Blame for README.md

---
license: other
language:
  - en
  - ko
  - zh
  - ja
base_model:
  - Comfy-Org/MiniMax-H3
pipeline_tag: image-to-video
library_name: comfyui
tags:
  - minimax-h3
  - video-generation
  - audio-video-generation
  - image-to-video
  - diffusion-transformer
  - quantization
  - ternary
  - balanced-base3
  - rung-omega
  - theoremflow-omega
  - comfyui
  - multilingual
---

# MiniMax-H3-Rung-Omega (`5.976 BPW`, `14.98 GiB` Joint-tANS / `16.22 GiB` Raw Base-243) + TheoremFlow-Ω (`8.92x` Speedup)

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> ☕ **Support Open Quantization & Inference Acceleration Research & Request Next Models**:
> * 🇺🇸 **Want another model quantized with `Rung Ω` (`4-Trit Base-243`) + `TheoremFlow-Ω` acceleration?** Leave your model request in a [**Buy Me a Coffee support note (`buymeacoffee.com/tripleyoung`)**](https://buymeacoffee.com/tripleyoung) (Priority Queue ⚡) or open a thread in our [**Hugging Face Community Discussions**](https://huggingface.co/yeowon/MiniMax-H3-Rung-Omega/discussions)!
> * 🇰🇷 **원하시는 다른 모델(비디오·오디오 DiT, LLM 등)의 `Rung Ω` 양자화 및 `TheoremFlow-Ω` 추론 가속이 필요하신가요?** [**Buy Me a Coffee 후원 메시지(`buymeacoffee.com/tripleyoung`)**](https://buymeacoffee.com/tripleyoung)에 모델명을 남겨주시면 **최우선 순위(Priority Queue ⚡)**로 양자화해 드립니다! ([**HF 커뮤니티 탭**](https://huggingface.co/yeowon/MiniMax-H3-Rung-Omega/discussions)에서도 요청 및 소통 가능 · [한국어 전체 문서](./README_ko.md))
> * 🇨🇳 **希望我们将其他模型（视频/音频 DiT、LLM 等）进行 `Rung Ω` 量化与 `TheoremFlow-Ω` 加速吗？** 欢迎在 [**Buy Me a Coffee 赞助留言 (`buymeacoffee.com/tripleyoung`)**](https://buymeacoffee.com/tripleyoung) 中写下您需要的模型名称（**赞助请求享最优先队列 ⚡**），或在 [**Hugging Face 社区讨论区 (Discussions)**](https://huggingface.co/yeowon/MiniMax-H3-Rung-Omega/discussions) 留言交流！([查看中文完整文档](./README_zh.md))
> * 🇯🇵 **他のモデル（動画・音声 DiT、LLM など）の `Rung Ω` 量子化や `TheoremFlow-Ω` 高速化をご希望ですか？** [**Buy Me a Coffee のサポートメッセージ (`buymeacoffee.com/tripleyoung`)**](https://buymeacoffee.com/tripleyoung) にご希望のモデル名をご記入いただければ、**最優先キュー (Priority Queue ⚡)** で量子化いたします！（[**HF コミュニティ Discussions**](https://huggingface.co/yeowon/MiniMax-H3-Rung-Omega/discussions) でもリクエスト受付中・[日本語ドキュメント全文](./README_ja.md)）

**`yeowon/MiniMax-H3-Rung-Omega`** provides the **50-Block Pure `Rung Ω` Balanced Base-3 (`4-Trit`, Base-243 5-in-4-byte)** quantized checkpoint and the **`TheoremFlow-Ω` Mathematical Floor Inference Engine** for the **MiniMax-H3 FL2VA (First/Last-Frame-to-Video+Audio, 33.1B Total / 20.11B Active Parameters)** multimodal Diffusion Transformer.

* **Zero Fallback (`200 / 200` Linear Layers in Pure `TensorCoreRungOmegaTernaryLayout`)**: Every single linear projection (`attn.qkv_proj`, `attn.out_proj`, `mlp.fc1`, `mlp.fc2`) across all 50 DiT blocks (`blocks.0` .. `blocks.49`) is quantized in the `Rung Ω` 4-Trit Balanced Base-3 lattice (`[-40, +40]` with `group_size=128` FP16 scales and `convrot_groupsize=256`), with **0 fallback NVFP4 layers**.
* **Smaller than Official `#1` Recommended `int8_convrot` (`19.53 GiB` → `14.98 GiB` Joint-tANS / `16.22 GiB` Raw `uint8`)**: Achieves **`2.50x` compression vs. BF16 Teacher (`37.46 GiB`)** and **`1.30x` compression vs. `int8_convrot` (`19.53 GiB`)**, while outperforming `int8_convrot` on Audio & Spatial-Temporal Top-1 Velocity Agreement.
* **TheoremFlow-Ω (`8.92x` End-to-End Sampling Speedup)**: Accelerates 20-step joint Video (`864×480`, `124` frames @ 24fps) + Stereo Audio (`32kHz`) flow-matching generation from **`625.4s` → `70.11s`** while preserving **`99.58%` Video Latent Cosine Similarity (`20.80 dB` SNR)** and crystal-clear synchronized speech/dialogue.

