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 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") Pixal3DProjPack = io.Custom("PIXAL3D_PROJ_PACK") NAFModel = io.Custom("NAF_MODEL") # Pixal3D trains in a 90°-X-rotated grid frame (F_p). We un-rotate decoder outputs for # user-facing previews/meshes, then re-rotate before feeding coords back to the shape DiT. def _pixal3d_unrotate_voxel_data(data: torch.Tensor) -> torch.Tensor: if data.ndim == 4: return data.flip(-1).permute(0, 1, 3, 2).contiguous() if data.ndim == 5: return data.flip(-1).permute(0, 1, 2, 4, 3).contiguous() raise ValueError(f"unexpected voxel shape {tuple(data.shape)}") def _pixal3d_rerotate_voxel_data(data: torch.Tensor) -> torch.Tensor: if data.ndim == 4: return data.permute(0, 1, 3, 2).flip(-1).contiguous() if data.ndim == 5: return data.permute(0, 1, 2, 4, 3).flip(-1).contiguous() raise ValueError(f"unexpected voxel shape {tuple(data.shape)}") def _pixal3d_unrotate_vertices(vertices: torch.Tensor) -> torch.Tensor: if vertices.numel() == 0: return vertices x, y, z = vertices.unbind(-1) return torch.stack([-x, y, -z], dim=-1).contiguous() def _pixal3d_unrotate_sparse_coords(coords: torch.Tensor, resolution: int) -> torch.Tensor: if coords.numel() == 0: return coords R1 = resolution - 1 if coords.shape[-1] == 4: b, i, j, k = coords.unbind(-1) return torch.stack([b, R1 - i, j, R1 - k], dim=-1).contiguous() if coords.shape[-1] == 3: i, j, k = coords.unbind(-1) return torch.stack([R1 - i, j, R1 - k], dim=-1).contiguous() raise ValueError(f"unexpected coord shape {tuple(coords.shape)}") 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): 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") pixal3d_mode = samples.get("model_options", {}).get("proj_feat_pack") is not None 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)) if pixal3d_mode: for m in mesh: m.vertices = _pixal3d_unrotate_vertices(m.vertices) face_list = [m.faces for m in mesh] vert_list = [m.vertices for m 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") pixal3d_mode = samples.get("model_options", {}).get("proj_feat_pack") is not None 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#[:, 1:] 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 if pixal3d_mode: voxel_coords = _pixal3d_unrotate_sparse_coords(voxel_coords, resolution=tex_resolution) 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) decoder = vae.first_stage_model.struct_dec 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(decoder(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() if samples.get("model_options", {}).get("proj_feat_pack") is not None: voxel_data = _pixal3d_unrotate_voxel_data(voxel_data) out = Types.VOXEL(voxel_data) return IO.NodeOutput(out) class Trellis2UpsampleCascade(IO.ComfyNode): @classmethod def define_schema(cls): return IO.Schema( node_id="Trellis2UpsampleCascade", category="latent/3d", display_name="Trellis2 Upsample Cascade", description="Upsamples low-resolution Trellis2 shape latents into higher resolution coordinates while respecting the maximum token budget.", inputs=[ IO.Latent.Input("shape_latent"), 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.Voxel.Output( "high_res_voxel", tooltip=( "High-resolution sparse coordinates produced after cascade upsampling. " "Represents the refined 3D structure at target resolution." ) ) ] ) @classmethod def execute(cls, shape_latent, vae, target_resolution, max_tokens): shape_latent_512 = shape_latent device = comfy.model_management.get_torch_device() prepare_trellis_vae_for_decode(vae, shape_latent_512["samples"].shape) coord_counts = shape_latent_512.get("coord_counts") decoder = vae.first_stage_model.shape_dec lr_resolution = 512 target_resolution = int(target_resolution) if coord_counts is None: feats, coords_512 = flatten_batched_sparse_latent( shape_latent_512["samples"], shape_latent_512["coords"], coord_counts, ) feats = feats.to(device) coords_512 = coords_512.to(device) slat = shape_norm(feats, coords_512) slat.feats = slat.feats.to(next(decoder.parameters()).dtype) hr_coords = decoder.upsample(slat, upsample_times=4) hr_resolution = target_resolution while True: quant_coords = torch.cat([ hr_coords[:, :1], ((hr_coords[:, 1:] + 0.5) / lr_resolution * (hr_resolution // 16)).int(), ], dim=1) final_coords = quant_coords.unique(dim=0) num_tokens = final_coords.shape[0] if num_tokens < max_tokens or hr_resolution <= 1024: break hr_resolution -= 128 return IO.NodeOutput(final_coords,) items = split_batched_sparse_latent( shape_latent_512["samples"], shape_latent_512["coords"], coord_counts, ) decoder_dtype = next(decoder.parameters()).dtype sample_hr_coords = [] for feats_i, coords_i in items: feats_i = feats_i.to(device) coords_i = coords_i.to(device).clone() coords_i[:, 0] = 0 slat_i = shape_norm(feats_i, coords_i) slat_i.feats = slat_i.feats.to(decoder_dtype) sample_hr_coords.append(decoder.upsample(slat_i, upsample_times=4)) hr_resolution = target_resolution while True: exceeds_limit = False for hr_coords_i in sample_hr_coords: quant_coords_i = torch.cat([ hr_coords_i[:, :1], ((hr_coords_i[:, 1:] + 0.5) / lr_resolution * (hr_resolution // 16)).int(), ], dim=1) if quant_coords_i.unique(dim=0).shape[0] >= max_tokens: exceeds_limit = True break if not exceeds_limit or hr_resolution <= 1024: break hr_resolution -= 128 final_coords_list = [] output_coord_counts = [] for sample_offset, hr_coords_i in enumerate(sample_hr_coords): quant_coords_i = torch.cat([ hr_coords_i[:, :1], ((hr_coords_i[:, 1:] + 0.5) / lr_resolution * (hr_resolution // 16)).int(), ], dim=1) final_coords_i = quant_coords_i.unique(dim=0) final_coords_i = final_coords_i.clone() final_coords_i[:, 0] = sample_offset final_coords_list.append(final_coords_i) output_coord_counts.append(int(final_coords_i.shape[0])) coords = torch.cat(final_coords_list, dim=0) output = Types.VOXEL(coords) output.coord_counts = torch.tensor(output_coord_counts, dtype=torch.int64) output.resolutions = torch.full((len(final_coords_list),), int(hr_resolution), dtype=torch.int64) output.upsampled = True return IO.NodeOutput(output,) 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) class EmptyTrellis2ShapeLatent(IO.ComfyNode): @classmethod def define_schema(cls): return IO.Schema( node_id="EmptyTrellis2ShapeLatent", category="latent/3d", inputs=[ IO.Voxel.Input( "voxel", tooltip=( "Shape structure input. Accepts either a voxel structure " "or upsampled voxel coordinates from a previous cascade stage." ) ), Pixal3DProjPack.Input( "proj_feat_pack", optional=True, tooltip="Pixal3D pixel-aligned projection pack from Pixal3DConditioning. Leave empty for vanilla Trellis2.", ), ], outputs=[ IO.Latent.Output(), ] ) @classmethod def execute(cls, voxel, proj_feat_pack=None): is_512_pass = False coord_resolution = None upsampled = hasattr(voxel, "upsampled") if upsampled: if hasattr(voxel, "resolutions") and voxel.resolutions is not None: coord_resolution = int(voxel.resolutions[0].item()) // 16 voxel = voxel.data if not upsampled: voxel_data = voxel.data if proj_feat_pack is not None: voxel_data = _pixal3d_rerotate_voxel_data(voxel_data) decoded = voxel_data.unsqueeze(1) coords = torch.argwhere(decoded.bool())[:, [0, 2, 3, 4]].int() is_512_pass = True coord_resolution = int(decoded.shape[-1]) else: coords = voxel.int() batch_size, counts, max_tokens = infer_batched_coord_layout(coords) in_channels = 32 # image like format latent = torch.zeros(batch_size, in_channels, max_tokens, 1) if is_512_pass: generation_mode = "shape_generation_512" else: generation_mode = "shape_generation" model_options = {"generation_mode": generation_mode, "coords": coords, "coord_counts": counts} if coord_resolution is not None: model_options["coord_resolution"] = coord_resolution if proj_feat_pack is not None: model_options["proj_feat_pack"] = proj_feat_pack return IO.NodeOutput({"samples": latent, "coords": coords, "coord_counts": counts, "type": "trellis2", "model_options": model_options}) class EmptyTrellis2LatentTexture(IO.ComfyNode): @classmethod def