FramePack: Run Locally to Create Long AI Videos on Laptops

Have you ever tried to generate AI videos only to run into frustrating hardware limitations? That's about to change with FramePack - a revolutionary approach that makes video generation feel as accessible as image generation.

FramePack solves two major challenges that have limited AI video generation until now:

1. Works on everyday hardware: Generate high-quality videos using just 6GB VRAM on a laptop GPU
2. Creates much longer videos: Produce videos up to 60 seconds (1800+ frames) at 30fps - far beyond the few seconds most tools manage

This means video generation is no longer exclusive to those with expensive, specialized hardware. Whether you're a creative professional, an indie filmmaker, or just curious about AI video creation, FramePack puts this technology within your reach.

What Makes FramePack Different?

A Simpler Way to Think About Video Generation

Traditional video generation is like trying to juggle all the frames at once - the more frames you add, the harder it becomes until you eventually drop everything. FramePack takes a smarter approach.

Imagine you're telling a story. Instead of memorizing the entire story before starting, you remember what you just said and use that to figure out what comes next. FramePack works similarly, focusing on predicting each new frame based on the previous ones.

FramePack's method is much more efficient because it doesn't need to process the entire video at once. This approach feels like image generation because each new frame builds naturally from the previous ones.

The Magic Behind FramePack's Efficiency

FramePack uses a clever system to compress previous frames in a way that preserves the most important information while using minimal memory:

• Newer frames get more detail (like remembering exactly what happened a moment ago)
• Older frames are more compressed but still provide context (like remembering the general idea of what happened earlier)

This is how FramePack maintains constant memory usage regardless of video length - a technical achievement called "O(1) computation complexity" that enables streaming video generation without growing memory requirements.

The system uses different "patchifying kernels" to encode each frame with varying levels of detail. For example, a 480p frame might use 1536 tokens with a smaller kernel for important frames, but only 192 tokens with a larger kernel for less important frames.

Smart Scheduling Options

FramePack offers flexible "scheduling" options for different video generation needs:

• Want to create a video from a single image? There's a schedule that gives more importance to your starting image
• Need consistent quality throughout a long video? Use a schedule that balances frame importance
• Creating a video with specific key moments? Prioritize those frames for better detail

These scheduling options give you control over how FramePack allocates resources to different parts of your video.

Solving the Drift Problem

One of the biggest challenges in AI video generation is "drift" - where quality deteriorates as the video gets longer, with characters changing appearance or scenes becoming unrecognizable.

FramePack addresses this with innovative "anti-drifting" techniques:

• Bi-directional sampling: Looking both forward and backward to maintain consistency
• Inverted anti-drifting: Especially useful for image-to-video generation, always keeping the first frame as a reference point

These methods break causality in the sampling process to fundamentally solve the drifting problem, rather than just applying temporary fixes that don't address the root cause.

Getting Started with FramePack

What You'll Need

Based on the official GitHub repository, FramePack runs on surprisingly modest hardware:

• GPU: NVIDIA GPU in RTX 30XX, 40XX, or 50XX series that supports fp16 and bf16 (GTX 10XX/20XX are not tested)
• OS: Windows or Linux
• Memory: At least 6GB GPU memory

To generate a 1-minute video (60 seconds) at 30fps (1800 frames) using the 13B model, you only need 6GB of GPU memory, which means laptop GPUs are perfectly capable.

Installation Options

FramePack offers two simple installation methods:

For Windows Users:

• Download the one-click package (CUDA 12.6 + PyTorch 2.6) from the official GitHub repository
• Extract the downloaded package
• Run update.bat to ensure you have the latest version (important to fix potential bugs)
• Run run.bat to launch the application

Note that models (over 30GB) will be downloaded automatically from HuggingFace when first needed.

For Linux Users:

• It's recommended to use Python 3.10
• Install PyTorch with CUDA support:

  bash
  1pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu126
  2pip install -r requirements.txt

• Launch the GUI with:

  bash
  1python demo_gradio.py

The software supports various attention mechanisms: PyTorch attention (default), xformers, flash-attn, and sage-attention. Advanced users can install these attention kernels for potential performance improvements.

Performance Expectations

FramePack delivers impressive generation speeds across different hardware setups:

• RTX 4090: ~2.5 seconds/frame (unoptimized) or ~1.5 seconds/frame (with TeaCache)
• Laptop GPUs (3070ti, 3060): About 4-8x slower than desktop GPUs

A major advantage is that you'll see frames being generated immediately as FramePack uses next-frame prediction - giving you visual feedback throughout the generation process rather than waiting for the entire video to complete.

Using the FramePack Interface

The FramePack interface is straightforward and user-friendly:

The interface is divided into two main sections:

• Left side: Upload an image and write your prompt
• Right side: View the generated videos and latent previews

As FramePack is a next-frame-section prediction model, you'll see your videos grow longer as more sections are generated. The interface displays:

• Progress bar for each section
• Latent preview for the next section
• Generated frames in real-time

Note that initial progress may be slower as your device warms up, with generation speed typically improving after the first few frames.

