# MiniMax-H3-QuantFunc-4bit
**4x compression, quality held.**
QuantFunc INT4 cuts MiniMax H3's core weight precision from 16-bit to 4-bit. Character detail, style fidelity and fast motion stay clear and coherent, while H3's native video+audio generation is fully preserved.
In our internal FL2VA evaluation, QuantFunc INT4 vs the BF16 baseline (same prompt, same seed) measures ~**23.7 dB PSNR**.
## Showcase
All clips below were generated by MiniMax-H3-QuantFunc-4bit — click a player to watch.
Live-action performance
Animated chase
High-speed car chase
Stylized character
> Poster frames are the first frame of each clip. Showcase spec: 896 × 1184, 124 frames, 24 FPS, ~5s.
## Same 124 frames, up to 3.19x FP8's per-step speed
On an **RTX 4090**, 768 × 768, 5s, 124 frames:
| Backend | Per-step time | Relative speed |
|---|---:|---:|
| **QuantFunc INT4** | **3.2s** | — |
| INT8 ConvRot | 8.5s | **2.66x faster** |
| FP8 | 10.2s | **3.19x faster** |
> Per-step core-model inference time only — excludes text encoder, VAE, audio processing and video saving. Actual speed varies with resolution, frame count, driver and software version.
## Swap one loader, keep the rest of your workflow
1. Install or update [ComfyUI-QuantFunc](https://github.com/QuantFunc/ComfyUI-QuantFunc).
2. Download the FL2VA 4-step or Ref2VA 8-step weights.
3. Swap your model loader for the QuantFunc loader and pick the matching weight file.
Prompts, reference images/videos, audio assets and the rest of your workflow nodes stay unchanged.
Both weight sets already have the acceleration LoRA, Token Refiner, INT4 Refiner and INT8 Conv Sidecar fused in — no extra components to attach.
## RTX 20-series through GB300, one build covers it all
Runs on every NVIDIA **SM75+** GPU: RTX 20/30/40/50-series, A100, H100, H200, B100, B200, GB300.
4-bit weights significantly cut the weight-bandwidth cost of loading and inference, making MiniMax H3 much easier to run on consumer GPUs. Actual VRAM needs depend on resolution, frame count, reference-asset count and the rest of your workflow.
## Choose a model
| Variant | File | Size | Recommended steps | Use case |
|---|---|---:|---:|---|
| **FL2VA** | `minimax_h3_fl2va_4step_quantfunc_int4_r128.safetensors` | 12.37 GB | **4 steps** | Text-to-audio/video, first-frame, last-frame, first+last-frame control |
| **Ref2VA** | `minimax_h3_ref2va_8steps_quantfunc_int4_r128.safetensors` | 12.37 GB | **8 steps** | Multimodal reference generation from images, video and audio |
FL2VA supports zero, one or two input images; Ref2VA targets more complex multimodal reference scenarios. See the [official MiniMax H3 repo](https://github.com/MiniMax-AI/MiniMax-H3) for details on both modes.
Use the matching scheduler for the model. Both weight sets already carry the acceleration LoRA fused in — no need to load it separately.
## Loading
Weights use QuantFunc's own sealed safetensors format (quantization parameters and metadata are sealed).
Load with **ComfyUI-QuantFunc** or the **QuantFunc inference engine** — this is not a drop-in Diffusers checkpoint. (This repo declares `library_name: diffusers` purely so Hugging Face counts real downloads of the flat `.safetensors` files above — see [HF's download-stats docs](https://huggingface.co/docs/hub/models-download-stats) — not because the files load via the `diffusers` package.)
If the model isn't recognized or fails to load, update ComfyUI-QuantFunc to the latest version and restart ComfyUI.
## Technical details
- INT4 weights × INT4 activations (W4A4)
- SVDQuant rank 128, group size 64
- INT4 Token Refiner + INT4 Refiner
- INT8 Conv Sidecar
- FL2VA measured max relative output error from fp16 adaLN folding: ~**7.58e-4**
- Minimum GPU architecture: NVIDIA SM75
## Source & license
This repository distributes derived quantized weights produced from [MiniMax H3](https://github.com/MiniMax-AI/MiniMax-H3).
MiniMax H3 and its derivative weights follow the **MiniMax H3 Community License Agreement**. QuantFunc's quantization tooling and implementation follow their own respective licenses. Please read and comply with the original model's license terms before use.
## Community
- 🌐 [QuantFunc website](https://www.quantfunc.com/)
- 🐙 [ComfyUI-QuantFunc on GitHub](https://github.com/QuantFunc/ComfyUI-QuantFunc)
- 🎮 [Discord](https://discord.gg/jCp9TpFWcn)