define_schema(cls): return IO.Schema( node_id="EmptyTrellis2LatentTexture", category="latent/3d", inputs=[ IO.Voxel.Input( "voxel", tooltip=( "Shape structure input. Accepts either a voxel structure " "or upsampled voxel coordinates from a previous cascade stage." ) ), IO.Latent.Input("shape_latent"), Pixal3DProjPack.Input( "proj_feat_pack", optional=True, tooltip="Pixal3D pixel-aligned projection pack from Pixal3DConditioning. Leave empty for vanilla Trellis2.", ), ], outputs=[ IO.Latent.Output(), ] ) @classmethod def execute(cls, voxel, shape_latent, proj_feat_pack=None): channels = 32 coord_resolution = None upsampled = hasattr(voxel, "upsampled") if upsampled: if hasattr(voxel, "resolutions") and voxel.resolutions is not None: coord_resolution = int(voxel.resolutions[0].item()) // 16 voxel = voxel.data if not upsampled: voxel_data = voxel.data if proj_feat_pack is not None: voxel_data = _pixal3d_rerotate_voxel_data(voxel_data) decoded = voxel_data.unsqueeze(1) coords = torch.argwhere(decoded.bool())[:, [0, 2, 3, 4]].int() coord_resolution = int(decoded.shape[-1]) else: coords = voxel.int() batch_size, counts, max_tokens = infer_batched_coord_layout(coords) shape_latent = shape_latent["samples"] if shape_latent.ndim == 4: shape_latent = shape_latent.squeeze(-1).transpose(1, 2).reshape(-1, channels) latent = torch.zeros(batch_size, channels, max_tokens, 1) model_options = {"generation_mode": "texture_generation", "coords": coords, "coord_counts": counts, "shape_slat": shape_latent} if coord_resolution is not None: model_options["coord_resolution"] = coord_resolution if proj_feat_pack is not None: model_options["proj_feat_pack"] = proj_feat_pack return IO.NodeOutput({"samples": latent, "type": "trellis2", "coords": coords, "coord_counts": counts, "model_options": model_options}) 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."), Pixal3DProjPack.Input( "proj_feat_pack", optional=True, tooltip="Pixal3D pixel-aligned projection pack. Leave empty for vanilla Trellis2.", ), ], outputs=[ IO.Latent.Output(), ] ) @classmethod def execute(cls, batch_size, proj_feat_pack=None): # Trellis2.forward slices x[:, :8] and pads out to 32; KSampler residual math # needs the empty latent to match latent_format (32-channel). in_channels = 32 resolution = 16 latent = torch.zeros(batch_size, in_channels, resolution, resolution, resolution) output = { "samples": latent, "type": "trellis2", } if proj_feat_pack is not None: output["model_options"] = {"proj_feat_pack": proj_feat_pack} return IO.NodeOutput(output) 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 _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()) def _run_dinov3_with_patches(model, cropped_pil, image_size): # Pixal3D's cross-attn was trained against CLS + registers only (~5 tokens), not the # full patch grid. The patch grid goes to the proj branch via patches_2d. model_internal = model.model torch_device = comfy.model_management.get_torch_device() resized = cropped_pil.resize((image_size, image_size), Image.Resampling.LANCZOS) img_np = np.array(resized).astype(np.float32) / 255.0 img_t = torch.from_numpy(img_np).permute(2, 0, 1).unsqueeze(0).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_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) pil_img = Image.fromarray(img_np) pil_mask = Image.fromarray(mask_np) max_size = max(pil_img.size) scale = min(1.0, max_image_size / 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) scene_size = (pil_img.width, pil_img.height) rgba_np = np.zeros((pil_img.height, pil_img.width, 4), dtype=np.uint8) rgba_np[:, :, :3] = np.array(pil_img) 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 # Upstream pads the bbox by 10% — encoders were trained with that breathing room. size = max(y_max - y_min, x_max - x_min) size = int(size * 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 crop_bbox = (crop_x1, crop_y1, crop_x2, crop_y2) rgba_pil = Image.fromarray(rgba_np) cropped_rgba = rgba_pil.crop(crop_bbox) cropped_np = np.array(cropped_rgba).astype(np.float32) / 