wangbo/ComfyUI
1
0
Fork 0
mirror of https://github.com/comfyanonymous/ComfyUI.git synced 2026-06-11 00:37:53 +08:00
Code Issues Actions 25 Packages Projects Releases Wiki Activity

remake

Browse Source
This commit is contained in:
Yousef Rafat 2026-05-16 19:49:51 +03:00
parent ca7fe65e7e
commit 178e859b1b
7 changed files with 788 additions and 760 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

18
comfy/ldm/trellis2/model.py
View File

@ -767,13 +767,17 @@ class Trellis2(nn.Module):
"model_channels":model_channels, "num_heads":num_heads, "mlp_ratio": mlp_ratio, "share_mod": share_mod,
"qk_rms_norm": qk_rms_norm, "qk_rms_norm_cross": qk_rms_norm_cross, "device": device, "dtype": dtype, "operations": operations
}
self.img2shape = SLatFlowModel(resolution=resolution, in_channels=in_channels, **args)
self.shape2txt = None
if init_txt_model:
txt_only = kwargs.get("txt_only", False)
if not txt_only:
self.img2shape = SLatFlowModel(resolution=resolution, in_channels=in_channels, **args)
self.shape2txt = None
if init_txt_model:
self.shape2txt = SLatFlowModel(resolution=resolution, in_channels=in_channels*2, **args)
self.img2shape_512 = SLatFlowModel(resolution=32, in_channels=in_channels, **args)
args.pop("out_channels")
self.structure_model = SparseStructureFlowModel(resolution=16, in_channels=8, out_channels=8, **args)
else:
self.shape2txt = SLatFlowModel(resolution=resolution, in_channels=in_channels*2, **args)
self.img2shape_512 = SLatFlowModel(resolution=32, in_channels=in_channels, **args)
args.pop("out_channels")
self.structure_model = SparseStructureFlowModel(resolution=16, in_channels=8, out_channels=8, **args)
self.guidance_interval = [0.6, 1.0]
self.guidance_interval_txt = [0.6, 0.9]
@ -787,7 +791,7 @@ class Trellis2(nn.Module):
if embeds is None:
raise ValueError("Trellis2.forward requires 'embeds' in kwargs")
is_1024 = self.img2shape.resolution == 1024
is_1024 = True#self.img2shape.resolution == 1024
coords = model_options.get("coords", None)
coord_counts = model_options.get("coord_counts", None)
mode = model_options.get("generation_mode", "structure_generation")

37
comfy/ldm/trellis2/vae.py
View File

@ -1387,10 +1387,10 @@ class SparseStructureDecoder(nn.Module):
return h
class Vae(nn.Module):
def __init__(self, init_txt_model, operations=None):
def __init__(self, init_txt_model, init_txt_model_only, operations=None):
super().__init__()
operations = operations or torch.nn
if init_txt_model:
if init_txt_model or init_txt_model_only:
self.txt_dec = SparseUnetVaeDecoder(
out_channels=6,
model_channels=[1024, 512, 256, 128, 64],
@ -1402,23 +1402,24 @@ class Vae(nn.Module):
pred_subdiv=False
)
self.shape_dec = FlexiDualGridVaeDecoder(
resolution=256,
model_channels=[1024, 512, 256, 128, 64],
latent_channels=32,
num_blocks=[4, 16, 8, 4, 0],
block_type=["SparseConvNeXtBlock3d"] * 5,
up_block_type=["SparseResBlockC2S3d"] * 4,
block_args=[{}, {}, {}, {}, {}],
)
if not init_txt_model_only:
self.shape_dec = FlexiDualGridVaeDecoder(
resolution=256,
model_channels=[1024, 512, 256, 128, 64],
latent_channels=32,
num_blocks=[4, 16, 8, 4, 0],
block_type=["SparseConvNeXtBlock3d"] * 5,
up_block_type=["SparseResBlockC2S3d"] * 4,
block_args=[{}, {}, {}, {}, {}],
)
self.struct_dec = SparseStructureDecoder(
out_channels=1,
latent_channels=8,
num_res_blocks=2,
num_res_blocks_middle=2,
channels=[512, 128, 32],
)
self.struct_dec = SparseStructureDecoder(
out_channels=1,
latent_channels=8,
num_res_blocks=2,
num_res_blocks_middle=2,
channels=[512, 128, 32],
)
self.register_buffer("resolution", torch.tensor(1024.0), persistent=False)
@torch.no_grad()

