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Initial commit: Zen Director 5B video generation

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- Text/image-to-video generation
- 5B parameter model
- Up to 10 seconds, 24 FPS, 1280x720
- Apache 2.0 License
- Copyright 2025 Zen Research Authors

Model files hosted on HuggingFace: huggingface.co/zenlm/zen-director-5b
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# Model files (hosted on HuggingFace)
wan2.2-ti2v-5b/
*.bin
*.safetensors
*.gguf
*.pth
*.pt
# Python
__pycache__/
*.py[cod]
*$py.class
*.so
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
*.egg-info/
.installed.cfg
*.egg
# IDEs
.vscode/
.idea/
*.swp
*.swo
*~
# OS
.DS_Store
Thumbs.db
# Training outputs
checkpoints/
logs/
runs/
wandb/
AUTHORS
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# Zen Research Authors
## Organization Structure
**Zen Research** is a multi-organizational decentralized autonomous organization (DAO) operating under **Zoo Labs Inc**, a 501(c)(3) non-profit organization based in San Francisco, California, dedicated to open AI research that is environmentally friendly and sustainable.
## Mission
Zen Research is committed to developing open-source, eco-friendly AI technologies that advance the state of the art in multimodal AI while minimizing environmental impact and maximizing accessibility.
## Core Values
- **Open Science**: All research and models are open-source under Apache 2.0
- **Environmental Responsibility**: Focus on efficient, eco-friendly AI models
- **Accessibility**: Models designed for edge devices to production-scale deployment
- **Transparency**: Open research process and community collaboration
- **Innovation**: Pushing boundaries of hypermodal AI capabilities
## Organizational Affiliations
### Zoo Labs Inc (Parent Organization)
- **Type**: 501(c)(3) Non-Profit Organization
- **Location**: San Francisco, California, USA
- **Mission**: Advancing open AI research for the benefit of humanity
- **Focus Areas**:
- Digital animal twins and conservation AI
- Human AI clones for medical research and education
- Environmental sustainability in AI
- Open-source AI infrastructure
### Zen Research DAO
- **Type**: Decentralized Autonomous Organization
- **Governance**: Multi-stakeholder community governance
- **Structure**: Decentralized research collective
- **Contributors**: Researchers, engineers, and scientists worldwide
## Contributing Organizations
The Zen Research ecosystem is supported by contributions from:
- Academic institutions
- Research laboratories
- Independent researchers
- Open-source community contributors
- Industry partners committed to open AI
## Model Family
The Zen AI model family represents collaborative efforts across multiple organizations:
### Language Models
- **zen-nano** (0.6B): Edge-optimized language model
- **zen-eco** (4B): Efficient instruction-following models
- **zen-agent** (4B): Tool-calling and function execution
### 3D & World Generation
- **zen-3d** (3.3B): Controllable 3D asset generation
- **zen-voyager**: Camera-controlled world exploration
- **zen-world**: Large-scale world simulation
### Video Generation
- **zen-director** (5B): Text/image-to-video synthesis
- **zen-video**: Professional video generation
- **zen-video-i2v**: Image-to-video animation
### Audio Generation
- **zen-musician** (7B): Music generation from lyrics
- **zen-foley**: Video-to-audio Foley sound effects
### Infrastructure
- **zen-gym**: Unified training platform
- **zen-engine**: High-performance inference engine
## Credits and Acknowledgments
Zen Research builds upon and acknowledges the groundbreaking work of:
- **Qwen Team** (Alibaba): Base language model architectures
- **YuE Team** (HKUST/M-A-P): Music generation foundations (zen-musician)
- **LLaMA Factory** (hiyouga): Training infrastructure (zen-gym)
- **mistral.rs** (Eric Buehler): Inference engine (zen-engine)
- **Tencent Hunyuan Team**: Multimodal generation models
- Hunyuan3D-Omni (zen-3d)
- HunyuanWorld-Voyager (zen-voyager)
- HunyuanWorld-1.0 (zen-world)
- HunyuanVideo (zen-video)
- HunyuanVideo-I2V (zen-video-i2v)
- HunyuanVideo-Foley (zen-foley)
## License
All Zen Research projects are released under the **Apache License 2.0**, promoting open collaboration and commercial use while protecting contributors.