TeaCache Optimization

The official documentation specifically notes that TeaCache is not lossless and can sometimes significantly impact results. About 30% of users may get noticeably different (sometimes worse) results when using TeaCache.

The developers recommend:

• Using TeaCache to quickly try out ideas and experiment
• Disabling TeaCache for final high-quality renders

This recommendation also applies to other optimizations like sage-attention, bnb quant, and gguf.

Creating Amazing Videos with FramePack

Crafting Effective Prompts

According to the official documentation, concise, motion-focused prompts work best with FramePack. The developers even share a ChatGPT template they personally use:

You are an assistant that writes short, motion-focused prompts for animating images.

When the user sends an image, respond with a single, concise prompt describing visual motion (such as human activity, moving objects, or camera movements). Focus only on how the scene could come alive and become dynamic using brief phrases.

Larger and more dynamic motions (like dancing, jumping, running, etc.) are preferred over smaller or more subtle ones (like standing still, sitting, etc.).

Describe subject, then motion, then other things. For example: "The girl dances gracefully, with clear movements, full of charm."

If there is something that can dance (like a man, girl, robot, etc.), then prefer to describe it as dancing.

Stay in a loop: one image in, one motion prompt out. Do not explain, ask questions, or generate multiple options.

Effective prompt examples from the official repository include:

• "The girl dances gracefully, with clear movements, full of charm."
• "The man dances powerfully, with clear movements, full of energy."
• "The girl suddenly took out a sign that said 'cute' using right hand"
• "The girl skateboarding, repeating the endless spinning and dancing and jumping on a skateboard, with clear movements, full of charm."

From Static Images to Dynamic Videos

One of FramePack's most impressive capabilities is turning single images into flowing videos. This transformation is made possible by the specialized "inverted anti-drifting sampling" method.

For best results when creating videos from images:

1. Choose a scheduling option that prioritizes the initial frame
2. Enable inverted anti-drifting to maintain fidelity to the original image
3. Start with shorter videos (5-10 seconds) before attempting longer ones

Long Video Generation

FramePack truly shines when creating longer videos. With the ability to generate up to 60 seconds (1800+ frames) at 30fps, it achieves what would be impossible with traditional approaches.

For optimal long video generation:

1. Use anti-drifting sampling
2. Consider breaking very long narratives into segments
3. Provide detailed prompts that describe the entire sequence of events

Real-World Examples

The official GitHub repository showcases impressive examples including:

• Image-to-5-seconds videos (150 frames at 30fps)
• Image-to-60-seconds videos (1800 frames at 30fps)

All these examples were generated on a 6GB RTX 3060 laptop GPU with a 13B model variant, demonstrating the accessibility of this technology.

See More Examples

For a comprehensive collection of video examples and to experience the full capabilities of FramePack, we highly recommend visiting:

1. Official GitHub Repository: github.com/lllyasviel/FramePack - Contains numerous example videos with corresponding prompts and source images. The repository includes a "Sanity Check" section that demonstrates the results you can expect from the system.

2. Project Page: lllyasviel.github.io/frame_pack_gitpage - Features additional examples including image-to-5-seconds and image-to-60-seconds demonstrations.

These resources provide not only visual examples but also practical guidance on achieving similar results with your own inputs. By studying these examples, you can better understand how different prompts and settings affect the final output.

Conclusion

FramePack represents a significant leap forward in making AI video generation practical for everyday users. By solving the core challenges of memory requirements and video length limitations, it opens up new creative possibilities without requiring expensive hardware upgrades.

Key advantages include:

• Accessibility: Works on consumer-grade laptops with modest GPUs
• Length: Generate videos up to 60 seconds or potentially longer
• Quality: Maintains consistency throughout the video with anti-drifting techniques
• Speed: Reasonable generation times, especially with optimization options

The best way to describe FramePack is: "Video diffusion, but feels like image diffusion." This perfectly captures how it has simplified a previously complex technology.

FAQ

How does FramePack achieve such low VRAM requirements?

FramePack compresses input frames using variable patchifying kernels, maintaining constant memory usage regardless of video length. This approach reduces computational complexity to O(1), keeping memory requirements at a fixed, manageable level of around 6GB.

What's the maximum video length possible with FramePack?

Videos up to 60 seconds (1800+ frames) at 30fps have been successfully generated on a laptop GPU. Theoretically, there's no hard limit due to the O(1) complexity approach - generation time and storage space are the primary practical limitations.

What is TeaCache and how does it help?

TeaCache is FramePack's optimization technique that improves generation speed by approximately 40%. It enables generation speeds of about 1.5 seconds per frame on an RTX 4090, compared to 2.5 seconds unoptimized. However, the developers note that it's not lossless and recommend using it for experimentation rather than final renders.

What types of videos work best with FramePack?

While FramePack supports various video types, it particularly excels at image-to-video generation. The system is especially effective at creating flowing, continuous motion from static images while maintaining fidelity to the original source.