ComfyUI/comfy_extras/nodes_trellis2.py

from typing_extensions import override
from comfy_api.latest import ComfyExtension, IO, Types, io
from comfy.ldm.trellis2.vae import SparseTensor
from comfy.ldm.trellis2.model import (
    _build_proj_transform_matrix, _project_points_to_image, compute_stage_proj_feats,
)
from comfy.ldm.trellis2.naf.model import build_naf_from_state_dict
from comfy_extras.nodes_mesh_postprocess import pack_variable_mesh_batch
import comfy.model_management
import comfy.utils
import folder_paths
from PIL import Image
import logging
import numpy as np
import math
import torch

ShapeSubdivides = io.Custom("SHAPE_SUBDIVIDES")
NAFModel = io.Custom("NAF_MODEL")


def prepare_trellis_vae_for_decode(vae, sample_shape):
    memory_required = vae.memory_used_decode(sample_shape, vae.vae_dtype)
    if len(sample_shape) == 5:
        memory_required *= max(1, int(sample_shape[4]))
    memory_required = max(1, int(memory_required))
    device = comfy.model_management.get_torch_device()
    comfy.model_management.load_models_gpu(
        [vae.patcher],
        memory_required=memory_required,
        force_full_load=getattr(vae, "disable_offload", False),
    )
    free_memory = vae.patcher.get_free_memory(device)
    batch_number = max(1, int(free_memory / memory_required))
    return batch_number

shape_slat_normalization = {
    "mean": torch.tensor([
        0.781296, 0.018091, -0.495192, -0.558457, 1.060530, 0.093252, 1.518149, -0.933218,
        -0.732996, 2.604095, -0.118341, -2.143904, 0.495076, -2.179512, -2.130751, -0.996944,
        0.261421, -2.217463, 1.260067, -0.150213, 3.790713, 1.481266, -1.046058, -1.523667,
        -0.059621, 2.220780, 1.621212, 0.877230, 0.567247, -3.175944, -3.186688, 1.578665
    ])[None],
    "std": torch.tensor([
        5.972266, 4.706852, 5.445010, 5.209927, 5.320220, 4.547237, 5.020802, 5.444004,
        5.226681, 5.683095, 4.831436, 5.286469, 5.652043, 5.367606, 5.525084, 4.730578,
        4.805265, 5.124013, 5.530808, 5.619001, 5.103930, 5.417670, 5.269677, 5.547194,
        5.634698, 5.235274, 6.110351, 5.511298, 6.237273, 4.879207, 5.347008, 5.405691
    ])[None]
}

tex_slat_normalization = {
    "mean": torch.tensor([
        3.501659, 2.212398, 2.226094, 0.251093, -0.026248, -0.687364, 0.439898, -0.928075,
        0.029398, -0.339596, -0.869527, 1.038479, -0.972385, 0.126042, -1.129303, 0.455149,
        -1.209521, 2.069067, 0.544735, 2.569128, -0.323407, 2.293000, -1.925608, -1.217717,
        1.213905, 0.971588, -0.023631, 0.106750, 2.021786, 0.250524, -0.662387, -0.768862
    ])[None],
    "std": torch.tensor([
        2.665652, 2.743913, 2.765121, 2.595319, 3.037293, 2.291316, 2.144656, 2.911822,
        2.969419, 2.501689, 2.154811, 3.163343, 2.621215, 2.381943, 3.186697, 3.021588,
        2.295916, 3.234985, 3.233086, 2.260140, 2.874801, 2.810596, 3.292720, 2.674999,
        2.680878, 2.372054, 2.451546, 2.353556, 2.995195, 2.379849, 2.786195, 2.775190
    ])[None]
}

def shape_norm(shape_latent, coords):
    std = shape_slat_normalization["std"].to(shape_latent)
    mean = shape_slat_normalization["mean"].to(shape_latent)
    samples = SparseTensor(feats = shape_latent, coords=coords)
    samples = samples * std + mean
    return samples


def infer_batched_coord_layout(coords):
    if coords.ndim != 2 or coords.shape[1] != 4:
        raise ValueError(f"Expected Trellis2 coords with shape [N, 4], got {tuple(coords.shape)}")

    if coords.shape[0] == 0:
        raise ValueError("Trellis2 coords can't be empty")

    batch_ids = coords[:, 0].to(torch.int64)
    if (batch_ids < 0).any():
        raise ValueError(f"Trellis2 batch ids must be non-negative, got {batch_ids.unique(sorted=True).tolist()}")
    batch_size = int(batch_ids.max().item()) + 1
    counts = torch.bincount(batch_ids, minlength=batch_size)

    if (counts == 0).any():
        raise ValueError(f"Non-contiguous Trellis2 batch ids in coords: {batch_ids.unique(sorted=True).tolist()}")

    max_tokens = int(counts.max().item())
    return batch_size, counts, max_tokens


def split_batched_coords(coords, coord_counts):
    if coord_counts.ndim != 1:
        raise ValueError(f"Trellis2 coord_counts must be 1D, got shape {tuple(coord_counts.shape)}")
    if (coord_counts < 0).any():
        raise ValueError(f"Trellis2 coord_counts must be non-negative, got {coord_counts.tolist()}")
    if int(coord_counts.sum().item()) != coords.shape[0]:
        raise ValueError(
            f"Trellis2 coord_counts total {int(coord_counts.sum().item())} does not match coords rows {coords.shape[0]}"
        )

    batch_ids = coords[:, 0].to(torch.int64)
    order = torch.argsort(batch_ids, stable=True)
    sorted_coords = coords.index_select(0, order)
    sorted_batch_ids = batch_ids.index_select(0, order)

    offsets = coord_counts.cumsum(0) - coord_counts
    items = []
    for i in range(coord_counts.shape[0]):
        count = int(coord_counts[i].item())
        start = int(offsets[i].item())
        coords_i = sorted_coords[start:start + count]
        ids_i = sorted_batch_ids[start:start + count]
        if coords_i.shape[0] != count or not torch.all(ids_i == i):
            raise ValueError(f"Trellis2 coords rows for batch {i} expected {count}, got {coords_i.shape[0]}")
        items.append(coords_i)
    return items

def flatten_batched_sparse_latent(samples, coords, coord_counts):
    samples = samples.squeeze(-1).transpose(1, 2)
    if coord_counts is None:
        return samples.reshape(-1, samples.shape[-1]), coords

    coords_items = split_batched_coords(coords, coord_counts)
    feat_list = []
    coord_list = []
    for i, coords_i in enumerate(coords_items):
        count = int(coord_counts[i].item())
        feat_list.append(samples[i, :count])
        coord_list.append(coords_i)

    return torch.cat(feat_list, dim=0), torch.cat(coord_list, dim=0)


def split_batched_sparse_latent(samples, coords, coord_counts):
    samples = samples.squeeze(-1).transpose(1, 2)
    if coord_counts is None:
        return [(samples.reshape(-1, samples.shape[-1]), coords)]

    coords_items = split_batched_coords(coords, coord_counts)
    items = []
    for i, coords_i in enumerate(coords_items):
        count = int(coord_counts[i].item())
        items.append((samples[i, :count], coords_i))
    return items

class VaeDecodeShapeTrellis(IO.ComfyNode):
    @classmethod
    def define_schema(cls):
        return IO.Schema(
            node_id="VaeDecodeShapeTrellis",
            category="latent/3d",
            inputs=[
                IO.Latent.Input("samples"),
                IO.Vae.Input("vae"),
            ],
            outputs=[
                IO.Mesh.Output("mesh"),
                ShapeSubdivides.Output(display_name = "shape_subdivides"),
            ]
        )

