---
library_name: fastvideo
license: other
license_name: source-model-licenses
license_link: https://huggingface.co/FastVideo/FastVideo-FastH3-Trim-8-Step
---
# FastH3 Trim 8-step MLX INT6

Converted from `FastVideo/FastVideo-FastH3-Trim-8-Step`, revision
`9edcb6511d5ff8dd1579e8eec8afee77e098b8d8`, from the released BF16 transformer.
The transformer uses MLX affine INT6 weights, retained VSA gates, and a cached
rank-16 AdaLN ladder. The native NVFP4 text encoder is stored in MLX layout
without requantization. The 26-layer lightweight video VAE and audio VAE are
included. `fastvideo_inference.json` supplies the eight-forward schedule.

Use the Spark/Mac code in hao-ai-lab/FastVideo#1920. After downloading this
snapshot to MODEL_ROOT, run:

```python
from fastvideo.mlx_runtime.minimax_h3_pipeline import MiniMaxH3MLXPipeline
from pathlib import Path
root = Path("MODEL_ROOT")
pipeline = MiniMaxH3MLXPipeline(
    model_root=root, mlx_dit_checkpoint=root / "transformer",
    conditioner_mode="nvfp4", vae_dtype="fp16", resident=False,
    metal_wired_limit_gib=27,
)
try:
    pipeline.generate("A red fox in snow.", output_path="clip.mp4",
                      width=832, height=480, num_frames=124, seed=1234,
                      num_steps=8, vsa=True, vsa_sparsity=0.8,
                      vsa_tile_size=64, vsa_impl="reference")
finally:
    pipeline.close()
```

Use reference attention for release generation. The optional SIMD Metal
backend has a known partial-tile correctness issue. Phased placement keeps one
heavy component loaded at a time on the 36 GiB Mac. Transformer storage is
15,355,458,984 bytes including its manifest, before the encoder and VAEs.

This is an experimental pruned model. Source-model licensing and acceptable-use
terms still apply. See the source repository and its parent model licenses.