diff --git a/nodes.py b/nodes.py
index 8fdc579..2d90617 100644
--- a/nodes.py
+++ b/nodes.py
@@ -1104,16 +1104,7 @@ class ToraEncodeTrajectory:
 
         vae = pipeline["pipe"].vae
         vae.enable_slicing()
-        vae._clear_fake_context_parallel_cache()
-
-        #get coordinates from string and convert to compatible range/format (has to be 256x256 for the model)
-        # coordinates = json.loads(coordinates.replace("'", '"'))
-        # coordinates = [(coord['x'], coord['y']) for coord in coordinates]
-        # traj_list_range_256 = scale_traj_list_to_256(coordinates, width, height)
-        print(f"Type of coordinates: {type(coordinates)}")
-        print(f"Structure of coordinates: {coordinates}")
-        print(len(coordinates))
-        
+        vae._clear_fake_context_parallel_cache()       
 
         if len(coordinates) < 10:
             coords_list = []
@@ -1301,7 +1292,7 @@ class CogVideoSampler:
             padding = torch.zeros((negative.shape[0], target_length - negative.shape[1], negative.shape[2]), device=negative.device)
             negative = torch.cat((negative, padding), dim=1)
 
-        autocastcondition = not pipeline["onediff"]
+        autocastcondition = not pipeline["onediff"] or not dtype == torch.float32
         autocast_context = torch.autocast(mm.get_autocast_device(device)) if autocastcondition else nullcontext()
         with autocast_context:
             latents = pipeline["pipe"](
@@ -1471,7 +1462,7 @@ class CogVideoXFunSampler:
 
         generator = torch.Generator(device=torch.device("cpu")).manual_seed(seed)
 
-        autocastcondition = not pipeline["onediff"]
+        autocastcondition = not pipeline["onediff"] or not dtype == torch.float32
         autocast_context = torch.autocast(mm.get_autocast_device(device)) if autocastcondition else nullcontext()
         with autocast_context:
             video_length = int((video_length - 1) // pipe.vae.config.temporal_compression_ratio * pipe.vae.config.temporal_compression_ratio) + 1 if video_length != 1 else 1
@@ -1566,7 +1557,7 @@ class CogVideoXFunVid2VidSampler:
 
         generator = torch.Generator(device=torch.device("cpu")).manual_seed(seed)
 
-        autocastcondition = not pipeline["onediff"]
+        autocastcondition = not pipeline["onediff"] or not dtype == torch.float32
         autocast_context = torch.autocast(mm.get_autocast_device(device)) if autocastcondition else nullcontext()
         with autocast_context:
             video_length = int((video_length - 1) // pipe.vae.config.temporal_compression_ratio * pipe.vae.config.temporal_compression_ratio) + 1 if video_length != 1 else 1
@@ -1813,7 +1804,7 @@ class CogVideoXFunControlSampler:
 
         generator = torch.Generator(device=torch.device("cpu")).manual_seed(seed)
 
-        autocastcondition = not pipeline["onediff"]
+        autocastcondition = not pipeline["onediff"] or not dtype == torch.float32
         autocast_context = torch.autocast(mm.get_autocast_device(device)) if autocastcondition else nullcontext()
         with autocast_context: