diff --git a/comfy_extras/nodes_mesh_postprocess.py b/comfy_extras/nodes_mesh_postprocess.py index cdca9e524..e26510f36 100644 --- a/comfy_extras/nodes_mesh_postprocess.py +++ b/comfy_extras/nodes_mesh_postprocess.py @@ -1,12 +1,100 @@ import torch import numpy as np from typing_extensions import override -from comfy_api.latest import ComfyExtension, IO +from comfy_api.latest import ComfyExtension, IO, Types import copy import comfy.utils import logging import scipy +def get_mesh_batch_item(mesh, index): + if hasattr(mesh, "vertex_counts") and mesh.vertex_counts is not None: + vertex_count = int(mesh.vertex_counts[index].item()) + face_count = int(mesh.face_counts[index].item()) + vertices = mesh.vertices[index, :vertex_count] + faces = mesh.faces[index, :face_count] + colors = None + if hasattr(mesh, "colors") and mesh.colors is not None: + if hasattr(mesh, "color_counts") and mesh.color_counts is not None: + color_count = int(mesh.color_counts[index].item()) + colors = mesh.colors[index, :color_count] + else: + colors = mesh.colors[index, :vertex_count] + return vertices, faces, colors + + colors = None + if hasattr(mesh, "colors") and mesh.colors is not None: + colors = mesh.colors[index] + return mesh.vertices[index], mesh.faces[index], colors + +def pack_variable_mesh_batch(vertices, faces, colors=None): + batch_size = len(vertices) + max_vertices = max(v.shape[0] for v in vertices) + max_faces = max(f.shape[0] for f in faces) + + packed_vertices = vertices[0].new_zeros((batch_size, max_vertices, vertices[0].shape[1])) + packed_faces = faces[0].new_zeros((batch_size, max_faces, faces[0].shape[1])) + vertex_counts = torch.tensor([v.shape[0] for v in vertices], device=vertices[0].device, dtype=torch.int64) + face_counts = torch.tensor([f.shape[0] for f in faces], device=faces[0].device, dtype=torch.int64) + + for i, (v, f) in enumerate(zip(vertices, faces)): + packed_vertices[i, :v.shape[0]] = v + packed_faces[i, :f.shape[0]] = f + + mesh = Types.MESH(packed_vertices, packed_faces) + mesh.vertex_counts = vertex_counts + mesh.face_counts = face_counts + + if colors is not None: + max_colors = max(c.shape[0] for c in colors) + packed_colors = colors[0].new_zeros((batch_size, max_colors, colors[0].shape[1])) + color_counts = torch.tensor([c.shape[0] for c in colors], device=colors[0].device, dtype=torch.int64) + for i, c in enumerate(colors): + packed_colors[i, :c.shape[0]] = c + mesh.vertex_colors = packed_colors + mesh.color_counts = color_counts + + return mesh + + +def paint_mesh_with_voxels(mesh, voxel_coords, voxel_colors, resolution): + """ + Generic function to paint a mesh using nearest-neighbor colors from a sparse voxel field. + """ + device = comfy.model_management.vae_offload_device() + + origin = torch.tensor([-0.5, -0.5, -0.5], device=device) + voxel_size = 1.0 / resolution + + # map voxels + voxel_pos = voxel_coords.to(device).float() * voxel_size + origin + verts = mesh.vertices.to(device).squeeze(0) + voxel_colors = voxel_colors.to(device) + + voxel_pos_np = voxel_pos.numpy() + verts_np = verts.numpy() + + tree = scipy.spatial.cKDTree(voxel_pos_np) + + # nearest neighbour k=1 + _, nearest_idx_np = tree.query(verts_np, k=1, workers=-1) + + nearest_idx = torch.from_numpy(nearest_idx_np).long() + v_colors = voxel_colors[nearest_idx] + + # to [0, 1] + srgb_colors = v_colors.clamp(0, 1)#(v_colors * 0.5 + 0.5).clamp(0, 1) + + # to Linear RGB (required for GLTF) + linear_colors = torch.pow(srgb_colors, 2.2) + + final_colors = linear_colors.unsqueeze(0) + + out_mesh = copy.deepcopy(mesh) + out_mesh.vertex_colors = final_colors + + return out_mesh + class PaintMesh(IO.ComfyNode): @classmethod def define_schema(cls): @@ -20,7 +108,7 @@ class PaintMesh(IO.ComfyNode): ), inputs=[ IO.Mesh.Input("mesh"), - IO.Voxel.Input("color_voxel") + IO.Voxel.Input("voxel_colors") ], outputs=[ IO.Mesh.Output("mesh"), @@ -28,523 +116,51 @@ class PaintMesh(IO.ComfyNode): ) @classmethod - def execute(cls, mesh, color_voxel): - """ - Generic function to paint a mesh using nearest-neighbor colors from a sparse voxel field. - """ - resolution = color_voxel.resolution - voxel_colors = color_voxel.voxel_colors - voxel_coords = color_voxel.data + def execute(cls, mesh, voxel_colors): + voxels = voxel_colors + coords = voxels.data + colors = voxels.voxel_colors + resolution = voxels.resolution + if coords.shape[0] == 0: + return IO.NodeOutput(paint_mesh_default_colors(mesh)) - device = comfy.model_management.vae_offload_device() + mesh_batch_size = mesh.vertices.shape[0] - origin = torch.tensor([-0.5, -0.5, -0.5], device=device) - voxel_size = 1.0 / resolution + if coords.shape[-1] == 4 and mesh_batch_size > 1: + batch_idx = coords[:, 0].long() + voxel_coords = coords[:, 1:] + mesh_batch_size = mesh.vertices.shape[0] - # map voxels - voxel_pos = voxel_coords.to(device).float() * voxel_size + origin - verts = mesh.vertices.to(device).squeeze(0) - voxel_colors = voxel_colors.to(device) + out_verts, out_faces, out_colors = [], [], [] + for i in range(mesh_batch_size): + sel = batch_idx == i + item_coords = voxel_coords[sel] + item_colors = colors[sel] + item_vertices, item_faces, _ = get_mesh_batch_item(mesh, i) + item_mesh = Types.MESH(vertices=item_vertices.unsqueeze(0), faces=item_faces.unsqueeze(0)) - voxel_pos_np = voxel_pos.numpy() - verts_np = verts.numpy() + if item_coords.shape[0] == 0: + painted = paint_mesh_default_colors(item_mesh) + else: + painted = paint_mesh_with_voxels(item_mesh, item_coords, item_colors, resolution=resolution) - tree = scipy.spatial.cKDTree(voxel_pos_np) + out_verts.append(painted.vertices.squeeze(0)) + out_faces.append(painted.faces.squeeze(0)) + out_colors.append(painted.vertex_colors.squeeze(0)) - # nearest neighbour k=1 - _, nearest_idx_np = tree.query(verts_np, k=1, workers=-1) - - nearest_idx = torch.from_numpy(nearest_idx_np).long() - v_colors = voxel_colors[nearest_idx] - - # to [0, 1] - srgb_colors = v_colors.clamp(0, 1)#(v_colors * 0.5 + 0.5).clamp(0, 1) - - # to Linear RGB (required for GLTF) - linear_colors = torch.pow(srgb_colors, 2.2) - - final_colors = linear_colors.unsqueeze(0) - - out_mesh = copy.deepcopy(mesh) - out_mesh.vertex_colors = final_colors + out_mesh = pack_variable_mesh_batch(out_verts, out_faces, out_colors) + return IO.NodeOutput(out_mesh) + out_mesh = paint_mesh_with_voxels(mesh, coords, colors, resolution=resolution) return IO.NodeOutput(out_mesh) - def paint_mesh_default_colors(mesh): out_mesh = copy.copy(mesh) vertex_count = mesh.vertices.shape[1] out_mesh.vertex_colors = mesh.vertices.new_zeros((1, vertex_count, 3)) return out_mesh -def _pytorch_edge_errors(verts, Q, edges, stabilizer, max_edge_length_sq, mesh_scale_sq): - n_edges = edges.shape[0] - if n_edges == 0: - return (torch.empty((0, 3), dtype=torch.float64, device=verts.device), - torch.empty((0,), dtype=torch.float64, device=verts.device), - torch.zeros((0,), dtype=torch.bool, device=verts.device)) - - device = verts.device - mesh_scale = (mesh_scale_sq) ** 0.5 - - va = edges[:, 0] - vb = edges[:, 1] - Q0 = Q[va] - Q1 = Q[vb] - Qe = Q0 + Q1 - - A = Qe[:, :3, :3] + torch.eye(3, device=device, dtype=torch.float64).unsqueeze(0) * stabilizer - b = -Qe[:, :3, 3].unsqueeze(-1) - - dets = torch.det(A) - good = dets.abs() > 1e-12 - opt = torch.zeros((n_edges, 3), dtype=torch.float64, device=device) - - if good.any(): - try: - sol = torch.linalg.solve(A[good], b[good]) - opt[good] = sol.squeeze(-1) - except Exception: - good = torch.zeros_like(good) - - if (~good).any(): - bad_idx = torch.nonzero(~good, as_tuple=True)[0] - opt[bad_idx] = (verts[va[bad_idx]] + verts[vb[bad_idx]]) * 0.5 - - pa = verts[va] - pb = verts[vb] - el = torch.norm(pb - pa, dim=-1) - dist_a = torch.norm(opt - pa, dim=-1) - dist_b = torch.norm(opt - pb, dim=-1) - wander_bad = (dist_a > 4.0 * el) | (dist_b > 4.0 * el) - - if wander_bad.any(): - bad_idx = torch.nonzero(wander_bad, as_tuple=True)[0] - opt[bad_idx] = (verts[va[bad_idx]] + verts[vb[bad_idx]]) * 0.5 - - v4 = torch.cat([opt, torch.ones((n_edges, 1), device=device, dtype=torch.float64)], dim=1) - err = torch.abs(torch.einsum("ei,eij,ej->e", v4, Qe, v4)) - - length_ok = el > mesh_scale * 1e-5 - error_ok = err < max_edge_length_sq - nan_ok = ~torch.isnan(opt).any(dim=-1) & ~torch.isnan(err) - valid = length_ok & error_ok & nan_ok - - return opt, err, valid - -def _build_quadrics(verts, faces): - v0 = verts[faces[:, 0]] - v1 = verts[faces[:, 1]] - v2 = verts[faces[:, 2]] - e1 = v1 - v0 - e2 = v2 - v0 - n = torch.cross(e1, e2, dim=-1) - area = torch.norm(n, dim=-1) - mask = area > 1e-12 - n_norm = torch.zeros_like(n) - n_norm[mask] = n[mask] / area[mask].unsqueeze(-1) - d = -(n_norm * v0).sum(dim=-1, keepdim=True) - p = torch.cat([n_norm, d], dim=-1) - K = torch.einsum("fi,fj->fij", p, p) - K = K * area[:, None, None] - V = verts.shape[0] - Q = torch.zeros((V, 4, 4), dtype=torch.float64, device=verts.device) - K_flat = K.reshape(-1, 16) - Q_flat = Q.reshape(V, 16) - for corner in range(3): - idx = faces[:, corner].unsqueeze(1).expand(-1, 16) - Q_flat.scatter_add_(0, idx, K_flat) - return Q_flat.reshape(V, 4, 4) - - -def _cleanup_mesh(verts, faces, min_angle_deg=0.5, max_aspect=100.0): - if faces.numel() == 0: - return verts, faces - - v0 = verts[faces[:, 0]] - v1 = verts[faces[:, 1]] - v2 = verts[faces[:, 2]] - e0 = v1 - v0 - e1 = v2 - v1 - e2 = v0 - v2 - l0 = torch.norm(e0, dim=-1) - l1 = torch.norm(e1, dim=-1) - l2 = torch.norm(e2, dim=-1) - n = torch.cross(e0, e2, dim=-1) - area = torch.norm(n, dim=-1) - - max_edge = torch.max(torch.max(l0, l1), l2) - aspect = max_edge * max_edge / (2.0 * area + 1e-12) - - cos_a = (l1 * l1 + l2 * l2 - l0 * l0) / (2 * l1 * l2 + 1e-12) - cos_b = (l0 * l0 + l2 * l2 - l1 * l1) / (2 * l0 * l2 + 1e-12) - cos_c = (l0 * l0 + l1 * l1 - l2 * l2) / (2 * l0 * l1 + 1e-12) - cos_all = torch.stack([cos_a, cos_b, cos_c], dim=-1) - angles = torch.acos(torch.clamp(cos_all, -1, 1)) * 180 / np.pi - - good = (aspect < max_aspect) & (angles.min(dim=1)[0] > min_angle_deg) & (area > 1e-12) - faces = faces[good] - - if faces.numel() == 0: - return verts, faces - - used = torch.zeros(verts.shape[0], dtype=torch.bool, device=verts.device) - used[faces[:, 0]] = True - used[faces[:, 1]] = True - used[faces[:, 2]] = True - - remap = torch.full((verts.shape[0],), -1, dtype=torch.int64, device=verts.device) - remap[used] = torch.arange(used.sum().item(), device=verts.device) - verts = verts[used] - faces = remap[faces] - return verts, faces - - -def _build_vertex_face_csr(faces, num_verts): - vf_verts = faces.view(-1) - vf_faces = torch.arange(faces.shape[0], device=faces.device).repeat_interleave(3) - - sort_idx = torch.argsort(vf_verts, stable=True) - sorted_verts = vf_verts[sort_idx] - sorted_faces = vf_faces[sort_idx] - - unique_verts, counts = torch.unique_consecutive(sorted_verts, return_counts=True) - - ptrs = torch.zeros(num_verts + 1, dtype=torch.int64, device=faces.device) - ptrs[unique_verts + 1] = counts - ptrs = torch.cumsum(ptrs, dim=0) - - return sorted_faces, ptrs - - -def _get_vertex_faces(v, face_indices, vert_ptrs): - start = vert_ptrs[v] - end = vert_ptrs[v + 1] - return face_indices[start:end] - -def _gpu_greedy_sampled(edges, errors, v_alive, max_select): - device = edges.device - n_edges = edges.shape[0] - - if n_edges == 0: - return torch.empty(0, dtype=torch.int64, device=device) - - # Sort by error - sorted_idx = torch.argsort(errors) - sorted_edges = edges[sorted_idx] - - # Sample K edges from the sorted list - # This gives us diverse edges spread across the mesh - K = min(max_select * 20, n_edges) - if K < n_edges: - sample_positions = torch.linspace(0, n_edges - 1, K, device=device).long() - sampled_edges = sorted_edges[sample_positions] - sampled_idx = sorted_idx[sample_positions] - else: - sampled_edges = sorted_edges - sampled_idx = sorted_idx - - # Greedy selection on GPU - used = torch.zeros(v_alive.shape[0], dtype=torch.bool, device=device) - used[~v_alive] = True - - selected = [] - batch_size = 8192 - - for start in range(0, sampled_edges.shape[0], batch_size): - end = min(start + batch_size, sampled_edges.shape[0]) - batch = sampled_edges[start:end] - batch_idx = sampled_idx[start:end] - - va = batch[:, 0] - vb = batch[:, 1] - - # Vectorized free check - free = ~used[va] & ~used[vb] - - if not free.any(): - continue - - # Get free edges - free_local = torch.nonzero(free, as_tuple=True)[0] - free_edges = batch[free_local] - free_idx = batch_idx[free_local] - - # Process free edges greedily but in larger chunks - # Transfer to CPU but only the small free subset - free_va = free_edges[:, 0].cpu().numpy() - free_vb = free_edges[:, 1].cpu().numpy() - free_edges_idx = free_idx.cpu().numpy() - - for i in range(len(free_va)): - a = int(free_va[i]) - b = int(free_vb[i]) - if not used[a].item() and not used[b].item(): - selected.append(int(free_edges_idx[i])) - used[a] = True - used[b] = True - if len(selected) >= max_select: - return torch.tensor(selected, dtype=torch.int64, device=device) - - if len(selected) == 0: - return torch.empty(0, dtype=torch.int64, device=device) - return torch.tensor(selected, dtype=torch.int64, device=device) - -def _qem_simplify(verts_np, faces_np, colors_np, target_faces, device, max_edge_length=None): - verts = torch.from_numpy(verts_np).to(device=device, dtype=torch.float64) - faces = torch.from_numpy(faces_np).to(device=device, dtype=torch.int64) - colors = ( - torch.from_numpy(colors_np).to(device=device, dtype=torch.float64) - if colors_np is not None - else None - ) - - num_verts = verts.shape[0] - num_faces = faces.shape[0] - - logging.debug(f"[QEM] Input: {num_verts} verts, {num_faces} faces, target={target_faces}") - - v_alive = torch.ones(num_verts, dtype=torch.bool, device=device) - f_alive = torch.ones(num_faces, dtype=torch.bool, device=device) - - Q = _build_quadrics(verts, faces) - - bbox = verts.max(dim=0)[0] - verts.min(dim=0)[0] - mesh_scale = torch.norm(bbox).item() - - if max_edge_length is None or max_edge_length <= 0: - max_edge_length = mesh_scale * 2.0 - - if max_edge_length < 1e-6: - max_edge_length = 1.0 - - stabilizer = mesh_scale * mesh_scale * 0.001 - max_edge_length_sq = max_edge_length * max_edge_length - mesh_scale_sq = mesh_scale * mesh_scale - - iteration = 0 - total_collapses = 0 - last_faces = num_faces - - while True: - n_faces = int(f_alive.sum().item()) - - if n_faces <= target_faces: - break - - alive_v = torch.nonzero(v_alive, as_tuple=True)[0] - alive_f = torch.nonzero(f_alive, as_tuple=True)[0] - - if alive_v.numel() <= 4 or alive_f.numel() == 0: - break - - # Compact active mesh - vmap = torch.full((num_verts,), -1, dtype=torch.int64, device=device) - vmap[alive_v] = torch.arange(alive_v.numel(), device=device) - - active_faces = faces[alive_f] - remapped = vmap[active_faces] - - # Extract edges - e0 = remapped[:, [0, 1]] - e1 = remapped[:, [1, 2]] - e2 = remapped[:, [2, 0]] - edges = torch.cat([e0, e1, e2], dim=0) - edges = torch.sort(edges, dim=1)[0] - edges = edges[(edges >= 0).all(dim=1)] - edges = edges[edges[:, 0] != edges[:, 1]] - - if edges.shape[0] == 0: - break - - edges_orig = alive_v[edges] - - # Filter by edge length - pa = verts[edges_orig[:, 0]] - pb = verts[edges_orig[:, 1]] - el = torch.norm(pb - pa, dim=-1) - short_enough = el < max_edge_length - - if not short_enough.any(): - max_edge_length = el.max().item() * 2.0 - max_edge_length_sq = max_edge_length * max_edge_length - short_enough = el < max_edge_length - if not short_enough.any(): - break - - edges_orig = edges_orig[short_enough] - if edges_orig.shape[0] == 0: - break - - # Sample edges for processing - n_edges_total = edges_orig.shape[0] - max_edges_to_process = 10_000_000 # 10M edges per iteration - - if n_edges_total > max_edges_to_process: - perm = torch.randperm(n_edges_total, device=device)[:max_edges_to_process] - edges_orig = edges_orig[perm] - n_edges = max_edges_to_process - else: - n_edges = n_edges_total - - optimal, err, valid = _pytorch_edge_errors( - verts, Q, edges_orig, stabilizer, max_edge_length_sq, mesh_scale_sq - ) - - if not valid.any(): - valid = torch.ones(n_edges, dtype=torch.bool, device=device) - - valid_idx = torch.nonzero(valid, as_tuple=True)[0] - edges_orig = edges_orig[valid_idx] - optimal = optimal[valid_idx] - err = err[valid_idx] - - # KEY: Much larger batch size - faces_to_remove = n_faces - target_faces - max_collapses = min(50000, max(1000, faces_to_remove // 20)) - - sel = _gpu_greedy_sampled(edges_orig, err, v_alive, max_collapses) - - if sel.numel() == 0: - break - - v_a = edges_orig[sel, 0] - v_b = edges_orig[sel, 1] - - # Build adjacency - face_indices, vert_ptrs = _build_vertex_face_csr(active_faces, num_verts) - - # Build (edge, face) pairs - pair_edge_idx = [] - pair_face_idx = [] - - va_cpu = v_a.cpu() - vb_cpu = v_b.cpu() - - for ei, (vai, vbi) in enumerate(zip(va_cpu, vb_cpu)): - f_va = _get_vertex_faces(vai.item(), face_indices, vert_ptrs) - f_vb = _get_vertex_faces(vbi.item(), face_indices, vert_ptrs) - - faces_vb = active_faces[f_vb] - mask_b = (faces_vb[:, 0] != vai) & (faces_vb[:, 1] != vai) & (faces_vb[:, 2] != vai) - f_vb_valid = f_vb[mask_b] - - faces_va = active_faces[f_va] - mask_a = (faces_va[:, 0] != vbi) & (faces_va[:, 1] != vbi) & (faces_va[:, 2] != vbi) - f_va_valid = f_va[mask_a] - - all_faces = torch.cat([f_vb_valid, f_va_valid]) - if all_faces.numel() > 0: - pair_edge_idx.extend([ei] * all_faces.numel()) - pair_face_idx.extend(all_faces.cpu().tolist()) - - keep_mask = torch.ones(v_a.numel(), dtype=torch.bool, device=device) - - if not keep_mask.any(): - break - - keep_idx = torch.nonzero(keep_mask, as_tuple=True)[0] - v_a = v_a[keep_idx] - v_b = v_b[keep_idx] - sel = sel[keep_idx] - - # Apply collapses - verts[v_a] = optimal[sel] - v_alive[v_b] = False - Q[v_a] += Q[v_b] - - if colors is not None: - colors[v_a] = (colors[v_a] + colors[v_b]) * 0.5 - - merge_map = torch.arange(num_verts, device=device) - merge_map[v_b] = v_a - faces = merge_map[faces] - - bad = ( - (faces[:, 0] == faces[:, 1]) - | (faces[:, 1] == faces[:, 2]) - | (faces[:, 2] == faces[:, 0]) - ) - f_alive &= ~bad - - total_collapses += v_a.numel() - iteration += 1 - - # Log only every 50 iterations to reduce sync overhead - if iteration % 50 == 0 or n_faces < last_faces * 0.9: - logging.debug(f"[QEM] Iter {iteration}: {total_collapses} collapses, {int(f_alive.sum().item())} faces, applied {v_a.numel()}") - last_faces = n_faces - - # Periodic compaction - if iteration % 5 == 0 and int(f_alive.sum().item()) < num_faces * 0.5: - faces = faces[f_alive] - f_alive = torch.ones(faces.shape[0], dtype=torch.bool, device=device) - num_faces = faces.shape[0] - - if iteration > 5000: - break - - # Finalize - final_v = verts[v_alive] - final_c = colors[v_alive] if colors is not None else None - - remap = torch.full((num_verts,), -1, dtype=torch.int64, device=device) - remap[v_alive] = torch.arange(int(v_alive.sum().item()), device=device) - - final_f_raw = faces[f_alive] - alive_mask = v_alive[final_f_raw].all(dim=1) - final_f_raw = final_f_raw[alive_mask] - final_f = remap[final_f_raw] - valid_faces = (final_f >= 0).all(dim=1) - final_f = final_f[valid_faces] - - if final_f.numel() > 0: - final_f = torch.unique(torch.sort(final_f, dim=1)[0], dim=0) - - final_v, final_f = _cleanup_mesh(final_v, final_f, min_angle_deg=0.5, max_aspect=100.0) - - return final_v, final_f, final_c - -def simplify_fn(vertices, faces, colors=None, target=100000, max_edge_length=None): - if vertices.ndim == 3: - v_list, f_list, c_list = [], [], [] - for i in range(vertices.shape[0]): - c_in = colors[i] if colors is not None else None - v_i, f_i, c_i = simplify_fn(vertices[i], faces[i], c_in, target, max_edge_length) - v_list.append(v_i) - f_list.append(f_i) - if c_i is not None: - c_list.append(c_i) - c_out = torch.stack(c_list) if len(c_list) > 0 else None - return torch.stack(v_list), torch.stack(f_list), c_out - - if faces.shape[0] <= target: - return vertices, faces, colors - - device = vertices.device - dtype = vertices.dtype - - verts_np = vertices.detach().cpu().numpy().astype(np.float64) - faces_np = faces.detach().cpu().numpy().astype(np.int64) - colors_np = ( - colors.detach().cpu().numpy().astype(np.float64) - if colors is not None - else None - ) - - out_v, out_f, out_c = _qem_simplify( - verts_np, faces_np, colors_np, target, device, max_edge_length - ) - - final_v = out_v.to(device=device, dtype=dtype) - final_f = out_f.to(device=device, dtype=faces.dtype) - final_c = ( - out_c.to(device=device, dtype=colors.dtype) - if out_c is not None - else None - ) - return final_v, final_f, final_c def fill_holes_fn(vertices, faces, max_perimeter=0.03): is_batched = vertices.ndim == 3 @@ -640,11 +256,411 @@ def make_double_sided(vertices, faces): faces_inv = faces[:, [0, 2, 1]] return vertices, torch.cat([faces, faces_inv], dim=0) +def _cleanup_mesh(verts, faces, min_angle_deg=0.5, max_aspect=100.0): + if faces.numel() == 0: + return verts, faces + + v0 = verts[faces[:, 0]] + v1 = verts[faces[:, 1]] + v2 = verts[faces[:, 2]] + e0 = v1 - v0 + e1 = v2 - v1 + e2 = v0 - v2 + l0 = torch.norm(e0, dim=-1) + l1 = torch.norm(e1, dim=-1) + l2 = torch.norm(e2, dim=-1) + n = torch.cross(e0, e2, dim=-1) + area = torch.norm(n, dim=-1) + + max_edge = torch.max(torch.max(l0, l1), l2) + aspect = max_edge * max_edge / (2.0 * area + 1e-12) + + cos_a = (l1 * l1 + l2 * l2 - l0 * l0) / (2 * l1 * l2 + 1e-12) + cos_b = (l0 * l0 + l2 * l2 - l1 * l1) / (2 * l0 * l2 + 1e-12) + cos_c = (l0 * l0 + l1 * l1 - l2 * l2) / (2 * l0 * l1 + 1e-12) + cos_all = torch.stack([cos_a, cos_b, cos_c], dim=-1) + angles = torch.acos(torch.clamp(cos_all, -1, 1)) * 180 / np.pi + + good = (aspect < max_aspect) & (angles.min(dim=1)[0] > min_angle_deg) & (area > 1e-12) + faces = faces[good] + + if faces.numel() == 0: + return verts, faces + + used = torch.zeros(verts.shape[0], dtype=torch.bool, device=verts.device) + used[faces[:, 0]] = True + used[faces[:, 1]] = True + used[faces[:, 2]] = True + + remap = torch.full((verts.shape[0],), -1, dtype=torch.int64, device=verts.device) + remap[used] = torch.arange(used.sum().item(), device=verts.device) + verts = verts[used] + faces = remap[faces] + return verts, faces + +def _pytorch_edge_errors_fast(verts, Q, edges, stabilizer, max_edge_length_sq, mesh_scale_sq): + n_edges = edges.shape[0] + dtype = verts.dtype + if n_edges == 0: + return (torch.empty((0, 3), dtype=dtype, device=verts.device), + torch.empty((0,), dtype=dtype, device=verts.device), + torch.zeros((0,), dtype=torch.bool, device=verts.device)) + + device = verts.device + mesh_scale = (mesh_scale_sq) ** 0.5 + + va = edges[:, 0] + vb = edges[:, 1] + Q0 = Q[va] + Q1 = Q[vb] + Qe = Q0 + Q1 + + A = Qe[:, :3, :3] + torch.eye(3, device=device, dtype=dtype).unsqueeze(0) * stabilizer + b = -Qe[:, :3, 3].unsqueeze(-1) + + dets = torch.det(A) + good = dets.abs() > 1e-12 + opt = torch.zeros((n_edges, 3), dtype=dtype, device=device) + + if good.any(): + try: + sol = torch.linalg.solve(A[good], b[good]) + opt[good] = sol.squeeze(-1) + except Exception: + good = torch.zeros_like(good) + + if (~good).any(): + bad_idx = torch.nonzero(~good, as_tuple=True)[0] + opt[bad_idx] = (verts[va[bad_idx]] + verts[vb[bad_idx]]) * 0.5 + + pa = verts[va] + pb = verts[vb] + el = torch.norm(pb - pa, dim=-1) + dist_a = torch.norm(opt - pa, dim=-1) + dist_b = torch.norm(opt - pb, dim=-1) + wander_bad = (dist_a > 4.0 * el) | (dist_b > 4.0 * el) + + if wander_bad.any(): + bad_idx = torch.nonzero(wander_bad, as_tuple=True)[0] + opt[bad_idx] = (verts[va[bad_idx]] + verts[vb[bad_idx]]) * 0.5 + + v4 = torch.cat([opt, torch.ones((n_edges, 1), device=device, dtype=dtype)], dim=1) + err = torch.abs(torch.einsum("ei,eij,ej->e", v4, Qe, v4)) + + length_ok = el > mesh_scale * 1e-5 + error_ok = err < max_edge_length_sq + nan_ok = ~torch.isnan(opt).any(dim=-1) & ~torch.isnan(err) + valid = length_ok & error_ok & nan_ok + + return opt, err, valid + + +def _build_quadrics_fast(verts, faces): + v0 = verts[faces[:, 0]] + v1 = verts[faces[:, 1]] + v2 = verts[faces[:, 2]] + e1 = v1 - v0 + e2 = v2 - v0 + n = torch.cross(e1, e2, dim=-1) + area = torch.norm(n, dim=-1) + mask = area > 1e-12 + n_norm = torch.zeros_like(n) + n_norm[mask] = n[mask] / area[mask].unsqueeze(-1) + d = -(n_norm * v0).sum(dim=-1, keepdim=True) + p = torch.cat([n_norm, d], dim=-1) + K = torch.einsum("fi,fj->fij", p, p) + K = K * area[:, None, None] + V = verts.shape[0] + Q = torch.zeros((V, 4, 4), dtype=verts.dtype, device=verts.device) + K_flat = K.reshape(-1, 16) + Q_flat = Q.reshape(V, 16) + for corner in range(3): + idx = faces[:, corner].unsqueeze(1).expand(-1, 16) + Q_flat.scatter_add_(0, idx, K_flat) + return Q_flat.reshape(V, 4, 4) + + +def _gpu_greedy_matching_fast(edges, err, v_alive, max_select): + """Vectorized greedy matching. + + Selects an independent set of edges (no two share a vertex) preferring + lowest