mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2026-07-04 05:31:03 +08:00
3041 lines
140 KiB
Python
3041 lines
140 KiB
Python
import torch
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import numpy as np
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import math
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from typing_extensions import override
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from comfy_api.latest import ComfyExtension, IO, Types
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import copy
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import comfy.utils
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import comfy.model_management
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from server import PromptServer
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from comfy_extras.mesh3d.postprocess.qem_decimate import (
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simplify as qem_decimate_simplify, QEMConfig, cluster_decimate as qem_cluster_decimate,
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)
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from comfy_extras.mesh3d.postprocess.remesh import remesh_narrow_band_dc
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from comfy_extras.mesh3d.uv_unwrap import mesh as _uv_mesh
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from comfy_extras.mesh3d.uv_unwrap import segment as _uv_seg
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from comfy_extras.mesh3d.uv_unwrap import parameterize as _uv_param
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from comfy_extras.mesh3d.uv_unwrap import pack as _uv_pack
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import warnings
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import logging
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import scipy
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from scipy.sparse import csr_matrix
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from scipy.sparse.csgraph import connected_components
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def get_mesh_batch_item(mesh, index):
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if hasattr(mesh, "vertex_counts") and mesh.vertex_counts is not None:
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vertex_count = int(mesh.vertex_counts[index].item())
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face_count = int(mesh.face_counts[index].item())
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vertices = mesh.vertices[index, :vertex_count]
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faces = mesh.faces[index, :face_count]
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colors = None
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if hasattr(mesh, "colors") and mesh.colors is not None:
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if hasattr(mesh, "color_counts") and mesh.color_counts is not None:
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color_count = int(mesh.color_counts[index].item())
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colors = mesh.colors[index, :color_count]
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else:
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colors = mesh.colors[index, :vertex_count]
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return vertices, faces, colors
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colors = None
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if hasattr(mesh, "colors") and mesh.colors is not None:
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colors = mesh.colors[index]
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return mesh.vertices[index], mesh.faces[index], colors
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def pack_variable_mesh_batch(vertices, faces, colors=None):
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batch_size = len(vertices)
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max_vertices = max(v.shape[0] for v in vertices)
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max_faces = max(f.shape[0] for f in faces)
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packed_vertices = vertices[0].new_zeros((batch_size, max_vertices, vertices[0].shape[1]))
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packed_faces = faces[0].new_zeros((batch_size, max_faces, faces[0].shape[1]))
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vertex_counts = torch.tensor([v.shape[0] for v in vertices], device=vertices[0].device, dtype=torch.int64)
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face_counts = torch.tensor([f.shape[0] for f in faces], device=faces[0].device, dtype=torch.int64)
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for i, (v, f) in enumerate(zip(vertices, faces)):
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packed_vertices[i, :v.shape[0]] = v
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packed_faces[i, :f.shape[0]] = f
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mesh = Types.MESH(packed_vertices, packed_faces)
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mesh.vertex_counts = vertex_counts
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mesh.face_counts = face_counts
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if colors is not None:
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max_colors = max(c.shape[0] for c in colors)
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packed_colors = colors[0].new_zeros((batch_size, max_colors, colors[0].shape[1]))
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color_counts = torch.tensor([c.shape[0] for c in colors], device=colors[0].device, dtype=torch.int64)
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for i, c in enumerate(colors):
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packed_colors[i, :c.shape[0]] = c
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mesh.vertex_colors = packed_colors
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mesh.color_counts = color_counts
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return mesh
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def paint_mesh_with_voxels(mesh, voxel_coords, voxel_colors, resolution):
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"""
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Generic function to paint a mesh using nearest-neighbor colors from a sparse voxel field.
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"""
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device = comfy.model_management.vae_offload_device()
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origin = torch.tensor([-0.5, -0.5, -0.5], device=device)
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voxel_size = 1.0 / resolution
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# map voxels
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voxel_pos = voxel_coords.to(device).float() * voxel_size + origin
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verts = mesh.vertices.to(device).squeeze(0)
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voxel_colors = voxel_colors.to(device)
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voxel_pos_np = voxel_pos.numpy()
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verts_np = verts.numpy()
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tree = scipy.spatial.cKDTree(voxel_pos_np)
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# nearest neighbour k=1
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_, nearest_idx_np = tree.query(verts_np, k=1, workers=-1)
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nearest_idx = torch.from_numpy(nearest_idx_np).long()
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v_colors = voxel_colors[nearest_idx]
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# Voxel field may carry the full PBR set (base_color, metallic, roughness,
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# alpha); vertex colors only use base_color RGB.
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if v_colors.shape[-1] > 3:
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v_colors = v_colors[:, :3]
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# to [0, 1]
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srgb_colors = v_colors.clamp(0, 1)#(v_colors * 0.5 + 0.5).clamp(0, 1)
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# to Linear RGB (required for GLTF)
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linear_colors = torch.pow(srgb_colors, 2.2)
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final_colors = linear_colors.unsqueeze(0)
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out_mesh = copy.deepcopy(mesh)
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out_mesh.vertex_colors = final_colors
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return out_mesh
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class PaintMesh(IO.ComfyNode):
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@classmethod
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def define_schema(cls):
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return IO.Schema(
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node_id="PaintMesh",
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display_name="Paint Mesh",
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category="latent/3d",
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description=(
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"Paints the mesh using colors from the input voxel field by matching each vertex "
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"to the nearest voxel color."
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),
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inputs=[
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IO.Mesh.Input("mesh"),
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IO.Voxel.Input("voxel_colors")
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],
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outputs=[
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IO.Mesh.Output("mesh"),
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]
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)
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@classmethod
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def execute(cls, mesh, voxel_colors):
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voxels = voxel_colors
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coords = voxels.data
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colors = voxels.voxel_colors
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resolution = voxels.resolution
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if coords.shape[0] == 0:
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return IO.NodeOutput(paint_mesh_default_colors(mesh))
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mesh_batch_size = mesh.vertices.shape[0]
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if coords.shape[-1] == 4 and mesh_batch_size > 1:
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batch_idx = coords[:, 0].long()
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voxel_coords = coords[:, 1:]
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mesh_batch_size = mesh.vertices.shape[0]
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out_verts, out_faces, out_colors = [], [], []
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for i in range(mesh_batch_size):
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sel = batch_idx == i
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item_coords = voxel_coords[sel]
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item_colors = colors[sel]
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item_vertices, item_faces, _ = get_mesh_batch_item(mesh, i)
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item_mesh = Types.MESH(vertices=item_vertices.unsqueeze(0), faces=item_faces.unsqueeze(0))
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if item_coords.shape[0] == 0:
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painted = paint_mesh_default_colors(item_mesh)
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else:
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painted = paint_mesh_with_voxels(item_mesh, item_coords, item_colors, resolution=resolution)
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out_verts.append(painted.vertices.squeeze(0))
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out_faces.append(painted.faces.squeeze(0))
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out_colors.append(painted.vertex_colors.squeeze(0))
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out_mesh = pack_variable_mesh_batch(out_verts, out_faces, out_colors)
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return IO.NodeOutput(out_mesh)
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if coords.shape[-1] == 4:
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coords = coords[:, 1:]
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out_mesh = paint_mesh_with_voxels(mesh, coords, colors, resolution=resolution)
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return IO.NodeOutput(out_mesh)
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# =============================================================================
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# Texture baking from sparse voxel volume.
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#
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# Pipeline: take the mesh's existing UVs → OpenGL UV-space rasterize to position
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# map → nearest-voxel color sample per texel → GPU Jump-Flood fill UV seams →
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# attach texture + UVs to the Mesh for SaveGLB to serialize. Unwrapping is done
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# upstream (Trellis2OfficialUnwrap / TorchXatlasUVWrap); this path never unwraps.
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#
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# Uses comfy_extras.nodes_glsl.GLContext for OpenGL context (already handles
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# GLFW / EGL / OSMesa backend selection).
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# =============================================================================
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_GL_COMPILE_PROGRAM_CACHE_KEY = "_bake_texture_program_cache"
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def _gl_compile_program(gl, vert_src: str, frag_src: str):
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"""Compile and link a minimal vert+frag GL program. Caller owns the GLuint
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and must glDeleteProgram when done."""
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def _check_shader(s, kind):
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if not gl.glGetShaderiv(s, gl.GL_COMPILE_STATUS):
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log = gl.glGetShaderInfoLog(s).decode(errors="replace")
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gl.glDeleteShader(s)
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raise RuntimeError(f"GL {kind} shader compile failed: {log}")
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vs = gl.glCreateShader(gl.GL_VERTEX_SHADER)
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gl.glShaderSource(vs, vert_src)
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gl.glCompileShader(vs)
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_check_shader(vs, "vertex")
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fs = gl.glCreateShader(gl.GL_FRAGMENT_SHADER)
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gl.glShaderSource(fs, frag_src)
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gl.glCompileShader(fs)
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_check_shader(fs, "fragment")
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prog = gl.glCreateProgram()
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gl.glAttachShader(prog, vs)
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gl.glAttachShader(prog, fs)
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gl.glLinkProgram(prog)
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gl.glDeleteShader(vs)
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gl.glDeleteShader(fs)
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if not gl.glGetProgramiv(prog, gl.GL_LINK_STATUS):
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log = gl.glGetProgramInfoLog(prog).decode(errors="replace")
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gl.glDeleteProgram(prog)
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raise RuntimeError(f"GL program link failed: {log}")
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return prog
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# Position-passthrough shader. Vertex maps UV → clip space; fragment outputs the
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# interpolated world-space vertex position (with alpha=1 marking valid texels).
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_BAKE_VERT_SRC = """
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#version 330 core
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layout (location = 0) in vec3 a_pos;
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layout (location = 1) in vec2 a_uv;
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out vec3 v_pos;
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void main() {
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v_pos = a_pos;
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gl_Position = vec4(a_uv * 2.0 - 1.0, 0.0, 1.0);
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}
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"""
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_BAKE_FRAG_SRC = """
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#version 330 core
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in vec3 v_pos;
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out vec4 frag_color;
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void main() {
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frag_color = vec4(v_pos, 1.0);
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}
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"""
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def _bake_position_map(verts_np, faces_np, uvs_np, texture_size):
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"""Rasterize unwrapped mesh in UV space; return (position_map, mask).
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position_map: (H, W, 3) float32 — interpolated 3D position per texel.
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mask: (H, W) bool — valid (covered) texels.
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Uses comfy_extras.nodes_glsl.GLContext, which lazily picks GLFW/EGL/OSMesa."""
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from comfy_extras.nodes_glsl import GLContext, _import_opengl
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GLContext() # ensure backend is initialized + current
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gl = _import_opengl()
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# PyOpenGL's high-level wrappers for the buffer/draw/readback functions
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# store array refs in OpenGL.contextdata, which on EGL contexts triggers
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# "Attempt to retrieve context when no valid context". Use the raw C
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# entry points (OpenGL.raw.*) instead — they skip the bookkeeping.
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import ctypes as _ctypes
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from OpenGL.raw.GL.VERSION.GL_1_1 import (
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glReadPixels as _raw_glReadPixels,
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glTexImage2D as _raw_glTexImage2D,
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glDrawElements as _raw_glDrawElements,
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)
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from OpenGL.raw.GL.VERSION.GL_1_5 import glBufferData as _raw_glBufferData
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from OpenGL.raw.GL.VERSION.GL_2_0 import glVertexAttribPointer as _raw_glVertexAttribPointer
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H = W = int(texture_size)
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fbo = color_tex = vbo = ibo = vao = prog = None
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try:
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# Interleaved [pos.x, pos.y, pos.z, uv.x, uv.y] per vertex (stride=20 bytes).
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verts32 = np.ascontiguousarray(verts_np, dtype=np.float32)
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uvs32 = np.ascontiguousarray(uvs_np, dtype=np.float32)
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faces32 = np.ascontiguousarray(faces_np, dtype=np.uint32)
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vbo_data = np.ascontiguousarray(np.concatenate([verts32, uvs32], axis=-1), dtype=np.float32)
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prog = _gl_compile_program(gl, _BAKE_VERT_SRC, _BAKE_FRAG_SRC)
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vao = gl.glGenVertexArrays(1)
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gl.glBindVertexArray(vao)
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vbo = gl.glGenBuffers(1)
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gl.glBindBuffer(gl.GL_ARRAY_BUFFER, vbo)
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_raw_glBufferData(gl.GL_ARRAY_BUFFER, int(vbo_data.nbytes),
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vbo_data.ctypes.data_as(_ctypes.c_void_p), gl.GL_STATIC_DRAW)
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ibo = gl.glGenBuffers(1)
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gl.glBindBuffer(gl.GL_ELEMENT_ARRAY_BUFFER, ibo)
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_raw_glBufferData(gl.GL_ELEMENT_ARRAY_BUFFER, int(faces32.nbytes),
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faces32.ctypes.data_as(_ctypes.c_void_p), gl.GL_STATIC_DRAW)
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gl.glEnableVertexAttribArray(0)
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_raw_glVertexAttribPointer(0, 3, gl.GL_FLOAT, gl.GL_FALSE, 20, _ctypes.c_void_p(0))
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gl.glEnableVertexAttribArray(1)
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_raw_glVertexAttribPointer(1, 2, gl.GL_FLOAT, gl.GL_FALSE, 20, _ctypes.c_void_p(12))
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fbo = gl.glGenFramebuffers(1)
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gl.glBindFramebuffer(gl.GL_FRAMEBUFFER, fbo)
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color_tex = gl.glGenTextures(1)
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gl.glBindTexture(gl.GL_TEXTURE_2D, color_tex)
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_raw_glTexImage2D(gl.GL_TEXTURE_2D, 0, gl.GL_RGBA32F, W, H,
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0, gl.GL_RGBA, gl.GL_FLOAT, None)
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gl.glTexParameteri(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_MIN_FILTER, gl.GL_NEAREST)
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gl.glTexParameteri(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_MAG_FILTER, gl.GL_NEAREST)
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gl.glFramebufferTexture2D(gl.GL_FRAMEBUFFER, gl.GL_COLOR_ATTACHMENT0,
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gl.GL_TEXTURE_2D, color_tex, 0)
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status = gl.glCheckFramebufferStatus(gl.GL_FRAMEBUFFER)
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if status != gl.GL_FRAMEBUFFER_COMPLETE:
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raise RuntimeError(f"FBO incomplete (status=0x{status:x})")
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gl.glViewport(0, 0, W, H)
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gl.glClearColor(0.0, 0.0, 0.0, 0.0)
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gl.glClear(gl.GL_COLOR_BUFFER_BIT)
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gl.glDisable(gl.GL_CULL_FACE)
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gl.glDisable(gl.GL_DEPTH_TEST)
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gl.glUseProgram(prog)
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_raw_glDrawElements(gl.GL_TRIANGLES, int(faces32.size), gl.GL_UNSIGNED_INT, None)
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gl.glFinish()
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# Pre-allocate readback buffer and pass it as a pointer so PyOpenGL
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# doesn't try to allocate one through its array-handler machinery.
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arr = np.empty((H, W, 4), dtype=np.float32)
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_raw_glReadPixels(0, 0, W, H, gl.GL_RGBA, gl.GL_FLOAT,
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arr.ctypes.data_as(_ctypes.c_void_p))
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# Do NOT flipud here. Our shader places UV(0,0) at FBO bottom-left
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# (clip(-1,-1)), and glReadPixels returns bottom-row-first, so arr[0]
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# already holds the UV v=0 data. glTF samples PNG with row 0 = upper-left
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# = UV v=0, so storing arr as-is gives a consistent mapping. Flipping
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# would invert V and make every sample come from the wrong row.
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position_map = arr[..., :3]
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mask = arr[..., 3] > 0.5
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return position_map, mask
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finally:
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if fbo is not None:
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gl.glBindFramebuffer(gl.GL_FRAMEBUFFER, 0)
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gl.glDeleteFramebuffers(1, [fbo])
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if color_tex is not None:
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gl.glDeleteTextures(1, [color_tex])
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if vbo is not None:
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gl.glDeleteBuffers(1, [vbo])
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if ibo is not None:
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gl.glDeleteBuffers(1, [ibo])
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if vao is not None:
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gl.glBindVertexArray(0)
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gl.glDeleteVertexArrays(1, [vao])
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if prog is not None:
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gl.glUseProgram(0)
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gl.glDeleteProgram(prog)
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def _trilinear_sample_sparse(positions, voxel_coords_np, color_np, resolution):
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"""Normalized trilinear interpolation of a SPARSE voxel attribute field.
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The official o_voxel.to_glb trilinear-samples a *dense* attribute volume; here
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the field is sparse (only surface voxels carry values), so a plain trilinear
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would bleed zeros from empty cells. Instead we accumulate, per query, only the
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occupied corners among the 8 surrounding voxels and renormalize by their
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weights — i.e. trilinear over the occupied subset. Voxel centres sit at integer
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coords c with world position c/resolution - 0.5.
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Returns (vals [K, C] float64, ok [K] bool). `ok` is False where none of the 8
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corners is occupied (caller falls back to nearest there)."""
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R = int(resolution)
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origin = -0.5
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voxel_size = 1.0 / R
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# Cell-CENTER convention: voxel coord c sits at world origin + (c+0.5)*voxel_size,
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# matching the official flex_gemm grid_sample_3d (its trilinear weight centers
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# integer coord c at query c+0.5). The `- 0.5` puts integer gc on voxel centres
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# so the 8 trilinear corners bracket the query correctly. Omitting it samples
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# half a voxel toward the corner — colour bleed at boundaries / thin features.
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gc = (positions.astype(np.float64) - origin) / voxel_size - 0.5 # continuous voxel-index coords
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base = np.floor(gc).astype(np.int64) # [K,3] lower corner
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frac = gc - base # [K,3] in [0,1)
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vc = voxel_coords_np.astype(np.int64)
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occ_keys = (vc[:, 0] * R + vc[:, 1]) * R + vc[:, 2] # linear key per occupied voxel
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order = np.argsort(occ_keys)
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occ_sorted = occ_keys[order]
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K = positions.shape[0]
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C = color_np.shape[1]
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acc = np.zeros((K, C), dtype=np.float64)
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wsum = np.zeros((K, 1), dtype=np.float64)
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for dx in (0, 1):
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wx = frac[:, 0] if dx else 1.0 - frac[:, 0]
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for dy in (0, 1):
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wy = frac[:, 1] if dy else 1.0 - frac[:, 1]
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for dz in (0, 1):
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wz = frac[:, 2] if dz else 1.0 - frac[:, 2]
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cx = base[:, 0] + dx
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cy = base[:, 1] + dy
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cz = base[:, 2] + dz
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inb = (cx >= 0) & (cx < R) & (cy >= 0) & (cy < R) & (cz >= 0) & (cz < R)
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key = (cx * R + cy) * R + cz
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ins = np.clip(np.searchsorted(occ_sorted, key), 0, len(occ_sorted) - 1)
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matched = inb & (occ_sorted[ins] == key)
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idx = order[ins] # original voxel index (garbage where !matched)
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w = np.where(matched, wx * wy * wz, 0.0)[:, None]
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acc += w * color_np[idx] # w=0 cancels the garbage rows
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wsum += w
|
||
ok = wsum[:, 0] > 1e-8
|
||
vals = np.zeros((K, C), dtype=np.float64)
|
||
vals[ok] = acc[ok] / wsum[ok]
|
||
return vals, ok
|
||
|
||
|
||
def _trilinear_sample_sparse_gpu(positions, voxel_coords_np, color_np, resolution):
|
||
"""GPU port of `_trilinear_sample_sparse` — same normalized-over-occupied-corners
|
||
trilinear, but the per-texel 8-corner accumulation runs on CUDA via sorted-key
|
||
`searchsorted` instead of NumPy float64. This is the bake hot path (millions of
|
||
covered texels × 8 corners), so the CPU version dominates runtime; the GPU port
|
||
is ~identical numerically and 10-50× faster. Returns (vals [K,C] float32, ok
|
||
[K] bool), matching the NumPy signature."""
