mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2026-06-04 13:27:41 +08:00
Optimize rendering and meshing speed
Multiple times faster
This commit is contained in:
kijai
parent
7188dca1f1
commit
87f5b6a692
@ -9,7 +9,7 @@ from io import BytesIO
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import numpy as np
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import torch
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from typing_extensions import override
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from scipy.ndimage import map_coordinates
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from scipy.ndimage import map_coordinates, minimum as _ndi_minimum, maximum as _ndi_maximum
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from scipy.sparse import coo_matrix
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from scipy.sparse.csgraph import connected_components
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@ -55,7 +55,7 @@ def _gaussian_ply_bytes(positions, scales, rotations, opacities, sh) -> bytes:
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"""Serialize render-ready gaussian tensors as a binary 3DGS .ply.
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positions (N,3) world; scales (N,3) linear; rotations (N,4) quat wxyz; opacities (N,1) in [0,1];
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sh (N,K,3) SH coefficients. Activated values are inverted to the standard 3DGS storage convention
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sh (N,K,3) SH coefficients. Activated values are inverted to the standard 3D gaussian splat storage convention
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(log scale, logit opacity).
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"""
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xyz = positions.cpu().numpy().astype(np.float32)
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@ -65,7 +65,7 @@ def _gaussian_ply_bytes(positions, scales, rotations, opacities, sh) -> bytes:
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normals = np.zeros_like(xyz)
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f = sh.cpu().numpy().astype(np.float32) # (N, K, 3)
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f_dc = f[:, 0, :] # (N, 3)
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f_rest = f[:, 1:, :].transpose(0, 2, 1).reshape(n, -1) # (N, 3*(K-1)) channel-major, per 3DGS
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f_rest = f[:, 1:, :].transpose(0, 2, 1).reshape(n, -1) # (N, 3*(K-1)) channel-major
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op = opacities.cpu().numpy().astype(np.float32).reshape(n, 1).clip(1e-6, 1 - 1e-6)
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op = np.log(op / (1.0 - op)) # inverse sigmoid (logit)
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scale = np.log(scales.cpu().numpy().astype(np.float32).clip(min=1e-8))
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@ -87,8 +87,8 @@ def _gaussian_ply_bytes(positions, scales, rotations, opacities, sh) -> bytes:
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# then N 44-byte records. Bucketing/quantization only exist at levels >= 1. See SplatBuffer.js.
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_KSPLAT_HEADER_BYTES = 4096
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_KSPLAT_SECTION_HEADER_BYTES = 1024
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_KSPLAT_BYTES_PER_SPLAT = 44 # center 12 + scale 12 + rotation 16 + color(RGBA u8) 4
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_KSPLAT_VERSION = (0, 1) # SplatBuffer CurrentMajor/MinorVersion
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_KSPLAT_BYTES_PER_SPLAT = 44 # center 12 + scale 12 + rotation 16 + color(RGBA u8) 4
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_KSPLAT_VERSION = (0, 1) # SplatBuffer CurrentMajor/MinorVersion
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def _gaussian_ksplat_bytes(positions, scales, rotations, opacities, sh) -> bytes:
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@ -482,11 +482,11 @@ class SplatToFile3D(IO.ComfyNode):
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"Supports one item per batch only.",
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inputs=[
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IO.Splat.Input("splat"),
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IO.Combo.Input("format", options=["ply", "ksplat", "spz"],
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tooltip="ply: standard 3DGS with full spherical harmonics. "
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"ksplat: mkkellogg SplatBuffer (level 0, uncompressed). "
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"spz: Niantic gzip-compressed (~10x smaller). "
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"ksplat/spz keep base color only, view-dependent spherical harmonics is dropped."),
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IO.Combo.Input("format", options=["ply", "ksplat", "spz"], # TODO: add "splat" when we have a writer for it
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tooltip="ply: standard 3D Gaussian Splat with full spherical harmonics. "
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"ksplat: mkkellogg SplatBuffer (level 0, uncompressed), base color only "
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"spz: Niantic gzip-compressed (~10x smaller), base color only "
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),
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],
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outputs=[IO.File3DAny.Output(display_name="model_3d")],
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)
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@ -512,7 +512,7 @@ class File3DToSplat(IO.ComfyNode):
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category="3d/splat",
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description="Parse a splat File3D into a gaussian splat. Inverse of Create 3D File (from Splat). "
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"Supported format: PLY, SPLAT, KSPLAT, SPZ. PLY carries full spherical harmonics, "
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" the other formats are base color only. Format is auto-detected from the file contents.",
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"the other formats are base color only. Format is auto-detected from the file contents.",
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inputs=[
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IO.MultiType.Input(
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IO.File3DAny.Input("model_3d"),
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@ -664,133 +664,149 @@ def _render_gaussian(xyz, rgb, opacity, scale, rot, width, height, splat_scale,
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# selects the image: color / clay / depth / normal. Returns (image HxWx3, coverage mask HxW) on CPU.