7
comfy/sd.py
View File

@ -516,15 +516,18 @@ class VAE:
self.first_stage_model = StageC_coder()
self.downscale_ratio = 32
self.latent_channels = 16
elif "shape_dec.blocks.1.16.to_subdiv.weight" in sd: # trellis2
elif "shape_dec.blocks.1.16.to_subdiv.weight" in sd or "txt_dec.blocks.3.4.conv2.weight" in sd: # trellis2 or trellis2 texture only
init_txt_model = False
init_txt_model_only = False
if "shape_dec.blocks.1.16.to_subdiv.weight" not in sd:
init_txt_model_only = True
if "txt_dec.blocks.1.16.norm1.weight" in sd:
init_txt_model = True
self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32]
# TODO
self.memory_used_decode = lambda shape, dtype: (2500 * shape[2] * shape[3]) * model_management.dtype_size(dtype)
self.memory_used_encode = lambda shape, dtype: (2500 * shape[2] * shape[3]) * model_management.dtype_size(dtype)
self.first_stage_model = comfy.ldm.trellis2.vae.Vae(init_txt_model)
self.first_stage_model = comfy.ldm.trellis2.vae.Vae(init_txt_model, init_txt_model_only= init_txt_model_only)
elif "decoder.conv_in.weight" in sd:
if sd['decoder.conv_in.weight'].shape[1] == 64:
ddconfig = {"block_out_channels": [128, 256, 512, 512, 1024, 1024], "in_channels": 3, "out_channels": 3, "num_res_blocks": 2, "ffactor_spatial": 32, "downsample_match_channel": True, "upsample_match_channel": True}

5
comfy_api/latest/_util/geometry_types.py
View File

@ -7,9 +7,10 @@ import torch
class VOXEL:
def __init__(self, data: torch.Tensor):
def __init__(self, data: torch.Tensor, voxel_colors=None, resolution=None):
self.data = data
self.voxel_colors = voxel_colors
self.resolution = resolution # each 3d model has its own resolution
class MESH:
def __init__(self, vertices: torch.Tensor, faces: torch.Tensor,

