FramePack: Open Source Image to Video Generation Software for Low GPU

In the rapidly evolving field of video generation, FramePack stands out as a groundbreaking implementation designed for next-frame prediction models. This innovative software allows users to generate videos progressively, making it a powerful tool for creators and researchers alike. The core concept behind FramePack is its ability to compress input contexts to a constant length, ensuring that the workload for video generation remains consistent regardless of the video's length. This feature is particularly beneficial for those working with extensive video datasets, as it simplifies the computational demands and enhances efficiency.

With FramePack, users can leverage a neural network structure that not only predicts the next frame but also does so with remarkable accuracy and speed. This blog post will delve into the functionalities, requirements, and installation process of FramePack, providing a comprehensive overview for potential users.

FramePack is designed to offer unique features that make it stand out from traditional video generation models. One of the key strengths of FramePack is its ability to handle a large number of frames using a 13B model, which even works on laptop GPUs. This opens up opportunities for creators who don’t have access to high-end desktop systems. The model’s architecture also enables training with a larger batch size, similar to image diffusion training, making the learning process more efficient. Additionally, FramePack uses a next-frame prediction technique, allowing real-time visual feedback during video creation—an advantage for iterative design processes. Finally, the design mimics image diffusion, making it more intuitive for users who are already familiar with image generation models.

Here are the main features of FramePack:

To effectively utilize FramePack, users must meet specific system requirements that ensure optimal performance. The software is compatible with Nvidia GPUs from the RTX 30XX, 40XX, and 50XX series, which support fp16 and bf16 precision. It's important to note that older GTX 10XX and 20XX series GPUs have not been tested, so users should consider upgrading if they wish to take full advantage of FramePack's capabilities. The minimum GPU memory required to generate a one-minute video at 30 frames per second (fps) is 6GB, which is quite accessible for many modern laptops. Below is a summary of the essential requirements for running FramePack:

On an RTX 4090 desktop, users can expect a generation speed of approximately 2.5 seconds per frame, which can be optimized to 1.5 seconds per frame with the use of TeaCache. However, on laptops like the 3070ti or 3060, the speed may be reduced by 4 to 8 times, highlighting the importance of hardware capabilities in achieving efficient video generation.

If you're using Windows, the easiest way to get started is with the FramePack One-Click Windows Installer, which comes pre-packaged with CUDA 12.6 and PyTorch 2.6. This setup requires no manual dependency installation—just download, unzip, and run. Follow the steps below to install and launch FramePack on your system.

1. Visit the GitHub Repository:
   FramePack Github Repository

2. Click on the FramePack One-Click Installer for Windows:
   Look for the link labeled:
   >>> Click Here to Download One-Click Package (CUDA 12.6 + Pytorch 2.6) <<<

3. After the download is complete, extract the ZIP file to a folder of your choice.

4. Double Click on the update.bat file

   
   This is critical to pull the latest version and fix any known bugs. Skipping this step may cause unexpected behavior due to outdated code.

5. Once the update finishes, be sure to close the terminal or command prompt window before proceeding.

6. Next, double-click the run.bat file to launch FramePack and begin downloading the necessary files.

   > Note: The models will be downloaded automatically—this process requires over 30GB of data from HuggingFace, so ensure you have a stable internet connection and sufficient disk space.

   Once the download is complete (this may take some time), the Gradio app will launch automatically, and you'll be ready to transform your images into stunning videos.

The Gradio UI provides an intuitive and user-friendly interface for interacting with FramePack. Once launched, you'll find a simple and efficient layout that allows you to easily upload an image, adjust settings, and preview results in real-time.

Framepack Gradio UI

The interface is designed to streamline the video generation process, making it accessible even for those with minimal technical expertise.

FramePack Gradio UI Settings Explained

FramePack offers several settings to help you customize the video generation process. These settings allow you to adjust rendering speed, video quality, and GPU memory usage to suit your needs and hardware capabilities. Here's a breakdown of the key options:

• Image Uploading: To upload your image, simply click the upload button in the interface or drag and drop an image onto the canvas.

• Prompt: Concise prompts work best, such as:

  • "The woman moves elegantly, with fluid motions, full of grace."

  • "The athlete runs swiftly, with strong strides, full of determination."

• Use TeaCache (Checkbox): TeaCache speeds up video rendering but may reduce visual quality. For better results, do not check the TeaCache box, but be aware that this will result in up to 2x longer rendering time.

• Total Video Length (seconds): This setting controls the maximum length of the generated video. The maximum allowed length is 120 seconds (or 2 minutes). You can adjust this to generate shorter or longer videos, depending on the input and desired output.

• Steps: The Steps setting controls the number of diffusion steps used during video generation. The default value is set to 25. It is not recommended to change this value unless you are experienced, as adjusting it can lead to suboptimal results or slow down the process. More steps typically lead to higher quality, but it also increases processing time.

• Distilled CFG Scale: The Distilled CFG Scale setting, with a default value of 10, impacts the strength of the conditioning during the video generation process. It is generally not recommended to change this setting. Adjusting it can cause unpredictable results and negatively affect the overall quality of the output.

• GPU Inference Preserved Memory (GB)
  This slider allows you to adjust the GPU memory allocation for inference. The default value should work for most users.

Note: If you encounter Out of Memory (OOM) errors during the video generation process, it's recommended to increase the GPU Inference Preserved Memory (GB) slider to 12GB or 16GB depending on your GPU's available memory.

FramePack allows you to easily convert images into dynamic videos. Below are a few examples that showcase how different prompts and images can result in unique video outputs, giving you a sense of the possibilities when using this tool. These examples highlight the versatility of FramePack, whether you're working with graceful movements or powerful actions.

Example 1: Realistic

Prompt: The woman moves gracefully, wind flowing through her hair, her smile soft and sensual, full of allure.

Example 2:

Prompt: The woman moves gracefully, slow shoulder movements, her smile soft and sensual.

For more examples, check out the official showcase page, which features a curated selection of safe-for-work demos. While the examples are censored, the tool itself is fully uncensored—giving you the freedom to generate content without restrictions, depending on your use case.

In conclusion, FramePack represents a significant advancement in the field of video generation, offering a robust and efficient solution for next-frame prediction. Its ability to compress input contexts, handle large frame counts, and provide real-time feedback makes it an invaluable tool for both researchers and content creators. As the software continues to evolve, we can expect further enhancements in its capabilities and performance. The accessibility of FramePack, particularly for users with mid-range hardware, democratizes video generation, allowing a broader audience to explore creative possibilities. For those interested in diving deeper into the world of video generation, FramePack is a must-try tool that promises to deliver high-quality results with minimal hassle. As the community grows and more examples are shared, the potential applications of FramePack will undoubtedly expand, paving the way for innovative projects in the realm of video content creation.