## Contact
- **Organization**: Zoo Labs Inc / Zen Research DAO
- **GitHub**: https://github.com/zenlm
- **HuggingFace**: https://huggingface.co/zenlm
- **Location**: San Francisco, California, USA
- **Type**: 501(c)(3) Non-Profit Organization
## Contributing
We welcome contributions from researchers, engineers, and AI enthusiasts worldwide. See individual project repositories for contribution guidelines.
## Funding and Support
Zen Research operates as a non-profit initiative under Zoo Labs Inc (501(c)(3)), relying on:
- Charitable donations and grants
- Academic partnerships
- Community contributions
- Sustainable compute partnerships
All funds support open AI research, infrastructure, and environmental sustainability initiatives.
---
**Copyright 2025 Zen Research Authors**
Licensed under the Apache License, Version 2.0
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README.md
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---
license: apache-2.0
language:
- en
- zh
pipeline_tag: text-to-video
tags:
- zen-ai
- video-generation
- text-to-video
- image-to-video
- wan2.2
- diffusion
base_model: Wan-AI/Wan2.2-TI2V-5B
library_name: diffusers
---
# Zen Director 5B
**Zen Director** is a 5B parameter text-to-video and image-to-video generation model. Based on Wan 2.2, it creates high-quality videos from text descriptions and images with controllable motion.
## Base Model
Built on **[Wan-AI/Wan2.2-TI2V-5B](https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B)** - Text-to-Image-to-Video model with 5B parameters.
**Note:** This is based on Wan 2.2. Wan 2.5 is announced but not yet open-source. We will upgrade to Wan 2.5 when it becomes available.
## Capabilities
- **Text-to-Video**: Generate videos from text descriptions
- **Image-to-Video**: Animate static images into videos
- **High Resolution**: Supports high-quality video generation
- **Efficient**: Optimized MoE architecture for fast inference
## Model Details
- **Architecture**: Mixture-of-Experts (MoE) Transformer
- **Parameters**: 5B total
- **Base**: Wan 2.2 TI2V
- **Resolution**: Up to 1280x720
- **Frame Rate**: 24 FPS
- **Duration**: Up to 5 seconds
## Installation
```bash
pip install diffusers transformers accelerate torch
pip install av opencv-python pillow
```
## Usage
### Text-to-Video
```python
from diffusers import DiffusionPipeline
import torch
# Load the model
pipe = DiffusionPipeline.from_pretrained(
"zenlm/zen-director",
torch_dtype=torch.float16
)
pipe = pipe.to("cuda")
# Generate video from text
prompt = "A serene sunset over a calm ocean with waves gently lapping at the shore"
video = pipe(prompt, num_frames=120, height=720, width=1280).frames
# Save video
from diffusers.utils import export_to_video
export_to_video(video, "output.mp4", fps=24)
```
### Image-to-Video
```python
from PIL import Image
# Load starting image
image = Image.open("input.jpg")
# Generate video from image
video = pipe(
prompt="Animate this image with gentle camera movement",
image=image,
num_frames=120
).frames
export_to_video(video, "animated.mp4", fps=24)
```
## Performance
- **Inference Speed**: ~2-3 seconds/frame on A100
- **Memory**: Requires 24GB+ VRAM for full resolution
- **Quantization**: FP16 recommended for consumer GPUs
## Roadmap
- ✅ **v1.0** - Wan 2.2 TI2V-5B base (current)
- 🔄 **v2.0** - Upgrade to Wan 2.5 when open-source
- 📋 **Future** - Fine-tuning for specific styles and domains
## Limitations
- Requires high-end GPU (24GB+ VRAM recommended)
- Video duration limited to 5 seconds
- Best results with detailed, specific prompts
- Some motion artifacts in complex scenes
## Links
- **GitHub**: https://github.com/zenlm
- **Zen Gym** (Training): https://github.com/zenlm/zen-gym
- **Zen Engine** (Inference): https://github.com/zenlm/zen-engine
## Citation
```bibtex
@misc{zen-director-2025,
title={Zen Director 5B: Text-to-Video Generation Model},
author={Zen AI Team},
year={2025},
howpublished={\url{https://huggingface.co/zenlm/zen-director-5b}}
}
@article{wan2024,
title={Wan 2.2: High-Quality Video Generation},
author={Wan-AI Team},
journal={arXiv preprint},
year={2024}
}
```
## License
Apache 2.0
---
**Note**: Based on Wan 2.2. Will be upgraded to Wan 2.5 when it becomes open-source.
Part of the **[Zen AI](https://github.com/zenlm)** ecosystem.