---

## 🎬 Demo Preview (Generated by Pure 50-Block `Rung Ω` + `TheoremFlow-Ω` in `70.11s`)

![Keyframes Preview](./assets/MiniMax_H3_TheoremFlow_Omega_Champion_keyframes.png)

### 🎬 Side-by-Side Audio & Video Comparison: Official int8_convrot (19.53 GiB, 434.1s) vs Ours Rung-Omega + TheoremFlow-Omega (14.96 GiB, 70.1s — Turn Sound ON 🔊)

<p align="center">
  <video controls autoplay loop muted playsinline width="100%" src="https://huggingface.co/yeowon/MiniMax-H3-Rung-Omega/resolve/main/assets/minimax_h3_viral_16x9.mp4"></video>
</p>



* **Full Video + Synchronized Korean Speech (`.mp4`)**: [`assets/MiniMax_H3_TheoremFlow_Omega_Champion.mp4`](./assets/MiniMax_H3_TheoremFlow_Omega_Champion.mp4)
* **Pure 50-Block 20-Step Baseline Video (`.mp4`)**: [`assets/MiniMax_H3_Rung_Omega_50blk_Champion.mp4`](./assets/MiniMax_H3_Rung_Omega_50blk_Champion.mp4)
* **Extracted 32kHz Stereo Audio Track (`.wav`)**: [`assets/MiniMax_H3_TheoremFlow_Omega_Champion_audio.wav`](./assets/MiniMax_H3_TheoremFlow_Omega_Champion_audio.wav)

---

## 📊 Benchmark Results

![MiniMax-H3-Rung-Omega Benchmark Overview](./assets/minimax_h3_rung_omega_benchmark_hero.png)

### 1. Full 50-Block End-to-End Velocity Fidelity vs. Official `#1` Recommended `int8_convrot`

Evaluated on held-out multimodal diffusion trajectories (`Video + Audio + Text + Conditioning Keyframe` across noise levels $\sigma \in [0.08, 0.92]$) against the unquantized `BF16` Teacher (`37.46 GiB`):

| Model / Format | Active Linear Layers | Effective BPW | Total Size (GiB) | Compression vs. BF16 | Video Spatial Top-1 (↑) | Audio Patch Top-1 (↑) | Video Velocity Cosine (↑) | Audio Velocity Cosine (↑) | Video $R^2$ (↑) | Audio $R^2$ (↑) |
| :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| **BF16 Teacher (`minimax_h3_fl2va_pruned_bf16`)** | 200 × BF16 | `16.000` | `37.46 GiB` | `1.00x` | `100.00%` | `100.00%` | `1.000000` | `1.000000` | `100.00%` | `100.00%` |
| **Official `#1` `int8_convrot` (`19.53 GiB`)** | 200 × INT8 | `8.003` | `19.53 GiB` | `1.92x` | `72.92%` | `77.60%` | `0.980832` | `0.985771` | `96.16%` | `97.10%` |
| **`Rung Ω` 4-Trit Pareto Champion (Ours)** | **200 × 4-Trit Base-243** | **`5.967` (`6.40` raw)** | **`14.96 GiB` (`16.22 GiB` raw)** | **`2.50x`** | **`78.12%` (`+5.20%p`)** | **`81.77%` (`+4.17%p`)** | **`0.976383`** | **`0.982006`** | **`95.28%`** | **`96.43%`** |

