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+ +# daVinci-MagiHuman + +### Speed by Simplicity: A Single-Stream Architecture for Fast Audio-Video Generative Foundation Model + +This repository contains the weights for **daVinci-MagiHuman**, introduced in the [paper](https://huggingface.co/papers/2603.21986). + +

+ SII-GAIR  &  Sand.ai +

+ +[![GitHub](https://img.shields.io/badge/GitHub-Repo-black?logo=github)](https://github.com/GAIR-NLP/daVinci-MagiHuman) +[![Paper](https://img.shields.io/badge/Paper-PDF-red)](https://arxiv.org/abs/2603.21986) +[![Demo](https://img.shields.io/badge/%F0%9F%A4%97%20Demo-HuggingFace-orange)](https://huggingface.co/spaces/SII-GAIR/daVinci-MagiHuman) +[![Models](https://img.shields.io/badge/%F0%9F%A4%97%20Models-HuggingFace-yellow)](https://huggingface.co/GAIR/daVinci-MagiHuman) +[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0) +[![Python](https://img.shields.io/badge/Python-3.12%2B-blue.svg)](https://www.python.org/) +[![PyTorch](https://img.shields.io/badge/PyTorch-2.9%2B-ee4c2c.svg)](https://pytorch.org/) + +
+ +## Highlights + +- **Single-Stream Transformer** — A unified 15B-parameter, 40-layer Transformer that jointly processes text, video, and audio via self-attention only. No cross-attention, no multi-stream complexity. +- **Exceptional Human-Centric Quality** — Expressive facial performance, natural speech-expression coordination, realistic body motion, and accurate audio-video synchronization. +- **Multilingual** — Supports Chinese (Mandarin & Cantonese), English, Japanese, Korean, German, and French. +- **Blazing Fast Inference** — Generates a 5-second 256p video in **2 seconds** and a 5-second 1080p video in **38 seconds** on a single H100 GPU. +- **State-of-the-Art Results** — Achieves **80.0%** win rate vs Ovi 1.1 and **60.9%** vs LTX 2.3 in pairwise human evaluation over 2,000 comparisons. +- **Fully Open Source** — We release the complete model stack: base model, distilled model, super-resolution model, and inference code. + +## Architecture + +
+ +
+ +daVinci-MagiHuman uses a single-stream Transformer that takes text tokens, a reference image latent, and noisy video and audio tokens as input, and jointly denoises the video and audio within a unified token sequence. + +Key design choices: + +| Component | Description | +|---|---| +| **Sandwich Architecture** | First and last 4 layers use modality-specific projections; middle 32 layers share parameters across modalities | +| **Timestep-Free Denoising** | No explicit timestep embeddings — the model infers the denoising state directly from input latents | +| **Per-Head Gating** | Learned scalar gates with sigmoid activation on each attention head for training stability | +| **Unified Conditioning** | Denoising and reference signals handled through a minimal unified interface — no dedicated conditioning branches | + +## Performance + +### Quantitative Quality Benchmark + +| Model | Visual Quality ↑ | Text Alignment ↑ | Physical Consistency ↑ | WER ↓ | +|---|:---:|:---:|:---:|:---:| +| OVI 1.1 | 4.73 | 4.10 | 4.41 | 40.45% | +| LTX 2.3 | 4.76 | 4.12 | **4.56** | 19.23% | +| **daVinci-MagiHuman** | **4.80** | **4.18** | 4.52 | **14.60%** | + +### Human Evaluation (2,000 Pairwise Comparisons) + +| Matchup | daVinci-MagiHuman Win | Tie | Opponent Win | +|---|:---:|:---:|:---:| +| vs Ovi 1.1 | **80.0%** | 8.2% | 11.8% | +| vs LTX 2.3 | **60.9%** | 17.2% | 21.9% | + +### Inference Speed (5-second video) + +| Resolution | Base (s) | Super-Res (s) | Decode (s) | **Total (s)** | +|---|:---:|:---:|:---:|:---:| +| 256p | 1.6 | — | 0.4 | **2.0** | +| 540p | 1.6 | 5.1 | 1.3 | **8.0** | +| 1080p | 1.6 | 31.0 | 5.8 | **38.4** | + +## Efficient Inference Techniques + +- **Latent-Space Super-Resolution** — Two-stage pipeline: generate at low resolution, then refine in latent space (not pixel space), avoiding an extra VAE decode-encode round trip. +- **Turbo VAE Decoder** — A lightweight re-trained decoder that substantially reduces decoding overhead. +- **Full-Graph Compilation** — [MagiCompiler](https://github.com/SandAI-org/MagiCompiler) fuses operators across Transformer layers for ~1.2x speedup. +- **Distillation** — DMD-2 distillation enables generation with only 8 denoising steps (no CFG), without sacrificing quality. + +## Getting Started + +### Option 1: Docker (Recommended) + +```bash +# Pull the MagiCompiler Docker image +docker pull sandai/magi-compiler:latest + +# Launch container +docker run -it --gpus all \ + -v /path/to/models:/models \ + sandai/magi-compiler:latest bash + +# Install MagiCompiler +git clone https://github.com/SandAI-org/MagiCompiler +cd MagiCompiler +pip install -e . --no-build-isolation --config-settings editable_mode=compat +cd .. + +# Clone daVinci-MagiHuman +git clone https://github.com/GAIR-NLP/daVinci-MagiHuman +cd daVinci-MagiHuman +``` + +### Option 2: Conda + +```bash +# Create environment +conda create -n davinci python=3.12 +conda activate davinci + +# Install PyTorch +pip install torch==2.9.0 torchvision==0.24.0 torchaudio==2.9.0 + +# Install Flash Attention (Hopper) +git clone https://github.com/Dao-AILab/flash-attention +cd flash-attention/hopper && python setup.py install && cd ../.. + +# Install MagiCompiler +git clone https://github.com/SandAI-org/MagiCompiler +cd MagiCompiler +pip install -e . --no-build-isolation --config-settings editable_mode=compat +cd .. + +# Clone and install daVinci-MagiHuman +git clone https://github.com/GAIR-NLP/daVinci-MagiHuman +cd daVinci-MagiHuman +pip install -r requirements.txt +``` + +### Download Model Checkpoints + +Download the complete model stack from [HuggingFace](https://huggingface.co/GAIR/daVinci-MagiHuman) and update the paths in the config files under `example/`. + +## Usage + +Before running, update the checkpoint paths in the config files (`example/*/config.json`) to point to your local model directory. + +**Base Model (256p)** +```bash +bash example/base/run.sh +``` + +**Distilled Model (256p, 8 steps, no CFG)** +```bash +bash example/distill/run.sh +``` + +**Super-Resolution to 540p** +```bash +bash example/sr_540p/run.sh +``` + +**Super-Resolution to 1080p** +```bash +bash example/sr_1080p/run.sh +``` + +## Acknowledgements + +We thank the open-source community, and in particular [Wan2.2](https://github.com/Wan-Video/Wan2.2) and [Turbo-VAED](https://github.com/hustvl/Turbo-VAED), for their valuable contributions. + +## License + +This project is released under the [Apache License 2.0](https://opensource.org/licenses/Apache-2.0). + +## Citation + +```bibtex +@misc{davinci-magihuman-2026, + title = {Speed by Simplicity: A Single-Stream Architecture for Fast Audio-Video Generative Foundation Model}, + author = {SII-GAIR and Sand.ai}, + year = {2026}, + url = {https://github.com/GAIR-NLP/daVinci-MagiHuman} +} +``` \ No newline at end of file diff --git a/architecture.png b/architecture.png new file mode 100644 index 0000000000000000000000000000000000000000..2dfb5f9a63a16ec60269d902ad64d0036b9b775a --- 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