79ef81988c
2 Commits
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m3ultra
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79ef81988c |
Real image -> occupancy grid, with a silhouette check and honest timings
image_to_occupancy() runs the structure stage on an actual photo: preprocess ->
DINOv3 -> proj back-projection -> ss_flow -> ss_dec -> 64^3 occupancy.
VERIFICATION THAT MATTERS: scripts/run_structure.py re-projects the occupied voxels
through the same camera and compares against the input alpha matte. On the upstream
sample that is silhouette IoU 0.842 with 12948 voxels occupied (4.94% of 64^3). This
is the model's own headline claim, so it is the right thing to assert — 'it ran
without crashing' would pass just as happily on a generic blob.
Two real bugs this phase found, neither visible without reading the shipped configs:
1. THE SAMPLER WAS MISSING guidance_rescale. The checkpoint's own pipeline.json sets
0.7 for the structure stage and 0.5 for shape_slat, so this fires at the model's
DEFAULT settings — omitting it silently overcooks every structure prediction. Now
implemented (Lin et al. CFG rescale) and diffed against upstream's
ClassifierFreeGuidanceSamplerMixin, run directly rather than reimplemented.
2. The sampler defaults were wrong: the real ss stage is steps=12 / rescale_t=5.0 /
guidance 7.5 / interval [0.6,1.0], not the steps=25 / rescale_t=3.0 the smoke test
assumed. All three stages' real params now live in pipeline.py, read from
pipeline.json rather than guessed.
TIMINGS, measured with interleaved reps after warmup (the first pass attributed the
same 11s of residual warmup to both 'rescale' and 'torch contention'; it was neither):
cold run 89.3s
warm, full settings 16.5s
warm, CFG off 9.2s -> CFG costs 1.80x, as expected for 10/12
steps falling inside the guidance interval
guidance_rescale ~0s -> free
torch/MPS contention ~0s -> DINOv3 can stay resident
peak memory 6.8GB
THE FINDING THAT SHAPES THE OPERATOR: warmup is ~71s against ~17s of actual compute,
i.e. 4x the work. A MODELBEAST operator MUST hold the models resident across jobs
rather than fork per job — the trellis2 lane shows the same shape (47.9s cold vs 2.5s
warm pipeline_load). Cost this in before optimising any kernel.
17/17 tests green (12 proj + 5 sampler).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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m3ultra
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bd0f3f1941 |
Proj conditioning: camera back-projection, grid_sample, DINOv3 (12/12 vs upstream)
The pixel-aligned conditioning is the ONLY thing separating this port from the
trellis2_mlx operator already in MODELBEAST — upstream's main branch is the
TRELLIS.2 backbone, so everything else here is TRELLIS.2 with a different head.
This lands that head.
proj.py ProjGrid, project_points, bilinear_sample, distance_from_fov — MLX
dino.py DINOv3 ViT-L/16 left in torch on MPS (run once per image, outside the
25-step loop; transformers gives exact parity for free)
cond.py encode_image_proj equivalent -> {'global','proj'} + zero uncond
The extractor has no sparse conv, so upstream RUNS on CPU torch here and is a real
oracle. All 12 checks diff against it, not against a transcription:
bilinear_sample vs grid_sample max diff 2.4e-07 corr 1.00000000
project_points pixels/depth/mask exact
ProjGrid forward (ss, 16^3) max diff 1.9e-05 corr 1.00000000
extractor global tokens max diff 0.0e+00 corr 1.00000000
extractor proj features max diff 4.8e-06 corr 1.00000000
Three details that a plain transcription gets wrong and eyeballing cannot catch:
grid_sample's align_corners=False maps a normalised coord to ((c+1)*size-1)/2, not
(c+1)/2*(size-1) — half a texel, invisible until you compare; padding_mode='border'
clamps the SOURCE INDEX before corners are taken, not the corners after, which
changes the weights on every silhouette edge (tested with deliberately out-of-range
grid coords); and the camera looks down -Z, so a sign slip still yields a plausible
grid that samples the mirror image.
Also corrects a shape assumption from the earlier smoke test: 'global' is CLS + 4
register tokens = [B,5,1024], NOT the 1370 image tokens. The patch tokens go to the
proj branch. That asymmetry IS the architecture.
Note the parameterless final layer_norm in extract_features — not model.norm, which
has weights. Same trap as the ss_flow bug: no checkpoint trace, 200x output error.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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