nodes.py

import os
import torch
import torchvision.transforms as transforms
from PIL import Image, ImageSequence, ImageOps
from pathlib import Path
import numpy as np
import json
import trimesh as Trimesh
from tqdm import tqdm

import folder_paths
import node_helpers
import hashlib
import cv2
import gc
import copy

import pymeshlab

import cumesh as CuMesh
import o_voxel

import meshlib.mrmeshnumpy as mrmeshnumpy
import meshlib.mrmeshpy as mrmeshpy

import nvdiffrast.torch as dr
from flex_gemm.ops.grid_sample import grid_sample_3d

import comfy.model_management as mm
from comfy.utils import load_torch_file, ProgressBar, common_upscale
import comfy.utils

from .trellis2.pipelines import Trellis2ImageTo3DPipeline
from .trellis2.representations import Mesh, MeshWithVoxel

script_directory = os.path.dirname(os.path.abspath(__file__))
comfy_path = os.path.dirname(os.path.dirname(os.path.dirname(__file__)))

to_pil = transforms.ToPILImage()

def pil2tensor(image):
    return torch.from_numpy(np.array(image).astype(np.float32) / 255.0)[None,]
    
def tensor2pil(image: torch.Tensor) -> Image.Image:
    """
    Accepts either:
      - (H,W,C)
      - (1,H,W,C)
    Returns a PIL RGB/RGBA image depending on channels.
    """
    if isinstance(image, torch.Tensor):
        t = image.detach().cpu()
        if t.ndim == 4:
            # Expect (B,H,W,C); allow only B==1 here
            if t.shape[0] != 1:
                raise ValueError(f"tensor2pil expects batch of 1, got batch={t.shape[0]}")
            t = t[0]
        elif t.ndim != 3:
            raise ValueError(f"tensor2pil expects (H,W,C) or (1,H,W,C), got shape={tuple(t.shape)}")

        arr = (t.numpy() * 255.0).clip(0, 255).astype(np.uint8)
        return Image.fromarray(arr)

    raise TypeError(f"tensor2pil expected torch.Tensor, got {type(image)}")    
    
def tensor_batch_to_pil_list(images: torch.Tensor, max_views: int = 4) -> list[Image.Image]:
    """
    Converts a ComfyUI IMAGE tensor (B,H,W,C) into a list of PIL images.
    Caps to max_views for safety.
    """
    if not isinstance(images, torch.Tensor):
        raise TypeError(f"Expected torch.Tensor for IMAGE, got {type(images)}")

    if images.ndim == 4:
        b = int(images.shape[0])
        n = min(b, int(max_views))
        return [tensor2pil(images[i:i+1]) for i in range(n)]

    if images.ndim == 3:
        return [tensor2pil(images)]

    raise ValueError(f"Unsupported IMAGE tensor shape: {tuple(images.shape)}")    
    
def convert_tensor_images_to_pil(images):
    pil_array = []
    
    for image in images:
        pil_array.append(tensor2pil(image))
        
    return pil_array
    
def simplify_with_meshlib(vertices, faces, target=1000000):
    current_faces_num = len(faces)
    print(f'Current Faces Number: {current_faces_num}')
    
    if current_faces_num<target:
        return

    settings = mrmeshpy.DecimateSettings()
    faces_to_delete = current_faces_num - target
    settings.maxDeletedFaces = faces_to_delete                        
    settings.packMesh = True
    
    print('Generating Meshlib Mesh ...')
    mesh = mrmeshnumpy.meshFromFacesVerts(faces, vertices)
    print('Packing Optimally ...')
    mesh.packOptimally()
    print('Decimating ...')
    mrmeshpy.decimateMesh(mesh, settings)
    
    new_vertices = mrmeshnumpy.getNumpyVerts(mesh)
    new_faces = mrmeshnumpy.getNumpyFaces(mesh.topology)               
    
    print(f"Reduced faces, resulting in {len(new_vertices)} vertices and {len(new_faces)} faces")
        
    return new_vertices, new_faces

def remove_floater(mesh):
    print('Removing floater ...')
    faces = mesh.faces.cpu().numpy()
    print(f"Current faces: {len(faces)}")
    mesh_set = pymeshlab.MeshSet()
    mesh_pymeshlab = pymeshlab.Mesh(vertex_matrix=mesh.vertices.cpu().numpy(), face_matrix=faces)
    mesh_set.add_mesh(mesh_pymeshlab, "converted_mesh")
    mesh_set = pymeshlab_remove_floater(mesh_set)
    
    mesh_pymeshlab = mesh_set.current_mesh()    
    
    new_faces = mesh_pymeshlab.face_matrix()
    print(f"After removing floater: {len(new_faces)}")
    
    new_vertices = torch.from_numpy(mesh_pymeshlab.vertex_matrix()).contiguous().float()
    new_faces = torch.from_numpy(new_faces).contiguous().int()   
    
    mesh.vertices = new_vertices
    mesh.faces = new_faces
    
    return mesh
    
def pymeshlab_remove_floater(mesh: pymeshlab.MeshSet):
    mesh.apply_filter("compute_selection_by_small_disconnected_components_per_face",
                      nbfaceratio=0.005)
    mesh.apply_filter("compute_selection_transfer_face_to_vertex", inclusive=False)
    mesh.apply_filter("meshing_remove_selected_vertices_and_faces")
    return mesh 
    
def _batched_unsigned_distance(bvh, positions, batch_size=100000, return_uvw=False):
    """
    Batch unsigned_distance queries to avoid GPU kernel timeout on large meshes.
    When processing high-resolution textures (e.g., 2048x2048 = ~4M pixels) on complex
    meshes, a single BVH query can cause GPU watchdog timeout. This function splits
    the query into smaller batches.
    Args:
        bvh: The BVH structure from cumesh
        positions: (N, 3) tensor of query positions
        batch_size: Maximum number of queries per batch (default 100K, matching
            the rasterization chunk size used elsewhere in this file)
        return_uvw: Whether to return barycentric coordinates
    Returns:
        Same as bvh.unsigned_distance()
    """
    import torch
    N = positions.shape[0]
    if N <= batch_size:
        return bvh.unsigned_distance(positions, return_uvw=return_uvw)

    distances_list = []
    face_id_list = []
    uvw_list = [] if return_uvw else None

    for i in range(0, N, batch_size):
        end = min(i + batch_size, N)
        d, f, u = bvh.unsigned_distance(positions[i:end], return_uvw=return_uvw)
        distances_list.append(d)
        face_id_list.append(f)
        if return_uvw:
            uvw_list.append(u)

    return (
        torch.cat(distances_list),
        torch.cat(face_id_list),
        torch.cat(uvw_list) if return_uvw else None
    )    

class Trellis2LoadModel:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "modelname": (["TRELLIS.2-4B"],),
                "backend": (["flash_attn","xformers"],{"default":"xformers"}),
                "device": (["cpu","cuda"],{"default":"cuda"}),
                "low_vram": ("BOOLEAN",{"default":True}),
                "keep_models_loaded": ("BOOLEAN", {"default":True}),
            },
        }

    RETURN_TYPES = ("TRELLIS2PIPELINE", )
    RETURN_NAMES = ("pipeline", )
    FUNCTION = "process"
    CATEGORY = "Trellis2Wrapper"
    OUTPUT_NODE = True

    def process(self, modelname, backend, device, low_vram, keep_models_loaded):
        os.environ['OPENCV_IO_ENABLE_OPENEXR'] = '1'
        os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"  # Can save GPU memory
        #os.environ["FLEX_GEMM_AUTOTUNE_CACHE_PATH"] = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'autotune_cache.json')
        #os.environ["FLEX_GEMM_AUTOTUNER_VERBOSE"] = '1'        
        os.environ['ATTN_BACKEND'] = backend
        
        torch.backends.cudnn.benchmark = False
        
        download_path = os.path.join(folder_paths.models_dir,"microsoft")
        model_path = os.path.join(download_path, modelname)
        
        hf_model_name = f"microsoft/{modelname}"
        
        if not os.path.exists(model_path):
            print(f"Downloading model to: {model_path}")
            from huggingface_hub import snapshot_download
            snapshot_download(
                repo_id=hf_model_name,
                local_dir=model_path,
                local_dir_use_symlinks=False,
            )
            
        dinov3_model_path = os.path.join(folder_paths.models_dir,"facebook","dinov3-vitl16-pretrain-lvd1689m","model.safetensors")
        if not os.path.exists(dinov3_model_path):
            raise Exception("Facebook Dinov3 model not found in models/facebook/dinov3-vitl16-pretrain-lvd1689m folder")
        
        trellis_image_large_path = os.path.join(folder_paths.models_dir,"microsoft","TRELLIS-image-large","ckpts","ss_dec_conv3d_16l8_fp16.safetensors")
        if not os.path.exists(trellis_image_large_path):
            print('Trellis-Image-Large ss_dec_conv3d_16l8_fp16 files not found. Trying to download the files from huggingface ...')
            import requests
            url = "https://huggingface.co/microsoft/TRELLIS-image-large/resolve/main/ckpts/ss_dec_conv3d_16l8_fp16.json?download=true"
            filename = os.path.join(folder_paths.models_dir,"microsoft","TRELLIS-image-large","ckpts","ss_dec_conv3d_16l8_fp16.json")
            path = Path(filename)
            path.parent.mkdir(parents=True, exist_ok=True)
            
