97dcdfb54a
The blocker for LATO.2 on Apple Silicon is one op, not the whole setup.sh --all CUDA stack. Measured: 5 of 7 checkpoints are fully dense, and every SparseConv3d in the model is constructed stride=1/padding=None, which upstream dispatches to spconv's SubMConv3d. No strided or inverse sparse conv is ever instantiated. - SubMConv3d in pure MLX via a sorted-key indice map (27 lookups/voxel vectorised, cached per coordinate set the way spconv uses indice_key) - SparseTensor container + subdivide upsampling - Converter handles the 5-D layout collision: spconv KRSC [O,kz,ky,kx,I] vs torch Conv3d [O,I,kz,ky,kx]. Rank alone is ambiguous; misreading it silently mangles the voxel encoder. - 7/7 tests pass vs an independent naive reference, max err 3e-7. spconv has no Metal build so there is no upstream oracle; the reference shares no indexing code. Kernel orientation (feats[c+d] vs feats[c-d]) remains unverified and is silent when wrong; --flip-kernel builds the mirror for an end-to-end A/B.
3.4 KiB
lato.2_mrp_mlx
An MLX port of LATO.2 — factorised 3D mesh generation (vertex flow, then connectivity flow) — so it runs natively on Apple Silicon.
Why
LATO.2 generates meshes to a controllable vertex budget, which is the interesting part:
it sidesteps the generate-dense-then-decimate loop that TRELLIS-style pipelines force on
you. But upstream inherits TRELLIS.2's setup.sh and hard-requires CUDA:
# upstream modules/sparse/__init__.py
BACKEND = 'spconv' # accepts only ['spconv', 'torchsparse'] — both CUDA-only
ATTN = 'flash_attn' # accepts only ['xformers', 'flash_attn'] — both CUDA-only
No SDPA fallback, no MPS path. Neither sparse backend has a Metal build.
The scope, once measured
The CUDA surface is far smaller than setup.sh --all implies. Of the seven released
checkpoints, five are entirely dense. Sparse code touches only vertex_autoencoder and
vertex_structured_flow, and every SparseConv3d in the model is constructed with the
defaults stride=1, padding=None — which upstream dispatches to SubMConv3d.
So the whole blocker is one operation: submanifold 3×3×3 convolution.
| Upstream | Here |
|---|---|
spconv.SubMConv3d |
lato_mlx/sparse/conv.py — gather/scatter over a sorted-key indice map |
SparseInverseConv3d |
never instantiated upstream; not needed |
| strided sparse conv | never used; upsampling is SparseSubdivide (coord expansion ×8) |
nn.Conv3d (voxel encoder) |
dense, maps to mlx.nn.Conv3d |
flash_attn / xformers |
attn_mode="full" everywhere → plain SDPA |
Status
SparseTensorcontainer +subdivideSubMConv3d(k=3 and k=1) — 7/7 correctness tests pass, max err 3e-7 vs an independent naive reference- Weight converter, all 7 checkpoints → MLX safetensors (3.3 GB)
- Remaining sparse ops:
SparseLinear,SparseGroupNorm32, activations, attention - Model graphs: V-VAE, V-Flow, T-VAE, T-Flow, encoders
- End-to-end inference
- Fleet benchmark (m1max / m2max / m4pro / m1ultra / m3ultra)
Correctness
spconv cannot be installed here — that is the reason this port exists — so there is no
numerical diff against upstream. Instead tests/test_sparse.py checks the vectorised
implementation against a deliberately naive one written straight from the definition
(dict lookup, per-voxel loop). They share no indexing code, so an off-by-one cannot hide
in both. Tests also cover batch isolation, isolated voxels, and indice-map hit rates.
One assumption remains unverified: whether spconv gathers feats[c+d]
(cross-correlation — the deep-learning convention, and what this implements) or
feats[c-d]. A flipped kernel is numerically silent. It gets settled end-to-end: the
V-VAE is an autoencoder, so a clean reconstruction confirms the orientation. Run the
converter with --flip-kernel to test the alternative without touching code.
Use
uv venv --python 3.12 .venv
VIRTUAL_ENV=.venv uv pip install mlx numpy torch trimesh
hf download 0x4c48/LATO.2 --local-dir ckpt # 3.3 GB upstream weights
.venv/bin/python -m lato_mlx.convert --ckpt ckpt --out weights
.venv/bin/python tests/test_sparse.py
Upstream source is vendored read-only under upstream/LATO.2 for reference.
Licence
Upstream LATO.2 is MIT (Copyright the LATO.2 authors); its sparse module carries Microsoft and VAST-AI-Research copyright, also MIT. This port is MIT on the same terms.