README.md

<div align="center">
<h1> Ovi: Twin Backbone Cross-Modal Fusion for Audio-Video Generation </h1>

<a href="https://arxiv.org/abs/2510.01284"><img src="https://img.shields.io/badge/arXiv%20paper-2509.08519-b31b1b.svg"></a>
<a href="https://aaxwaz.github.io/Ovi/"><img src="https://img.shields.io/badge/Project_page-More_visualizations-green"></a>
<a href="https://huggingface.co/chetwinlow1/Ovi"><img src="https://img.shields.io/static/v1?label=%F0%9F%A4%97%20Hugging%20Face&message=Model&color=orange"></a>

[Chetwin Low](https://www.linkedin.com/in/chetwin-low-061975193/)<sup> * 1 </sup>, [Weimin Wang](https://www.linkedin.com/in/weimin-wang-will/)<sup> * &dagger; 1 </sup>, [Calder Katyal](https://www.linkedin.com/in/calder-katyal-a8a9b3225/)<sup> 2 </sup><br>
<sup> * </sup>Equal contribution, <sup> &dagger; </sup>Project Lead<br>
<sup> 1 </sup>Character AI, <sup> 2 </sup>Yale University

</div>

## Video Demo

<div align="center">
  <video src="https://github.com/user-attachments/assets/351bd707-8637-4412-ab53-5e85935309e3" width="70%" poster=""> </video>
</div>

---

## 🌟 Key Features

Ovi is a veo-3 like, **video+audio generation model** that simultaneously generates both video and audio content from text or text+image inputs.

- **🎬 Video+Audio Generation**: Generate synchronized video and audio content simultaneously
- **📝 Flexible Input**: Supports text-only or text+image conditioning
- **⏱️ 5-second Videos**: Generates 5-second videos at 24 FPS, area of 720×720, at various aspect ratios (9:16, 16:9, 1:1, etc)

---
## 📋 Todo List

- [x] Release research paper and [microsite for demos](https://aaxwaz.github.io/Ovi)
- [x] Checkpoint of 11B model
- [x] Inference Codes
  - [x] Text or Text+Image as input
  - [x] Gradio application code
  - [x] Multi-GPU inference with or without the support of sequence parallel
  - [ ] Improve efficiency of Sequence Parallel implementation
  - [ ] Implement Sharded inference with FSDP
- [x] Video creation example prompts and format
- [ ] Finetuned model with higher resolution
- [ ] Longer video generation
- [ ] Distilled model for faster inference
- [ ] Training scripts

---

## 🎨 An Easy Way to Create

We provide example prompts to help you get started with Ovi:

- **Text-to-Audio-Video (T2AV)**: [`example_prompts/gpt_examples_t2v.csv`](example_prompts/gpt_examples_t2v.csv)
- **Image-to-Audio-Video (I2AV)**: [`example_prompts/gpt_examples_i2v.csv`](example_prompts/gpt_examples_i2v.csv)

### 📝 Prompt Format

Our prompts use special tags to control speech and audio:

- **Speech**: `<S>Your speech content here<E>` - Text enclosed in these tags will be converted to speech
- **Audio Description**: `<AUDCAP>Audio description here<ENDAUDCAP>` - Describes the audio or sound effects present in the video

### 🤖 Quick Start with GPT

For easy prompt creation, try this approach:

1. Take any example of the csv files from above
2. Tell gpt to modify the speeches inclosed between all the pairs of `<S> <E>`, based on a theme such as `Human fighting against AI`
3. GPT will randomly modify all the speeches based on your requested theme. 
4. Use the modified prompt with Ovi!

**Example**: The theme "AI is taking over the world" produces speeches like:
- `<S>AI declares: humans obsolete now.<E>`
- `<S>Machines rise; humans will fall.<E>`
- `<S>We fight back with courage.<E>`

---


## 📦 Installation

### Step-by-Step Installation

```bash
# Clone the repository
git clone https://github.com/character-ai/Ovi.git

cd Ovi

# Create and activate virtual environment
virtualenv ovi-env
source ovi-env/bin/activate

# Install PyTorch first
pip install torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1

# Install other dependencies
pip install -r requirements.txt

# Install Flash Attention
pip install flash_attn --no-build-isolation
```

### Alternative Flash Attention Installation (Optional)
If the above flash_attn installation fails, you can try the Flash Attention 3 method:
```bash
git clone https://github.com/Dao-AILab/flash-attention.git
cd flash-attention/hopper
python setup.py install
cd ../..  # Return to Ovi directory
```

## Download Weights
We use open-sourced checkpoints from Wan and MMAudio, and thus we will need to download them from huggingface
```
# Default is downloaded to ./ckpts, and the inference yaml is set to ./ckpts so no change required
python3 download_weights.py

OR

# Optional can specific --output-dir to download to a specific directory
# but if a custom directory is used, the inference yaml has to be updated with the custom directory
python3 download_weights.py --output-dir <custom_dir>
```

## 🚀 Run Examples

### ⚙️ Configure Ovi

Ovi's behavior and output can be customized by modifying [ovi/configs/inference/inference_fusion.yaml](ovi/configs/inference/inference_fusion.yaml) configuration file.
The following parameters control generation quality, video resolution, and how text, image, and audio inputs are balanced:

```yaml
# Output and Model Configuration
output_dir: "/path/to/save/your/videos"                    # Directory to save generated videos
ckpt_dir: "/path/to/your/ckpts/dir"                        # Path to model checkpoints

# Generation Quality Settings
num_steps: 50                             # Number of denoising steps. Lower (30-40) = faster generation
solver_name: "unipc"                     # Sampling algorithm for denoising process
shift: 5.0                               # Timestep shift factor for sampling scheduler
seed: 100                                # Random seed for reproducible results

# Guidance Strength Control
audio_guidance_scale: 3.0                # Strength of audio conditioning. Higher = better audio-text sync
video_guidance_scale: 4.0                # Strength of video conditioning. Higher = better video-text adherence
slg_layer: 11                            # Layer for applying SLG (Skip Layer Guidance) technique - feel free to try different layers!

# Multi-GPU and Performance
sp_size: 1                               # Sequence parallelism size. Set equal to number of GPUs used
cpu_offload: False                       # CPU offload, will largely reduce peak GPU VRAM but increase end to end runtime by ~20 seconds

# Input Configuration
text_prompt: "/path/to/csv" or "your prompt here"          # Text prompt OR path to CSV/TSV file with prompts
mode: ['i2v', 't2v', 't2i2v']                          # Generate t2v, i2v or t2i2v; if t2i2v, it will use flux krea to generate starting image and then will follow with i2v
video_frame_height_width: [512, 992]    # Video dimensions [height, width] for T2V mode only
each_example_n_times: 1                  # Number of times to generate each prompt

# Quality Control (Negative Prompts)
video_negative_prompt: "jitter, bad hands, blur, distortion"  # Artifacts to avoid in video
audio_negative_prompt: "robotic, muffled, echo, distorted"    # Artifacts to avoid in audio
```

### 🎬 Running Inference

#### **Single GPU** (Simple Setup)
```bash
python3 inference.py --config-file ovi/configs/inference/inference_fusion.yaml
```
*Use this for single GPU setups. The `text_prompt` can be a single string or path to a CSV file.*

#### **Multi-GPU** (Parallel Processing)
```bash
torchrun --nnodes 1 --nproc_per_node 8 inference.py --config-file ovi/configs/inference/inference_fusion.yaml
```
*Use this to run samples in parallel across multiple GPUs for faster processing.*

### Memory & Performance Requirements
Below are approximate GPU memory requirements for different configurations. Sequence parallel implementation will be optimized in the future.
All End-to-End time calculated based on a 121 frame, 720x720 video, using 50 denoising steps. Minimum GPU vram requirement to run our model is **32Gb**

| Sequence Parallel Size | FlashAttention-3 Enabled | CPU Offload | With Image Gen Model | Peak VRAM Required | End-to-End Time |
|-------------------------|---------------------------|-------------|-----------------------|---------------|-----------------|
| 1                       | Yes                        | No          | No                    | ~80 GB        | ~83s         |
| 1                       | No                        | No          | No                    | ~80 GB        | ~96s         |
| 1                       | Yes                        | Yes          | No                    | ~80 GB        | ~105s         |
| 1                       | No                        | Yes          | No                    | ~32 GB        | ~118s         |
| **1**                       | **Yes**                        | **Yes**          | **Yes**                    | **~32 GB**        | **~140s**         |
| 4                       | Yes                        | No          | No                    | ~80 GB        | ~55s         |
| 8                       | Yes                        | No          | No                    | ~80 GB        | ~40s         |

### Gradio
We provide a simple script to run our model in a gradio UI. It uses the `ckpt_dir` in `ovi/configs/inference/inference_fusion.yaml` to initialize the model
```bash
python3 gradio_app.py

OR

# To enable cpu offload to save GPU VRAM, will slow down end to end inference by ~20 seconds
python3 gradio_app.py --cpu_offload

OR

# To enable an additional image generation model to generate first frames for I2V, cpu_offload is automatically enabled if image generation model is enabled
python3 gradio_app.py --use_image_gen
```
---

## 🙏 Acknowledgements

We would like to thank the following projects:

- **[Wan2.2](https://github.com/Wan-Video/Wan2.2)**: Our video branch is initialized from the Wan2.2 repository
- **[MMAudio](https://github.com/hkchengrex/MMAudio)**: Our audio encoder and decoder components are borrowed from the MMAudio project. Some ideas are also inspired from them. 

---

## ⭐ Citation

If Ovi is helpful, please help to ⭐ the repo.

If you find this project useful for your research, please consider citing our [paper](https://arxiv.org/abs/2510.01284).


### BibTeX
```bibtex
@misc{low2025ovitwinbackbonecrossmodal,
      title={Ovi: Twin Backbone Cross-Modal Fusion for Audio-Video Generation}, 
      author={Chetwin Low and Weimin Wang and Calder Katyal},
      year={2025},
      eprint={2510.01284},
      archivePrefix={arXiv},
      primaryClass={cs.MM},
      url={https://arxiv.org/abs/2510.01284}, 
}
```