745
comfy_extras/nodes_mesh_postprocess.py Normal file
View File

@ -0,0 +1,745 @@
import torch
import numpy as np
from typing_extensions import override
from comfy_api.latest import ComfyExtension, IO
import copy
import comfy.utils
import logging
import scipy
class PaintMesh(IO.ComfyNode):
@classmethod
def define_schema(cls):
return IO.Schema(
node_id="PaintMesh",
display_name="Paint Mesh",
category="latent/3d",
description=(
"Paints the mesh using colors from the input voxel field by matching each vertex "
"to the nearest voxel color."
),
inputs=[
IO.Mesh.Input("mesh"),
IO.Voxel.Input("color_voxel")
],
outputs=[
IO.Mesh.Output("mesh"),
]
)
@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
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 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
if is_batched:
v_list, f_list = [],[]
for i in range(vertices.shape[0]):
v_i, f_i = fill_holes_fn(vertices[i], faces[i], max_perimeter)
v_list.append(v_i)
f_list.append(f_i)
return torch.stack(v_list), torch.stack(f_list)
device = vertices.device
v = vertices
f = faces
if f.numel() == 0:
return v, f
edges = torch.cat([f[:, [0, 1]], f[:, [1, 2]], f[:, [2, 0]]], dim=0)
edges_sorted, _ = torch.sort(edges, dim=1)
max_v = v.shape[0]
packed_undirected = edges_sorted[:, 0].long() * max_v + edges_sorted[:, 1].long()
unique_packed, counts = torch.unique(packed_undirected, return_counts=True)
boundary_packed = unique_packed[counts == 1]
if boundary_packed.numel() == 0:
return v, f
packed_directed_sorted = edges[:, 0].min(edges[:, 1]).long() * max_v + edges[:, 0].max(edges[:, 1]).long()
is_boundary = torch.isin(packed_directed_sorted, boundary_packed)
b_edges = edges[is_boundary]
adj = {u.item(): v_idx.item() for u, v_idx in b_edges}
loops =[]
visited = set()
for start_node in adj.keys():
if start_node in visited:
continue
curr = start_node
loop = []
while curr not in visited:
visited.add(curr)
loop.append(curr)
curr = adj.get(curr, -1)
if curr == -1:
loop = []
break
if curr == start_node:
loops.append(loop)
break
new_verts =[]
new_faces = []
v_idx = v.shape[0]
for loop in loops:
loop_t = torch.tensor(loop, device=device, dtype=torch.long)
loop_v = v[loop_t]
diffs = loop_v - torch.roll(loop_v, shifts=-1, dims=0)
perimeter = torch.norm(diffs, dim=1).sum().item()
if perimeter <= max_perimeter:
new_verts.append(loop_v.mean(dim=0))
for i in range(len(loop)):
new_faces.append([loop[(i + 1) % len(loop)], loop[i], v_idx])
v_idx += 1
if new_verts:
v = torch.cat([v, torch.stack(new_verts)], dim=0)
f = torch.cat([f, torch.tensor(new_faces, device=device, dtype=torch.long)], dim=0)
return v, f
def make_double_sided(vertices, faces):
is_batched = vertices.ndim == 3
if is_batched:
f_list = []
for i in range(faces.shape[0]):
f_inv = faces[i][:, [0, 2, 1]]
f_list.append(torch.cat([faces[i], f_inv], dim=0))
return vertices, torch.stack(f_list)