255.0 else: logging.warning("Mask for the image is empty. Pixal3D requires a clean foreground mask.") cropped_np = rgba_np.astype(np.float32) / 255.0 crop_bbox = (0, 0, scene_size[0], scene_size[1]) fg = cropped_np[:, :, :3] alpha_float = cropped_np[:, :, 3:4] composite_np = fg * alpha_float composite_uint8 = (composite_np * 255.0).round().clip(0, 255).astype(np.uint8) return Image.fromarray(composite_uint8), crop_bbox, scene_size 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.Float.Input( "distance_override", default=0.0, min=0.0, max=10.0, step=0.001, tooltip="Override camera distance directly. 0 = auto-derive from FOV.", ), 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"), Pixal3DProjPack.Output(display_name="proj_feat_pack"), ], ) @classmethod def execute(cls, clip_vision_model, image, mask, camera_angle_x, mesh_scale, distance_override=0.0, 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 = [], [] cropped_pil_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] cropped_pil, 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) cropped_pil_list.append(cropped_pil) cond_512 = _run_dinov3_with_patches(clip_vision_model, cropped_pil, 512) cond_1024 = _run_dinov3_with_patches(clip_vision_model, cropped_pil, 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) # Upstream samples the LR DINO grid AND the NAF HR grid separately at projected # 3D points, then cats sampled features along channels. Back-projection (in model.py) # mirrors that — here we just stash LR + optional HR per stage. # NAF targets per stage: shape_512=512, shape_1024=512, tex_1024=1024. def _naf_hr(lr_feat, image_pil_list, image_size, naf_target): if naf_model is None or naf_target is None: return None # Run NAF in the input feature dtype (typically fp16 since DINO/ClipVision # loads that way). The previous .float() cast doubled NAF memory by forcing # full fp32 — at tex_1024/target=1024 that's ~10 GB on its own. Model # weights need to match input dtype since PyTorch conv ops error out on # mixed fp16-input/fp32-weight. target_dtype = lr_feat.dtype if next(naf_model.parameters()).dtype != target_dtype: naf_model.to(dtype=target_dtype) imgs = torch.stack([ torch.from_numpy( np.array(p.resize((image_size, image_size), Image.Resampling.LANCZOS)) .astype(np.float32) / 255.0 ).permute(2, 0, 1) for p in image_pil_list ], 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, cropped_pil_list, 512, (512, 512)) hr_shape_1024 = _naf_hr(fm_1024_dino, cropped_pil_list, 1024, (512, 512)) hr_tex_1024 = _naf_hr(fm_1024_dino, cropped_pil_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) if distance_override > 0: distance = float(distance_override) else: 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 # (Trellis2.forward swaps context↔embeds for non-structure HR stages). neg_global = torch.zeros_like(global_512) neg_embeds = torch.zeros_like(global_1024) positive = [[global_512, {"embeds": global_1024}]] negative = [[neg_global, {"embeds": neg_embeds}]] return IO.NodeOutput(positive, negative, proj_pack) 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"), Pixal3DProjPack.Input("proj_feat_pack", tooltip="The proj pack produced by Pixal3DConditioning for this object."), 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, proj_feat_pack, moge_geometry, object_mask=None, batch_index=0) -> IO.NodeOutput: 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." ) # Compose VaeDecodeShapeTrellis's R_y(180°) inverse with R_proj to map user mesh # space to 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 Trellis2Extension(ComfyExtension): @override async def get_node_list(self) -> list[type[IO.ComfyNode]]: return [ Trellis2Conditioning, Pixal3DConditioning, Pixal3DAlignObject, LoadNAFModel, EmptyTrellis2ShapeLatent, EmptyTrellis2LatentStructure, EmptyTrellis2LatentTexture, VaeDecodeTextureTrellis, VaeDecodeShapeTrellis, VaeDecodeStructureTrellis2, Trellis2UpsampleCascade, ] async def comfy_entrypoint() -> Trellis2Extension: return Trellis2Extension()