diff --git a/comfy_extras/nodes_trellis2.py b/comfy_extras/nodes_trellis2.py
index e133761e0..f424213e9 100644
--- a/comfy_extras/nodes_trellis2.py
+++ b/comfy_extras/nodes_trellis2.py
@@ -302,7 +302,7 @@ class VaeDecodeTextureTrellis(IO.ComfyNode):
             node_id="VaeDecodeTextureTrellis",
             category="latent/3d",
             inputs=[
-                IO.Mesh.Input("shape_mesh"),
+                IO.Mesh.Input("mesh"),
                 IO.Latent.Input("samples"),
                 IO.Vae.Input("vae"),
                 IO.AnyType.Input("shape_subs"),
@@ -314,8 +314,8 @@ class VaeDecodeTextureTrellis(IO.ComfyNode):
         )
 
     @classmethod
-    def execute(cls, shape_mesh, samples, vae, shape_subs, resolution):
-
+    def execute(cls, mesh, samples, vae, shape_subs, resolution):
+        shape_mesh = mesh
         sample_tensor = samples["samples"]
         resolution = int(resolution)
         device = comfy.model_management.get_torch_device()
@@ -549,7 +549,6 @@ class Trellis2Conditioning(IO.ComfyNode):
                 IO.ClipVision.Input("clip_vision_model"),
                 IO.Image.Input("image"),
                 IO.Mask.Input("mask"),
-                IO.Combo.Input("background_color", options=["black", "gray", "white"], default="black")
             ],
             outputs=[
                 IO.Conditioning.Output(display_name="positive"),
@@ -558,7 +557,7 @@ class Trellis2Conditioning(IO.ComfyNode):
         )
 
     @classmethod
-    def execute(cls, clip_vision_model, image, mask, background_color) -> IO.NodeOutput:
+    def execute(cls, clip_vision_model, image, mask) -> IO.NodeOutput:
         # Normalize to batched form so per-image conditioning loop below is uniform.
         if image.ndim == 3:
             image = image.unsqueeze(0)
@@ -617,8 +616,7 @@ class Trellis2Conditioning(IO.ComfyNode):
                 logging.warning("Mask for the image is empty. Trellis2 requires an image with a mask for the best mesh quality.")
                 cropped_np = rgba_np.astype(np.float32) / 255.0
 
-            bg_colors = {"black":[0.0, 0.0, 0.0], "gray":[0.5, 0.5, 0.5], "white":[1.0, 1.0, 1.0]}
-            bg_rgb = np.array(bg_colors.get(background_color, [0.0, 0.0, 0.0]), dtype=np.float32)
+            bg_rgb = np.array([0.0, 0.0, 0.0], dtype=np.float32)
 
             fg = cropped_np[:, :, :3]
             alpha_float = cropped_np[:, :, 3:4]
@@ -649,17 +647,17 @@ class EmptyTrellis2ShapeLatent(IO.ComfyNode):
             node_id="EmptyTrellis2ShapeLatent",
             category="latent/3d",
             inputs=[
-                IO.AnyType.Input("structure_or_coords"),
                 IO.Model.Input("model"),
+                IO.AnyType.Input("structure_or_coords"),
             ],
             outputs=[
+                IO.Model.Output(),
                 IO.Latent.Output(),
-                IO.Model.Output()
             ]
         )
 
     @classmethod
-    def execute(cls, structure_or_coords, model):
+    def execute(cls, model, structure_or_coords):
         # to accept the upscaled coords
         is_512_pass = False
 
@@ -690,7 +688,7 @@ class EmptyTrellis2ShapeLatent(IO.ComfyNode):
             model.model_options["transformer_options"]["generation_mode"] = "shape_generation_512"
         else:
             model.model_options["transformer_options"]["generation_mode"] = "shape_generation"
-        return IO.NodeOutput({"samples": latent, "coords": coords, "coords_counts": counts, "type": "trellis2"}, model)
+        return IO.NodeOutput(model, {"samples": latent, "coords": coords, "coords_counts": counts, "type": "trellis2"})
 
 class EmptyTrellis2LatentTexture(IO.ComfyNode):
     @classmethod
@@ -699,18 +697,18 @@ class EmptyTrellis2LatentTexture(IO.ComfyNode):
             node_id="EmptyTrellis2LatentTexture",
             category="latent/3d",
             inputs=[
+                IO.Model.Input("model"),
                 IO.Voxel.Input("structure_or_coords"),
                 IO.Latent.Input("shape_latent"),
-                IO.Model.Input("model"),
             ],
             outputs=[
+                IO.Model.Output(),
                 IO.Latent.Output(),
-                IO.Model.Output()
             ]
         )
 
     @classmethod
-    def execute(cls, structure_or_coords, shape_latent, model):
+    def execute(cls, model, structure_or_coords, shape_latent):
         channels = 32
         if hasattr(structure_or_coords, "data") and structure_or_coords.data.ndim == 4:
             decoded = structure_or_coords.data.unsqueeze(1)
@@ -736,7 +734,7 @@ class EmptyTrellis2LatentTexture(IO.ComfyNode):
         model.model_options["transformer_options"]["coords"] = coords
         model.model_options["transformer_options"]["generation_mode"] = "texture_generation"
         model.model_options["transformer_options"]["shape_slat"] = shape_latent
-        return IO.NodeOutput({"samples": latent, "coords": coords, "coords_counts": counts, "type": "trellis2"}, model)
+        return IO.NodeOutput(model, {"samples": latent, "coords": coords, "coords_counts": counts, "type": "trellis2"})
 
 
 class EmptyTrellis2LatentStructure(IO.ComfyNode):
@@ -1346,13 +1344,12 @@ class PostProcessMesh(IO.ComfyNode):
                                 "Set to 0 to disable hole filling."))
             ],
             outputs=[
-                IO.Mesh.Output("output_mesh"),
+                IO.Mesh.Output("mesh"),
             ]
         )
 
     @classmethod
     def execute(cls, mesh, target_face_count, fill_holes_perimeter):
-        # input should be comfy.NestedTensor
         mesh = copy.deepcopy(mesh)
 
         def process_single(v, f, c, bar):
@@ -1368,7 +1365,6 @@ class PostProcessMesh(IO.ComfyNode):
             bar.update(1)
             return v, f, c
 
-        # Check if batch is Jagged (List) or Uniform (3D Tensor)
         is_list = isinstance(mesh.vertices, list)
         is_batched_tensor = not is_list and mesh.vertices.ndim == 3