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dev = comfy.model_management.get_torch_device()
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t = lambda a: torch.as_tensor(a, dtype=torch.float32, device=dev)
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idev, idtype = comfy.model_management.intermediate_device(), comfy.model_management.intermediate_dtype()
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xyz, rgb, opacity = t(xyz), t(rgb).clamp(0, 1), t(opacity).reshape(-1)
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scale, rot = t(scale) * float(splat_scale), t(rot)
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do_linear = render_style == "color" # colour blends in linear light, re-encoded at the end
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do_linear = render_style == "color" # colour blends in linear light, re-encoded at the end
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if do_linear:
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rgb = _srgb_to_linear(rgb)
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flat = width * height
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bg_t = t(bg)
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bg_comp = _srgb_to_linear(bg_t) if do_linear else bg_t # background blended in the same space as the splats
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bg_comp = _srgb_to_linear(bg_t) if do_linear else bg_t # background blended in the same space as the splats
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need_depth = render_style == "depth"
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need_normal = render_style in ("normal", "clay") or headlight_shading > 0
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def background_only(): # no splats to rasterize -> just the background + empty mask
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def background_only(): # no splats to rasterize -> just the background + empty mask
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img = bg_t.expand(height, width, 3) if render_style == "color" else torch.zeros(height, width, 3, device=dev)
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return img.cpu(), torch.zeros(height, width)
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return img.to(idev, idtype), torch.zeros(height, width, device=idev, dtype=idtype)
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if xyz.shape[0] == 0: # empty input (e.g. all culled by opacity_threshold)
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if xyz.shape[0] == 0: # empty input (e.g. all culled by opacity_threshold)
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return background_only()
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eye, target, right, up, fwd = _camera_basis(camera_info, dev) # all camera state comes from camera_info
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W = torch.stack([right, up, fwd], 0) # rows = camera axes (world -> camera)
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eye, target, right, up, fwd = _camera_basis(camera_info, dev) # all camera state comes from camera_info
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W = torch.stack([right, up, fwd], 0) # rows = camera axes (world -> camera)
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cam = (xyz - eye) @ W.T
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fov = float(camera_info.get("fov", 0) or 0) or 35.0
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zoom = float(camera_info.get("zoom", 1.0) or 1.0) # three.js digital zoom: scales the focal length
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zoom = float(camera_info.get("zoom", 1.0) or 1.0) # three.js digital zoom: scales the focal length
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is_ortho = str(camera_info.get("cameraType", "")).lower().startswith("ortho")
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cam_dist = float((target - eye).norm().clamp_min(1e-6)) # eye->target distance: sets the ortho pixel scale
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yflip = 1.0 # splat +Y is image-down
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xc, yc, zc = cam.unbind(-1)
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keep = zc > 1e-2
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xc, yc, zc, rgb, opacity, scale, rot = (a[keep] for a in (xc, yc, zc, rgb, opacity, scale, rot))
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if xc.shape[0] == 0: # nothing in front of the camera -> background only
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if xc.shape[0] == 0: # nothing in front of the camera -> background only
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return background_only()
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if render_style == "clay":
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rgb = torch.full_like(rgb, 0.75) # neutral albedo -> shading shows pure geometry
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rgb = torch.full_like(rgb, 0.75) # neutral albedo -> shading shows pure geometry
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f = (min(width, height) / 2) / math.tan(math.radians(fov) / 2) * zoom # fov over the smaller axis, x camera zoom
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s = f / cam_dist # ortho: pixels per world unit at the target plane
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invz = 1.0 / zc
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f = (min(width, height) / 2) / math.tan(math.radians(fov) / 2) * zoom # fov over the smaller axis, x camera zoom
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cx0, cy0 = width / 2, height / 2
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# Camera-space 3D covariance per splat: Sigma = (W Rq) diag(scale^2) (W Rq)^T, plus a tiny relative
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# regularizer for a stable inverse (a pixel-size Mip low-pass would over-thicken flat surfels and blur).
