From d2b97b510c3ebec8c918dbf8b0495767e756ae2b Mon Sep 17 00:00:00 2001 From: Yousef Rafat <81116377+yousef-rafat@users.noreply.github.com> Date: Thu, 14 May 2026 15:07:50 +0300 Subject: [PATCH] remove triton version --- comfy_extras/nodes_trellis2.py | 452 +-------------------------------- 1 file changed, 8 insertions(+), 444 deletions(-) diff --git a/comfy_extras/nodes_trellis2.py b/comfy_extras/nodes_trellis2.py index aa2f7b3ee..c3b8a8183 100644 --- a/comfy_extras/nodes_trellis2.py +++ b/comfy_extras/nodes_trellis2.py @@ -4,9 +4,7 @@ from comfy.ldm.trellis2.vae import SparseTensor import comfy.model_management from PIL import Image import numpy as np -import triton.language as tl import logging -import triton import torch import scipy import copy @@ -761,227 +759,6 @@ class EmptyTrellis2LatentStructure(IO.ComfyNode): } return IO.NodeOutput(output) -@triton.jit -def qem_edge_errors_kernel( - verts_ptr, Q_ptr, edges_ptr, optimal_ptr, error_ptr, wander_ptr, - n_edges, stabilizer, max_edge_length_sq, mesh_scale_sq, - BLOCK_SIZE: tl.constexpr -): - pid = tl.program_id(0) - offs = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE) - mask = offs < n_edges - - va = tl.load(edges_ptr + offs * 2, mask=mask, other=0).to(tl.int64) - vb = tl.load(edges_ptr + offs * 2 + 1, mask=mask, other=0).to(tl.int64) - - vax = tl.load(verts_ptr + va * 3 + 0, mask=mask, other=0.0) - vay = tl.load(verts_ptr + va * 3 + 1, mask=mask, other=0.0) - vaz = tl.load(verts_ptr + va * 3 + 2, mask=mask, other=0.0) - vbx = tl.load(verts_ptr + vb * 3 + 0, mask=mask, other=0.0) - vby = tl.load(verts_ptr + vb * 3 + 1, mask=mask, other=0.0) - vbz = tl.load(verts_ptr + vb * 3 + 2, mask=mask, other=0.0) - - ex = vbx - vax - ey = vby - vay - ez = vbz - vaz - el_sq = ex * ex + ey * ey + ez * ez - el = tl.sqrt(el_sq) - - Qa_base = Q_ptr + va * 16 - Qb_base = Q_ptr + vb * 16 - - qe0 = tl.load(Qa_base + 0, mask=mask, other=0.0) + tl.load(Qb_base + 0, mask=mask, other=0.0) - qe1 = tl.load(Qa_base + 1, mask=mask, other=0.0) + tl.load(Qb_base + 1, mask=mask, other=0.0) - qe2 = tl.load(Qa_base + 2, mask=mask, other=0.0) + tl.load(Qb_base + 2, mask=mask, other=0.0) - qe3 = tl.load(Qa_base + 3, mask=mask, other=0.0) + tl.load(Qb_base + 3, mask=mask, other=0.0) - qe4 = tl.load(Qa_base + 4, mask=mask, other=0.0) + tl.load(Qb_base + 4, mask=mask, other=0.0) - qe5 = tl.load(Qa_base + 5, mask=mask, other=0.0) + tl.load(Qb_base + 5, mask=mask, other=0.0) - qe6 = tl.load(Qa_base + 6, mask=mask, other=0.0) + tl.load(Qb_base + 6, mask=mask, other=0.0) - qe7 = tl.load(Qa_base + 7, mask=mask, other=0.0) + tl.load(Qb_base + 7, mask=mask, other=0.0) - qe8 = tl.load(Qa_base + 8, mask=mask, other=0.0) + tl.load(Qb_base + 8, mask=mask, other=0.0) - qe9 = tl.load(Qa_base + 9, mask=mask, other=0.0) + tl.load(Qb_base + 9, mask=mask, other=0.0) - qe10 = tl.load(Qa_base + 10, mask=mask, other=0.0) + tl.load(Qb_base + 10, mask=mask, other=0.0) - qe11 = tl.load(Qa_base + 11, mask=mask, other=0.0) + tl.load(Qb_base + 11, mask=mask, other=0.0) - qe12 = tl.load(Qa_base + 