Framepack AI

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

1. Prepare input image
2. Configure generation parameters
3. Start video generation
4. 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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• 収集時間：2025-09-29
• 価格設定モデル： Free

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