remove triton version

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
+        optimal, err, valid = _pytorch_edge_errors(
-        if use_triton and torch.cuda.is_available():
+            verts, Q, edges_orig, stabilizer, max_edge_length_sq, mesh_scale_sq
-            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
-            )
 
         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)