diff --git a/nodes_sampler.py b/nodes_sampler.py
index 32176fa..a53023c 100644
--- a/nodes_sampler.py
+++ b/nodes_sampler.py
@@ -3080,10 +3080,36 @@ class WanVideoSampler:
             "samples": callback_latent.unsqueeze(0).cpu() if callback is not None else None,
         })
 
+class WanVideoSamplerSettings(WanVideoSampler):
+    RETURN_TYPES = ("SAMPLER_ARGS",)
+    RETURN_NAMES = ("sampler_inputs", )
+    DESCRIPTION = "Node to output all settings and inputs for the WanVideoSamplerFromSettings -node"
+    def process(self, *args, **kwargs):
+        import inspect
+        params = inspect.signature(WanVideoSampler.process).parameters
+        args_dict = {name: kwargs.get(name, param.default if param.default is not inspect.Parameter.empty else None)
+                     for name, param in params.items() if name != "self"}
+        return args_dict,
+
+class WanVideoSamplerFromSettings(WanVideoSampler):
+    DESCRIPTION = "Utility node with no other functionality than to look cleaner, useful for the live preview as the main sampler node has become a messy monster"
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "sampler_inputs": ("SAMPLER_ARGS",),},
+        }
+
+    def process(self, sampler_inputs):
+        return super().process(**sampler_inputs)
 
 NODE_CLASS_MAPPINGS = {
     "WanVideoSampler": WanVideoSampler,
+    "WanVideoSamplerSettings": WanVideoSamplerSettings,
+    "WanVideoSamplerFromSettings": WanVideoSamplerFromSettings,
     }
 NODE_DISPLAY_NAME_MAPPINGS = {
     "WanVideoSampler": "WanVideo Sampler",
+    "WanVideoSamplerSettings": "WanVideo Sampler Settings",
+    "WanVideoSamplerFromSettings": "WanVideo Sampler From Settings",
 }
diff --git a/wanvideo/modules/model.py b/wanvideo/modules/model.py
index 977c705..c602fe4 100644
--- a/wanvideo/modules/model.py
+++ b/wanvideo/modules/model.py
@@ -2398,7 +2398,7 @@ class WanModel(torch.nn.Module):
                 block.self_attn.mask_map = MaskMap(video_token_num=seq_len, num_frame=F+1)
 
         if self.ref_conv is not None and fun_ref is not None:
-            fun_ref = self.ref_conv(fun_ref).flatten(2).transpose(1, 2)
+            fun_ref = self.ref_conv(fun_ref.to(self.ref_conv.weight.dtype)).flatten(2).transpose(1, 2)
             grid_sizes = torch.stack([torch.tensor([u[0] + 1, u[1], u[2]]) for u in grid_sizes]).to(grid_sizes.device)
             seq_len += fun_ref.size(1)
             F += 1
diff --git a/wanvideo/wan_video_vae.py b/wanvideo/wan_video_vae.py
index cb2fd58..740822c 100644
--- a/wanvideo/wan_video_vae.py
+++ b/wanvideo/wan_video_vae.py
@@ -5,19 +5,11 @@ import torch.nn as nn
 import torch.nn.functional as F
 from tqdm import tqdm
 from comfy.utils import ProgressBar
+import comfy.ops
+ops = comfy.ops.disable_weight_init
 
 CACHE_T = 2
 
-# Workaround for increased memory usage in Conv3D with bfloat16 in torch 2.9.0 stable and up
-try:
-    torch_cudnn_bug = (
-        hasattr(torch.backends.cudnn, 'version') and
-        torch.backends.cudnn.version() >= 90800 and
-        torch.__version__ in ["2.9.0+cu126", "2.9.0+cu128", "2.9.0+cu130"] or torch.__version__.startswith("2.10.")
-    )
-except:
-    torch_cudnn_bug = False
-
 def check_is_instance(model, module_class):
     if isinstance(model, module_class):
         return True
@@ -26,7 +18,7 @@ def check_is_instance(model, module_class):
     return False
 
 
-class CausalConv3d(nn.Conv3d):
+class CausalConv3d(ops.Conv3d):
     """
     Causal 3d convolusion.
     """
@@ -45,21 +37,6 @@ class CausalConv3d(nn.Conv3d):
             padding[4] -= cache_x.shape[2]
         x = F.pad(x, padding)
 
-        # Convert to float32 only if this would trigger cuDNN bug
-        if (
-            torch_cudnn_bug and
-            x.dtype in (torch.bfloat16, torch.half) and
-            len(self.weight.shape) == 5 and
-            any(self.weight.shape[i] != 1 for i in range(2, 5))
-        ):
-            self.weight.data = self.weight.data.float()
-            if self.bias is not None:
-                self.bias.data = self.bias.data.float()
-            
-            result = super().forward(x.float())
-                
-            return result.to(x.dtype)
-
         return super().forward(x)