    @classmethod
    def execute(cls, samples, vae):
        # Mesh grid_size must match the actual coord resolution the upstream
        # stage was run at (1024 cascade -> 64, 1536 cascade -> 96). The VAE's
        # built-in `.resolution` buffer defaults to 1024 and is otherwise stale;
        # take coord_resolution from the latent dict if the stage node set it.
        coord_resolution = samples.get("coord_resolution")
        if coord_resolution is not None:
            resolution = int(coord_resolution) * 16
        else:
            resolution = int(vae.first_stage_model.resolution.item())
        sample_tensor = samples["samples"]
        device = comfy.model_management.get_torch_device()
        coords = samples["coords"]
        prepare_trellis_vae_for_decode(vae, sample_tensor.shape)
        trellis_vae = vae.first_stage_model
        coord_counts = samples.get("coord_counts")

        samples = samples["samples"]
        if coord_counts is None:
            samples, coords = flatten_batched_sparse_latent(samples, coords, coord_counts)
            samples = shape_norm(samples.to(device), coords.to(device))
            mesh, subs = trellis_vae.decode_shape_slat(samples, resolution)
        else:
            split_items = split_batched_sparse_latent(samples, coords, coord_counts)
            mesh = []
            subs_per_sample = []
            for feats_i, coords_i in split_items:
                coords_i = coords_i.to(device).clone()
                coords_i[:, 0] = 0
                sample_i = shape_norm(feats_i.to(device), coords_i)
                mesh_i, subs_i = trellis_vae.decode_shape_slat(sample_i, resolution)
                mesh.append(mesh_i[0])
                subs_per_sample.append(subs_i)

            subs = []
            for stage_index in range(len(subs_per_sample[0])):
                stage_tensors = [sample_subs[stage_index] for sample_subs in subs_per_sample]
                feats_list = [stage_tensor.feats for stage_tensor in stage_tensors]
                coords_list = [stage_tensor.coords for stage_tensor in stage_tensors]
                subs.append(SparseTensor.from_tensor_list(feats_list, coords_list))

        vert_list = [v.float() for v, f in mesh]
        face_list = [f.int() for v, f in mesh]
        if all(v.shape == vert_list[0].shape for v in vert_list) and all(f.shape == face_list[0].shape for f in face_list):
            mesh = Types.MESH(vertices=torch.stack(vert_list), faces=torch.stack(face_list))
        else:
            mesh = pack_variable_mesh_batch(vert_list, face_list)
        return IO.NodeOutput(mesh, subs)

class VaeDecodeTextureTrellis(IO.ComfyNode):
    @classmethod
    def define_schema(cls):
        return IO.Schema(
            node_id="VaeDecodeTextureTrellis",
            category="latent/3d",
            inputs=[
                IO.Latent.Input("samples"),
                IO.Vae.Input("vae"),
                ShapeSubdivides.Input("shape_subdivides",
                                 tooltip=(
                                    "Shape information used to guide higher-detail reconstruction during decoding. "
                                    "Helps preserve structure consistency at higher resolutions."
                                 )),
            ],
            outputs=[
                IO.Voxel.Output("voxel_colors"),
            ]
        )

    @classmethod
    def execute(cls, samples, vae, shape_subdivides):
        sample_tensor = samples["samples"]
        device = comfy.model_management.get_torch_device()
        coords = samples["coords"]
        prepare_trellis_vae_for_decode(vae, sample_tensor.shape)
        trellis_vae = vae.first_stage_model
        coord_counts = samples.get("coord_counts")

        samples = samples["samples"]
        samples, coords = flatten_batched_sparse_latent(samples, coords, coord_counts)
        samples = samples.to(device)
        std = tex_slat_normalization["std"].to(samples)
        mean = tex_slat_normalization["mean"].to(samples)
        samples = SparseTensor(feats = samples, coords=coords.to(device))
        samples = samples * std + mean

        voxel = trellis_vae.decode_tex_slat(samples, shape_subdivides)
        color_feats = voxel.feats[:, :3]
        voxel_coords = voxel.coords

        if voxel_coords.numel() > 0 and voxel_coords.shape[-1] >= 3:
            spatial = voxel_coords[:, -3:] if voxel_coords.shape[-1] == 4 else voxel_coords
            max_idx = int(spatial.max().item()) + 1
            tex_resolution = next((r for r in (256, 512, 1024, 1536, 2048) if r >= max_idx), max_idx)
        else:
            tex_resolution = 1024

        voxel = Types.VOXEL(voxel_coords, color_feats, tex_resolution)
        return IO.NodeOutput(voxel)

class VaeDecodeStructureTrellis2(IO.ComfyNode):
    @classmethod
    def define_schema(cls):
        return IO.Schema(
            node_id="VaeDecodeStructureTrellis2",
            category="latent/3d",
            inputs=[
                IO.Latent.Input("samples"),
                IO.Vae.Input("vae"),
                IO.Combo.Input("resolution", options=["32", "64"], default="32"),
            ],
            outputs=[
                IO.Voxel.Output("voxel"),
            ]
        )

    @classmethod
    def execute(cls, samples, vae, resolution):
        resolution = int(resolution)
        sample_tensor = samples["samples"]
        sample_tensor = sample_tensor[:, :8]
        batch_number = prepare_trellis_vae_for_decode(vae, sample_tensor.shape)
        shape_vae = vae.first_stage_model
        load_device = comfy.model_management.get_torch_device()
        decoded_batches = []
        for start in range(0, sample_tensor.shape[0], batch_number):
            sample_chunk = sample_tensor[start:start + batch_number].to(load_device)
            decoded_batches.append(shape_vae.decode_structure(sample_chunk) > 0)
        decoded = torch.cat(decoded_batches, dim=0)
        current_res = decoded.shape[2]

        if current_res != resolution:
            ratio = current_res // resolution
            decoded = torch.nn.functional.max_pool3d(decoded.float(), ratio, ratio, 0) > 0.5
        voxel_data = decoded.squeeze(1).float()
        return IO.NodeOutput(Types.VOXEL(voxel_data))

class Trellis2UpsampleStage(IO.ComfyNode):
    """Cascade-upsamples a 512-resolution shape latent into high-resolution
    sparse coords and sets up the second shape-stage sampling pass at the
    target resolution, attaching per-stage metadata to the conditioning for
    the model to consume via extra_conds. target_resolution is reduced in
    128-step decrements until the unique upsampled coord count fits under
    max_tokens (floor 1024)."""
    @classmethod
    def define_schema(cls):
        return IO.Schema(
            node_id="Trellis2UpsampleStage",
            category="latent/3d",
            display_name="Trellis2 Upsample Stage",
            inputs=[
                IO.Conditioning.Input("positive"),
                IO.Conditioning.Input("negative"),
                IO.Latent.Input("shape_latent", tooltip="The 512-resolution shape latent output from the first shape-stage KSampler."),
                IO.Vae.Input("vae"),
                IO.Combo.Input("target_resolution", options=["1024", "1536"], default="1024", tooltip="Controls output detail level for upsampling."),
                IO.Int.Input("max_tokens", default=49152, min=1024, max=100000,
                             tooltip=(
                                "Maximum number of output elements (coordinates) allowed after upsampling. "
                                "Used to limit memory usage and control mesh density."
                            )),
            ],
            outputs=[
                IO.Conditioning.Output(display_name="positive"),
                IO.Conditioning.Output(display_name="negative"),
                IO.Latent.Output(),
            ]
        )