faces_inv = faces[:, [0, 2, 1]]
return vertices, torch.cat([faces, faces_inv], dim=0)
class PostProcessMesh(IO.ComfyNode):
@classmethod
def define_schema(cls):
return IO.Schema(
node_id="PostProcessMesh",
category="latent/3d",
description=(
"Applies a sequence of mesh post-processing operations including optional hole filling"
" and mesh simplification to a target face count."
),
inputs=[
IO.Mesh.Input("mesh"),
IO.Int.Input("target_face_count", default=1_000_000, min=0, max=50_000_000,
tooltip="Target maximum number of faces after mesh simplification. Set to 0 to disable simplification."),
IO.Float.Input("fill_holes_perimeter", default=0.03, min=0.0, step=0.0001,
tooltip=(
"Maximum hole perimeter threshold for filling holes in the mesh. "
"Smaller values only fill tiny holes, larger values fill larger gaps. "
"Set to 0 to disable hole filling."))
],
outputs=[
IO.Mesh.Output("mesh"),
]
)
@classmethod
def execute(cls, mesh, target_face_count, fill_holes_perimeter):
mesh = copy.deepcopy(mesh)
def process_single(v, f, c, bar):
if fill_holes_perimeter > 0:
v, f = fill_holes_fn(v, f, max_perimeter=fill_holes_perimeter)
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)
bar.update(1)
v, f = make_double_sided(v, f)
bar.update(1)
return v, f, c
is_list = isinstance(mesh.vertices, list)
is_batched_tensor = not is_list and mesh.vertices.ndim == 3
if is_list or is_batched_tensor:
out_v, out_f, out_c = [], [],[]
bsz = len(mesh.vertices) if is_list else mesh.vertices.shape[0]
bar = comfy.utils.ProgressBar(3 * bsz)
for i in range(bsz):
v_i = mesh.vertices[i]
f_i = mesh.faces[i]
# Safely grab colors if they exist
c_i = None
if hasattr(mesh, 'colors') and mesh.colors is not None:
c_i = mesh.colors[i] if (isinstance(mesh.colors, list) or mesh.colors.ndim == 3) else mesh.colors
v_i, f_i, c_i = process_single(v_i, f_i, c_i, bar)
out_v.append(v_i)
out_f.append(f_i)
if c_i is not None:
out_c.append(c_i)
# If the output meshes happen to have the exact same shape, stack them nicely.
# Otherwise, just leave them as a List! (ComfyUI native standard)
if all(v.shape == out_v[0].shape for v in out_v) and all(f.shape == out_f[0].shape for f in out_f):
mesh.vertices = torch.stack(out_v)
mesh.faces = torch.stack(out_f)
if out_c:
mesh.vertex_colors = torch.stack(out_c)
else:
mesh.vertices = out_v
mesh.faces = out_f
if out_c:
mesh.vertex_colors = out_c
else:
# Single Unbatched Mesh[V, 3]
c = mesh.colors if hasattr(mesh, 'colors') and mesh.colors is not None else None
v, f, c = process_single(mesh.vertices, mesh.faces, c)
mesh.vertices = v
mesh.faces = f
if c is not None:
mesh.vertex_colors = c
return IO.NodeOutput(mesh)
class PostProcessMeshExtension(ComfyExtension):
@override
async def get_node_list(self) -> list[type[IO.ComfyNode]]:
return [
PostProcessMesh,
PaintMesh
]
async def comfy_entrypoint() -> PostProcessMeshExtension:
return PostProcessMeshExtension()

