# Generic utility nodes for the SPLAT type (3D gaussian splats) import gzip import logging import math import struct from io import BytesIO import numpy as np import torch from typing_extensions import override from scipy.ndimage import map_coordinates, minimum as _ndi_minimum, maximum as _ndi_maximum from scipy.sparse import coo_matrix from scipy.sparse.csgraph import connected_components import comfy.model_management import comfy.utils from comfy_api.latest import ComfyExtension, IO, Types from comfy_extras.nodes_save_3d import pack_variable_mesh_batch from server import PromptServer _C0 = 0.28209479177387814 # SH band-0 constant: DC coefficient -> base RGB def _srgb_to_linear(c): return torch.where(c <= 0.04045, c / 12.92, ((c.clamp_min(0) + 0.055) / 1.055) ** 2.4) def _linear_to_srgb(c): return torch.where(c <= 0.0031308, c * 12.92, 1.055 * c.clamp_min(0) ** (1 / 2.4) - 0.055) def _real_len(g: Types.SPLAT, i: int) -> int: # Real splat count of batch item i (honors variable-length `counts`). return int(g.counts[i].item()) if g.counts is not None else g.positions.shape[1] def _hex_to_rgb(h: str) -> tuple[float, float, float]: # "#RRGGBB" -> (r,g,b) in [0,1]; falls back to black. h = h.lstrip("#") if len(h) != 6: return (0.0, 0.0, 0.0) return tuple(int(h[i:i + 2], 16) / 255.0 for i in (0, 2, 4)) def _quantile(x, q): # torch.quantile errors above 2**24 elements; stride-subsample large inputs for the estimate. lim = 1 << 24 if x.numel() > lim: x = x[:: x.numel() // lim + 1] return torch.quantile(x, q) def _gaussian_ply_bytes(positions, scales, rotations, opacities, sh) -> bytes: """Serialize render-ready gaussian tensors as a binary 3DGS .ply. positions (N,3) world; scales (N,3) linear; rotations (N,4) quat wxyz; opacities (N,1) in [0,1]; sh (N,K,3) SH coefficients. Activated values are inverted to the standard 3D gaussian splat storage convention (log scale, logit opacity). """ xyz = positions.cpu().numpy().astype(np.float32) n = xyz.shape[0] if n == 0: raise ValueError("SplatToFile3D: gaussian is empty") normals = np.zeros_like(xyz) f = sh.cpu().numpy().astype(np.float32) # (N, K, 3) f_dc = f[:, 0, :] # (N, 3) f_rest = f[:, 1:, :].transpose(0, 2, 1).reshape(n, -1) # (N, 3*(K-1)) channel-major op = opacities.cpu().numpy().astype(np.float32).reshape(n, 1).clip(1e-6, 1 - 1e-6) op = np.log(op / (1.0 - op)) # inverse sigmoid (logit) scale = np.log(scales.cpu().numpy().astype(np.float32).clip(min=1e-8)) rot = rotations.cpu().numpy().astype(np.float32) # (N, 4) attrs = (['x', 'y', 'z', 'nx', 'ny', 'nz'] + [f'f_dc_{i}' for i in range(3)] + [f'f_rest_{i}' for i in range(f_rest.shape[1])] + ['opacity'] + [f'scale_{i}' for i in range(3)] + [f'rot_{i}' for i in range(4)]) elements = np.empty(n, dtype=[(a, 'f4') for a in attrs]) elements[:] = list(map(tuple, np.concatenate([xyz, normals, f_dc, f_rest, op, scale, rot], axis=1))) header = "ply\nformat binary_little_endian 1.0\n" + f"element vertex {n}\n" header += "".join(f"property float {a}\n" for a in attrs) + "end_header\n" return header.encode('ascii') + elements.tobytes() # .ksplat (mkkellogg SplatBuffer) level 0, SH degree 0: 4096-byte header, one 1024-byte section header, # then N 44-byte records. Bucketing/quantization only exist at levels >= 1. See SplatBuffer.js. _KSPLAT_HEADER_BYTES = 4096 _KSPLAT_SECTION_HEADER_BYTES = 1024 _KSPLAT_BYTES_PER_SPLAT = 44 # center 12 + scale 12 + rotation 16 + color(RGBA u8) 4 _KSPLAT_VERSION = (0, 1) # SplatBuffer CurrentMajor/MinorVersion def _gaussian_ksplat_bytes(positions, scales, rotations, opacities, sh) -> bytes: """Serialize gaussian tensors as a level-0, SH degree-0 .ksplat (linear scale, opacity in color alpha). positions (N,3) world; scales (N,3) linear; rotations (N,4) wxyz; opacities (N,1) in [0,1]; sh (N,K,3). """ xyz = positions.cpu().numpy().astype(np.float32) n = xyz.shape[0] if n == 0: raise ValueError("SplatToFile3D: gaussian is empty") scale = scales.cpu().numpy().astype(np.float32) rot = rotations.cpu().numpy().astype(np.float32) # wxyz, mirrors the .ply rot order rot = rot / np.linalg.norm(rot, axis=1, keepdims=True).clip(1e-12) rgb = np.clip(sh[:, 0, :].cpu().numpy().astype(np.float32) * _C0 + 0.5, 0, 1) op = opacities.cpu().numpy().astype(np.float32).reshape(n, 1).clip(0, 1) rgba = np.round(np.concatenate([rgb, op], axis=1) * 255.0).astype(np.uint8) # (N, 4) RGBA # 44-byte record: float center(3) + scale(3) + rot(4), then uint8 rgba(4). floats = np.concatenate([xyz, scale, rot], axis=1).astype(' bytes: """Serialize gaussian tensors as a gzip-compressed .spz (Niantic v2, SH degree 0, base color only). positions (N,3) world; scales (N,3) linear; rotations (N,4) wxyz; opacities (N,1) in [0,1]; sh (N,K,3). """ xyz = positions.cpu().numpy().astype(np.float32) n = xyz.shape[0] if n == 0: raise ValueError("SplatToFile3D: gaussian is empty") # Positions: fixed point, masked to 24 bits, little-endian 3-byte words. fixed = 1 << _SPZ_FRACTIONAL_BITS qi = np.clip(np.round(xyz * fixed), -(1 << 23), (1 << 23) - 1).astype(np.int32) qu = (qi & 0xFFFFFF).astype(np.uint32) pos = np.stack([qu & 0xFF, (qu >> 8) & 0xFF, (qu >> 16) & 0xFF], axis=-1).reshape(n, 9).astype(np.uint8) alpha = np.round(opacities.cpu().numpy().astype(np.float32).reshape(n) * 255.0).clip(0, 255).astype(np.uint8) rgb = sh[:, 0, :].cpu().numpy().astype(np.float32) * _C0 + 0.5 col = np.round(((rgb - 0.5) / _SPZ_COLOR_SCALE + 0.5) * 255.0).clip(0, 255).astype(np.uint8) # (N,3) sln = np.log(scales.cpu().numpy().astype(np.float32).clip(min=1e-9)) scb = np.round((sln + 10.0) * 16.0).clip(0, 255).astype(np.uint8) # (N,3) inverts exp(b/16-10) rot = rotations.cpu().numpy().astype(np.float32) # wxyz rot = rot / np.linalg.norm(rot, axis=1, keepdims=True).clip(1e-12) rot[rot[:, 0] < 0] *= -1.0 # canonical w >= 0 (w dropped on decode) rotb = np.round((rot[:, 1:4] + 1.0) * 127.5).clip(0, 255).astype(np.uint8) # (N,3) x,y,z header = bytearray(16) struct.pack_into(' (positions, scales linear, rotations wxyz, opacities [0,1], sh (N,K,3)) ---- # Inverse of the writers above and of spark's loaders. ksplat/splat/spz carry base color only (SH degree 0 # -> K=1); .ply round-trips full SH. None of the formats flip axes, so import is the identity of export. _PLY_DTYPES = {'char': 'i1', 'uchar': 'u1', 'short': 'i2', 'ushort': 'u2', 'int': 'i4', 'uint': 'u4', 'float': 'f4', 'double': 'f8', 'int8': 'i1', 'uint8': 'u1', 'int16': 'i2', 'uint16': 'u2', 'int32': 'i4', 'uint32': 'u4', 'float32': 'f4', 'float64': 'f8'} _KSPLAT_COMPRESSION = { # level -> (bytesPerCenter, scale, rotation, color, shComponent, defaultScaleRange) 0: (12, 12, 16, 4, 4, 1), 1: (6, 6, 8, 4, 2, 32767), 2: (6, 6, 8, 4, 1, 32767)} _KSPLAT_SH_COMPONENTS = {0: 0, 1: 9, 2: 24, 3: 45} def _rgb_to_sh_dc(rgb): return ((np.asarray(rgb, np.float32) - 0.5) / _C0)[:, None, :] # (N,3) base color -> (N,1,3) SH DC def _norm_quat(q): return q / np.linalg.norm(q, axis=1, keepdims=True).clip(1e-12) def _parse_ply_gaussian(data: bytes): end = data.find(b'end_header') if end < 0: raise ValueError("File3DToSplat: not a PLY (missing end_header)") header = data[:end].decode('ascii', 'replace') body = end + len(b'end_header') body += 2 if data[body:body + 2] == b'\r\n' else 1 count, props, in_vertex = 0, [], False for line in header.splitlines(): p = line.split() if not p: continue if p[0] == 'format' and p[1] != 'binary_little_endian': raise ValueError(f"File3DToSplat: unsupported PLY format '{p[1]}' (need binary_little_endian)") if p[0] == 'element': in_vertex = p[1] == 'vertex' if in_vertex: count = int(p[2]) elif p[0] == 'property' and in_vertex: if p[1] == 'list': raise ValueError("File3DToSplat: PLY vertex has list properties (unsupported)") props.append((p[2], '<' + _PLY_DTYPES[p[1]])) arr = np.frombuffer(data, np.dtype(props), count=count, offset=body) names = arr.dtype.names c = lambda k: arr[k].astype(np.float32) n = count xyz = np.stack([c('x'), c('y'), c('z')], 1) if 'scale_0' in names: scale = np.exp(np.stack([c('scale_0'), c('scale_1'), c('scale_2')], 1)) # 3DGS stores log scale else: scale = np.full((n, 3), 0.01, np.float32) if 'rot_0' in names: rot = _norm_quat(np.stack([c('rot_0'), c('rot_1'), c('rot_2'), c('rot_3')], 1)) # wxyz else: rot = np.tile(np.array([1, 0, 0, 0], np.float32), (n, 1)) opacity = 1.0 / (1.0 + np.exp(-c('opacity'))) if 'opacity' in names else np.ones(n, np.float32) if 'f_dc_0' in names: dc = np.stack([c('f_dc_0'), c('f_dc_1'), c('f_dc_2')], 1) # (N,3) rest = sorted((k for k in names if k.startswith('f_rest_')), key=lambda s: int(s.split('_')[-1])) if rest: r = np.stack([c(k) for k in rest], 1) # (N, 3*(K-1)) channel-major kk = r.shape[1] // 3 + 1 r = r.reshape(n, 3, kk - 1).transpose(0, 2, 1) # -> (N, K-1, 3) sh = np.concatenate([dc[:, None, :], r], 1) else: sh = dc[:, None, :] elif 'red' in names: sh = _rgb_to_sh_dc(np.stack([c('red'), c('green'), c('blue')], 1) / 255.0) else: sh = np.zeros((n, 1, 3), np.float32) return xyz, scale, rot, opacity, sh def _parse_splat_gaussian(data: bytes): # antimatter15 .splat: 32-byte records (f32 xyz, f32 scale, u8 rgba, u8 quat as (b-128)/128 wxyz). if len(data) % 32 != 0: raise ValueError("File3DToSplat: .splat size is not a multiple of 32 bytes") rec = np.frombuffer(data, np.dtype([('xyz', ' 0: ct, ft = (' full_splats: lengths = np.frombuffer(data, '> 30) & 3 q = np.zeros((n, 4), np.float32) # x,y,z,w remaining, sumsq = combined.copy(), np.zeros(n, np.float64) for comp in (3, 2, 1, 0): active = comp != largest value = (remaining & 0x1FF).astype(np.float64) sign = (remaining >> 9) & 1 remaining = np.where(active, remaining >> 10, remaining) val = (1.0 / math.sqrt(2)) * (value / 0x1FF) val = np.where(sign == 1, -val, val) q[active, comp] = val[active] sumsq += np.where(active, val * val, 0.0) q[np.arange(n), largest] = np.sqrt(np.clip(1.0 - sumsq, 0, None)) rot = _norm_quat(np.stack([q[:, 3], q[:, 0], q[:, 1], q[:, 2]], 1)) # xyzw -> wxyz else: qb = np.frombuffer(raw, np.uint8, count=n * 3, offset=off).reshape(n, 3).astype(np.float32) xq = qb / 127.5 - 1.0 w = np.sqrt(np.clip(1.0 - (xq ** 2).sum(1), 0, None)) rot = _norm_quat(np.concatenate([w[:, None], xq], 1)) # wxyz return xyz, scale, rot, alpha, _rgb_to_sh_dc(rgb) _GAUSSIAN_PARSERS = {"ply": _parse_ply_gaussian, "splat": _parse_splat_gaussian, "ksplat": _parse_ksplat_gaussian, "spz": _parse_spz_gaussian} def _detect_splat_format(data: bytes) -> str: if data[:3] == b'ply': return "ply" if data[:2] == b'\x1f\x8b': # gzip -> spz return "spz" if len(data) >= 2 and data[0] == 0 and data[1] >= 1: # ksplat version 0.x header return "ksplat" if len(data) % 32 == 0: return "splat" raise ValueError("File3DToSplat: could not determine splat format from contents") def _gaussian_item(g: Types.SPLAT, i: int, device): # Slice batch item i to its real length, as float32 torch tensors on `device` (SH DC -> base RGB). end = _real_len(g, i) to = lambda a: a.to(device=device, dtype=torch.float32) xyz = to(g.positions[i, :end]) rgb = (to(g.sh[i, :end, 0, :]) * _C0 + 0.5).clamp(0, 1) opacity = to(g.opacities[i, :end]).reshape(-1) scale = to(g.scales[i, :end]) rot = to(g.rotations[i, :end]) return xyz, rgb, opacity, scale, rot def _quat_to_mat(q): # q: (N, 4) wxyz, normalized -> (N, 3, 3) q = q / q.norm(dim=-1, keepdim=True).clamp_min(1e-12) w, x, y, z = q.unbind(-1) return torch.stack([ 1 - 2 * (y * y + z * z), 2 * (x * y - w * z), 2 * (x * z + w * y), 2 * (x * y + w * z), 1 - 2 * (x * x + z * z), 2 * (y * z - w * x), 2 * (x * z - w * y), 2 * (y * z + w * x), 1 - 2 * (x * x + y * y), ], dim=-1).reshape(-1, 3, 3) def _quat_mul(a, b): # Hamilton product a (x) b, wxyz. aw, ax, ay, az = a.unbind(-1) bw, bx, by, bz = b.unbind(-1) return torch.stack([ aw * bw - ax * bx - ay * by - az * bz, aw * bx + ax * bw + ay * bz - az * by, aw * by - ax * bz + ay * bw + az * bx, aw * bz + ax * by - ay * bx + az * bw, ], dim=-1) def _euler_to_quat(rx, ry, rz): # Degrees, applied as Rz @ Ry @ Rx (rotate about X, then Y, then Z in world). Returns wxyz. c, s = np.cos(np.radians([rx, ry, rz]) / 2.0), np.sin(np.radians([rx, ry, rz]) / 2.0) qx = torch.tensor([c[0], s[0], 0.0, 0.0], dtype=torch.float32) qy = torch.tensor([c[1], 0.0, s[1], 0.0], dtype=torch.float32) qz = torch.tensor([c[2], 0.0, 0.0, s[2]], dtype=torch.float32) return _quat_mul(_quat_mul(qz, qy), qx) def _mat_to_quat(m): # Rotation matrix (..., 3, 3) -> quaternion (..., 4) wxyz. Batched; builds the four candidate quaternions # and keeps the one with the largest component (numerically stable across all rotations). m00, m11, m22 = m[..., 0, 0], m[..., 1, 1], m[..., 2, 2] m21, m12 = m[..., 2, 1], m[..., 1, 2] m02, m20 = m[..., 0, 2], m[..., 2, 0] m10, m01 = m[..., 1, 0], m[..., 0, 1] q2 = torch.stack([1 + m00 + m11 + m22, 1 + m00 - m11 - m22, 1 - m00 + m11 - m22, 1 - m00 - m11 + m22], -1) # 4 * (w^2, x^2, y^2, z^2) cand = torch.stack([ torch.stack([q2[..., 0], m21 - m12, m02 - m20, m10 - m01], -1), torch.stack([m21 - m12, q2[..., 1], m10 + m01, m02 + m20], -1), torch.stack([m02 - m20, m10 + m01, q2[..., 2], m12 + m21], -1), torch.stack([m10 - m01, m02 + m20, m12 + m21, q2[..., 3]], -1), ], -2) # (...,4,4) candidates, rows = wxyz sel = q2.argmax(-1) q = torch.gather(cand, -2, sel[..., None, None].expand(sel.shape + (1, 4)))[..., 0, :] return q / q.norm(dim=-1, keepdim=True).clamp_min(1e-12) class SplatToFile3D(IO.ComfyNode): @classmethod def define_schema(cls): return IO.Schema( node_id="SplatToFile3D", display_name="Create 3D File (from Splat)", search_aliases=["gaussian to ply", "splat to file", "export gaussian"], category="3d/splat", description="Serialize a gaussian splat to a File3D object for Save / Preview 3D nodes. " "Supports one item per batch only.", inputs=[ IO.Splat.Input("splat"), IO.Combo.Input("format", options=["ply", "ksplat", "spz"], # TODO: add "splat" when we have a writer for it tooltip="ply: standard 3D Gaussian Splat with full spherical harmonics. " "ksplat: mkkellogg SplatBuffer (level 0, uncompressed), base color only " "spz: Niantic gzip-compressed (~10x smaller), base color only " ), ], outputs=[IO.File3DSplatAny.Output(display_name="model_3d")], ) @classmethod def execute(cls, splat, format="ply") -> IO.NodeOutput: if splat.positions.shape[0] > 1: logging.warning("SplatToFile3D supports one item per batch only. Got %d; using first.", splat.positions.shape[0]) end = _real_len(splat, 0) writer = {"ksplat": _gaussian_ksplat_bytes, "spz": _gaussian_spz_bytes}.get(format, _gaussian_ply_bytes) data = writer(splat.positions[0, :end], splat.scales[0, :end], splat.rotations[0, :end], splat.opacities[0, :end], splat.sh[0, :end]) return IO.NodeOutput(Types.File3D(BytesIO(data), file_format=format)) class File3DToSplat(IO.ComfyNode): @classmethod def define_schema(cls): return IO.Schema( node_id="File3DToSplat", display_name="Get Splat", search_aliases=["load splat", "ply to splat", "import splat", "file to splat"], category="3d/splat", description="Parse a splat File3D into a gaussian splat. Inverse of Create 3D File (from Splat). " "Supported format: PLY, SPLAT, KSPLAT, SPZ. PLY carries full spherical harmonics, " "the other formats are base color only. Format is auto-detected from the file contents.", inputs=[ IO.MultiType.Input( IO.File3DAny.Input("model_3d"), types=[IO.File3DSplatAny, IO.File3DPLY, IO.File3DSPLAT, IO.File3DKSPLAT, IO.File3DSPZ], tooltip="A gaussian splat 3D file", ), ], outputs=[IO.Splat.Output(display_name="splat")], ) @classmethod def execute(cls, model_3d: Types.File3D) -> IO.NodeOutput: data = model_3d.get_bytes() fmt = (model_3d.format or "").lower() parser = _GAUSSIAN_PARSERS.get(fmt) or _GAUSSIAN_PARSERS[_detect_splat_format(data)] xyz, scale, rot, opacity, sh = parser(data) t = lambda a: torch.from_numpy(np.ascontiguousarray(a)).float() splat = Types.SPLAT( t(xyz)[None], # (1, N, 3) t(scale)[None], # (1, N, 3) linear t(rot)[None], # (1, N, 4) wxyz t(opacity).reshape(1, -1, 1), # (1, N, 1) t(sh)[None], # (1, N, K, 3) ) return IO.NodeOutput(splat) def _view_matrix_t(yaw_deg, pitch_deg, device): y, p = math.radians(yaw_deg), math.radians(pitch_deg) cy, sy, cp, sp = math.cos(y), math.sin(y), math.cos(p), math.sin(p) Ry = torch.tensor([[cy, 0, sy], [0, 1, 0], [-sy, 0, cy]], device=device) Rx = torch.tensor([[1, 0, 0], [0, cp, -sp], [0, sp, cp]], device=device) return Rx @ Ry def _camera_basis(camera_info, dev): # Look-at basis in the splat frame, named by their projection rows: right = image +x, up = image +y # (down, since yflip=1), fwd = view/depth axis (eye -> scene). Load3D is three.js (right-handed, Y-up, # camera looks down -Z); the splat is 3DGS (Y-down, Z-forward). World -> splat is a 180 deg rotation # about X: (x, y, z) -> (x, -y, -z) (det +1, no mirror, no axis swap). pos, tgt = camera_info.get("position", {}), camera_info.get("target", {}) m = lambda d: torch.tensor([float(d.get("x", 0.0)), -float(d.get("y", 0.0)), -float(d.get("z", 0.0))], device=dev) eye, target = m(pos), m(tgt) mv = lambda v: torch.stack([v[0], -v[1], -v[2]]) # same world->splat map, for direction vectors n = lambda v: v / v.norm().clamp_min(1e-8) q = camera_info.get("quaternion") if q: # exact camera world rotation (incl. roll) qwxyz = torch.tensor([float(q.get("w", 1.0)), float(q.get("x", 0.0)), float(q.get("y", 0.0)), float(q.get("z", 0.0))], device=dev) R = _quat_to_mat(qwxyz[None])[0] # columns = camera world axes; looks down local -Z right = n(mv(R[:, 0])) # camera +X -> image right up = n(mv(-R[:, 1])) # camera +Y is image up; image-down row is its negative fwd = n(mv(-R[:, 2])) # camera looks down local -Z -> view direction return eye, target, right, up, fwd fwd = n(target - eye) # no quaternion: orbit-consistent, roll-free yaw = math.degrees(math.atan2(-float(fwd[0]), float(fwd[2]))) pitch = math.degrees(math.asin(max(-1.0, min(1.0, float(fwd[1]))))) W = _view_matrix_t(yaw, pitch, dev) return eye, target, W[0], W[1], W[2] def _lookat_quat_wxyz(position, target, dev): # three.js lookAt in world frame: camera local +Z = (eye - target), up = world +Y. Returns wxyz. z = position - target z = z / z.norm().clamp_min(1e-8) up0 = torch.tensor([0.0, 1.0, 0.0], device=dev) if z.dot(up0).abs() > 0.999: # looking straight up/down up0 = torch.tensor([0.0, 0.0, 1.0], device=dev) x = torch.linalg.cross(up0, z) x = x / x.norm().clamp_min(1e-8) y = torch.linalg.cross(z, x) R = torch.stack([x, y, z], dim=1) # columns = camera world axes return _mat_to_quat(R[None])[0] def _lookat_camera_info(position, target, fov, dev, zoom=1.0, camera_type="perspective", roll=0.0): # Build a camera_info from a world-space (right-handed, Y-up) eye + look-at target; up = world +Y. pos = torch.as_tensor(position, dtype=torch.float32, device=dev) tgt = torch.as_tensor(target, dtype=torch.float32, device=dev) q = _lookat_quat_wxyz(pos, tgt, dev) if roll: # roll about the view axis (camera local Z) a = math.radians(roll) qz = torch.tensor([math.cos(a / 2), 0.0, 0.0, math.sin(a / 2)], device=dev) q = _quat_mul(q[None], qz[None])[0] xyz = lambda v: {"x": float(v[0]), "y": float(v[1]), "z": float(v[2])} return {"position": xyz(pos), "target": xyz(tgt), "quaternion": {"x": float(q[1]), "y": float(q[2]), "z": float(q[3]), "w": float(q[0])}, "fov": float(fov), "cameraType": str(camera_type), "zoom": float(zoom)} def _quat_camera_info(position, quat_xyzw, fov, dev, zoom=1.0, camera_type="perspective"): # camera_info from an explicit world position + camera-rotation quaternion (three.js: looks down local -Z). pos = torch.as_tensor(position, dtype=torch.float32, device=dev) qx, qy, qz, qw = (float(c) for c in quat_xyzw) qwxyz = torch.tensor([qw, qx, qy, qz], dtype=torch.float32, device=dev) qwxyz = qwxyz / qwxyz.norm().clamp_min(1e-8) R = _quat_to_mat(qwxyz[None])[0] tgt = pos - R[:, 2] # look one unit down local -Z xyz = lambda v: {"x": float(v[0]), "y": float(v[1]), "z": float(v[2])} return {"position": xyz(pos), "target": xyz(tgt), "quaternion": {"x": float(qwxyz[1]), "y": float(qwxyz[2]), "z": float(qwxyz[3]), "w": float(qwxyz[0])}, "fov": float(fov), "cameraType": str(camera_type), "zoom": float(zoom)} def _orbit_camera_info(yaw, pitch, distance, fov, pivot_splat, dev): # Orbit helper for RenderSplat's default camera: yaw/pitch about `pivot_splat` (splat frame) at `distance`. # World<->splat is the (x,-y,-z) map, so _camera_basis recovers exactly _view_matrix_t(yaw, pitch). y, p = math.radians(yaw), math.radians(pitch) cy, sy, cp, sp = math.cos(y), math.sin(y), math.cos(p), math.sin(p) fwd_splat = torch.tensor([-cp * sy, sp, cp * cy], device=dev) # == _view_matrix_t(yaw, pitch)[2] m = lambda v: torch.stack([v[0], -v[1], -v[2]]) # splat<->world (its own inverse) return _lookat_camera_info(m(pivot_splat - distance * fwd_splat), m(pivot_splat), fov, dev) def _orbit_camera_info_yaw(camera_info, angle_deg, dev): # Turntable: rigidly rotate a camera_info about world +Y around its target by angle_deg. Returns a new dict. a = math.radians(angle_deg) ca, sa = math.cos(a), math.sin(a) v = lambda d: torch.tensor([float(d.get("x", 0.0)), float(d.get("y", 0.0)), float(d.get("z", 0.0))], device=dev) pos, tgt = v(camera_info.get("position", {})), v(camera_info.get("target", {})) Ry = torch.tensor([[ca, 0.0, sa], [0.0, 1.0, 0.0], [-sa, 0.0, ca]], device=dev) new_pos = tgt + Ry @ (pos - tgt) q = camera_info.get("quaternion") or {} qcur = torch.tensor([float(q.get("w", 1.0)), float(q.get("x", 0.0)), float(q.get("y", 0.0)), float(q.get("z", 0.0))], device=dev) qy = torch.tensor([math.cos(a / 2), 0.0, math.sin(a / 2), 0.0], device=dev) # world +Y rotation qn = _quat_mul(qy[None], qcur[None])[0] xyz = lambda t: {"x": float(t[0]), "y": float(t[1]), "z": float(t[2])} return {**camera_info, "position": xyz(new_pos), "quaternion": {"x": float(qn[1]), "y": float(qn[2]), "z": float(qn[3]), "w": float(qn[0])}} def _gauss_blur(x, sigma, dev): # Separable Gaussian blur of (1, C, H, W). Used to denoise the screen-space normal map. r = max(1, int(round(3 * sigma))) k = torch.exp(-0.5 * (torch.arange(-r, r + 1, device=dev, dtype=torch.float32) / sigma) ** 2) k = k / k.sum() c = x.shape[1] x = torch.nn.functional.conv2d(x, k.view(1, 1, 1, -1).expand(c, 1, 1, -1), padding=(0, r), groups=c) x = torch.nn.functional.conv2d(x, k.view(1, 1, -1, 1).expand(c, 1, -1, 1), padding=(r, 0), groups=c) return x def _render_gaussian(xyz, rgb, opacity, scale, rot, width, height, splat_scale, bg, camera_info, sharpen=1.0, headlight_shading=0.0, render_style="color"): # Perspective-correct anisotropic gaussian splat rasterizer. Each splat is weighted by its 3D Gaussian's # peak along each pixel's ray (AAA / Hahlbohm), composited front-to-back across depth slabs. `render_style` # selects the image: color / clay / depth / normal. Returns (image HxWx3, coverage mask HxW) on CPU. dev = comfy.model_management.get_torch_device() t = lambda a: torch.as_tensor(a, dtype=torch.float32, device=dev) idev, idtype = comfy.model_management.intermediate_device(), comfy.model_management.intermediate_dtype() xyz, rgb, opacity = t(xyz), t(rgb).clamp(0, 1), t(opacity).reshape(-1) scale, rot = t(scale) * float(splat_scale), t(rot) do_linear = render_style == "color" # colour blends in linear light, re-encoded at the end if do_linear: rgb = _srgb_to_linear(rgb) flat = width * height bg_t = t(bg) bg_comp = _srgb_to_linear(bg_t) if do_linear else bg_t # background blended in the same space as the splats need_depth = render_style == "depth" need_normal = render_style in ("normal", "clay") or headlight_shading > 0 def background_only(): # no splats to rasterize -> just the background + empty mask img = bg_t.expand(height, width, 3) if render_style == "color" else torch.zeros(height, width, 3, device=dev) return img.to(idev, idtype), torch.zeros(height, width, device=idev, dtype=idtype) if xyz.shape[0] == 0: # empty input (e.g. all culled by opacity_threshold) return background_only() eye, target, right, up, fwd = _camera_basis(camera_info, dev) # all camera state comes from camera_info W = torch.stack([right, up, fwd], 0) # rows = camera axes (world -> camera) cam = (xyz - eye) @ W.T fov = float(camera_info.get("fov", 0) or 0) or 35.0 zoom = float(camera_info.get("zoom", 1.0) or 1.0) # three.js digital zoom: scales the focal length is_ortho = str(camera_info.get("cameraType", "")).lower().startswith("ortho") xc, yc, zc = cam.unbind(-1) keep = zc > 1e-2 xc, yc, zc, rgb, opacity, scale, rot = (a[keep] for a in (xc, yc, zc, rgb, opacity, scale, rot)) if xc.shape[0] == 0: # nothing in front of the camera -> background only return background_only() if render_style == "clay": rgb = torch.full_like(rgb, 0.75) # neutral albedo -> shading shows pure geometry f = (min(width, height) / 2) / math.tan(math.radians(fov) / 2) * zoom # fov over the smaller axis, x camera zoom cx0, cy0 = width / 2, height / 2 # Camera-space 3D covariance per splat: Sigma = (W Rq) diag(scale^2) (W Rq)^T, plus a tiny relative # regularizer for a stable inverse (a pixel-size Mip low-pass would over-thicken flat surfels and blur). Mw = W[None] @ _quat_to_mat(rot) # (N,3,3) world -> camera cam_cov = (Mw * scale.square()[:, None, :]) @ Mw.transpose(1, 2) cam_cov = cam_cov + (cam_cov.diagonal(dim1=-2, dim2=-1).mean(-1) * 1e-3)[:, None, None] * torch.eye(3, device=dev) # Perspective-correct weighting: peak of the 3D Gaussian along each pixel ray. Precompute Si, Si@mu, mu^T Si mu. mu = torch.stack([xc, yc, zc], -1) si = torch.linalg.inv(cam_cov) simu = (si @ mu[:, :, None])[:, :, 0] # (N,3) musimu = (mu * simu).sum(-1) # (N,) s00, s01, s02 = si[:, 0, 0], si[:, 0, 1], si[:, 0, 2] s11, s12, s22 = si[:, 1, 1], si[:, 1, 2], si[:, 2, 2] simu0, simu1, simu2 = simu.unbind(-1) if need_normal: # surfel normal = thinnest axis, oriented toward camera nrm = Mw[torch.arange(Mw.shape[0], device=dev), :, scale.argmin(-1)] # (N,3) camera-space normal nrm = nrm * torch.where(nrm[:, 2:3] > 0, -1.0, 1.0) # flip so nz <= 0 (faces camera) # Screen centre (exact) + footprint radius from the affine 2D projection (used only to size the kernel). # The image is +y-down, so the projection's y row is unflipped - it matches the splat frame's +Y. jm = torch.zeros(xc.shape[0], 2, 3, device=dev) if is_ortho: # parallel projection: screen = s * (xc, yc) s = f / float((target - eye).norm().clamp_min(1e-6)) # pixels per world unit at the target plane cx, cy = cx0 + s * xc, cy0 + s * yc jm[:, 0, 0] = s jm[:, 1, 1] = s else: # perspective: screen = f * (xc, yc) / zc invz = 1.0 / zc cx, cy = cx0 + f * xc * invz, cy0 + f * yc * invz jm[:, 0, 0], jm[:, 0, 2] = f * invz, -f * xc * invz.square() jm[:, 1, 1], jm[:, 1, 2] = f * invz, -f * yc * invz.square() cov2 = jm @ cam_cov @ jm.transpose(1, 2) a, b, c = cov2[:, 0, 0], cov2[:, 0, 1], cov2[:, 1, 1] max_eig = (a + c) * 0.5 + (((a - c) * 0.5).square() + b * b).clamp_min(0).sqrt() radius = 3.0 * max_eig.clamp_min(1e-8).sqrt() K = int(min(max(24, min(width, height) // 16), max(2, math.ceil(_quantile(radius, 0.995).item())))) # Per-splat kernel size: bucket splats by radius into a coarse ladder of window sizes (global K stays the cap) so # small splats (the bulk of it) use a small window. levels = [L for L in (16, 64, 256) if L < K] + [K] levels_t = torch.tensor(levels, device=dev, dtype=torch.float32) grids = [] for L in levels: rng = torch.arange(-L, L + 1, device=dev, dtype=torch.float32) gy, gx = torch.meshgrid(rng, rng, indexing="ij") grids.append((gx.reshape(-1), gy.reshape(-1))) blevel = torch.bucketize(radius * (4.0 / 