    @staticmethod
    def _quantize_unique(hr_coords: torch.Tensor, lr_resolution: int, hr_resolution: int) -> torch.Tensor:
        # Fold the two scalar divisions into one and chain the float math in-place
        # to avoid 3 full M*3 fp32 transients per call.
        scale = (hr_resolution // 16) / lr_resolution
        spatial = hr_coords[:, 1:].float()
        spatial.add_(0.5).mul_(scale)
        quant = torch.cat([hr_coords[:, :1], spatial.int()], dim=1)
        return quant.unique(dim=0)

    @classmethod
    def execute(cls, positive, negative, shape_latent, vae, target_resolution, max_tokens):
        device = comfy.model_management.get_torch_device()
        prepare_trellis_vae_for_decode(vae, shape_latent["samples"].shape)

        coord_counts = shape_latent.get("coord_counts")
        shape_vae = vae.first_stage_model
        lr_resolution = 512
        target_resolution = int(target_resolution)

        # Decode each sample's HR coords, then search for the largest hr_resolution
        # that fits under max_tokens across all samples.
        if coord_counts is None:
            feats, coords_512 = flatten_batched_sparse_latent(
                shape_latent["samples"], shape_latent["coords"], coord_counts,
            )
            slat = shape_norm(feats.to(device), coords_512.to(device))
            sample_hr_coords = [shape_vae.upsample_shape(slat, upsample_times=4)]
        else:
            items = split_batched_sparse_latent(
                shape_latent["samples"], shape_latent["coords"], coord_counts,
            )
            sample_hr_coords = []
            for feats_i, coords_i in items:
                coords_i = coords_i.to(device).clone()
                coords_i[:, 0] = 0
                slat_i = shape_norm(feats_i.to(device), coords_i)
                sample_hr_coords.append(shape_vae.upsample_shape(slat_i, upsample_times=4))

        # Resolution search — cache the final iteration's quantized unique tensors
        # so we don't recompute .unique() per sample after picking hr_resolution.
        hr_resolution = target_resolution
        quant_unique_list = []
        while True:
            quant_unique_list = []
            exceeds_limit = False
            for hr_coords_i in sample_hr_coords:
                qu = cls._quantize_unique(hr_coords_i, lr_resolution, hr_resolution)
                quant_unique_list.append(qu)
                if qu.shape[0] >= max_tokens:
                    exceeds_limit = True
                    break
            if not exceeds_limit:
                break
            if hr_resolution <= 1024:
                for k in range(len(quant_unique_list), len(sample_hr_coords)):
                    quant_unique_list.append(
                        cls._quantize_unique(sample_hr_coords[k], lr_resolution, hr_resolution)
                    )
                break
            hr_resolution -= 128

        # Rewrite batch column to match per-sample offset and concat.
        per_sample_counts = []
        for sample_offset, qu in enumerate(quant_unique_list):
            qu[:, 0] = sample_offset
            per_sample_counts.append(int(qu.shape[0]))
        coords = torch.cat(quant_unique_list, dim=0)
        counts = torch.tensor(per_sample_counts, dtype=torch.int64)
        coord_resolution = hr_resolution // 16

        batch_size, _, max_tokens_out = infer_batched_coord_layout(coords)
        latent = torch.zeros(batch_size, 32, max_tokens_out, 1)

        extras = {
            "trellis2_generation_mode": "shape_generation",
            "trellis2_coords": coords,
            "trellis2_coord_counts": counts,
        }
        proj_pack = _proj_pack_from_conditioning(positive)
        if proj_pack is not None:
            extras["trellis2_proj_feats"] = compute_stage_proj_feats(
                proj_pack, "shape_1024", coords=coords, coord_resolution=coord_resolution,
            )
        positive_out = _conditioning_set_extras(positive, extras)
        negative_out = _conditioning_set_extras(negative, extras)
        out_latent = {"samples": latent, "coords": coords, "coord_counts": counts,
                      "coord_resolution": coord_resolution, "type": "trellis2"}
        return IO.NodeOutput(positive_out, negative_out, out_latent)

dino_mean = torch.tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1)
dino_std = torch.tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1)

def run_conditioning(model, cropped_img_tensor, include_1024=True):
    model_internal = model.model
    device = comfy.model_management.intermediate_device()
    torch_device = comfy.model_management.get_torch_device()

    def prepare_tensor(pil_img, size):
        resized_pil = pil_img.resize((size, size), Image.Resampling.LANCZOS)
        img_np = np.array(resized_pil).astype(np.float32) / 255.0
        img_t = torch.from_numpy(img_np).permute(2, 0, 1).unsqueeze(0).to(torch_device)
        return (img_t - dino_mean.to(torch_device)) / dino_std.to(torch_device)

    model_internal.image_size = 512
    input_512 = prepare_tensor(cropped_img_tensor, 512)
    cond_512 = model_internal(input_512, skip_norm_elementwise=True)[0]

    cond_1024 = None
    if include_1024:
        model_internal.image_size = 1024
        input_1024 = prepare_tensor(cropped_img_tensor, 1024)
        cond_1024 = model_internal(input_1024, skip_norm_elementwise=True)[0]

    conditioning = {
        'cond_512': cond_512.to(device),
        'neg_cond': torch.zeros_like(cond_512).to(device),
    }
    if cond_1024 is not None:
        conditioning['cond_1024'] = cond_1024.to(device)

    return conditioning
class Trellis2Conditioning(IO.ComfyNode):
    @classmethod
    def define_schema(cls):
        return IO.Schema(
            node_id="Trellis2Conditioning",
            category="conditioning/video_models",
            inputs=[
                IO.ClipVision.Input("clip_vision_model"),
                IO.Image.Input("image"),
                IO.Mask.Input("mask"),
            ],
            outputs=[
                IO.Conditioning.Output(display_name="positive"),
                IO.Conditioning.Output(display_name="negative"),
            ]
        )

    @classmethod
    @classmethod
    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)
        elif image.ndim == 4:
            if image.shape[1] in [1, 3, 4] and image.shape[-1] not in [1, 3, 4]:
                image = image.permute(0, 2, 3, 1)

        # normalize mask to standard [B, H, W] (handling 2D, 3D, and 4D variants)
        if mask.ndim == 4:
            if mask.shape[1] == 1:
                mask = mask.squeeze(1)
            elif mask.shape[-1] == 1:
                mask = mask.squeeze(-1)
            else:
                mask = mask[:, :, :, 0] # take first channel as fallback

        if mask.ndim == 3:
            if mask.shape[-1] == 1:
                mask = mask.squeeze(-1).unsqueeze(0)
        elif mask.ndim == 2:
            mask = mask.unsqueeze(0)

        batch_size = image.shape[0]
        if mask.shape[0] == 1 and batch_size > 1:
            mask = mask.expand(batch_size, -1, -1)
        elif mask.shape[0] != batch_size:
            raise ValueError(f"Trellis2Conditioning mask batch {mask.shape[0]} does not match image batch {batch_size}")

        cond_512_list = []
        cond_1024_list = []

        for b in range(batch_size):
            item_image = image[b]
            item_mask = mask[b] if mask.size(0) > 1 else mask[0]

            img_np = (item_image.cpu().numpy() * 255).clip(0, 255).astype(np.uint8)
            mask_np = (item_mask.cpu().numpy() * 255).clip(0, 255).astype(np.uint8)

            # Ensure img_np is either 2D (grayscale) or 3D (RGB/RGBA)
            if img_np.ndim == 3 and img_np.shape[-1] == 1:
                img_np = img_np.squeeze(-1)

            mask_np = mask_np.squeeze()

            # detect inverted mask
            border_pixels = np.concatenate([
                mask_np[0, :], mask_np[-1, :], mask_np[:, 0], mask_np[:, -1]
            ])
            if np.mean(border_pixels) > 127:
                mask_np = 255 - mask_np

            mask_np[mask_np < 35] = 0

            border_shave = 4
            mask_np[:border_shave, :] = 0
            mask_np[-border_shave:, :] = 0
            mask_np[:, :border_shave] = 0
            mask_np[:, -border_shave:] = 0

            pil_img = Image.fromarray(img_np)
            pil_mask = Image.fromarray(mask_np)

            max_size = max(pil_img.size)
            scale = min(1.0, 1024 / max_size)
            if scale < 1.0:
                new_w, new_h = int(pil_img.width * scale), int(pil_img.height * scale)
                pil_img = pil_img.resize((new_w, new_h), Image.Resampling.LANCZOS)
                pil_mask = pil_mask.resize((new_w, new_h), Image.Resampling.NEAREST)

            rgba_np = np.zeros((pil_img.height, pil_img.width, 4), dtype=np.uint8)
            rgba_np[:, :, :3] = np.array(pil_img.convert("RGB"))
            rgba_np[:, :, 3] = np.array(pil_mask)

            alpha = rgba_np[:, :, 3]
            bbox_coords = np.argwhere(alpha > 0.8 * 255)

            if len(bbox_coords) > 0:
                y_min, x_min = np.min(bbox_coords[:, 0]), np.min(bbox_coords[:, 1])
                y_max, x_max = np.max(bbox_coords[:, 0]), np.max(bbox_coords[:, 1])

                center_y, center_x = (y_min + y_max) / 2.0, (x_min + x_max) / 2.0
                size = max(y_max - y_min, x_max - x_min)

                crop_x1 = int(center_x - size // 2)
                crop_y1 = int(center_y - size // 2)
                crop_x2 = int(center_x + size // 2)
                crop_y2 = int(center_y + size // 2)

                rgba_pil = Image.fromarray(rgba_np)
                cropped_rgba = rgba_pil.crop((crop_x1, crop_y1, crop_x2, crop_y2))
                cropped_np = np.array(cropped_rgba).astype(np.float32) / 255.0
            else:
                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_rgb = np.array([0.0, 0.0, 0.0], dtype=np.float32)

            fg = cropped_np[:, :, :3]
            alpha_float = cropped_np[:, :, 3:4]
            composite_np = fg * alpha_float + bg_rgb * (1.0 - alpha_float)

            # Keep the image as 4-channel RGBA to force TRELLIS to bypass its internal background remover
            rgb_uint8 = (composite_np * 255.0).round().clip(0, 255).astype(np.uint8)
            alpha_uint8 = (alpha_float.squeeze(-1) * 255.0).round().clip(0, 255).astype(np.uint8)

            rgba_composite = np.zeros((cropped_np.shape[0], cropped_np.shape[1], 4), dtype=np.uint8)
            rgba_composite[:, :, :3] = rgb_uint8
            rgba_composite[:, :, 3] = alpha_uint8

            cropped_pil = Image.fromarray(rgba_composite, mode="RGBA")

            # Convert to RGB to ensure the CLIP/DINO model receives a 3-channel image
            item_conditioning = run_conditioning(clip_vision_model, cropped_pil.convert("RGB"), include_1024=True)
            cond_512_list.append(item_conditioning["cond_512"])
            cond_1024_list.append(item_conditioning["cond_1024"])

        cond_512_batched = torch.cat(cond_512_list, dim=0)
        cond_1024_batched = torch.cat(cond_1024_list, dim=0)
        neg_cond_batched = torch.zeros_like(cond_512_batched)
        neg_embeds_batched = torch.zeros_like(cond_1024_batched)

        positive = [[cond_512_batched, {"embeds": cond_1024_batched}]]
        negative = [[neg_cond_batched, {"embeds": neg_embeds_batched}]]
        return IO.NodeOutput(positive, negative)

def _proj_pack_from_conditioning(conditioning):
    """Return the proj_feat_pack dict embedded in a Pixal3D conditioning (or None
    for vanilla Trellis2 / no conditioning connected). Pixal3DConditioning ships
    the pack in cond[0][1]["proj_feat_pack"]; Trellis2Conditioning doesn't set it."""
    if not conditioning:
        return None
    entry = conditioning[0]
    if not isinstance(entry, (list, tuple)) or len(entry) < 2 or not isinstance(entry[1], dict):
        return None
    return entry[1].get("proj_feat_pack")


def _conditioning_set_extras(conditioning, extras: dict):
    """Return a copy of `conditioning` with `extras` merged into each entry's
    dict — same shallow-copy pattern ControlNetApplyAdvanced uses. The dicts
    are copied so we don't mutate upstream conditioning."""
    out = []
    for entry in conditioning:
        if isinstance(entry, (list, tuple)) and len(entry) >= 2 and isinstance(entry[1], dict):
            new_dict = entry[1].copy()
            new_dict.update(extras)
            out.append([entry[0], new_dict])
        else:
            out.append(entry)
    return out


class Trellis2ShapeStage(IO.ComfyNode):
    """Sets up the first shape-stage sampling pass: extracts sparse coords from
    the dense structure voxel produced by VaeDecodeStructureTrellis2, builds an
    empty sparse latent, and attaches per-stage metadata to the conditioning so
    the model reads it via extra_conds at sample time. For the second shape pass
    (post-upsample), use Trellis2UpsampleStage instead — it combines the cascade
    and the second-pass stage setup."""
    @classmethod
    def define_schema(cls):
        return IO.Schema(
            node_id="Trellis2ShapeStage",
            category="latent/3d",
            inputs=[
                IO.Conditioning.Input("positive"),
                IO.Conditioning.Input("negative"),
                IO.Voxel.Input(
                    "voxel",
                    tooltip="Dense structure voxel from VaeDecodeStructureTrellis2.",
                ),
            ],
            outputs=[
                IO.Conditioning.Output(display_name="positive"),
                IO.Conditioning.Output(display_name="negative"),
                IO.Latent.Output(),
            ]
        )

    @classmethod
    def execute(cls, positive, negative, voxel):
        decoded = voxel.data.unsqueeze(1)
        coords = torch.argwhere(decoded.bool())[:, [0, 2, 3, 4]].int()
        coord_resolution = int(decoded.shape[-1])

