c42f066723
- run.py: force HF_HUB_DISABLE_XET=1 (xet chunked downloader fails on BFL repos with 'Unable to parse string as hex hash value'; HTTP path is reliable) - BENCHMARKS.md: warm generation times for all 5 local FLUX models on M3 Ultra (Klein 4B 9.1s, schnell-4bit 18.5s, Klein 9B 18.7s, schnell 20.4s, dev 108s). Verdict: Klein 9B = best hero-asset quality (beats schnell at same speed), Klein 4B = volume workhorse. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
53 lines
4.1 KiB
Markdown
53 lines
4.1 KiB
Markdown
# MODELBEAST Benchmarks (M3 Ultra, 256GB)
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First measurements on this machine, recorded 2026-07-12. Fixtures are synthetic
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(Blender-rendered Suzanne), so quality numbers are not representative of real
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photography — these validate that the pipeline *runs* and how fast.
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## Scan track (fully validated, no gated weights)
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| Stage | Input | Settings | Result |
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|---|---|---|---|
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| `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 |
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| `brush_train` | above colmap_dataset | 1500 steps, max_res 800, sh 2 | ~30–60s wall, 465KB splat.ply, renders in the in-app SplatViewer |
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A full-quality `brush_train` run is 30000 steps (the default) — expect minutes,
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and a much crisper splat than the 1500-step preview above.
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## Image generation — local FLUX lineup (M3 Ultra, mflux/MLX, 1024×1024, validated 2026-07-13)
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All five installed and generating. **Warm** = weights cached (the real per-image cost):
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| Model | Steps | Warm gen | Peak MLX mem | Gated? | Best for |
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|---|---|---|---|---|---|
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| **FLUX.2 Klein 4B** | 4 | **9.1s** | 18.0 GB | no (Apache) | volume / sprites — the default |
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| FLUX.1 schnell-4bit | 4 | 18.5s | 19.1 GB | no (community quant) | fast draft, FLUX.1 look |
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| **FLUX.2 Klein 9B** | 4 | **18.7s** | 28.4 GB | yes | **hero assets — best object accuracy of the whole lineup, cloud included** |
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| FLUX.1 schnell | 4 | 20.4s | 25.0 GB | yes | fast draft (Klein 9B beats it at same speed) |
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| FLUX.1 dev | 25 | 108.1s | 25.1 GB | yes | cinematic mood / DoF when you can wait ~2 min |
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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.
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**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.
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**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).
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## Mesh-gen (installed; first real run blocked on owner HuggingFace auth)
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| Operator | Install | Runtime status |
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|---|---|---|
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| `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. |
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| `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). |
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| `fal_*` (trellis / trellis2 / hunyuan3d / rodin) | none (API) | Gated on `FAL_KEY`. Verified param surfaces; ~1s–1min server-side per fal docs. |
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## To unblock the gated local operators
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1. Accept the model licenses on HuggingFace (one-time, usually instant):
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- https://huggingface.co/stabilityai/stable-fast-3d
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- https://huggingface.co/facebook/dinov3-vitl16-pretrain-lvd1689m
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- https://huggingface.co/briaai/RMBG-2.0
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2. Either `huggingface-cli login` on the machine, or paste an HF token into
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Settings → "HuggingFace token" (injected as `HF_TOKEN` for the operators).
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## Method
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Timings are wall-clock from the job runner (`started_at`→`finished_at`), single
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job at a time (gpu lane = 1). Re-run `tests/smoke.sh` for the framework
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regression suite (12 checks, ~30s).
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