FramePackAI Video Generator

Diffuse 1800+ frames at 30fps using 13B models on consumer GPUs

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Adopted by 5000+ creators & researchers worldwide

Why Choose FramePack AI?

Laptop-Friendly 13B Models

Generate 60-second videos (1800 frames) on 6GB GPUs using FramePack AI's revolutionary memory compression

Seamless ComfyUI Integration

Access FramePack AI workflows through popular ComfyUI nodes with automatic model downloads

Anti-Drifting Technology

Bi-directional sampling ensures stable long-form generation - 10x longer videos without quality decay

Open-Source Foundation

Modify and extend FramePack AI through our active GitHub repository with 2K+ stars

Real-Time Preview System

Watch frames generate live with FramePack AI's progressive next-section prediction

Enterprise-Grade Batch Training

Train video models at batch size 64 on single-node A100/H100 clusters

FramePack AI Expert Insights

How does FramePack AI achieve 6GB VRAM usage for 13B models?

FramePack AI's patented context packing algorithm compresses input frames to constant-length latent representations through adaptive patchification. This FramePack technology enables O(1) memory complexity regardless of video length, making 13B model inference feasible on mobile GPUs.

Can I use FramePack AI for commercial video production?

Yes! FramePack AI's GitHub repository (Apache 2.0 license) allows commercial use. Our ComfyUI integration enables direct pipeline deployment for studios. The FramePack architecture supports 4K resolution through scalable patchification strategies.

What makes FramePack AI different from stable video diffusion?

FramePack AI introduces three innovations: 1) Context-aware frame packing 2) Bi-directional anti-drifting sampling 3) Section-based progressive generation. These enable 10x longer videos than conventional video diffusion models at equal compute resources.

How to install FramePack AI on Windows?

Windows users can download our one-click package containing CUDA 12.6 + PyTorch 2.6. After unzipping, run update.bat and launch via run.bat. The FramePack GitHub repository provides detailed troubleshooting guides for driver configurations.

Does FramePack AI support AMD GPUs?

Currently FramePack AI optimizes for NVIDIA GPUs (30XX/40XX/50XX series) with fp16/bf16 support. While ROCm compatibility is experimental, we recommend using FramePack ComfyUI implementation through WSL2 for AMD users.

How to achieve best results with FramePack text-to-video?

For FramePack AI text-to-video: 1) Use explicit motion descriptors ('dancing with 3 rotations') 2) Maintain temporal consistency through TeaCache 3) Start with 128-frame generations 4) Use our SigCLIP prompt enhancer. Visit our GitHub for example prompts.

What's the difference between TeaCache and full diffusion?

FramePack's TeaCache is an attention optimization that speeds generation by 40% but may reduce fidelity. For final renders, disable TeaCache and use full Eularian sampling. Our GitHub benchmarks show 0.78 FVD improvement with full diffusion.

Can I train custom models with FramePack architecture?

Absolutely! FramePack AI's GitHub provides training scripts supporting 8xA100 node configurations. The frame packing scheduler API allows custom compression patterns. Community models are shared via HuggingFace - check our 'framepack-models' organization.

How to handle video drifting in long generations?

Enable FramePack AI's anti-drifting sampler in advanced settings. This implements our paper's inverted bidirectional sampling that anchors to initial frames. Combined with CFG=7.5 and 50-step diffusion, you can achieve 5-minute stable generations.

What's the relation between FramePack and HunyuanVideo?

FramePack AI is the open-source evolution of HunyuanVideo's core technology. We've decoupled the framework while maintaining compatibility - you can load HunyuanVideo checkpoints directly through FramePack GitHub implementation or ComfyUI nodes.