        # Dispatch based on the upstream voxel resolution, mirroring upstream's
        # pipeline_type → ss_res table:
        #   coord_res == 32 → first cascade shape pass OR pure-512 pipeline
        #                     (img2shape_512 + shape_512 proj stage, 512 DINO).
        #   coord_res >  32 → pure-1024 non-cascade pipeline
        #                     (img2shape + shape_1024 proj stage, 1024 DINO).
        if coord_resolution <= 32:
            mode = "shape_generation_512"
            stage = "shape_512"
        else:
            mode = "shape_generation"
            stage = "shape_1024"

        batch_size, counts, max_tokens = infer_batched_coord_layout(coords)
        latent = torch.zeros(batch_size, 32, max_tokens, 1)

        extras = {
            "trellis2_generation_mode": mode,
            "trellis2_coords": coords,
            "trellis2_coord_counts": counts,
        }
        proj_pack = _proj_pack_from_conditioning(positive)
        if proj_pack is not None:
            extras["trellis2_proj_feats"] = compute_stage_proj_feats(
                proj_pack, stage, coords=coords, coord_resolution=coord_resolution,
            )
        positive_out = _conditioning_set_extras(positive, extras)
        negative_out = _conditioning_set_extras(negative, extras)
        out_latent = {"samples": latent, "coords": coords, "coord_counts": counts,
                      "coord_resolution": coord_resolution, "type": "trellis2"}
        return IO.NodeOutput(positive_out, negative_out, out_latent)

class Trellis2TextureStage(IO.ComfyNode):
    """Sets up the texture-stage sampling pass. Reads coords / coord_counts /
    coord_resolution and the shape_slat (the per-voxel shape latent) from the
    incoming shape_latent dict — set there by Trellis2ShapeStage or
    Trellis2UpsampleStage. Builds an empty sparse latent at the same coord
    layout and attaches per-stage metadata to the conditioning."""
    @classmethod
    def define_schema(cls):
        return IO.Schema(
            node_id="Trellis2TextureStage",
            category="latent/3d",
            inputs=[
                IO.Conditioning.Input("positive"),
                IO.Conditioning.Input("negative"),
                IO.Latent.Input("shape_latent"),
            ],
            outputs=[
                IO.Conditioning.Output(display_name="positive"),
                IO.Conditioning.Output(display_name="negative"),
                IO.Latent.Output(),
            ]
        )

    @classmethod
    def execute(cls, positive, negative, shape_latent):
        channels = 32
        coords = shape_latent["coords"]
        coord_resolution = shape_latent.get("coord_resolution")

        batch_size, counts, max_tokens = infer_batched_coord_layout(coords)

        shape_slat = shape_latent["samples"]
        if shape_slat.ndim == 4:
            shape_slat = shape_slat.squeeze(-1).transpose(1, 2).reshape(-1, channels)

        latent = torch.zeros(batch_size, channels, max_tokens, 1)
        extras = {
            "trellis2_generation_mode": "texture_generation",
            "trellis2_coords": coords,
            "trellis2_coord_counts": counts,
            "trellis2_shape_slat": shape_slat,
        }
        proj_pack = _proj_pack_from_conditioning(positive)
        if proj_pack is not None and coord_resolution is not None:
            extras["trellis2_proj_feats"] = compute_stage_proj_feats(
                proj_pack, "tex_1024", coords=coords, coord_resolution=coord_resolution,
            )

        positive_out = _conditioning_set_extras(positive, extras)
        negative_out = _conditioning_set_extras(negative, extras)
        out_latent = {"samples": latent, "type": "trellis2", "coords": coords, "coord_counts": counts}
        if coord_resolution is not None:
            out_latent["coord_resolution"] = coord_resolution
        return IO.NodeOutput(positive_out, negative_out, out_latent)


class EmptyTrellis2LatentStructure(IO.ComfyNode):
    @classmethod
    def define_schema(cls):
        return IO.Schema(
            node_id="EmptyTrellis2LatentStructure",
            category="latent/3d",
            inputs=[
                IO.Int.Input("batch_size", default=1, min=1, max=4096, tooltip="The number of latent images in the batch."),
            ],
            outputs=[
                IO.Latent.Output(),
            ]
        )
    @classmethod
    def execute(cls, batch_size):
        in_channels = 32
        resolution = 16
        latent = torch.zeros(batch_size, in_channels, resolution, resolution, resolution)
        return IO.NodeOutput({"samples": latent, "type": "trellis2"})

def _dinov3_patches_to_2d(tokens, image_size, patch_size=16):
    h_p = w_p = image_size // patch_size
    n_patches = h_p * w_p
    n_reg = tokens.shape[1] - 1 - n_patches
    if n_reg < 0 or tokens.shape[1] != 1 + n_reg + n_patches:
        raise ValueError(
            f"_dinov3_patches_to_2d: got {tokens.shape[1]} tokens, expected "
            f"1 (CLS) + N_reg + {h_p}*{w_p}={n_patches} patches at image_size={image_size}, "
            f"patch_size={patch_size}. Inferred N_reg={n_reg} which is invalid."
        )
    start = 1 + n_reg
    patches = tokens[:, start:start + n_patches]
    return patches.transpose(1, 2).reshape(tokens.shape[0], -1, h_p, w_p).contiguous()


def _run_dinov3_with_patches(model, composite, image_size):
    model_internal = model.model
    torch_device = comfy.model_management.get_torch_device()
    img_t = comfy.utils.common_upscale(composite, image_size, image_size, "lanczos", "disabled")
    img_t = img_t.to(torch_device)
    img_t = (img_t - dino_mean.to(torch_device)) / dino_std.to(torch_device)
    model_internal.image_size = image_size
    tokens = model_internal(img_t, skip_norm_elementwise=True)[0]
    patches = _dinov3_patches_to_2d(tokens, image_size)
    h_p = w_p = image_size // 16
    n_reg = tokens.shape[1] - 1 - h_p * w_p
    global_tokens = tokens[:, :1 + n_reg]
    return {"tokens": global_tokens, "patches_2d": patches}


def _crop_image_with_mask(item_image, item_mask, max_image_size=1024):
    img = item_image.permute(2, 0, 1).unsqueeze(0).cpu().float()
    mask = item_mask.unsqueeze(0).unsqueeze(0).cpu().float()
    # Upstream went float→PIL uint8 implicitly; match that to keep composite bit-exact.
    img = (img.clamp(0, 1) * 255.0).to(torch.uint8).float() / 255.0
    mask = (mask.clamp(0, 1) * 255.0).to(torch.uint8).float() / 255.0

    H, W = img.shape[-2:]
    if max(H, W) > max_image_size:
        scale = max_image_size / max(H, W)
        new_w, new_h = int(W * scale), int(H * scale)
        img  = comfy.utils.common_upscale(img,  new_w, new_h, "lanczos",       "disabled")
        mask = comfy.utils.common_upscale(mask, new_w, new_h, "nearest-exact", "disabled")
        H, W = new_h, new_w
    scene_size = (W, H)

    alpha_u8 = (mask[0, 0].clamp(0, 1) * 255.0).to(torch.uint8)
    fg_pixels = (alpha_u8 > 204).nonzero()
    if fg_pixels.numel() > 0:
        y_min, x_min = fg_pixels.min(dim=0).values.tolist()
        y_max, x_max = fg_pixels.max(dim=0).values.tolist()
        center_y, center_x = (y_min + y_max) / 2.0, (x_min + x_max) / 2.0
        size = int(max(y_max - y_min, x_max - x_min) * 1.1)
        half = size // 2
        crop_x1 = int(center_x - half)
        crop_y1 = int(center_y - half)
        crop_x2 = crop_x1 + 2 * half
        crop_y2 = crop_y1 + 2 * half
    else:
        logging.warning("Mask for the image is empty. Pixal3D requires a clean foreground mask.")
        crop_x1, crop_y1, crop_x2, crop_y2 = 0, 0, W, H
    crop_bbox = (crop_x1, crop_y1, crop_x2, crop_y2)