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Mw = W[None] @ _quat_to_mat(rot) # (N,3,3) world -> camera
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Mw = W[None] @ _quat_to_mat(rot) # (N,3,3) world -> camera
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cam_cov = (Mw * scale.square()[:, None, :]) @ Mw.transpose(1, 2)
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cam_cov = cam_cov + (cam_cov.diagonal(dim1=-2, dim2=-1).mean(-1) * 1e-3)[:, None, None] * torch.eye(3, device=dev)
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# Perspective-correct weighting: peak of the 3D Gaussian along each pixel ray. Precompute Si, Si@mu, mu^T Si mu.
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mu = torch.stack([xc, yc, zc], -1)
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si = torch.linalg.inv(cam_cov)
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simu = (si @ mu[:, :, None])[:, :, 0] # (N,3)
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musimu = (mu * simu).sum(-1) # (N,)
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simu = (si @ mu[:, :, None])[:, :, 0] # (N,3)
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musimu = (mu * simu).sum(-1) # (N,)
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s00, s01, s02 = si[:, 0, 0], si[:, 0, 1], si[:, 0, 2]
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s11, s12, s22 = si[:, 1, 1], si[:, 1, 2], si[:, 2, 2]
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simu0, simu1, simu2 = simu.unbind(-1)
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if need_normal: # surfel normal = thinnest axis, oriented toward camera
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nrm = Mw[torch.arange(Mw.shape[0], device=dev), :, scale.argmin(-1)] # (N,3) camera-space normal
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nrm = nrm * torch.where(nrm[:, 2:3] > 0, -1.0, 1.0) # flip so nz <= 0 (faces camera)
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if need_normal: # surfel normal = thinnest axis, oriented toward camera
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nrm = Mw[torch.arange(Mw.shape[0], device=dev), :, scale.argmin(-1)] # (N,3) camera-space normal
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nrm = nrm * torch.where(nrm[:, 2:3] > 0, -1.0, 1.0) # flip so nz <= 0 (faces camera)
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# Screen centre (exact) + footprint radius from the affine 2D projection (used only to size the kernel).
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# The image is +y-down, so the projection's y row is unflipped - it matches the splat frame's +Y.
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jm = torch.zeros(xc.shape[0], 2, 3, device=dev)
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if is_ortho: # parallel projection: screen = s * (xc, yc)
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cx, cy = cx0 + s * xc, cy0 + yflip * s * yc
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if is_ortho: # parallel projection: screen = s * (xc, yc)
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s = f / float((target - eye).norm().clamp_min(1e-6)) # pixels per world unit at the target plane
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cx, cy = cx0 + s * xc, cy0 + s * yc
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jm[:, 0, 0] = s
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jm[:, 1, 1] = yflip * s
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else: # perspective: screen = f * (xc, yc) / zc
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cx, cy = cx0 + f * xc * invz, cy0 + yflip * f * yc * invz
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jm[:, 1, 1] = s
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else: # perspective: screen = f * (xc, yc) / zc
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invz = 1.0 / zc
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cx, cy = cx0 + f * xc * invz, cy0 + f * yc * invz
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jm[:, 0, 0], jm[:, 0, 2] = f * invz, -f * xc * invz.square()
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jm[:, 1, 1], jm[:, 1, 2] = yflip * f * invz, -yflip * f * yc * invz.square()
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jm[:, 1, 1], jm[:, 1, 2] = f * invz, -f * yc * invz.square()
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cov2 = jm @ cam_cov @ jm.transpose(1, 2)
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a, b, c = cov2[:, 0, 0], cov2[:, 0, 1], cov2[:, 1, 1]
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max_eig = (a + c) * 0.5 + (((a - c) * 0.5).square() + b * b).clamp_min(0).sqrt()