735
comfy_extras/nodes_trellis2.py
View File

@ -4,11 +4,7 @@ from comfy.ldm.trellis2.vae import SparseTensor
import comfy.model_management
from PIL import Image
import numpy as np
import comfy.utils
import logging
import torch
import scipy
import copy
ShapeSubdivides = io.Custom("SHAPE_SUBDIVIDES")
HighResVoxel = io.Custom("HIGH_RES_VOXEL")
@ -192,52 +188,6 @@ def split_batched_sparse_latent(samples, coords, coord_counts):
items.append((samples[i, :count], coords_i))
return items
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)
# TODO: generic independent node? if so: figure how pass the resolution parameter
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
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
class VaeDecodeShapeTrellis(IO.ComfyNode):
@classmethod
def define_schema(cls):
@ -304,7 +254,6 @@ class VaeDecodeTextureTrellis(IO.ComfyNode):
node_id="VaeDecodeTextureTrellis",
category="latent/3d",
inputs=[
IO.Mesh.Input("mesh"),
IO.Latent.Input("samples"),
IO.Vae.Input("vae"),
ShapeSubdivides.Input("shape_subdivides",
@ -314,13 +263,12 @@ class VaeDecodeTextureTrellis(IO.ComfyNode):
)),
],
outputs=[
IO.Mesh.Output("mesh"),
IO.Voxel.Output("color_voxel"),
]
)
@classmethod
def execute(cls, mesh, samples, vae, shape_subdivides):
shape_mesh = mesh
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()
@ -340,31 +288,9 @@ class VaeDecodeTextureTrellis(IO.ComfyNode):
voxel = trellis_vae.decode_tex_slat(samples, shape_subdivides)
color_feats = voxel.feats[:, :3]
voxel_coords = voxel.coords[:, 1:]
voxel_batch_idx = voxel.coords[:, 0]
mesh_batch_size = shape_mesh.vertices.shape[0]
if mesh_batch_size > 1:
out_verts, out_faces, out_colors = [], [], []
for i in range(mesh_batch_size):
sel = voxel_batch_idx == i
item_coords = voxel_coords[sel]
item_colors = color_feats[sel]
item_vertices, item_faces, _ = get_mesh_batch_item(shape_mesh, i)
item_mesh = Types.MESH(vertices=item_vertices.unsqueeze(0), faces=item_faces.unsqueeze(0))
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)
out_verts.append(painted.vertices.squeeze(0))
out_faces.append(painted.faces.squeeze(0))
out_colors.append(painted.colors.squeeze(0))
out_mesh = pack_variable_mesh_batch(out_verts, out_faces, out_colors)
else:
if voxel_coords.shape[0] == 0:
out_mesh = paint_mesh_default_colors(shape_mesh)
else:
out_mesh = paint_mesh_with_voxels(shape_mesh, voxel_coords, color_feats, resolution=resolution)
return IO.NodeOutput(out_mesh)
voxel = Types.VOXEL(voxel_coords, color_feats, resolution)
return IO.NodeOutput(voxel)
class VaeDecodeStructureTrellis2(IO.ComfyNode):
@classmethod
@ -772,658 +698,6 @@ class EmptyTrellis2LatentStructure(IO.ComfyNode):
}
return IO.NodeOutput(output)
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
if is_batched:
v_list, f_list = [],[]
for i in range(vertices.shape[0]):
v_i, f_i = fill_holes_fn(vertices[i], faces[i], max_perimeter)
v_list.append(v_i)
f_list.append(f_i)
return torch.stack(v_list), torch.stack(f_list)
device = vertices.device
v = vertices
f = faces
if f.numel() == 0:
return v, f
edges = torch.cat([f[:, [0, 1]], f[:, [1, 2]], f[:, [2, 0]]], dim=0)
edges_sorted, _ = torch.sort(edges, dim=1)
max_v = v.shape[0]
packed_undirected = edges_sorted[:, 0].long() * max_v + edges_sorted[:, 1].long()
unique_packed, counts = torch.unique(packed_undirected, return_counts=True)
boundary_packed = unique_packed[counts == 1]
if boundary_packed.numel() == 0:
return v, f
packed_directed_sorted = edges[:, 0].min(edges[:, 1]).long() * max_v + edges[:, 0].max(edges[:, 1]).long()
is_boundary = torch.isin(packed_directed_sorted, boundary_packed)
b_edges = edges[is_boundary]
adj = {u.item(): v_idx.item() for u, v_idx in b_edges}
loops =[]
visited = set()
for start_node in adj.keys():
if start_node in visited:
continue
curr = start_node
loop = []
while curr not in visited:
visited.add(curr)
loop.append(curr)
curr = adj.get(curr, -1)
if curr == -1:
loop = []
break
if curr == start_node:
loops.append(loop)