    # Zero-pad out-of-bounds slice (PIL.crop semantics).
    pad_l = max(0, -crop_x1)
    pad_t = max(0, -crop_y1)
    pad_r = max(0, crop_x2 - W)
    pad_b = max(0, crop_y2 - H)
    if pad_l or pad_t or pad_r or pad_b:
        img  = torch.nn.functional.pad(img,  (pad_l, pad_r, pad_t, pad_b), value=0.0)
        mask = torch.nn.functional.pad(mask, (pad_l, pad_r, pad_t, pad_b), value=0.0)
        crop_x1 += pad_l; crop_x2 += pad_l
        crop_y1 += pad_t; crop_y2 += pad_t
    cropped_img  = img [..., crop_y1:crop_y2, crop_x1:crop_x2]
    cropped_mask = mask[..., crop_y1:crop_y2, crop_x1:crop_x2]

    composite = (cropped_img * cropped_mask).clamp(0, 1)
    composite = (composite * 255.0).round().clamp(0, 255).to(torch.uint8).float() / 255.0
    return composite, crop_bbox, scene_size

def _fov_from_moge_intrinsics(moge_intrinsics: torch.Tensor) -> float:
    fx = moge_intrinsics[..., 0, 0].float()
    fov = 2.0 * torch.atan(0.5 / fx.clamp(min=1e-4))
    return float(fov.mean().item())

class Pixal3DConditioning(IO.ComfyNode):

    @classmethod
    def define_schema(cls):
        return IO.Schema(
            node_id="Pixal3DConditioning",
            category="conditioning/video_models",
            inputs=[
                IO.ClipVision.Input("clip_vision_model", tooltip="DINOv3 ViT-L/16 ClipVision."),
                IO.Image.Input("image"),
                IO.Mask.Input("mask"),
                IO.Float.Input(
                    "camera_angle_x", default=0.2, min=0.0175, max=2.9671, step=0.001,
                    tooltip="Horizontal FOV in radians (upstream demo default 0.2). "
                            "Overridden by moge_geometry if connected.",
                ),
                IO.Float.Input(
                    "mesh_scale", default=1.0, min=0.1, max=4.0, step=0.01,
                    tooltip="Mesh scale; 1.0 means unit cube.",
                ),
                io.Custom("MOGE_GEOMETRY").Input(
                    "moge_geometry",
                    optional=True,
                    tooltip="If connected, camera_angle_x is recovered from MoGe.",
                ),
                NAFModel.Input(
                    "naf_model",
                    optional=True,
                    tooltip="Optional NAF feature upsampler. Required for shape/texture stages "
                            "to match upstream's trained feature distribution.",
                ),
            ],
            outputs=[
                IO.Conditioning.Output(display_name="positive"),
                IO.Conditioning.Output(display_name="negative"),
            ],
        )

    @classmethod
    def execute(cls, clip_vision_model, image, mask, camera_angle_x, mesh_scale,
                moge_geometry=None, naf_model=None) -> IO.NodeOutput:
        if image.ndim == 3:
            image = image.unsqueeze(0)
        if mask.ndim == 2:
            mask = mask.unsqueeze(0)
        batch_size = image.shape[0]
        if mask.shape[0] == 1 and batch_size > 1:
            mask = mask.expand(batch_size, -1, -1)
        elif mask.shape[0] != batch_size:
            raise ValueError(f"Pixal3DConditioning mask batch {mask.shape[0]} != image batch {batch_size}")

        if moge_geometry is not None and "intrinsics" in moge_geometry:
            camera_angle_x = _fov_from_moge_intrinsics(moge_geometry["intrinsics"])

        device = comfy.model_management.intermediate_device()

        cond_512_list, cond_1024_list = [], []
        patches_512_list, patches_1024_list = [], []
        composite_list = []
        crop_bbox_list, scene_size_list = [], []

        torch_device = comfy.model_management.get_torch_device()
        for b in range(batch_size):
            item_image = image[b]
            item_mask = mask[b] if mask.size(0) > 1 else mask[0]
            composite, crop_bbox, scene_size = _crop_image_with_mask(
                item_image, item_mask, max_image_size=1024)
            crop_bbox_list.append(crop_bbox)
            scene_size_list.append(scene_size)
            composite_list.append(composite)

            cond_512 = _run_dinov3_with_patches(clip_vision_model, composite, 512)
            cond_1024 = _run_dinov3_with_patches(clip_vision_model, composite, 1024)
            cond_512_list.append(cond_512["tokens"].to(device))
            cond_1024_list.append(cond_1024["tokens"].to(device))
            patches_512_list.append(cond_512["patches_2d"].to(device))
            patches_1024_list.append(cond_1024["patches_2d"].to(device))

        global_512 = torch.cat(cond_512_list, dim=0)
        global_1024 = torch.cat(cond_1024_list, dim=0)

        fm_512_dino = torch.cat(patches_512_list, dim=0)
        fm_1024_dino = torch.cat(patches_1024_list, dim=0)

        # The LR DINO grid AND the NAF HR grid are sampled separately
        # NAF targets per stage: shape_512=512, shape_1024=512, tex_1024=1024.
        def _naf_hr(lr_feat, composites, image_size, naf_target):
            if naf_model is None or naf_target is None:
                return None
            target_dtype = lr_feat.dtype
            if next(naf_model.parameters()).dtype != target_dtype:
                naf_model.to(dtype=target_dtype)
            imgs = torch.cat([
                comfy.utils.common_upscale(c, image_size, image_size, "lanczos", "disabled")
                for c in composites
            ], dim=0).to(torch_device).to(target_dtype)
            hr = naf_model(imgs, lr_feat.to(torch_device).to(target_dtype), naf_target)
            return hr.to(device)

        hr_shape_512  = _naf_hr(fm_512_dino,  composite_list, 512,  (512, 512))
        hr_shape_1024 = _naf_hr(fm_1024_dino, composite_list, 1024, (512, 512))
        hr_tex_1024   = _naf_hr(fm_1024_dino, composite_list, 1024, (1024, 1024))

