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TR-2026-0012技术报告同行评审: 未经同行评审证据强度: 实验性状态: 已发布

Fully-sovereign text-to-video on Apple Silicon (Wan2.1), parity to diffusers

Ran Tao (Octoryn Research)

本文未经同行评审。请将其视为工作文档,而非经验证的结果。

摘要

A fully-sovereign text-to-video pipeline for Wan2.1-T2V-1.3B on Apple Silicon: a prompt string runs end-to-end in-process (tokenizer, UMT5 encode, UniPC schedule, DiT denoise with CFG, 3D-VAE decode to frames), every linear via Apple's Accelerate BLAS, no third-party ML framework (no diffusers, no torch) in path. Against an fp32 diffusers reference under matched seed/schedule it reaches per-step latent cosine 1.0 and frames cosine 1.0 at PSNR 73.3 dB. A single-prompt numerical-parity check, not a throughput, quality, or larger-variant claim; the naive 3D-VAE causal conv dominates wall time.

本 Research Object 以其原始语言(英文)发表。

Contribution

Claimed. We demonstrate a fully-sovereign text-to-video inference pipeline for Wan2.1-T2V-1.3B on Apple Silicon in which a prompt string is processed end-to-end in-process — tokenizer -> UMT5 text encode -> UniPC multistep schedule -> DiT denoise with classifier-free guidance (CFG) -> 3D-VAE decode -> frames — with every linear running through Apple's Accelerate BLAS and no third-party ML framework (no diffusers, no torch) in the path. Against an fp32 diffusers reference with matched seed and schedule, this pipeline reaches per-step latent cosine 1.0 across the denoise schedule and output frames cosine 1.0 at PSNR 73.3 dB.

NOT claimed. This is a numerical-parity / correctness-validation result, not a novel algorithm and not a performance result. We do not claim throughput or latency competitiveness — the 3D-VAE causal convolution is a naive implementation and dominates wall time. We do not claim perceptual generation quality beyond the measured numeric parity (cosine/PSNR against the reference is not a human-quality judgment). We do not claim coverage of larger Wan variants, longer clips, or other video models/backends. The altitude here is an engineering re-implementation validated for bit-faithful agreement with an established reference, not a research advance in modeling.

Methods

FieldValue
HardwareApple Silicon Mac (M-series)
StackApple Accelerate BLAS for all linears; recent Swift toolchain
Oracle / baselineAn fp32 diffusers reference pipeline (recent diffusers / transformers / torch versions)
ModelWan2.1-T2V-1.3B
ProcedureIn-process pipeline: prompt string -> tokenizer -> UMT5 text encode -> UniPC multistep schedule -> DiT denoise w/ CFG -> 3D-VAE decode -> frames; compared per-step latent and final frames against the fp32 diffusers reference under matched seed/schedule
Precisionfp32 throughout (sovereign linears use Accelerate BLAS, same fp32 math as the reference)
Metric + gateCosine similarity (latent per-step and frames) and PSNR (frames); no explicit numeric gate threshold recorded in source notes
Stimulus / inputsSingle prompt-string -> bounded decode; exact prompt text and step count not recorded in source notes

Results

MeasurementValue
Per-step latent cosine vs fp32 diffusers reference1.0 (across the denoise schedule)
Output frames cosine vs fp32 diffusers reference1.0
Output frames PSNR vs fp32 diffusers reference73.3 dB
Slowest operation3D-VAE causal convolution (naive; dominates wall time on a bounded decode)
UMT5 encoder linear refactorstrided cache-hostile loop -> Accelerate BLAS matmul; per-layer cosine stayed 1.0 (no parity change)