            response = requests.get(url)
            if response.status_code == 200:
                with open(filename, "wb") as f:
                    f.write(response.content)
                print("Download ss_dec_conv3d_16l8_fp16.json complete!")
            else:
                raise Exception("Cannot download Trellis-Image-Large file ss_dec_conv3d_16l8_fp16.json")
            
            url = "https://huggingface.co/microsoft/TRELLIS-image-large/resolve/main/ckpts/ss_dec_conv3d_16l8_fp16.safetensors?download=true"
            filename = os.path.join(folder_paths.models_dir,"microsoft","TRELLIS-image-large","ckpts","ss_dec_conv3d_16l8_fp16.safetensors")

            response = requests.get(url)
            if response.status_code == 200:
                with open(filename, "wb") as f:
                    f.write(response.content)
                print("Download ss_dec_conv3d_16l8_fp16.safetensors complete!")
            else:
                raise Exception("Cannot download Trellis-Image-Large file ss_dec_conv3d_16l8_fp16.safetensors")
        
        pipeline = Trellis2ImageTo3DPipeline.from_pretrained(model_path, keep_models_loaded = keep_models_loaded)
        pipeline.low_vram = low_vram
        
        if device=="cuda":
            if low_vram:
                pipeline.cuda()
            else:
                pipeline.to(device)
        else:
            pipeline.to(device)
        
        return (pipeline,)
        
class Trellis2MeshWithVoxelGenerator:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "pipeline": ("TRELLIS2PIPELINE",),
                "image": ("IMAGE",),                
                "seed": ("INT", {"default": 0, "min": 0, "max": 0x7fffffff}),
                "pipeline_type": (["512","1024","1024_cascade","1536_cascade"],{"default":"1024_cascade"}),
                "sparse_structure_steps": ("INT",{"default":12, "min":1, "max":100},),
                "shape_steps": ("INT",{"default":12, "min":1, "max":100},),
                "texture_steps": ("INT",{"default":12, "min":1, "max":100},),
                "max_num_tokens": ("INT",{"default":49152,"min":0,"max":999999}),
                "max_views": ("INT", {"default": 4, "min": 1, "max": 16}),
                "sparse_structure_resolution": ("INT", {"default":32,"min":8,"max":128,"step":8}),
                "generate_texture_slat": ("BOOLEAN", {"default":True}),
                "use_tiled_decoder": ("BOOLEAN", {"default":True}),
            },
        }

    RETURN_TYPES = ("MESHWITHVOXEL", )
    RETURN_NAMES = ("mesh", )
    FUNCTION = "process"
    CATEGORY = "Trellis2Wrapper"
    OUTPUT_NODE = True

    def process(self, pipeline, image, seed, pipeline_type, sparse_structure_steps, shape_steps, texture_steps, max_num_tokens, max_views, sparse_structure_resolution, generate_texture_slat, use_tiled_decoder):
        images = tensor_batch_to_pil_list(image, max_views=max_views)
        image_in = images[0] if len(images) == 1 else images
        
        sparse_structure_sampler_params = {"steps":sparse_structure_steps}
        shape_slat_sampler_params = {"steps":shape_steps}
        tex_slat_sampler_params = {"steps":texture_steps}
        
        if generate_texture_slat:
            num_steps = 5
        else:
            num_steps = 4

        pbar = ProgressBar(num_steps)        
        
        mesh = pipeline.run(image=image_in, seed=seed, pipeline_type=pipeline_type, sparse_structure_sampler_params = sparse_structure_sampler_params, shape_slat_sampler_params = shape_slat_sampler_params, tex_slat_sampler_params = tex_slat_sampler_params, max_num_tokens = max_num_tokens, sparse_structure_resolution = sparse_structure_resolution, max_views = max_views, generate_texture_slat = generate_texture_slat, use_tiled=use_tiled_decoder, pbar=pbar)[0]
        
        return (mesh,)    

class Trellis2LoadImageWithTransparency:
    @classmethod
    def INPUT_TYPES(s):
        input_dir = folder_paths.get_input_directory()
        files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))]
        files = folder_paths.filter_files_content_types(files, ["image"])
        return {"required":
                    {"image": (sorted(files), {"image_upload": True})},
                }

    CATEGORY = "Trellis2Wrapper"

    RETURN_TYPES = ("IMAGE", "MASK", "IMAGE", )
    RETURN_NAMES = ("image", "mask", "image_with_alpha")
    FUNCTION = "load_image"
    def load_image(self, image):
        image_path = folder_paths.get_annotated_filepath(image)

        img = node_helpers.pillow(Image.open, image_path)

        output_images = []
        output_masks = []
        output_images_ori = []
        w, h = None, None

        excluded_formats = ['MPO']

        for i in ImageSequence.Iterator(img):
            i = node_helpers.pillow(ImageOps.exif_transpose, i)
            
            output_images_ori.append(pil2tensor(i))

            if i.mode == 'I':
                i = i.point(lambda i: i * (1 / 255))
            image = i.convert("RGB")

            if len(output_images) == 0:
                w = image.size[0]
                h = image.size[1]

            if image.size[0] != w or image.size[1] != h:
                continue

            image = np.array(image).astype(np.float32) / 255.0
            image = torch.from_numpy(image)[None,]
            if 'A' in i.getbands():
                mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
                mask = 1. - torch.from_numpy(mask)
            elif i.mode == 'P' and 'transparency' in i.info:
                mask = np.array(i.convert('RGBA').getchannel('A')).astype(np.float32) / 255.0
                mask = 1. - torch.from_numpy(mask)
            else:
                mask = torch.zeros((64,64), dtype=torch.float32, device="cpu")
            output_images.append(image)
            output_masks.append(mask.unsqueeze(0))

        if len(output_images) > 1 and img.format not in excluded_formats:
            output_image = torch.cat(output_images, dim=0)
            output_mask = torch.cat(output_masks, dim=0)
            output_image_ori = torch.cat(output_images_ori, dim=0)
        else:
            output_image = output_images[0]
            output_mask = output_masks[0]
            output_image_ori = output_images_ori[0]

        return (output_image, output_mask, output_image_ori)

    @classmethod
    def IS_CHANGED(s, image):
        image_path = folder_paths.get_annotated_filepath(image)
        m = hashlib.sha256()
        with open(image_path, 'rb') as f:
            m.update(f.read())
        return m.digest().hex()

    @classmethod
    def VALIDATE_INPUTS(s, image):
        if not folder_paths.exists_annotated_filepath(image):
            return "Invalid image file: {}".format(image)

        return True  

class Trellis2SimplifyMesh:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "mesh": ("MESHWITHVOXEL",),
                "target_face_num": ("INT",{"default":1000000,"min":1,"max":30000000}),
                "method": (["Cumesh","Meshlib"],{"default":"Cumesh"}),
            },
        }

    RETURN_TYPES = ("MESHWITHVOXEL", )
    RETURN_NAMES = ("mesh", )
    FUNCTION = "process"
    CATEGORY = "Trellis2Wrapper"
    OUTPUT_NODE = True

    def process(self, mesh, target_face_num, method):        
        mesh_copy = copy.deepcopy(mesh)
        if method=="Cumesh":
            mesh_copy.simplify_with_cumesh(target = target_face_num)
        elif method=="Meshlib":
            mesh_copy.simplify_with_meshlib(target = target_face_num)
        else:
            raise Exception("Unknown simplification method")             
        
        return (mesh_copy,)          
        
class Trellis2MeshWithVoxelToTrimesh:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "mesh": ("MESHWITHVOXEL",),
            },
        }

    RETURN_TYPES = ("TRIMESH", )
    RETURN_NAMES = ("trimesh", )
    FUNCTION = "process"
    CATEGORY = "Trellis2Wrapper"
    OUTPUT_NODE = True

    def process(self, mesh):       
        vertices_np = mesh.vertices.cpu().numpy()
        vertices_np[:, 1], vertices_np[:, 2] = vertices_np[:, 2], -vertices_np[:, 1]        
        
        trimesh = Trimesh.Trimesh(
            vertices=vertices_np,
            faces=mesh.faces.cpu().numpy(),
            process=False
        )
        
        return (trimesh,)
        
class Trellis2ExportMesh:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "trimesh": ("TRIMESH",),
                "filename_prefix": ("STRING", {"default": "3D/Trellis2"}),
                "file_format": (["glb", "obj", "ply", "stl", "3mf", "dae"],),
            },
            "optional": {
                "save_file": ("BOOLEAN", {"default": True}),
            },
        }

    RETURN_TYPES = ("STRING",)
    RETURN_NAMES = ("glb_path",)
    FUNCTION = "process"
    CATEGORY = "Trellis2Wrapper"
    OUTPUT_NODE = True

    def process(self, trimesh, filename_prefix, file_format, save_file=True):
        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, folder_paths.get_output_directory())
        output_glb_path = Path(full_output_folder, f'{filename}_{counter:05}_.{file_format}')
        output_glb_path.parent.mkdir(exist_ok=True)
        if save_file:
            trimesh.export(output_glb_path, file_type=file_format, include_normals=True)
            relative_path = Path(subfolder) / f'{filename}_{counter:05}_.{file_format}'
        else:
            temp_file = Path(full_output_folder, f'hy3dtemp_.{file_format}')
            trimesh.export(temp_file, file_type=file_format,include_normals=True)
            relative_path = Path(subfolder) / f'hy3dtemp_.{file_format}'
        
        return (str(relative_path), )        
        
class Trellis2PostProcessMesh:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "mesh": ("MESHWITHVOXEL",),                
                "fill_holes": ("BOOLEAN", {"default":True}),
                "fill_holes_max_perimeter": ("FLOAT",{"default":0.03,"min":0.001,"max":99.999,"step":0.001}),
                "remove_duplicate_faces": ("BOOLEAN",{"default":True}),
                "repair_non_manifold_edges": ("BOOLEAN", {"default":True}),
                "remove_non_manifold_faces": ("BOOLEAN", {"default":True}),
                "remove_small_connected_components": ("BOOLEAN", {"default":True}),
                "remove_small_connected_components_size": ("FLOAT", {"default":0.00001,"min":0.00001,"max":9.99999,"step":0.00001}),
                "unify_faces_orientation": ("BOOLEAN", {"default":True}),
                "remove_floaters": ("BOOLEAN",{"default":True}),
            },
        }

