FramePack AI Model Ecosystem

13B parameter video diffusion models running on consumer hardware - 6GB VRAM support via patented context packing technology

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3-Step FramePack AI Workflow

From installation to Hollywood-quality video generation

  1. Download from FramePack GitHub: Get one-click installers for Windows/Linux with automatic HuggingFace model fetching
  2. Configure via ComfyUI: Implement custom nodes for FramePack text-to-video pipelines with automatic mixed precision
  3. Generate & Export: Create 4K videos using FramePack AI's anti-drifting sampler with live latent space previews

FramePack Model Architecture Deep Dive

How does FramePack AI's context compression enable 13B models on 6GB GPUs?

FramePack AI implements dynamic token allocation through our Temporal Patch Compression (TPC) system. By adaptively resizing frame representations from 1536 tokens (1x2x2 patch) to 192 tokens (2x4x4), FramePack models maintain constant VRAM usage. The GitHub repository includes memory schedulers for RTX 3060/4090 optimization.

What video formats/resolutions does FramePack AI support?

FramePack AI models natively output 480p videos at 30fps through our HunyuanVideo-based architecture. Through FramePack ComfyUI nodes, users can upscale to 4K using integrated ESRGAN implementations. The GitHub version supports custom aspect ratios via latent space reshaping.

How to integrate FramePack AI with existing ComfyUI workflows?

Use our dedicated FramePack ComfyUI wrapper: 1) Install via GitHub community nodes 2) Load either fp8_e4m3fn or bf16 model variants 3) Connect SigCLIP text encoder nodes. The FramePack scheduler node allows real-time compression ratio adjustments during generation.

What's the difference between FramePack and traditional video diffusion?

Unlike stable video diffusion's fixed UNet architecture, FramePack AI employs: 1) Bidirectional attention blocks 2) Section-based latent caching 3) Adaptive patchification. This allows 1800-frame generations vs standard 24-frame limits. Our GitHub paper comparison shows 68% lower VRAM usage.

Can I combine FramePack AI with ControlNet?

Yes! The FramePack GitHub repository includes experimental depth/normal ControlNet adapters. Through ComfyUI, users can: 1) Load FramePack base model 2) Connect ControlNet annotations 3) Enable cross-attention guidance. This enables pose-consistent character animation across 1000+ frames.

How to troubleshoot CUDA errors in FramePack AI?

For common issues: 1) Verify CUDA 12.6+ via GitHub install guide 2) Disable xformers if using 10XX GPUs 3) Allocate 5GB+ VRAM buffer. The FramePack ComfyUI implementation includes automatic memory scaling - reduce 'max_frames' parameter if encountering OOM errors during video generation.