### 2. Multi-Track Pareto Ladder (10-Block Calibration & Held-Out Benchmark)

![Rung Ω Rate-Distortion Pareto Frontier](./assets/minimax_h3_pareto_frontier_chart.png)

| Track | Trits / Codebook | BPW (Joint-tANS) | Total Size (GiB) | Compression vs. BF16 | Combined Top-1 (↑) | Combined SNR (dB) | Video Cosine (↑) | Audio Cosine (↑) |
| :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| `baseline_nvfp4_rtn` | NVFP4 | `4.500` | `11.67 GiB` | `3.21x` | `97.53%` | `33.61 dB` | `0.999810` | `0.999676` |
| **`omega_base15_ultra`** | 3-Trit (`[-7..+7]`, 2 w/byte) | **`3.432`** | **`9.27 GiB`** | **`4.04x`** | **`99.48%`** | **`44.31 dB`** | **`0.999984`** | **`0.999968`** |
| **`omega_4trit_sub45`** | 4-Trit ECSQ (`[-40..+40]`) | **`4.488`** | **`11.64 GiB`** | **`3.22x`** | **`99.48%`** | **`47.77 dB`** | **`0.999992`** | **`0.999990`** |
| **`omega_4trit_pareto` (Released Checkpoint)** | 4-Trit Full-Range (`[-40..+40]`) | **`5.977`** | **`14.98 GiB` (`16.22 GiB` raw)** | **`2.50x`** | **`99.87%`** | **`50.62 dB`** | **`0.999995`** | **`0.999996`** |
| **`omega_5trit_supreme`** | 5-Trit (`[-121..+121]`) | **`7.575`** | **`18.57 GiB`** | **`2.02x`** | **`99.48%`** | **`52.60 dB`** | **`0.999997`** | **`0.999998`** |

### 3. `TheoremFlow-Ω` Inference Acceleration (`864×480`, `124` Frames + Stereo Audio, 20 Steps)

| Engine / Schedule | Sampling Time (↓) | Speedup vs. Baseline | Video Latent Cosine | Video Latent SNR | Audio Latent Cosine |
| :--- | :---: | :---: | :---: | :---: | :---: |
| Standard 20-Step Full 50-Block Sampling | `625.40s` | `1.00x` | `1.000000` | `∞` | `1.000000` |
| **`TheoremFlow-Ω` (`6-Exact + 14-Fast`, Floors 1–5)** | **`70.11s`** | **`8.92x`** | **`0.995812`** | **`20.80 dB`** | **`0.971400`** |

---

## 📦 Repository Structure

```text
yeowon/MiniMax-H3-Rung-Omega/
├── diffusion_models/
│   └── minimax_h3_fl2va_pruned_rung_omega.safetensors   # 16.22 GiB (50-Block Pure Rung Ω 4-Trit Base-243 Checkpoint)
├── src/
│   ├── register_comfyui_rung_omega.py                   # ComfyUI TensorCoreRungOmegaTernaryLayout registration patch
│   ├── theoremflow_omega.py                             # TheoremFlow-Ω (Floors 1-5) 8.92x inference acceleration engine
│   └── generate_minimax_h3_rung_omega_video.py          # Standalone 3-stage Video+Audio generator with 5k+2 bounded VAE decode
├── benchmarks/
│   ├── rung_omega_minimax_h3_50blk_results.json         # Full 50-block evaluation metrics vs. int8_convrot
│   └── rung_omega_minimax_h3_results.json               # Multi-track Pareto ladder benchmark metrics
└── assets/
    ├── minimax_h3_rung_omega_benchmark_hero.png         # 50-Block Size, Velocity Top-1 & TheoremFlow-Ω 8.92x Speedup Chart
    ├── minimax_h3_pareto_frontier_chart.png             # Rate-Distortion Pareto Frontier vs. NVFP4 & int8_convrot Chart
    ├── MiniMax_H3_TheoremFlow_Omega_Champion.mp4        # 70.11s TheoremFlow-Ω generated video + Korean speech
    ├── MiniMax_H3_Rung_Omega_50blk_Champion.mp4         # Full 20-step generated video + Korean speech
    ├── MiniMax_H3_TheoremFlow_Omega_Champion_audio.wav  # 32kHz stereo audio output
    └── MiniMax_H3_TheoremFlow_Omega_Champion_keyframes.png
```

---

## 🚀 Quickstart

### 1. Register `TensorCoreRungOmegaTernaryLayout` in ComfyUI

Patch your ComfyUI installation (`comfy/quant_ops.py` and `comfy/ops.py`) so `UNETLoader` natively recognizes `"format": "rung_omega_ternary"`:

```bash
python3 src/register_comfyui_rung_omega.py
```

### 2. Place Checkpoint in ComfyUI `diffusion_models/`

```bash
ln -s /path/to/diffusion_models/minimax_h3_fl2va_pruned_rung_omega.safetensors \
      /path/to/ComfyUI/models/diffusion_models/minimax_h3_fl2va_pruned_rung_omega.safetensors
```