    RETURN_TYPES = ("MESHWITHVOXEL",)
    RETURN_NAMES = ("mesh",)
    FUNCTION = "process"
    CATEGORY = "Trellis2Wrapper"
    OUTPUT_NODE = True

    def process(self, mesh, fill_holes, fill_holes_max_perimeter, remove_duplicate_faces, repair_non_manifold_edges, remove_non_manifold_faces, remove_small_connected_components, remove_small_connected_components_size,unify_faces_orientation,remove_floaters):
        mesh_copy = copy.deepcopy(mesh)

        if remove_floaters:
            mesh_copy = remove_floater(mesh_copy)

        vertices = mesh_copy.vertices
        faces = mesh_copy.faces

        # Move data to GPU
        vertices = vertices.cuda()
        faces = faces.cuda()
        
        # Initialize CUDA mesh handler
        cumesh = CuMesh.CuMesh()
        cumesh.init(vertices, faces)
        print(f"Current vertices: {cumesh.num_vertices}, faces: {cumesh.num_faces}")
        
        if fill_holes:
            cumesh.fill_holes(max_hole_perimeter=fill_holes_max_perimeter)
            print(f"After filling holes: {cumesh.num_vertices} vertices, {cumesh.num_faces} faces")
            
        if remove_duplicate_faces:
            print('Removing duplicate faces ...')
            cumesh.remove_duplicate_faces()
            
        if repair_non_manifold_edges:
            print('Repairing non manifold edges ...')
            cumesh.repair_non_manifold_edges()
            
        if remove_non_manifold_faces:
            print('Removing non manifold faces ...')
            cumesh.remove_non_manifold_faces()
            
        if remove_small_connected_components:
            print('Removing small connected components ...')
            cumesh.remove_small_connected_components(remove_small_connected_components_size)        
        
        if unify_faces_orientation:
            print('Unifying faces orientation ...')
            cumesh.unify_face_orientations()
        
        print(f"After initial cleanup: {cumesh.num_vertices} vertices, {cumesh.num_faces} faces")                                   
                
        
        new_vertices, new_faces = cumesh.read()
        
        mesh_copy.vertices = new_vertices.to(mesh_copy.device)
        mesh_copy.faces = new_faces.to(mesh_copy.device) 
        
        del cumesh
        gc.collect()     
                
        return (mesh_copy,)
       
class Trellis2UnWrapAndRasterizer:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "mesh": ("MESHWITHVOXEL",),
                "mesh_cluster_threshold_cone_half_angle_rad": ("FLOAT",{"default":90.0,"min":0.0,"max":359.9}),
                "mesh_cluster_refine_iterations": ("INT",{"default":0}),
                "mesh_cluster_global_iterations": ("INT",{"default":1}),
                "mesh_cluster_smooth_strength": ("INT",{"default":1}),                
                "texture_size": ("INT",{"default":1024, "min":512, "max":16384}),
                "texture_alpha_mode": (["OPAQUE","MASK","BLEND"],{"default":"OPAQUE"}),
                "double_side_material": ("BOOLEAN",{"default":True}),
            },
        }

    RETURN_TYPES = ("TRIMESH","IMAGE","IMAGE",)
    RETURN_NAMES = ("trimesh","base_color_texture", "metallic_roughness_texture",)
    FUNCTION = "process"
    CATEGORY = "Trellis2Wrapper"
    OUTPUT_NODE = True

    def process(self, mesh, mesh_cluster_threshold_cone_half_angle_rad, mesh_cluster_refine_iterations, mesh_cluster_global_iterations, mesh_cluster_smooth_strength, texture_size, texture_alpha_mode, double_side_material):
        mesh_copy = copy.deepcopy(mesh)
        
        aabb = [[-0.5, -0.5, -0.5], [0.5, 0.5, 0.5]]
        
        vertices = mesh_copy.vertices
        faces = mesh_copy.faces
        attr_volume = mesh_copy.attrs
        coords = mesh_copy.coords
        attr_layout = mesh_copy.layout
        voxel_size = mesh_copy.voxel_size  
        
        mesh_cluster_threshold_cone_half_angle_rad = np.radians(mesh_cluster_threshold_cone_half_angle_rad)

        # --- Input Normalization (AABB, Voxel Size, Grid Size) ---
        if isinstance(aabb, (list, tuple)):
            aabb = np.array(aabb)
        if isinstance(aabb, np.ndarray):
            aabb = torch.tensor(aabb, dtype=torch.float32, device=coords.device)

        # Calculate grid dimensions based on AABB and voxel size                
        if voxel_size is not None:
            if isinstance(voxel_size, float):
                voxel_size = [voxel_size, voxel_size, voxel_size]
            if isinstance(voxel_size, (list, tuple)):
                voxel_size = np.array(voxel_size)
            if isinstance(voxel_size, np.ndarray):
                voxel_size = torch.tensor(voxel_size, dtype=torch.float32, device=coords.device)
            grid_size = ((aabb[1] - aabb[0]) / voxel_size).round().int()
        else:
            if isinstance(grid_size, int):
                grid_size = [grid_size, grid_size, grid_size]
            if isinstance(grid_size, (list, tuple)):
                grid_size = np.array(grid_size)
            if isinstance(grid_size, np.ndarray):
                grid_size = torch.tensor(grid_size, dtype=torch.int32, device=coords.device)
            voxel_size = (aabb[1] - aabb[0]) / grid_size       
        
            print(f"Original mesh: {vertices.shape[0]} vertices, {faces.shape[0]} faces")        
        
        vertices = vertices.cuda()
        faces = faces.cuda()        
        
        cumesh = CuMesh.CuMesh()
        cumesh.init(vertices, faces)
        
        # Build BVH for the current mesh to guide remeshing
        print(f"Building BVH for current mesh...")
        bvh = CuMesh.cuBVH(vertices, faces)        
        
        print('Unwrapping ...')        
        out_vertices, out_faces, out_uvs, out_vmaps = cumesh.uv_unwrap(
            compute_charts_kwargs={
                "threshold_cone_half_angle_rad": mesh_cluster_threshold_cone_half_angle_rad,
                "refine_iterations": mesh_cluster_refine_iterations,
                "global_iterations": mesh_cluster_global_iterations,
                "smooth_strength": mesh_cluster_smooth_strength,
            },
            return_vmaps=True,
            verbose=True,
        )
        
        out_vertices = out_vertices.cuda()
        out_faces = out_faces.cuda()
        out_uvs = out_uvs.cuda()
        out_vmaps = out_vmaps.cuda()
        cumesh.compute_vertex_normals()
        out_normals = cumesh.read_vertex_normals()[out_vmaps]        

        print("Sampling attributes...")
        # Setup differentiable rasterizer context
        ctx = dr.RasterizeCudaContext()
        # Prepare UV coordinates for rasterization (rendering in UV space)
        uvs_rast = torch.cat([out_uvs * 2 - 1, torch.zeros_like(out_uvs[:, :1]), torch.ones_like(out_uvs[:, :1])], dim=-1).unsqueeze(0)
        rast = torch.zeros((1, texture_size, texture_size, 4), device='cuda', dtype=torch.float32)
        
        # Rasterize in chunks to save memory
        for i in range(0, out_faces.shape[0], 100000):
            rast_chunk, _ = dr.rasterize(
                ctx, uvs_rast, out_faces[i:i+100000],
                resolution=[texture_size, texture_size],
            )
            mask_chunk = rast_chunk[..., 3:4] > 0
            rast_chunk[..., 3:4] += i # Store face ID in alpha channel
            rast = torch.where(mask_chunk, rast_chunk, rast)
        
        # Mask of valid pixels in texture
        mask = rast[0, ..., 3] > 0
        
        # Interpolate 3D positions in UV space (finding 3D coord for every texel)
        pos = dr.interpolate(out_vertices.unsqueeze(0), rast, out_faces)[0][0]
        valid_pos = pos[mask]
        
        # Map these positions back to the *original* high-res mesh to get accurate attributes
        # This corrects geometric errors introduced by simplification/remeshing
        _, face_id, uvw = bvh.unsigned_distance(valid_pos, return_uvw=True)
        orig_tri_verts = vertices[faces[face_id.long()]] # (N_new, 3, 3)
        valid_pos = (orig_tri_verts * uvw.unsqueeze(-1)).sum(dim=1)
        
