The structure stage now runs end to end on Metal: noise -> ss_flow (12-step Euler)
-> latent -> ss_dec -> 64^3 occupancy grid. 759 ms/step for the 1.3B DiT, 50 ms for
the decoder. Output lands at 1.36% occupancy, which is the right order for a surface
in a 64^3 grid.
Sampler details worth recording, both from upstream:
- CFG is a LERP (g*pos + (1-g)*neg), NOT neg + g*(pos-neg). Those differ non-linearly
in strength rather than failing outright, so it is silent when wrong. Tested.
- Guidance interval forces strength to 1 outside its window, which halves model calls
there: 6 calls for 4 steps rather than 8. Tested by counting.
Schedule matches upstream to 1e-16 and constant velocity integrates exactly at any
step count.
Remaining for a real image->3D run is CONDITIONING, not models: DINOv3 features plus
Pixal3D's camera back-projection for the view-aligned 'proj' half. Deliberately not
porting DINOv3 - it is a stock ViT run once per image, outside the denoising loop, so
torch on MPS is the right tool and transformers gives exact parity for free.
All seven Pixal3D models now load and run. shape_dec 292/292, tex_dec 284/284, both
with zero missing/unmapped/mismatched keys - the 8-param difference between them is
exactly the four to_subdiv layers, since tex_dec has pred_subdiv=False.
Behaviour is right: 12 latent voxels grow SELECTIVELY through four stages
(12 -> 91 -> 193 -> 358 -> 1150) rather than x8 each time, which would have reached
49,152. Scale lands at 1/16. shape_dec emits the hardcoded 7 channels and its vertex
head produces offsets inside the [-0.5, 1.5] band its sigmoid+voxel_margin allows.
tex_dec, guided by shape_dec's masks, reproduces exactly the same voxel count.
VERIFICATION CAVEAT, recorded in the README: these two are the only models using sparse
conv, so upstream cannot run here and there is no numerical oracle. Unlike the five
models verified at correlation 1.0, these are checked structurally and behaviourally
only. Weaker evidence, and labelled as such rather than presented alongside the
verified results.
slat_flow joins ss_flow: max abs diff 9.3e-6, 700/700 params, against upstream running
on CPU torch. All three SLAT checkpoints load clean and run (img2shape 512/1024 and
imgshape2tex, the last taking 64 in-channels since shape is concatenated).
The SLAT flows differ from ss_flow in two ways, both handled in the shared core:
tokens are a SparseTensor's voxels so attention runs per batch item, and RoPE phases
are NOT shipped - positions are the input's own coordinates, so they are derived at
call time by rope_phases_from_coords.
tests/oracle_slat.py keeps the CPU-torch oracle harness: it patches both the dense and
sparse flash-attn kernels with SDPA equivalents. Use it rather than reasoning about
correctness - it has already caught three bugs a perfect 700/700 key match did not.
