FastVideo-Minimax-FastH3

About

What FastVideo-Minimax-FastH3 is

FastVideo-Minimax-FastH3-Preview-v0.2 A few-step (4-step) distillation preview of MiniMax-H3, the 33B dual-modality (video + audio) diffusion transformer — distilled with data-free DMD2 by the FastVideo team. The base model samples with 50 denoising steps; this student walks a 4-step grid on the release's shift-12 rectified-flow schedule (12.5× fewer transformer evaluations), generating synchronized video and audio in one pipeline call. Preview status (v0.2): step 2900 of a 4000-step run — the same run that produced v0.1 (step 1400), carried 1500 steps further. Sample quality is still maturing, most visibly on high-motion detail. What's new since v0.1 - 1500 more distillation steps (1400 → 2900) on the same data-free DMD2 run: sharper still detail and steadier audio/video sync. - Corrected sampling contract in the card. v0.1 documented numinferencesteps=4, which makes the scheduler build its own 4-point sigma grid — 3 forwards on native spacing, not the 4 trained jump points. Sample with the explicit trained ladder instead (see Usage). The same off-operating-point mismatch affected this run's in-training validation renders, so judge the student by fresh samples on the ladder below, not by earlier validation clips. - Fixed the repo id in the usage snippet (v0.1's card had it doubled). What's in the repo Diffusers-format (modular pipeline) layout. Only the transformer/ weights differ from the base release — the distilled student, in bf16. All other components (Qwen3-VL text encoder, video/audio VAEs, tokenizer, processor, schedulers) are unmodified copies of the base release, included so the repo is self-contained. The student was trained with block-sparse video attention (VSA, 64-token tiles, 90% sparsity) and carries its trained sparse-gate parameters (attn.togatecompress); it can be run dense (default) or with VSA for additional inference speedup. Usage (FastVideo) Sample on the trained ladder — [999, 749, 500, 250] on the shared 1000-step grid, one forward per entry, each scheduler applying its own shift: The ladder can also be set without touching code: To run the student under the sparse attention it was trained with, select the VSA-H3 backend and match both

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

How video models work

Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys fastvideo-minimax-fasth3 for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (fastvideo-minimax-fasth3 below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/videos/generations \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"fastvideo-minimax-fasth3","prompt":"a drone shot over a forest"}'

Details

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Commercial licence needed

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Weights & code

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