12, mask=mask, other=0.0) + tl.load(Qb_base + 12, mask=mask, other=0.0) - qe13 = tl.load(Qa_base + 13, mask=mask, other=0.0) + tl.load(Qb_base + 13, mask=mask, other=0.0) - qe14 = tl.load(Qa_base + 14, mask=mask, other=0.0) + tl.load(Qb_base + 14, mask=mask, other=0.0) - qe15 = tl.load(Qa_base + 15, mask=mask, other=0.0) + tl.load(Qb_base + 15, mask=mask, other=0.0) - - a11 = qe0 + stabilizer - a12 = qe1 - a13 = qe2 - a21 = qe4 - a22 = qe5 + stabilizer - a23 = qe6 - a31 = qe8 - a32 = qe9 - a33 = qe10 + stabilizer - - b1 = -qe3 - b2 = -qe7 - b3 = -qe11 - - det = (a11 * (a22 * a33 - a23 * a32) - - a12 * (a21 * a33 - a23 * a31) - + a13 * (a21 * a32 - a22 * a31)) - - det_good = tl.abs(det) > 1e-12 - - det_x = (b1 * (a22 * a33 - a23 * a32) - - a12 * (b2 * a33 - a23 * b3) - + a13 * (b2 * a32 - a22 * b3)) - det_y = (a11 * (b2 * a33 - a23 * b3) - - b1 * (a21 * a33 - a23 * a31) - + a13 * (a21 * b3 - b2 * a31)) - det_z = (a11 * (a22 * b3 - b2 * a32) - - a12 * (a21 * b3 - b2 * a31) - + b1 * (a21 * a32 - a22 * a31)) - - ox = tl.where(det_good, det_x / det, (vax + vbx) * 0.5) - oy = tl.where(det_good, det_y / det, (vay + vby) * 0.5) - oz = tl.where(det_good, det_z / det, (vaz + vbz) * 0.5) - - dist_a_sq = (ox - vax) * (ox - vax) + (oy - vay) * (oy - vay) + (oz - vaz) * (oz - vaz) - dist_b_sq = (ox - vbx) * (ox - vbx) + (oy - vby) * (oy - vby) + (oz - vbz) * (oz - vbz) - wander_thresh = 16.0 * el_sq - wander_bad = (dist_a_sq > wander_thresh) | (dist_b_sq > wander_thresh) - - ox = tl.where(wander_bad & (el > 0.0), (vax + vbx) * 0.5, ox) - oy = tl.where(wander_bad & (el > 0.0), (vay + vby) * 0.5, oy) - oz = tl.where(wander_bad & (el > 0.0), (vaz + vbz) * 0.5, oz) - - v4_0 = ox - v4_1 = oy - v4_2 = oz - v4_3 = 1.0 - - qv0 = qe0 * v4_0 + qe1 * v4_1 + qe2 * v4_2 + qe3 * v4_3 - qv1 = qe4 * v4_0 + qe5 * v4_1 + qe6 * v4_2 + qe7 * v4_3 - qv2 = qe8 * v4_0 + qe9 * v4_1 + qe10 * v4_2 + qe11 * v4_3 - qv3 = qe12 * v4_0 + qe13 * v4_1 + qe14 * v4_2 + qe15 * v4_3 - - err = tl.abs(v4_0 * qv0 + v4_1 * qv1 + v4_2 * qv2 + v4_3 * qv3) - - tl.store(optimal_ptr + offs * 3 + 0, ox, mask=mask) - tl.store(optimal_ptr + offs * 3 + 1, oy, mask=mask) - tl.store(optimal_ptr + offs * 3 + 2, oz, mask=mask) - tl.store(error_ptr + offs, err, mask=mask) - tl.store(wander_ptr + offs, wander_bad.to(tl.int32), mask=mask) - - -@triton.jit -def validate_faces_kernel( - verts_ptr, faces_ptr, va_ptr, vb_ptr, opt_ptr, pair_edge_ptr, pair_face_ptr, - n_pairs, area_thresh, keep_mask_ptr, - BLOCK_SIZE: tl.constexpr -): - pid = tl.program_id(0) - offs = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE) - mask = offs < n_pairs - - ei = tl.load(pair_edge_ptr + offs, mask=mask, other=0).to(tl.int64) - fi = tl.load(pair_face_ptr + offs, mask=mask, other=0).to(tl.int64) - - f0 = tl.load(faces_ptr + fi * 3 + 0, mask=mask, other=0).to(tl.int64) - f1 = tl.load(faces_ptr + fi * 3 + 1, mask=mask, other=0).to(tl.int64) - f2 = tl.load(faces_ptr + fi * 3 + 2, mask=mask, other=0).to(tl.int64) - - vai = tl.load(va_ptr + ei, mask=mask, other=0).to(tl.int64) - vbi = tl.load(vb_ptr + ei, mask=mask, other=0).to(tl.int64) - - optx = tl.load(opt_ptr + ei * 3 + 0, mask=mask, other=0.0) - opty = tl.load(opt_ptr + ei * 3 + 1, mask=mask, other=0.0) - optz = tl.load(opt_ptr + ei * 3 + 2, mask=mask, other=0.0) - - v0x = tl.load(verts_ptr + f0 * 3 + 0, mask=mask, other=0.0) - v0y = tl.load(verts_ptr + f0 * 3 + 1, mask=mask, other=0.0) - v0z = tl.load(verts_ptr + f0 * 3 + 2, mask=mask, other=0.0) - v1x = tl.load(verts_ptr + f1 * 3 + 0, mask=mask, other=0.0) - v1y = tl.load(verts_ptr + f1 * 3 + 1, mask=mask, other=0.0) - v1z = tl.load(verts_ptr + f1 * 3 + 2, mask=mask, other=0.0) - v2x = tl.load(verts_ptr + f2 * 3 + 0, mask=mask, other=0.0) - v2y = tl.load(verts_ptr + f2 * 3 + 1, mask=mask, other=0.0) - v2z = tl.load(verts_ptr + f2 * 3 + 2, mask=mask, other=0.0) - - is_v0_a = (f0 == vai) | (f0 == vbi) - is_v1_a = (f1 == vai) | (f1 == vbi) - is_v2_a = (f2 == vai) | (f2 == vbi) - - n0x = tl.where(is_v0_a, optx, v0x) - n0y = tl.where(is_v0_a, opty, v0y) - n0z = tl.where(is_v0_a, optz, v0z) - n1x = tl.where(is_v1_a, optx, v1x) - n1y = tl.where(is_v1_a, opty, v1y) - n1z = tl.where(is_v1_a, optz, v1z) - n2x = tl.where(is_v2_a, optx, v2x) - n2y = tl.where(is_v2_a, opty, v2y) - n2z = tl.where(is_v2_a, optz, v2z) - - e1x_old = v1x - v0x - e1y_old = v1y - v0y - e1z_old = v1z - v0z - e2x_old = v2x - v0x - e2y_old = v2y - v0y - e2z_old = v2z - v0z - - nx_old = e1y_old * e2z_old - e1z_old * e2y_old - ny_old = e1z_old * e2x_old - e1x_old * e2z_old - nz_old = e1x_old * e2y_old - e1y_old * e2x_old - area_old_sq = nx_old * nx_old + ny_old * ny_old + nz_old * nz_old - area_old = tl.sqrt(area_old_sq) - - e1x_new = n1x - n0x - e1y_new = n1y - n0y - e1z_new = n1z - n0z - e2x_new = n2x - n0x - e2y_new = n2y - n0y - e2z_new = n2z - n0z - - nx_new = e1y_new * e2z_new - e1z_new * e2y_new - ny_new = e1z_new * e2x_new - e1x_new * e2z_new - nz_new = e1x_new * e2y_new - e1y_new * e2x_new - area_new_sq = nx_new * nx_new + ny_new * ny_new + nz_new * nz_new - area_new = tl.sqrt(area_new_sq) - - area_bad = area_new_sq < area_thresh * area_thresh - dot = nx_old * nx_new + ny_old * ny_new + nz_old * nz_new - flip_bad = dot < -0.2 * area_old * area_new - - e0x_new = n1x - n0x - e0y_new = n1y - n0y - e0z_new = n1z - n0z - e1x_new2 = n2x - n1x - e1y_new2 = n2y - n1y - e1z_new2 = n2z - n1z - e2x_new2 = n0x - n2x - e2y_new2 = n0y - n2y - e2z_new2 = n0z - n2z - - l0_new_sq = e0x_new * e0x_new + e0y_new * e0y_new + e0z_new * e0z_new - l1_new_sq = e1x_new2 * e1x_new2 + e1y_new2 * e1y_new2 + e1z_new2 * e1z_new2 - l2_new_sq = e2x_new2 * e2x_new2 + e2y_new2 * e2y_new2 + e2z_new2 * e2z_new2 - max_new_sq = tl.maximum(tl.maximum(l0_new_sq, l1_new_sq), l2_new_sq) - - e0x_old = v1x - v0x - e0y_old = v1y - v0y - e0z_old = v1z - v0z - e1x_old2 = v2x - v1x - e1y_old2 = v2y - v1y - e1z_old2 = v2z - v1z - e2x_old2 = v0x - v2x - e2y_old2 = v0y - v2y - e2z_old2 = v0z - v2z - - l0_old_sq = e0x_old * e0x_old + e0y_old * e0y_old + e0z_old * e0z_old - l1_old_sq = e1x_old2 * e1x_old2 + e1y_old2 * e1y_old2 + e1z_old2 * e1z_old2 - l2_old_sq = e2x_old2 * e2x_old2 + e2y_old2 * e2y_old2 + e2z_old2 * e2z_old2 - max_old_sq = tl.maximum(tl.maximum(l0_old_sq, l1_old_sq), l2_old_sq) - - stretch_bad = max_new_sq > 6.25 * max_old_sq - - any_bad = area_bad | flip_bad | stretch_bad - tl.store(keep_mask_ptr + offs, any_bad.to(tl.int32), mask=mask) - def _pytorch_edge_errors(verts, Q, edges, stabilizer, max_edge_length_sq, mesh_scale_sq): n_edges = edges.shape[0] if n_edges == 0: @@ -1037,161 +814,6 @@ def _pytorch_edge_errors(verts, Q, edges, stabilizer, max_edge_length_sq, mesh_s return opt, err, valid - -def _triton_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 - - optimal = torch.empty((n_edges, 3), dtype=torch.float64, device=device) - error = torch.empty((n_edges,), dtype=torch.float64, device=device) - wander = torch.empty((n_edges,), dtype=torch.int32, device=device) - - BLOCK_SIZE = 256 - grid = (triton.cdiv(n_edges, BLOCK_SIZE),) - - try: - qem_edge_errors_kernel[grid]( - verts, Q, edges, optimal, error, wander, - n_edges, stabilizer, max_edge_length_sq, mesh_scale_sq, - BLOCK_SIZE=BLOCK_SIZE - ) - except Exception: - return _pytorch_edge_errors(verts, Q, edges, stabilizer, max_edge_length_sq, mesh_scale_sq) - - has_nan = torch.isnan(optimal).any() or torch.isnan(error).any() - has_inf = torch.isinf(error).any() - - if has_nan or has_inf: - return _pytorch_edge_errors(verts, Q, edges, stabilizer, max_edge_length_sq, mesh_scale_sq) - - pa = verts[edges[:, 0]] - pb = verts[edges[:, 1]] - el = torch.norm(pb - pa, dim=-1) - mesh_scale = (mesh_scale_sq) ** 0.5 - - length_ok = el > mesh_scale * 1e-5 - error_ok = error < max_edge_length_sq - nan_ok = ~torch.isnan(optimal).any(dim=-1) & ~torch.isnan(error) - valid = length_ok & error_ok & nan_ok - - return optimal, error, valid - -def _pytorch_validate_faces(verts, faces, v_a, v_b, opt_pos, pair_edge_idx, pair_face_idx, area_thresh): - n_pairs = len(pair_edge_idx) - if n_pairs == 0: - return torch.ones(v_a.numel(), dtype=torch.bool, device=verts.device) - - device = verts.device - - old_faces = faces[pair_face_idx] - v0_old = verts[old_faces[:, 0]] - v1_old = verts[old_faces[:, 1]] - v2_old = verts[old_faces[:, 2]] - - v0_new = v0_old.clone() - v1_new = v1_old.clone() - v2_new = v2_old.clone() - - va_t = v_a[pair_edge_idx] - vb_t = v_b[pair_edge_idx] - opt_t = opt_pos[pair_edge_idx] - - mask0 = (old_faces[:, 0] == va_t) | (old_faces[:, 0] == vb_t) - mask1 = (old_faces[:, 1] == va_t) | (old_faces[:, 1] == vb_t) - mask2 = (old_faces[:, 2] == va_t) | (old_faces[:, 2] == vb_t) - - v0_new[mask0] = opt_t[mask0] - v1_new[mask1] = opt_t[mask1] - v2_new[mask2] = opt_t[mask2] - - e1_old = v1_old - v0_old - e2_old = v2_old - v0_old - n_old = torch.cross(e1_old, e2_old, dim=-1) - - e1_new = v1_new - v0_new - e2_new = v2_new - v0_new - n_new = torch.cross(e1_new, e2_new, dim=-1) - - area_new = torch.norm(n_new, dim=-1) - area_bad = area_new < area_thresh - - n_old_norm = n_old / (torch.norm(n_old, dim=-1, keepdim=True) + 1e-12) - n_new_norm = n_new / (torch.norm(n_new, dim=-1, keepdim=True) + 1e-12) - dots = (n_old_norm * n_new_norm).sum(dim=-1) - flip_bad = dots < -0.2 - - old_edges = torch.stack([ - torch.norm(v1_old - v0_old, dim=-1), - torch.norm(v2_old - v1_old, dim=-1), - torch.norm(v0_old - v2_old, dim=-1), - ], dim=1).max(dim=1)[0] - - new_edges = torch.stack([ - torch.norm(v1_new - v0_new, dim=-1), - torch.norm(v2_new - v1_new, dim=-1), - torch.norm(v0_new - v2_new, dim=-1), - ], dim=1).max(dim=1)[0] - - stretch_bad = new_edges > 2.5 * old_edges - - def face_angles(v0, v1, v2): - 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) - 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) - return torch.acos(torch.clamp(cos_all, -1, 1)) * 180 / np.pi - - new_angles = face_angles(v0_new, v1_new, v2_new) - angle_bad = (new_angles < 1.0).any(dim=-1) | (new_angles > 178.0).any(dim=-1) - - any_bad = area_bad | flip_bad | stretch_bad | angle_bad - - keep_mask = torch.ones(v_a.numel(), dtype=torch.bool, device=device) - if any_bad.any(): - bad_edges = pair_edge_idx[any_bad] - keep_mask.scatter_(0, bad_edges, False) - return keep_mask - - -def _triton_validate_faces(verts, faces, v_a, v_b, opt_pos, pair_edge_idx, pair_face_idx, area_thresh): - n_pairs = len(pair_edge_idx) - if n_pairs == 0: - return torch.ones(v_a.numel(), dtype=torch.bool, device=verts.device) - - device = verts.device - - pair_bad = torch.empty(n_pairs, dtype=torch.int32, device=device) - - BLOCK_SIZE = 256 - grid = (triton.cdiv(n_pairs, BLOCK_SIZE),) - - try: - validate_faces_kernel[grid]( - verts, faces, v_a, v_b, opt_pos, pair_edge_idx, pair_face_idx, - n_pairs, area_thresh, pair_bad, - BLOCK_SIZE=BLOCK_SIZE - ) - except Exception: - return _pytorch_validate_faces(verts, faces, v_a, v_b, opt_pos, pair_edge_idx, pair_face_idx, area_thresh) - - keep_mask = torch.ones(v_a.numel(), dtype=torch.bool, device=device) - bad_mask = pair_bad.bool() - if bad_mask.any(): - bad_edges = pair_edge_idx[bad_mask] - keep_mask.scatter_(0, bad_edges, False) - return keep_mask - def _build_quadrics(verts, faces): v0 = verts[faces[:, 0]] v1 = verts[faces[:, 1]] @@ -1350,7 +972,7 @@ def _gpu_greedy_sampled(edges, errors, v_alive, max_select): 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, use_triton=False, fast_mode=False): +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 = ( @@ -1362,7 +984,7 @@ def _qem_simplify(verts_np, faces_np, colors_np, target_faces, device, max_edge_ num_verts = verts.shape[0] num_faces = faces.shape[0] - logging.debug(f"[QEM] Input: {num_verts} verts, {num_faces} faces, target={target_faces}, fast={fast_mode}") + 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) @@ -1379,7 +1001,6 @@ def _qem_simplify(verts_np, faces_np, colors_np, target_faces, device, max_edge_ max_edge_length = 1.0 stabilizer = mesh_scale * mesh_scale * 0.001 - area_thresh = mesh_scale * mesh_scale * 1e-10 max_edge_length_sq = max_edge_length * max_edge_length mesh_scale_sq = mesh_scale * mesh_scale @@ -1448,15 +1069,9 @@ def _qem_simplify(verts_np, faces_np, colors_np, target_faces, device, max_edge_ else: n_edges = n_edges_total - # Compute edge errors - if use_triton and torch.cuda.is_available(): - optimal, err, valid = _triton_edge_errors( - verts, Q, edges_orig, stabilizer, max_edge_length_sq, mesh_scale_sq - ) - else: - optimal, err, valid = _pytorch_edge_errors( - verts, Q, edges_orig, stabilizer, max_edge_length_sq, mesh_scale_sq - ) + 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) @@ -1477,7 +1092,6 @@ def _qem_simplify(verts_np, faces_np, colors_np, target_faces, device, max_edge_ v_a = edges_orig[sel, 0] v_b = edges_orig[sel, 1] - opt_pos = optimal[sel] # Build adjacency face_indices, vert_ptrs = _build_vertex_face_csr(active_faces, num_verts) @@ -1508,56 +1122,6 @@ def _qem_simplify(verts_np, faces_np, colors_np, target_faces, device, max_edge_ keep_mask = torch.ones(v_a.numel(), dtype=torch.bool, device=device) - # Face validation (skip in fast_mode) - if not fast_mode and len(pair_edge_idx) > 0: - pair_edge_idx_t = torch.tensor(pair_edge_idx, dtype=torch.int64, device=device) - pair_face_idx_t = torch.tensor(pair_face_idx, dtype=torch.int64, device=device) - - if use_triton and torch.cuda.is_available(): - keep_mask = _triton_validate_faces( - verts, active_faces, v_a, v_b, opt_pos, - pair_edge_idx_t, pair_face_idx_t, area_thresh - ) - else: - keep_mask = _pytorch_validate_faces( - verts, active_faces, v_a, v_b, opt_pos, - pair_edge_idx_t, pair_face_idx_t, area_thresh - ) - - # Link condition (skip in fast_mode for massive speedup) - if not fast_mode: - # Vectorized link condition using GPU operations - link_keep = torch.ones(v_a.numel(), dtype=torch.bool, device=device) - - # Build neighbor sets for va and vb using GPU operations - for ei in range(v_a.numel()): - vai = v_a[ei].item() - vbi = v_b[ei].item() - - f_va = _get_vertex_faces(vai, face_indices, vert_ptrs) - f_vb = _get_vertex_faces(vbi, face_indices, vert_ptrs) - - if f_va.numel() == 0 or f_vb.numel() == 0: - continue - - faces_va = active_faces[f_va] - verts_va = faces_va[faces_va != vai] - verts_va = verts_va[verts_va != vbi] - - faces_vb = active_faces[f_vb] - verts_vb = faces_vb[faces_vb != vbi] - verts_vb = verts_vb[verts_vb != vai] - - if verts_va.numel() == 0 or verts_vb.numel() == 0: - continue - - # Use torch.intersect1d for GPU-native intersection - common = torch.intersect1d(verts_va, verts_vb) - if common.numel() > 2: - link_keep[ei] = False - - keep_mask &= link_keep - if not keep_mask.any(): break @@ -1623,12 +1187,12 @@ def _qem_simplify(verts_np, faces_np, colors_np, target_faces, device, max_edge_ return final_v, final_f, final_c -def simplify_fn(vertices, faces, colors=None, target=100000, max_edge_length=None, use_triton=True, fast_mode=True): +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, use_triton, fast_mode) + 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: @@ -1651,7 +1215,7 @@ def simplify_fn(vertices, faces, colors=None, target=100000, max_edge_length=Non ) out_v, out_f, out_c = _qem_simplify( - verts_np, faces_np, colors_np, target, device, max_edge_length, use_triton, fast_mode + verts_np, faces_np, colors_np, target, device, max_edge_length ) final_v = out_v.to(device=device, dtype=dtype)