FramePack is an innovative neural network structure designed for next-frame (or next-frame-section) prediction in video generation. It compresses input contexts to a constant length, making the generation workload invariant to video length. This allows processing a large number of frames even on laptop GPUs with 13B models. FramePack can be trained with batch sizes similar to image diffusion training, making video diffusion as practical as image diffusion.
Key features:
- Processes very large numbers of frames efficiently
- Constant workload regardless of video length
- Can run on laptop GPUs (minimum 6GB VRAM)
- Generates videos progressively (next-frame prediction)
- Supports various attention mechanisms (PyTorch, xformers, flash-attn, sage-attention)
- Includes a user-friendly GUI for easy operation
Use cases:
- Video generation from single images
- Creating dynamic content from static inputs
- AI-assisted video production
- Research in video diffusion models
