Fast-SAM3D +  ·  HiCache

Image-to-3D, faster — Fast-SAM3D with a tree-aware HiCache cache on its slat-stage flow-matching.

Skip the dynamics network on most slat-stage solver steps and forecast the cached velocity with a scaled-Hermite polynomial. Training-free, geometry-preserving, native (no monkey-patching).

         

When to use this repo

These repos are complementary accelerators, not competing solutions — each speeds up a different base generator, and the + / ++ suffix is a method choice, not a rival product. Pick by (1) which base model you run, then (2) which forecast basis you want:

base generator	+ = HiCache (Hermite)	++ = HiCache++ (DMD)
Hunyuan3D-2.1	hunyuan2.1-plus	hunyuan2.1-plus-plus
Hunyuan3D-2 mini	hunyuan2-plus	hunyuan2-plus-plus
SAM 3D Objects	sam3d-plus	sam3d-plus-plus
Fast-SAM3D	fastsam3d-plus	fastsam3d-plus-plus
TRELLIS (v1)	faster-trellis	faster-trellis-plus-plus
TRELLIS.2-4B (v2)	hermit-trellis2	hermit-trellis2-plus-plus

• + (HiCache / scaled-Hermite): the published polynomial velocity-forecast basis — conservative, reproduces the HiCache paper. Use it to deploy the established method.
• ++ (HiCache++ / DMD exponential): our Dynamic-Mode-Decomposition basis — the same near-lossless quality at wider skip intervals, where the polynomial diverges. Use it when you push the cache interval for more speed.
• standalone / model-agnostic: hicache-plus-plus — the forecaster itself, to add DMD caching to your own diffusion/flow model.
• fast-trellis2 = the TaylorSeer baseline fork (the upstream "Fast" accel) — the v2 reference point, not a HiCache variant.

This repo: fastsam3d-plus — Fast-SAM3D × HiCache (Hermite) — on the TaylorSeer-cached Fast-SAM3D pipeline.

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What it is

A fork of Fast-SAM3D (single-image → textured 3D mesh, itself a TaylorSeer-style accelerated SAM3D) that adds HiCache — a feature cache — to the slat-stage flow-matching sampler.

SAM3D generates in two flow-matching stages; the second (slat) stage integrates an ODE dx/dt = v_θ(x, t) with a Euler solver, where each v_θ call is an expensive DiT forward. HiCache runs the network on a sparse schedule and, on every skipped step, forecasts the (CFG-combined) velocity from cached anchors instead of calling v_θ. SAM3D's velocities are torch.utils._pytree structures (not single tensors), so this fork's HiCache is tree-aware: the Hermite/finite-difference coefficients are scalars shared across all leaves, and a forecast is one tree_map per order. The companion Adaptive-CFG drops the unconditional pass once it aligns with the conditional one. Wiring is native — the solver and CFG modules call the helpers directly; there is no runtime patching.

Method — scaled-Hermite velocity forecasting

HiCache (arXiv:2508.16984) caches the velocity at compute steps and extrapolates it to skipped steps with a dual-scaled physicist's Hermite polynomial H̃ₙ(x) = σⁿ Hₙ(σx). From the cached anchors it forms finite-difference "derivatives" Δ⁰…Δᵐ of the velocity tree and predicts the velocity k steps past the last compute step as a Hermite-weighted sum of those derivatives. The Hermite basis tempers the high-order extrapolation that makes a plain Taylor (monomial) cache diverge, so it stays lossless at a larger skip interval than TaylorSeer — but, being a polynomial, it still drifts as the skip grows. The exponential successor that pushes the lossless skip further lives in fastsam3d-plus-plus.

Enable it (real API)

The cache attaches to the slat-stage FlowMatching generator's Euler solver. The model methods are chainable (they return the model):

# `fm` is the slat-stage FlowMatching generator inside the Fast-SAM3D pipeline.

# HiCache: forecast the velocity on skipped slat steps with the scaled-Hermite polynomial.
fm.enable_hicache(
    interval=3,        # run the DiT 1 step in `interval`; forecast the other (interval-1)
    max_order=1,       # highest finite-difference order used in the Hermite forecast
    first_enhance=2,   # always run full for the first N steps (warm-up)
    end_enhance=None,  # always run full for the final steps (defaults to the last step)
    sigma=0.5,         # Hermite scale σ ∈ (0,1)
)

# ... run the pipeline / sampler as usual ...

fm.disable_hicache()   # back to the dense (uncached) schedule

Under the hood enable_hicache stores the config on the Euler solver (ODESolver.enable_hicache); on each step the solver calls hicache_decide and, when it returns "forecast", replaces the dynamics_fn (DiT) call with hicache_forecast_tree — see accel.py, solver.py, and model.py.

