rootonchair/MiniMax-H3-nunchaku-lite-nvfp4

MiniMax-H3 transformer — Nunchaku Lite NVFP4

NVFP4 SVDQuant quantizations of the 30.8B MiniMax-H3 video+audio transformer (the transformer/ partition, i.e. the t2va / fl2va workflows), packed in the Nunchaku Lite kernel layout. This is the transformer component only — pair it with the other components from MiniMaxAI/MiniMax-H3.

This repo holds two checkpoints that differ in calibration and packaging. Pick one:

Both are ~19 GB (vs ~66 GB bf16) and both now load with a one-line from_pretrained (the calibrated one via subfolder=).

1. Data-free checkpoint (default from_pretrained path)

Data-free SVDQuant: weight-span smoothing + rank-32 SVD low-rank branch + NVFP4 group quantization (group size 16), no calibration data. Loads through diffusers' pre-quantized fast path — the extra single-file checkpoint in this repo is ignored, since from_pretrained reads only the shards named in diffusion_pytorch_model.safetensors.index.json.

import torch
from diffusers import MiniMaxH3Transformer3DModel

transformer = MiniMaxH3Transformer3DModel.from_pretrained(
    "rootonchair/MiniMax-H3-nunchaku-lite-nvfp4", dtype=torch.bfloat16, device_map="cuda",
)

2. Calibrated 8×20 checkpoint

Calibrated SVDQuant produced with diffuse-compressor (examples/text_to_video/quantize_minimax_h3.py); calibration is 8 prompts × 20 denoising steps at the default 768-short-edge canvas. Same quantization is provided two ways.

Recommended — diffusers-native (`subfolder=`): repackaged into the same pre-quantized format as the data-free build, loads in one line.

import torch
from diffusers import MiniMaxH3Transformer3DModel

transformer = MiniMaxH3Transformer3DModel.from_pretrained(
    "rootonchair/MiniMax-H3-nunchaku-lite-nvfp4", subfolder="calibrated-8x20",
    dtype=torch.bfloat16, device_map="cuda",
)

Alternative — raw single-file export. The original svdq-nvfp4_r32-minimax-h3-t2va.safetensors (embedded nunchaku_lite.runtime_manifest v1, precision mixed: fp4 SVDQ + int4 AWQ, plus a .config.yaml sidecar) is also kept. It is not diffusers-native, so it is applied by patching a meta-constructed transformer:

import torch
from huggingface_hub import hf_hub_download
from diffusers.models.transformers.transformer_minimax_h3 import (
    MiniMaxH3RotaryPosEmbed, MiniMaxH3Transformer3DModel)
from nunchaku_lite.core import _patch_component

ckpt = hf_hub_download("rootonchair/MiniMax-H3-nunchaku-lite-nvfp4",
                       "svdq-nvfp4_r32-minimax-h3-t2va.safetensors")

config = MiniMaxH3Transformer3DModel.load_config("MiniMaxAI/MiniMax-H3", subfolder="transformer")
with torch.device("meta"):
    transformer = MiniMaxH3Transformer3DModel.from_config(config)

# rope.inv_freq is a non-persistent buffer (absent from the checkpoint), so the
# meta-assign load would leave it on the meta device — rebuild it off-meta first.
transformer.rope.inv_freq = MiniMaxH3RotaryPosEmbed(
    rope_freq_dim=transformer.config.rope_freq_dim,
    rope_theta=transformer.config.rope_theta).inv_freq

_patch_component(transformer, ckpt, target="manifest", precision="fp4",
                 torch_dtype=torch.bfloat16, device="cuda", strict=True,
                 adapter_options=None, assign=True)

Requirements (both checkpoints)

Requires the kernels package, DIFFUSERS_TRUST_REMOTE_KERNELS=true, and a Blackwell-or-newer NVIDIA GPU — NVFP4 kernels need sm_120, PyTorch >= 2.7 with CUDA >= 12.8. Keep the transformer fully resident on the GPU (~19 GB) rather than offloading, for best throughput. (For non-Blackwell GPUs, use the int4 build at rootonchair/MiniMax-H3-nunchaku-lite-int4.)