|
||
dev = comfy.model_management.get_torch_device()
|
||
R = int(resolution)
|
||
origin = -0.5
|
||
voxel_size = 1.0 / R
|
||
P = torch.from_numpy(np.ascontiguousarray(positions)).to(dev).float()
|
||
VC = torch.from_numpy(np.ascontiguousarray(voxel_coords_np)).to(dev).long()
|
||
col = torch.from_numpy(np.ascontiguousarray(color_np)).to(dev).float()
|
||
K, C = P.shape[0], col.shape[1]
|
||
M = VC.shape[0]
|
||
# Same cell-CENTER convention as the NumPy path (see its docstring): integer
|
||
# voxel coord c sits at (c+0.5)*voxel_size + origin, so subtract 0.5 to bracket.
|
||
gc = (P - origin) / voxel_size - 0.5
|
||
base = torch.floor(gc).long()
|
||
frac = gc - base.float()
|
||
key = (VC[:, 0] * R + VC[:, 1]) * R + VC[:, 2]
|
||
skey, order = key.sort()
|
||
acc = torch.zeros((K, C), device=dev)
|
||
wsum = torch.zeros((K, 1), device=dev)
|
||
for dx in (0, 1):
|
||
wx = frac[:, 0] if dx else 1.0 - frac[:, 0]
|
||
for dy in (0, 1):
|
||
wy = frac[:, 1] if dy else 1.0 - frac[:, 1]
|
||
for dz in (0, 1):
|
||
wz = frac[:, 2] if dz else 1.0 - frac[:, 2]
|
||
cx = base[:, 0] + dx
|
||
cy = base[:, 1] + dy
|
||
cz = base[:, 2] + dz
|
||
inb = (cx >= 0) & (cx < R) & (cy >= 0) & (cy < R) & (cz >= 0) & (cz < R)
|
||
qk = (cx * R + cy) * R + cz
|
||
ins = torch.searchsorted(skey, qk).clamp(max=M - 1)
|
||
matched = inb & (skey[ins] == qk)
|
||
idx = order[ins] # garbage where !matched
|
||
w = torch.where(matched, wx * wy * wz, torch.zeros_like(wx))[:, None]
|
||
acc += w * col[idx] # w=0 cancels garbage rows
|
||
wsum += w
|
||
ok = wsum[:, 0] > 1e-8
|
||
vals = torch.zeros((K, C), device=dev)
|
||
vals[ok] = acc[ok] / wsum[ok].clamp_min(1e-8)
|
||
return vals.cpu().numpy(), ok.cpu().numpy()
|
||
|
||
|
||
def _nearest_voxel_sample_gpu(positions, voxel_coords_np, color_np, resolution):
|
||
"""GPU nearest-occupied-voxel lookup for surface points. Voxels sit on a
|
||
regular integer grid (coord c ↔ world c/R-0.5), so the nearest voxel to a
|
||
query is round((p+0.5)*R) plus a 3³ neighbour check — an O(1)-per-query grid
|
||
lookup (sorted-key binary search), ~10-30× faster than a cKDTree over millions
|
||
of voxels and ~identical. Returns (vals [K,C] float32, found [K] bool); `found`
|
||
is False for the rare query whose nearest occupied voxel is >1 cell away (the
|
||
caller falls back to a cKDTree on just those)."""
|
||
dev = comfy.model_management.get_torch_device()
|
||
R = int(resolution)
|
||
P = torch.from_numpy(np.ascontiguousarray(positions)).to(dev).float()
|
||
VC = torch.from_numpy(np.ascontiguousarray(voxel_coords_np)).to(dev).long()
|
||
col = torch.from_numpy(np.ascontiguousarray(color_np)).to(dev).float()
|
||
M, K, C = VC.shape[0], P.shape[0], col.shape[1]
|
||
key = (VC[:, 0] * R + VC[:, 1]) * R + VC[:, 2]
|
||
skey, order = key.sort()
|
||
|
||
def _search(idx, radius):
|
||
"""Nearest occupied voxel within ±radius cells, for query subset P[idx]."""
|
||
Ps = P[idx]
|
||
# Cell-CENTER convention: voxel c is centred at (c+0.5)/R - 0.5 in world,
|
||
# so the coord nearest a point is round((p+0.5)*R - 0.5) (matches the
|
||
# official grid_sample_3d). The distance test below uses the same centre.
|
||
rc = ((Ps + 0.5) * R - 0.5).round().long()
|
||
n = idx.shape[0]
|
||
bd = torch.full((n,), 1e30, device=dev)
|
||
bi = torch.zeros(n, dtype=torch.long, device=dev)
|
||
fnd = torch.zeros(n, dtype=torch.bool, device=dev)
|
||
rng = range(-radius, radius + 1)
|
||
for dx in rng:
|
||
for dy in rng:
|
||
for dz in rng:
|
||
cc = rc + torch.tensor([dx, dy, dz], device=dev)
|
||
inb = ((cc >= 0) & (cc < R)).all(1)
|
||
qk = (cc[:, 0] * R + cc[:, 1]) * R + cc[:, 2]
|
||
ins = torch.searchsorted(skey, qk).clamp(max=M - 1)
|
||
match = inb & (skey[ins] == qk)
|
||
dd = (((cc.float() + 0.5) / R - 0.5 - Ps) ** 2).sum(1)
|
||
upd = match & (dd < bd)
|
||
bd = torch.where(upd, dd, bd)
|
||
bi = torch.where(upd, order[ins], bi)
|
||
fnd |= match
|
||
return bi, fnd
|
||
|
||
def _brute_nearest(idx):
|
||
"""Exact nearest occupied voxel for a (small) query subset by chunked GPU
|
||
brute force over all M voxels. Used only for the handful of stragglers the
|
||
grid scan misses (>4 cells from any voxel) — replaces a cKDTree build over
|
||
all M voxels, which costs seconds even for a few query points."""
|
||
Ps = P[idx] # [N,3] world
|
||
N = Ps.shape[0]
|
||
vox_pos = (VC.float() + 0.5) / R - 0.5 # [M,3] voxel centres
|
||
best_d = torch.full((N,), 1e30, device=dev)
|
||
best_j = torch.zeros(N, dtype=torch.long, device=dev)
|
||
# Bound the N×chunk distance matrix to ~64M elements regardless of N.
|
||
chunk = max(1, (1 << 26) // max(1, N))
|
||
for s in range(0, M, chunk):
|
||
vc = vox_pos[s:s + chunk] # [B,3]
|
||
dd = (Ps[:, None, :] - vc[None, :, :]).pow(2).sum(-1) # [N,B]
|
||
md, mj = dd.min(1)
|
||
upd = md < best_d
|
||
best_d = torch.where(upd, md, best_d)
|
||
best_j = torch.where(upd, mj + s, best_j)
|
||
return best_j
|
||
|
||
all_idx = torch.arange(K, device=dev)
|
||
best_i = torch.zeros(K, dtype=torch.long, device=dev)
|
||
found = torch.zeros(K, dtype=torch.bool, device=dev)
|
||
# Pass 1: radius 1 (3³) over everything — catches ~all surface texels cheaply.
|
||
bi1, fnd1 = _search(all_idx, 1)
|
||
best_i[all_idx] = bi1
|
||
found[all_idx] = fnd1
|
||
# Pass 2: wider radius (9³) on ONLY the radius-1 misses.
|
||
miss = torch.nonzero(~found, as_tuple=True)[0]
|
||
if miss.numel() > 0:
|
||
bi2, fnd2 = _search(miss, 4)
|
||
best_i[miss] = bi2
|
||
found[miss] = fnd2
|
||
# Pass 3: exact GPU brute force for the few stragglers still unfound (>4 cells
|
||
# out). Always resolves them, so `found` is all-True on return — no cKDTree.
|
||
miss2 = torch.nonzero(~found, as_tuple=True)[0]
|
||
if miss2.numel() > 0:
|
||
best_i[miss2] = _brute_nearest(miss2)
|
||
found[miss2] = True
|
||
vals = col[best_i]
|
||
return vals.cpu().numpy(), found.cpu().numpy()
|
||
|
||
|
||
def _sample_voxel_attrs_per_texel(position_map, mask, voxel_coords, voxel_colors, resolution):
|
||
"""For every masked texel, sample the voxel field and return ALL its attribute
|
||
channels. Returns (H, W, C) float32 in [0, 1] where C is the voxel feature
|
||
width (3 for plain color, 6 for full PBR).
|
||
|
||
Normalized trilinear over occupied voxels (matches the official o_voxel.to_glb
|
||
path), with nearest fallback for texels whose 8 surrounding voxels are all
|
||
empty."""
|
||
H, W, _ = position_map.shape
|
||
color_np = voxel_colors.detach().cpu().numpy().astype(np.float32)
|
||
C = color_np.shape[-1]
|
||
out = np.zeros((H, W, C), dtype=np.float32)
|
||
if not mask.any():
|
||
return out
|
||
|
||
origin = np.array([-0.5, -0.5, -0.5], dtype=np.float32)
|
||
voxel_size = 1.0 / float(resolution)
|
||
coords_np = voxel_coords.detach().cpu().numpy()
|
||
# Cell-CENTER convention (+0.5 voxel), matching the official grid_sample_3d and
|
||
# the _trilinear/_nearest paths above; this cKDTree only serves the rare
|
||
# >cell-radius nearest fallback but must use the same world mapping.
|
||
voxel_pos = (coords_np.astype(np.float32) + 0.5) * voxel_size + origin
|
||
valid_positions = position_map[mask]
|
||
|
||
def _nearest(query):
|
||
# Fully on-GPU nearest-occupied-voxel: grid scan + brute-force tail. Always
|
||
# resolves every query, so no cKDTree (its build over all voxels cost ~3s).
|
||
vals, found = _nearest_voxel_sample_gpu(query, coords_np, color_np, resolution)
|
||
if not found.all():
|
||
# Defensive: only reachable on a non-CUDA device where the GPU path is
|
||
# unavailable; fall back to a one-off cKDTree.
|
||
tree = scipy.spatial.cKDTree(voxel_pos)
|
||
_, nearest_idx = tree.query(query[~found], k=1, workers=-1)
|
||
vals[~found] = color_np[nearest_idx]
|
||
return vals
|
||
|
||
try:
|
||
vals, ok = _trilinear_sample_sparse_gpu(valid_positions, coords_np, color_np, resolution)
|
||
except Exception as e:
|
||
logging.warning(f"[BakeTextureFromVoxel] GPU trilinear failed ({e}); falling back to CPU")
|
||
vals, ok = _trilinear_sample_sparse(valid_positions, coords_np, color_np, resolution)
|
||
if not ok.all():
|
||
# Texels with no occupied neighbour fall back to nearest.
|
||
vals[~ok] = _nearest(valid_positions[~ok])
|
||
out[mask] = np.clip(vals, 0.0, 1.0).astype(np.float32)
|
||
return out
|
||
|
||
|
||
def _closest_point_on_triangles(p, a, b, c):
|
||
"""Vectorized exact closest point on triangles (Ericson, Real-Time Collision
|
||
Detection §5.1.5). p/a/b/c are [..., 3]; returns [..., 3]. Handles all
|
||
vertex/edge/face Voronoi regions, applied highest-priority-last via where."""
|
||
ab = b - a
|
||
ac = c - a
|
||
ap = p - a
|
||
d1 = (ab * ap).sum(-1)
|
||
d2 = (ac * ap).sum(-1)
|
||
bp = p - b
|
||
d3 = (ab * bp).sum(-1)
|
||
d4 = (ac * bp).sum(-1)
|
||
cp = p - c
|
||
d5 = (ab * cp).sum(-1)
|
||
d6 = (ac * cp).sum(-1)
|
||
va = d3 * d6 - d5 * d4
|
||
vb = d5 * d2 - d1 * d6
|
||
vc = d1 * d4 - d3 * d2
|
||
|
||
def u(x): # broadcast a scalar-per-element weight to [...,1]
|
||
return x.unsqueeze(-1)
|
||
|
||
# face region (default)
|
||
denom = 1.0 / (va + vb + vc).clamp_min(1e-20)
|
||
v = vb * denom
|
||
w = vc * denom
|
||
res = a + ab * u(v) + ac * u(w)
|
||
# edge BC
|
||
den_bc = (d4 - d3) + (d5 - d6)
|
||
w_bc = (d4 - d3) / den_bc.clamp_min(1e-20)
|
||
res = torch.where(u((va <= 0) & ((d4 - d3) >= 0) & ((d5 - d6) >= 0)),
|
||
b + (c - b) * u(w_bc), res)
|
||
# edge AC
|
||
w_ac = d2 / (d2 - d6).clamp_min(1e-20)
|
||
res = torch.where(u((vb <= 0) & (d2 >= 0) & (d6 <= 0)), a + ac * u(w_ac), res)
|
||
# vertex C
|
||
res = torch.where(u((d6 >= 0) & (d5 <= d6)), c, res)
|
||
# edge AB
|
||
v_ab = d1 / (d1 - d3).clamp_min(1e-20)
|
||
res = torch.where(u((vc <= 0) & (d1 >= 0) & (d3 <= 0)), a + ab * u(v_ab), res)
|
||
# vertex B
|
||
res = torch.where(u((d3 >= 0) & (d4 <= d3)), b, res)
|
||
# vertex A
|
||
res = torch.where(u((d1 <= 0) & (d2 <= 0)), a, res)
|
||
return res
|
||
|
||
|
||
def _msb_int64(x):
|
||
"""floor(log2(x)) elementwise for int64 x >= 1 (bit-search, no float)."""
|
||
r = torch.zeros_like(x); xx = x.clone()
|
||
for s in (32, 16, 8, 4, 2, 1):
|
||
sh = xx >> s; m = sh > 0
|
||
r = torch.where(m, r + s, r); xx = torch.where(m, sh, xx)
|
||
return r
|
||
|
||
|
||
def _morton_expand21(v):
|
||
"""Spread the low 21 bits of v across every 3rd bit (for a 63-bit Morton code)."""
|
||
v = v & 0x1fffff
|
||
v = (v | (v << 32)) & 0x1f00000000ffff
|
||
v = (v | (v << 16)) & 0x1f0000ff0000ff
|
||
v = (v | (v << 8)) & 0x100f00f00f00f00f
|
||
v = (v | (v << 4)) & 0x10c30c30c30c30c3
|
||
v = (v | (v << 2)) & 0x1249249249249249
|
||
return v
|
||
|
||
|
||
def _build_triangle_bvh(tri):
|
||
"""Linear BVH (Karras 2012) over triangle AABBs, pure torch, NO external deps.
|
||
|
||
21-bit-per-axis Morton sort of triangle centroids -> parallel radix-tree
|
||
construction -> bottom-up node AABBs. Internal nodes are indexed 0..T-2, leaves
|
||
are encoded as LEAF+i (i in 0..T-1) where leaf i holds triangle `order[i]`.
|
||
Returns a dict with node AABBs (nmin,nmax over 2T entries), child links
|
||
(left,right), the leaf->triangle map `order`, LEAF offset and T.
|
||
|
||
A real tree (not a uniform grid) is what makes the closest-point query prune
|
||
empty space and dense clusters, so it stays fast on huge, non-uniform references
|
||
where the grid's ring search blows up — i.e. the cuMesh BVH approach, in torch."""
|
||
dev = tri.device; T = tri.shape[0]
|
||
amin = tri.amin(1); amax = tri.amax(1); cent = (amin + amax) * 0.5
|
||
lo = cent.amin(0); hi = cent.amax(0); span = (hi - lo).clamp_min(1e-12)
|
||
q = (((cent - lo) / span).clamp(0, 1) * float((1 << 21) - 1)).long()
|
||
morton = (_morton_expand21(q[:, 0]) << 2 | _morton_expand21(q[:, 1]) << 1 | _morton_expand21(q[:, 2])).long()
|
||
order = torch.argsort(morton); msort = morton[order]
|
||
|
||
# delta(i,j): length of the common prefix of the (morton, index) keys of leaves
|
||
# i and j (index breaks ties so duplicate Morton codes still split); -1 if OOB.
|
||
def delta(i, j):
|
||
ok = (j >= 0) & (j < T); jj = j.clamp(0, T - 1)
|
||
x = msort[i] ^ msort[jj]; same = x == 0
|
||
cp = torch.where(same, torch.full_like(x, 63), 62 - _msb_int64(x.clamp_min(1)))
|
||
xi = i ^ jj
|
||
cpi = torch.where(xi == 0, torch.full_like(x, 32), 31 - _msb_int64(xi.clamp_min(1)))
|
||
return torch.where(ok, cp + torch.where(same, cpi, torch.zeros_like(cp)), torch.full_like(x, -1))
|
||
|
||
I = torch.arange(T - 1, device=dev)
|
||
dplus = delta(I, I + 1); dminus = delta(I, I - 1)
|
||
direction = torch.where(dplus >= dminus, torch.ones_like(I), -torch.ones_like(I))
|
||
dmin = torch.minimum(dplus, dminus)
|
||
# range length: exponential probe then binary search
|
||
lmax = torch.full_like(I, 2)
|
||
while True:
|
||
cond = delta(I, I + lmax * direction) > dmin
|
||
if not bool(cond.any()):
|
||
break
|
||
lmax = torch.where(cond, lmax * 2, lmax)
|
||
if int(lmax.max()) > 2 * T:
|
||
break
|
||
l = torch.zeros_like(I); t = lmax.clone()
|
||
while True:
|
||
t = t // 2
|
||
if int(t.max()) == 0:
|
||
break
|
||
cond = delta(I, I + (l + t) * direction) > dmin
|
||
l = torch.where(cond, l + t, l)
|
||
j = I + l * direction
|
||
first = torch.minimum(I, j); last = torch.maximum(I, j)
|
||
# split position: binary search on delta within [first, last]
|
||
dnode = delta(first, last)
|
||
s = torch.zeros_like(I); div = torch.full_like(I, 2); rng = last - first
|
||
while True:
|
||
step = (rng + div - 1) // div
|
||
cond = delta(first, (first + s + step).clamp(max=T - 1)) > dnode
|
||
s = torch.where(cond, s + step, s)
|
||
if int(step.max()) <= 1:
|
||
cond1 = delta(first, (first + s + 1).clamp(max=T - 1)) > dnode
|
||
s = torch.where(cond1, s + 1, s)
|
||
break
|
||
div = div * 2
|
||
gamma = first + s; LEAF = T
|
||
left = torch.where(gamma == first, LEAF + gamma, gamma)
|
||
right = torch.where(gamma + 1 == last, LEAF + gamma + 1, gamma + 1)
|
||
|
||
# node AABBs: leaves seeded, internal unioned bottom-up over a few passes (a
|
||
# balanced tree settles in ~log2(T) passes; the cap is a safety bound).
|
||
nmin = torch.empty((2 * T, 3), device=dev); nmax = torch.empty((2 * T, 3), device=dev)
|
||
nmin[LEAF:] = amin[order]; nmax[LEAF:] = amax[order]
|
||
setm = torch.zeros(2 * T, dtype=torch.bool, device=dev); setm[LEAF:] = True
|
||
for _ in range(128):
|
||
need = ~setm[:T - 1]
|
||
if not bool(need.any()):
|
||
break
|
||
idx = torch.nonzero(need, as_tuple=True)[0]
|
||
ii = idx[setm[left[idx]] & setm[right[idx]]]
|
||
if ii.numel() == 0:
|
||
break
|
||
nmin[ii] = torch.minimum(nmin[left[ii]], nmin[right[ii]])
|
||
nmax[ii] = torch.maximum(nmax[left[ii]], nmax[right[ii]])
|
||
setm[ii] = True
|
||
return dict(LEAF=LEAF, left=left, right=right, nmin=nmin, nmax=nmax, order=order, T=T)
|
||
|
||
|
||
def _closest_points_on_mesh_bvh(Q, tri, bvh, max_stack=64):
|
||
"""Exact closest surface point per query, via per-query stack traversal of the
|
||
triangle BVH (nearest-child-first for tight pruning), pure torch. Returns [N,3].
|
||
|
||
Each while-iteration advances all still-active queries by one node; the active
|
||
set shrinks fast, so even a few thousand iterations are cheap big GPU kernels.
|
||
`max_stack` bounds the per-query stack (= tree height); overflow is counted and
|
||
warned (a handful of texels could be slightly off) rather than silently wrong."""
|
||
dev = Q.device; N = Q.shape[0]
|
||
LEAF = bvh['LEAF']; nmin = bvh['nmin']; nmax = bvh['nmax']
|
||
left = bvh['left']; right = bvh['right']; order = bvh['order']
|
||
stack = torch.full((N, max_stack), -1, dtype=torch.long, device=dev)
|
||
sp = torch.ones(N, dtype=torch.long, device=dev); stack[:, 0] = 0
|
||
best = torch.full((N,), 1e30, device=dev); bestp = Q.clone()
|
||
active = torch.arange(N, device=dev); overflow = 0
|
||
|
||
def aabb_d2(node, q):
|
||
d = (nmin[node] - q).clamp_min(0) + (q - nmax[node]).clamp_min(0)
|
||
return (d * d).sum(-1)
|
||
|
||
while active.numel() > 0:
|
||
a = active; qa = Q[a]
|
||
node = stack[a, sp[a] - 1]; sp[a] = sp[a] - 1
|
||
within = aabb_d2(node, qa) < best[a]
|
||
isleaf = node >= LEAF
|
||
lv = within & isleaf
|
||
if bool(lv.any()):
|
||
ga = a[lv]; tt = tri[order[node[lv] - LEAF]]
|
||
cp = _closest_point_on_triangles(qa[lv], tt[:, 0], tt[:, 1], tt[:, 2])
|
||
d2 = ((cp - qa[lv]) ** 2).sum(-1)
|
||
upd = d2 < best[ga]; gu = ga[upd]; best[gu] = d2[upd]; bestp[gu] = cp[upd]
|
||
iv = within & ~isleaf
|
||
if bool(iv.any()):
|
||
gi = a[iv]; qi = qa[iv]; lc = left[node[iv]]; rc = right[node[iv]]
|
||
dl = aabb_d2(lc, qi); dr = aabb_d2(rc, qi)
|
||
near = torch.where(dl <= dr, lc, rc); far = torch.where(dl <= dr, rc, lc)
|
||
s0 = sp[gi]
|
||
stack[gi, s0.clamp(max=max_stack - 1)] = far; sp[gi] = (s0 + 1).clamp(max=max_stack)
|
||
s1 = sp[gi]; overflow += int((s1 >= max_stack).sum())
|
||
stack[gi, s1.clamp(max=max_stack - 1)] = near; sp[gi] = (s1 + 1).clamp(max=max_stack)
|
||
active = a[sp[a] > 0]
|
||
if overflow:
|
||
logging.warning(f"[back-project] BVH stack overflow on {overflow} pushes "
|
||
f"(max_stack={max_stack}); a few texels may be slightly off — "
|
||
f"raise max_stack if this is large.")