break
new_verts =[]
new_faces = []
v_idx = v.shape[0]
for loop in loops:
loop_t = torch.tensor(loop, device=device, dtype=torch.long)
loop_v = v[loop_t]
diffs = loop_v - torch.roll(loop_v, shifts=-1, dims=0)
perimeter = torch.norm(diffs, dim=1).sum().item()
if perimeter <= max_perimeter:
new_verts.append(loop_v.mean(dim=0))
for i in range(len(loop)):
new_faces.append([loop[(i + 1) % len(loop)], loop[i], v_idx])
v_idx += 1
if new_verts:
v = torch.cat([v, torch.stack(new_verts)], dim=0)
f = torch.cat([f, torch.tensor(new_faces, device=device, dtype=torch.long)], dim=0)
return v, f
def make_double_sided(vertices, faces):
is_batched = vertices.ndim == 3
if is_batched:
f_list = []
for i in range(faces.shape[0]):
f_inv = faces[i][:, [0, 2, 1]]
f_list.append(torch.cat([faces[i], f_inv], dim=0))
return vertices, torch.stack(f_list)
faces_inv = faces[:, [0, 2, 1]]
return vertices, torch.cat([faces, faces_inv], dim=0)
class PostProcessMesh(IO.ComfyNode):
@classmethod
def define_schema(cls):
return IO.Schema(
node_id="PostProcessMesh",
category="latent/3d",
description=(
"Applies a sequence of mesh post-processing operations including optional hole filling"
" and mesh simplification to a target face count."
),
inputs=[
IO.Mesh.Input("mesh"),
IO.Int.Input("target_face_count", default=1_000_000, min=0, max=50_000_000,
tooltip="Target maximum number of faces after mesh simplification. Set to 0 to disable simplification."),
IO.Float.Input("fill_holes_perimeter", default=0.03, min=0.0, step=0.0001,
tooltip=(
"Maximum hole perimeter threshold for filling holes in the mesh. "
"Smaller values only fill tiny holes, larger values fill larger gaps. "
"Set to 0 to disable hole filling."))
],
outputs=[
IO.Mesh.Output("mesh"),
]
)
@classmethod
def execute(cls, mesh, target_face_count, fill_holes_perimeter):
mesh = copy.deepcopy(mesh)
def process_single(v, f, c, bar):
if fill_holes_perimeter > 0:
v, f = fill_holes_fn(v, f, max_perimeter=fill_holes_perimeter)
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)
bar.update(1)
v, f = make_double_sided(v, f)
bar.update(1)
return v, f, c
is_list = isinstance(mesh.vertices, list)
is_batched_tensor = not is_list and mesh.vertices.ndim == 3
if is_list or is_batched_tensor:
out_v, out_f, out_c = [], [],[]
bsz = len(mesh.vertices) if is_list else mesh.vertices.shape[0]
bar = comfy.utils.ProgressBar(3 * bsz)
for i in range(bsz):
v_i = mesh.vertices[i]
f_i = mesh.faces[i]
# Safely grab colors if they exist
c_i = None
if hasattr(mesh, 'colors') and mesh.colors is not None:
c_i = mesh.colors[i] if (isinstance(mesh.colors, list) or mesh.colors.ndim == 3) else mesh.colors
v_i, f_i, c_i = process_single(v_i, f_i, c_i, bar)
out_v.append(v_i)
out_f.append(f_i)
if c_i is not None:
out_c.append(c_i)
# If the output meshes happen to have the exact same shape, stack them nicely.
# Otherwise, just leave them as a List! (ComfyUI native standard)
if all(v.shape == out_v[0].shape for v in out_v) and all(f.shape == out_f[0].shape for f in out_f):
mesh.vertices = torch.stack(out_v)
mesh.faces = torch.stack(out_f)
if out_c:
mesh.vertex_colors = torch.stack(out_c)
else:
mesh.vertices = out_v
mesh.faces = out_f
if out_c:
mesh.vertex_colors = out_c
else:
# Single Unbatched Mesh[V, 3]
c = mesh.colors if hasattr(mesh, 'colors') and mesh.colors is not None else None
v, f, c = process_single(mesh.vertices, mesh.faces, c)
mesh.vertices = v
mesh.faces = f
if c is not None:
mesh.vertex_colors = c
return IO.NodeOutput(mesh)
class Trellis2Extension(ComfyExtension):
@override
async def get_node_list(self) -> list[type[IO.ComfyNode]]:
@ -1436,7 +710,6 @@ class Trellis2Extension(ComfyExtension):
VaeDecodeShapeTrellis,
VaeDecodeStructureTrellis2,
Trellis2UpsampleCascade,
PostProcessMesh
]

1
nodes.py
View File

@ -2429,6 +2429,7 @@ async def init_builtin_extra_nodes():
"nodes_replacements.py",
"nodes_nag.py",
"nodes_trellis2.py",
"nodes_mesh_postprocess.py",
"nodes_sdpose.py",
"nodes_math.py",
"nodes_number_convert.py",