3.0), levels_t).clamp_(max=len(levels) - 1) # window >= ~4 sigma n = zc.shape[0] ns = int(min(256, max(1, n // 1000))) # depth slabs: 1 per ~1000 splats, capped nl = len(levels) order = torch.argsort(zc) # front (small zc) -> back -> defines the slabs bounds = torch.linspace(0, n, ns + 1, device=dev).round().long() rank = torch.empty(n, dtype=torch.long, device=dev) rank[order] = torch.arange(n, device=dev) # depth rank of each splat slab_id = (torch.searchsorted(bounds, rank, right=True) - 1).clamp_(0, ns - 1) key = slab_id * nl + blevel # group by slab, then kernel level (order-free within) order = torch.argsort(key) key = key[order] cxr, cyr = cx[order].round(), cy[order].round() s00, s01, s02 = s00[order], s01[order], s02[order] s11, s12, s22 = s11[order], s12[order], s22[order] s01b, s02b, s12b = s01 * 2, s02 * 2, s12 * 2 # doubled cross terms for the fused quadratic forms simu0, simu1, simu2, musimu = simu0[order], simu1[order], simu2[order], musimu[order] opacity, rgb = opacity[order], rgb[order] zc_o = zc[order] if need_depth else None nrm_o = nrm[order] if need_normal else None mux_o, muy_o, muz_o = (xc[order], yc[order], zc[order]) if is_ortho else (None, None, None) # Pack the per-splat scalars into one tensor so each chunk slices once common = [cxr, cyr, s00, s11, s22, s01b, s02b, s12b, opacity] pstack = torch.stack(common + ([s02, s12, mux_o, muy_o, muz_o] if is_ortho else [simu0, simu1, simu2, musimu])) # Precompute the (slab, level) run table on-GPU and pull it to the CPU once starts = torch.cat([torch.zeros(1, dtype=torch.long, device=dev), (key[1:] != key[:-1]).nonzero().flatten() + 1]) ks = key[starts] run_lo = starts.tolist() + [n] run_lev = (ks % nl).tolist() run_slab = torch.div(ks, nl, rounding_mode="floor").tolist() slab_runs = [[] for _ in range(ns)] for r in range(len(run_lev)): slab_runs[run_slab[r]].append((run_lo[r], run_lo[r + 1], run_lev[r])) def splat(lo, hi, ox, oy): # -> pixel idx (m,M), alpha (m,M); weight = 3D Gaussian peak along each pixel's ray cols = pstack[:, lo:hi, None].unbind(0) cxr_, cyr_, a00, a11, a22, b01, b02, b12, opa = cols[:9] # a* = Si components; b* = 2 * cross terms px = cxr_ + ox[None, :] py = cyr_ + oy[None, :] valid = (px >= 0) & (px < width) & (py >= 0) & (py < height) if is_ortho: # parallel ray (0,0,1) from screen point (X, Y, 0); rz constant per splat c02, c12, mx, my, mz = cols[9:] rx = (px - cx0) / s - mx ry = (py - cy0) / s - my rz = -mz a22rz = a22 * rz inx = torch.addcmul(b02 * rz, a00, rx).addcmul_(b01, ry) # a00 rx + b01 ry + b02 rz rSr = torch.addcmul(a22rz * rz, rx, inx).addcmul_(ry, torch.addcmul(b12 * rz, a11, ry)) dsr = torch.addcmul(a22rz, c02, rx).addcmul_(c12, ry) q = torch.addcdiv(rSr, dsr * dsr, a22.clamp_min(1e-12), value=-1).clamp_min_(0) else: # perspective ray (dx,dy,1) through the camera origin su0, su1, su2, mus = cols[9:] dx, dy = (px - cx0) / f, (py - cy0) / f dsid = torch.addcmul(a22, dx, torch.addcmul(b02, a00, dx)) # a22 + dx*(a00 dx + b02) dsid = dsid.addcmul_(dy, torch.addcmul(b12, a11, dy)) # + dy*(a11 dy + b12) dsid = dsid.addcmul_(b01 * dx, dy) # + (2 s01) dx dy dsimu = torch.addcmul(su2, dx, su0).addcmul_(dy, su1) q = torch.addcdiv(mus, dsimu * dsimu, dsid.clamp_min(1e-12), value=-1).clamp_min_(0) alpha = (opa * torch.exp(-0.5 * q) * valid).clamp_(0, 0.999) idx = py.long().clamp(0, height - 1) * width + px.long().clamp(0, width - 1) return idx, alpha # Front-to-back compositing over the depth slabs set up above. Within a slab the accumulation is a pure # sum (order-independent), so splats are grouped by kernel level and each level uses its own tight window. sharp = sharpen != 1.0 # winner-take-more colour blend: dominant splat shows more cacc = torch.zeros((flat, 3), device=dev) trans = torch.ones((flat,), device=dev) a_buf = torch.zeros((flat,), device=dev) # sum alpha -> colour/depth/normal weight (alpha-weighted mean) tau_buf = torch.zeros((flat,), device=dev) # sum -ln(1-alpha) -> slab opacity = 1-prod(1-alpha) crgb = torch.zeros((flat, 3), device=dev) # sum alpha^p * rgb -> slab colour wbuf = torch.zeros((flat,), device=dev) if sharp else None # sum alpha^p -> colour normalizer (sharp only) dacc = torch.zeros((flat,), device=dev) if need_depth else None # front-weighted depth nacc = torch.zeros((flat, 3), device=dev) if need_normal else None # front-weighted camera-space normal zslab = torch.zeros((flat,), device=dev) if need_depth else None nslab = torch.zeros((flat, 3), device=dev) if need_normal else None stale = 0 # consecutive fully-occluded slabs -> early-out for si in range(ns): runs = slab_runs[si] if not runs: continue a_buf.zero_() tau_buf.zero_() crgb.zero_() if sharp: wbuf.zero_() if need_depth: zslab.zero_() if need_normal: nslab.zero_() for r_lo, r_hi, li in runs: # contiguous same-kernel-level runs in this slab ox, oy = grids[li] ch = max(2048, 10_000_000 // ox.shape[0]) # splats/chunk, bounded by this level's kernel size for lo in range(r_lo, r_hi, ch): hi = min(lo + ch, r_hi) idx, alpha = splat(lo, hi, ox, oy) idx, af = idx.reshape(-1), alpha.reshape(-1) a_buf.index_add_(0, idx, af) tau_buf.index_add_(0, idx, (-torch.log1p(-alpha)).reshape(-1)) # -ln(1-alpha), correct opacity merge apw = alpha.pow(sharpen) if sharp else alpha # bias colour toward the highest-alpha splat crgb.index_add_(0, idx, (apw[:, :, None] * rgb[lo:hi, None, :]).reshape(-1, 3)) if sharp: wbuf.index_add_(0, idx, apw.reshape(-1)) if need_depth: zslab.index_add_(0, idx, (alpha * zc_o[lo:hi, None]).reshape(-1)) if need_normal: nslab.index_add_(0, idx, (alpha[:, :, None] * nrm_o[lo:hi, None, :]).reshape(-1, 3)) slab_a = 1 - torch.exp(-tau_buf) # 1 - prod(1-alpha): true opacity of the slab's splats front = trans * slab_a denom = wbuf if sharp else a_buf cacc.addcmul_(front[:, None], crgb / denom.clamp_min(1e-8)[:, None]) # cacc += front * (crgb/denom) if need_depth or need_normal: ainv = a_buf.clamp_min(1e-8) # alpha-weighted-mean normalizer (depth/normal only) if need_depth: dacc.addcmul_(front, zslab / ainv) if need_normal: nacc.addcmul_(front[:, None], nslab / ainv[:, None]) trans.mul_(1 - slab_a) if si % 8 == 7: # checkpoint every 8 slabs (a per-slab GPU sync would cost more) if float(front.max()) < 1e-3: # this checkpoint slab is fully occluded by what is in front stale += 1 if stale >= 2: # two occluded checkpoints running -> the rest are too -> stop break else: stale = 0 cov = 1 - trans covg = cov.reshape(height, width) covm = covg > 0.5 if render_style in ("depth", "normal") else None # silhouette mask (depth/normal styles only) depth_map = (dacc / cov.clamp_min(1e-6)).reshape(height, width) if need_depth else None nrm_map = None if need_normal: # Per-splat surfel normals are jittery, so do a masked blur nb = nacc.reshape(height, width, 3).permute(2, 0, 1)[None] cb = cov.reshape(1, 1, height, width) nb, cb = _gauss_blur(nb, 1.2, dev), _gauss_blur(cb, 1.2, dev) normal = (nb / cb.clamp_min(1e-6))[0].permute(1, 2, 0) nrm_map = normal / normal.norm(dim=-1, keepdim=True).clamp_min(1e-6) if render_style == "depth": # near = bright, far = dark, 0 off-object d = torch.zeros(height, width, device=dev) if bool(covm.any()): lo, hi = depth_map[covm].min(), depth_map[covm].max() d = torch.where(covm, ((hi - depth_map) / (hi - lo).clamp_min(1e-6)).clamp(0, 1), d) img = d[:, :, None].expand(height, width, 3) elif render_style == "normal": # OpenGL normal map: +X right, +Y up, +Z to viewer enc = (nrm_map * t([1.0, -1.0, -1.0]) * 0.5 + 0.5).clamp(0, 1) img = enc * covm[:, :, None] else: # color / clay img = cacc.reshape(height, width, 3) if render_style == "clay": # studio key light + ambient -> sculpted matte look kl = t([-0.4, -0.7, -0.6]) # key from screen upper-left, angled toward the viewer kl = kl / kl.norm() hl = (0.5 * (nrm_map * kl).sum(-1) + 0.5).clamp(0, 1) # half-Lambert: soft terminator, no harsh dark side img = img * (0.35 + 0.65 * hl * hl)[:, :, None] # ambient floor + diffuse key elif headlight_shading > 0: # camera headlight: darken faces turned from view k = float(headlight_shading) ndotl = (-nrm_map[:, :, 2]).clamp(0, 1) img = img * (1 - 0.6 * k + 0.6 * k * ndotl)[:, :, None] img = img.addcmul_(trans.reshape(height, width, 1), bg_comp) if do_linear: # back to display space after linear compositing img = _linear_to_srgb(img) return img.clamp(0, 1).to(idev, idtype), covg.clamp(0, 1).to(idev, idtype) class RenderSplat(IO.ComfyNode): @classmethod def define_schema(cls): return IO.Schema( node_id="RenderSplat", display_name="Render Splat", search_aliases=["splat to image", "render splat", "gaussian turntable"], category="3d/splat", description="Render a gaussian splat as an image with an anisotropic EWA rasterizer (oriented " "elliptical splats, antialiased, depth-sorted front-to-back). The camera comes from a " "camera_info input (Load / Preview 3D, or a Create Camera Info node); leave it empty to " "auto-frame the splat. Set frames greater than 1 for a turntable batch of images to feed a Video node.", inputs=[ IO.Splat.Input("splat"), IO.Int.Input("width", default=1024, min=64, max=2048, step=8), IO.Int.Input("height", default=1024, min=64, max=2048, step=8), IO.Int.Input("frames", default=1, min=-240, max=240, tooltip="-1, 