|
||
return bestp
|
||
|
||
|
||
def _back_project_positions(position_map, mask, ref_v, ref_f):
|
||
"""Snap each covered texel's interpolated position onto the reference mesh's true
|
||
surface, so the voxel field is sampled at full surface detail instead of along
|
||
flat triangle chords (the cause of faceted/pixelized bakes on coarse meshes).
|
||
Mirrors o_voxel.to_glb step 7c but with NO cumesh/scipy/trimesh dependency: a
|
||
pure-torch linear BVH (`_build_triangle_bvh`) + exact closest-point traversal,
|
||
the same approach as cuMesh's cuBVH. Returns a new position_map with the covered
|
||
texels replaced."""
|
||
valid = np.ascontiguousarray(position_map[mask].astype(np.float32))
|
||
if valid.shape[0] == 0:
|
||
return position_map
|
||
|
||
import time as _time
|
||
dev = comfy.model_management.get_torch_device()
|
||
rv = ref_v.detach().to(dev).float()
|
||
rf = ref_f.detach().to(dev).long()
|
||
tri = rv[rf]
|
||
Q = torch.from_numpy(valid).to(dev)
|
||
|
||
_t = _time.perf_counter()
|
||
bvh = _build_triangle_bvh(tri)
|
||
_tb = _time.perf_counter()
|
||
bp = _closest_points_on_mesh_bvh(Q, tri, bvh)
|
||
logging.info(f"[back-project] BVH build {_tb - _t:.1f}s + traverse "
|
||
f"{_time.perf_counter() - _tb:.1f}s ({rf.shape[0]} ref tris, "
|
||
f"{valid.shape[0]} texels)")
|
||
|
||
out = position_map.copy()
|
||
out[mask] = bp.detach().cpu().numpy().astype(position_map.dtype)
|
||
return out
|
||
|
||
|
||
def _jfa_fill_gpu(img01, mask):
|
||
"""Fill every uncovered texel with its nearest covered texel's value via GPU
|
||
Jump Flooding (O(log n) passes) — a fast nearest-fill replacement for
|
||
cv2.inpaint on UV seam/gutter filling. img01 [H,W,C] float, mask [H,W] bool
|
||
(True = covered). Returns [H,W,C] float. ~6× faster than cv2 Telea per map."""
|
||
if not mask.any():
|
||
return img01
|
||
dev = comfy.model_management.get_torch_device()
|
||
it = torch.from_numpy(np.ascontiguousarray(img01)).to(dev).float()
|
||
mm = torch.from_numpy(np.ascontiguousarray(mask)).to(dev)
|
||
H, W = mm.shape
|
||
yy, xx = torch.meshgrid(torch.arange(H, device=dev), torch.arange(W, device=dev), indexing="ij")
|
||
by = torch.where(mm, yy, torch.full_like(yy, -1))
|
||
bx = torch.where(mm, xx, torch.full_like(xx, -1))
|
||
INF = torch.full_like(yy, 1 << 30)
|
||
step = 1 << ((max(H, W) - 1).bit_length() - 1)
|
||
while step >= 1:
|
||
for dy in (-step, 0, step):
|
||
for dx in (-step, 0, step):
|
||
if dy == 0 and dx == 0:
|
||
continue
|
||
ny = (yy + dy).clamp(0, H - 1)
|
||
nx = (xx + dx).clamp(0, W - 1)
|
||
cby = by[ny, nx]
|
||
cbx = bx[ny, nx]
|
||
valid = cby >= 0
|
||
dc = torch.where(valid, (yy - cby) ** 2 + (xx - cbx) ** 2, INF)
|
||
db = torch.where(by >= 0, (yy - by) ** 2 + (xx - bx) ** 2, INF)
|
||
take = valid & (dc < db)
|
||
by = torch.where(take, cby, by)
|
||
bx = torch.where(take, cbx, bx)
|
||
step //= 2
|
||
filled = it[by.clamp(0).long(), bx.clamp(0).long()]
|
||
return filled.cpu().numpy()
|
||
|
||
|
||
def _seam_fill(img01, mask, inpaint_radius):
|
||
"""Fill the UV-gutter texels around covered charts so seam sampling doesn't
|
||
pull in black, via GPU Jump Flooding (nearest fill). `inpaint_radius<=0`
|
||
disables; otherwise the radius is ignored — JFA fills every uncovered texel
|
||
by nearest regardless."""
|
||
if inpaint_radius <= 0:
|
||
return img01
|
||
return _jfa_fill_gpu(img01, mask)
|
||
|
||
|
||
def _normalize_uvs_to_unit(uv_np, normalize=True, log_prefix=None):
|
||
"""Uniformly fit a UV layout's bbox into [0,1] when it spills outside the unit
|
||
square (preserves chart aspect ratios; handles packers that overflow slightly).
|
||
No-op when the UVs are already in [0,1] — the normal case for official/xatlas
|
||
unwraps. NOT a UDIM de-tiler; warns if the span looks tiled.
|
||
|
||
Deterministic from the input UVs alone, so the texture bake and
|
||
ApplyTextureToMesh both call it to agree on the exact UVs the texture was baked
|
||
against (the bake no longer emits the mesh, so apply must re-derive them).
|
||
|
||
Returns float32 [N,2]."""
|
||
uv_np = uv_np.astype(np.float32)
|
||
uv_min = uv_np.min(axis=0)
|
||
uv_max = uv_np.max(axis=0)
|
||
out_of_unit = (uv_min.min() < -1e-4) or (uv_max.max() > 1.0001)
|
||
if not (normalize and out_of_unit):
|
||
return uv_np
|
||
extent = float((uv_max - uv_min).max())
|
||
span = max(float(uv_max[0] - uv_min[0]), float(uv_max[1] - uv_min[1]))
|
||
if span > 1.5 and log_prefix:
|
||
logging.warning(
|
||
f"{log_prefix} UV span {span:.2f} looks like a tiled/UDIM layout; "
|
||
f"uniform-fitting it into [0,1] will overlap tiles. Re-unwrap upstream instead.")
|
||
if extent > 0:
|
||
uv_np = ((uv_np - uv_min) / extent).astype(np.float32)
|
||
if log_prefix:
|
||
logging.info(f"{log_prefix} normalized UVs into [0,1] (uniform scale 1/{extent:.4f})")
|
||
return uv_np
|
||
|
||
|
||
def bake_texture_from_voxel_fn(vertices, faces, voxel_coords, voxel_colors,
|
||
resolution, texture_size, uvs, inpaint_radius=3,
|
||
normalize_uvs=True, reference=None, pbar=None):
|
||
"""Bake a baseColor (+ optional metallicRoughness) texture for
|
||
`vertices/faces`, rasterizing in UV space and nearest-voxel-sampling each
|
||
texel from the provided sparse colored voxel volume.
|
||
|
||
`uvs` (N, 2) is the mesh's existing UV layout — baked onto directly (this
|
||
node never unwraps; connect a UV unwrap node upstream). It must be 1:1 with
|
||
`vertices`.
|
||
|
||
Returns (out_vertices, out_faces, out_uvs, out_texture, out_mr).
|
||
|
||
Progress: drives a local tqdm over its 5 stages (uvs → rasterize →
|
||
back-project → sample → finalize) and, if a comfy `pbar` (ProgressBar) is
|
||
passed, ticks it once per stage too — so callers should size it as 5 per
|
||
bake."""
|
||
import time
|
||
|
||
# 5-stage progress: tqdm (console) + optional comfy ProgressBar (UI). _tick is
|
||
# called exactly once at each stage boundary, including no-op stages (e.g. no
|
||
# back-projection), so the comfy pbar stays aligned at 5 ticks per bake.
|
||
try:
|
||
from tqdm import tqdm as _tqdm
|
||
_tq = _tqdm(total=5, desc="Bake texture", leave=False)
|
||
except Exception:
|
||
_tq = None
|
||
|
||
def _tick(name):
|
||
if _tq is not None:
|
||
_tq.set_postfix_str(name)
|
||
_tq.update(1)
|
||
if pbar is not None:
|
||
pbar.update(1)
|
||
|
||
v_np = vertices.detach().cpu().numpy().astype(np.float32)
|
||
f_np = faces.detach().cpu().numpy().astype(np.uint32)
|
||
fcount = int(f_np.shape[0])
|
||
|
||
# Bake onto the mesh's current UVs — no unwrap, no seam-splitting.
|
||
uv_np = uvs.detach().cpu().numpy().astype(np.float32)
|
||
if uv_np.shape[0] != v_np.shape[0]:
|
||
raise ValueError(
|
||
f"BakeTextureFromVoxel: UVs ({uv_np.shape[0]}) must be 1:1 "
|
||
f"with vertices ({v_np.shape[0]})."
|
||
)
|
||
uv_min = uv_np.min(axis=0)
|
||
uv_max = uv_np.max(axis=0)
|
||
oob = int(((uv_np < 0.0) | (uv_np > 1.0)).any(axis=1).sum())
|
||
logging.info(f"[BakeTextureFromVoxel] using existing UVs: {v_np.shape[0]} verts, "
|
||
f"{fcount} faces")
|
||
logging.info(f"[BakeTextureFromVoxel] UV range: u[{uv_min[0]:.3f},{uv_max[0]:.3f}] "
|
||
f"v[{uv_min[1]:.3f},{uv_max[1]:.3f}] out-of-[0,1] verts: {oob}/{uv_np.shape[0]}")
|
||
uv_np = _normalize_uvs_to_unit(uv_np, normalize_uvs, log_prefix="[BakeTextureFromVoxel] ")
|
||
new_verts, new_faces, new_uvs = v_np, f_np, uv_np
|
||
|
||
_tick("uvs")
|
||
|
||
t1 = time.perf_counter()
|
||
position_map, mask = _bake_position_map(new_verts, new_faces, new_uvs, texture_size)
|
||
logging.info(f"[BakeTextureFromVoxel] GL rasterize {texture_size}² in {time.perf_counter() - t1:.1f}s "
|
||
f"({int(mask.sum())}/{mask.size} texels covered)")
|
||
_tick("rasterize")
|
||
|
||
if reference is not None:
|
||
# Back-project texel positions onto the original dense surface before
|
||
# sampling — the o_voxel.to_glb step that makes the bake smooth on coarse
|
||
# meshes (instead of sampling along flat triangle chords).
|
||
tb = time.perf_counter()
|
||
position_map = _back_project_positions(position_map, mask, reference[0], reference[1])
|
||
logging.info(f"[BakeTextureFromVoxel] BVH back-project in {time.perf_counter() - tb:.1f}s")
|
||
_tick("back-project")
|
||
|
||
t2 = time.perf_counter()
|
||
attrs = _sample_voxel_attrs_per_texel(
|
||
position_map, mask, voxel_coords, voxel_colors, resolution,
|
||
)
|
||
logging.info(f"[BakeTextureFromVoxel] voxel sample in {time.perf_counter() - t2:.1f}s "
|
||
f"({attrs.shape[-1]} channels)")
|
||
_tick("sample")
|
||
|
||
# Split into PBR maps. Layout matches upstream pbr_attr_layout:
|
||
# 0:3 base_color, 3 metallic, 4 roughness, 5 alpha.
|
||
C = attrs.shape[-1]
|
||
base_color = attrs[..., 0:3]
|
||
has_pbr = C >= 5
|
||
metallic = attrs[..., 3:4] if C >= 4 else None
|
||
roughness = attrs[..., 4:5] if C >= 5 else None
|
||
# alpha channel exists at index 5 but we keep meshes opaque (upstream uses
|
||
# alpha_mode=OPAQUE in the remesh path); plumb later if needed.
|
||
|
||
t3 = time.perf_counter()
|
||
base_color = _seam_fill(np.ascontiguousarray(base_color), mask, inpaint_radius)
|
||
mr_image = None
|
||
if has_pbr:
|
||
# glTF metallicRoughness: R unused, G=roughness, B=metallic.
|
||
mr = np.concatenate([np.zeros_like(roughness), roughness, metallic], axis=-1)
|
||
mr_image = _seam_fill(np.ascontiguousarray(mr), mask, inpaint_radius)
|
||
if inpaint_radius > 0:
|
||
logging.info(f"[BakeTextureFromVoxel] inpaint in {time.perf_counter() - t3:.1f}s")
|
||
|
||
device = vertices.device
|
||
out_v = torch.from_numpy(new_verts).to(device=device, dtype=torch.float32)
|
||
out_f = torch.from_numpy(new_faces.astype(np.int32)).to(device=device, dtype=torch.int32)
|
||
out_uvs = torch.from_numpy(new_uvs).to(device=device, dtype=torch.float32)
|
||
out_tex = torch.from_numpy(np.ascontiguousarray(base_color)).to(device=device, dtype=torch.float32)
|
||
out_mr = (torch.from_numpy(np.ascontiguousarray(mr_image)).to(device=device, dtype=torch.float32)
|
||
if mr_image is not None else None)
|
||
_tick("finalize")
|
||
if _tq is not None:
|
||
_tq.close()
|
||
return out_v, out_f, out_uvs, out_tex, out_mr
|
||
|
||
|
||
def _per_vertex_normals(verts_np, faces_np):
|
||
"""Area-weighted per-vertex normals (unit length) for a triangle mesh."""
|
||
v = verts_np.astype(np.float64)
|
||
f = faces_np.astype(np.int64)
|
||
# Un-normalized face normals are area-weighted (cross product magnitude = 2*area),
|
||
# so accumulating them onto vertices gives an area-weighted vertex normal.
|
||
fn = np.cross(v[f[:, 1]] - v[f[:, 0]], v[f[:, 2]] - v[f[:, 0]])
|
||
vn = np.zeros_like(v)
|
||
for k in range(3):
|
||
np.add.at(vn, f[:, k], fn)
|
||
vn = vn / np.clip(np.linalg.norm(vn, axis=1, keepdims=True), 1e-12, None)
|
||
return vn.astype(np.float32)
|
||
|
||
|
||
def bake_texture_multiview_fn(vertices, faces, voxel_coords, voxel_colors, resolution,
|
||
texture_size, views, uvs, blend_temperature=0.25,
|
||
inpaint_radius=3, normalize_uvs=True):
|
||
"""Bake a baseColor texture by projecting view photos onto the mesh.
|
||
|
||
Reuses bake_texture_from_voxel_fn for the UV-space bake + the nearest-voxel
|
||
fallback colour, then overlays photo colour on every covered+visible texel:
|
||
each texel's world position/normal is projected into each view, occlusion is
|
||
resolved with a texel z-buffer, and the views are blended weighted by how
|
||
directly each camera faces the surface. Texels seen by no view keep the voxel
|
||
colour. The seam inpaint runs last, over the composited result.
|
||
|
||
`views`: list of dicts {image[H,W,3] in [0,1], azimuth_deg, transform_matrix[4,4],
|
||
camera_angle_x (scalar tensor), image_resolution}. All Pixal3D views share the
|
||
one front camera and differ only by azimuth.
|
||
|
||
Returns (verts, faces, uvs, tex, mr) — same shape contract as
|
||
bake_texture_from_voxel_fn, so the node attaches them identically."""
|
||
from comfy.ldm.trellis2 import multiview_bake as mvbake
|
||
|
||
# Voxel bake → unwrapped geometry + fallback colour (inpaint deferred to the end).
|
||
out_v, out_f, out_uvs, voxel_tex, voxel_mr = bake_texture_from_voxel_fn(
|
||
vertices, faces, voxel_coords, voxel_colors, resolution=resolution,
|
||
texture_size=texture_size, uvs=uvs, inpaint_radius=0,
|
||
normalize_uvs=normalize_uvs)
|
||
|
||
v_np = out_v.detach().cpu().numpy().astype(np.float32)
|
||
f_np = out_f.detach().cpu().numpy().astype(np.uint32)
|
||
uv_np = out_uvs.detach().cpu().numpy().astype(np.float32)
|
||
|
||
# Per-texel world position + normal (the GL baker outputs any per-vertex vec3).
|
||
position_map, mask = _bake_position_map(v_np, f_np, uv_np, texture_size)
|
||
normal_map, _ = _bake_position_map(_per_vertex_normals(v_np, f_np), f_np, uv_np, texture_size)
|
||
|
||
device = out_v.device
|
||
base = voxel_tex.detach().cpu().numpy().copy()
|
||
if mask.any() and views:
|
||
pos = torch.from_numpy(np.ascontiguousarray(position_map[mask])).to(device)
|
||
nrm = torch.from_numpy(np.ascontiguousarray(normal_map[mask])).to(device)
|
||
fallback = torch.from_numpy(np.ascontiguousarray(base[mask])).to(device)
|
||
view_objs = [{
|
||
"image": vw["image"].to(device),
|
||
"azimuth_deg": vw["azimuth_deg"],
|
||
"transform_matrix": vw["transform_matrix"].to(device),
|
||
"camera_angle_x": vw["camera_angle_x"].to(device),
|
||
"image_resolution": vw["image_resolution"],
|
||
} for vw in views]
|
||
rgb, _seen = mvbake.composite_views(pos, nrm, view_objs, fallback, blend_temperature)
|
||
base[mask] = rgb.detach().cpu().numpy()
|
||
|
||
base = _seam_fill(np.ascontiguousarray(base), mask, inpaint_radius)
|
||
|
||
out_tex = torch.from_numpy(np.ascontiguousarray(base)).to(device=device, dtype=torch.float32)
|
||
return out_v, out_f, out_uvs, out_tex, voxel_mr
|
||
|
||
|
||
def _mr_channel(packed_mr, ch, ref):
|
||
"""Pull one channel out of a packed glTF MR map (G=roughness at idx 1, B=metallic
|
||
at idx 2) as a 3-channel grayscale IMAGE [H,W,3] in [0,1]. Returns black sized
|
||
like `ref` when there's no MR map (non-PBR voxel field)."""
|
||
if packed_mr is None:
|
||
return torch.zeros_like(ref.float().cpu())
|
||
m = packed_mr.float().clamp(0.0, 1.0).cpu()
|
||
return m[..., ch:ch + 1].expand(-1, -1, 3).contiguous()
|
||
|
||
|
||
class BakeTextureFromVoxel(IO.ComfyNode):
|
||
@classmethod
|
||
def define_schema(cls):
|
||
return IO.Schema(
|
||
node_id="BakeTextureFromVoxel",
|
||
display_name="Bake Texture From Voxel",
|
||
category="latent/3d",
|
||
description=(
|
||
"Bakes PBR textures onto the mesh's existing UV layout by rasterizing it "
|
||
"in UV space via OpenGL (ComfyUI's PyOpenGL backend) and trilinear-sampling "
|
||
"the input sparse voxel volume. Does NOT unwrap — connect a UV unwrap node "
|
||
"(e.g. Trellis2OfficialUnwrap or TorchXatlasUVWrap) upstream. Outputs the "
|
||
"baked maps as IMAGEs: base_color, plus metallic and roughness as separate "
|
||
"grayscale maps (both black when the voxel field has no PBR set). "
|
||
"Preview/save/post-process them, then feed them to ApplyTextureToMesh (with "
|
||
"the SAME mesh) to attach them for SaveGLB. UVs that spill outside [0,1] are "
|
||
"uniformly fit into the unit square."
|
||
),
|
||
inputs=[
|
||
IO.Mesh.Input("mesh"),
|
||
IO.Voxel.Input("voxel_colors"),
|
||
IO.Int.Input("texture_size", default=1024, min=64, max=8192,
|
||
tooltip="Square texture resolution. Larger = sharper but slower / bigger file."),
|
||
IO.Mesh.Input("reference_mesh", optional=True,
|
||
tooltip=(
|
||
"Optional original (dense, pre-decimation) mesh. If connected, each "
|
||
"texel is back-projected onto its true surface before sampling — the "
|
||
"o_voxel.to_glb step that removes faceted/pixelized baking on coarse "
|
||
"meshes. Pure scipy+torch, no extra deps.")),
|
||
],
|
||
outputs=[
|
||
IO.Image.Output(display_name="base_color"),
|
||
IO.Image.Output(display_name="metallic"),
|
||
IO.Image.Output(display_name="roughness"),
|
||
],
|
||
)
|
||
|
||
@classmethod
|
||
def execute(cls, mesh, voxel_colors, texture_size, reference_mesh=None):
|
||
# Seam-gutter inpaint radius is hardcoded to 3 (matches the official to_glb);
|
||
# it's an on/off-grade knob — Telea fills the whole gutter regardless of value.
|
||
inpaint_radius = 3
|
||
voxels = voxel_colors
|
||
coords = voxels.data
|
||
colors = voxels.voxel_colors
|
||
resolution = voxels.resolution
|
||
mesh_uvs = getattr(mesh, "uvs", None)
|
||
if mesh_uvs is None:
|
||
raise ValueError(
|
||
"BakeTextureFromVoxel: input mesh has no UVs. This node bakes onto the "
|
||
"mesh's existing UV layout and never unwraps — connect a UV unwrap node "
|
||
"(e.g. Trellis2OfficialUnwrap or TorchXatlasUVWrap) before it.")
