioning = False
+
+            if pose_conditioning:
+                with open(pose_path, "r", encoding="utf-8") as file:
+                    pose_paths = [self.data_root.joinpath(line.strip()) for line in file.readlines() if len(line.strip()) > 0]
+        else:
+            pose_paths = None 
+
         if not prompt_path.exists() or not prompt_path.is_file():
             raise ValueError(
                 "Expected `--caption_column` to be path to a file in `--data_root` containing line-separated text prompts."
@@ -188,16 +200,13 @@ class ImageOrVideoDataset(Dataset):
             raise ValueError(
                 "Expected `--video_column` to be path to a file in `--data_root` containing line-separated paths to video data in the same directory."
             )
-        if not pose_path.exists() or not pose_path.is_file():
+
-            raise ValueError(
-                "Expected `--pose_column` to be path to a file in `--data_root` containing line-separated paths to video data in the same directory."
-            )
         with open(prompt_path, "r", encoding="utf-8") as file:
             prompts = [line.strip() for line in file.readlines() if len(line.strip()) > 0]
         with open(video_path, "r", encoding="utf-8") as file:
             video_paths = [self.data_root.joinpath(line.strip()) for line in file.readlines() if len(line.strip()) > 0]
-        with open(pose_path, "r", encoding="utf-8") as file:
+
-            pose_paths = [self.data_root.joinpath(line.strip()) for line in file.readlines() if len(line.strip()) > 0]
+       
 
         if any(not path.is_file() for path in video_paths):
             raise ValueError(

finetrainers/ltx_video/lora.py

@@ -181,7 +181,7 @@ def post_latent_preparation(
     return {"latents": latents, "num_frames": num_frames, "height": height, "width": width}
 
 
-def collate_fn_t2v(batch: List[List[Dict[str, torch.Tensor]]]) -> Dict[str, torch.Tensor]:
+def collate_fn_t2v_cond(batch: List[List[Dict[str, torch.Tensor]]]) -> Dict[str, torch.Tensor]:
     return {
         "prompts": [x["prompt"] for x in batch[0]],
         "videos": torch.stack([x["video"] for x in batch[0]]),
@@ -189,6 +189,13 @@ def collate_fn_t2v(batch: List[List[Dict[str, torch.Tensor]]]) -> Dict[str, torc
         "img_refs": torch.stack([x["img_ref"] for x in batch[0]])
     }
 
+def collate_fn_t2v(batch: List[List[Dict[str, torch.Tensor]]]) -> Dict[str, torch.Tensor]:
+    return {
+        "prompts": [x["prompt"] for x in batch[0]],
+        "videos": torch.stack([x["video"] for x in batch[0]]),
+    }
+
+
 
 def forward_pass(
     transformer: LTXVideoTransformer3DModel,