Threats to validity / Limitations

  • Sample size is a correctness smoke test, not a benchmark. The parity is established over a single prompt / bounded decode and a single denoise schedule. This validates numerical agreement, not behavior across a prompt distribution, seeds, resolutions, or clip lengths.
  • Single run, no variance. Cosine/PSNR figures are reported as point values with no repetition statistics, confidence intervals, or seed sweep recorded in source notes. There are no wall-time numbers recorded at all, so even the qualitative "VAE conv is slowest" claim is not backed by tabulated timings in the source.
  • Parity != quality. Cosine 1.0 / PSNR 73.3 dB measures agreement with the fp32 diffusers reference. It says nothing about perceptual or generation quality; if the reference itself is imperfect, this pipeline faithfully reproduces those imperfections.
  • Reference-bounded. All claims are relative to one specific fp32 diffusers configuration with matched seed/schedule. The exact reference version and the exact scheduler/step settings are not recorded in source notes — so the parity is only as trustworthy as that under-specified oracle.
  • Scope / quantization bounds. Validated only for Wan2.1-T2V-1.3B at fp32 on Apple Silicon. No claim holds for larger Wan variants, quantized paths, longer clips, or non-Accelerate backends.
  • Hardware/version under-specification. The exact Apple Silicon chip, RAM, OS, toolchain, and reference framework versions are not recorded in source notes, which limits independent reproducibility.
  • Evidence level. The source describes the parity result but does not, in these notes, document an independent second-party reproduction with full version pins. A skeptical reader should treat the level as "validated against an under-specified reference" until the pins and a re-run are recorded.

Reproducibility / Provenance

  • Build / run commands: standard Swift release build and test.
  • Sovereignty: zero third-party ML framework calls (Accelerate BLAS only; no diffusers, no torch).
  • Version pins: recent Swift toolchain and recent diffusers / transformers / torch on the oracle side; OS, Accelerate, and Wan2.1 weights revision not recorded in source notes.
  • Validate-the-oracle caveat: the parity is meaningful only if the fp32 diffusers reference is itself correctly configured (matched seed, scheduler, step count, fp32 throughout). Reproducers should pin and verify the reference configuration before trusting the cosine/PSNR figures.
  • Known refactor of record: the UMT5 encoder linear was moved from a strided cache-hostile loop to an Accelerate BLAS matmul; per-layer cosine remained 1.0, confirming the optimization did not perturb parity.

声明边界

作者对范围的明确界定——本工作证明了什么、未证明什么——沿用自 Octoryn Research 的发表模型。

证明

  • A fully-sovereign Apple-Silicon text-to-video pipeline (string to tokenizer to UMT5 to UniPC to DiT+CFG to 3D-VAE) reaches per-step latent cosine 1.0 and output-frames cosine 1.0 / PSNR 73.3 dB versus an fp32 diffusers reference under matched seed and schedule.
  • The whole inference path uses Apple's Accelerate BLAS only, with no third-party ML framework (no diffusers, no torch) anywhere in the path.

未证明

  • Throughput or latency competitiveness: the 3D-VAE causal convolution is a naive implementation and dominates wall time, and no timings are tabulated.
  • Larger Wan variants, quantized paths, or longer clips.
  • Perceptual or generation quality beyond the measured numeric parity against the reference.
  • Behavior beyond a single prompt and single denoise schedule: this is a single-run correctness smoke test with no variance, seed sweep, or confidence intervals.

适用于

  • Sovereign Wan2.1-T2V-1.3B fp32 inference on Apple Silicon, evaluated for numerical parity against an fp32 diffusers reference with matched seed and schedule.

不适用于

  • Performance or throughput claims, other video models or backends, quantized or larger Wan variants, longer clips, or any differently-configured or unpinned diffusers reference.

作者

  • Ran Tao — 调查研究, 写作

引用本文

引用格式

Tao, R., Octoryn Research. (2026). Fully-sovereign text-to-video on Apple Silicon (Wan2.1), parity to diffusers (TR-2026-0012). Octopus Research Institute.

BibTeX

@techreport{oritr20260012,
  title       = {Fully-sovereign text-to-video on Apple Silicon (Wan2.1), parity to diffusers},
  author      = {Tao, Ran and {Octoryn Research}},
  institution = {Octopus Research Institute},
  year        = {2026},
  note        = {Permanent ID TR-2026-0012. Not peer reviewed.}
}

披露

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