        # Trilinear sampling from the attribute volume (Color, Material props)
        attrs = torch.zeros(texture_size, texture_size, attr_volume.shape[1], device='cuda')
        attrs[mask] = grid_sample_3d(
            attr_volume,
            torch.cat([torch.zeros_like(coords[:, :1]), coords], dim=-1),
            shape=torch.Size([1, attr_volume.shape[1], *grid_size.tolist()]),
            grid=((valid_pos - aabb[0]) / voxel_size).reshape(1, -1, 3),
            mode='trilinear',
        )
        
        # --- Texture Post-Processing & Material Construction ---
        print("Finalizing mesh...")
        
        mask = mask.cpu().numpy()
        
        # Extract channels based on layout (BaseColor, Metallic, Roughness, Alpha)
        base_color = np.clip(attrs[..., attr_layout['base_color']].cpu().numpy() * 255, 0, 255).astype(np.uint8)
        metallic = np.clip(attrs[..., attr_layout['metallic']].cpu().numpy() * 255, 0, 255).astype(np.uint8)
        roughness = np.clip(attrs[..., attr_layout['roughness']].cpu().numpy() * 255, 0, 255).astype(np.uint8)
        alpha = np.clip(attrs[..., attr_layout['alpha']].cpu().numpy() * 255, 0, 255).astype(np.uint8)
        alpha_mode = texture_alpha_mode
        
        # Inpainting: fill gaps (dilation) to prevent black seams at UV boundaries
        mask_inv = (~mask).astype(np.uint8)
        base_color = cv2.inpaint(base_color, mask_inv, 3, cv2.INPAINT_TELEA)
        metallic = cv2.inpaint(metallic, mask_inv, 1, cv2.INPAINT_TELEA)[..., None]
        roughness = cv2.inpaint(roughness, mask_inv, 1, cv2.INPAINT_TELEA)[..., None]
        alpha = cv2.inpaint(alpha, mask_inv, 1, cv2.INPAINT_TELEA)[..., None]
        
        # Create PBR material
        # Standard PBR packs Metallic and Roughness into Blue and Green channels
        baseColorTexture_np = Image.fromarray(np.concatenate([base_color, alpha], axis=-1))
        metallicRoughnessTexture_np = Image.fromarray(np.concatenate([np.zeros_like(metallic), roughness, metallic], axis=-1))
        
        material = Trimesh.visual.material.PBRMaterial(
            baseColorTexture=baseColorTexture_np,
            baseColorFactor=np.array([255, 255, 255, 255], dtype=np.uint8),
            metallicRoughnessTexture=metallicRoughnessTexture_np,
            metallicFactor=1.0,
            roughnessFactor=1.0,
            alphaMode=alpha_mode,
            doubleSided=double_side_material,
        )        
        
        vertices_np = out_vertices.cpu().numpy()
        faces_np = out_faces.cpu().numpy()
        uvs_np = out_uvs.cpu().numpy()
        normals_np = out_normals.cpu().numpy()
        
        # Swap Y and Z axes, invert Y (common conversion for GLB compatibility)
        vertices_np[:, 1], vertices_np[:, 2] = vertices_np[:, 2], -vertices_np[:, 1]
        normals_np[:, 1], normals_np[:, 2] = normals_np[:, 2], -normals_np[:, 1]
        uvs_np[:, 1] = 1 - uvs_np[:, 1] # Flip UV V-coordinate
        
        textured_mesh = Trimesh.Trimesh(
            vertices=vertices_np,
            faces=faces_np,
            vertex_normals=normals_np,
            process=False,
            visual=Trimesh.visual.TextureVisuals(uv=uvs_np,material=material)
        )   

        del cumesh
        gc.collect()    

        baseColorTexture = pil2tensor(baseColorTexture_np)
        metallicRoughnessTexture = pil2tensor(metallicRoughnessTexture_np)
                
        return (textured_mesh, baseColorTexture, metallicRoughnessTexture, )
        
class Trellis2MeshWithVoxelAdvancedGenerator:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "pipeline": ("TRELLIS2PIPELINE",),
                "image": ("IMAGE",),
                "seed": ("INT", {"default": 0, "min": 0, "max": 0x7fffffff}),
                "pipeline_type": (["512","1024","1024_cascade","1536_cascade"],{"default":"1024_cascade"}),
                "sparse_structure_steps": ("INT",{"default":12, "min":1, "max":100},),
                "sparse_structure_guidance_strength": ("FLOAT",{"default":7.50}),
                "sparse_structure_guidance_rescale": ("FLOAT",{"default":0.70}),
                "sparse_structure_rescale_t": ("FLOAT",{"default":5.00}),
                "shape_steps": ("INT",{"default":12, "min":1, "max":100},),
                "shape_guidance_strength": ("FLOAT",{"default":7.50}),
                "shape_guidance_rescale": ("FLOAT",{"default":0.50}),
                "shape_rescale_t": ("FLOAT",{"default":3.00}),                
                "texture_steps": ("INT",{"default":12, "min":1, "max":100},),
                "texture_guidance_strength": ("FLOAT",{"default":1.00}),
                "texture_guidance_rescale": ("FLOAT",{"default":0.00}),
                "texture_rescale_t": ("FLOAT",{"default":3.00}),                
                "max_num_tokens": ("INT",{"default":49152,"min":0,"max":999999}),
                "max_views": ("INT", {"default": 4, "min": 1, "max": 16}),
                "sparse_structure_resolution": ("INT", {"default":32,"min":8,"max":128,"step":8}),
                "generate_texture_slat": ("BOOLEAN", {"default":True}),
                "sparse_structure_guidance_interval_start": ("FLOAT",{"default":0.30,"min":0.00,"max":1.00,"step":0.01}),
                "sparse_structure_guidance_interval_end": ("FLOAT",{"default":1.00,"min":0.00,"max":1.00,"step":0.01}),
                "shape_guidance_interval_start": ("FLOAT",{"default":0.30,"min":0.00,"max":1.00,"step":0.01}),
                "shape_guidance_interval_end": ("FLOAT",{"default":1.00,"min":0.00,"max":1.00,"step":0.01}),
                "texture_guidance_interval_start": ("FLOAT",{"default":0.60,"min":0.00,"max":1.00,"step":0.01}),
                "texture_guidance_interval_end": ("FLOAT",{"default":0.90,"min":0.00,"max":1.00,"step":0.01}),
                "use_tiled_decoder": ("BOOLEAN", {"default":True}),
            },
        }

    RETURN_TYPES = ("MESHWITHVOXEL", )
    RETURN_NAMES = ("mesh", )
    FUNCTION = "process"
    CATEGORY = "Trellis2Wrapper"
    OUTPUT_NODE = True

    def process(self, pipeline, image, seed, pipeline_type, sparse_structure_steps, 
        sparse_structure_guidance_strength, 
        sparse_structure_guidance_rescale,
        sparse_structure_rescale_t,
        shape_steps, 
        shape_guidance_strength, 
        shape_guidance_rescale,
        shape_rescale_t,        
        texture_steps, 
        texture_guidance_strength, 
        texture_guidance_rescale,
        texture_rescale_t,        
        max_num_tokens,
        max_views,
        sparse_structure_resolution,
        generate_texture_slat,
        sparse_structure_guidance_interval_start,
        sparse_structure_guidance_interval_end,
        shape_guidance_interval_start,
        shape_guidance_interval_end,
        texture_guidance_interval_start,
        texture_guidance_interval_end,
        use_tiled_decoder):

        images = tensor_batch_to_pil_list(image, max_views=max_views)
        image_in = images[0] if len(images) == 1 else images
        
        sparse_structure_guidance_interval = [sparse_structure_guidance_interval_start,sparse_structure_guidance_interval_end]
        shape_guidance_interval = [shape_guidance_interval_start,shape_guidance_interval_end]
        texture_guidance_interval = [texture_guidance_interval_start,texture_guidance_interval_end]
        
        sparse_structure_sampler_params = {"steps":sparse_structure_steps,"guidance_strength":sparse_structure_guidance_strength,"guidance_rescale":sparse_structure_guidance_rescale,"guidance_interval":sparse_structure_guidance_interval,"rescale_t":sparse_structure_rescale_t}        
        shape_slat_sampler_params = {"steps":shape_steps,"guidance_strength":shape_guidance_strength,"guidance_rescale":shape_guidance_rescale,"guidance_interval":shape_guidance_interval,"rescale_t":shape_rescale_t}       
        tex_slat_sampler_params = {"steps":texture_steps,"guidance_strength":texture_guidance_strength,"guidance_rescale":texture_guidance_rescale,"guidance_interval":texture_guidance_interval,"rescale_t":texture_rescale_t}
            
        if generate_texture_slat:
            num_steps = 5
        else:
            num_steps = 4

        pbar = ProgressBar(num_steps)
        
        mesh = pipeline.run(image=image_in, seed=seed, pipeline_type=pipeline_type, sparse_structure_sampler_params = sparse_structure_sampler_params, shape_slat_sampler_params = shape_slat_sampler_params, tex_slat_sampler_params = tex_slat_sampler_params, max_num_tokens = max_num_tokens, sparse_structure_resolution = sparse_structure_resolution, max_views = max_views, generate_texture_slat=generate_texture_slat, use_tiled=use_tiled_decoder, pbar=pbar)[0]         
        
        return (mesh,)    

class Trellis2PostProcessAndUnWrapAndRasterizer:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "mesh": ("MESHWITHVOXEL",),
                "mesh_cluster_threshold_cone_half_angle_rad": ("FLOAT",{"default":90.0,"min":0.0,"max":359.9}),
                "mesh_cluster_refine_iterations": ("INT",{"default":0}),
                "mesh_cluster_global_iterations": ("INT",{"default":1}),
                "mesh_cluster_smooth_strength": ("INT",{"default":1}),                
                "texture_size": ("INT",{"default":2048, "min":512, "max":16384}),
                "remesh": ("BOOLEAN",{"default":True}),
                "remesh_band": ("FLOAT",{"default":1.0}),
                "remesh_project": ("FLOAT",{"default":0.0}),
                "target_face_num": ("INT",{"default":2000000,"min":1,"max":16000000}),
                "simplify_method": (["Cumesh","Meshlib"],{"default":"Cumesh"}),
                "fill_holes": ("BOOLEAN", {"default":True}),
                "fill_holes_max_perimeter": ("FLOAT",{"default":0.03,"min":0.001,"max":99.999,"step":0.001}),
                "texture_alpha_mode": (["OPAQUE","MASK","BLEND"],{"default":"OPAQUE"}),
                "dual_contouring_resolution": (["Auto","128","256","512","1024","2048"],{"default":"512"}),
                "double_side_material": ("BOOLEAN",{"default":True}),
                "remove_floaters": ("BOOLEAN",{"default":True}),
            },
        }