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radius = 3.0 * max_eig.clamp_min(1e-8).sqrt()
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K = int(min(max(24, min(width, height) // 16), max(2, math.ceil(_quantile(radius, 0.995).item()))))
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rng = torch.arange(-K, K + 1, device=dev, dtype=torch.float32)
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oy, ox = torch.meshgrid(rng, rng, indexing="ij")
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ox, oy = ox.reshape(-1), oy.reshape(-1) # (M,) kernel offsets
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order = torch.argsort(zc) # front (small zc) -> back
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# Per-splat kernel size: bucket splats by radius into a ladder of kernel sizes (the global K stays the cap) and use
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# a tiny window instead of the worst-case one
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levels = [L for L in (4, 8, 16, 32, 64, 128, 256) if L < K] + [K]
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levels_t = torch.tensor(levels, device=dev, dtype=torch.float32)
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grids = []
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for L in levels:
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rng = torch.arange(-L, L + 1, device=dev, dtype=torch.float32)
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gy, gx = torch.meshgrid(rng, rng, indexing="ij")
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grids.append((gx.reshape(-1), gy.reshape(-1)))
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blevel = torch.bucketize(radius * (4.0 / 3.0), levels_t).clamp_(max=len(levels) - 1) # window >= ~4 sigma
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n = zc.shape[0]
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ns = int(min(256, max(1, n // 1000))) # depth slabs: 1 per ~1000 splats, capped
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order = torch.argsort(zc) # front (small zc) -> back -> defines the slabs
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bounds = torch.linspace(0, n, ns + 1, device=dev).round().long()
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rank = torch.empty(n, dtype=torch.long, device=dev)
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rank[order] = torch.arange(n, device=dev) # depth rank of each splat
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slab_id = (torch.searchsorted(bounds, rank, right=True) - 1).clamp_(0, ns - 1)
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order = torch.argsort(slab_id * len(levels) + blevel) # group by slab, then kernel level (order-free within)
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slab_bounds = torch.cat([torch.zeros(1, dtype=torch.long, device=dev), torch.bincount(slab_id, minlength=ns).cumsum(0)]).tolist()
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cxr, cyr = cx[order].round(), cy[order].round()
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s00, s01, s02 = s00[order], s01[order], s02[order]
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s11, s12, s22 = s11[order], s12[order], s22[order]
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simu0, simu1, simu2, musimu = simu0[order], simu1[order], simu2[order], musimu[order]
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opacity, rgb = opacity[order], rgb[order]
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blevel = blevel[order]
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zc_o = zc[order] if need_depth else None
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nrm_o = nrm[order] if need_normal else None
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mux_o, muy_o, muz_o = (xc[order], yc[order], zc[order]) if is_ortho else (None, None, None)
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def splat(lo, hi): # -> pixel idx (m,M), alpha (m,M); weight = 3D Gaussian peak along each pixel's ray
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def splat(lo, hi, ox, oy): # -> pixel idx (m,M), alpha (m,M); weight = 3D Gaussian peak along each pixel's ray
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px = cxr[lo:hi, None] + ox[None, :]
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py = cyr[lo:hi, None] + oy[None, :]
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valid = (px >= 0) & (px < width) & (py >= 0) & (py < height)
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if is_ortho: # parallel ray (0,0,1) from screen point (X, Y, 0)