### 3. Run End-to-End Video + Audio Generation with `TheoremFlow-Ω` (`8.92x` Speedup)

```bash
python3 src/generate_minimax_h3_rung_omega_video.py \
  --unet-name minimax_h3_fl2va_pruned_rung_omega.safetensors \
  --first-frame /path/to/input_frame.png \
  --width 864 --height 480 --length 124 --steps 20 \
  --theoremflow \
  --output-mp4 output_theoremflow_omega.mp4
```

### 4. Programmatic Usage of `TheoremFlow-Ω` on a Loaded ComfyUI Model

```python
import torch
from theoremflow_omega import install_floor1_static_int8_engine, install_theoremflow_omega_schedule

# 1. Install Floor 1: Gauge-folded Base-243 -> Static-Workspace INT8 MMA + Fused SwiGLU + NHD SageAttention
f1_info = install_floor1_static_int8_engine(
    model.model.diffusion_model,
    max_seq_len=16384,
    device=torch.device("cuda:0"),
)

# 2. Install Floors 2, 3, and 5: Step-0 Constant Folding + Bi-Time Secant Boundary-Pinned Trunk Schedule
tf_state = install_theoremflow_omega_schedule(
    model.model.diffusion_model,
    exact_steps={0, 1, 2, 7, 13, 19},
    head_blocks=1,
    tail_blocks=1,
)
```

---

## 🔬 Technical Architecture Overview

1. **Bijective 5-in-4 Base-243 Packing (`rung_omega_ternary`)**:
   * Each linear weight matrix $W \in \mathbb{R}^{M \times K}$ is rotated in 256-element blocks via the DC-balanced regular orthogonal Hadamard matrix $H_{256} = H_4^{\otimes 4}$ and mapped onto the symmetric 4-Trit Balanced Base-3 lattice $q \in \{-40, \dots, +40\}$ with `FP16` group scales (`group_size = 128`).
   * Five 4-trit codes $(u_0, u_1, u_2, u_3, u_4) \in \{0, \dots, 80\}^5$ are bijectively packed into 4 bytes $b_i = u_i + 81 \cdot \text{trit}_i(u_4) \in \{0, \dots, 242\}$ (`6.40` raw storage bits/weight, `5.976` Joint-tANS entropy bits/weight).
2. **TheoremFlow-Ω Inference Engine (`src/theoremflow_omega.py`)**:
   * **Floor 1 (Hardware Execution Roofline)**: Unpacks Base-243 weights once at load time into gauge-folded INT8 tensors (`-3.55 GiB` VRAM saved), pre-allocates a static contiguous CUDA workspace (eliminating 16,000+ `cudaMalloc`/`cudaFree` calls), fuses `SwiGLU + ConvRot64 + Rowwise INT8 Quantize` in `MLP.fc2`, and runs zero-transpose `NHD` `sageattn_qk_int8_pv_fp16_cuda`.
   * **Floor 2 & 3 (Bi-Time Intrinsic Flow & Boundary-Pinned Trunk Splitting)**: Evaluates exact boundary blocks and `FinalLayer` on the live Step-$k$ state while bridging the interior trunk via modality-decoupled Bi-Time Secant extrapolation along $(t_v, t_a)$.
   * **Floor 5 (Zero-Recompute Step-0 Constant Folding)**: Folds `PackedLayout`, `rope_rotation_table`, `TokenRefiner(condition_proj(text_states))`, and `video_patch_proj(cond_video_rows)` into Step-0 constants (`0.0 ms` overhead on Steps `1..19`).

---

## ☕ Support Our Research & Request Custom Model Quantization

We release **`MiniMax-H3-Rung-Omega`** and **`TheoremFlow-Ω`** openly with zero gated access barriers so the community can run high-fidelity multimodal Video+Audio generation faster and in smaller VRAM footprints.

### 🎯 Want Another Model Quantized? (`Rung Ω` + `TheoremFlow-Ω`)
Have a large Video/Audio Diffusion Transformer (Wan, HunyuanVideo, LTX, Sora-class DiT) or LLM you want compressed to **`Rung Ω` (`3-Trit` / `4-Trit` / `5-Trit` Base-243)** with **`TheoremFlow-Ω` acceleration**?
1. **⚡ Priority Supporter Queue**: Leave the target model name in your [**Buy Me a Coffee support note (`buymeacoffee.com/tripleyoung`)**](https://buymeacoffee.com/tripleyoung) — supporter requests are prioritized first!
2. **💬 Community Channel & Model Requests**: Open a discussion thread anytime in the [**Hugging Face Community Tab (`Discussions`)**](https://huggingface.co/yeowon/MiniMax-H3-Rung-Omega/discussions) to request models, ask questions, or talk directly with us.

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