diff --git a/nodes.py b/nodes.py
index 73a22a9..3a21576 100644
--- a/nodes.py
+++ b/nodes.py
@@ -178,7 +178,7 @@ class DownloadAndLoadCogVideoGGUFModel:
                 ),
             "vae_precision": (["fp16", "fp32", "bf16"], {"default": "bf16", "tooltip": "VAE dtype"}),
             "fp8_fastmode": ("BOOLEAN", {"default": False, "tooltip": "only supported on 4090 and later GPUs"}),
-            "compile": (["disabled","onediff","torch"], {"tooltip": "UNTESTED WITH GGUF"}),
+            "load_device": (["main_device", "offload_device"], {"default": "main_device"}),
             },
         }
 
@@ -187,7 +187,7 @@ class DownloadAndLoadCogVideoGGUFModel:
     FUNCTION = "loadmodel"
     CATEGORY = "CogVideoWrapper"
 
-    def loadmodel(self, model, vae_precision, compile, fp8_fastmode):
+    def loadmodel(self, model, vae_precision, fp8_fastmode, load_device):
         device = mm.get_torch_device()
         offload_device = mm.unet_offload_device()
         mm.soft_empty_cache()
@@ -227,20 +227,10 @@ class DownloadAndLoadCogVideoGGUFModel:
 
             transformer.to(torch.float8_e4m3fn)
             transformer = mz_gguf_loader.quantize_load_state_dict(transformer, sd, device="cpu")
-            transformer.to(device)
-        
-        # transformer
-        # if fp8_transformer == "fastmode":
-        #     if "2b" in model:
-        #         for name, param in transformer.named_parameters():
-        #             if name != "pos_embedding":
-        #                 param.data = param.data.to(torch.float8_e4m3fn)
-        #     elif "I2V" in model:
-        #         for name, param in transformer.named_parameters():
-        #             if "patch_embed" not in name:
-        #                 param.data = param.data.to(torch.float8_e4m3fn)
-        #     else:
-        #         transformer.to(torch.float8_e4m3fn)
+            if load_device == "offload_device":
+                transformer.to(offload_device)
+            else:
+                transformer.to(device)
         
         if fp8_fastmode:
            from .fp8_optimization import convert_fp8_linear
@@ -586,16 +576,16 @@ class CogVideoSampler:
         offload_device = mm.unet_offload_device()
         pipe = pipeline["pipe"]
         dtype = pipeline["dtype"]
-        
+        scheduler_config = pipeline["scheduler_config"]
         
         if not pipeline["cpu_offloading"]:
             pipe.transformer.to(device)
         generator = torch.Generator(device=device).manual_seed(seed)
 
         if scheduler == "DDIM" or scheduler == "DDIM_tiled":
-            pipe.scheduler = CogVideoXDDIMScheduler.from_pretrained(base_path, subfolder="scheduler")
+            pipe.scheduler = CogVideoXDDIMScheduler.from_config(scheduler_config)
         elif scheduler == "DPM":
-            pipe.scheduler = CogVideoXDPMScheduler.from_pretrained(base_path, subfolder="scheduler")
+            pipe.scheduler = CogVideoXDPMScheduler.from_config(scheduler_config)
 
         if negative.shape[1] < positive.shape[1]:
             target_length = positive.shape[1]
@@ -674,7 +664,7 @@ class CogVideoDecode:
         latents = latents.to(vae.dtype)
         latents = latents.permute(0, 2, 1, 3, 4)  # [batch_size, num_channels, num_frames, height, width]
         latents = 1 / vae.config.scaling_factor * latents
-       
+        vae._clear_fake_context_parallel_cache()
         frames = vae.decode(latents).sample
         vae.disable_tiling()
         if not pipeline["cpu_offloading"]: