Windows support for T2V scripts (#48)

Author: a-r-r-o-w

training/cogvideox_image_to_video_lora.py

@@ -202,11 +202,11 @@ def log_validation(
 
 
 class CollateFunction:
-    def __init__(self, weight_dtype, load_tensors):
+    def __init__(self, weight_dtype: torch.dtype, load_tensors: bool) -> None:
         self.weight_dtype = weight_dtype
         self.load_tensors = load_tensors
 
-    def __call__(self, data):
+    def __call__(self, data: Dict[str, Any]) -> Dict[str, torch.Tensor]:
         prompts = [x["prompt"] for x in data[0]]
 
         if self.load_tensors:
@@ -519,13 +519,13 @@ def load_model_hook(models, input_dir):
             video_reshape_mode=args.video_reshape_mode, **dataset_init_kwargs
         )
 
-    collate_fn_instance = CollateFunction(weight_dtype, args.load_tensors)
+    collate_fn = CollateFunction(weight_dtype, args.load_tensors)
 
     train_dataloader = DataLoader(
         train_dataset,
         batch_size=1,
         sampler=BucketSampler(train_dataset, batch_size=args.train_batch_size, shuffle=True),
-        collate_fn=collate_fn_instance,
+        collate_fn=collate_fn,
         num_workers=args.dataloader_num_workers,
         pin_memory=args.pin_memory,
     )

training/cogvideox_text_to_video_lora.py

@@ -198,6 +198,26 @@ def log_validation(
     return videos
 
 
+class CollateFunction:
+    def __init__(self, weight_dtype: torch.dtype, load_tensors: bool) -> None:
+        self.weight_dtype = weight_dtype
+        self.load_tensors = load_tensors
+
+    def __call__(self, data: Dict[str, Any]) -> Dict[str, torch.Tensor]:
+        prompts = [x["prompt"] for x in data[0]]
+
+        if self.load_tensors:
+            prompts = torch.stack(prompts).to(dtype=self.weight_dtype, non_blocking=True)
+
+        videos = [x["video"] for x in data[0]]
+        videos = torch.stack(videos).to(dtype=self.weight_dtype, non_blocking=True)
+
+        return {
+            "videos": videos,
+            "prompts": prompts,
+        }
+
+
 def main(args):
     if args.report_to == "wandb" and args.hub_token is not None:
         raise ValueError(
@@ -491,19 +511,7 @@ def load_model_hook(models, input_dir):
             video_reshape_mode=args.video_reshape_mode, **dataset_init_kwargs
         )
 
-    def collate_fn(data):
-        prompts = [x["prompt"] for x in data[0]]
-
-        if args.load_tensors:
-            prompts = torch.stack(prompts).to(dtype=weight_dtype, non_blocking=True)
-
-        videos = [x["video"] for x in data[0]]
-        videos = torch.stack(videos).to(dtype=weight_dtype, non_blocking=True)
-
-        return {
-            "videos": videos,
-            "prompts": prompts,
-        }
+    collate_fn = CollateFunction(weight_dtype, args.load_tensors)
 
     train_dataloader = DataLoader(
         train_dataset,

training/cogvideox_text_to_video_sft.py

@@ -188,6 +188,26 @@ def log_validation(
     return videos
 
 
+class CollateFunction:
+    def __init__(self, weight_dtype: torch.dtype, load_tensors: bool) -> None:
+        self.weight_dtype = weight_dtype
+        self.load_tensors = load_tensors
+
+    def __call__(self, data: Dict[str, Any]) -> Dict[str, torch.Tensor]:
+        prompts = [x["prompt"] for x in data[0]]
+
+        if self.load_tensors:
+            prompts = torch.stack(prompts).to(dtype=self.weight_dtype, non_blocking=True)
+
+        videos = [x["video"] for x in data[0]]
+        videos = torch.stack(videos).to(dtype=self.weight_dtype, non_blocking=True)
+
+        return {
+            "videos": videos,
+            "prompts": prompts,
+        }
+
+
 def main(args):
     if args.report_to == "wandb" and args.hub_token is not None:
         raise ValueError(
@@ -457,19 +477,7 @@ def load_model_hook(models, input_dir):
             video_reshape_mode=args.video_reshape_mode, **dataset_init_kwargs
         )
 
-    def collate_fn(data):
-        prompts = [x["prompt"] for x in data[0]]
-
-        if args.load_tensors:
-            prompts = torch.stack(prompts).to(dtype=weight_dtype, non_blocking=True)
-
-        videos = [x["video"] for x in data[0]]
-        videos = torch.stack(videos).to(dtype=weight_dtype, non_blocking=True)
-
-        return {
-            "videos": videos,
-            "prompts": prompts,
-        }
+    collate_fn = CollateFunction(weight_dtype, args.load_tensors)
 
     train_dataloader = DataLoader(
         train_dataset,