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README.md

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-title: MMAudio — generating synchronized audio from video/text
+title: WAN 2.1 FAST VIDEO with AUDIO
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-
-# [Taming Multimodal Joint Training for High-Quality Video-to-Audio Synthesis](https://hkchengrex.github.io/MMAudio)
-
-[Ho Kei Cheng](https://hkchengrex.github.io/), [Masato Ishii](https://scholar.google.co.jp/citations?user=RRIO1CcAAAAJ), [Akio Hayakawa](https://scholar.google.com/citations?user=sXAjHFIAAAAJ), [Takashi Shibuya](https://scholar.google.com/citations?user=XCRO260AAAAJ), [Alexander Schwing](https://www.alexander-schwing.de/), [Yuki Mitsufuji](https://www.yukimitsufuji.com/)
-
-University of Illinois Urbana-Champaign, Sony AI, and Sony Group Corporation
-
-
-[[Paper (being prepared)]](https://hkchengrex.github.io/MMAudio) [[Project Page]](https://hkchengrex.github.io/MMAudio)
-
-
-**Note: This repository is still under construction. Single-example inference should work as expected. The training code will be added. Code is subject to non-backward-compatible changes.**
-
-## Highlight
-
-MMAudio generates synchronized audio given video and/or text inputs.
-Our key innovation is multimodal joint training which allows training on a wide range of audio-visual and audio-text datasets.
-Moreover, a synchronization module aligns the generated audio with the video frames.
-
-
-## Results
-
-(All audio from our algorithm MMAudio)
-
-Videos from Sora: 
-
-https://github.com/user-attachments/assets/82afd192-0cee-48a1-86ca-bd39b8c8f330
-
-
-Videos from MovieGen/Hunyuan Video/VGGSound: 
-
-https://github.com/user-attachments/assets/29230d4e-21c1-4cf8-a221-c28f2af6d0ca
-
-For more results, visit https://hkchengrex.com/MMAudio/video_main.html.
-
-## Installation
-
-We have only tested this on Ubuntu.
-
-### Prerequisites
-
-We recommend using a [miniforge](https://github.com/conda-forge/miniforge) environment.
-
-- Python 3.8+
-- PyTorch **2.5.1+** and corresponding torchvision/torchaudio (pick your CUDA version https://pytorch.org/)
-- ffmpeg<7 ([this is required by torchaudio](https://pytorch.org/audio/master/installation.html#optional-dependencies), you can install it in a miniforge environment with `conda install -c conda-forge 'ffmpeg<7'`)
-
-**Clone our repository:**
-
-```bash
-git clone https://github.com/hkchengrex/MMAudio.git
-```
-
-**Install with pip:**
-
-```bash
-cd MMAudio
-pip install -e .
-```
-
-(If you encounter the File "setup.py" not found error, upgrade your pip with pip install --upgrade pip)
-
-**Pretrained models:**
-
-The models will be downloaded automatically when you run the demo script. MD5 checksums are provided in `mmaudio/utils/download_utils.py`
-
-| Model    | Download link | File size |
-| -------- | ------- | ------- |
-| Flow prediction network, small 16kHz | <a href="https://databank.illinois.edu/datafiles/k6jve/download" download="mmaudio_small_16k.pth">mmaudio_small_16k.pth</a> | 601M |
-| Flow prediction network, small 44.1kHz | <a href="https://databank.illinois.edu/datafiles/864ya/download" download="mmaudio_small_44k.pth">mmaudio_small_44k.pth</a> | 601M |
-| Flow prediction network, medium 44.1kHz | <a href="https://databank.illinois.edu/datafiles/pa94t/download" download="mmaudio_medium_44k.pth">mmaudio_medium_44k.pth</a> | 2.4G |
-| Flow prediction network, large 44.1kHz **(recommended)** | <a href="https://databank.illinois.edu/datafiles/4jx76/download" download="mmaudio_large_44k.pth">mmaudio_large_44k.pth</a> | 3.9G |
-| 16kHz VAE | <a href="https://github.com/hkchengrex/MMAudio/releases/download/v0.1/v1-16.pth">v1-16.pth</a> | 655M |
-| 16kHz BigVGAN vocoder |<a href="https://github.com/hkchengrex/MMAudio/releases/download/v0.1/best_netG.pt">best_netG.pt</a> | 429M |
-| 44.1kHz VAE |<a href="https://github.com/hkchengrex/MMAudio/releases/download/v0.1/v1-44.pth">v1-44.pth</a> | 1.2G | 
-| Synchformer visual encoder |<a href="https://github.com/hkchengrex/MMAudio/releases/download/v0.1/synchformer_state_dict.pth">synchformer_state_dict.pth</a> | 907M |
-
-The 44.1kHz vocoder will be downloaded automatically.
-
-The expected directory structure (full):
-
-```bash
-MMAudio
-├── ext_weights
-│   ├── best_netG.pt
-│   ├── synchformer_state_dict.pth
-│   ├── v1-16.pth
-│   └── v1-44.pth
-├── weights
-│   ├── mmaudio_small_16k.pth
-│   ├── mmaudio_small_44k.pth
-│   ├── mmaudio_medium_44k.pth
-│   └── mmaudio_large_44k.pth
-└── ...
-```
-
-The expected directory structure (minimal, for the recommended model only):
-
-```bash
-MMAudio
-├── ext_weights
-│   ├── synchformer_state_dict.pth
-│   └── v1-44.pth
-├── weights
-│   └── mmaudio_large_44k.pth
-└── ...
-```
-
-## Demo
-
-By default, these scripts use the `large_44k` model. 
-In our experiments, inference only takes around 6GB of GPU memory (in 16-bit mode) which should fit in most modern GPUs.
-
-### Command-line interface
-
-With `demo.py`
-```bash
-python demo.py --duration=8 --video=<path to video> --prompt "your prompt" 
-```
-The output (audio in `.flac` format, and video in `.mp4` format) will be saved in `./output`.
-See the file for more options.
-Simply omit the `--video` option for text-to-audio synthesis.
-The default output (and training) duration is 8 seconds. Longer/shorter durations could also work, but a large deviation from the training duration may result in a lower quality.
-
-
-### Gradio interface
-
-Supports video-to-audio and text-to-audio synthesis.
-
-```
-python gradio_demo.py
-```
-
-### Known limitations
-
-1. The model sometimes generates undesired unintelligible human speech-like sounds
-2. The model sometimes generates undesired background music
-3. The model struggles with unfamiliar concepts, e.g., it can generate "gunfires" but not "RPG firing".
-
-We believe all of these three limitations can be addressed with more high-quality training data.
-
-## Training
-Work in progress.
-
-## Evaluation
-Work in progress.
-
-## Acknowledgement
-Many thanks to:
-- [Make-An-Audio 2](https://github.com/bytedance/Make-An-Audio-2) for the 16kHz BigVGAN pretrained model
-- [BigVGAN](https://github.com/NVIDIA/BigVGAN)
-- [Synchformer](https://github.com/v-iashin/Synchformer) 
-