diff --git a/train.py b/train.py
index d5d9292..1ee4ca6 100644
--- a/train.py
+++ b/train.py
@@ -263,6 +263,18 @@ def cast_to_gpu_and_type(model_list, accelerator, weight_dtype):
     for model in model_list:
         if model is not None: model.to(accelerator.device, dtype=weight_dtype)
 
+def get_cached_latent_dir(c_dir):
+    from omegaconf import ListConfig
+
+    if isinstance(c_dir, str):
+        return os.path.abspath(c_dir) if c_dir is not None else None
+    
+    if isinstance(c_dir, ListConfig):
+        c_dir = OmegaConf.to_object(c_dir)
+        return c_dir
+
+    return None
+
 def handle_cache_latents(
         should_cache, 
         output_dir, 
@@ -270,33 +282,42 @@ def handle_cache_latents(
         train_batch_size, 
         vae, 
         cached_latent_dir=None,
-        shuffle=False
+        shuffle=False,
+        minimum_required_frames = 0
     ):
 
     # Cache latents by storing them in VRAM. 
     # Speeds up training and saves memory by not encoding during the train loop.
     if not should_cache: return None
-    vae.to('cuda', dtype=torch.float16)
+    vae.to('cuda', dtype=torch.float32)
     vae.enable_slicing()
     
-    cached_latent_dir = (
-        os.path.abspath(cached_latent_dir) if cached_latent_dir is not None else None 
-        )
+    cached_latent_dir = get_cached_latent_dir(cached_latent_dir)
 
     if cached_latent_dir is None:
         cache_save_dir = f"{output_dir}/cached_latents"
         os.makedirs(cache_save_dir, exist_ok=True)
 
         for i, batch in enumerate(tqdm(train_dataloader, desc="Caching Latents.")):
+            if batch['pixel_values'].shape[1] > 2 and batch['pixel_values'].shape[1] < minimum_required_frames:
+                print(f"""
+                    Batch item at index {i} does not meet required minimum frames: {minimum_required_frames}.
+                    Seeing this error means that some of your video lengths are too short, but training will continue.
+                    """
+                )
+                continue
 
             save_name = f"cached_{i}"
             full_out_path =  f"{cache_save_dir}/{save_name}.pt"
 
-            pixel_values = batch['pixel_values'].to('cuda', dtype=torch.float16)
-            batch['pixel_values'] = tensor_to_vae_latent(pixel_values, vae)
-            for k, v in batch.items(): batch[k] = v[0]
         
+            pixel_values = batch['pixel_values'].to('cuda', dtype=torch.float32)
+            batch['pixel_values'] = tensor_to_vae_latent(pixel_values, vae)
+            for k, v in batch.items(): 
+                batch[k] = v[0]
+
             torch.save(batch, full_out_path)
+            
             del pixel_values
             del batch
 
@@ -305,13 +326,31 @@ def handle_cache_latents(
     else:
         cache_save_dir = cached_latent_dir
         
+    # Convert string to list of strings for processing if we have more than.
+    cache_save_dir = (
+                [cache_save_dir] if not isinstance(cache_save_dir, list) 
+            else 
+                cache_save_dir
+        )
+
+    cached_dataset_list = []
+
+    for save_dir in cache_save_dir:
+        cached_dataset = CachedDataset(cache_dir=save_dir)
+        cached_dataset_list.append(cached_dataset)
 
+    if len(cached_dataset_list) > 1:
+        print(f"Found {len(cached_dataset_list)} cached datasets. Merging...")
+        new_cached_dataset = torch.utils.data.ConcatDataset(cached_dataset_list)
+    else:
+        new_cached_dataset = cached_dataset_list[0] 
+        
     return torch.utils.data.DataLoader(
-        CachedDataset(cache_dir=cache_save_dir), 
-        batch_size=train_batch_size, 
-        shuffle=shuffle,
-        num_workers=0
-    ) 
+                new_cached_dataset,
+                batch_size=train_batch_size, 
+                shuffle=shuffle,
+                num_workers=0,
+            )
 
 def handle_trainable_modules(model, trainable_modules=None, is_enabled=True, negation=None):
     global already_printed_trainables
@@ -650,7 +689,9 @@ def main(
         train_dataloader, 
         train_batch_size, 
         vae,
-        cached_latent_dir
+        cached_latent_dir,
+        shuffle=shuffle,
+        minimum_required_frames=kwargs.get("minimum_required_frames", 0)
     ) 
 
     if cached_data_loader is not None: