modelbeast/BENCHMARKS.md

MODELBEAST Benchmarks (M3 Ultra, 256GB)

First measurements on this machine, recorded 2026-07-12. Fixtures are synthetic (Blender-rendered Suzanne), so quality numbers are not representative of real photography — these validate that the pipeline runs and how fast.

Scan track (fully validated, no gated weights)

Stage	Input	Settings	Result
colmap_poses	48 frames @ 800×600	sequential matcher, global (GLOMAP) mapper, OPENCV, CPU SIFT	48/48 images registered, 1479 points, 0.60px mean reprojection error; global mapper step ~1.0s; full job a few seconds
brush_train	above colmap_dataset	1500 steps, max_res 800, sh 2	~30–60s wall, 465KB splat.ply, renders in the in-app SplatViewer

A full-quality brush_train run is 30000 steps (the default) — expect minutes, and a much crisper splat than the 1500-step preview above.

Image generation — local FLUX lineup (M3 Ultra, mflux/MLX, 1024×1024, validated 2026-07-13)

All five installed and generating. Warm = weights cached (the real per-image cost):

Model	Steps	Warm gen	Peak MLX mem	Gated?	Best for
FLUX.2 Klein 4B	4	9.1s	18.0 GB	no (Apache)	volume / sprites — the default
FLUX.1 schnell-4bit	4	18.5s	19.1 GB	no (community quant)	fast draft, FLUX.1 look
FLUX.2 Klein 9B	4	18.7s	28.4 GB	yes	hero assets — best object accuracy of the whole lineup, cloud included
FLUX.1 schnell	4	20.4s	25.0 GB	yes	fast draft (Klein 9B beats it at same speed)
FLUX.1 dev	25	108.1s	25.1 GB	yes	cinematic mood / DoF when you can wait ~2 min

First-run downloads (one-time): schnell/dev ~31GB & ~16–19 min each, Klein 9B ~32GB, Klein 4B ~15GB. Believed among the first published M3 Ultra mflux FLUX.1/FLUX.2 numbers.

Sweet spot: Klein 9B. Same ~19s as schnell but far better object coherence → it dominates schnell. Klein 4B when speed matters (2× faster), dev only when you want the cinematic atmosphere. Note the hf_xet chunked downloader fails on these repos ("Unable to parse string as hex hash value") — the operator sets HF_HUB_DISABLE_XET=1 to force the reliable HTTP path.

First A/B (same prompt, 2026-07-12): FLUX.2 Klein 4B local (8s, $0) vs nano-banana via OpenRouter (google/gemini-2.5-flash-image, 7.7s, $0.0387 exact-billed). Klein: cleaner product-photo subject. nano-banana: richer scene dressing (books/inkwell/quill, dust motes) + finer engraving detail. Verdict: Klein is the volume workhorse; nano-banana wins on scene storytelling per prompt-adherence expectations (Elo 1154 vs ~1083).

Mesh-gen (local, validated 2026-07-13)

Operator	Config	Result
sf3d	image → GLB, MPS, tex 1024	~5s, ~9GB peak, 1.5MB GLB. Fast draft tier — good on solid objects, struggles on thin/open geometry. Needs OMP_NUM_THREADS=1+KMP_DUPLICATE_LIB_OK (segfaults otherwise).
trellis_mac	TRELLIS.2-4B, pipeline 1024, tex 2048, MPS	289s (~4.8 min) generation + 16s bake, 18.4MB GLB with PBR. SOTA-tier local quality — clean coherent geometry even on a thin-ringed astrolabe (dramatically better than SF3D). First run adds a one-time ~15GB download (~30 min); cached after. Needs HF_HUB_DISABLE_XET=1 + the OMP guards.
hunyuan3d_mlx	Hunyuan3D 2.1, native MLX (fp16), shape+PBR, tex 2048, remesh 40k	260s (~4.3 min) total (shape 149s + texture 112s), peak 20.2GB, 7.6MB GLB (40k faces, 2048² baseColor+MR PBR). Weights public — no HF login. Needs diffusers+fast_simplification in the venv. MLX-native → the one local 3D op that runs on M1 Ultra (trellis_mac's torch-MPS bf16 is unverified there).
bg_remove_local	RMBG-2.0, MPS, 1024	seconds; clean transparent cutout. Run before SF3D for a big geometry improvement. Note: keeps original RGB under alpha (upscale before cutout).

Head-to-head, same mermaid cutout (M3 Ultra, 2026-07-16)

	speed	GLB	faces	face/detail quality
trellis_mac	318s	23MB	175,842	sharper — defined eyes/nose/mouth, individually raised tail scales, vivid colors
hunyuan3d_mlx	260s	7.6MB	40,000	softer — melted face, smoothed scales, muted texture

Verdict: at defaults trellis_mac wins on quality (crisper face + geometry, richer color); hunyuan3d_mlx is faster, ~3× lighter, and the only local 3D op that runs on M1. sf3d stays the ~5s draft tier. All free/offline; fal cloud for on-demand SOTA without the local wait.

Phase D — hunyuan Studio tuning (2026-07-16, M3 Ultra) → new operator defaults

Raised the config from the laptop-tuned defaults to octree_resolution 384 + remesh_faces 120000 + texture_size 4096. 4096² bake works on the Studio GPU — no Metal command-buffer watchdog (the existing extract_textiles tiling handles it; uv_feature_map never needed patching). Result: 380s (shape 160 + tex 221), peak 20.2GB, 21.5MB GLB, 78k verts / 120k faces, 4096² baseColor+MR. Quality jump is real — the melted face gains defined eyes + structure, tail geometry sharpens, textures crisper; closes most of the gap to trellis (trellis still edges the face). Cost: ~46% slower + ~3× file size vs the 40k/2048 default. These are now the hunyuan3d_mlx operator defaults (all still param-overridable; drop to remesh_faces 40000/texture_size 2048 for fast drafts). 4096 on the M1 Ultra: verifying.

Mesh-gen — earlier install notes (superseded by the table above)

Operator	Install	Runtime status
sf3d	venv + Metal texture_baker/uv_unwrapper kernels compiled OK; torch 2.13 MPS available	Runs end-to-end; weights gated — stabilityai/stable-fast-3d returns GatedRepoError until the owner accepts the license + sets an HF token. Then expect seconds-to-a-minute on MPS.
trellis_mac	setup.sh built .venv (py3.11) + mtl* Metal kernels; torch 2.13 MPS available	Runs end-to-end; weights gated — needs HF access to facebook/dinov3-vitl16-pretrain-lvd1689m + briaai/RMBG-2.0. Expect ~3–5 min/gen once authed (M4 Pro reference; M3 Ultra should match or beat).
fal_* (trellis / trellis2 / hunyuan3d / rodin)	none (API)	Gated on FAL_KEY. Verified param surfaces; ~1s–1min server-side per fal docs.

To unblock the gated local operators

1. Accept the model licenses on HuggingFace (one-time, usually instant):
   • https://huggingface.co/stabilityai/stable-fast-3d
   • https://huggingface.co/facebook/dinov3-vitl16-pretrain-lvd1689m
   • https://huggingface.co/briaai/RMBG-2.0
2. Either huggingface-cli login on the machine, or paste an HF token into Settings → "HuggingFace token" (injected as HF_TOKEN for the operators).

Method

Timings are wall-clock from the job runner (started_at→finished_at), single job at a time (gpu lane = 1). Re-run tests/smoke.sh for the framework regression suite (12 checks, ~30s).