FramePack Studio

FramePack Studio is an enhanced version of the FramePack demo script, designed to create intricate video scenes with improved prompt adherence. This is very much a work in progress, expect some bugs and broken features.

Current Features

• Timestamped Prompts: Define different prompts for specific time segments in your video
• Basic LoRA Support: Works with most (all?) hunyuan LoRAs but the implementation is a bit rough around the edges
• Queue System: Process multiple generation jobs without blocking the interface
• Metadata Saving/Import: Prompt and seed are encoded into the output PNG, all other generation metadata is saved in a JSON file

Fresh Installation

Prerequisites

• Python 3.8+
• CUDA-compatible GPU with at least 8GB VRAM (16GB+ recommended)

Setup

1. Clone the repository:

   git clone https://github.com/yourusername/framepack-studio.git
   cd framepack-studio

2. Install dependencies:

   pip install -r requirements.txt

Add to an Existing FramePack Installation

Setup

1. Drop studio.py, the 'modules' folder and requirements.txt into the root of your FramePack installation.

2. Install dependencies:

   pip install -r requirements.txt

Usage

Run the studio interface:

python studio.py

Additional command line options:

• --share: Create a public Gradio link to share your interface
• --server: Specify the server address (default: 0.0.0.0)
• --port: Specify a custom port
• --inbrowser: Automatically open the interface in your browser

LoRAs

Add LoRAs to the /loras/ folder at the root of the installation. Each LoRA in the folder will be loaded when Studio loads. Then you can set the weight of each LoRA for each generation job, LoRAs and their weights are saved with the other metadata in a job's JSON file.

Working with Timestamped Prompts

You can create videos with changing prompts over time using the following syntax:

[0s] A serene forest with sunlight filtering through the trees
[5s] A deer appears in the clearing
[10s] The deer drinks from a small stream

Each timestamp defines when that prompt should start influencing the generation. The system will (hopefully) smoothly transition between prompts for a cohesive video.

Credits

Many thanks to Lvmin Zhang for the absolutely amazing work on the original FramePack code!

@article{zhang2025framepack,
    title={Packing Input Frame Contexts in Next-Frame Prediction Models for Video Generation},
    author={Lvmin Zhang and Maneesh Agrawala},
    journal={Arxiv},
    year={2025}
}