# MagiHuman Generation

MagiHuman is an advanced, omni-modality model that generates both high-quality video and lip-synced audio from a text prompt.

Because MagiHuman is a very large model featuring a powerful DiT MoE backbone and a ~9B parameter T5Gemma text encoder, it natively supports **Tensor Parallelism (TP)** in vLLM-Omni to run efficiently across multi-GPU setups, reducing device memory bottlenecks.

## Setup

### Install MagiCompiler (recommended)

MagiHuman relies on [MagiCompiler](https://github.com/SandAI-org/MagiCompiler) for custom-op registration used by the DiT attention kernels. While the pipeline can fall back to stub implementations, installing MagiCompiler is **strongly recommended** for correct behaviour.

```bash
# Clone the repo
git clone https://github.com/SandAI-org/MagiCompiler.git
cd MagiCompiler

# System dependencies (optional, for FX graph visualization; Debian/Ubuntu)
sudo apt update && sudo apt install -y graphviz

# Python dependencies
pip install -r requirements.txt

# Install MagiCompiler
pip install .          # end users (recommended)
# pip install -e .     # developers (editable install)
```

### Hardware requirements

Ensure your hardware has enough VRAM. For a standard node with 80GB GPUs, running with `--tensor-parallel-size 4` is recommended to shard both the MoE weights and the T5Gemma text encoder across 4 GPUs, reducing the per-GPU peak VRAM overhead significantly (by roughly ~13.5GB per GPU compared to single-device inference).

Please refer to the [stage configuration documentation](https://docs.vllm.ai/projects/vllm-omni/en/latest/configuration/stage_configs/) for further details on allocating memory.

## Run Examples

Get into the example folder:
```bash
cd examples/offline_inference/magi_human
```

### End-to-End Generation (Text to Video+Audio)

Generate a video with synchronized speech natively generated by the model.

```bash
python end2end.py \
    --model /proj-tango-pvc/users/zhipeng.wang/workspace/models/daVinci-MagiHuman \
    --prompt "A young woman with long, wavy golden blonde hair..." \
    --tensor-parallel-size 4 \
    --output output_magihuman.mp4
```

## Common Parameters

| Parameter | Default | Description |
|-----------|---------|-------------|
| `--model` | *(Required)* | Local model path or HuggingFace ID |
| `--prompt` | *(built-in demo prompt)* | Highly detailed text prompt dictating visual look and dialogue text |
| `--tensor-parallel-size` | `4` | Tensor parallelism size (Number of GPUs) |
| `--height` | `256` | Initial resolution height |
| `--width` | `448` | Initial resolution width |
| `--num-inference-steps` | `8` | Denoising steps |
| `--seed` | `52` | Random seed |
| `--output` | `output_magihuman.mp4` | Output video with audio path |

## Example materials

??? abstract "end2end.py"
    ``````py
    --8<-- "examples/offline_inference/magi_human/end2end.py"
    ``````