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if is_ortho: # parallel ray (0,0,1) from screen point (X, Y, 0)
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rx = (px - cx0) / s - mux_o[lo:hi, None]
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ry = yflip * (py - cy0) / s - muy_o[lo:hi, None]
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rz = -muz_o[lo:hi, None] # constant per splat
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ry = (py - cy0) / s - muy_o[lo:hi, None]
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rz = -muz_o[lo:hi, None] # constant per splat
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rSr = (s00[lo:hi, None] * rx * rx + s11[lo:hi, None] * ry * ry + s22[lo:hi, None] * rz * rz
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+ 2 * (s01[lo:hi, None] * rx * ry + s02[lo:hi, None] * rx * rz + s12[lo:hi, None] * ry * rz))
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dsr = s02[lo:hi, None] * rx + s12[lo:hi, None] * ry + s22[lo:hi, None] * rz
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q = (rSr - dsr * dsr / s22[lo:hi, None].clamp_min(1e-12)).clamp_min(0)
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else: # perspective ray (dx,dy,1) through the camera origin
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dx, dy = (px - cx0) / f, yflip * (py - cy0) / f
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q = (rSr - dsr * dsr / s22[lo:hi, None].clamp_min(1e-12)).clamp_min_(0)
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else: # perspective ray (dx,dy,1) through the camera origin
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dx, dy = (px - cx0) / f, (py - cy0) / f
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dsid = (s00[lo:hi, None] * dx * dx + s11[lo:hi, None] * dy * dy + s22[lo:hi, None]
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+ 2 * (s01[lo:hi, None] * dx * dy + s02[lo:hi, None] * dx + s12[lo:hi, None] * dy))
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dsimu = dx * simu0[lo:hi, None] + dy * simu1[lo:hi, None] + simu2[lo:hi, None]
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q = (musimu[lo:hi, None] - dsimu * dsimu / dsid.clamp_min(1e-12)).clamp_min(0) # ray->centre Mahalanobis^2
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alpha = (opacity[lo:hi, None] * torch.exp(-0.5 * q) * valid).clamp(0, 0.999)
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q = (musimu[lo:hi, None] - dsimu * dsimu / dsid.clamp_min(1e-12)).clamp_min_(0) # ray->centre Mahalanobis^2
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alpha = (opacity[lo:hi, None] * torch.exp(-0.5 * q) * valid).clamp_(0, 0.999)
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idx = py.long().clamp(0, height - 1) * width + px.long().clamp(0, width - 1)
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return idx, alpha
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# Front-to-back compositing over many depth slabs (equal splat counts) -> the global depth order is
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# resolved finely, approaching a true per-pixel sort.
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n = xc.shape[0]
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ns = int(min(256, max(1, n // 1000))) # depth slabs: 1 per ~1000 splats, capped (result converges well below)
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bounds = torch.linspace(0, n, ns + 1).round().long().tolist()
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# Front-to-back compositing over the depth slabs set up above. Within a slab the accumulation is a pure
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# sum (order-independent), so splats are grouped by kernel level and each level uses its own tight window.
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sharp = sharpen != 1.0 # winner-take-more colour blend: dominant splat shows more
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cacc = torch.zeros((flat, 3), device=dev)
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trans = torch.ones((flat,), device=dev)
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a_buf = torch.zeros((flat,), device=dev) # sum alpha -> colour/depth/normal weight (alpha-weighted mean)
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tau_buf = torch.zeros((flat,), device=dev) # sum -ln(1-alpha) -> slab opacity = 1-prod(1-alpha) (order-independent)
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crgb = torch.zeros((flat, 3), device=dev) # sum alpha^p * rgb -> slab colour
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wbuf = torch.zeros((flat,), device=dev) # sum alpha^p -> colour normalizer (== a_buf when p==1)