0, 1 = single still image; >1 = turntable, the camera orbits over a full " "360 turn (works with any camera_info). Negative value orbits the other way."), IO.Float.Input("splat_scale", default=1.0, min=0.1, max=5.0, step=0.05, advanced=True, tooltip="Multiplier on each splat's projected footprint (lower = crisper points, " "higher = softer/fuller surface)."), IO.Float.Input("sharpen", default=2.0, min=1.0, max=8.0, step=0.5, tooltip="Sharpen overlapping splats: 1.0 = physically-correct blend; higher biases " "each pixel toward its dominant (nearest) splat for crisper texture, without " "shrinking splats or opening gaps. Non-physical above 1."), IO.Float.Input("headlight_shading", default=0.0, min=0.0, max=3.0, step=0.05, advanced=True, tooltip="Diffuse shading from a light at the camera (headlight), using the splat surfel " "normals: darkens surfaces that turn away from view to reveal form/curvature. " "0 = flat albedo, 1 = strongest shading."), IO.Float.Input("opacity_threshold", default=0.0, min=0.0, max=1.0, step=0.01, advanced=True, tooltip="Cull gaussians with opacity below this (removes faint floaters)."), IO.Combo.Input("render_style", options=["color", "clay", "depth", "normal"], tooltip="What the image output shows: color, clay (neutral-albedo shaded), " "depth (near=bright), normal (OpenGL normal map)."), IO.Color.Input("background", default="#000000"), IO.Image.Input("bg_image", optional=True, tooltip="Optional background plate composited behind the splat (overrides the solid " "background colour). Resized to the render size; a batch is used per frame, " "a single image for all. color/clay only."), IO.Load3DCamera.Input("camera_info", optional=True, tooltip="Camera to render from - a Load3D / Preview3D camera or a Create Camera " "Info node. If empty, the splat is auto-framed from a default 3/4 view."), ], outputs=[IO.Image.Output(display_name="image"), IO.Mask.Output(display_name="mask")], ) @classmethod def execute(cls, splat, width, height, frames, splat_scale, sharpen, headlight_shading, opacity_threshold, background, render_style, camera_info=None, bg_image=None) -> IO.NodeOutput: bg = _hex_to_rgb(background) bg_imgs = None if bg_image is not None: # resize the plate(s) to the render size: (B,H,W,3) bi = bg_image[... , :3].movedim(-1, 1) # (B,3,H,W) bi = comfy.utils.common_upscale(bi, width, height, "bicubic", "disabled") bg_imgs = bi.movedim(1, -1).clamp(0, 1) n_frames = abs(int(frames)) or 1 # magnitude = frame count (0 -> single still) orbit_dir = -1.0 if frames < 0 else 1.0 # sign = orbit direction imgs, masks = [], [] device = comfy.model_management.get_torch_device() total = splat.positions.shape[0] * n_frames pbar = comfy.utils.ProgressBar(total) if total > 1 else None k = 0 for i in range(splat.positions.shape[0]): xyz, rgb, opacity, scale, rot = _gaussian_item(splat, i, device) if opacity_threshold > 0: keep = opacity >= opacity_threshold xyz, rgb, opacity, scale, rot = xyz[keep], rgb[keep], opacity[keep], scale[keep], rot[keep] base_cam = camera_info if base_cam is None: # no camera -> default 3/4 view, auto-framed on the splat center = xyz.mean(0) if xyz.shape[0] else torch.zeros(3, device=device) extent = (_quantile((xyz - center).norm(dim=-1), 0.99).clamp_min(1e-4) if xyz.shape[0] else torch.tensor(1.0, device=device)) dist = float(extent / (math.tan(math.radians(35.0) / 2) * 0.9)) base_cam = _orbit_camera_info(35.0, 30.0, dist, 35.0, center, device) for fr in range(n_frames): cam_fr = (base_cam if n_frames == 1 else _orbit_camera_info_yaw(base_cam, orbit_dir * 360.0 * fr / n_frames, device)) bg_k = bg_imgs[k % bg_imgs.shape[0]] if bg_imgs is not None else bg # per-frame plate, or solid colour img, mask = _render_gaussian(xyz, rgb, opacity, scale, rot, width, height, splat_scale, bg_k, cam_fr, sharpen=sharpen, headlight_shading=headlight_shading, render_style=render_style) imgs.append(img) masks.append(mask) k += 1 if pbar is not None: pbar.update(1) return IO.NodeOutput(torch.stack(imgs), torch.stack(masks)) class CreateCameraInfo(IO.ComfyNode): # TODO: move to better file @classmethod def define_schema(cls): return IO.Schema( node_id="CreateCameraInfo", display_name="Create Camera Info", search_aliases=["camera position", "make camera info", "orbit camera", "look at camera"], category="3d", description="Build a camera_info" "Mode 'orbit' aims with yaw/pitch/distance around the target; " "'look_at' places the camera at world position. Coordinates are the viewer's world space (right-handed,Y-up).", inputs=[ IO.DynamicCombo.Input("mode", options=[ IO.DynamicCombo.Option("orbit", [ IO.Float.Input("yaw", default=35.0, min=-360.0, max=360.0, step=1.0), IO.Float.Input("pitch", default=30.0, min=-89.0, max=89.0, step=1.0), IO.Float.Input("distance", default=4.0, min=0.01, max=1000.0, step=0.01, tooltip="Camera distance from the target."), ]), IO.DynamicCombo.Option("look_at", [ IO.Float.Input("position_x", default=4.0, min=-1000.0, max=1000.0, step=0.01, tooltip="Camera position in world space (right-handed, Y-up)."), IO.Float.Input("position_y", default=4.0, min=-1000.0, max=1000.0, step=0.01), IO.Float.Input("position_z", default=4.0, min=-1000.0, max=1000.0, step=0.01), ]), IO.DynamicCombo.Option("quaternion", [ IO.Float.Input("position_x", default=4.0, min=-1000.0, max=1000.0, step=0.01, tooltip="Camera position in world space (right-handed, Y-up)."), IO.Float.Input("position_y", default=4.0, min=-1000.0, max=1000.0, step=0.01), IO.Float.Input("position_z", default=4.0, min=-1000.0, max=1000.0, step=0.01), IO.Float.Input("quat_x", default=0.0, min=-1.0, max=1.0, step=0.001), IO.Float.Input("quat_y", default=0.0, min=-1.0, max=1.0, step=0.001), IO.Float.Input("quat_z", default=0.0, min=-1.0, max=1.0, step=0.001), IO.Float.Input("quat_w", default=1.0, min=-1.0, max=1.0, step=0.001, tooltip="Camera world-rotation quaternion (three.js: looks down local -Z). Normalized for you."), ]), ], tooltip="How to define the camera: orbit angles, an explicit position, or a position + quaternion."), IO.Float.Input("target_x", default=0.0, min=-1000.0, max=1000.0, step=0.01, advanced=True, tooltip="Look-at point (orbit pivot / aim). In orbit mode, move it to pan/translate the " "whole camera. Ignored in quaternion mode. Defaults to the origin."), IO.Float.Input("target_y", default=0.0, min=-1000.0, max=1000.0, step=0.01, advanced=True), IO.Float.Input("target_z", default=0.0, min=-1000.0, max=1000.0, step=0.01, advanced=True), IO.Float.Input("roll", default=0.0, min=-180.0, max=180.0, step=1.0, tooltip="Camera roll about the view axis, degrees."), IO.Float.Input("fov", default=35.0, min=1.0, max=120.0, step=1.0, tooltip="Vertical field of view in degrees."), IO.Float.Input("zoom", default=1.0, min=0.01, max=100.0, step=0.01, tooltip="Digital zoom (focal-length multiplier). >1 zooms in without moving the camera."), IO.Combo.Input("camera_type", options=["perspective", "orthographic"], tooltip="Projection used by Render Splat: perspective (foreshortening) or orthographic (parallel)."), ], outputs=[IO.Load3DCamera.Output(display_name="camera_info")], ) @classmethod def execute(cls, mode, target_x, target_y, target_z, roll, fov, zoom=1.0, camera_type="perspective") -> IO.NodeOutput: dev = comfy.model_management.get_torch_device() kind = mode["mode"] if kind == "quaternion": # explicit world position + camera rotation position = [mode["position_x"], mode["position_y"], mode["position_z"]] quat = [mode["quat_x"], mode["quat_y"], mode["quat_z"], mode["quat_w"]] return IO.NodeOutput(_quat_camera_info(position, quat, fov, dev, zoom=zoom, camera_type=camera_type)) target = [target_x, target_y, target_z] # orbit pivot / aim; move it to pan the whole camera if kind == "orbit": # yaw/pitch/distance about the target (world Y-up) y, p = math.radians(mode["yaw"]), math.radians(mode["pitch"]) cy, sy, cp, sp = math.cos(y), math.sin(y), math.cos(p), math.sin(p) d = mode["distance"] position = [target_x + d * cp * sy, target_y + d * sp, target_z + d * cp * cy] else: # look_at: explicit world-space camera position position = [mode["position_x"], mode["position_y"], mode["position_z"]] return IO.NodeOutput(_lookat_camera_info(position, target, fov, dev, zoom=zoom, camera_type=camera_type, roll=roll)) class TransformSplat(IO.ComfyNode): @classmethod def define_schema(cls): return IO.Schema( node_id="TransformSplat", display_name="Transform Splat", search_aliases=["move splat", "rotate splat", "scale splat", "gaussian transform"], category="3d/splat", description="Translate, rotate, and scale a gaussian splat. " "Non-uniform scale also reshapes every individual splat, slower process.", inputs=[ IO.Splat.Input("splat"), IO.Float.Input("translate_x", default=0.0, min=-100.0, max=100.0, step=0.01), IO.Float.Input("translate_y", default=0.0, min=-100.0, max=100.0, step=0.01), IO.Float.Input("translate_z", default=0.0, min=-100.0, max=100.0, step=0.01), IO.Float.Input("rotate_x", default=0.0, min=-360.0, max=360.0, step=1.0), IO.Float.Input("rotate_y", default=0.0, min=-360.0, max=360.0, step=1.0), IO.Float.Input("rotate_z", default=0.0, min=-360.0, max=360.0, step=1.0), IO.Float.Input("scale_x", default=1.0, min=0.01, max=100.0, step=0.01), IO.Float.Input("scale_y", default=1.0, min=0.01, max=100.0, step=0.01), IO.Float.Input("scale_z", default=1.0, min=0.01, max=100.0, step=0.01), ], outputs=[IO.Splat.Output(display_name="splat")], ) @classmethod def execute(cls, splat, translate_x, translate_y, translate_z, rotate_x, rotate_y, rotate_z, scale_x, scale_y, scale_z) -> IO.NodeOutput: pos = splat.positions dev, dt = pos.device, pos.dtype q_rot = _euler_to_quat(rotate_x, rotate_y, rotate_z).to(device=dev, dtype=dt) R = _quat_to_mat(q_rot[None])[0] # (3, 3) node rotation D = torch.tensor([scale_x, scale_y, scale_z], dtype=dt, device=dev) A = D[:, None] * R # diag(D) @ R: per-axis scale after rotation t = torch.tensor([translate_x, translate_y, translate_z], dtype=dt, device=dev) positions = pos @ A.T + t # rotate, scale per-axis, then translate if scale_x == scale_y == scale_z: # uniform: rotation/scale factor out cleanly scales = splat.scales * scale_x rotations = _quat_mul(q_rot.expand_as(splat.rotations), splat.rotations) rotations = rotations / rotations.norm(dim=-1, keepdim=True).clamp_min(1e-12) else: # non-uniform: transform Sigma = A R s^2 R^T A^T, re-extract rg = _quat_to_mat(splat.rotations.reshape(-1, 4)) # (M,3,3) per-splat rotation s2 = splat.scales.reshape(-1, 3).square() cov = (rg * s2[:, None, :]) @ rg.transpose(-1, -2) # Sigma cov = A @ cov @ A.T # A Sigma A^T (A broadcast over splats) lam, V = torch.linalg.eigh(cov) # symmetric -> eigenvalues (asc), orthonormal axes V = V * torch.where(torch.linalg.det(V) < 0, -1.0, 1.0)[..., None, None] # keep a proper rotation scales = lam.clamp_min(0).sqrt().reshape(splat.scales.shape) rotations = _mat_to_quat(V).reshape(splat.rotations.shape) out = Types.SPLAT(positions, scales, rotations, splat.opacities, splat.sh, counts=getattr(splat, "counts", None)) return IO.NodeOutput(out) class GetSplatCount(IO.ComfyNode): @classmethod def define_schema(cls): return IO.Schema( node_id="GetSplatCount", display_name="Get Splat Count", search_aliases=["splat count", "gaussian count", "number of splats", "splat info"], category="3d/splat", description="Returns the number of splats summed across the batch.", inputs=[IO.Splat.Input("splat")], outputs=[IO.Splat.Output(display_name="splat"), IO.Int.Output(display_name="count"), ], hidden=[IO.Hidden.unique_id], ) @classmethod def execute(cls, splat) -> IO.NodeOutput: count = sum(_real_len(splat, i) for i in range(splat.positions.shape[0])) if cls.hidden.unique_id: # show the count inline on the node PromptServer.instance.send_progress_text(f"{count:,} splats", cls.hidden.unique_id) return IO.NodeOutput(splat, count) def _pad_stack(items, n): # Stack a list of (Lᵢ, *tail) tensors into (B, n, *tail), zero-padding each row up to n. tail = items[0].shape[1:] out = items[0].new_zeros((len(items), n, *tail)) for i, t in enumerate(items): out[i, :t.shape[0]] = t return out def _merge_gaussians(gaussians: list) -> Types.SPLAT: # Concatenate SPLAT batches along the splat dimension (per item), padding SH to the highest degree. gs = [g for g in gaussians if g is not None] if not gs: raise ValueError("MergeSplat: no gaussians to merge") b = gs[0].positions.shape[0] for g in gs: if g.positions.shape[0] != b: raise ValueError(f"MergeSplat: batch size mismatch ({b} vs {g.positions.shape[0]}).") max_k = max(g.sh.shape[2] for g in gs) pos_b, scl_b, rot_b, op_b, sh_b, lengths = [], [], [], [], [], [] for i in range(b): pos_i, scl_i, rot_i, op_i, sh_i = [], [], [], [], [] for g in gs: end = _real_len(g, i) pos_i.append(g.positions[i, :end]) scl_i.append(g.scales[i, :end]) rot_i.append(g.rotations[i, :end]) op_i.append(g.opacities[i, :end]) sh = g.sh[i, :end] # (end, K, 3) if sh.shape[1] < max_k: # zero-pad lower-degree SH sh = torch.cat([sh, sh.new_zeros(sh.shape[0], max_k - sh.shape[1], sh.shape[2])], dim=1) sh_i.append(sh) pos_b.append(torch.cat(pos_i)) scl_b.append(torch.cat(scl_i)) rot_b.append(torch.cat(rot_i)) op_b.append(torch.cat(op_i)) sh_b.append(torch.cat(sh_i)) lengths.append(pos_b[-1].shape[0]) n = max(lengths) counts = None if len(set(lengths)) > 1: counts = torch.tensor(lengths, device=gs[0].positions.device, dtype=torch.int64) return Types.SPLAT(_pad_stack(pos_b, n), _pad_stack(scl_b, n), _pad_stack(rot_b, n), _pad_stack(op_b, n), _pad_stack(sh_b, n), counts=counts) class MergeSplat(IO.ComfyNode): @classmethod def define_schema(cls): # Autogrow: a splat0/splat1/... input list that grows a fresh slot as you connect splats. splats = IO.Autogrow.TemplatePrefix(IO.Splat.Input("splat"), prefix="splat", min=2, max=32) return IO.Schema( node_id="MergeSplat", display_name="Merge Splats", search_aliases=["union splat", "densify gaussian", "combine splat", "merge gaussian"], category="3d/splat", description="Concatenate any number of gaussian splats into one. Unioning several decodes of the same " "latent at different seeds densifies the surface, this can improve surface quality when meshing.", inputs=[IO.Autogrow.Input("splats", template=splats)], outputs=[IO.Splat.Output(display_name="splat")], ) @classmethod def execute(cls, splats: IO.Autogrow.Type) -> IO.NodeOutput: gs = [v for v in splats.values() if v is not None] if not gs: raise ValueError("MergeSplat: connect at least one splat.") return IO.NodeOutput(_merge_gaussians(gs)) def _inverse_covariance(scale, quat): # Per-splat Sigma^-1 = R diag(1/s^2) R^T. scale (N,3) linear std, quat (N,4) wxyz -> (N,3,3). q = quat / quat.norm(dim=1, keepdim=True).clamp_min(1e-12) w, x, y, z = q.unbind(-1) R = torch.stack([ 1 - 2 * (y * y + z * z), 2 * (x * y - w * z), 2 * (x * z + w * y), 2 * (x * y + w * z), 1 - 2 * (x * x + z * z), 2 * (y * z - w * x), 2 * (x * z - w * y), 2 * (y * z + w * x), 1 - 2 * (x * x + y * y), ], dim=1).reshape(-1, 3, 3) inv_s2 = 1.0 / scale.clamp_min(1e-8) ** 2 # (N, 3) return torch.einsum("nij,nj,nkj->nik", R, inv_s2, R) def _splat_density(xyz, opacity, scale, quat, rgb, res, kernel, device, color_sharpen=1.0, chunk=4096, progress=None, col_dtype=torch.float16): # Splat each gaussian as its oriented-covariance disk (3-sigma, opacity-weighted) into a density grid, # plus a colour volume. Each gaussian uses a voxel window sized to its OWN 3-sigma (capped at `kernel`). # Colour is weighted by w^color_sharpen: >1 biases each voxel toward its dominant gaussian (crisper # texture). Returns (density, colour numerator, colour normaliser, origin, voxel). pad = 4.0 * scale.median() lo = xyz.amin(0) - pad hi = xyz.amax(0) + pad voxel = ((hi - lo).max() / res).clamp_min(1e-8) dx, dy, dz = (torch.ceil((hi - lo) / voxel).long() + 1).tolist() sinv = _inverse_covariance(scale, quat) kreq = torch.ceil(3.0 * scale.amax(-1) / voxel).long().clamp(1, int(kernel)) # per-gaussian half-width sharp = color_sharpen != 1.0 vol = torch.zeros(dx * dy * dz, device=device) # Sum(w) density (surface) colvol = torch.zeros(dx * dy * dz, 3, device=device, dtype=col_dtype) # Sum(w^p * rgb) colour numerator wcol = torch.zeros(dx * dy * dz, device=device, dtype=col_dtype) if sharp else None # Sum(w^p) normaliser (p>1) n, done = xyz.shape[0], 0 for k in range(1, int(kernel) + 1): sel = (kreq == k).nonzero(as_tuple=True)[0] if sel.numel() == 0: continue rng = torch.arange(-k, k + 1, device=device, dtype=torch.float32) off = torch.stack(torch.meshgrid(rng, rng, rng, indexing="ij"), -1).reshape(-1, 3) # (M, 3) for st in range(0, sel.numel(), chunk): gi = sel[st:st + chunk] cc = xyz[gi] idx = ((cc - lo) / voxel).round()[:, None, :] + off[None] # (b, M, 3) voxel coords d = (lo + idx * voxel) - cc[:, None, :] # world offset to voxel center quad = torch.einsum("bmi,bij,bmj->bm", d, sinv[gi], d) wgt = opacity[gi, None] * torch.exp(-0.5 * quad) wgt = torch.where(quad < 9.0, wgt, torch.zeros_like(wgt)) # clip beyond 3 sigma ii = idx.long() ix = ii[..., 0].clamp(0, dx - 1) iy = ii[..., 1].clamp(0, dy - 1) iz = ii[..., 2].clamp(0, dz - 1) flat = (ix * (dy * dz) + iy * dz + iz).reshape(-1) vol.index_add_(0, flat, wgt.reshape(-1)) wp = wgt.pow(color_sharpen) if sharp else wgt # winner-take-more colour weight colvol.index_add_(0, flat, (wp[..., None] * rgb[gi, None, :]).reshape(-1, 3).to(col_dtype)) if sharp: wcol.index_add_(0, flat, wp.reshape(-1).to(col_dtype)) done += gi.numel() if progress is not None: progress(min(1.0, done / max(1, n))) colnorm = (wcol if sharp else vol).reshape(dx, dy, dz) # p==1 -> Sum(w) == density return vol.reshape(dx, dy, dz), colvol.reshape(dx, dy, dz, 3), colnorm, lo.cpu().numpy(), float(voxel) def _connected_components_gpu(faces, nv): # FastSV connected components: grandparent hooking + shortcutting, ~O(log nv) iterations. # Returns per-vertex component labels (min node id, not densified). a = torch.cat([faces[:, 0], faces[:, 1]]) # 2F edge endpoints: (v0,v1),(v1,v2) b = torch.cat([faces[:, 1], faces[:, 2]]) f = torch.arange(nv, device=faces.device) while True: gp = f[f] # grandparent ga, gb = gp[a], gp[b] new = f.clone() new.scatter_reduce_(0, f[a], gb, "amin", include_self=True) # stochastic hooking onto roots