FramePack: O(1) Video Diffusion on Consumer GPUs

Community Article • Published 2025‑04‑17

• GitHub https://github.com/lllyasviel/FramePack
• Paper Packing Input Frame Context in Next‑Frame Prediction Models for Video Generation (arXiv, 2025)
• Project Page / Demo https://lllyasviel.github.io/frame_pack_gitpage/
• Website https://framepack.ai/

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Table of Contents

• Introduction
• Technical Innovation
• Key Features
  • O(1) Context Packing
  • Anti‑Drifting Bidirectional Sampling
  • Laptop‑Friendly Performance
  • Open‑Source Desktop App
• Practical Applications
• Ethical Considerations
• Technical Specifications
• Conclusion
• References

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Introduction

FramePack is a next‑frame (or next‑frame‑section) prediction framework that shrinks the memory cost of video diffusion to a constant, independent of clip length. It can generate thousands of 30 fps frames on as little as 6 GB VRAM, turning “video diffusion” into an experience as lightweight as image diffusion.

Compared with autoregressive video models (error accumulation) or conventional diffusion pipelines (memory explosion), FramePack compresses spatio‑temporal context before each sampling step. A 13 B‑parameter variant therefore runs smoothly on laptops while still scaling to batch 64 training on a single 8 × A100/H100 node.

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Technical Innovation

1. Constant‑Length Context Packing
   Every past frame is tokenized with a variable patch size so the total token count stays capped. Compute therefore scales O(1) regardless of video length.

2. FramePack Scheduling
   Built‑in schedules let you decide which frames get more tokens—e.g. emphasize the first frame for image‑to‑video tasks—without breaking the constant‑cost guarantee.

3. Anti‑Drifting & Inverted Anti‑Drifting Sampling
   Two bidirectional sampling strategies periodically re‑anchor generation to the first frame, removing long‑horizon drift.

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Key Features

O(1) Context Packing

• Compress arbitrary‑length context to a fixed token budget.
• Train batch 64, 13 B models on one 8‑GPU server.

Anti‑Drifting Bidirectional Sampling

• Breaks strict causality to resample past frames and prevent quality decay over hundreds or thousands of frames.

Laptop‑Friendly Performance

• 6 GB VRAM → 60 s (1 800 frames) at 30 fps with a 13 B model.
• RTX 4090 → 1.5 s / frame (TeaCache) or 2.5 s / frame unoptimized.

Open‑Source Desktop App

• Gradio GUI: upload initial frame + prompt and watch the clip extend in real time.
• Supports PyTorch attention, xformers, flash‑attn, Sage‑Attention, and convenient CLI flags (--share, --port, …).

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Practical Applications

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Ethical Considerations

• Copyright & Style → Make sure you own (or are licensed to use) the input frames and any style references.
• Deepfake Risk → Re‑anchoring to the first frame preserves identity well; always obtain explicit consent.
• Disclosure → Clearly label AI‑generated footage and note any artifacts.

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Technical Specifications

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Conclusion

FramePack collapses the gap between image and video diffusion: constant‑cost context packing, bidirectional anti‑drift sampling, and an easy desktop GUI push 30 fps long‑form generation onto everyday hardware. Whether you’re an indie creator, graduate student, or industry researcher, FramePack offers a playground for hours of coherent AI video without the usual memory wall.

Try the demo, star the repo, and share your experiments—let’s make long‑video generation as accessible as Stable Diffusion made images.

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References

• Zhang L., & Agrawala M. (2025). Packing Input Frame Contexts in Next‑Frame Prediction Models for Video Generation. arXiv.
• FramePack GitHub Repository. https://github.com/lllyasviel/FramePack
• FramePack Project Page & Demos. https://lllyasviel.github.io/frame_pack_gitpage/