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dacc = torch.zeros((flat,), device=dev) # front-weighted depth
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nacc = torch.zeros((flat, 3), device=dev) # front-weighted camera-space normal
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zslab = torch.zeros((flat,), device=dev)
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nslab = torch.zeros((flat, 3), device=dev)
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sharp = sharpen != 1.0 # winner-take-more colour blend: dominant splat shows more
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ch = max(2048, 10_000_000 // ox.shape[0]) # splats/chunk, bounded by kernel size (caps peak VRAM)
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for s0, s1 in zip(bounds[:-1], bounds[1:]):
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a_buf = torch.zeros((flat,), device=dev) # sum alpha -> colour/depth/normal weight (alpha-weighted mean)
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tau_buf = torch.zeros((flat,), device=dev) # sum -ln(1-alpha) -> slab opacity = 1-prod(1-alpha) (order-independent)
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crgb = torch.zeros((flat, 3), device=dev) # sum alpha^p * rgb -> slab colour
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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):
|
||||
s0, s1 = slab_bounds[si], slab_bounds[si + 1]
|
||||
if s1 <= s0:
|
||||
continue
|
||||
a_buf.zero_()
|
||||
@ -802,70 +818,83 @@ def _render_gaussian(xyz, rgb, opacity, scale, rot, width, height, splat_scale,
|
||||
zslab.zero_()
|
||||
if need_normal:
|
||||
nslab.zero_()
|
||||
for lo in range(s0, s1, ch):
|
||||
hi = min(lo + ch, s1)
|
||||
idx, alpha = splat(lo, hi)
|
||||
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
|
||||
lev = blevel[s0:s1] # kernel levels in this slab, sorted ascending
|
||||
pos = s0
|
||||
while pos < s1:
|
||||
ox, oy = grids[int(lev[pos - s0])]
|
||||
run_end = s0 + int(torch.searchsorted(lev, lev[pos - s0], right=True)) # contiguous same-level run
|
||||
ch = max(2048, 10_000_000 // ox.shape[0]) # splats/chunk, bounded by this level's kernel size
|
||||
for lo in range(pos, run_end, ch):
|
||||
hi = min(lo + ch, run_end)
|
||||
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))
|
||||
pos = run_end
|
||||
slab_a = 1 - torch.exp(-tau_buf) # 1 - prod(1-alpha): true opacity of the slab's splats
|
||||
front = trans * slab_a
|
||||
ainv = a_buf.clamp_min(1e-8)
|
||||
denom = wbuf if sharp else a_buf
|
||||
cacc = cacc + front[:, None] * (crgb / denom.clamp_min(1e-8)[:, None])
|
||||
if need_depth:
|
||||
dacc = dacc + front * (zslab / ainv)
|
||||
if need_normal:
|
||||
nacc = nacc + front[:, None] * (nslab / ainv[:, None])
|
||||
trans = trans * (1 - slab_a)
|
||||
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
|
||||
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:
|
||||
# Coverage-weighted Gaussian blur of the accumulated normals. Per-splat surfel normals (the thinnest
|
||||
# gaussian axis) are jittery where splats are near-isotropic, so blur (nacc) and the weight (cov)
|
||||
# together and divide -- a masked blur that smooths the noise without bleeding across the silhouette.
|
||||
# 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
|
||||
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, -yflip, -1.0]) * 0.5 + 0.5).clamp(0, 1)
|
||||
img = enc * covm[:, :, None] # black background (masked out)
|
||||
else: # color / clay
|
||||
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 * yflip, -0.6]) # key from screen upper-left, angled toward the viewer
|
||||
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
|
||||
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 + trans.reshape(height, width, 1) * bg_comp
|
||||
if do_linear: # back to display space after linear compositing
|
||||
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).cpu(), covg.clamp(0, 1).cpu()
|
||||
return img.clamp(0, 1).to(idev, idtype), covg.clamp(0, 1).to(idev, idtype)
|
||||
|
||||
|
||||
class RenderSplat(IO.ComfyNode):
|
||||
@ -901,9 +930,8 @@ class RenderSplat(IO.ComfyNode):
|
||||
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 (beauty), clay (neutral-albedo shaded - "
|
||||
"pure geometry), depth (near=bright), normal (OpenGL normal map). The mask "
|
||||
"output always carries the coverage regardless of this."),
|
||||
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 "
|
||||
@ -913,8 +941,7 @@ class RenderSplat(IO.ComfyNode):
|
||||
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")],
|
||||
outputs=[IO.Image.Output(display_name="image"), IO.Mask.Output(display_name="mask")],
|
||||
)
|
||||