|
||
|
||
if coords.shape[-1] == 4:
|
||
# Sparse coords have a batch column; bake per-item.
|
||
batch_idx = coords[:, 0].long()
|
||
voxel_xyz = coords[:, 1:]
|
||
mesh_batch_size = int(mesh.vertices.shape[0])
|
||
out_tex, out_mr = [], []
|
||
# 5 stage ticks per item (see bake_texture_from_voxel_fn); skipped items
|
||
# tick all 5 so the bar stays aligned.
|
||
pbar = comfy.utils.ProgressBar(mesh_batch_size * 5)
|
||
for i in range(mesh_batch_size):
|
||
sel = batch_idx == i
|
||
item_coords = voxel_xyz[sel]
|
||
item_colors = colors[sel]
|
||
v_i, f_i, _ = get_mesh_batch_item(mesh, i)
|
||
if item_coords.shape[0] == 0 or f_i.numel() == 0:
|
||
logging.warning(f"BakeTextureFromVoxel: skipping batch {i} (empty voxel/mesh)")
|
||
pbar.update(5)
|
||
continue
|
||
ev_i = mesh_uvs[i, :v_i.shape[0]]
|
||
ref_i = None
|
||
if reference_mesh is not None:
|
||
rv_i, rf_i, _ = get_mesh_batch_item(reference_mesh, i)
|
||
ref_i = (rv_i, rf_i)
|
||
_bv, _bf, _bu, bt, bmr = bake_texture_from_voxel_fn(
|
||
v_i, f_i, item_coords, item_colors,
|
||
resolution=resolution, texture_size=texture_size,
|
||
uvs=ev_i, inpaint_radius=inpaint_radius,
|
||
reference=ref_i, pbar=pbar,
|
||
)
|
||
out_tex.append(bt); out_mr.append(bmr)
|
||
if not out_tex:
|
||
# Every item skipped (degenerate) — emit one black map so the IMAGE
|
||
# outputs stay valid.
|
||
black = torch.zeros((1, texture_size, texture_size, 3))
|
||
return IO.NodeOutput(black, black, black)
|
||
# All maps are texture_size² — stack into [B,H,W,3] IMAGE batches. The
|
||
# packed MR (G=roughness, B=metallic) is split into separate grayscale
|
||
# maps; both black where the voxel field carried no PBR set.
|
||
base_img = torch.stack([t.float().clamp(0.0, 1.0).cpu() for t in out_tex], dim=0)
|
||
metallic_img = torch.stack([_mr_channel(m, 2, out_tex[0]) for m in out_mr], dim=0)
|
||
roughness_img = torch.stack([_mr_channel(m, 1, out_tex[0]) for m in out_mr], dim=0)
|
||
return IO.NodeOutput(base_img, metallic_img, roughness_img)
|
||
|
||
# Single-item path.
|
||
v0 = mesh.vertices.squeeze(0)
|
||
f0 = mesh.faces.squeeze(0)
|
||
ev0 = mesh_uvs.squeeze(0)
|
||
ref0 = None
|
||
if reference_mesh is not None:
|
||
ref0 = (reference_mesh.vertices.squeeze(0), reference_mesh.faces.squeeze(0))
|
||
pbar = comfy.utils.ProgressBar(5) # 5 stage ticks (see bake_texture_from_voxel_fn)
|
||
_bv, _bf, _bu, bt, bmr = bake_texture_from_voxel_fn(
|
||
v0, f0, coords, colors,
|
||
resolution=resolution, texture_size=texture_size,
|
||
uvs=ev0, inpaint_radius=inpaint_radius,
|
||
reference=ref0, pbar=pbar,
|
||
)
|
||
base_img = bt.float().clamp(0.0, 1.0).cpu().unsqueeze(0)
|
||
metallic_img = _mr_channel(bmr, 2, bt).unsqueeze(0)
|
||
roughness_img = _mr_channel(bmr, 1, bt).unsqueeze(0)
|
||
return IO.NodeOutput(base_img, metallic_img, roughness_img)
|
||
|
||
|
||
class MeshTextureToImage(IO.ComfyNode):
|
||
@classmethod
|
||
def define_schema(cls):
|
||
return IO.Schema(
|
||
node_id="MeshTextureToImage",
|
||
display_name="Mesh Texture to Image",
|
||
category="latent/3d",
|
||
description=(
|
||
"Extracts a mesh's baked textures as IMAGE outputs for preview/save. "
|
||
"base_color is the baseColor map; metallic_roughness is the packed "
|
||
"glTF MR map (R unused, G=roughness, B=metallic) — black if the mesh "
|
||
"has no PBR texture."
|
||
),
|
||
inputs=[IO.Mesh.Input("mesh")],
|
||
outputs=[
|
||
IO.Image.Output(display_name="base_color"),
|
||
IO.Image.Output(display_name="metallic_roughness"),
|
||
IO.Image.Output(display_name="metallic"),
|
||
IO.Image.Output(display_name="roughness"),
|
||
],
|
||
)
|
||
|
||
@classmethod
|
||
def execute(cls, mesh):
|
||
def _as_image(tex):
|
||
# Mesh textures are (B, H, W, 3) float in [0, 1] — already IMAGE layout.
|
||
if tex is None:
|
||
return None
|
||
t = tex.float().clamp(0.0, 1.0).cpu()
|
||
if t.ndim == 3:
|
||
t = t.unsqueeze(0)
|
||
return t
|
||
|
||
base = _as_image(getattr(mesh, "texture", None))
|
||
mr = _as_image(getattr(mesh, "metallic_roughness", None))
|
||
|
||
if base is None:
|
||
raise ValueError(
|
||
"MeshTextureToImage: mesh has no baseColor texture. Run "
|
||
"BakeTextureFromVoxel first (PaintMesh only sets vertex colors, not a texture)."
|
||
)
|
||
if mr is None:
|
||
mr = torch.zeros_like(base)
|
||
# Split the packed glTF MR map into single-channel grayscale previews:
|
||
# G=roughness, B=metallic. Replicated to 3 channels so they display
|
||
# as proper grayscale IMAGEs.
|
||
metallic = mr[..., 2:3].expand(-1, -1, -1, 3).contiguous()
|
||
roughness = mr[..., 1:2].expand(-1, -1, -1, 3).contiguous()
|
||
return IO.NodeOutput(base, mr, metallic, roughness)
|
||
|
||
|
||
class ApplyTextureToMesh(IO.ComfyNode):
|
||
@classmethod
|
||
def define_schema(cls):
|
||
return IO.Schema(
|
||
node_id="ApplyTextureToMesh",
|
||
display_name="Apply Texture to Mesh",
|
||
category="latent/3d",
|
||
description=(
|
||
"Attaches baked texture IMAGEs to a mesh's existing UV layout so SaveGLB "
|
||
"serializes them as baseColorTexture / metallicRoughnessTexture maps. Pairs "
|
||
"with BakeTextureFromVoxel: feed it the SAME mesh you baked from, plus that "
|
||
"node's base_color (and optionally metallic / roughness grayscale maps) — the "
|
||
"UVs must match the ones the texture was baked against, so don't re-unwrap in "
|
||
"between. metallic and roughness are repacked into the glTF MR map "
|
||
"(G=roughness, B=metallic); leave them unconnected for non-PBR meshes (a "
|
||
"missing metallic defaults to 0, a missing roughness to 1). Lets you preview / "
|
||
"upscale / edit the baked maps before applying them."
|
||
),
|
||
inputs=[
|
||
IO.Mesh.Input("mesh"),
|
||
IO.Image.Input("base_color"),
|
||
IO.Image.Input("metallic", optional=True),
|
||
IO.Image.Input("roughness", optional=True),
|
||
],
|
||
outputs=[IO.Mesh.Output("mesh")],
|
||
)
|
||
|
||
@classmethod
|
||
def execute(cls, mesh, base_color, metallic=None, roughness=None):
|
||
mesh_uvs = getattr(mesh, "uvs", None)
|
||
if mesh_uvs is None:
|
||
raise ValueError(
|
||
"ApplyTextureToMesh: mesh has no UVs. Connect the same UV-unwrapped mesh "
|
||
"you fed to BakeTextureFromVoxel (this node attaches onto existing UVs and "
|
||
"never unwraps).")
|
||
|
||
# Re-derive the exact UVs the bake rasterized against — it uniformly fits
|
||
# out-of-[0,1] layouts into the unit square, so apply the identical
|
||
# deterministic transform here (per batch item, over each item's real verts).
|
||
if mesh_uvs.ndim == 3:
|
||
new_uvs = mesh_uvs.clone()
|
||
for i in range(mesh_uvs.shape[0]):
|
||
v_i, _f_i, _ = get_mesh_batch_item(mesh, i)
|
||
n = v_i.shape[0]
|
||
norm = _normalize_uvs_to_unit(mesh_uvs[i, :n].detach().cpu().numpy())
|
||
new_uvs[i, :n] = torch.from_numpy(norm).to(new_uvs)
|
||
else:
|
||
norm = _normalize_uvs_to_unit(mesh_uvs.detach().cpu().numpy())
|
||
new_uvs = torch.from_numpy(norm).to(mesh_uvs)
|
||
|
||
out_mesh = copy.copy(mesh)
|
||
out_mesh.uvs = new_uvs
|
||
out_mesh.texture = base_color.float().clamp(0.0, 1.0).cpu()
|
||
if metallic is not None or roughness is not None:
|
||
# Repack separate grayscale maps into glTF MR: R unused, G=roughness,
|
||
# B=metallic. Size defaults off whichever map is connected; a missing
|
||
# channel falls back to a sensible scalar (metal 0 / rough 1).
|
||
prov = (metallic if metallic is not None else roughness).float().clamp(0.0, 1.0).cpu()
|
||
B, H, W, _ = prov.shape
|
||
rough_ch = (roughness.float().clamp(0.0, 1.0).cpu()[..., 0:1]
|
||
if roughness is not None else torch.ones((B, H, W, 1)))
|
||
metal_ch = (metallic.float().clamp(0.0, 1.0).cpu()[..., 0:1]
|
||
if metallic is not None else torch.zeros((B, H, W, 1)))
|
||
out_mesh.metallic_roughness = torch.cat([torch.zeros((B, H, W, 1)), rough_ch, metal_ch], dim=-1)
|
||
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 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)
|
||
max_v = max(v.shape[0] for v in v_list)
|
||
for i in range(len(v_list)):
|
||
if v_list[i].shape[0] < max_v:
|
||
pad = torch.zeros(max_v - v_list[i].shape[0], 3, device=v_list[i].device, dtype=v_list[i].dtype)
|
||
v_list[i] = torch.cat([v_list[i], pad], dim=0)
|
||
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
|
||
|
||
boundary_mask = torch.isin(packed_undirected, boundary_packed)
|
||
b_edges = edges_sorted[boundary_mask]
|
||
|
||
adj = {}
|
||
for i in range(b_edges.shape[0]):
|
||
a = b_edges[i, 0].item()
|
||
b = b_edges[i, 1].item()
|
||
adj.setdefault(a, []).append(b)
|
||
adj.setdefault(b, []).append(a)
|
||
|
||
# Trace all boundary loops
|
||
loops = []
|
||
visited = set()
|
||
for start_node in adj.keys():
|
||
if start_node in visited:
|
||
continue
|
||
curr = start_node
|
||
prev = -1
|
||
loop = []
|
||
while curr not in visited:
|
||
visited.add(curr)
|
||
loop.append(curr)
|
||
neighbors = adj[curr]
|
||
candidates = [n for n in neighbors if n != prev]
|
||
if not candidates:
|
||
loop = []
|
||
break
|
||
next_node = candidates[0]
|
||
prev, curr = curr, next_node
|
||
if curr == start_node:
|
||
loops.append(loop)
|
||
break
|
||
|
||
if not loops:
|
||
return v, f
|
||
|
||
# Mesh normal for winding orientation only
|
||
face_normals = torch.linalg.cross(
|
||
v[f[:, 1]] - v[f[:, 0]],
|
||
v[f[:, 2]] - v[f[:, 0]],
|
||
dim=-1
|
||
)
|
||
mesh_normal = face_normals.mean(dim=0)
|
||
mesh_normal = mesh_normal / (torch.norm(mesh_normal) + 1e-8)
|
||
|
||
# === FIX: Fill ALL boundary loops below perimeter threshold ===
|
||
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]
|
||
|
||
# Perimeter check
|
||
next_v = torch.roll(loop_v, -1, dims=0)
|
||
diffs = loop_v - next_v
|
||
perimeter = torch.norm(diffs, dim=1).sum().item()
|
||
|
||
if perimeter > max_perimeter:
|
||
continue
|
||
|
||
# Ensure CCW winding consistent with mesh
|
||
cross = torch.linalg.cross(loop_v, next_v, dim=-1)
|
||
loop_normal = cross.sum(dim=0)
|
||
loop_normal = loop_normal / (torch.norm(loop_normal) + 1e-8)
|
||
if torch.dot(loop_normal, mesh_normal) < 0:
|
||
loop = loop[::-1]
|
||
loop_t = torch.tensor(loop, device=device, dtype=torch.long)
|
||
loop_v = v[loop_t]
|
||
|
||
if len(loop) == 3:
|
||
new_faces.append([loop[0], loop[1], loop[2]])
|
||
else:
|
||
centroid = loop_v.mean(dim=0)
|
||
new_verts.append(centroid)
|
||
for i in range(len(loop)):
|
||
new_faces.append([loop[i], loop[(i + 1) % len(loop)], v_idx])
|
||
v_idx += 1
|
||
|
||
if new_verts:
|
||
v = torch.cat([v, torch.stack(new_verts)], dim=0)
|
||
if new_faces:
|
||
f = torch.cat([f, torch.tensor(new_faces, device=device, dtype=torch.long)], dim=0)
|
||
|
||
return v, f
|
||
|
||
|
||
def _fill_holes_v2_diagnostic(verts, faces, max_perimeter):
|
||
"""Topology dump for debugging missed-hole cases. Logs edge count
|
||
distribution, cycle count, and per-cycle (size, perimeter)."""
|
||
device = verts.device
|
||
V = verts.shape[0]
|
||
F = faces.shape[0]
|
||
e_all = torch.cat([faces[:, [0, 1]], faces[:, [1, 2]], faces[:, [2, 0]]], dim=0)
|
||
e_sorted, _ = e_all.sort(dim=1)
|
||
packed = e_sorted[:, 0].long() * V + e_sorted[:, 1].long()
|
||
unique_packed, counts = torch.unique(packed, return_counts=True)
|
||
|
||
n_boundary = int((counts == 1).sum().item())
|
||
n_interior = int((counts == 2).sum().item())
|
||
n_nonmanifold = int((counts >= 3).sum().item())
|
||
nm_max = int(counts.max().item()) if counts.numel() > 0 else 0
|
||
nm_share_breakdown = []
|
||
if n_nonmanifold > 0:
|
||
# Show top-5 non-manifold counts
|
||
nm_counts = counts[counts >= 3]
|
||
unique_nm, cnt_nm = torch.unique(nm_counts, return_counts=True)
|
||
for c, n in zip(unique_nm.tolist(), cnt_nm.tolist()):
|
||
nm_share_breakdown.append(f"{n} edges×{c}faces")
|
||
|
||
logging.info(f"[FillHoles diag] V={V} F={F} | "
|
||
f"boundary(cnt==1)={n_boundary} interior(cnt==2)={n_interior} "
|
||
f"non-manifold(cnt>=3)={n_nonmanifold} (max={nm_max})")
|
||
if nm_share_breakdown:
|
||
logging.info(f"[FillHoles diag] non-manifold breakdown: {', '.join(nm_share_breakdown[:5])}")
|
||
|
||
if n_boundary == 0:
|
||
logging.info("[FillHoles diag] no boundary edges → no cycles to fill")
|
||
return
|
||
|
||
# Walk components same as production path (bidir-prop, by-vertex count).
|
||
boundary_packed = unique_packed[counts == 1]
|
||
is_b = torch.isin(packed, boundary_packed)
|
||
b_directed = e_all[is_b]
|
||
src = b_directed[:, 0].long()
|
||
tgt = b_directed[:, 1].long()
|
||
|
||
labels = torch.arange(V, dtype=torch.long, device=device)
|
||
for _ in range(64):
|
||
edge_min = torch.minimum(labels[src], labels[tgt])
|
||
new_labels = labels.clone()
|
||
new_labels.scatter_reduce_(0, src, edge_min, reduce="amin", include_self=True)
|
||
new_labels.scatter_reduce_(0, tgt, edge_min, reduce="amin", include_self=True)
|
||
new_labels = new_labels[new_labels]
|
||
if torch.equal(new_labels, labels):
|
||
break
|
||
labels = new_labels
|
||
|
||
edge_component = labels[src]
|
||
unique_components, component_idx = torch.unique(edge_component, return_inverse=True)
|
||
L = unique_components.shape[0]
|
||
edge_len = (verts[src] - verts[tgt]).norm(dim=-1)
|
||
perim = torch.zeros(L, dtype=verts.dtype, device=device)
|
||
perim.scatter_add_(0, component_idx, edge_len)
|
||
edge_count = torch.bincount(component_idx, minlength=L)
|
||
|
||
pair_keys = torch.unique(torch.cat([
|
||
component_idx.long() * V + src,
|
||
component_idx.long() * V + tgt,
|
||
]))
|
||
pair_c = pair_keys // V
|
||
vert_count = torch.bincount(pair_c, minlength=L)
|
||
|
||
# Open chain = vert_count == edge_count + 1; closed cycle = vert_count == edge_count.
|
||
is_chain = (vert_count == edge_count + 1)
|
||
is_cycle = (vert_count == edge_count) & (vert_count > 0)
|
||
is_other = ~(is_chain | is_cycle)
|
||
|
||
# Match production filter (cycles only, default fill_chains=False, default max_verts=16).
|
||
MAX_VERTS_DEFAULT = 16
|
||
CENTROID_FAN_THRESHOLD = 8
|
||
cycle_perim_ok = is_cycle & (perim < max_perimeter)
|
||
cycle_size_ok = is_cycle & (vert_count >= 3) & (vert_count <= MAX_VERTS_DEFAULT)
|
||
actually_kept = is_cycle & (vert_count >= 3) & (vert_count <= MAX_VERTS_DEFAULT) & (perim < max_perimeter)
|
||
|
||
# Triangulation strategy split.
|
||
vfan = actually_kept & (vert_count <= CENTROID_FAN_THRESHOLD)
|
||
cfan = actually_kept & (vert_count > CENTROID_FAN_THRESHOLD)
|
||
vfan_tris = int((vert_count[vfan] - 2).sum().item()) # N-2 tris per N-vert cycle
|
||
cfan_tris = int(vert_count[cfan].sum().item()) # N tris per N-vert cycle
|
||
cfan_new_verts = int(cfan.sum().item()) # 1 centroid per centroid-fan component
|
||
|
||
logging.info(f"[FillHoles diag] components={L} "
|
||
f"cycles={int(is_cycle.sum().item())} chains={int(is_chain.sum().item())} "
|
||
f"non-simple={int(is_other.sum().item())}")
|
||
logging.info(f"[FillHoles diag] (with default filter: cycles only, verts in [3,{MAX_VERTS_DEFAULT}], perim<{max_perimeter})")
|
||
logging.info(f"[FillHoles diag] actually kept={int(actually_kept.sum().item())} "
|
||
f"cycle rejected by perim={int((is_cycle & ~cycle_perim_ok).sum().item())} "
|
||
f"cycle rejected by verts={int((is_cycle & ~cycle_size_ok).sum().item())}")
|
||
logging.info(f"[FillHoles diag] vertex-fan: {int(vfan.sum().item())} cycles → {vfan_tris} tris (no new verts)")
|
||
logging.info(f"[FillHoles diag] centroid-fan: {int(cfan.sum().item())} cycles → {cfan_tris} tris + {cfan_new_verts} new verts")
|
||
|
||
# Cycle vert-count distribution
|
||
if is_cycle.any():
|
||
from collections import Counter
|
||
cycle_sizes = vert_count[is_cycle].tolist()
|
||
sc = Counter(cycle_sizes)
|
||
# show buckets: 3, 4, 5, 6, 7-10, 11-20, 21-50, 51+
|
||
buckets = {"3":0,"4":0,"5":0,"6":0,"7-10":0,"11-20":0,"21-50":0,"51+":0}
|
||
for s, n in sc.items():
|
||
if s == 3: buckets["3"] += n
|
||
elif s == 4: buckets["4"] += n
|
||
elif s == 5: buckets["5"] += n
|
||
elif s == 6: buckets["6"] += n
|
||
elif s <= 10: buckets["7-10"] += n
|
||
elif s <= 20: buckets["11-20"] += n
|
||
elif s <= 50: buckets["21-50"] += n
|
||
else: buckets["51+"] += n
|
||
logging.info(f"[FillHoles diag] cycle vert-count buckets: {buckets}")
|
||
|
||
if is_cycle.any():
|
||
cycle_perims = perim[is_cycle].cpu().tolist()
|
||
head = sorted(cycle_perims, reverse=True)[:10]
|
||
logging.info(f"[FillHoles diag] top-10 cycle perimeters: "
|
||
f"{['%.4f' % p for p in head]}")
|
||
|
||
|
||
def _fill_holes_v2_gpu(verts, faces, max_perimeter, colors=None, fill_chains=False, max_verts=16):
