diff --git a/comfy/ldm/hunyuan3dv2_1/vae.py b/comfy/ldm/hunyuan3dv2_1/vae.py
index 35986e917..219bc430e 100644
--- a/comfy/ldm/hunyuan3dv2_1/vae.py
+++ b/comfy/ldm/hunyuan3dv2_1/vae.py
@@ -5,7 +5,6 @@
 import torch
 from torch import Tensor
 import math
-import trimesh
 import numpy as np
 from skimage import measure
 from dataclasses import dataclass
@@ -607,8 +606,8 @@ class PointCrossAttention(nn.Module):
 @dataclass
 class Latent2MeshOutput():
     # mesh for vertices and faces
-    mesh_v: None
-    mesh_f: None
+    vertices: None
+    faces: None
 
 class SufraceExtractor():
     def compute_box_stat(self, bounds, octree_resolution: int):
@@ -644,7 +643,7 @@ class SufraceExtractor():
                 vertices, faces = self.run(grid_logits[i], **kwds)
                 vertices = vertices.astype(np.float32)
                 faces = np.ascontiguousarray(faces)
-                outputs.append(Latent2MeshOutput(mesh_v = vertices, mesh_f = faces))
+                outputs.append(Latent2MeshOutput(vertices = vertices, faces = faces))
 
             except Exception:
                 import traceback
@@ -687,24 +686,6 @@ class VanillaVolumeDecoder():
 
         return grid_logits
 
-def export_to_trimesh(mesh_output):
-    import trimesh
-    
-    if isinstance(mesh_output, list):
-        outputs = []
-        for mesh in mesh_output:
-            if mesh is None:
-                outputs.append(None)
-            else:
-                mesh.mesh_f = mesh.mesh_f[:, ::-1]
-                mesh_output = trimesh.Trimesh(mesh.mesh_v, mesh.mesh_f)
-                outputs.append(mesh_output)
-        return outputs
-    else:
-        mesh_output.mesh_f = mesh_output.mesh_f[:, ::-1]
-        mesh_output = trimesh.Trimesh(mesh_output.mesh_v, mesh_output.mesh_f)
-        return mesh_output
-
 def normalize_mesh(mesh, scale = 0.9999):
     """Normalize mesh to fit in [-scale, scale]. Translate mesh so its center is [0,0,0]"""
 
@@ -767,6 +748,8 @@ def sharp_sample_pointcloud(mesh, num = 16384):
 def load_surface_sharpedge(mesh, num_points=4096, num_sharp_points=4096, sharpedge_flag = True, device = "cuda"):
     """Load a surface with optional sharp-edge annotations from a trimesh mesh."""
 
+    import trimesh
+
     try:
         mesh_full = trimesh.util.concatenate(mesh.dump())
     except Exception:
@@ -837,6 +820,7 @@ class SharpEdgeSurfaceLoader:
 
     @staticmethod
     def _load_mesh(mesh_input):
+        import trimesh
 
         if isinstance(mesh_input, str):
             mesh = trimesh.load(mesh_input, force="mesh", merge_primitives = True)
diff --git a/comfy_extras/nodes_hunyuan3d.py b/comfy_extras/nodes_hunyuan3d.py
index 1034f7fbc..e91b48a99 100644
--- a/comfy_extras/nodes_hunyuan3d.py
+++ b/comfy_extras/nodes_hunyuan3d.py
@@ -110,17 +110,15 @@ class VAEDecodeHunyuan3D:
                 }),
                 "num_chunks": ("INT", {
                     "default": 8000, "min": 1000, "max": 500000,
-                    "visible_if": {"version": "2.0"}
                 }),
                 "octree_resolution": ("INT", {
                     "default": 256, "min": 16, "max": 512,
-                    "visible_if": {"version": "2.0"}
                 }),
             }
         }
 
-    RETURN_TYPES = ("VOXEL", "SDF_FUNCTION")
-    RETURN_NAMES = ("voxel", "sdf")
+    RETURN_TYPES = ("VOXEL", "MESH")
+    RETURN_NAMES = ("voxel", "mesh")
 
     FUNCTION = "decode"
     CATEGORY = "latent/3d"
@@ -137,6 +135,12 @@ class VAEDecodeHunyuan3D:
         mesh = vae.decode(samples["samples"], to_mesh = True,
                          num_chunks = num_chunks,
                          octree_resolution = octree_resolution)
+        
+        # ensure batch dim
+        if mesh.verticies.ndim == 2:
+            mesh.verticies = mesh.verticies[np.newaxis, ...]
+            mesh.faces = mesh.faces[np.newaxis, ...]
+
         return (None, mesh)
     
 def voxel_to_mesh(voxels, threshold=0.5, device=None):
@@ -495,7 +499,7 @@ class VoxelToMesh:
         return (MESH(torch.stack(vertices), torch.stack(faces)), )
 
 
-def save_glb(vertices, faces, filepath, metadata=None, numpy_ready = False):
+def save_glb(vertices, faces, filepath, metadata=None):
     """
     Save PyTorch tensor vertices and faces as a GLB file without external dependencies.
 
@@ -506,7 +510,7 @@ def save_glb(vertices, faces, filepath, metadata=None, numpy_ready = False):
     """
 
     # Convert tensors to numpy arrays
-    if not numpy_ready:
+    if isinstance(vertices, torch.tensor) and isinstance(faces, torch.tensor):
         vertices_np = vertices.cpu().numpy().astype(np.float32)
         faces_np = faces.cpu().numpy().astype(np.uint32)
     else: