ConsisID-preview
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Paper | arxiv.org/abs/2411.17440 |
| Framework | Diffusers, ONNX |
| Task | Image-to-Video Generation |
What is ConsisID-preview?
ConsisID-preview is an innovative AI model designed for identity-preserving text-to-video generation. Developed by the Yuan Group at PKU, it employs a unique frequency decomposition approach to maintain consistent identity features throughout generated videos.
Implementation Details
The model utilizes the Diffusers framework and supports ONNX runtime optimization. It's implemented with Safetensors for efficient model weight storage and focuses on maintaining identity consistency through specialized frequency decomposition techniques.
- Frequency-based identity preservation
- ONNX runtime support for optimization
- Safetensors implementation for efficient weight storage
- English language support
Core Capabilities
- High-fidelity identity preservation in video generation
- Text-guided video synthesis
- Image-to-video conversion
- Consistent identity maintenance across video frames
Frequently Asked Questions
Q: What makes this model unique?
ConsisID-preview stands out for its frequency decomposition approach to maintaining identity consistency in generated videos, making it particularly effective for applications requiring stable identity preservation.
Q: What are the recommended use cases?
The model is ideal for applications requiring identity-preserved video generation from images, such as character animation, digital content creation, and personalized video synthesis.