    RETURN_TYPES = ("TRIMESH","IMAGE","IMAGE",)
    RETURN_NAMES = ("trimesh","base_color_texture","metallic_roughness_texture",)
    FUNCTION = "process"
    CATEGORY = "Trellis2Wrapper"
    OUTPUT_NODE = True

    def process(self, mesh, mesh_cluster_threshold_cone_half_angle_rad, mesh_cluster_refine_iterations, mesh_cluster_global_iterations, mesh_cluster_smooth_strength, texture_size, remesh, remesh_band, remesh_project, target_face_num, simplify_method, fill_holes, fill_holes_max_perimeter, texture_alpha_mode, dual_contouring_resolution, double_side_material,remove_floaters):
        pbar = ProgressBar(5)
        mesh_copy = copy.deepcopy(mesh)
        
        aabb = [[-0.5, -0.5, -0.5], [0.5, 0.5, 0.5]]
        
        attr_volume = mesh_copy.attrs
        coords = mesh_copy.coords
        attr_layout = mesh_copy.layout
        voxel_size = mesh_copy.voxel_size  
        
        mesh_cluster_threshold_cone_half_angle_rad = np.radians(mesh_cluster_threshold_cone_half_angle_rad)

        # --- Input Normalization (AABB, Voxel Size, Grid Size) ---
        if isinstance(aabb, (list, tuple)):
            aabb = np.array(aabb)
        if isinstance(aabb, np.ndarray):
            aabb = torch.tensor(aabb, dtype=torch.float32, device=coords.device)

        # Calculate grid dimensions based on AABB and voxel size                
        if voxel_size is not None:
            if isinstance(voxel_size, float):
                voxel_size = [voxel_size, voxel_size, voxel_size]
            if isinstance(voxel_size, (list, tuple)):
                voxel_size = np.array(voxel_size)
            if isinstance(voxel_size, np.ndarray):
                voxel_size = torch.tensor(voxel_size, dtype=torch.float32, device=coords.device)
            grid_size = ((aabb[1] - aabb[0]) / voxel_size).round().int()
        else:
            if isinstance(grid_size, int):
                grid_size = [grid_size, grid_size, grid_size]
            if isinstance(grid_size, (list, tuple)):
                grid_size = np.array(grid_size)
            if isinstance(grid_size, np.ndarray):
                grid_size = torch.tensor(grid_size, dtype=torch.int32, device=coords.device)
            voxel_size = (aabb[1] - aabb[0]) / grid_size
        
        if remove_floaters:
            mesh_copy = remove_floater(mesh_copy)
            
        vertices = mesh_copy.vertices
        faces = mesh_copy.faces
        
        vertices = vertices.cuda()
        faces = faces.cuda()                
        
        # Initialize CUDA mesh handler
        cumesh = CuMesh.CuMesh()
        cumesh.init(vertices, faces)
        print(f"Current vertices: {cumesh.num_vertices}, faces: {cumesh.num_faces}")
        
        # --- Initial Mesh Cleaning ---
        # Fills holes as much as we can before processing
        if fill_holes:
            cumesh.fill_holes(max_hole_perimeter=fill_holes_max_perimeter)
            print(f"After filling holes: {cumesh.num_vertices} vertices, {cumesh.num_faces} faces")        
            
        # Build BVH for the current mesh to guide remeshing
        print(f"Building BVH for current mesh...")
        bvh = CuMesh.cuBVH(vertices, faces)
        pbar.update(1)
            
        print("Cleaning mesh...")        
        # --- Branch 1: Standard Pipeline (Simplification & Cleaning) ---
        if not remesh:            
            if simplify_method == 'Cumesh':
                cumesh.simplify(target_face_num * 3, verbose=True)
            elif simplify_method == 'Meshlib':
                 # GPU -> CPU -> Meshlib -> CPU -> GPU
                v, f = cumesh.read()
                new_vertices, new_faces = simplify_with_meshlib(v.cpu().numpy(), f.cpu().numpy(), target_face_num)
                cumesh.init(torch.from_numpy(new_vertices).float().cuda(), torch.from_numpy(new_faces).int().cuda())        
            
            cumesh.remove_duplicate_faces()
            cumesh.repair_non_manifold_edges()
            cumesh.remove_small_connected_components(1e-5)
            
            if fill_holes:
                cumesh.fill_holes(max_hole_perimeter=fill_holes_max_perimeter)
            
            if simplify_method == 'Cumesh':
                cumesh.simplify(target_face_num, verbose=True)
            elif simplify_method == 'Meshlib':
                 # GPU -> CPU -> Meshlib -> CPU -> GPU
                v, f = cumesh.read()
                new_vertices, new_faces = simplify_with_meshlib(v.cpu().numpy(), f.cpu().numpy(), target_face_num)
                cumesh.init(torch.from_numpy(new_vertices).float().cuda(), torch.from_numpy(new_faces).int().cuda())
            
            cumesh.remove_duplicate_faces()
            cumesh.repair_non_manifold_edges()
            cumesh.remove_small_connected_components(1e-5) 

            if fill_holes:
                cumesh.fill_holes(max_hole_perimeter=fill_holes_max_perimeter)            
            
            print(f"After initial cleanup: {cumesh.num_vertices} vertices, {cumesh.num_faces} faces")                            
                
            # Step 2: Unify face orientations
            cumesh.unify_face_orientations()
        
        # --- Branch 2: Remeshing Pipeline ---
        else:
            center = aabb.mean(dim=0)
            scale = (aabb[1] - aabb[0]).max().item()
            
            if dual_contouring_resolution == "Auto":
                resolution = grid_size.max().item()
                print(f"Dual Contouring resolution: {resolution}")
            else:
                resolution = int(dual_contouring_resolution)
            
            print('Performing Dual Contouring ...')
            # Perform Dual Contouring remeshing (rebuilds topology)
            cumesh.init(*CuMesh.remeshing.remesh_narrow_band_dc(
                vertices, faces,
                center = center,
                scale = 1.0, # old calculation : (resolution + 3 * remesh_band) / resolution * scale,
                resolution = resolution,
                band = remesh_band,
                project_back = remesh_project, # Snaps vertices back to original surface
                verbose = True,
                bvh = bvh,
            ))
            
            print(f"After remeshing: {cumesh.num_vertices} vertices, {cumesh.num_faces} faces")
            
            # Step 2: Unify face orientations
            #cumesh.unify_face_orientations()            

            if simplify_method == 'Cumesh':
                cumesh.simplify(target_face_num, verbose=True)
            elif simplify_method == 'Meshlib':
                 # GPU -> CPU -> Meshlib -> CPU -> GPU
                v, f = cumesh.read()
                new_vertices, new_faces = simplify_with_meshlib(v.cpu().numpy(), f.cpu().numpy(), target_face_num)
                cumesh.init(torch.from_numpy(new_vertices).float().cuda(), torch.from_numpy(new_faces).int().cuda())

        print(f"After simplifying: {cumesh.num_vertices} vertices, {cumesh.num_faces} faces")            
        pbar.update(1)
        
        print('Unwrapping ...')        
        out_vertices, out_faces, out_uvs, out_vmaps = cumesh.uv_unwrap(
            compute_charts_kwargs={
                "threshold_cone_half_angle_rad": mesh_cluster_threshold_cone_half_angle_rad,
                "refine_iterations": mesh_cluster_refine_iterations,
                "global_iterations": mesh_cluster_global_iterations,
                "smooth_strength": mesh_cluster_smooth_strength,
            },
            return_vmaps=True,
            verbose=True,
        )
        pbar.update(1)
        
        out_vertices = out_vertices.cuda()
        out_faces = out_faces.cuda()
        out_uvs = out_uvs.cuda()
        out_vmaps = out_vmaps.cuda()
        cumesh.compute_vertex_normals()
        out_normals = cumesh.read_vertex_normals()[out_vmaps]        

        print("Sampling attributes...")
        # Setup differentiable rasterizer context
        ctx = dr.RasterizeCudaContext()
        # Prepare UV coordinates for rasterization (rendering in UV space)
        uvs_rast = torch.cat([out_uvs * 2 - 1, torch.zeros_like(out_uvs[:, :1]), torch.ones_like(out_uvs[:, :1])], dim=-1).unsqueeze(0)
        rast = torch.zeros((1, texture_size, texture_size, 4), device='cuda', dtype=torch.float32)
        
