# 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. |
| `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). |

**Local image→3D verdict:** `trellis_mac` for hero assets (~5 min, SOTA), `sf3d` for fast drafts (~5s). Both free/offline; fal cloud (`fal_trellis2` etc.) for on-demand SOTA without the local wait.

## 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).