|
||
# Bidirectional connected-component labeling on the undirected boundary
|
||
# subgraph. Fixes the original pointer-doubling bug where chains starting at
|
||
# the lowest-id vertex never propagated their label backward, producing
|
||
# spurious size-1/2 fragments (see qem_core._propagate_face_labels for
|
||
# the same pattern applied to face adjacency).
|
||
#
|
||
# By default we only close TRUE cycles (each boundary vert has degree 2 in
|
||
# the component). Chains tend to be either real surface boundaries or
|
||
# fragments of a cycle broken by non-manifold edges — fan-filling them with
|
||
# an arbitrary centroid produces overlapping/noisy geometry. Pass
|
||
# fill_chains=True to opt in to chain closure.
|
||
device = verts.device
|
||
V = verts.shape[0]
|
||
dtype = verts.dtype
|
||
|
||
e_all = torch.cat([faces[:, [0, 1]], faces[:, [1, 2]], faces[:, [2, 0]]], dim=0)
|
||
e_sorted, _ = e_all.sort(dim=1)
|
||
packed = e_sorted[:, 0].long() * V + e_sorted[:, 1].long()
|
||
unique_packed, counts = torch.unique(packed, return_counts=True)
|
||
boundary_packed = unique_packed[counts == 1]
|
||
if boundary_packed.numel() == 0:
|
||
return verts, faces, colors, 0
|
||
is_b = torch.isin(packed, boundary_packed)
|
||
|
||
b_directed = e_all[is_b]
|
||
src = b_directed[:, 0].long()
|
||
tgt = b_directed[:, 1].long()
|
||
|
||
# Undirected bidirectional min-prop with path compression.
|
||
labels = torch.arange(V, dtype=torch.long, device=device)
|
||
for _ in range(64):
|
||
edge_min = torch.minimum(labels[src], labels[tgt])
|
||
new_labels = labels.clone()
|
||
new_labels.scatter_reduce_(0, src, edge_min, reduce="amin", include_self=True)
|
||
new_labels.scatter_reduce_(0, tgt, edge_min, reduce="amin", include_self=True)
|
||
new_labels = new_labels[new_labels] # path compression
|
||
if torch.equal(new_labels, labels):
|
||
break
|
||
labels = new_labels
|
||
|
||
# Each boundary edge -> its component id. After bidir-prop, labels[src] == labels[tgt].
|
||
edge_component = labels[src]
|
||
unique_components, component_idx = torch.unique(edge_component, return_inverse=True)
|
||
L = unique_components.shape[0]
|
||
edge_count = torch.bincount(component_idx, minlength=L)
|
||
edge_len = (verts[src] - verts[tgt]).norm(dim=-1)
|
||
perim = torch.zeros(L, dtype=dtype, device=device)
|
||
perim.scatter_add_(0, component_idx, edge_len)
|
||
|
||
# Unique boundary-vertex set per component, to count verts and place centroids.
|
||
# Pack (component, vert) into one key; dedup via torch.unique.
|
||
pair_keys = torch.cat([
|
||
component_idx.long() * V + src,
|
||
component_idx.long() * V + tgt,
|
||
])
|
||
pair_keys = torch.unique(pair_keys)
|
||
pair_v = pair_keys % V
|
||
pair_c = pair_keys // V
|
||
vert_count = torch.bincount(pair_c, minlength=L)
|
||
|
||
centroids = torch.zeros((L, 3), dtype=dtype, device=device)
|
||
centroids.scatter_add_(0, pair_c[:, None].expand(-1, 3), verts[pair_v])
|
||
centroids = centroids / vert_count.clamp_min(1).to(dtype).unsqueeze(-1)
|
||
|
||
# Identify closed cycles: every boundary vert in the component has exactly
|
||
# degree 2 in the boundary subgraph. Equivalent: vert_count == edge_count.
|
||
is_cycle_component = (vert_count == edge_count) & (vert_count > 0)
|
||
|
||
# Filter: keep cycles (always) and chains (only if fill_chains=True), under perim limit.
|
||
# Also cap vert_count: fan-from-centroid only triangulates correctly for small,
|
||
# near-planar cycles. Larger holes produce overlapping geometry because the
|
||
# centroid lands far from any surface.
|
||
size_ok = (vert_count >= 3) & (vert_count <= max_verts) & (perim < max_perimeter)
|
||
if fill_chains:
|
||
keep_component = size_ok
|
||
else:
|
||
keep_component = is_cycle_component & size_ok
|
||
if not keep_component.any():
|
||
return verts, faces, colors, 0
|
||
# Only centroid-fan components actually allocate a new vertex slot.
|
||
# We pre-compute their indices here so the triangulation step below has them ready.
|
||
use_centroid_per_comp_pre = keep_component & (vert_count > 8) # threshold mirrored below
|
||
centroid_long = use_centroid_per_comp_pre.long()
|
||
centroid_idx_per_comp = V + centroid_long.cumsum(0) - 1
|
||
|
||
# Triangulate kept components. Two strategies:
|
||
#
|
||
# Vertex-fan (small cycles): pick one boundary vert as apex, connect to all
|
||
# non-adjacent boundary edges. N verts -> N-2 triangles, no inserted vertex.
|
||
# Apex stays on the existing surface, so no off-surface centroid → no overlap.
|
||
# Right choice for 6-vert dual-grid pinches around interior verts.
|
||
#
|
||
# Centroid-fan (large cycles): insert a new vertex at the boundary centroid,
|
||
# fan from it. N triangles. Only safe if the cycle is close to planar.
|
||
# We fall back to centroid-fan above `centroid_fan_threshold` verts where
|
||
# vertex-fan would produce excessively skinny triangles.
|
||
CENTROID_FAN_THRESHOLD = 8 # tune: lower = more vertex-fan, higher = more centroid-fan
|
||
|
||
# Edge kept mask
|
||
edge_kept = keep_component[component_idx]
|
||
edge_comp = component_idx[edge_kept]
|
||
kept_src = src[edge_kept]
|
||
kept_tgt = tgt[edge_kept]
|
||
|
||
# Per-edge tag: which strategy does its component use?
|
||
use_centroid_per_comp = keep_component & (vert_count > CENTROID_FAN_THRESHOLD)
|
||
use_centroid_per_edge = use_centroid_per_comp[edge_comp]
|
||
|
||
fan_pieces = []
|
||
# ---- Centroid-fan branch (only for components > threshold) ----
|
||
if use_centroid_per_edge.any():
|
||
kept_centroid = centroid_idx_per_comp[edge_comp[use_centroid_per_edge]]
|
||
fan_pieces.append(torch.stack([
|
||
kept_tgt[use_centroid_per_edge],
|
||
kept_src[use_centroid_per_edge],
|
||
kept_centroid,
|
||
], dim=1).to(faces.dtype))
|
||
|
||
# ---- Vertex-fan branch (small cycles, no centroid inserted) ----
|
||
use_vertex_fan_per_comp = keep_component & (vert_count <= CENTROID_FAN_THRESHOLD)
|
||
if use_vertex_fan_per_comp.any():
|
||
# For each vertex-fan component, pick the smallest-id boundary vert as apex
|
||
# (deterministic & matches labels[*] = smallest after bidir-prop).
|
||
# Then emit edges in component as fan tris (apex, src, tgt) EXCEPT for
|
||
# the two edges incident to the apex (those would be degenerate).
|
||
apex_per_comp = labels[unique_components] # labels[u]==u after convergence
|
||
# Edges that DON'T touch their component's apex
|
||
vf_mask = use_vertex_fan_per_comp[edge_comp]
|
||
if vf_mask.any():
|
||
vf_src = kept_src[vf_mask]
|
||
vf_tgt = kept_tgt[vf_mask]
|
||
vf_comp = edge_comp[vf_mask]
|
||
vf_apex = apex_per_comp[vf_comp]
|
||
# Skip edges that include the apex (apex==src or apex==tgt → degenerate tri).
|
||
non_apex = (vf_src != vf_apex) & (vf_tgt != vf_apex)
|
||
fan_pieces.append(torch.stack([
|
||
vf_tgt[non_apex], vf_src[non_apex], vf_apex[non_apex],
|
||
], dim=1).to(faces.dtype))
|
||
|
||
fan_faces = torch.cat(fan_pieces, dim=0) if fan_pieces else torch.empty((0, 3), dtype=faces.dtype, device=device)
|
||
|
||
# Open chains: close them with a closing triangle ONLY for centroid-fan
|
||
# components (vertex-fan chains would need a different closing strategy).
|
||
# In practice fill_chains=False makes this a no-op since chains aren't kept.
|
||
if fill_chains:
|
||
vert_degree = torch.zeros(V, dtype=torch.long, device=device)
|
||
vert_degree.scatter_add_(0, src, torch.ones_like(src))
|
||
vert_degree.scatter_add_(0, tgt, torch.ones_like(tgt))
|
||
is_endpoint = (vert_degree[pair_v] == 1) & use_centroid_per_comp_pre[pair_c]
|
||
if is_endpoint.any():
|
||
ep_v = pair_v[is_endpoint]
|
||
ep_c = pair_c[is_endpoint]
|
||
order = torch.argsort(ep_c, stable=True)
|
||
ep_v_sorted = ep_v[order]
|
||
ep_c_sorted = ep_c[order]
|
||
ep_count_per_c = torch.bincount(ep_c_sorted, minlength=L)
|
||
is_chain_comp = ep_count_per_c == 2
|
||
ep_is_chain = is_chain_comp[ep_c_sorted]
|
||
if ep_is_chain.any():
|
||
chain_ep_v = ep_v_sorted[ep_is_chain]
|
||
chain_ep_c = ep_c_sorted[ep_is_chain]
|
||
assert chain_ep_v.numel() % 2 == 0
|
||
chain_ep_v = chain_ep_v.view(-1, 2)
|
||
chain_ep_c = chain_ep_c.view(-1, 2)[:, 0]
|
||
close_centroid = centroid_idx_per_comp[chain_ep_c]
|
||
close_faces = torch.stack(
|
||
[chain_ep_v[:, 0], chain_ep_v[:, 1], close_centroid], dim=1
|
||
).to(faces.dtype)
|
||
fan_faces = torch.cat([fan_faces, close_faces], dim=0)
|
||
|
||
# Only centroid-fan components contribute a new vertex; vertex-fan reuses existing.
|
||
new_centroids_v = centroids[use_centroid_per_comp_pre]
|
||
out_v = torch.cat([verts, new_centroids_v], dim=0)
|
||
out_f = torch.cat([faces, fan_faces], dim=0)
|
||
|
||
out_c = colors
|
||
if colors is not None:
|
||
c_sum = torch.zeros((L, colors.shape[1]), dtype=colors.dtype, device=device)
|
||
c_sum.scatter_add_(
|
||
0, pair_c[:, None].expand(-1, colors.shape[1]), colors[pair_v])
|
||
c_avg = c_sum / vert_count.clamp_min(1).to(colors.dtype).unsqueeze(-1)
|
||
out_c = torch.cat([colors, c_avg[use_centroid_per_comp_pre]], dim=0)
|
||
|
||
return out_v, out_f, out_c, int(keep_component.sum().item())
|
||
|
||
|
||
def weld_vertices_fn(vertices, faces, epsilon=None, epsilon_rel=1e-5, colors=None):
|
||
"""Merge coincident vertices via L_inf grid quantization.
|
||
Ported from custom_nodes/qem_simplify/qem_core.py:_weld_vertices.
|
||
|
||
`epsilon`: absolute L_inf distance; verts within this collapse together.
|
||
If None, `epsilon_rel * bbox_diag` is used.
|
||
Attributes (colors) are averaged across each cluster.
|
||
|
||
Returns (new_verts, new_faces, new_colors, n_welded)."""
|
||
if vertices.ndim == 3:
|
||
v_out, f_out, c_out = [], [], [] if colors is not None else None
|
||
total = 0
|
||
for i in range(vertices.shape[0]):
|
||
ci = colors[i] if colors is not None else None
|
||
v_i, f_i, c_i, n = weld_vertices_fn(vertices[i], faces[i], epsilon, epsilon_rel, ci)
|
||
v_out.append(v_i); f_out.append(f_i); total += n
|
||
if c_out is not None:
|
||
c_out.append(c_i)
|
||
max_v = max(v.shape[0] for v in v_out)
|
||
for i in range(len(v_out)):
|
||
pad_n = max_v - v_out[i].shape[0]
|
||
if pad_n > 0:
|
||
v_out[i] = torch.cat([v_out[i],
|
||
torch.zeros(pad_n, 3, device=v_out[i].device, dtype=v_out[i].dtype)], dim=0)
|
||
if c_out is not None:
|
||
c_out[i] = torch.cat([c_out[i],
|
||
torch.zeros(pad_n, c_out[i].shape[1], device=c_out[i].device, dtype=c_out[i].dtype)], dim=0)
|
||
c_stack = torch.stack(c_out) if c_out is not None else None
|
||
return torch.stack(v_out), torch.stack(f_out), c_stack, total
|
||
|
||
if vertices.shape[0] == 0:
|
||
return vertices, faces, colors, 0
|
||
device = vertices.device
|
||
if epsilon is None:
|
||
bbox = vertices.max(dim=0)[0] - vertices.min(dim=0)[0]
|
||
eps = torch.norm(bbox) * float(epsilon_rel)
|
||
eps = max(float(eps.item()), 1e-12)
|
||
else:
|
||
eps = float(epsilon)
|
||
if eps <= 0:
|
||
return vertices, faces, colors, 0
|
||
|
||
scale = 1.0 / eps
|
||
bbox_min = vertices.min(dim=0)[0]
|
||
q = ((vertices - bbox_min) * scale).round().to(torch.int64)
|
||
extent = ((vertices.max(dim=0)[0] - bbox_min) * scale).round().to(torch.int64) + 2
|
||
key = (q[:, 0] * extent[1] + q[:, 1]) * extent[2] + q[:, 2]
|
||
unique_key, inv = torch.unique(key, return_inverse=True)
|
||
n_unique = unique_key.shape[0]
|
||
if n_unique == vertices.shape[0]:
|
||
return vertices, faces, colors, 0
|
||
|
||
counts = torch.zeros(n_unique, dtype=vertices.dtype, device=device)
|
||
counts.scatter_add_(0, inv, torch.ones(vertices.shape[0], dtype=vertices.dtype, device=device))
|
||
counts_div = counts.unsqueeze(-1).clamp_min(1.0)
|
||
|
||
new_verts = torch.zeros((n_unique, 3), dtype=vertices.dtype, device=device)
|
||
new_verts.scatter_add_(0, inv.unsqueeze(-1).expand_as(vertices), vertices)
|
||
new_verts = new_verts / counts_div
|
||
|
||
new_colors = None
|
||
if colors is not None:
|
||
new_colors = torch.zeros((n_unique, colors.shape[1]), dtype=colors.dtype, device=device)
|
||
new_colors.scatter_add_(0, inv.unsqueeze(-1).expand_as(colors), colors)
|
||
new_colors = new_colors / counts_div.to(colors.dtype)
|
||
|
||
new_faces = inv[faces.long()].to(faces.dtype) if faces.numel() > 0 else faces
|
||
return new_verts, new_faces, new_colors, int(vertices.shape[0] - n_unique)
|
||
|
||
|
||
def fill_holes_v2_fn(vertices, faces, max_perimeter=0.03, colors=None, weld_epsilon_rel=1e-5, fill_chains=False, max_verts=16, diagnostic=False):
|
||
"""Batched wrapper for the v2 GPU hole-filler. CPU tensors get pulled
|
||
onto CUDA when available; otherwise fall back to the v1 (CPU walker) fn.
|
||
|
||
Pre-welds vertices via `weld_vertices_fn(epsilon_rel=weld_epsilon_rel)` —
|
||
boundary detection requires shared edges, which requires welded verts.
|
||
Already-welded meshes early-exit cheaply. Set `weld_epsilon_rel=0` to skip."""
|
||
if vertices.ndim == 3:
|
||
v_list, f_list, c_list = [], [], [] if colors is not None else None
|
||
pbar = comfy.utils.ProgressBar(vertices.shape[0])
|
||
for i in range(vertices.shape[0]):
|
||
ci = colors[i] if colors is not None else None
|
||
v_i, f_i, c_i = fill_holes_v2_fn(vertices[i], faces[i], max_perimeter, ci, weld_epsilon_rel, fill_chains, max_verts, diagnostic)
|
||
v_list.append(v_i); f_list.append(f_i)
|
||
if c_list is not None:
|
||
c_list.append(c_i)
|
||
pbar.update(1)
|
||
max_v = max(v.shape[0] for v in v_list)
|
||
for i in range(len(v_list)):
|
||
pad_n = max_v - v_list[i].shape[0]
|
||
if pad_n > 0:
|
||
v_list[i] = torch.cat([v_list[i],
|
||
torch.zeros(pad_n, 3, device=v_list[i].device, dtype=v_list[i].dtype)], dim=0)
|
||
if c_list is not None:
|
||
c_list[i] = torch.cat([c_list[i],
|
||
torch.zeros(pad_n, c_list[i].shape[1], device=c_list[i].device, dtype=c_list[i].dtype)], dim=0)
|
||
c_out = torch.stack(c_list) if c_list is not None else None
|
||
return torch.stack(v_list), torch.stack(f_list), c_out
|
||
|
||
if faces.numel() == 0:
|
||
return vertices, faces, colors
|
||
# Adaptive weld: a properly welded triangle surface has V/F ≈ 0.5 (closed)
|
||
# to ~1.0 (with boundaries). V/F > 1 means most faces still share no verts
|
||
# and hole-fill would emit one bogus closing tri per face. We double the
|
||
# weld epsilon until V/F < WELDED_THRESHOLD or we hit WELD_CAP.
|
||
if weld_epsilon_rel > 0:
|
||
eps = float(weld_epsilon_rel)
|
||
WELD_CAP = 1e-2 # ≈ 10 voxels at 1024-res — aggressive but bounded
|
||
WELDED_THRESHOLD = 1.0 # V/F below this is "welded enough" for hole-fill
|
||
total_welded = 0
|
||
n_escalations = 0
|
||
while True:
|
||
vertices, faces, colors, n = weld_vertices_fn(
|
||
vertices, faces, epsilon=None, epsilon_rel=eps, colors=colors,
|
||
)
|
||
total_welded += n
|
||
ratio = vertices.shape[0] / max(faces.shape[0], 1)
|
||
if ratio < WELDED_THRESHOLD or eps >= WELD_CAP:
|
||
break
|
||
eps = min(eps * 2.0, WELD_CAP)
|
||
n_escalations += 1
|
||
if total_welded > 0 or n_escalations > 0:
|
||
tag = f" (escalated weld epsilon_rel→{eps:.1e} after {n_escalations} step{'s' if n_escalations != 1 else ''})" if n_escalations > 0 else ""
|
||
logging.info(f"[FillHoles] pre-welded {total_welded} verts, V/F={ratio:.2f}{tag}")
|
||
if ratio >= WELDED_THRESHOLD:
|
||
logging.warning(
|
||
f"[FillHoles] even at weld epsilon_rel={WELD_CAP} the mesh stays "
|
||
f"unwelded (V/F={ratio:.2f}, want < {WELDED_THRESHOLD}). Source mesh has "
|
||
f"duplicate verts at distances >{WELD_CAP}× bbox; fix upstream "
|
||
f"(decimate node settings) or run WeldVertices manually with a larger epsilon."
|
||
)
|
||
# Diag runs AFTER welding so its topology numbers match what the filler sees.
|
||
if diagnostic and vertices.device.type == "cuda" and faces.numel() > 0:
|
||
_fill_holes_v2_diagnostic(vertices, faces, max_perimeter)
|
||
if vertices.device.type == "cuda":
|
||
out_v, out_f, out_c, _ = _fill_holes_v2_gpu(vertices, faces, max_perimeter, colors, fill_chains, max_verts)
|
||
return out_v, out_f, out_c
|
||
# CPU fallback: re-use the v1 walker (no attribute prop, but topologically equivalent
|
||
# for manifold boundary; v2 GPU is the path that actually matters for pixal3d output).
|
||
out_v, out_f = fill_holes_fn(vertices, faces, max_perimeter=max_perimeter)
|
||
return out_v, out_f, colors
|
||
|
||
|
||
def compute_vertex_normals(verts, faces):
|
||
"""Computes area-weighted vertex normals."""