error. Replaces _gpu_greedy_sampled's Python per-edge loop with + two scatter_reduce calls. + """ + device = edges.device + n_edges = edges.shape[0] + if n_edges == 0: + return torch.empty(0, dtype=torch.int64, device=device) + + va = edges[:, 0] + vb = edges[:, 1] + num_verts = v_alive.shape[0] + + # Pack (error_bits, edge_idx) into one int64 so amin gives a unique winner. + # err is non-negative finite float32 -> IEEE bits are monotonic. + err32 = err.to(torch.float32).clamp(min=0).contiguous() + err_bits = err32.view(torch.int32).to(torch.int64) & 0xFFFFFFFF + edge_idx = torch.arange(n_edges, device=device, dtype=torch.int64) + key = (err_bits << 32) | edge_idx + + INT64_MAX = torch.iinfo(torch.int64).max + best_key = torch.full((num_verts,), INT64_MAX, dtype=torch.int64, device=device) + best_key.scatter_reduce_(0, va, key, reduce='amin', include_self=True) + best_key.scatter_reduce_(0, vb, key, reduce='amin', include_self=True) + + # An edge wins iff it is the min-key edge incident to BOTH its endpoints + # AND both endpoints are still alive. + is_winner = (key == best_key[va]) & (key == best_key[vb]) & v_alive[va] & v_alive[vb] + + sel = torch.nonzero(is_winner, as_tuple=True)[0] + + if sel.numel() > max_select: + sel_err = err[sel] + top = torch.topk(sel_err, max_select, largest=False).indices + sel = sel[top] + + return sel + + +def _qem_simplify_fast(vertices, faces_in, colors_in, target_faces, device, max_edge_length=None): + # Use float32 instead of float64. RTX-class consumer GPUs run FP32 ~32-64x + # faster than FP64, and QEM only needs the stabilizer for conditioning. + # Always copy=True so we can safely mutate verts/colors in-place. + verts = vertices.detach().to(device=device, dtype=torch.float32, copy=True) + faces = faces_in.detach().to(device=device, dtype=torch.int64) + colors = ( + colors_in.detach().to(device=device, dtype=torch.float32, copy=True) + if colors_in is not None + else None + ) + + num_verts = verts.shape[0] + num_faces = faces.shape[0] + + logging.debug(f"[QEM-fast] Input: {num_verts} verts, {num_faces} faces, target={target_faces}") + + v_alive = torch.ones(num_verts, dtype=torch.bool, device=device) + f_alive = torch.ones(num_faces, dtype=torch.bool, device=device) + + Q = _build_quadrics_fast(verts, faces) + + bbox = verts.max(dim=0)[0] - verts.min(dim=0)[0] + mesh_scale = torch.norm(bbox).item() + + if max_edge_length is None or max_edge_length <= 0: + max_edge_length = mesh_scale * 2.0 + + if max_edge_length < 1e-6: + max_edge_length = 1.0 + + stabilizer = mesh_scale * mesh_scale * 0.001 + max_edge_length_sq = max_edge_length * max_edge_length + mesh_scale_sq = mesh_scale * mesh_scale + + iteration = 0 + total_collapses = 0 + last_faces = num_faces + + while True: + n_faces = int(f_alive.sum().item()) + + if n_faces <= target_faces: + break + + alive_v = torch.nonzero(v_alive, as_tuple=True)[0] + alive_f = torch.nonzero(f_alive, as_tuple=True)[0] + + if alive_v.numel() <= 4 or alive_f.numel() == 0: + break + + # Compact active mesh + vmap = torch.full((num_verts,), -1, dtype=torch.int64, device=device) + vmap[alive_v] = torch.arange(alive_v.numel(), device=device) + + active_faces = faces[alive_f] + remapped = vmap[active_faces] + + # Extract edges + e0 = remapped[:, [0, 1]] + e1 = remapped[:, [1, 2]] + e2 = remapped[:, [2, 0]] + edges = torch.cat([e0, e1, e2], dim=0) + edges = torch.sort(edges, dim=1)[0] + edges = edges[(edges >= 0).all(dim=1)] + edges = edges[edges[:, 0] != edges[:, 1]] + + if edges.shape[0] == 0: + break + + # Deduplicate edges (each interior edge appears in two adjacent faces). + # Pack (low, high) into a single int64 key and call torch.unique once. + num_compact = alive_v.numel() + packed = edges[:, 0].long() * num_compact + edges[:, 1].long() + packed = torch.unique(packed) + edges = torch.stack([packed // num_compact, packed % num_compact], dim=1) + + edges_orig = alive_v[edges] + + # Filter by edge length + pa = verts[edges_orig[:, 0]] + pb = verts[edges_orig[:, 1]] + el = torch.norm(pb - pa, dim=-1) + short_enough = el < max_edge_length + + if not short_enough.any(): + max_edge_length = el.max().item() * 2.0 + max_edge_length_sq = max_edge_length * max_edge_length + short_enough = el < max_edge_length + if not short_enough.any(): + break + + edges_orig = edges_orig[short_enough] + if edges_orig.shape[0] == 0: + break + + # Sample edges for processing + n_edges_total = edges_orig.shape[0] + max_edges_to_process = 10_000_000 # 10M edges per iteration + + if n_edges_total > max_edges_to_process: + # Random sample without building a full permutation. + perm = torch.randint(0, n_edges_total, (max_edges_to_process,), device=device) + edges_orig = edges_orig[perm] + n_edges = max_edges_to_process + else: + n_edges = n_edges_total + + optimal, err, valid = _pytorch_edge_errors_fast( + verts, Q, edges_orig, stabilizer, max_edge_length_sq, mesh_scale_sq + ) + + if not valid.any(): + valid = torch.ones(n_edges, dtype=torch.bool, device=device) + + valid_idx = torch.nonzero(valid, as_tuple=True)[0] + edges_orig = edges_orig[valid_idx] + optimal = optimal[valid_idx] + err = err[valid_idx] + + # Vectorized greedy matching can safely collapse many independent + # edges per iteration, so allow a much larger batch. + faces_to_remove = n_faces - target_faces + max_collapses = min(1_000_000, max(10_000, faces_to_remove // 4)) + + sel = _gpu_greedy_matching_fast(edges_orig, err, v_alive, max_collapses) + + if sel.numel() == 0: + break + + v_a = edges_orig[sel, 0] + v_b = edges_orig[sel, 1] + + # NOTE: original PostProcessMesh built a CSR (face_indices/vert_ptrs) and + # iterated v_a/v_b in Python here to populate pair_edge_idx/pair_face_idx + # arrays that were then discarded (keep_mask was unconditionally all-True). + # That dead block was the dominant cost on Trellis-sized meshes and has + # been removed in this fast variant. + + # Apply collapses + verts[v_a] = optimal[sel] + v_alive[v_b] = False + Q[v_a] += Q[v_b] + + if colors is not None: + colors[v_a] = (colors[v_a] + colors[v_b]) * 0.5 + + merge_map = torch.arange(num_verts, device=device) + merge_map[v_b] = v_a + faces = merge_map[faces] + + bad = ( + (faces[:, 0] == faces[:, 1]) + | (faces[:, 1] == faces[:, 2]) + | (faces[:, 2] == faces[:, 0]) + ) + f_alive &= ~bad + + total_collapses += v_a.numel() + iteration += 1 + + # Log only every 50 iterations to reduce sync overhead + if iteration % 50 == 0 or n_faces < last_faces * 0.9: + logging.debug(f"[QEM-fast] Iter {iteration}: {total_collapses} collapses, {int(f_alive.sum().item())} faces, applied {v_a.numel()}") + last_faces = n_faces + + # Periodic compaction + if iteration % 5 == 0 and int(f_alive.sum().item()) < num_faces * 0.5: + faces = faces[f_alive] + f_alive = torch.ones(faces.shape[0], dtype=torch.bool, device=device) + num_faces = faces.shape[0] + + if iteration > 5000: + break + + # Finalize + final_v = verts[v_alive] + final_c = colors[v_alive] if colors is not None else None + + remap = torch.full((num_verts,), -1, dtype=torch.int64, device=device) + remap[v_alive] = torch.arange(int(v_alive.sum().item()), device=device) + + final_f_raw = faces[f_alive] + alive_mask = v_alive[final_f_raw].all(dim=1) + final_f_raw = final_f_raw[alive_mask] + final_f = remap[final_f_raw] + valid_faces = (final_f >= 0).all(dim=1) + final_f = final_f[valid_faces] + + if final_f.numel() > 0: + final_f = torch.unique(torch.sort(final_f, dim=1)[0], dim=0) + + final_v, final_f = _cleanup_mesh(final_v, final_f, min_angle_deg=0.5, max_aspect=100.0) + + return final_v, final_f, final_c + + +def simplify_fn_fast(vertices, faces, colors=None, target=100000, max_edge_length=None): + if vertices.ndim == 3: + v_list, f_list, c_list = [], [], [] + for i in range(vertices.shape[0]): + c_in = colors[i] if colors is not None else None + v_i, f_i, c_i = simplify_fn_fast(vertices[i], faces[i], c_in, target, max_edge_length) + v_list.append(v_i) + f_list.append(f_i) + if c_i is not None: + c_list.append(c_i) + c_out = torch.stack(c_list) if len(c_list) > 0 else None + return torch.stack(v_list), torch.stack(f_list), c_out + + if faces.shape[0] <= target: + return vertices, faces, colors + + device = vertices.device + dtype = vertices.dtype + face_dtype = faces.dtype + color_dtype = colors.dtype if colors is not None else None + + # Pass tensors directly; _qem_simplify_fast handles dtype/device + copy. + out_v, out_f, out_c = _qem_simplify_fast( + vertices, faces, colors, target, device, max_edge_length + ) + + final_v = out_v.to(device=device, dtype=dtype) + final_f = out_f.to(device=device, dtype=face_dtype) + final_c = ( + out_c.to(device=device, dtype=color_dtype) + if out_c is not None + else None + ) + return final_v, final_f, final_c + + class PostProcessMesh(IO.ComfyNode): @classmethod def define_schema(cls): return IO.Schema( node_id="PostProcessMesh", + display_name="Post Process Mesh", category="latent/3d", description=( "Applies a sequence of mesh post-processing operations including optional hole filling" @@ -675,7 +691,7 @@ class PostProcessMesh(IO.ComfyNode): bar.update(1) if target_face_count > 0 and f.shape[0] > target_face_count: - v, f, c = simplify_fn(v, f, colors=c, target=target_face_count) + v, f, c = simplify_fn_fast(v, f, colors=c, target=target_face_count) bar.update(1) v, f = make_double_sided(v, f) diff --git a/comfy_extras/nodes_trellis2.py b/comfy_extras/nodes_trellis2.py index 39bfe1e3a..3702ed511 100644 --- a/comfy_extras/nodes_trellis2.py +++ b/comfy_extras/nodes_trellis2.py @@ -1,6 +1,7 @@ from typing_extensions import override from comfy_api.latest import ComfyExtension, IO, Types, io from comfy.ldm.trellis2.vae import SparseTensor +from comfy_extras.nodes_mesh_postprocess import pack_variable_mesh_batch import comfy.model_management from PIL import Image import numpy as np @@ -24,57 +25,6 @@ def prepare_trellis_vae_for_decode(vae, sample_shape): batch_number = max(1, int(free_memory / memory_required)) return batch_number - -def pack_variable_mesh_batch(vertices, faces, colors=None): - batch_size = len(vertices) - max_vertices = max(v.shape[0] for v in vertices) - max_faces = max(f.shape[0] for f in faces) - - packed_vertices = vertices[0].new_zeros((batch_size, max_vertices, vertices[0].shape[1])) - packed_faces = faces[0].new_zeros((batch_size, max_faces, faces[0].shape[1])) - vertex_counts = torch.tensor([v.shape[0] for v in vertices], device=vertices[0].device, dtype=torch.int64) - face_counts = torch.tensor([f.shape[0] for f in faces], device=faces[0].device, dtype=torch.int64) - - for i, (v, f) in enumerate(zip(vertices, faces)): - packed_vertices[i, :v.shape[0]] = v - packed_faces[i, :f.shape[0]] = f - - mesh = Types.MESH(packed_vertices, packed_faces) - mesh.vertex_counts = vertex_counts - mesh.face_counts = face_counts - - if colors is not None: - max_colors = max(c.shape[0] for c in colors) - packed_colors = colors[0].new_zeros((batch_size, max_colors, colors[0].shape[1])) - color_counts = torch.tensor([c.shape[0] for c in colors], device=colors[0].device, dtype=torch.int64) - for i, c in enumerate(colors): - packed_colors[i, :c.shape[0]] = c - mesh.vertex_colors = packed_colors - mesh.color_counts = color_counts - - return mesh - - -def get_mesh_batch_item(mesh, index): - if hasattr(mesh, "vertex_counts"): - vertex_count = int(mesh.vertex_counts[index].item()) - face_count = int(mesh.face_counts[index].item()) - vertices = mesh.vertices[index, :vertex_count] - faces = mesh.faces[index, :face_count] - colors = None - if hasattr(mesh, "colors") and mesh.colors is not None: - if hasattr(mesh, "color_counts"): - color_count = int(mesh.color_counts[index].item()) - colors = mesh.colors[index, :color_count] - else: - colors = mesh.colors[index, :vertex_count] - return vertices, faces, colors - - colors = None - if hasattr(mesh, "colors") and mesh.colors is not None: - colors = mesh.colors[index] - return mesh.vertices[index], mesh.faces[index], colors - shape_slat_normalization = { "mean": torch.tensor([ 0.781296, 0.018091, -0.495192, -0.558457, 1.060530, 0.093252, 1.518149, -0.933218, @@ -263,14 +213,13 @@ class VaeDecodeTextureTrellis(IO.ComfyNode): )), ], outputs=[ - IO.Voxel.Output("color_voxel"), + IO.Voxel.Output("voxel_colors"), ] ) @classmethod def execute(cls, samples, vae, shape_subdivides): sample_tensor = samples["samples"] - resolution = int(vae.first_stage_model.resolution.item()) device = comfy.model_management.get_torch_device() coords = samples["coords"] prepare_trellis_vae_for_decode(vae, sample_tensor.shape) @@ -287,9 +236,9 @@ class VaeDecodeTextureTrellis(IO.ComfyNode): voxel = trellis_vae.decode_tex_slat(samples, shape_subdivides) color_feats = voxel.feats[:, :3] - voxel_coords = voxel.coords[:, 1:] + voxel_coords = voxel.coords#[:, 1:] - voxel = Types.VOXEL(voxel_coords, color_feats, resolution) + voxel = Types.VOXEL(voxel_coords, color_feats, 1024) return IO.NodeOutput(voxel) class VaeDecodeStructureTrellis2(IO.ComfyNode): @@ -607,6 +556,9 @@ class EmptyTrellis2ShapeLatent(IO.ComfyNode): # to accept the upscaled coords is_512_pass = False + if isinstance(voxel, dict): + voxel = voxel["coords"] + if hasattr(voxel, "data") and voxel.data.ndim == 4: decoded = voxel.data.unsqueeze(1) coords = torch.argwhere(decoded.bool())[:, [0, 2, 3, 4]].int() @@ -627,8 +579,8 @@ class EmptyTrellis2ShapeLatent(IO.ComfyNode): generation_mode = "shape_generation_512" else: generation_mode = "shape_generation" - return IO.NodeOutput({"samples": latent, "coords": coords, "coords_counts": counts, "type": "trellis2", - "model_options": {"generation_mode": generation_mode, "coords": coords, "coords_counts": counts}}) + return IO.NodeOutput({"samples": latent, "coords": coords, "coord_counts": counts, "type": "trellis2", + "model_options": {"generation_mode": generation_mode, "coords": coords, "coord_counts": counts}}) class EmptyTrellis2LatentTexture(IO.ComfyNode): @classmethod @@ -655,6 +607,8 @@ class EmptyTrellis2LatentTexture(IO.ComfyNode): @classmethod def execute(cls, voxel, shape_latent): channels = 32 + if isinstance(voxel, dict): + voxel = voxel["coords"] if hasattr(voxel, "data") and voxel.data.ndim == 4: decoded = voxel.data.unsqueeze(1) coords = torch.argwhere(decoded.bool())[:, [0, 2, 3, 4]].int() @@ -669,9 +623,9 @@ class EmptyTrellis2LatentTexture(IO.ComfyNode): shape_latent = shape_latent.squeeze(-1).transpose(1, 2).reshape(-1, channels) latent = torch.zeros(batch_size, channels, max_tokens, 1) - return IO.NodeOutput({"samples": latent, "type": "trellis2", "coords": coords, "coords_counts": counts, + return IO.NodeOutput({"samples": latent, "type": "trellis2", "coords": coords, "coord_counts": counts, "model_options": {"generation_mode": "texture_generation", - "coords": coords, "coords_counts": counts, "shape_slat": shape_latent}}) + "coords": coords, "coord_counts": counts, "shape_slat": shape_latent}}) class EmptyTrellis2LatentStructure(IO.ComfyNode):