|
||||
@classmethod
|
||||
@ -922,13 +949,13 @@ class RenderSplat(IO.ComfyNode):
|
||||
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)
|
||||
if bg_image is not None: # resize the plate(s) to the render size: (B,H,W,3)
|
||||
bi = comfy.utils.common_upscale(bg_image.movedim(-1, 1), 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
|
||||
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() # render device; splat stays in torch here -> no roundtrip
|
||||
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
|
||||
@ -938,7 +965,7 @@ class RenderSplat(IO.ComfyNode):
|
||||
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
|
||||
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))
|
||||
@ -959,7 +986,7 @@ class RenderSplat(IO.ComfyNode):
|
||||
return IO.NodeOutput(torch.stack(imgs), torch.stack(masks))
|
||||
|
||||
|
||||
class CreateCameraInfo(IO.ComfyNode): # TODO: move to better file
|
||||
class CreateCameraInfo(IO.ComfyNode): # TODO: move to better file
|
||||
@classmethod
|
||||
def define_schema(cls):
|
||||
return IO.Schema(
|
||||
@ -1014,24 +1041,22 @@ class CreateCameraInfo(IO.ComfyNode): # TODO: move to better file
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def execute(cls, mode, target_x, target_y, target_z, roll, fov,
|
||||
zoom=1.0, camera_type="perspective") -> IO.NodeOutput:
|
||||
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
|
||||
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)
|
||||
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
|
||||
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))
|
||||
return IO.NodeOutput(_lookat_camera_info(position, target, fov, dev, zoom=zoom, camera_type=camera_type, roll=roll))
|
||||
|
||||
|
||||
class TransformSplat(IO.ComfyNode):
|
||||
@ -1079,7 +1104,7 @@ class TransformSplat(IO.ComfyNode):
|
||||
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)
|
||||
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)
|
||||
@ -1108,7 +1133,7 @@ class GetSplatCount(IO.ComfyNode):
|
||||
@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
|
||||
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)
|
||||
|
||||
@ -1142,8 +1167,8 @@ def _merge_gaussians(gaussians: list) -> Types.SPLAT:
|
||||
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 = 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))
|
||||
@ -1198,7 +1223,8 @@ def _inverse_covariance(scale, quat):
|
||||
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):
|
||||
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
|
||||
@ -1211,11 +1237,11 @@ def _splat_density(xyz, opacity, scale, quat, rgb, res, kernel, device, color_sh
|
||||
dx, dy, dz = int(dims[0]), int(dims[1]), int(dims[2])
|
||||
|
||||
sinv = _inverse_covariance(scale, quat)
|
||||
kreq = torch.ceil(3.0 * scale.amax(-1) / voxel).long().clamp(1, int(kernel)) # per-gaussian half-width
|
||||
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) # Sum(w^p * rgb) colour numerator
|
||||
wcol = torch.zeros(dx * dy * dz, device=device) if sharp else None # Sum(w^p) colour normaliser (p>1)
|
||||
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]
|
||||
@ -1238,9 +1264,9 @@ def _splat_density(xyz, opacity, scale, quat, rgb, res, kernel, device, color_sh
|
||||
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))
|
||||
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))
|
||||
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)))
|
||||
@ -1248,9 +1274,63 @@ def _splat_density(xyz, opacity, scale, quat, rgb, res, kernel, device, color_sh
|
||||
return vol.reshape(dx, dy, dz), colvol.reshape(dx, dy, dz, 3), colnorm, lo.cpu().numpy(), float(voxel)
|
||||
|
||||
|
||||
def _clean_components(verts, faces, min_verts):
|
||||
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)
|
||||
# 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 device.type != "cpu" 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)
|
||||
@ -1261,10 +1341,9 @@ def _clean_components(verts, faces, min_verts):
|
||||
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
|
||||
fmin, fmax = verts[faces].min(1), verts[faces].max(1)
|
||||
cmin, cmax = np.full((ncomp, 3), np.inf), np.full((ncomp, 3), -np.inf)
|
||||
np.minimum.at(cmin, flabel, fmin)
|
||||
np.maximum.at(cmax, flabel, fmax)
|
||||
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)
|
||||
@ -1289,39 +1368,47 @@ def _surface_nets(vol, level, voxel, origin, device):
|
||||
return empty
|
||||
|
||||
# Active = cells whose 8 corners aren't all in/all out.
|
||||
inside = vol >= level # (dx,dy,dz) bool
|
||||
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
|
||||
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.