        # Rasterize in chunks to save memory
        for i in range(0, out_faces.shape[0], 100000):
            rast_chunk, _ = dr.rasterize(
                ctx, uvs_rast, out_faces[i:i+100000],
                resolution=[texture_size, texture_size],
            )
            mask_chunk = rast_chunk[..., 3:4] > 0
            rast_chunk[..., 3:4] += i # Store face ID in alpha channel
            rast = torch.where(mask_chunk, rast_chunk, rast)
        
        # Mask of valid pixels in texture
        mask = rast[0, ..., 3] > 0
        
        # Interpolate 3D positions in UV space (finding 3D coord for every texel)
        pos = dr.interpolate(out_vertices.unsqueeze(0), rast, out_faces)[0][0]
        valid_pos = pos[mask]
        
        # Map these positions back to the *original* high-res mesh to get accurate attributes
        # This corrects geometric errors introduced by simplification/remeshing
        _, face_id, uvw = bvh.unsigned_distance(valid_pos, return_uvw=True)
        orig_tri_verts = vertices[faces[face_id.long()]] # (N_new, 3, 3)
        valid_pos = (orig_tri_verts * uvw.unsqueeze(-1)).sum(dim=1)
        
        # Trilinear sampling from the attribute volume (Color, Material props)
        attrs = torch.zeros(texture_size, texture_size, attr_volume.shape[1], device='cuda')
        attrs[mask] = grid_sample_3d(
            attr_volume,
            torch.cat([torch.zeros_like(coords[:, :1]), coords], dim=-1),
            shape=torch.Size([1, attr_volume.shape[1], *grid_size.tolist()]),
            grid=((valid_pos - aabb[0]) / voxel_size).reshape(1, -1, 3),
            mode='trilinear',
        )
        
        # --- Texture Post-Processing & Material Construction ---
        print("Finalizing mesh...")
        pbar.update(1)
        
        mask = mask.cpu().numpy()
        
        # Extract channels based on layout (BaseColor, Metallic, Roughness, Alpha)
        base_color = np.clip(attrs[..., attr_layout['base_color']].cpu().numpy() * 255, 0, 255).astype(np.uint8)
        metallic = np.clip(attrs[..., attr_layout['metallic']].cpu().numpy() * 255, 0, 255).astype(np.uint8)
        roughness = np.clip(attrs[..., attr_layout['roughness']].cpu().numpy() * 255, 0, 255).astype(np.uint8)
        alpha = np.clip(attrs[..., attr_layout['alpha']].cpu().numpy() * 255, 0, 255).astype(np.uint8)
        alpha_mode = texture_alpha_mode
        
        # Inpainting: fill gaps (dilation) to prevent black seams at UV boundaries
        mask_inv = (~mask).astype(np.uint8)
        base_color = cv2.inpaint(base_color, mask_inv, 3, cv2.INPAINT_TELEA)
        metallic = cv2.inpaint(metallic, mask_inv, 1, cv2.INPAINT_TELEA)[..., None]
        roughness = cv2.inpaint(roughness, mask_inv, 1, cv2.INPAINT_TELEA)[..., None]
        alpha = cv2.inpaint(alpha, mask_inv, 1, cv2.INPAINT_TELEA)[..., None]
        
        # Create PBR material
        # Standard PBR packs Metallic and Roughness into Blue and Green channels
        baseColorTexture_np = Image.fromarray(np.concatenate([base_color, alpha], axis=-1))
        metallicRoughnessTexture_np = Image.fromarray(np.concatenate([np.zeros_like(metallic), roughness, metallic], axis=-1))
        material = Trimesh.visual.material.PBRMaterial(
            baseColorTexture=baseColorTexture_np,
            baseColorFactor=np.array([255, 255, 255, 255], dtype=np.uint8),
            metallicRoughnessTexture=metallicRoughnessTexture_np,
            metallicFactor=1.0,
            roughnessFactor=1.0,
            alphaMode=alpha_mode,
            doubleSided=double_side_material,
        )        
        
        vertices_np = out_vertices.cpu().numpy()
        faces_np = out_faces.cpu().numpy()
        uvs_np = out_uvs.cpu().numpy()
        normals_np = out_normals.cpu().numpy()
        
        # Swap Y and Z axes, invert Y (common conversion for GLB compatibility)
        vertices_np[:, 1], vertices_np[:, 2] = vertices_np[:, 2], -vertices_np[:, 1]
        normals_np[:, 1], normals_np[:, 2] = normals_np[:, 2], -normals_np[:, 1]
        uvs_np[:, 1] = 1 - uvs_np[:, 1] # Flip UV V-coordinate
        
        textured_mesh = Trimesh.Trimesh(
            vertices=vertices_np,
            faces=faces_np,
            vertex_normals=normals_np,
            process=False,
            visual=Trimesh.visual.TextureVisuals(uv=uvs_np,material=material)
        )
        pbar.update(1)        
        
        del cumesh
        gc.collect()         

        baseColorTexture = pil2tensor(baseColorTexture_np)
        metallicRoughnessTexture = pil2tensor(metallicRoughnessTexture_np)
        
        return (textured_mesh, baseColorTexture, metallicRoughnessTexture, )    

class Trellis2Remesh:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "mesh": ("MESHWITHVOXEL",),
                "remesh_band": ("FLOAT",{"default":1.0}),
                "remesh_project": ("FLOAT",{"default":0.0}),
                "fill_holes": ("BOOLEAN", {"default":True}),
                "fill_holes_max_perimeter": ("FLOAT",{"default":0.03,"min":0.001,"max":99.999,"step":0.001}),
                "dual_contouring_resolution": (["Auto","128","256","512","1024","2048"],{"default":"Auto"}),
                "remove_floaters": ("BOOLEAN",{"default":True}), 
            },
        }

    RETURN_TYPES = ("MESHWITHVOXEL",)
    RETURN_NAMES = ("mesh",)
    FUNCTION = "process"
    CATEGORY = "Trellis2Wrapper"
    OUTPUT_NODE = True

    def process(self, mesh, remesh_band, remesh_project, fill_holes, fill_holes_max_perimeter, dual_contouring_resolution, remove_floaters):
        mesh_copy = copy.deepcopy(mesh)
        
        if remove_floaters:
            mesh_copy = remove_floater(mesh_copy)
        
        aabb = [[-0.5, -0.5, -0.5], [0.5, 0.5, 0.5]]
        
        vertices = mesh_copy.vertices
        faces = mesh_copy.faces
        attr_volume = mesh_copy.attrs
        coords = mesh_copy.coords
        attr_layout = mesh_copy.layout
        voxel_size = mesh_copy.voxel_size        
        
        # --- Input Normalization (AABB, Voxel Size, Grid Size) ---
        if isinstance(aabb, (list, tuple)):
            aabb = np.array(aabb)
        if isinstance(aabb, np.ndarray):
            aabb = torch.tensor(aabb, dtype=torch.float32, device='cuda')

        # Calculate grid dimensions based on AABB and voxel size                
        if voxel_size is not None:
            if isinstance(voxel_size, float):
                voxel_size = [voxel_size, voxel_size, voxel_size]
            if isinstance(voxel_size, (list, tuple)):
                voxel_size = np.array(voxel_size)
            if isinstance(voxel_size, np.ndarray):
                voxel_size = torch.tensor(voxel_size, dtype=torch.float32, device='cuda')
            grid_size = ((aabb[1] - aabb[0]) / voxel_size).round().int()
        else:
            if isinstance(grid_size, int):
                grid_size = [grid_size, grid_size, grid_size]
            if isinstance(grid_size, (list, tuple)):
                grid_size = np.array(grid_size)
            if isinstance(grid_size, np.ndarray):
                grid_size = torch.tensor(grid_size, dtype=torch.int32, device='cuda')
            voxel_size = (aabb[1] - aabb[0]) / grid_size

        # Move data to GPU
        vertices = vertices.cuda()
        faces = faces.cuda()
        
        # Initialize CUDA mesh handler
        cumesh = CuMesh.CuMesh()
        cumesh.init(vertices, faces)
        print(f"Current vertices: {cumesh.num_vertices}, faces: {cumesh.num_faces}")
        
        # --- Initial Mesh Cleaning ---
        # Fills holes as much as we can before processing
        if fill_holes:
            cumesh.fill_holes(max_hole_perimeter=fill_holes_max_perimeter)
            print(f"After filling holes: {cumesh.num_vertices} vertices, {cumesh.num_faces} faces")
        
        vertices, faces = cumesh.read()
            
        # Build BVH for the current mesh to guide remeshing
        print(f"Building BVH for current mesh...")
        bvh = CuMesh.cuBVH(vertices, faces)
            
        print("Cleaning mesh...")        
        center = aabb.mean(dim=0)
        scale = (aabb[1] - aabb[0]).max().item()
        
        if dual_contouring_resolution == "Auto":
            resolution = grid_size.max().item()
            print(f"Dual Contouring resolution: {resolution}")
        else:
            resolution = int(dual_contouring_resolution)
        
        print('Performing Dual Contouring ...')
        # Perform Dual Contouring remeshing (rebuilds topology)
        cumesh.init(*CuMesh.remeshing.remesh_narrow_band_dc(
            vertices, faces,
            center = center,
            scale = 1.0, # old calculation (resolution + 3 * remesh_band) / resolution * scale,
            resolution = resolution,
            band = remesh_band,
            project_back = remesh_project, # Snaps vertices back to original surface
            verbose = True,
            bvh = bvh,
        ))
        
        print(f"After remeshing: {cumesh.num_vertices} vertices, {cumesh.num_faces} faces")                          
        