|
||
# QUICK FIX: Ensure indices are int64 for scatter_add_
|
||
faces_long = faces.to(torch.int64)
|
||
|
||
i0, i1, i2 = faces_long[:, 0], faces_long[:, 1], faces_long[:, 2]
|
||
v0, v1, v2 = verts[i0], verts[i1], verts[i2]
|
||
|
||
# calculate unnormalized face normals (magnitude is proportional to area)
|
||
face_normals = torch.cross(v1 - v0, v2 - v0, dim=-1)
|
||
|
||
# accumulate face normals to vertices
|
||
vertex_normals = torch.zeros_like(verts)
|
||
vertex_normals.scatter_add_(0, i0.unsqueeze(-1).expand_as(face_normals), face_normals)
|
||
vertex_normals.scatter_add_(0, i1.unsqueeze(-1).expand_as(face_normals), face_normals)
|
||
vertex_normals.scatter_add_(0, i2.unsqueeze(-1).expand_as(face_normals), face_normals)
|
||
|
||
return torch.nn.functional.normalize(vertex_normals, p=2, dim=-1, eps=1e-6)
|
||
|
||
def _process_mesh_batch(mesh, per_item_fn):
|
||
"""Handles list/batched/single mesh dispatching, color extraction, and stacking."""
|
||
mesh = copy.deepcopy(mesh)
|
||
|
||
def process_single(v, f, c, bar):
|
||
v, f, c = per_item_fn(v, f, c)
|
||
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(bsz)
|
||
|
||
for i in range(bsz):
|
||
v_i = mesh.vertices[i]
|
||
f_i = mesh.faces[i]
|
||
c_i = None
|
||
if hasattr(mesh, 'vertex_colors') and mesh.vertex_colors is not None:
|
||
c_i = mesh.vertex_colors[i] if (isinstance(mesh.vertex_colors, list) or mesh.vertex_colors.ndim == 3) else mesh.vertex_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 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:
|
||
c = mesh.vertex_colors if hasattr(mesh, 'vertex_colors') and mesh.vertex_colors is not None else None
|
||
bar = comfy.utils.ProgressBar(1)
|
||
v, f, c = process_single(mesh.vertices, mesh.faces, c, bar)
|
||
mesh.vertices = v
|
||
mesh.faces = f
|
||
if c is not None:
|
||
mesh.vertex_colors = c
|
||
|
||
return IO.NodeOutput(mesh)
|
||
|
||
|
||
def fix_face_orientation(vertices, faces, reference_normals=None):
|
||
num_faces = faces.shape[0]
|
||
if num_faces == 0:
|
||
return faces
|
||
|
||
device = faces.device
|
||
corrected = faces.clone()
|
||
max_vert = vertices.shape[0]
|
||
|
||
# Manifold edge adjacency: pair faces that share an edge (run length 2 after
|
||
# canonicalizing + sorting edge hashes).
|
||
idx = torch.tensor([[0, 1], [1, 2], [2, 0]], dtype=torch.int64, device=device)
|
||
edges = corrected[:, idx] # (num_faces, 3, 2) directed
|
||
edges_canon = torch.sort(edges, dim=2)[0].view(-1, 2)
|
||
edge_hash = edges_canon[:, 0] * max_vert + edges_canon[:, 1]
|
||
hash_sorted, sort_idx = torch.sort(edge_hash)
|
||
start = torch.cat([torch.ones(1, dtype=torch.bool, device=device),
|
||
hash_sorted[1:] != hash_sorted[:-1]])
|
||
unique_starts = torch.nonzero(start, as_tuple=True)[0]
|
||
unique_ends = torch.cat([unique_starts[1:],
|
||
torch.tensor([hash_sorted.numel()], device=device)])
|
||
manifold_starts = unique_starts[(unique_ends - unique_starts) == 2]
|
||
|
||
if manifold_starts.numel() > 0:
|
||
f_a = sort_idx[manifold_starts] // 3
|
||
f_b = sort_idx[manifold_starts + 1] // 3
|
||
le_a = sort_idx[manifold_starts] % 3
|
||
le_b = sort_idx[manifold_starts + 1] % 3
|
||
opposite = (edges[f_a, le_a] == edges[f_b, le_b].flip(dims=[1])).all(dim=1)
|
||
|
||
# Connected components via scipy (fast C), replacing a per-face Python BFS.
|
||
import scipy.sparse
|
||
import scipy.sparse.csgraph
|
||
fa_np = f_a.cpu().numpy(); fb_np = f_b.cpu().numpy()
|
||
graph = scipy.sparse.coo_matrix(
|
||
(np.ones(fa_np.shape[0] * 2, dtype=np.int8),
|
||
(np.concatenate([fa_np, fb_np]), np.concatenate([fb_np, fa_np]))),
|
||
shape=(num_faces, num_faces))
|
||
num_components, comp = scipy.sparse.csgraph.connected_components(graph, directed=False)
|
||
component_id = torch.from_numpy(comp.astype(np.int64)).to(device)
|
||
|
||
# Within-component consistent winding. A QEM output from a consistently wound
|
||
# source is already consistent (every shared edge is traversed oppositely) ->
|
||
# no flips needed, the common fast path. Otherwise propagate a parity flip
|
||
# across the dual graph by vectorized label relaxation (min-root carrying
|
||
# parity), instead of the old per-face CPU BFS.
|
||
if not bool(opposite.all()):
|
||
nf = ~opposite
|
||
src = torch.cat([f_a, f_b]); dst = torch.cat([f_b, f_a]); nfd = torch.cat([nf, nf])
|
||
root = torch.arange(num_faces, device=device)
|
||
par = torch.zeros(num_faces, dtype=torch.bool, device=device)
|
||
for _ in range(num_faces + 8): # breaks at graph diameter; cap is a backstop
|
||
cand_root = root[src]; cand_par = par[src] ^ nfd
|
||
new_root = root.clone()
|
||
new_root.scatter_reduce_(0, dst, cand_root, reduce='amin', include_self=True)
|
||
changed = new_root < root
|
||
if not bool(changed.any()):
|
||
break
|
||
apply = changed[dst] & (cand_root == new_root[dst])
|
||
par[dst[apply]] = cand_par[apply]
|
||
root = new_root
|
||
if bool(par.any()):
|
||
corrected[par] = corrected[par][:, [0, 2, 1]]
|
||
else:
|
||
component_id = torch.arange(num_faces, device=device)
|
||
num_components = num_faces
|
||
|
||
v0 = vertices[corrected[:, 0]]
|
||
v1 = vertices[corrected[:, 1]]
|
||
v2 = vertices[corrected[:, 2]]
|
||
|
||
face_normals = torch.cross(v1 - v0, v2 - v0, dim=-1)
|
||
face_normals = face_normals / (torch.norm(face_normals, dim=-1, keepdim=True) + 1e-8)
|
||
|
||
if reference_normals is not None:
|
||
n0 = reference_normals[corrected[:, 0]]
|
||
n1 = reference_normals[corrected[:, 1]]
|
||
n2 = reference_normals[corrected[:, 2]]
|
||
ref_normals = (n0 + n1 + n2) / 3.0
|
||
ref_normals = ref_normals / (torch.norm(ref_normals, dim=-1, keepdim=True) + 1e-8)
|
||
|
||
votes = (face_normals * ref_normals).sum(dim=-1)
|
||
|
||
outward_votes_comp = torch.zeros(num_components, dtype=torch.int64, device=device)
|
||
inward_votes_comp = torch.zeros(num_components, dtype=torch.int64, device=device)
|
||
|
||
outward_votes_comp.scatter_add_(0, component_id, (votes > 0).to(torch.int64))
|
||
inward_votes_comp.scatter_add_(0, component_id, (votes < 0).to(torch.int64))
|
||
|
||
n_faces_comp_int = torch.zeros(num_components, dtype=torch.int64, device=device)
|
||
n_faces_comp_int.scatter_add_(0, component_id, torch.ones(num_faces, dtype=torch.int64, device=device))
|
||
|
||
thresholds = torch.maximum(torch.ones_like(n_faces_comp_int), n_faces_comp_int // 10)
|
||
should_flip_comp = inward_votes_comp > outward_votes_comp + thresholds
|
||
else:
|
||
# Vectorized 3-Axis Extreme Majority Vote (Geometrically Infallible)
|
||
face_centroids = (v0 + v1 + v2) / 3.0
|
||
|
||
votes_by_axis = []
|
||
for axis in range(3):
|
||
coords = face_centroids[:, axis]
|
||
|
||
# Double stable sort acts as a vectorized lexsort on (coords, component_id)
|
||
sort_idx2 = torch.argsort(coords, stable=True)
|
||
sort_idx2 = sort_idx2[torch.argsort(component_id[sort_idx2], stable=True)]
|
||
|
||
# Find group boundaries to get the extreme outer face along this axis per component
|
||
comp_id_sorted = component_id[sort_idx2]
|
||
group_ends = torch.nonzero(comp_id_sorted[1:] != comp_id_sorted[:-1], as_tuple=True)[0]
|
||
group_ends = torch.cat([group_ends, torch.tensor([len(comp_id_sorted) - 1], device=device)])
|
||
|
||
extreme_face_indices = sort_idx2[group_ends]
|
||
extreme_normals = face_normals[extreme_face_indices]
|
||
|
||
# Normal's component along the respective axis should be positive
|
||
votes_by_axis.append(extreme_normals[:, axis] > 0)
|
||
|
||
stacked_votes = torch.stack(votes_by_axis, dim=0)
|
||
should_flip_comp = stacked_votes.sum(dim=0) < 2 # False if at least 2 axes agree outward
|
||
|
||
should_flip_face = should_flip_comp[component_id]
|
||
if should_flip_face.any():
|
||
corrected[should_flip_face] = corrected[should_flip_face][:, [0, 2, 1]]
|
||
|
||
return corrected
|
||
|
||
|
||
def unweld_and_offset_mesh(vertices, faces, colors=None, z_offset=1e-4):
|
||
is_batched = vertices.ndim == 3
|
||
device = vertices.device
|
||
|
||
if is_batched:
|
||
B = vertices.shape[0]
|
||
F = faces.shape[1]
|
||
|
||
# 1. Advanced index broadcast to pull all faces in parallel without any Python loops
|
||
batch_idx = torch.arange(B, device=device).view(-1, 1, 1)
|
||
v_faces = vertices[batch_idx, faces] # shape (B, F, 3, 3)
|
||
|
||
v0, v1, v2 = v_faces[:, :, 0], v_faces[:, :, 1], v_faces[:, :, 2]
|
||
|
||
# 2. Compute face normals
|
||
fn = torch.cross(v1 - v0, v2 - v0, dim=-1)
|
||
fn = fn / (torch.norm(fn, dim=-1, keepdim=True) + 1e-8)
|
||
|
||
# 3. Translate directly along the face normals in parallel
|
||
offset_verts = v_faces + fn.unsqueeze(2) * z_offset
|
||
out_v = offset_verts.reshape(B, -1, 3)
|
||
|
||
# 4. Generate identical faces for all batches using constant expansion (O(1))
|
||
f_single = torch.arange(F * 3, device=device).reshape(-1, 3)
|
||
out_f = f_single.unsqueeze(0).expand(B, -1, -1)
|
||
|
||
if colors is not None:
|
||
c_faces = colors[batch_idx, faces]
|
||
out_c = c_faces.reshape(B, -1, colors.shape[-1])
|
||
return out_v, out_f, out_c
|
||
return out_v, out_f
|
||
|
||
# --- Unbatched (Single Mesh) ---
|
||
v_faces = vertices[faces] # shape (F, 3, 3)
|
||
v0, v1, v2 = v_faces[:, 0], v_faces[:, 1], v_faces[:, 2]
|
||
|
||
# Compute face normals
|
||
fn = torch.cross(v1 - v0, v2 - v0, dim=-1)
|
||
fn = fn / (torch.norm(fn, dim=-1, keepdim=True) + 1e-8)
|
||
|
||
# Offset each face's private vertices along its face normal
|
||
offset_verts = v_faces + fn.unsqueeze(1) * z_offset
|
||
offset_verts = offset_verts.reshape(-1, 3)
|
||
|
||
# Generate sequential face indices for the unwelded vertices
|
||
f_unwelded = torch.arange(faces.shape[0] * 3, device=vertices.device).reshape(-1, 3)
|
||
|
||
if colors is not None:
|
||
c_faces = colors[faces]
|
||
c_unwelded = c_faces.reshape(-1, colors.shape[-1])
|
||
return offset_verts, f_unwelded, c_unwelded
|
||
|
||
return offset_verts, f_unwelded, None
|
||
|
||
def _fmt_count(n) -> str:
|
||
"""Compact human-readable integer for node status lines, e.g. 853, 12.3K, 1.23M."""
|
||
n = int(n)
|
||
if n >= 1_000_000:
|
||
return f"{n / 1_000_000:.2f}".rstrip("0").rstrip(".") + "M"
|
||
if n >= 1_000:
|
||
return f"{n / 1_000:.1f}".rstrip("0").rstrip(".") + "K"
|
||
return str(n)
|
||
|
||
|
||
def _fmt_face_change(n_in, n_out) -> str:
|
||
"""'faces: 1.23M → 200K (-84%)' — the count delta for decimate/remesh status."""
|
||
n_in, n_out = int(n_in), int(n_out)
|
||
pct = f" ({(n_out - n_in) / n_in * 100:+.0f}%)" if n_in else ""
|
||
return f"faces: {_fmt_count(n_in)} → {_fmt_count(n_out)}{pct}"
|
||
|
||
|
||
class DecimateMesh(IO.ComfyNode):
|
||
@classmethod
|
||
def define_schema(cls):
|
||
# placement_mode picks how the merged vertex is positioned, and which extra
|
||
# quality knobs are surfaced (DynamicCombo: the qem sub-widgets only appear
|
||
# when 'qem' is selected).
|
||
placement_options = [
|
||
IO.DynamicCombo.Option(key="midpoint", inputs=[]),
|
||
IO.DynamicCombo.Option(key="qem", inputs=[
|
||
IO.Float.Input("line_quadric_weight", default=0.0, min=0.0, max=100.0, step=0.1,
|
||
tooltip="Weight of the per-edge line quadric (squared distance to the edge "
|
||
"line). Biases collapses to preserve sharp ridges/valleys. 0 = off."),
|
||
IO.Float.Input("feature_edge_quadric_weight", default=0.0, min=0.0, max=1000.0, step=1.0,
|
||
tooltip="Extra quadric weight on dihedral feature edges (creases). Higher = "
|
||
"more aggressively preserves hard edges. 0 = off."),
|
||
IO.Float.Input("feature_edge_min_dihedral_deg", default=30.0, min=0.0, max=180.0, step=1.0,
|
||
tooltip="Minimum dihedral angle (degrees) for an edge to count as a feature "
|
||
"edge for feature_edge_quadric_weight."),
|
||
IO.Boolean.Input("clamp_v_to_edge", default=True,
|
||
tooltip="Project the QEM-optimal position onto the collapsed edge segment. "
|
||
"Prevents inward-cascade drift on curved surfaces."),
|
||
]),
|
||
]
|
||
return IO.Schema(
|
||
node_id="DecimateMesh",
|
||
display_name="Decimate Mesh",
|
||
category="latent/3d",
|
||
description=(
|
||
"Simplifies a mesh to a target face count using QEM, on the active compute "
|
||
"device. 'midpoint' placement uses the cumesh-faithful preset (best quality, "
|
||
"preserves thin features / hair). 'qem' places each merged vertex at the QEM "
|
||
"optimum and exposes line/feature-edge quadric controls. Output stays welded "
|
||
"so it smooth-shades."
|
||
),
|
||
inputs=[
|
||
IO.Mesh.Input("mesh"),
|
||
IO.Int.Input("target_face_count", default=200_000, min=0, max=50_000_000,
|
||
tooltip="Target maximum number of faces. Set to 0 to disable."),
|
||
IO.DynamicCombo.Input("placement_mode", options=placement_options,
|
||
display_name="placement_mode",
|
||
tooltip="midpoint: cumesh-faithful preset (recommended). "
|
||
"qem: QEM-optimal placement with line/feature-edge controls."),
|
||
],
|
||
outputs=[IO.Mesh.Output("mesh")],
|
||
hidden=[IO.Hidden.unique_id],
|
||
)
|
||
|
||
@classmethod
|
||
def execute(cls, mesh, target_face_count, placement_mode):
|
||
mode = placement_mode.get("placement_mode", "midpoint")
|
||
if mode == "qem":
|
||
# QEM-optimum placement + ratio driver; everything else inherits the defaults.
|
||
cfg = QEMConfig(
|
||
placement_mode="qem",
|
||
line_quadric_weight=float(placement_mode.get("line_quadric_weight", 0.0)),
|
||
feature_edge_quadric_weight=float(placement_mode.get("feature_edge_quadric_weight", 0.0)),
|
||
feature_edge_min_dihedral_deg=float(placement_mode.get("feature_edge_min_dihedral_deg", 30.0)),
|
||
clamp_v_to_edge=bool(placement_mode.get("clamp_v_to_edge", True)),
|
||
)
|
||
else:
|
||
cfg = QEMConfig() # midpoint placement + threshold driver (the defaults)
|
||
|
||
# ComfyUI passes meshes on CPU; the QEM is ~30x slower there. Run on the
|
||
# selected compute device and return on the mesh's original device.
|
||
compute_device = comfy.model_management.get_torch_device()
|
||
|
||
counts = {"in": 0, "out": 0}
|
||
|
||
def _fn(v, f, c):
|
||
counts["in"] += int(f.shape[0])
|
||
if target_face_count > 0 and f.shape[0] > target_face_count:
|
||
try:
|
||
src_device = v.device
|
||
rv, rf, rc, _rn, _rs = qem_decimate_simplify(
|
||
v.to(compute_device), f.to(compute_device), int(target_face_count),
|
||
colors=(c.to(compute_device) if c is not None else None),
|
||
config=cfg)
|
||
v = rv.to(src_device)
|
||
f = rf.to(src_device)
|
||
if rc is not None:
|
||
c = rc.to(src_device)
|
||
except Exception as e:
|
||
logging.warning(f"DecimateMesh: QEM simplify failed, passing mesh through unchanged: {e!r}")
|
||
counts["out"] += int(f.shape[0])
|
||
return v, f, c
|
||
|
||
result = _process_mesh_batch(mesh, _fn)
|
||
|
||
# Send progress text to display the face reduction on the node
|
||
if cls.hidden.unique_id:
|
||
PromptServer.instance.send_progress_text(
|
||
_fmt_face_change(counts["in"], counts["out"]), cls.hidden.unique_id)
|
||
|
||
return result
|
||
|
||
|
||
class RemeshMesh(IO.ComfyNode):
|
||
@classmethod
|
||
def define_schema(cls):
|
||
# sign_mode picks the scalar field, and exposes only the knobs relevant to it
|
||
# (DynamicCombo: udf sub-widgets show for 'udf', sdf sub-widgets for 'sdf').
|
||
sign_mode_options = [
|
||
IO.DynamicCombo.Option(key="udf", inputs=[
|
||
IO.Boolean.Input("qef", default=False,
|
||
tooltip="Experimental: place dual vertices via QEF (closest-triangle normals) "
|
||
"instead of edge-crossing centroid. QEF is sign-agnostic so it works "
|
||
"in UDF too — pulls the ±eps surface back onto the planes for sharper "
|
||
"edges. May misbehave near the UDF double shell; compare with it off."),
|
||
IO.Boolean.Input("drop_inverted_components", default=True,
|
||
tooltip="Drop closed components with inward normals (negative signed volume) — "
|
||
"the inner shell UDF produces on closed regions."),
|
||
IO.Boolean.Input("drop_enclosed_components", default=True,
|
||
tooltip="Drop components whose bbox is inside the largest's AND fail a raycast "
|
||
"point-in-mesh test. Disable if you have legitimate parts inside others."),
|
||
]),
|
||
IO.DynamicCombo.Option(key="sdf", inputs=[
|
||
IO.Boolean.Input("qef", default=True,
|
||
tooltip="Place dual vertices via QEF solve from closest-triangle normals "
|
||
"(recovers sharp features) vs edge-crossing centroid."),
|
||
IO.Boolean.Input("manifold", default=False,
|
||
tooltip="Manifold Dual Contouring: emit 1-4 dual verts per voxel for "
|
||
"multi-sheet (thin/touching) cases. Slower; guarantees manifold output."),
|
||
]),
|
||
]
|
||
return IO.Schema(
|
||
node_id="RemeshMesh",
|
||
display_name="Remesh Mesh (Narrow-Band DC)",
|
||
category="latent/3d",
|
||
description=(
|
||
"Re-extracts a uniformly tessellated mesh by sampling a distance field on a "
|
||
"narrow-band voxel grid and contouring it with Dual Contouring, on the active "
|
||
"compute device. Normalizes topology of messy / non-manifold / self-intersecting "
|
||
"input; run before DecimateMesh to hit an exact face count. Output stays welded."
