Main Features
- Fixed-Length Context Compression: Compresses all input frames into fixed-length context 'notes', preventing memory usage from scaling with video length
- Minimal Hardware Requirements: Generate 60-120 second 30fps high-quality videos with only 6GB VRAM, compatible with RTX 30XX, 40XX, and 50XX series NVIDIA GPUs
- Efficient Generation: Approximately 2.5 seconds per frame on RTX 4090, reducible to 1.5 seconds per frame using teacache optimization
- Strong Anti-Drift Capabilities: Progressive compression and differential handling of frames by importance mitigates the 'drift' phenomenon
- Multiple Attention Mechanisms: Support for PyTorch attention, xformers, flash-attn, and sage-attention
Technical Features
- Based on next-frame prediction neural network structure
- Computational load decoupled from video length
- Supports FP16 and BF16 data formats
- Open-source and freely available on GitHub
Target Users
- Content creators
- Video production professionals
- AI researchers
- Users with consumer-grade GPUs
Core Advantages
- Extremely low VRAM requirements (6GB sufficient)
- Capable of generating long videos (60-120 seconds)
- Open-source and free with no usage restrictions
- Runs locally on devices, protecting privacy
Usage Workflow
- Prepare input image
- Configure generation parameters
- Start video generation
- Export high-quality video
FAQs
Q: What is Framepack AI?
A: A specialized neural network structure for AI video generation using 'next frame prediction' technology, compressing input context information to fixed length, making computational load independent of video length.
Q: What are the hardware requirements?
A: Requires NVIDIA RTX 30XX, 40XX, or 50XX series GPU with at least 6GB VRAM, compatible with Windows and Linux systems.
Q: How long can generated videos be?
A: Can generate 60-120 second 30fps high-quality videos depending on hardware configuration and optimization techniques used.
Q: What makes it different from other video generation models?
A: Main innovation is fixed-length context compression, avoiding the linear growth of context length with video time faced by traditional models, significantly reducing VRAM requirements and computational costs.
Q: Is it open-source?
A: Yes, developed by ControlNet creator Lvmin Zhang and Stanford professor Maneesh Agrawala, code and models are publicly available on GitHub.
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액세스
：
<5K
- 수집 시간：2025-09-29
-
가격 모델：
Free
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