Results

On Fast-SAM3D's slat-stage FlowMatching, HiCache (Hermite) is geometry-lossless (F1 = 1.000) out to interval-3 (~1.4× over the uncached slat schedule). Past interval-3 the polynomial basis starts to drift. For the exponential (DMD) forecaster that holds quality two intervals further on the same FlowMatching substrate, see fastsam3d-plus-plus and the standalone library hicache-plus-plus.

The Hermite ⇄ Taylor swap on the SS stage is a wash — that stage already runs a fixed TaylorSeer stride, so the basis change there doesn't move latency. HiCache's gain is on the slat stage, where it forecasts the skipped velocities.

Sign-convention update (2026-06-10)

The vendored Hermite forecast evaluated the basis at x = -k; the corrected convention from hicache-plus-plus 1.2.0 is x = +k (the upstream TaylorSeer distance convention; -k flips every odd-order term, extrapolating backwards). This fork now ships the corrected forecast in both Hermite sites (flow_matching/accel.py, the slat-stage forecaster, and forecast_basis.py, the pluggable SS-stage basis). The published numbers above were measured with the as-released code and remain valid as-measured.

The slat-stage result has been re-validated with the corrected forecast on the published protocol (this repo's runnable InferencePipelinePointMap, real weights, seed 42, F1@0.05 vs the uncached baseline): corrected HiCache i3/o2 stays F1 = 1.000 (CD 0.0121 vs 0.0125 as-released), and wider probes i5/o3 and i6/o3 also hold F1 = 1.000 under both conventions. Verdict: same at the published interval. The corrected-vs-as-released table is in sam3d-plus (identical vendored port, same harness). Re-validation of the SS-stage Taylor-vs-Hermite wash with the corrected basis is pending.

Attribution

• Fast-SAM3D — © wlfeng0509 (arXiv:2602.05293); built on SAM3D (© Meta). The upstream README is preserved in this fork's git history and its license/attribution is unchanged. The SAM 3D weights this fork runs on remain under the Meta SAM License — research/responsible-use, publication-acknowledgement, and trade-control / ITAR / sanctions restrictions apply; it is not permissive or unconditionally commercial.
• HiCache — scaled-Hermite velocity forecasting, arXiv:2508.16984. This fork is a clean tree-aware reimplementation on Fast-SAM3D's slat flow-matching.
• Adaptive-CFG — Adaptive Guidance, arXiv:2312.12487.
• HiCache++ (the exponential successor) — fastsam3d-plus-plus · standalone hicache-plus-plus.

Citation

If you use this fork, please cite the base model and the acceleration methods it builds on.

Fast-SAM3D (base model):

@misc{feng2026fastsam3d3dfyimagesfaster,
      title={Fast-SAM3D: 3Dfy Anything in Images but Faster}, 
      author={Weilun Feng and Mingqiang Wu and Zhiliang Chen and Chuanguang Yang and Haotong Qin and Yuqi Li and Xiaokun Liu and Guoxin Fan and Zhulin An and Libo Huang and Yulun Zhang and Michele Magno and Yongjun Xu},
      year={2026},
      eprint={2602.05293},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2602.05293}, 
}

HiCache (scaled-Hermite velocity forecasting):

@misc{hicache2025,
      title={HiCache: Training-free Acceleration of Diffusion Models via Hermite Polynomial Feature Forecasting},
      eprint={2508.16984},
      archivePrefix={arXiv},
      year={2025}
}

TaylorSeer (the cache the base model accelerates with):

@misc{taylorseer2025,
      title={From Reusing to Forecasting: Accelerating Diffusion Models with TaylorSeers},
      eprint={2503.06923},
      archivePrefix={arXiv},
      year={2025}
}

Adaptive Guidance:

@misc{adaptiveguidance2023,
      title={Adaptive Guidance: Training-free Acceleration of Conditional Diffusion Models},
      eprint={2312.12487},
      archivePrefix={arXiv},
      year={2023}
}

Weights & data

Model weights and demo/example assets are not committed to this repo — only the acceleration architecture (code + integration). Download the base-model weights from the upstream project, wlfeng0509/Fast-SAM3D, per its instructions, and point the loader at them (see the code / upstream README). This keeps the repository lightweight and avoids redistributing third-party weights.

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Family

Part of the HiCache++ acceleration family.

• Family hub: hicache-plus-plus — the basis library behind this adapter.
• Sibling: fastsam3d-plus-plus — the same base model with the HiCache++ (Dynamic Mode Decomposition / Prony) exponential-forecast variant.