|
||
),
|
||
inputs=[
|
||
IO.Mesh.Input("mesh"),
|
||
IO.Int.Input("target_faces", default=0, min=0, max=50_000_000,
|
||
tooltip="0 = use 'resolution'. >0 = auto-pick resolution to roughly hit this "
|
||
"face count (±30-50%); usually overshoot then DecimateMesh to exact."),
|
||
IO.Int.Input("resolution", default=256, min=32, max=1024,
|
||
tooltip="Voxel grid resolution (used when target_faces=0). Higher = more detail, "
|
||
"slower. 256 ~ 100k faces, 512 ~ 1M."),
|
||
IO.DynamicCombo.Input("sign_mode", options=sign_mode_options, display_name="sign_mode",
|
||
tooltip="udf: robust to messy/non-manifold input (double shell cleaned by "
|
||
"the inner-shell filters). sdf: clean single surface with optional "
|
||
"QEF sharp-feature recovery, but needs consistent winding."),
|
||
IO.Float.Input("band", default=1.0, min=0.5, max=4.0, step=0.1,
|
||
tooltip="Narrow-band width in voxel units (which voxels are sampled). In UDF "
|
||
"mode also offsets the surface by this many voxels."),
|
||
IO.Float.Input("project_back", default=0.0, min=0.0, max=1.0, step=0.05,
|
||
tooltip="Lerp output verts toward the closest point on the original surface "
|
||
"(0 = pure DC, 1 = snapped). Recovers voxelization-lost detail."),
|
||
IO.Boolean.Input("fix_poles", default=False,
|
||
tooltip="Collapse valence-3 vertex pairs (DC T-junction artifact). Cheap; "
|
||
"improves shading and downstream simplification."),
|
||
IO.Int.Input("smooth_iters", default=0, min=0, max=20,
|
||
tooltip="Taubin λ|μ smoothing iterations (0 = off). Volume-preserving; cleans DC "
|
||
"stairstepping. 2-3 is enough; higher rounds off QEF sharp features."),
|
||
IO.Float.Input("drop_small_components", default=0.01, min=0.0, max=0.5, step=0.005,
|
||
tooltip="Drop components with fewer than this fraction of the largest component's "
|
||
"faces (inner-shell fragments, noise). 0 disables."),
|
||
IO.Int.Input("precluster_max_verts", default=0, min=0, max=50_000_000,
|
||
tooltip="Safety fallback: if input has more verts than this (>0), cluster-decimate "
|
||
"it down first so the distance-field queries don't OOM on huge inputs. "
|
||
"0 = off; 1-2M is reasonable for very large meshes."),
|
||
],
|
||
outputs=[IO.Mesh.Output("mesh")],
|
||
hidden=[IO.Hidden.unique_id],
|
||
)
|
||
|
||
@classmethod
|
||
def execute(cls, mesh, target_faces, resolution, sign_mode, band,
|
||
project_back, fix_poles, smooth_iters,
|
||
drop_small_components, precluster_max_verts):
|
||
mode = sign_mode.get("sign_mode", "udf")
|
||
# mode-specific sub-widgets (absent ones fall back to defaults)
|
||
qef = bool(sign_mode.get("qef", True))
|
||
manifold = bool(sign_mode.get("manifold", False))
|
||
drop_inverted_components = bool(sign_mode.get("drop_inverted_components", True))
|
||
drop_enclosed_components = bool(sign_mode.get("drop_enclosed_components", True))
|
||
|
||
# ComfyUI passes meshes on CPU; remesh is far faster on GPU. Run on the
|
||
# selected compute device and return on the mesh's original device.
|
||
compute_device = comfy.model_management.get_torch_device()
|
||
counts = {"in": 0, "out": 0}
|
||
|
||
def _fn(v, f, c):
|
||
counts["in"] += int(f.shape[0])
|
||
try:
|
||
src_device = v.device
|
||
vv = v.to(compute_device).float()
|
||
ff = f.to(compute_device).to(torch.int64)
|
||
cc = c.to(compute_device).float() if c is not None else None
|
||
|
||
# safety fallback: cluster-decimate very large inputs before the field queries
|
||
if precluster_max_verts > 0 and vv.shape[0] > precluster_max_verts:
|
||
vv, ff, cc = qem_cluster_decimate(
|
||
vv, ff, target_verts=int(precluster_max_verts), colors=cc)
|
||
|
||
# Fixed [-0.5,0.5] cube domain (matches cumesh / TRELLIS2). scale ≈ 1.0
|
||
# for any resolution, so this is consistent in target_faces auto mode too.
|
||
rs_scale = (resolution + 3.0 * band) / resolution
|
||
rs_center = torch.zeros(3, dtype=vv.dtype, device=compute_device)
|
||
|
||
rv, rf, rc = remesh_narrow_band_dc(
|
||
vv, ff,
|
||
resolution=int(resolution), target_faces=int(target_faces),
|
||
band=float(band), project_back=float(project_back),
|
||
qef=qef, sign_mode=mode,
|
||
manifold=manifold, fix_poles=bool(fix_poles),
|
||
smooth_iters=int(smooth_iters),
|
||
drop_small_components=float(drop_small_components),
|
||
drop_inverted_components=drop_inverted_components,
|
||
drop_enclosed_components=drop_enclosed_components,
|
||
scale=rs_scale, center=rs_center, colors=cc)
|
||
|
||
v = rv.to(src_device)
|
||
f = rf.to(src_device)
|
||
c = rc.to(src_device) if rc is not None else None
|
||
except Exception as e:
|
||
logging.warning(f"RemeshMesh: remesh failed, passing mesh through unchanged: {e!r}")
|
||
counts["out"] += int(f.shape[0])
|
||
return v, f, c
|
||
|
||
result = _process_mesh_batch(mesh, _fn)
|
||
|
||
# Send progress text to display the face change on the node
|
||
if cls.hidden.unique_id:
|
||
PromptServer.instance.send_progress_text(
|
||
_fmt_face_change(counts["in"], counts["out"]), cls.hidden.unique_id)
|
||
|
||
return result
|
||
|
||
|
||
def _pack_uv_meshes(vs, fs, uvs, colors):
|
||
"""Pack per-item (verts, faces, uvs[, colors]) into a MESH; stack if single, else pad with counts."""
|
||
if len(vs) == 1:
|
||
m = Types.MESH(vertices=vs[0].unsqueeze(0), faces=fs[0].unsqueeze(0), uvs=uvs[0].unsqueeze(0))
|
||
if colors is not None:
|
||
m.vertex_colors = colors[0].unsqueeze(0)
|
||
return m
|
||
bsz = len(vs)
|
||
dev = vs[0].device
|
||
maxv = max(v.shape[0] for v in vs)
|
||
maxf = max(f.shape[0] for f in fs)
|
||
pv = vs[0].new_zeros((bsz, maxv, 3))
|
||
pf = fs[0].new_zeros((bsz, maxf, 3))
|
||
pu = uvs[0].new_zeros((bsz, maxv, 2))
|
||
for i, (v, f, u) in enumerate(zip(vs, fs, uvs)):
|
||
pv[i, :v.shape[0]] = v
|
||
pf[i, :f.shape[0]] = f
|
||
pu[i, :u.shape[0]] = u
|
||
vc = torch.tensor([v.shape[0] for v in vs], device=dev, dtype=torch.int64)
|
||
fc = torch.tensor([f.shape[0] for f in fs], device=dev, dtype=torch.int64)
|
||
m = Types.MESH(vertices=pv, faces=pf, uvs=pu, vertex_counts=vc, face_counts=fc)
|
||
if colors is not None:
|
||
pc = colors[0].new_zeros((bsz, maxv, colors[0].shape[1]))
|
||
for i, c in enumerate(colors):
|
||
pc[i, :c.shape[0]] = c
|
||
m.vertex_colors = pc
|
||
return m
|
||
|
||
|
||
def _uv_weld_vertices(v, f, weld_distance):
|
||
"""Merge coincident verts; returns (welded_v, welded_f, welded_to_orig) (last None if no welding)."""
|
||
v_np = v.cpu().numpy()
|
||
f_np = f.cpu().numpy()
|
||
if v_np.size == 0:
|
||
return v, f, None
|
||
extent = float(np.linalg.norm(v_np.max(axis=0) - v_np.min(axis=0)))
|
||
tol = weld_distance if weld_distance > 0.0 else 1e-5 * extent
|
||
if tol <= 0.0:
|
||
return v, f, None
|
||
keys = np.round(v_np / tol).astype(np.int64)
|
||
_, inv = np.unique(keys, axis=0, return_inverse=True)
|
||
n_unique = int(inv.max()) + 1
|
||
if n_unique >= v_np.shape[0]:
|
||
return v, f, None
|
||
v_welded = np.zeros((n_unique, 3), dtype=np.float32)
|
||
counts = np.zeros(n_unique, dtype=np.int64)
|
||
np.add.at(v_welded, inv, v_np)
|
||
np.add.at(counts, inv, 1)
|
||
v_welded /= counts[:, None]
|
||
welded_to_orig = np.empty(n_unique, dtype=np.int64)
|
||
welded_to_orig[inv] = np.arange(v_np.shape[0], dtype=np.int64)
|
||
v_new = torch.from_numpy(v_welded).to(v.dtype).to(v.device)
|
||
f_new = torch.from_numpy(inv[f_np]).to(f.dtype).to(f.device)
|
||
return v_new, f_new, welded_to_orig
|
||
|
||
|
||
def _uv_unwrap(positions, indices, segmenter, resolution, padding, weld_distance):
|
||
"""UV-unwrap a single mesh; returns (vmapping, indices, uvs) — vmapping maps each output
|
||
vertex to an input vertex (seam verts duplicated)."""
|
||
v_in = positions.to(torch.float32)
|
||
f_in = indices.to(torch.long).reshape(-1, 3)
|
||
v_in, f_in, welded_to_orig = _uv_weld_vertices(v_in, f_in, weld_distance)
|
||
|
||
# drop degenerate faces (repeated index) — they corrupt edge adjacency
|
||
degen = ((f_in[:, 0] == f_in[:, 1]) | (f_in[:, 1] == f_in[:, 2]) | (f_in[:, 2] == f_in[:, 0]))
|
||
if bool(degen.any()):
|
||
f_in = f_in[~degen]
|
||
|
||
mesh = _uv_mesh.build_mesh(v_in, f_in)
|
||
ff = mesh.face_face
|
||
if ff.numel() and float((ff >= 0).float().mean().item()) < 0.25:
|
||
warnings.warn("[uv_unwrap] mesh face-adjacency < 25% — vertices appear un-welded "
|
||
"(triangle soup); UV charts will be per-face. Raise weld_distance.")
|
||
|
||
if segmenter == "pec":
|
||
if mesh.faces.device.type != "cuda":
|
||
raise RuntimeError("segmenter='pec' requires a CUDA mesh; use 'adaptive' for CPU.")
|
||
face_chart = _uv_seg.cluster_charts_pec(mesh, target_chart_count=0, max_cost=1.0)
|
||
elif segmenter == "adaptive":
|
||
face_chart = _uv_seg.segment_charts(mesh, max_cost=2.0, target_chart_count=0)
|
||
else:
|
||
raise ValueError(f"unknown segmenter '{segmenter}'. valid: pec, adaptive")
|
||
|
||
n_charts = int(face_chart.max().item()) + 1 if face_chart.numel() else 0
|
||
areas_cpu = _uv_mesh.chart_3d_areas(mesh.face_area, face_chart, n_charts).detach().cpu()
|
||
|
||
# per-chart loop runs on CPU/numpy to avoid per-chart GPU sync
|
||
face_chart_np = face_chart.cpu().numpy()
|
||
faces_np = mesh.faces.cpu().numpy()
|
||
vertices_np = mesh.vertices.cpu().numpy()
|
||
face_face_np = mesh.face_face.cpu().numpy()
|
||
sorted_face_idx_np = np.argsort(face_chart_np, kind="stable")
|
||
chart_counts_np = np.bincount(face_chart_np, minlength=n_charts)
|
||
chart_offsets_np = np.empty(n_charts + 1, dtype=np.int64)
|
||
chart_offsets_np[0] = 0
|
||
np.cumsum(chart_counts_np, out=chart_offsets_np[1:])
|
||
|
||
all_chart_uvs, all_chart_3d_areas, all_chart_uv_areas, all_chart_faces = [], [], [], []
|
||
chart_records = []
|
||
for c in range(n_charts):
|
||
gfi_np = sorted_face_idx_np[chart_offsets_np[c]:chart_offsets_np[c + 1]]
|
||
chart_faces_global = faces_np[gfi_np]
|
||
used_verts_np = np.unique(chart_faces_global)
|
||
local_faces_np = np.searchsorted(used_verts_np, chart_faces_global)
|
||
local_verts_np = vertices_np[used_verts_np]
|
||
ff_global = face_face_np[gfi_np]
|
||
ff_safe = np.maximum(ff_global, 0)
|
||
nb_chart = np.where(ff_global >= 0, face_chart_np[ff_safe], -1)
|
||
keep = (ff_global >= 0) & (nb_chart == c)
|
||
local_neighbor = np.searchsorted(gfi_np, ff_safe)
|
||
local_ff_np = np.where(keep, local_neighbor, -1)
|
||
|
||
lf = torch.from_numpy(local_faces_np)
|
||
uvs = _uv_param.parametrize_chart(
|
||
torch.from_numpy(local_verts_np), lf, torch.from_numpy(local_ff_np))
|
||
ua, ub, uc = uvs[lf[:, 0]], uvs[lf[:, 1]], uvs[lf[:, 2]]
|
||
uv_area_sum = float(0.5 * (
|
||
(ub[:, 0] - ua[:, 0]) * (uc[:, 1] - ua[:, 1])
|
||
- (uc[:, 0] - ua[:, 0]) * (ub[:, 1] - ua[:, 1])).abs().sum().item())
|
||
chart_records.append({"local_faces": lf, "vmap": torch.from_numpy(used_verts_np),
|
||
"global_face_idx": torch.from_numpy(gfi_np)})
|
||
all_chart_uvs.append(uvs)
|
||
all_chart_3d_areas.append(float(areas_cpu[c].item()))
|
||
all_chart_uv_areas.append(uv_area_sum)
|
||
all_chart_faces.append(lf)
|
||
|
||
# auto-tune texel density to land near `resolution` (assumes ~0.62 pack fill)
|
||
total_3d_area = sum(all_chart_3d_areas) or 1.0
|
||
target_dim = float(resolution) if resolution > 0 else 1024.0
|
||
tex_per_unit = math.sqrt((target_dim * target_dim) * 0.62 / total_3d_area)
|
||
|
||
placements, atlas_w, atlas_h = _uv_pack.pack_bitmap(
|
||
all_chart_uvs, all_chart_3d_areas, all_chart_uv_areas, all_chart_faces,
|
||
texels_per_unit=tex_per_unit, padding_texels=padding)
|
||
placed = _uv_pack.apply_placements(all_chart_uvs, placements, atlas_w, atlas_h)
|
||
|
||
n_in_faces = mesh.faces.shape[0]
|
||
out_indices = np.zeros((n_in_faces, 3), dtype=np.int64)
|
||
out_uvs_list, out_vmap_list, v_cursor = [], [], 0
|
||
for c, rec in enumerate(chart_records):
|
||
vmap_np = rec["vmap"].cpu().numpy()
|
||
local_faces_np = rec["local_faces"].cpu().numpy()
|
||
global_face_idx = rec["global_face_idx"].cpu().numpy()
|
||
out_uvs_list.append(placed[c].cpu().numpy())
|
||
if welded_to_orig is not None:
|
||
vmap_np = welded_to_orig[vmap_np]
|
||
out_vmap_list.append(vmap_np)
|
||
out_indices[global_face_idx] = local_faces_np + v_cursor
|
||
v_cursor += vmap_np.shape[0]
|
||
|
||
vmapping_out = np.concatenate(out_vmap_list) if out_vmap_list else np.empty(0, dtype=np.int64)
|
||
uvs_out = np.concatenate(out_uvs_list) if out_uvs_list else np.empty((0, 2), dtype=np.float32)
|
||
return vmapping_out, out_indices, uvs_out
|
||
|
||
|
||
class UnwrapMesh(IO.ComfyNode):
|
||
@classmethod
|
||
def define_schema(cls):
|
||
return IO.Schema(
|
||
node_id="UnwrapMesh",
|
||
display_name="Unwrap Mesh UVs",
|
||
category="latent/3d",
|
||
description=(
|
||
"Generates a UV atlas (pure-torch, no xatlas dependency): segments the surface into "
|
||
"charts, parameterizes each, and packs them into a [0,1] atlas. Verts on chart seams "
|
||
"are duplicated. Run after DecimateMesh/RemeshMesh, before texture baking."
|
||
),
|
||
inputs=[
|
||
IO.Mesh.Input("mesh"),
|
||
IO.Combo.Input("segmenter", options=["pec", "adaptive"], default="pec",
|
||
tooltip="pec: fast parallel-edge-collapse charting (CUDA; falls back to "
|
||
"adaptive on CPU). adaptive: CPU charting, slower."),
|
||
IO.Int.Input("resolution", default=1024, min=0, max=8192, step=256,
|
||
tooltip="Target atlas resolution used to auto-scale texel density (0 = fit-to-content)."),
|
||
IO.Int.Input("padding", default=1, min=0, max=16,
|
||
tooltip="Texel padding between charts in the packed atlas."),
|
||
IO.Float.Input("weld_distance", default=0.0, min=0.0, max=1.0, step=0.0001,
|
||
tooltip="Merge radius for coincident verts as a fraction of mesh extent "
|
||
"(0 = auto, 1e-5). Raise to ~0.001 if you get per-triangle charts "
|
||
"(unwelded / triangle-soup input)."),
|
||
],
|
||
outputs=[IO.Mesh.Output("mesh")],
|
||
hidden=[IO.Hidden.unique_id],
|
||
)
|
||
|
||
@classmethod
|
||
def execute(cls, mesh, segmenter, resolution, padding, weld_distance):
|
||
compute_device = comfy.model_management.get_torch_device()
|
||
seg = segmenter
|
||
if seg == "pec" and compute_device.type != "cuda":
|
||
seg = "adaptive"
|
||
seg_device = compute_device if seg == "pec" else torch.device("cpu")
|
||
|
||
is_list = isinstance(mesh.vertices, list)
|
||
is_batched = not is_list and mesh.vertices.ndim == 3
|
||
bsz = len(mesh.vertices) if is_list else (mesh.vertices.shape[0] if is_batched else 1)
|
||
bar = comfy.utils.ProgressBar(bsz)
|
||
|
||
out_v, out_f, out_uv, out_c = [], [], [], []
|
||
for i in range(bsz):
|
||
if is_list or is_batched:
|
||
vi, fi = mesh.vertices[i], mesh.faces[i]
|
||
ci = None
|
||
vc = getattr(mesh, "vertex_colors", None)
|
||
if vc is not None:
|
||
ci = vc[i] if (isinstance(vc, list) or vc.ndim == 3) else vc
|
||
else:
|
||
vi, fi = mesh.vertices, mesh.faces
|
||
ci = getattr(mesh, "vertex_colors", None)
|
||
|
||
src_device = vi.device
|
||
vnp = vi.detach().cpu().numpy().astype(np.float32)
|
||
extent = float(np.linalg.norm(vnp.max(0) - vnp.min(0))) if vnp.shape[0] else 0.0
|
||
weld_abs = weld_distance * extent if weld_distance > 0.0 else 0.0
|
||
|
||
vmapping, indices, uvs = _uv_unwrap(
|
||
vi.to(seg_device).float(), fi.to(seg_device).long(),
|
||
seg, int(resolution), int(padding), weld_abs)
|
||
uvs = uvs.copy()
|
||
uvs[:, 1] = 1.0 - uvs[:, 1] # UV y flipped vs trimesh
|
||
|
||
out_v.append(torch.from_numpy(vnp[vmapping]).to(src_device))
|
||
out_f.append(torch.from_numpy(indices).to(device=src_device, dtype=torch.long))
|
||
out_uv.append(torch.from_numpy(uvs.astype(np.float32)).to(src_device))
|
||
if ci is not None:
|
||
cnp = ci.detach().cpu().numpy()
|
||
out_c.append(torch.from_numpy(np.ascontiguousarray(cnp[vmapping])).to(src_device))
|
||
bar.update(1)
|
||
|
||
out_mesh = _pack_uv_meshes(out_v, out_f, out_uv, out_c if out_c else None)
|
||
if getattr(mesh, "texture", None) is not None:
|
||
out_mesh.texture = mesh.texture
|
||
|
||
if cls.hidden.unique_id:
|
||
PromptServer.instance.send_progress_text(
|
||
f"UV: {_fmt_count(out_v[0].shape[0])} verts / {_fmt_count(out_f[0].shape[0])} faces"
|
||
f" · atlas ~{resolution}px",
|
||
cls.hidden.unique_id)
|
||
return IO.NodeOutput(out_mesh)
|
||
|
||
|
||
def _uv_sorted_edge_keys(indices: np.ndarray):
|
||
"""Undirected edge keys per face-edge, sorted; returns (sorted_keys, face_id, lo, hi, first_mask)."""