        # distance_from_fov: grid_point (-1, 0, 0) projects to pixel (0, image_resolution-1).
        camera_angle_x = float(camera_angle_x)
        distance = 0.5 / math.tan(camera_angle_x / 2.0) / float(mesh_scale)
        cam_angle_t = torch.tensor([camera_angle_x] * batch_size, device=device, dtype=torch.float32)
        dist_t = torch.tensor([distance] * batch_size, device=device, dtype=torch.float32)
        scale_t = torch.tensor([float(mesh_scale)] * batch_size, device=device, dtype=torch.float32)
        T = _build_proj_transform_matrix(dist_t, batch_size, device=device, dtype=torch.float32)

        proj_pack = {
            "stages": {
                "ss":         {"feature_map": fm_512_dino,    "feature_map_hr": None,         "image_resolution": 512},
                "shape_512":  {"feature_map": fm_512_dino,    "feature_map_hr": hr_shape_512, "image_resolution": 512},
                "shape_1024": {"feature_map": fm_1024_dino,   "feature_map_hr": hr_shape_1024,"image_resolution": 1024},
                "tex_1024":   {"feature_map": fm_1024_dino,   "feature_map_hr": hr_tex_1024,  "image_resolution": 1024},
            },
            "transform_matrix": T,
            "camera_angle_x": cam_angle_t,
            "mesh_scale": scale_t,
            "distance": dist_t,
            "patch_size": 16,
            "crop_bboxes": crop_bbox_list,
            "scene_sizes": scene_size_list,
        }

        # global_512 → SS/shape_512 cross-attn; global_1024 → shape_1024/tex_1024.
        # proj_feat_pack rides in the conditioning dict (same place embeds, ControlNet
        # hints etc. live); the sampler auto-promotes it to a model.forward kwarg via
        # Trellis2.extra_conds. The same pack object is shared between pos/neg —
        # CONDConstant.can_concat sees them equal and concats to a single dict, then
        # Trellis2.forward zeros proj for the uncond slots via cond_or_uncond.
        # Pre-compute the SS-stage proj features (dense 16³ grid) once here — the
        # shape/texture stages do their own computes in their respective stage nodes.
        # proj_pack lives on intermediate (CPU); force the compute onto cuda so
        # the bilinear-sampling step doesn't run on CPU.
        ss_proj_feats = compute_stage_proj_feats(
            proj_pack, "ss", dense_grid_resolution=16, batch_size=batch_size,
            device=torch_device,
        )
        neg_global = torch.zeros_like(global_512)
        neg_embeds = torch.zeros_like(global_1024)
        base_extras = {
            "embeds": global_1024, "proj_feat_pack": proj_pack,
            "trellis2_proj_feats": ss_proj_feats,
        }
        neg_extras = {
            "embeds": neg_embeds, "proj_feat_pack": proj_pack,
            "trellis2_proj_feats": ss_proj_feats,
        }
        positive = [[global_512, base_extras]]
        negative = [[neg_global, neg_extras]]
        return IO.NodeOutput(positive, negative)


def _project_vertices_to_image_uv(vertices_world, transform_matrix, camera_angle_x, image_resolution):
    points = vertices_world.unsqueeze(0).float()
    T = transform_matrix.unsqueeze(0).float() if transform_matrix.ndim == 2 else transform_matrix.float()
    cam = camera_angle_x.unsqueeze(0) if camera_angle_x.ndim == 0 else camera_angle_x
    uv_pix, depth, valid = _project_points_to_image(points, T, cam.float(), image_resolution)
    uv = uv_pix.squeeze(0) / image_resolution
    return uv, depth.squeeze(0), valid.squeeze(0)


def _crop_uv_to_scene_pixels(uv_crop, crop_bbox, scene_image_size):
    crop_x1, crop_y1, crop_x2, crop_y2 = crop_bbox
    crop_w = max(1, crop_x2 - crop_x1)
    crop_h = max(1, crop_y2 - crop_y1)
    px = uv_crop[:, 0] * crop_w + crop_x1
    py = uv_crop[:, 1] * crop_h + crop_y1
    W, H = scene_image_size
    return torch.stack([px.clamp(0, W - 1), py.clamp(0, H - 1)], dim=-1)


class Pixal3DAlignObject(IO.ComfyNode):
    """Pixal3D paper §3.3 Global Alignment for a single object.

    Solves (scale, translation) aligning the mesh to MoGe's per-pixel point map. Requires
    MoGe to have been computed on the same resized scene image as Pixal3DConditioning."""

    @classmethod
    def define_schema(cls):
        return IO.Schema(
            node_id="Pixal3DAlignObject",
            category="latent/3d",
            inputs=[
                IO.Mesh.Input("mesh"),
                IO.Conditioning.Input("positive", tooltip="The positive conditioning from Pixal3DConditioning for this object — Pixal3DAlignObject reads transform_matrix / camera_angle_x / mesh_scale / crop_bboxes out of its proj_feat_pack."),
                io.Custom("MOGE_GEOMETRY").Input("moge_geometry", tooltip="MoGe geometry computed on the original scene image."),
                IO.Mask.Input(
                    "object_mask",
                    optional=True,
                    tooltip="Optional per-object scene-space mask. If connected, only vertices whose projected pixel falls inside the mask contribute to the alignment solve.",
                ),
                IO.Int.Input(
                    "batch_index",
                    default=0, min=0, max=1024,
                    tooltip="Which batch slot of the proj_feat_pack/MoGe geometry corresponds to this object.",
                ),
            ],
            outputs=[
                IO.Mesh.Output("aligned_mesh"),
                IO.Float.Output(display_name="scale"),
            ],
        )

    @classmethod
    def execute(cls, mesh, positive, moge_geometry, object_mask=None, batch_index=0) -> IO.NodeOutput:
        proj_feat_pack = _proj_pack_from_conditioning(positive)
        if proj_feat_pack is None:
            raise ValueError("Pixal3DAlignObject: positive conditioning has no proj_feat_pack — connect a Pixal3DConditioning output.")
        vertices = mesh.vertices
        faces = mesh.faces
        if vertices.ndim == 3:
            vertices_one = vertices[0]
            faces_one = faces[0]
        else:
            vertices_one = vertices
            faces_one = faces

        T = proj_feat_pack["transform_matrix"][batch_index:batch_index + 1]
        cam_angle = proj_feat_pack["camera_angle_x"][batch_index:batch_index + 1]
        mesh_scale = proj_feat_pack["mesh_scale"][batch_index]
        image_resolution = int(proj_feat_pack.get("image_resolution", 1024))
        crop_bbox = proj_feat_pack["crop_bboxes"][batch_index]
        pack_scene_size = proj_feat_pack.get("scene_sizes", [None] * (batch_index + 1))[batch_index]
        moge_points = moge_geometry["points"]
        moge_mask = moge_geometry["mask"]
        if moge_points.ndim != 4:
            raise ValueError(f"MoGe points expected [B, H, W, 3]; got {tuple(moge_points.shape)}")
        scene_H, scene_W = moge_points.shape[1], moge_points.shape[2]
        if pack_scene_size is not None and pack_scene_size != (scene_W, scene_H):
            raise ValueError(
                f"Pixal3DAlignObject: MoGe geometry was computed on a {scene_W}x{scene_H} image, "
                f"but the proj_feat_pack's bbox lives in a {pack_scene_size[0]}x{pack_scene_size[1]} "
                "image. Run MoGe on the same resized scene image Pixal3DConditioning used."
            )

