MiniMax-H3-PTD-LoRAs

模型介绍

We apply **PTD (Parametric Trajectory Distillation)** to MiniMax-H3, distilling the Ref2VA (reference-image → video + audio) trajectory into a **few-step** student. We release an **8-step** and a **4-step** accelerator for the Ref2VA task. > [!TIP] > **Want better quality or lower latency?** Check out `MiniMax-H3-PTD-RefAsymOPD-LoRAs` — the upgraded sibling trained with **PTD + RefAsymOPD** (Reference-Asymmetric On-Policy Distillation). > It ships an **8-step** and a new **6-step** variant, both delivering noticeably better **background plausibility, sharpness and realism** — surpassing the plain PTD checkpoints in this repo as well as the base model at 40 NFE. > Same checkpoint format and the same `apply_ptd_lora` call — a one-line filename swap. Unlike a single shared acceleration LoRA, PTD trains **one independent low-rank adapter per inference step** ("per-hop" LoRA) on the diffusion backbone, on top of the per-interval output heads introduced by Parallel Decoding Distillation (PDD). At inference, each denoising step selects its own hop's adapter and fuses that step's output head, so an 8-hop checkpoint runs in **8** transformer evaluations (NFE) and a 4-hop checkpoint in **4**.

主要特点

特点摘要待依据来源补齐，不凭模型名称推断能力。

局限与注意事项

暂无足够来源支持具体缺点结论。请重点验证实际任务表现、时延、数据政策、许可与版本生命周期。

来源报价与费用分析

同一模型在不同渠道、地区和配置下可能采用不同价格。下表独立保留各渠道报价，不将渠道价格视为厂商官方统一价格。

公开评测与运行配置

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

常见问题

资料来源与可追溯记录

许可证：other。模型介绍与第三方评测均以原来源的具体版本和方法为准。