Framepackai
Framepack AI is an open-source neural network architecture for next-frame video generation that compresses input frames into fixed-length context notes, enabling generation of high-quality long-form videos (up to 60–120s at 30fps) on consumer NVIDIA GPUs with as little as 6GB VRAM.
Framepackai is video generation software teams evaluate for creative & design. Use this page to review pricing, integration signals, and the best alternatives before you commit.
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Quick Overview
Best for: Creative & Design
What it does
Video Generation software for decision-makers comparing workflow fit and alternatives.
Best fit
Creative & Design
Pricing snapshot
Free from Free
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Framepackai
Framepack AI is a specialized neural network structure designed for AI video generation using a next-frame prediction approach and a novel fixed-length context compression strategy. By compressing input frames into fixed-length "notes" and evaluating frame importance progressively, Framepack prevents memory usage from scaling with video length, dramatically reducing VRAM requirements compared with traditional video generation methods. It targets creators, researchers, and developers who need to generate long-form, consistent videos on consumer-grade NVIDIA GPUs.
Developed and released as open-source by Lvmin Zhang (ControlNet creator) and Maneesh Agrawala (Stanford), Framepack provides tools and models on GitHub and an active community ecosystem. The project emphasizes accessibility (minimal hardware needs), efficient generation, anti-drift mechanisms for consistent long videos, and flexible attention backends for hardware optimization.
Framepack AI is an open-source neural network architecture for next-frame video generation that compresses input frames into fixed-length context notes, enabling generation of high-quality long-form videos (up to 60–120s at 30fps) on consumer NVIDIA GPUs with as little as 6GB VRAM.
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Fixed-Length Context Compression
Compresses all input frames into fixed-length context 'notes' so memory usage does not grow linearly with video length. This enables generation of long videos without proportionally increasing VRAM.
Minimal Hardware Requirements
Capable of generating high-quality videos up to 60–120 seconds at 30fps on consumer GPUs with as little as 6GB of VRAM. Supported GPUs include NVIDIA RTX 30XX, 40XX, and 50XX series.
Efficient Generation
Frame generation speed is approximately 2.5 seconds per frame on an RTX 4090 desktop GPU, with optimizations (teacache) that can reduce generation to ~1.5 seconds per frame.
Strong Anti-Drift Capabilities
Uses progressive compression and differential handling of frames based on importance to mitigate drift, maintaining consistent quality across long videos.
Multiple Attention Mechanisms
Supports multiple attention backends (PyTorch attention, xformers, flash-attn, and sage-attention) to allow flexible optimization for different hardware and performance requirements.
Open-Source and Community-Driven
Fully open-source with code and models available on GitHub. Developed by well-known contributors and supported by an active community and ecosystem.
Pricing
Framepack AI is fully open-source and free to use; code and models are publicly available on GitHub.
Open Source
Free- Full access to code and models on GitHub
- Community support and ecosystem
- Local execution on supported hardware
Use Cases
Long-form AI video generation
Create consistent high-quality videos up to 60–120 seconds at 30fps without requiring server-scale VRAM, useful for storytelling, demonstrations, and content creation.
GPU-constrained workflows
Enable creators and hobbyists with consumer NVIDIA GPUs (6GB VRAM) to generate long videos that would otherwise require much larger memory budgets.
Research and model development
Researchers and engineers can experiment with next-frame prediction, compression strategies, and attention backends in an open-source codebase.
Integration and experimentation
Developers can integrate Framepack models and optimizations into existing pipelines or combine with other open-source tools and community models.
Integrations
PyTorch
Primary deep learning framework support for model execution and training.
xformers
Optional attention backend for performance and memory optimizations on supported hardware.
flash-attn
High-performance attention implementation that can accelerate generation on compatible GPUs.
sage-attention
Additional attention backend supported to provide flexibility for different setups and performance trade-offs.
NVIDIA RTX GPUs
Optimizations and compatibility targeting RTX 30XX, 40XX, and 50XX series GPUs for efficient local generation.
Benefits
Limitations
Frequently Asked Questions
What is Framepack AI?
What are Framepack AI's hardware requirements?
How long can videos generated by Framepack AI be?
What makes Framepack AI unique?
Is Framepack AI open-source?
Getting Started
- 1 Visit the Framepack AI website or the project's GitHub repository to access code, models, and demos.
- 2 Ensure you have a supported NVIDIA GPU (RTX 30XX/40XX/50XX) with at least 6GB VRAM and install required dependencies (PyTorch and recommended attention backends).
- 3 Clone the repository, follow the project README for setup and example commands, and try the provided demos or example scripts to generate videos.
Support
Docs
Project documentation and README available via the project's GitHub repository (link available on the website).
Community
Active community ecosystem and social media discussion (tweets and community posts) for examples, demos, and help.
GitHub issues
Report bugs, request features, and engage with developers via the project's GitHub issues tracker.
API
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