new.scatter_reduce_(0, f[b], ga, "amin", include_self=True) new.scatter_reduce_(0, a, gb, "amin", include_self=True) # aggressive hooking, both directions new.scatter_reduce_(0, b, ga, "amin", include_self=True) new = new[new] # shortcut (path compression) if torch.equal(new, f): return f f = new def _clean_components_gpu(verts, faces, min_verts, device): # GPU port of _clean_components: FastSV components + scatter reductions. Byte-identical to the numpy path vt = torch.as_tensor(verts, device=device) ft = torch.as_tensor(faces, device=device) nv = vt.shape[0] _, label = torch.unique(_connected_components_gpu(ft, nv), return_inverse=True) # dense 0..ncomp-1 ncomp = int(label.max()) + 1 flabel = label[ft[:, 0]] # component id per face keep = torch.bincount(label, minlength=ncomp) >= min_verts # per-component vertex-count gate if int(keep.sum()) > 1: fcount = torch.bincount(flabel, minlength=ncomp) largest = int(torch.where(keep, fcount, fcount.new_tensor(-1)).argmax()) v0, v1, v2 = vt[ft[:, 0]], vt[ft[:, 1]], vt[ft[:, 2]] cvol = torch.zeros(ncomp, device=device).scatter_add_(0, flabel, (v0 * torch.linalg.cross(v1, v2)).sum(-1)) idx3 = label[:, None].expand(-1, 3) # per-component vertex bbox cmin = torch.full((ncomp, 3), float("inf"), device=device).scatter_reduce_(0, idx3, vt, "amin", include_self=True) cmax = torch.full((ncomp, 3), float("-inf"), device=device).scatter_reduce_(0, idx3, vt, "amax", include_self=True) tol = 1e-4 * (cmax[largest] - cmin[largest]).max() enclosed = (cmin >= cmin[largest] - tol).all(1) & (cmax <= cmax[largest] + tol).all(1) inner = enclosed & (torch.sign(cvol) != torch.sign(cvol[largest])) & (torch.arange(ncomp, device=device) != largest) keep &= ~inner faces_k = ft[keep[flabel]] if faces_k.shape[0] == 0: return verts[:0], faces[:0] used = torch.unique(faces_k) # sorted, matches np.unique remap = torch.full((nv,), -1, dtype=torch.int64, device=device) remap[used] = torch.arange(used.shape[0], device=device) return vt[used].cpu().numpy(), remap[faces_k].cpu().numpy() def _clean_components(verts, faces, min_verts, device=None): # Drop floaters (components with < min_verts vertices) and inner shells - the surfel shell density # extracts a double wall (outer + inner cavity surface). GPU path (FastSV CC + scatter reductions, ~13x # faster) when an accelerator has headroom; else numpy/scipy. Both produce byte-identical output. if device is not None and not comfy.model_management.is_device_cpu(device) and \ comfy.model_management.get_free_memory(device) > 10 * faces.size * 8: # peak ~8.4x faces bytes return _clean_components_gpu(verts, faces, min_verts, device) nv = len(verts) e = np.concatenate([faces[:, [0, 1]], faces[:, [1, 2]], faces[:, [0, 2]]], 0) ncomp, label = connected_components(coo_matrix((np.ones(len(e)), (e[:, 0], e[:, 1])), shape=(nv, nv)), directed=False) flabel = label[faces[:, 0]] # component id per face keep = np.bincount(label, minlength=ncomp) >= min_verts # per-component vertex-count gate if keep.sum() > 1: fcount = np.bincount(flabel, minlength=ncomp) largest = np.where(keep, fcount, -1).argmax() v0, v1, v2 = verts[faces[:, 0]], verts[faces[:, 1]], verts[faces[:, 2]] cvol = np.bincount(flabel, weights=np.einsum("ij,ij->i", v0, np.cross(v1, v2)), minlength=ncomp) # 6*signed vol cidx = np.arange(ncomp) # per-component vertex bbox via ndimage (~6x faster than ufunc.at) cmin = np.stack([_ndi_minimum(verts[:, a], label, cidx) for a in range(3)], 1) cmax = np.stack([_ndi_maximum(verts[:, a], label, cidx) for a in range(3)], 1) tol = 1e-4 * (cmax[largest] - cmin[largest]).max() enclosed = (cmin >= cmin[largest] - tol).all(1) & (cmax <= cmax[largest] + tol).all(1) inner = enclosed & (np.sign(cvol) != np.sign(cvol[largest])) & (np.arange(ncomp) != largest) keep &= ~inner faces = faces[keep[flabel]] if len(faces) == 0: return verts[:0], faces used = np.unique(faces) remap = np.full(nv, -1, np.int64) remap[used] = np.arange(len(used)) return verts[used], remap[faces] def _surface_nets(vol, level, voxel, origin, device): # Vectorized Surface Nets: one dual vertex per sign-changing cell at its edge-crossing mean, quads wound CCW-outward. # Returns verts (V,3), faces (F,3). vol = vol.to(device=device, dtype=torch.float32) dx, dy, dz = vol.shape origin_t = torch.as_tensor(origin, device=device, dtype=torch.float32) empty = (np.zeros((0, 3), np.float32), np.zeros((0, 3), np.int64)) if dx < 2 or dy < 2 or dz < 2: return empty # Active = cells whose 8 corners aren't all in/all out. inside = vol >= level # (dx,dy,dz) bool cs8 = [inside[ox:ox + dx - 1, oy:oy + dy - 1, oz:oz + dz - 1] for ox, oy, oz in ((0, 0, 0), (1, 0, 0), (0, 1, 0), (1, 1, 0), (0, 0, 1), (1, 0, 1), (0, 1, 1), (1, 1, 1))] any_in = cs8[0] | cs8[1] | cs8[2] | cs8[3] | cs8[4] | cs8[5] | cs8[6] | cs8[7] all_in = cs8[0] & cs8[1] & cs8[2] & cs8[3] & cs8[4] & cs8[5] & cs8[6] & cs8[7] active = any_in & ~all_in # (cx,cy,cz) straddling cells nv = int(active.sum()) if nv == 0: return empty # Active cells only (a thin shell): each dual vertex = mean of its 12 edges' zero-crossings. del any_in, all_in, cs8 # corner bool grids no longer needed ac = active.nonzero(as_tuple=False) # (nv,3) cell min-corner indices offs = torch.tensor([[0, 0, 0], [1, 0, 0], [0, 1, 0], [1, 1, 0], [0, 0, 1], [1, 0, 1], [0, 1, 1], [1, 1, 1]], device=device) offf = offs.to(torch.float32) edges = torch.tensor([[0, 1], [0, 2], [0, 4], [1, 3], [1, 5], [2, 3], [2, 6], [3, 7], [4, 5], [4, 6], [5, 7], [6, 7]], device=device) e0, e1 = edges[:, 0], edges[:, 1] oe0, oe1 = offf[e0], offf[e1] # (12,3) edge endpoints cstep = 1 << 18 # chunk to bound peak memory (CPU RAM too) loc = [] for st in range(0, nv, cstep): ci = ac[st:st + cstep, None, :] + offs[None] # (m,8,3) cval = vol[ci[..., 0], ci[..., 1], ci[..., 2]] # (m,8) corner values csl = cval >= level v0, v1 = cval[:, e0], cval[:, e1] # (m,12) cross = (csl[:, e0] != csl[:, e1])[..., None].to(torch.float32) denom = v1 - v0 t = torch.where(denom.abs() > 1e-12, (level - v0) / denom, torch.full_like(denom, 0.5)).clamp(0, 1) pts = torch.lerp(oe0, oe1, t[..., None]) # (m,12,3) local crossings (fused interp) loc.append((pts * cross).sum(1) / cross.sum(1).clamp_min(1.0)) # (m,3) in [0,1] local = torch.cat(loc, 0) if len(loc) > 1 else loc[0] # (nv,3) verts = origin_t + (ac.to(torch.float32) + local) * voxel # world space del loc, local, ac vid = torch.full((dx - 1, dy - 1, dz - 1), -1, dtype=torch.int32, device=device) vid[active] = torch.arange(nv, dtype=torch.int32, device=device) del active # Each straddling grid edge -> one quad from its 4 cells; `sol` (low-end sign) picks outward winding. faces = [] def emit(cr, sol, a, b, d, c): valid = cr & (a >= 0) & (b >= 0) & (c >= 0) & (d >= 0) if not bool(valid.any()): return a, b, c, d, sol = a[valid], b[valid], c[valid], d[valid], sol[valid] p2, p4 = torch.where(sol, b, c), torch.where(sol, c, b) # reverse quad winding where ~sol faces.append(torch.stack([a, p2, d], 1)) faces.append(torch.stack([a, d, p4], 1)) a = inside[0:dx - 1, 1:dy - 1, 1:dz - 1] emit(a != inside[1:dx, 1:dy - 1, 1:dz - 1], a, vid[:, 0:dy - 2, 0:dz - 2], vid[:, 1:dy - 1, 0:dz - 2], vid[:, 1:dy - 1, 1:dz - 1], vid[:, 0:dy - 2, 1:dz - 1]) a = inside[1:dx - 1, 0:dy - 1, 1:dz - 1] emit(a != inside[1:dx - 1, 1:dy, 1:dz - 1], a, vid[0:dx - 2, :, 0:dz - 2], vid[0:dx - 2, :, 1:dz - 1], vid[1:dx - 1, :, 1:dz - 1], vid[1:dx - 1, :, 0:dz - 2]) a = inside[1:dx - 1, 1:dy - 1, 0:dz - 1] emit(a != inside[1:dx - 1, 1:dy - 1, 1:dz], a, vid[0:dx - 2, 0:dy - 2, :], vid[1:dx - 1, 0:dy - 2, :], vid[1:dx - 1, 1:dy - 1, :], vid[0:dx - 2, 1:dy - 1, :]) if not faces: return empty return verts.cpu().numpy().astype(np.float32), torch.cat(faces, 0).cpu().numpy().astype(np.int64) def _otsu_level(values, bins=256): # Otsu threshold: the density value that best splits inside/outside (max between-class variance). hist, edges = np.histogram(values, bins=bins) hist = hist.astype(np.float64) centers = (edges[:-1] + edges[1:]) * 0.5 w = np.cumsum(hist) # background-class weight at each split mu = np.cumsum(hist * centers) wf = w[-1] - w # foreground-class weight mb = mu / np.where(w > 0, w, 1.0) mf = (mu[-1] - mu) / np.where(wf > 0, wf, 1.0) var_b = w * wf * (mb - mf) ** 2 # between-class variance var_b[(w <= 0) | (wf <= 0)] = -1.0 return float(centers[int(np.argmax(var_b))]) def _taubin_smooth(verts, faces, iters, lam=0.5, mu=-0.53): # Taubin lambda|mu smoothing: low-pass the mesh surface without the shrinkage of a Laplacian blur # (the mu inflation pass cancels the lambda pass's volume loss). Uniform (umbrella) weights. if iters <= 0 or len(verts) == 0 or len(faces) == 0: return verts nv = len(verts) e = np.concatenate([faces[:, [0, 1]], faces[:, [1, 2]], faces[:, [0, 2]]], 0) e = np.concatenate([e, e[:, ::-1]], 0) # symmetric adjacency adj = coo_matrix((np.ones(len(e), np.float32), (e[:, 0], e[:, 1])), shape=(nv, nv)).tocsr() adj.data[:] = 1.0 deg = np.clip(np.asarray(adj.sum(1)).ravel(), 1.0, None).astype(np.float32)[:, None] v = verts.astype(np.float32) # fp32 matvec: ~2x faster, sub-micron drift on unit-scale verts for _ in range(int(iters)): for fac in (lam, mu): v = v + np.float32(fac) * ((adj @ v) / deg - v) # fac * (mean(neighbours) - v) return np.ascontiguousarray(v) def _sample_vertex_colours_gpu(colvol, colnorm, verts, origin, voxel, device): # GPU trilinear sampling of the colour numerator (3ch) and normaliser (1ch) at vertex grid-coords # reproduces scipy map_coordinates(order=1, mode='nearest'). Returns col (V,3) numpy. dx, dy, dz = colnorm.shape vt = torch.as_tensor(verts, device=device, dtype=torch.float32) org = torch.as_tensor(origin, device=device, dtype=torch.float32) gi = (vt - org) / voxel # (V,3) grid-index coords (x,y,z) size = torch.tensor([dx, dy, dz], device=device, dtype=torch.float32) g = 2.0 * gi / (size - 1).clamp_min(1.0) - 1.0 # -> [-1,1] (align_corners) grid = torch.stack([g[:, 2], g[:, 1], g[:, 0]], -1)[None, None, None] # (1,1,1,V,3): grid_sample order (W=z,H=y,D=x) def samp(v): # (dx,dy,dz,C) cpu fp16 -> (C,V) fp32 on device inp = v.to(device).permute(3, 0, 1, 2)[None].float() o = torch.nn.functional.grid_sample(inp, grid, mode="bilinear", padding_mode="border", align_corners=True) return o[0, :, 0, 0, :] num = samp(colvol) # (3,V) den = samp(colnorm[..., None]) # (1,V) return (num / den.clamp_min(1e-8)).T.cpu().numpy() # (V,3) def _gaussian_to_mesh(g: Types.SPLAT, i, res, kernel, taubin, level_bias, min_component, min_opacity, color_sharpen, device, progress=None): # Mesh one splat: density + colour grids -> Surface Nets -> floater removal -> Taubin smoothing -> # volume-sampled colours. Returns (verts, faces int64, colors in [0,1]), or None if no surface. rep = progress if progress is not None else (lambda *_: None) end = _real_len(g, i) xyz = g.positions[i, :end].to(device=device, dtype=torch.float32) scale = g.scales[i, :end].to(device=device, dtype=torch.float32) quat = g.rotations[i, :end].to(device=device, dtype=torch.float32) opacity = g.opacities[i, :end].reshape(-1).to(device=device, dtype=torch.float32) rgb = (g.sh[i, :end, 0, :].to(device=device, dtype=torch.float32) * _C0 + 0.5).clamp(0, 1) keep = opacity >= min_opacity xyz, scale, quat, opacity, rgb = xyz[keep], scale[keep], quat[keep], opacity[keep], rgb[keep] if xyz.shape[0] == 0: return None vol, colvol, colnorm, origin, voxel = _splat_density(xyz, opacity, scale, quat, rgb, res, kernel, device, color_sharpen=color_sharpen, progress=lambda f: rep(0.25 * f)) # density build: 0 -> 25% # Colour: sample on the GPU (grid_sample) when there's headroom colour_gpu = not comfy.model_management.is_device_cpu(device) and comfy.model_management.get_free_memory(device) > 6 * vol.numel() * 4 if colour_gpu: colvol_cpu, colnorm_cpu = colvol.cpu(), colnorm.half().cpu() # park colours (fp16) off-GPU during meshing colvol_np = colnorm_np = None else: colvol_np = colvol.cpu().numpy().astype(np.float32) # Sum(w^p * rgb) colour numerator (fp16 grid -> fp32) colnorm_np = colnorm.cpu().numpy().astype(np.float32) # Sum(w^p) colour normaliser del colvol, colnorm # free the colour grids before iso-surfacing rep(0.40) vmin, vmax = float(vol.min()), float(vol.max()) occ = vol[vol > vmax * 1e-3] # occupied voxels (skip the empty-space peak) if occ.numel() == 0: return None # Otsu picks the inside/outside split principledly; `level_bias` nudges it (1.0 = auto). Clamp strictly # inside the data range so a bias can't push the iso off the histogram. level = min(max(_otsu_level(occ.cpu().numpy()) * level_bias, vmin + 1e-6 * (vmax - vmin)), vmax - 1e-6 * (vmax - vmin)) # Iso-surface on the accelerator when there's headroom: ~15x faster than CPU, identical output. Chunked # Surface Nets peaks at ~3-3.5x the density grid, so fall back to CPU for large grids / tight VRAM. sn_dev = device if not comfy.model_management.is_device_cpu(device) and comfy.model_management.get_free_memory(device) < 6 * vol.numel() * 4: sn_dev = torch.device("cpu") vol = vol.cpu() verts, faces = _surface_nets(vol, level, voxel, origin, sn_dev) del vol rep(0.55) if min_component > 0 and len(faces) > 0: verts, faces = _clean_components(verts, faces, min_component, device) if len(verts) == 0 or len(faces) == 0: return None # Taubin smooths the blocky iso without shrinking it (unlike blurring the density, which rounds features). verts = _taubin_smooth(verts, faces, taubin) rep(0.7) # Colour each vertex from the co-splatted colour volume: trilinearly sample the numerator Sum(w^p*rgb) # and normaliser Sum(w^p) separately, then divide. Normalising AFTER interpolation keeps zero-density # edge voxels from pulling colours toward black, and matches the gaussians that formed the surface. if colour_gpu: col = _sample_vertex_colours_gpu(colvol_cpu, colnorm_cpu, verts, origin, voxel, device) else: coords = ((verts - origin) / voxel).T # (3, V) grid-index coords, matching volume axes num = np.stack([map_coordinates(colvol_np[..., c], coords, order=1, mode="nearest") for c in range(3)], -1) den = map_coordinates(colnorm_np, coords, order=1, mode="nearest") col = num / np.clip(den, 1e-8, None)[:, None] rep(1.0) # The unlit material's COLOR_0 is linear and the viewer sRGB-encodes it on output; the splat colours # are display (sRGB) values, so convert sRGB -> linear here to land at the same brightness as the splat. col = np.clip(col, 0, 1) col = np.where(col <= 0.04045, col / 12.92, ((col + 0.055) / 1.055) ** 2.4).astype(np.float32) # Splat +Y is glTF's -Y: rotate 180 deg about X (negate Y,Z) to land upright. Proper rotation, so # winding is kept; done after colouring (which works in the splat frame). verts = np.ascontiguousarray(verts * np.array([1.0, -1.0, -1.0], dtype=np.float32)) return (torch.from_numpy(verts), torch.from_numpy(faces), torch.from_numpy(col)) class SplatToMesh(IO.ComfyNode): @classmethod def define_schema(cls): return IO.Schema( node_id="SplatToMesh", display_name="Extract Mesh from Splat", search_aliases=["splat to mesh", "gaussian surface nets", "splat surface", "mesh splat"], category="3d/splat", description="Extract a coloured mesh from a gaussian splat.", inputs=[ IO.Splat.Input("splat"), IO.Int.Input("resolution", default=384, min=64, max=768, step=16, tooltip="Density-grid resolution along the longest axis. Higher = finer surface, " "more VRAM/time (grows with resolution^3)."), IO.Int.Input("kernel", default=5, min=1, max=8, tooltip="Max splat half-width in voxels. Each gaussian is rasterized over a window " "sized to its own 3-sigma, capped here - small surfels stay cheap, large ones " "aren't truncated. Raise if sparse splats leave gaps."), IO.Int.Input("smooth", default=0, min=0, max=60, advanced = True, tooltip="Taubin mesh-smoothing iterations. Smooths the surface without shrinking it " "(volume-preserving), unlike blurring the density. 0 = raw surface."), IO.Float.Input("level", default=0.4, min=0.0, max=2.0, step=0.01, tooltip="Iso-surface level. Auto-picked by Otsu; this biases it (1.0 = auto, lower = " "fatter/more-connected surface, higher = thinner/tighter)."), IO.Int.Input("min_component", default=500, min=0, max=100000, step=50, advanced=True, tooltip="Drop connected components smaller than this many vertices (0 = keep all). " "Removes detached floater blobs and the inner shell of the double wall."), IO.Float.Input("min_opacity", default=0.02, min=0.0, max=1.0, step=0.01, advanced=True, tooltip="Ignore gaussians fainter than this before meshing."), IO.Float.Input("color_sharpen", default=2.0, min=1.0, max=8.0, step=0.5, tooltip="Crisp up the vertex texture: 1.0 = physically-correct blend; higher biases " "each voxel's colour toward its dominant gaussian instead of averaging " "neighbours (de-smears the texture). Colour only - geometry is unchanged."), ], outputs=[IO.Mesh.Output(display_name="mesh")], ) @classmethod def execute(cls, splat, resolution, kernel, smooth, level, min_component, min_opacity, color_sharpen) -> IO.NodeOutput: device = comfy.model_management.get_torch_device() b = splat.positions.shape[0] prec = 1000 # each splat owns a 0..prec block of the bar; its callback advances within that block pbar = comfy.utils.ProgressBar(b * prec) verts_l, faces_l, colors_l = [], [], [] for i in range(b): cb = lambda f, base=i * prec: pbar.update_absolute(base + int(min(max(f, 0.0), 1.0) * prec)) res = _gaussian_to_mesh(splat, i, resolution, kernel, smooth, level, min_component, min_opacity, color_sharpen, device, cb) if res is None: logging.warning("SplatToMesh: splat %d produced no surface; emitting an empty mesh.", i) v, f, c = torch.zeros((0, 3)), torch.zeros((0, 3), dtype=torch.int64), torch.zeros((0, 3)) else: v, f, c = res verts_l.append(v) faces_l.append(f) colors_l.append(c) pbar.update_absolute((i + 1) * prec) # snap to block end (covers empty / early-out splats) # unlit: render flat (emissive-like) so SaveGLB matches the splat instead of lighting/washing it. return IO.NodeOutput(pack_variable_mesh_batch(verts_l, faces_l, colors=colors_l, unlit=True)) class GaussianExtension(ComfyExtension): @override async def get_node_list(self) -> list[type[IO.ComfyNode]]: return [SplatToFile3D, File3DToSplat, RenderSplat, CreateCameraInfo, TransformSplat, GetSplatCount, MergeSplat, SplatToMesh] async def comfy_entrypoint() -> GaussianExtension: return GaussianExtension()