Embed viral audio/video comparison demo in README.md

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---
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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</p>

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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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* **💬 Community & Model Request Board**: [https://huggingface.co/yeowon/MiniMax-H3-Rung-Omega/discussions](https://huggingface.co/yeowon/MiniMax-H3-Rung-Omega/discussions)

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	---
	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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	<strong>🌐 Language / 언어 / 语言 / 言語:</strong>
	<a href="./README.md"><strong>🇺🇸 English</strong></a>  \|
	<a href="./README_ko.md"><strong>🇰🇷 한국어</strong></a>  \|
	<a href="./README_zh.md"><strong>🇨🇳 中文 (简体)</strong></a>  \|
	<a href="./README_ja.md"><strong>🇯🇵 日本語</strong></a>
	</p>

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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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