        # Step 2: Unify face orientations
        #cumesh.unify_face_orientations()        
        
        new_vertices, new_faces = cumesh.read()
        
        mesh_copy.vertices = new_vertices.to(mesh_copy.device)
        mesh_copy.faces = new_faces.to(mesh_copy.device) 
        
        del cumesh
        gc.collect()          
                
        return (mesh_copy,)
        
class Trellis2MeshTexturing:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "pipeline": ("TRELLIS2PIPELINE",),
                "image": ("IMAGE",),
                "trimesh": ("TRIMESH",),
                "seed": ("INT", {"default": 0, "min": 0, "max": 0x7fffffff}),
                "texture_steps": ("INT",{"default":12, "min":1, "max":100},),
                "texture_guidance_strength": ("FLOAT",{"default":1.0}),
                "texture_guidance_rescale": ("FLOAT",{"default":0.0}),
                "texture_rescale_t": ("FLOAT",{"default":3.0}),
                "resolution": ([512,1024],{"default":1024}),
                "texture_size": ("INT",{"default":2048,"min":512,"max":16384}),
                "texture_alpha_mode": (["OPAQUE","MASK","BLEND"],{"default":"OPAQUE"}),
                "double_side_material": ("BOOLEAN",{"default":True}), 
                "texture_guidance_interval_start": ("FLOAT",{"default":0.60,"min":0.00,"max":1.00,"step":0.01}),
                "texture_guidance_interval_end": ("FLOAT",{"default":0.90,"min":0.00,"max":1.00,"step":0.01}),                
            },
        }

    RETURN_TYPES = ("TRIMESH","IMAGE","IMAGE",)
    RETURN_NAMES = ("trimesh","base_color_texture","metallic_roughness_texture",)
    FUNCTION = "process"
    CATEGORY = "Trellis2Wrapper"
    OUTPUT_NODE = True

    def process(self, pipeline, image, trimesh, seed, texture_steps, texture_guidance_strength, texture_guidance_rescale, texture_rescale_t, resolution, texture_size, texture_alpha_mode, double_side_material, texture_guidance_interval_start, texture_guidance_interval_end):
        #image = tensor2pil_v2(image)
        image = tensor2pil(image)
        
        texture_guidance_interval = [texture_guidance_interval_start,texture_guidance_interval_end]                
        
        tex_slat_sampler_params = {"steps":texture_steps,"guidance_strength":texture_guidance_strength,"guidance_rescale":texture_guidance_rescale,"guidance_interval":texture_guidance_interval,"rescale_t":texture_rescale_t}

        textured_mesh, baseColorTexture_np, metallicRoughnessTexture_np = pipeline.texture_mesh(mesh=trimesh, 
            image=image, 
            seed=seed, 
            tex_slat_sampler_params = tex_slat_sampler_params,
            resolution = resolution,
            texture_size = texture_size,
            texture_alpha_mode = texture_alpha_mode,
            double_side_material = double_side_material
        )
            

        baseColorTexture = pil2tensor(baseColorTexture_np)
        metallicRoughnessTexture = pil2tensor(metallicRoughnessTexture_np)
        
        return (textured_mesh, baseColorTexture, metallicRoughnessTexture, )
        
class Trellis2LoadMesh:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "glb_path": ("STRING", {"default": "", "tooltip": "The glb path with mesh to load."}), 
            }
        }
    RETURN_TYPES = ("TRIMESH",)
    RETURN_NAMES = ("trimesh",)
    OUTPUT_TOOLTIPS = ("The glb model with mesh to texturize.",)
    
    FUNCTION = "load"
    CATEGORY = "Trellis2Wrapper"
    DESCRIPTION = "Loads a glb model from the given path."

    def load(self, glb_path):
        if not os.path.exists(glb_path):
            glb_path = os.path.join(folder_paths.get_input_directory(), glb_path)
        
        trimesh = Trimesh.load(glb_path, force="mesh")
        
        return (trimesh,)  
        
class Trellis2PreProcessImage:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "image": ("IMAGE",), 
            }
        }
    RETURN_TYPES = ("IMAGE",)
    RETURN_NAMES = ("image",)
    
    FUNCTION = "process"
    CATEGORY = "Trellis2Wrapper"

    def process(self, image):
        image = tensor2pil(image)
        image = self.preprocess_image(image)
        image = pil2tensor(image)
        
        return (image,)         


    def preprocess_image(self, input: Image.Image) -> Image.Image:
        """
        Preprocess the input image.
        """
        # if has alpha channel, use it directly; otherwise, remove background
        has_alpha = False
        if input.mode == 'RGBA':
            alpha = np.array(input)[:, :, 3]
            if not np.all(alpha == 255):
                has_alpha = True
        max_size = max(input.size)
        scale = min(1, 2048 / max_size)
        if scale < 1:
            input = input.resize((int(input.width * scale), int(input.height * scale)), Image.Resampling.LANCZOS)
        # if has_alpha:
            # output = input
        # else:
            # input = input.convert('RGB')
            # if self.low_vram:
                # self.rembg_model.to(self.device)
            # output = self.rembg_model(input)
            # if self.low_vram:
                # self.rembg_model.cpu()
        output = input
        output_np = np.array(output)
        alpha = output_np[:, :, 3]
        bbox = np.argwhere(alpha > 0.8 * 255)
        bbox = np.min(bbox[:, 1]), np.min(bbox[:, 0]), np.max(bbox[:, 1]), np.max(bbox[:, 0])
        center = (bbox[0] + bbox[2]) / 2, (bbox[1] + bbox[3]) / 2
        size = max(bbox[2] - bbox[0], bbox[3] - bbox[1])
        size = int(size * 1)
        bbox = center[0] - size // 2, center[1] - size // 2, center[0] + size // 2, center[1] + size // 2
        output = output.crop(bbox)  # type: ignore
        output = np.array(output).astype(np.float32) / 255
        output = output[:, :, :3] * output[:, :, 3:4]
        output = Image.fromarray((output * 255).astype(np.uint8))
        return output    

class Trellis2MeshRefiner:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "pipeline": ("TRELLIS2PIPELINE",),
                "trimesh": ("TRIMESH",),
                "image": ("IMAGE",),
                "seed": ("INT", {"default": 0, "min": 0, "max": 0x7fffffff}),
                "resolution": ([512,1024,1536],{"default":1024}),
                "shape_steps": ("INT",{"default":12, "min":1, "max":100},),
                "shape_guidance_strength": ("FLOAT",{"default":7.50}),
                "shape_guidance_rescale": ("FLOAT",{"default":0.50}),
                "shape_rescale_t": ("FLOAT",{"default":3.00}),                
                "texture_steps": ("INT",{"default":12, "min":1, "max":100},),
                "texture_guidance_strength": ("FLOAT",{"default":1.00}),
                "texture_guidance_rescale": ("FLOAT",{"default":0.00}),
                "texture_rescale_t": ("FLOAT",{"default":3.00}),                
                "max_num_tokens": ("INT",{"default":49152,"min":0,"max":999999}),
                "generate_texture_slat": ("BOOLEAN", {"default":True}),
                "downsampling":([16,32,64],{"default":16}),
                "shape_guidance_interval_start": ("FLOAT",{"default":0.30,"min":0.00,"max":1.00,"step":0.01}),
                "shape_guidance_interval_end": ("FLOAT",{"default":1.00,"min":0.00,"max":1.00,"step":0.01}),
                "texture_guidance_interval_start": ("FLOAT",{"default":0.60,"min":0.00,"max":1.00,"step":0.01}),
                "texture_guidance_interval_end": ("FLOAT",{"default":0.90,"min":0.00,"max":1.00,"step":0.01}),
                "use_tiled_decoder": ("BOOLEAN", {"default":True}),
            },
        }

    RETURN_TYPES = ("MESHWITHVOXEL", )
    RETURN_NAMES = ("mesh", )
    FUNCTION = "process"
    CATEGORY = "Trellis2Wrapper"
    OUTPUT_NODE = True

    def process(self, pipeline, trimesh, image, seed, resolution,
        shape_steps, 
        shape_guidance_strength, 
        shape_guidance_rescale,
        shape_rescale_t,        
        texture_steps, 
        texture_guidance_strength, 
        texture_guidance_rescale,
        texture_rescale_t,        
        max_num_tokens,
        generate_texture_slat,
        downsampling,
        shape_guidance_interval_start,
        shape_guidance_interval_end,
        texture_guidance_interval_start,
        texture_guidance_interval_end,
        use_tiled_decoder):

        image = tensor2pil(image)
        
        shape_guidance_interval = [shape_guidance_interval_start,shape_guidance_interval_end]
        texture_guidance_interval = [texture_guidance_interval_start,texture_guidance_interval_end]        
        
        shape_slat_sampler_params = {"steps":shape_steps,"guidance_strength":shape_guidance_strength,"guidance_rescale":shape_guidance_rescale,"guidance_interval":shape_guidance_interval,"rescale_t":shape_rescale_t}       
        tex_slat_sampler_params = {"steps":texture_steps,"guidance_strength":texture_guidance_strength,"guidance_rescale":texture_guidance_rescale,"guidance_interval":texture_guidance_interval,"rescale_t":texture_rescale_t}
        
        mesh = pipeline.refine_mesh(mesh = trimesh, image=image, seed=seed, shape_slat_sampler_params = shape_slat_sampler_params, tex_slat_sampler_params = tex_slat_sampler_params, resolution = resolution, max_num_tokens = max_num_tokens, generate_texture_slat=generate_texture_slat, downsampling=downsampling, use_tiled=use_tiled_decoder)[0]         
        
        return (mesh,)

class Trellis2PostProcess2:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "mesh": ("MESHWITHVOXEL",),
                "fill_holes": ("BOOLEAN", {"default":True}),
                "fix_normals": ("BOOLEAN", {"default":False}),
                "fix_face_orientation": ("BOOLEAN", {"default":True}),
                "remove_duplicate_faces": ("BOOLEAN",{"default":True}),
                "remove_infinite_values": ("BOOLEAN",{"default":True}),
            },
        }