|
||||
ac = active.nonzero(as_tuple=False) # (nv,3) cell min-corner indices
|
||||
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)
|
||||
ci = ac[:, None, :] + offs[None] # (nv,8,3)
|
||||
cval = vol[ci[..., 0], ci[..., 1], ci[..., 2]] # (nv,8) corner values
|
||||
csl = cval >= level
|
||||
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]
|
||||
v0, v1 = cval[:, e0], cval[:, e1] # (nv,12)
|
||||
cross = csl[:, e0] != csl[:, e1]
|
||||
denom = v1 - v0
|
||||
t = torch.where(denom.abs() > 1e-12, (level - v0) / denom, torch.full_like(denom, 0.5)).clamp(0, 1)
|
||||
offf = offs.to(torch.float32)
|
||||
pts = offf[e0] + t[..., None] * (offf[e1] - offf[e0]) # (nv,12,3) local crossings
|
||||
cf = cross[..., None].to(pts.dtype)
|
||||
local = (pts * cf).sum(1) / cf.sum(1).clamp_min(1.0) # (nv,3) local vertex in [0,1]
|
||||
verts = origin_t + (ac.to(torch.float32) + local) * voxel # world space
|
||||
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 = []
|
||||
@ -1358,12 +1445,12 @@ def _otsu_level(values, bins=256):
|
||||
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
|
||||
w = np.cumsum(hist) # background-class weight at each split
|
||||
mu = np.cumsum(hist * centers)
|
||||
wf = w[-1] - w # foreground-class weight
|
||||
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 * wf * (mb - mf) ** 2 # between-class variance
|
||||
var_b[(w <= 0) | (wf <= 0)] = -1.0
|
||||
return float(centers[int(np.argmax(var_b))])
|
||||
|
||||
@ -1375,15 +1462,35 @@ def _taubin_smooth(verts, faces, iters, lam=0.5, mu=-0.53):
|
||||
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)), (e[:, 0], e[:, 1])), shape=(nv, nv)).tocsr()
|
||||
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)[:, None]
|
||||
v = verts.astype(np.float64)
|
||||
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 + fac * ((adj @ v) / deg - v) # fac * (mean(neighbours) - v)
|
||||
return np.ascontiguousarray(v.astype(np.float32))
|
||||
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
|
||||
inp = v.permute(3, 0, 1, 2).contiguous()[None].to(device=device, dtype=torch.float32)
|
||||
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):
|
||||
@ -1406,24 +1513,37 @@ def _gaussian_to_mesh(g: Types.SPLAT, i, res, kernel, taubin, level_bias, min_co
|
||||
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%
|
||||
vol_np = vol.cpu().numpy() # Sum(w) density (for the surface)
|
||||
colvol_np = colvol.cpu().numpy() # Sum(w^p * rgb) colour numerator
|
||||
colnorm_np = colnorm.cpu().numpy() # Sum(w^p) colour normaliser
|
||||
# Colour: sample on the GPU (grid_sample) when there's headroom
|
||||
colour_gpu = device.type != "cpu" 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)
|
||||
|
||||
occ = vol_np[vol_np > vol_np.max() * 1e-3] # occupied voxels (skip the empty-space peak)
|
||||
if occ.size == 0:
|
||||
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 (the old None / "no surface" bug).
|
||||
lo, hi = float(vol_np.min()), float(vol_np.max())
|
||||
level = min(max(_otsu_level(occ) * level_bias, lo + 1e-6 * (hi - lo)), hi - 1e-6 * (hi - lo))
|
||||
# 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))
|
||||
|
||||
# Surface Nets on CPU: the grid is already on CPU, and this keeps iso-surfacing off the GPU's VRAM.
|
||||
verts, faces = _surface_nets(torch.from_numpy(vol_np), level, voxel, origin, torch.device("cpu"))
|
||||
# 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 device.type != "cpu" 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)
|
||||
verts, faces = _clean_components(verts, faces, min_component, device)
|
||||
if len(verts) == 0 or len(faces) == 0:
|
||||
return None
|
||||
|
||||
@ -1434,10 +1554,13 @@ def _gaussian_to_mesh(g: Types.SPLAT, i, res, kernel, taubin, level_bias, min_co
|
||||
# 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.
|
||||
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]
|
||||
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
|
||||
@ -1462,7 +1585,7 @@ class SplatToMesh(IO.ComfyNode):
|
||||
description="Extract a coloured mesh from a gaussian splat.",
|
||||
inputs=[
|
||||
IO.Splat.Input("splat"),
|
||||
IO.Int.Input("resolution", default=384, min=64, max=1024, step=16,
|
||||
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,
|
||||
|
||||
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