        # Vertices come out of VaeDecodeShapeTrellis in the Pixal3D model frame
        # (no un-rotation). Apply _PROJ_GRID_ROTATION = R_x(-90°) to map model
        # frame → ProjGrid world: (X, Y, Z) -> (X, -Z, Y).
        v = vertices_one.float()
        verts_world = torch.stack([v[..., 0], -v[..., 2], v[..., 1]], dim=-1)
        verts_world = verts_world / float(mesh_scale.item())
        uv_crop, _depth, valid = _project_vertices_to_image_uv(
            verts_world, T[0], cam_angle[0], image_resolution)
        scene_pixels = _crop_uv_to_scene_pixels(uv_crop, crop_bbox, (scene_W, scene_H))
        in_scene = ((scene_pixels[:, 0] >= 0) & (scene_pixels[:, 0] < scene_W) &
                    (scene_pixels[:, 1] >= 0) & (scene_pixels[:, 1] < scene_H))
        sx = scene_pixels[:, 0].long().clamp(0, scene_W - 1)
        sy = scene_pixels[:, 1].long().clamp(0, scene_H - 1)
        moge_per_vertex = moge_points[batch_index, sy, sx]
        moge_mask_per_vertex = moge_mask[batch_index, sy, sx]
        keep = valid & in_scene & moge_mask_per_vertex
        if object_mask is not None:
            om = object_mask if object_mask.ndim == 2 else object_mask[batch_index]
            keep = keep & (om[sy, sx] > 0.5)

        finite = torch.isfinite(moge_per_vertex).all(dim=-1)
        keep = keep & finite

        kept = int(keep.sum().item())
        if kept < 8:
            scale = 1.0
            aligned = vertices_one
        else:
            P = vertices_one[keep].float()
            Q = moge_per_vertex[keep].float()
            p_mean = P.mean(dim=0, keepdim=True)
            q_mean = Q.mean(dim=0, keepdim=True)
            P_c = P - p_mean
            Q_c = Q - q_mean
            num = (P_c * Q_c).sum()
            den = (P_c * P_c).sum().clamp(min=1e-8)
            scale = float((num / den).item())
            if not (scale > 0):
                # Negative scale would mirror the mesh; treat as a camera-convention mismatch.
                logging.warning(
                    f"Pixal3DAlignObject: computed scale={scale:.4f} <= 0; "
                    "refusing to apply mirroring. Check camera convention alignment.")
                scale = 1.0
                aligned = vertices_one
            else:
                t = q_mean - scale * p_mean
                aligned = scale * vertices_one + t

        if vertices.ndim == 3:
            aligned = aligned.unsqueeze(0)
            out_mesh = Types.MESH(vertices=aligned, faces=faces)
        else:
            out_mesh = Types.MESH(vertices=aligned, faces=faces_one)
        return IO.NodeOutput(out_mesh, float(scale))


class LoadNAFModel(IO.ComfyNode):

    @classmethod
    def define_schema(cls):
        return IO.Schema(
            node_id="LoadNAFModel",
            display_name="Load NAF Model",
            category="loaders",
            inputs=[
                IO.Combo.Input(
                    "naf_name",
                    options=folder_paths.get_filename_list("upscale_models"),
                    tooltip="NAF safetensors checkpoint (e.g. naf_release.safetensors).",
                ),
            ],
            outputs=[NAFModel.Output(display_name="naf_model")],
        )

    @classmethod
    def execute(cls, naf_name) -> IO.NodeOutput:
        path = folder_paths.get_full_path_or_raise("upscale_models", naf_name)
        sd = comfy.utils.load_torch_file(path, safe_load=True)
        model = build_naf_from_state_dict(sd)
        device = comfy.model_management.get_torch_device()
        model = model.to(device).eval()
        return IO.NodeOutput(model)


class CFGGuidanceInterval(IO.ComfyNode):
    """Generic model patch: apply CFG only during [start_percent, end_percent] of
    the sampling schedule. Outside that window, skip the uncond computation and
    collapse to effective cfg=1 — same idea as upstream Trellis2 / Pixal3D's
    guidance_interval_mixin, but lives at the sampler level (via
    sampler_calc_cond_batch_function) so it works for any model.

    Percents use ComfyUI's standard convention: 0.0 = start of sampling
    (max-noise step), 1.0 = end of sampling (clean step). Conversion to sigma
    is done via model_sampling.percent_to_sigma so the window is portable
    across schedules (flow / EDM / discrete) and shift settings.

    Defaults are full-range (no bypass). For Trellis2's upstream behavior,
    wire (start_percent=0.0, end_percent=0.667) on the SS / shape KSamplers;
    texture defaults to cfg=1 so the node is moot there."""
    @classmethod
    def define_schema(cls):
        return IO.Schema(
            node_id="CFGGuidanceInterval",
            category="model_patches/sampling",
            inputs=[
                IO.Model.Input("model"),
                IO.Float.Input("start_percent", default=0.0, min=0.0, max=1.0, step=0.001,
                               tooltip="Fraction of sampling at which CFG turns ON (0 = beginning)."),
                IO.Float.Input("end_percent", default=1.0, min=0.0, max=1.0, step=0.001,
                               tooltip="Fraction of sampling at which CFG turns OFF (1 = end)."),
            ],
            outputs=[IO.Model.Output()],
        )

    @classmethod
    def execute(cls, model, start_percent, end_percent):
        import comfy.samplers

        model_sampling = model.get_model_object("model_sampling")
        # percent_to_sigma is monotonically decreasing: percent=0 -> sigma_max,
        # percent=1 -> sigma_min. So start_percent < end_percent in user space
        # means sigma_start > sigma_end. "Inside the window" is sigma in
        # [sigma_end, sigma_start].
        sigma_start = float(model_sampling.percent_to_sigma(start_percent))
        sigma_end = float(model_sampling.percent_to_sigma(end_percent))

        def calc_cond_batch_with_interval(args):
            sigma_val = args["sigma"][0].item()
            conds = args["conds"]
            input_x = args["input"]
            timestep = args["sigma"]
            model_ref = args["model"]
            model_opts = args["model_options"]

            # conds is typically [cond, uncond]; uncond may be None when ComfyUI's
            # global cfg=1 optimization has already pruned it.
            cond = conds[0]
            uncond = conds[1] if len(conds) > 1 else None
            inside = sigma_end <= sigma_val <= sigma_start

            if uncond is None or inside:
                return comfy.samplers.calc_cond_batch(model_ref, conds, input_x, timestep, model_opts)
            # Outside the window: compute cond only, mirror it into the uncond slot
            # so the downstream cfg_function collapses to `cond` (effective cfg=1).
            out = comfy.samplers.calc_cond_batch(model_ref, [cond], input_x, timestep, model_opts)
            return [out[0], out[0]]

        m = model.clone()
        m.model_options["sampler_calc_cond_batch_function"] = calc_cond_batch_with_interval
        return IO.NodeOutput(m)


class Trellis2Extension(ComfyExtension):
    @override
    async def get_node_list(self) -> list[type[IO.ComfyNode]]:
        return [
            Trellis2Conditioning,
            Pixal3DConditioning,
            Pixal3DAlignObject,
            LoadNAFModel,
            Trellis2ShapeStage,
            EmptyTrellis2LatentStructure,
            Trellis2TextureStage,
            VaeDecodeTextureTrellis,
            VaeDecodeShapeTrellis,
            VaeDecodeStructureTrellis2,
            Trellis2UpsampleStage,
            CFGGuidanceInterval,
        ]


async def comfy_entrypoint() -> Trellis2Extension:
    return Trellis2Extension()