    RETURN_TYPES = ("MESHWITHVOXEL",)
    RETURN_NAMES = ("mesh",)
    FUNCTION = "process"
    CATEGORY = "Trellis2Wrapper"
    OUTPUT_NODE = True

    def process(self, mesh, fill_holes, fix_normals, fix_face_orientation, remove_duplicate_faces, remove_infinite_values):
        mesh_copy = copy.deepcopy(mesh)
        
        vertices_np = mesh_copy.vertices.cpu().numpy()
        faces_np = mesh_copy.faces.cpu().numpy()
        
        trimesh = Trimesh.Trimesh(vertices=vertices_np,faces=faces_np)
        
        print(f"Initial mesh: {len(trimesh.faces)} faces")
        print(f"Is winding consistent? {trimesh.is_winding_consistent}")        
        
        if fix_normals:
            print('Fixing normals ...')
            trimesh.fix_normals()       
            
        if fix_face_orientation:
            if trimesh.is_watertight:
                print('Mesh is watertight, fixing inversion ...')
                Trimesh.repair.fix_inversion(trimesh)
            else:
                print('Mesh is not watertight, cannot fix inversion')

        if remove_duplicate_faces:
            print('Removing duplicate faces ...')
            trimesh.remove_duplicate_faces()
        
        if remove_infinite_values:
            print('Removing infinite values ...')
            trimesh.remove_infinite_values()
        
        if fill_holes:
            print('Filling holes ...')
            trimesh.fill_holes()  
        
        new_vertices = torch.from_numpy(trimesh.vertices).float()
        new_faces = torch.from_numpy(trimesh.faces).int()                
        
        mesh_copy.vertices = new_vertices.to(mesh_copy.device)
        mesh_copy.faces = new_faces.to(mesh_copy.device) 
        
        del trimesh
        gc.collect()
                
        return (mesh_copy,)    

class Trellis2OvoxelExportToGLB:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "mesh": ("MESHWITHVOXEL",),
                "resolution": ([512,1024],{"default":1024}),
                "texture_size": ([512,1024,2048,4096],{"default":2048}),
                "target_face_num": ("INT",{"default":2000000,"min":500,"max":16000000}),
            },
        }

    RETURN_TYPES = ("TRIMESH",)
    RETURN_NAMES = ("trimesh",)
    FUNCTION = "process"
    CATEGORY = "Trellis2Wrapper"
    OUTPUT_NODE = True

    def process(self, mesh, resolution, texture_size, target_face_num):
        mesh_copy = copy.deepcopy(mesh)

        glb = o_voxel.postprocess.to_glb(
            vertices=mesh_copy.vertices,
            faces=mesh_copy.faces,
            attr_volume=mesh_copy.attrs,
            coords=mesh_copy.coords,
            attr_layout=mesh_copy.layout,
            grid_size=resolution,
            aabb=[[-0.5, -0.5, -0.5], [0.5, 0.5, 0.5]],
            decimation_target=target_face_num,
            texture_size=texture_size,
            remesh=True,
            remesh_band=1,
            remesh_project=0,
            use_tqdm=True,
        )
                
        return (glb,)

class Trellis2TrimeshToMeshWithVoxel:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "trimesh": ("TRIMESH",),
                "resolution": ([512,1024],{"default":1024}),
            },
        }

    RETURN_TYPES = ("MESHWITHVOXEL", )
    RETURN_NAMES = ("mesh", )
    FUNCTION = "process"
    CATEGORY = "Trellis2Wrapper"
    OUTPUT_NODE = True

    def process(self, trimesh, resolution):       
        mesh_copy = trimesh.copy()
        
        mvoxel = self.get_voxelmesh_from_trimesh(mesh_copy, resolution)
        
        return (mvoxel,)        
        
    def get_voxelmesh_from_trimesh(self, mesh, resolution):
        vertices = torch.from_numpy(mesh.vertices).float()
        faces = torch.from_numpy(mesh.faces).long()
        
        voxel_indices, dual_vertices, intersected = o_voxel.convert.mesh_to_flexible_dual_grid(
            vertices.cpu(), faces.cpu(),
            grid_size=resolution,
            aabb=[[-0.5,-0.5,-0.5],[0.5,0.5,0.5]],
            face_weight=1.0,
            boundary_weight=0.2,
            regularization_weight=1e-2,
            timing=True,
        )
        
        coords = torch.cat([torch.zeros_like(voxel_indices[:, 0:1]), voxel_indices], dim=-1)                
        coords = coords.cpu()

        del voxel_indices
        del dual_vertices
        del intersected
        gc.collect()
            
        pbr_attr_layout = {
            'base_color': slice(0, 3),
            'metallic': slice(3, 4),
            'roughness': slice(4, 5),
            'alpha': slice(5, 6),
        }

        mvoxel = MeshWithVoxel(
                    vertices, faces,
                    origin = [-0.5, -0.5, -0.5],
                    voxel_size = 1 / resolution,
                    coords = coords,
                    attrs = None,
                    voxel_shape = None,
                    layout=pbr_attr_layout
                    )
                    
        return mvoxel

NODE_CLASS_MAPPINGS = {
    "Trellis2LoadModel": Trellis2LoadModel,
    "Trellis2MeshWithVoxelGenerator": Trellis2MeshWithVoxelGenerator,
    "Trellis2LoadImageWithTransparency": Trellis2LoadImageWithTransparency,
    "Trellis2SimplifyMesh": Trellis2SimplifyMesh,
    "Trellis2MeshWithVoxelToTrimesh": Trellis2MeshWithVoxelToTrimesh,
    "Trellis2ExportMesh": Trellis2ExportMesh,
    "Trellis2PostProcessMesh": Trellis2PostProcessMesh,
    "Trellis2UnWrapAndRasterizer": Trellis2UnWrapAndRasterizer,
    "Trellis2MeshWithVoxelAdvancedGenerator": Trellis2MeshWithVoxelAdvancedGenerator,
    "Trellis2PostProcessAndUnWrapAndRasterizer": Trellis2PostProcessAndUnWrapAndRasterizer,
    "Trellis2Remesh": Trellis2Remesh,
    "Trellis2MeshTexturing": Trellis2MeshTexturing,
    "Trellis2LoadMesh": Trellis2LoadMesh,
    "Trellis2PreProcessImage": Trellis2PreProcessImage,
    "Trellis2MeshRefiner": Trellis2MeshRefiner,
    "Trellis2PostProcess2": Trellis2PostProcess2,
    "Trellis2OvoxelExportToGLB": Trellis2OvoxelExportToGLB,
    "Trellis2TrimeshToMeshWithVoxel": Trellis2TrimeshToMeshWithVoxel,
    }

NODE_DISPLAY_NAME_MAPPINGS = {
    "Trellis2LoadModel": "Trellis2 - LoadModel",
    "Trellis2MeshWithVoxelGenerator": "Trellis2 - Mesh With Voxel Generator",
    "Trellis2LoadImageWithTransparency": "Trellis2 - Load Image with Transparency",
    "Trellis2SimplifyMesh": "Trellis2 - Simplify Mesh",
    "Trellis2MeshWithVoxelToTrimesh": "Trellis2 - Mesh With Voxel To Trimesh",
    "Trellis2ExportMesh": "Trellis2 - Export Mesh",
    "Trellis2PostProcessMesh": "Trellis2 - PostProcess Mesh",
    "Trellis2UnWrapAndRasterizer": "Trellis2 - UV Unwrap and Rasterize",
    "Trellis2MeshWithVoxelAdvancedGenerator": "Trellis2 - Mesh With Voxel Advanced Generator",
    "Trellis2PostProcessAndUnWrapAndRasterizer": "Trellis2 - Post Process/UnWrap and Rasterize",
    "Trellis2Remesh": "Trellis2 - Remesh",
    "Trellis2MeshTexturing": "Trellis2 - Mesh Texturing",
    "Trellis2LoadMesh": "Trellis2 - Load Mesh",
    "Trellis2PreProcessImage": "Trellis2 - PreProcess Image",
    "Trellis2MeshRefiner": "Trellis2 - Mesh Refiner",
    "Trellis2PostProcess2": "Trellis2 - PostProcess Mesh 2",
    "Trellis2OvoxelExportToGLB": "Trellis2 - Ovoxel Export to GLB",
    "Trellis2TrimeshToMeshWithVoxel": "Trellis2 - Trimesh to Mesh with Voxel",
    }