HunyuanVideo Keyframe Control LoRA
| Property | Value |
|---|---|
| Author | dashtoon |
| Model Type | Video Generation LoRA Adapter |
| Framework | Diffusion Transformer (DiT) |
| Repository | Hugging Face |
What is hunyuan-video-keyframe-control-lora?
HunyuanVideo Keyframe Control LoRA is an innovative adapter designed to enhance video generation capabilities through precise keyframe control. Built on top of the HunyuanVideo T2V model, it introduces sophisticated modifications to the architecture that enable users to define start and end frames for more controlled video generation outcomes.
Implementation Details
The model implements several technical innovations, including modified input patch embedding projection layers and comprehensive Low-Rank Adaptation across all linear layers. This architecture enables efficient processing of image inputs within the Diffusion Transformer framework while maintaining model efficiency through reduced parameter training.
- Modified input patch embedding for keyframe integration
- LoRA implementation across all linear and convolutional input layers
- Optimized for specific video resolutions: 720x1280, 544x960, 1280x720, 960x544
- Support for 33-97 frames, with potential extension to 121 frames
Core Capabilities
- Precise keyframe-based video generation control
- Optimized performance for human subjects
- Flexible prompt handling for enhanced generation guidance
- Efficient inference with adjustable step counts (30-50 recommended)
Frequently Asked Questions
Q: What makes this model unique?
This model's uniqueness lies in its ability to provide precise control over video generation through keyframe conditioning, while maintaining efficiency through LoRA adaptation. It specifically excels at handling human subjects and offers flexibility in resolution and frame count settings.
Q: What are the recommended use cases?
The model is best suited for generating videos featuring single human subjects, with optimal performance at specific resolutions (720x1280, 544x960, 1280x720, 960x544). It works effectively with both simple and detailed prompts, making it versatile for various video generation applications.