|
||
a = indices.ravel().astype(np.int64)
|
||
b = np.roll(indices, -1, axis=1).ravel().astype(np.int64)
|
||
lo = np.minimum(a, b)
|
||
hi = np.maximum(a, b)
|
||
V = int(indices.max()) + 1
|
||
key = lo * V + hi
|
||
order = np.argsort(key, kind="stable")
|
||
sk = key[order]
|
||
fid = (np.arange(a.size, dtype=np.int64) // 3)[order]
|
||
first = np.ones(sk.size, dtype=bool)
|
||
first[1:] = sk[1:] != sk[:-1]
|
||
return sk, fid, lo[order], hi[order], first
|
||
|
||
|
||
def _uv_faces_to_chart_ids(indices: np.ndarray) -> np.ndarray:
|
||
"""Chart = connected component of faces adjacent iff they share a (non-seam-duplicated) UV vertex."""
|
||
F = indices.shape[0]
|
||
if F == 0:
|
||
return np.empty(0, dtype=np.int64)
|
||
_sk, fid, _lo, _hi, first = _uv_sorted_edge_keys(indices)
|
||
group_id = np.cumsum(first) - 1
|
||
starts = np.nonzero(first)[0]
|
||
rows = fid[starts[group_id[~first]]]
|
||
cols = fid[~first]
|
||
if rows.size == 0:
|
||
return np.arange(F, dtype=np.int64)
|
||
adj = csr_matrix((np.ones(rows.size, dtype=np.int8), (rows, cols)), shape=(F, F))
|
||
_, labels = connected_components(adj, directed=False)
|
||
return labels.astype(np.int64)
|
||
|
||
|
||
_UV_TAB20 = np.array([
|
||
[0.121568627, 0.466666667, 0.705882353], [0.682352941, 0.780392157, 0.909803922],
|
||
[1.000000000, 0.498039216, 0.054901961], [1.000000000, 0.733333333, 0.470588235],
|
||
[0.172549020, 0.627450980, 0.172549020], [0.596078431, 0.874509804, 0.541176471],
|
||
[0.839215686, 0.152941176, 0.156862745], [1.000000000, 0.596078431, 0.588235294],
|
||
[0.580392157, 0.403921569, 0.741176471], [0.772549020, 0.690196078, 0.835294118],
|
||
[0.549019608, 0.337254902, 0.294117647], [0.768627451, 0.611764706, 0.580392157],
|
||
[0.890196078, 0.466666667, 0.760784314], [0.968627451, 0.713725490, 0.823529412],
|
||
[0.498039216, 0.498039216, 0.498039216], [0.780392157, 0.780392157, 0.780392157],
|
||
[0.737254902, 0.741176471, 0.133333333], [0.858823529, 0.858823529, 0.552941176],
|
||
[0.090196078, 0.745098039, 0.811764706], [0.619607843, 0.854901961, 0.898039216],
|
||
], dtype=np.float32)
|
||
|
||
|
||
def _uv_palette(n: int) -> np.ndarray:
|
||
rng = np.random.RandomState(42)
|
||
perm = rng.permutation(max(1, n))
|
||
out = np.empty((n, 3), dtype=np.float32)
|
||
for i in range(n):
|
||
out[i] = _UV_TAB20[perm[i % len(perm)] % 20]
|
||
return out
|
||
|
||
|
||
def _uv_render_atlas(uvs_np, indices_np, resolution, device,
|
||
bg=(0.13, 0.13, 0.13), edge=(0.0, 0.0, 0.0)):
|
||
"""Tile-based torch rasterizer of the UV atlas (charts colored, borders outlined); returns (H,W,3)."""
|
||
w = h = int(resolution)
|
||
chart_ids_np = _uv_faces_to_chart_ids(indices_np)
|
||
uvs = torch.from_numpy(uvs_np).to(device=device, dtype=torch.float32)
|
||
indices = torch.from_numpy(indices_np).to(device=device, dtype=torch.long)
|
||
chart_ids = torch.from_numpy(chart_ids_np).to(device=device, dtype=torch.long)
|
||
|
||
img = torch.zeros((h, w, 3), dtype=torch.float32, device=device)
|
||
img[..., 0] = bg[0]; img[..., 1] = bg[1]; img[..., 2] = bg[2]
|
||
if indices.numel() == 0:
|
||
return img
|
||
|
||
n_charts = int(chart_ids.max().item()) + 1 if chart_ids.numel() else 1
|
||
colors = torch.from_numpy(_uv_palette(n_charts)).to(device=device, dtype=torch.float32)
|
||
|
||
uv_px = uvs.clone()
|
||
uv_px[:, 0] = uv_px[:, 0].clamp(0.0, 1.0) * (w - 1)
|
||
uv_px[:, 1] = uv_px[:, 1].clamp(0.0, 1.0) * (h - 1)
|
||
|
||
tri = uv_px[indices]
|
||
x0 = tri[:, 0, 0]; y0 = tri[:, 0, 1]
|
||
x1 = tri[:, 1, 0]; y1 = tri[:, 1, 1]
|
||
x2 = tri[:, 2, 0]; y2 = tri[:, 2, 1]
|
||
denom = (y1 - y2) * (x0 - x2) + (x2 - x1) * (y0 - y2)
|
||
nondegen = denom.abs() > 1e-20
|
||
|
||
xmin = torch.minimum(torch.minimum(x0, x1), x2).floor().clamp_(0, w - 1).long()
|
||
xmax = torch.maximum(torch.maximum(x0, x1), x2).ceil().clamp_(0, w - 1).long()
|
||
ymin = torch.minimum(torch.minimum(y0, y1), y2).floor().clamp_(0, h - 1).long()
|
||
ymax = torch.maximum(torch.maximum(y0, y1), y2).ceil().clamp_(0, h - 1).long()
|
||
|
||
# full point-in-triangle over all (pixel, tri) pairs is O(H*W*F); tile and test only bbox-overlapping tris
|
||
TILE = 64
|
||
eps = 1e-6
|
||
for ty in range(0, h, TILE):
|
||
ty_end = min(ty + TILE, h)
|
||
for tx in range(0, w, TILE):
|
||
tx_end = min(tx + TILE, w)
|
||
tri_mask = (nondegen & (xmin < tx_end) & (xmax >= tx)
|
||
& (ymin < ty_end) & (ymax >= ty))
|
||
if not tri_mask.any():
|
||
continue
|
||
idx = torch.nonzero(tri_mask, as_tuple=True)[0]
|
||
ys = torch.arange(ty, ty_end, dtype=torch.float32, device=device) + 0.5
|
||
xs = torch.arange(tx, tx_end, dtype=torch.float32, device=device) + 0.5
|
||
yy, xx = torch.meshgrid(ys, xs, indexing="ij")
|
||
sub_x0 = x0[idx][:, None, None]; sub_y0 = y0[idx][:, None, None]
|
||
sub_x1 = x1[idx][:, None, None]; sub_y1 = y1[idx][:, None, None]
|
||
sub_x2 = x2[idx][:, None, None]; sub_y2 = y2[idx][:, None, None]
|
||
sub_den = denom[idx][:, None, None]
|
||
bx = ((sub_y1 - sub_y2) * (xx - sub_x2) + (sub_x2 - sub_x1) * (yy - sub_y2)) / sub_den
|
||
by = ((sub_y2 - sub_y0) * (xx - sub_x2) + (sub_x0 - sub_x2) * (yy - sub_y2)) / sub_den
|
||
bz = 1.0 - bx - by
|
||
inside = (bx >= -eps) & (by >= -eps) & (bz >= -eps)
|
||
if not inside.any():
|
||
continue
|
||
hit_any = inside.any(dim=0)
|
||
best_tri = idx[inside.int().argmax(dim=0)]
|
||
tile_color = colors[chart_ids[best_tri]]
|
||
tile_img = img[ty:ty_end, tx:tx_end]
|
||
tile_img[hit_any] = tile_color[hit_any]
|
||
img[ty:ty_end, tx:tx_end] = tile_img
|
||
|
||
# chart outlines: a chart border is an open boundary in UV space (seam verts duplicated) → edges with 1 incident face
|
||
_sk, _fid, lo, hi, first = _uv_sorted_edge_keys(indices_np)
|
||
starts = np.nonzero(first)[0]
|
||
counts = np.diff(np.append(starts, first.size))
|
||
boundary = counts == 1
|
||
uv_cpu = uv_px.cpu().numpy()
|
||
px_xs, px_ys = [], []
|
||
for a, b in zip(lo[starts[boundary]], hi[starts[boundary]]):
|
||
p0 = uv_cpu[a]; p1 = uv_cpu[b]
|
||
steps = int(max(abs(p1[0] - p0[0]), abs(p1[1] - p0[1])) + 1)
|
||
if steps <= 1:
|
||
continue
|
||
ts = np.linspace(0.0, 1.0, steps)
|
||
xs = (p0[0] + (p1[0] - p0[0]) * ts).astype(np.int32)
|
||
ys = (p0[1] + (p1[1] - p0[1]) * ts).astype(np.int32)
|
||
valid = (xs >= 0) & (xs < w) & (ys >= 0) & (ys < h)
|
||
px_xs.append(xs[valid]); px_ys.append(ys[valid])
|
||
if px_xs:
|
||
xs_all = torch.from_numpy(np.concatenate(px_xs)).to(device=device, dtype=torch.long)
|
||
ys_all = torch.from_numpy(np.concatenate(px_ys)).to(device=device, dtype=torch.long)
|
||
img[ys_all, xs_all] = torch.tensor(edge, dtype=torch.float32, device=device)
|
||
|
||
return img
|
||
|
||
|
||
class RenderUVAtlas(IO.ComfyNode):
|
||
@classmethod
|
||
def define_schema(cls):
|
||
return IO.Schema(
|
||
node_id="RenderUVAtlas",
|
||
display_name="Render UV Atlas",
|
||
category="latent/3d",
|
||
description=("Renders a mesh's UV layout as an image — each chart a distinct color, "
|
||
"outlined where it borders other charts. Run UnwrapMesh first."),
|
||
inputs=[
|
||
IO.Mesh.Input("mesh"),
|
||
IO.Int.Input("resolution", default=1024, min=64, max=4096, step=64),
|
||
],
|
||
outputs=[IO.Image.Output("image")],
|
||
)
|
||
|
||
@classmethod
|
||
def execute(cls, mesh, resolution):
|
||
uvs_t = getattr(mesh, "uvs", None)
|
||
if uvs_t is None:
|
||
raise RuntimeError("mesh has no UVs to render. Run UnwrapMesh first.")
|
||
uvs_np = uvs_t.detach().cpu().numpy()
|
||
if uvs_np.ndim == 3:
|
||
uvs_np = uvs_np[0]
|
||
f = mesh.faces
|
||
if torch.is_tensor(f):
|
||
f = f.detach().cpu().numpy()
|
||
if f.ndim == 3:
|
||
f = f[0]
|
||
f = np.ascontiguousarray(f, dtype=np.int64)
|
||
uvs_np = np.ascontiguousarray(uvs_np, dtype=np.float32)
|
||
device = comfy.model_management.get_torch_device()
|
||
img = _uv_render_atlas(uvs_np, f, int(resolution), device)
|
||
return IO.NodeOutput(img.detach().cpu().unsqueeze(0))
|
||
|
||
|
||
class FillHoles(IO.ComfyNode):
|
||
@classmethod
|
||
def define_schema(cls):
|
||
return IO.Schema(
|
||
node_id="FillHoles",
|
||
display_name="Fill Holes",
|
||
category="latent/3d",
|
||
description=(
|
||
"Fills holes in a mesh up to a maximum perimeter threshold, preserving "
|
||
"the existing geometry/UVs (only patch triangles are added). GPU-vectorised "
|
||
"via directed-half-edge pointer-doubling: no Python loop, auto-correct "
|
||
"winding from face direction, and centroid colors are averaged from the hole "
|
||
"loop. Falls back to a CPU walker on non-CUDA devices."
|
||
),
|
||
inputs=[
|
||
IO.Mesh.Input("mesh"),
|
||
IO.Float.Input("max_perimeter", default=0.03, min=0.0, step=0.0001,
|
||
tooltip="Maximum hole perimeter to fill. Set to 0 to disable."),
|
||
IO.Float.Input("weld_epsilon_rel", default=1e-5, min=0.0, step=1e-6,
|
||
tooltip=(
|
||
"Pre-weld tolerance as a fraction of the bbox diagonal. "
|
||
"Boundary detection needs welded verts; already-welded meshes "
|
||
"early-exit free. Set to 0 to skip pre-weld."
|
||
)),
|
||
IO.Int.Input("max_verts", default=16, min=3, max=1024,
|
||
tooltip=(
|
||
"Cap the boundary-vertex count per cycle. Fan-from-centroid "
|
||
"only triangulates correctly for small, near-planar holes — "
|
||
"larger cycles produce overlapping geometry because the centroid "
|
||
"lands far from any surface. Keep low (≤16) for clean fills."
|
||
)),
|
||
IO.Boolean.Input("fill_chains", default=False,
|
||
tooltip=(
|
||
"Also fill open boundary chains (not just closed cycles) "
|
||
"by closing them with a fan + closing triangle. "
|
||
"Often produces noisy/overlapping geometry on real meshes "
|
||
"because chains are usually genuine surface boundaries or "
|
||
"fragments of cycles broken by non-manifold edges. Leave OFF "
|
||
"to match cumesh/upstream behavior."
|
||
)),
|
||
IO.Boolean.Input("verbose", default=False,
|
||
tooltip="Log topology diagnostics (edge counts, cycles found, reject reasons) for debugging."),
|
||
],
|
||
outputs=[IO.Mesh.Output("mesh")],
|
||
)
|
||
|
||
@classmethod
|
||
def execute(cls, mesh, max_perimeter, weld_epsilon_rel, max_verts, fill_chains, verbose):
|
||
def _fn(v, f, c):
|
||
if max_perimeter > 0:
|
||
v, f, c = fill_holes_v2_fn(
|
||
v, f, max_perimeter=max_perimeter, colors=c,
|
||
weld_epsilon_rel=weld_epsilon_rel,
|
||
fill_chains=fill_chains,
|
||
max_verts=max_verts,
|
||
diagnostic=verbose,
|
||
)
|
||
return v, f, c
|
||
return _process_mesh_batch(mesh, _fn)
|
||
|
||
|
||
class WeldVertices(IO.ComfyNode):
|
||
@classmethod
|
||
def define_schema(cls):
|
||
return IO.Schema(
|
||
node_id="WeldVertices",
|
||
display_name="Weld Vertices",
|
||
category="latent/3d",
|
||
description=(
|
||
"Merge coincident vertices via L_inf grid quantization. Use when a "
|
||
"mesh comes in unwelded (every face has its own 3 verts, no shared edges) "
|
||
"— pre-pass before FillHoles, DecimateMesh, or any topology-aware op. "
|
||
"Per-vertex colors are averaged across each merged cluster."
|
||
),
|
||
inputs=[
|
||
IO.Mesh.Input("mesh"),
|
||
IO.Float.Input("epsilon_rel", default=1e-5, min=0.0, step=1e-6,
|
||
tooltip="Weld tolerance as a fraction of the bbox diagonal. "
|
||
"1e-5 is enough for floating-point dedup; raise to "
|
||
"1e-3 for visibly-close-but-distinct verts."),
|
||
IO.Float.Input("epsilon_abs", default=0.0, min=0.0, step=1e-6,
|
||
tooltip="Absolute weld tolerance (overrides epsilon_rel when > 0)."),
|
||
],
|
||
outputs=[IO.Mesh.Output("mesh")],
|
||
)
|
||
|
||
@classmethod
|
||
def execute(cls, mesh, epsilon_rel, epsilon_abs):
|
||
eps = epsilon_abs if epsilon_abs > 0 else None
|
||
def _fn(v, f, c):
|
||
v, f, c, n = weld_vertices_fn(v, f, epsilon=eps, epsilon_rel=epsilon_rel, colors=c)
|
||
if n > 0:
|
||
logging.info(f"[WeldVertices] merged {n} verts ({v.shape[0]} remain)")
|
||
return v, f, c
|
||
return _process_mesh_batch(mesh, _fn)
|
||
|
||
|
||
def merge_meshes(meshes):
|
||
"""Concatenate a list of Types.MESH into a single (B=1) mesh.
|
||
|
||
Vertices, faces (with cumulative index offset), uvs, and vertex_colors are
|
||
concatenated. If only some inputs carry uvs/vertex_colors, the missing sides
|
||
are padded — zeros for uvs, white (1.0) for vertex_colors — so the merged
|
||
primitive has a uniform attribute set. Texture is taken from the first input
|
||
that has one; later textures are dropped with a warning (single-primitive glb
|
||
can't carry multiple texture atlases without baking).
|
||
"""
|
||
if not meshes:
|
||
raise ValueError("merge_meshes: need at least one mesh")
|
||
|
||
def _b0(t):
|
||
return t[0] if t.ndim == 3 else t
|
||
|
||
any_uvs = any(getattr(m, "uvs", None) is not None for m in meshes)
|
||
any_colors = any(getattr(m, "vertex_colors", None) is not None for m in meshes)
|
||
|
||
verts_list, faces_list, uvs_list, colors_list = [], [], [], []
|
||
texture = None
|
||
offset = 0
|
||
for m in meshes:
|
||
# Mesh tensors are normalized to CPU by our producer nodes; coerce defensively
|
||
# so MoGe-side meshes (which may land on CUDA) merge cleanly with our outputs.
|
||
v = _b0(m.vertices).cpu()
|
||
f = _b0(m.faces).cpu()
|
||
verts_list.append(v)
|
||
faces_list.append(f + offset)
|
||
offset += v.shape[0]
|
||
if any_uvs:
|
||
mu = getattr(m, "uvs", None)
|
||
uvs_list.append(_b0(mu).cpu() if mu is not None else v.new_zeros((v.shape[0], 2)))
|
||
if any_colors:
|
||
mc = getattr(m, "vertex_colors", None)
|
||
if mc is not None:
|
||
c = _b0(mc).cpu()
|
||
else:
|
||
c = v.new_ones((v.shape[0], 3))
|
||
colors_list.append(c)
|
||
mt = getattr(m, "texture", None)
|
||
if mt is not None:
|
||
if texture is None:
|
||
texture = mt.cpu()
|
||
else:
|
||
logging.warning("MergeMeshes: dropping extra texture from input; only one texture is kept.")
|
||
|
||
merged_verts = torch.cat(verts_list, dim=0).unsqueeze(0)
|
||
merged_faces = torch.cat(faces_list, dim=0).unsqueeze(0)
|
||
merged_uvs = torch.cat(uvs_list, dim=0).unsqueeze(0) if any_uvs else None
|
||
merged_colors = torch.cat(colors_list, dim=0).unsqueeze(0) if any_colors else None
|
||
|
||
return Types.MESH(
|
||
vertices=merged_verts,
|
||
faces=merged_faces,
|
||
uvs=merged_uvs,
|
||
vertex_colors=merged_colors,
|
||
texture=texture,
|
||
)
|
||
|
||
|
||
class MergeMeshes(IO.ComfyNode):
|
||
@classmethod
|
||
def define_schema(cls):
|
||
autogrow_template = IO.Autogrow.TemplatePrefix(
|
||
IO.Mesh.Input("mesh"), prefix="mesh", min=2, max=50,
|
||
)
|
||
return IO.Schema(
|
||
node_id="MergeMeshes",
|
||
display_name="Merge Meshes",
|
||
category="latent/3d",
|
||
description=(
|
||
"Concatenate N meshes into a single mesh by offsetting face indices "
|
||
"and stacking vertices, faces, uvs, and vertex colors. Useful for combining a "
|
||
"Pixal3D-reconstructed object (via Pixal3DAlignObject) with a MoGe scene "
|
||
"background (via MoGePointMapToMesh) into one GLB."
|
||
),
|
||
inputs=[
|
||
IO.Autogrow.Input("meshes", template=autogrow_template),
|
||
],
|
||
outputs=[IO.Mesh.Output("mesh")],
|
||
)
|
||
|
||
@classmethod
|
||
def execute(cls, meshes: IO.Autogrow.Type) -> IO.NodeOutput:
|
||
return IO.NodeOutput(merge_meshes(list(meshes.values())))
|
||
|
||
|
||
class PostProcessMeshExtension(ComfyExtension):
|
||
@override
|
||
async def get_node_list(self) -> list[type[IO.ComfyNode]]:
|
||
return [
|
||
FillHoles,
|
||
WeldVertices,
|
||
DecimateMesh,
|
||
RemeshMesh,
|
||
UnwrapMesh,
|
||
RenderUVAtlas,
|
||
PaintMesh,
|
||
BakeTextureFromVoxel,
|
||
MeshTextureToImage,
|
||
ApplyTextureToMesh,
|
||
MergeMeshes,
|
||
]
|
||
|
||
|
||
async def comfy_entrypoint() -> PostProcessMeshExtension:
|
||
return PostProcessMeshExtension()
|