Next-Generation AI Video Creation Platform
Text to Video
Convert your text descriptions into stunning video content with just a few clicks.
Scene Generation
Create complex scenes and environments with detailed AI-generated visuals.
Audio Integration
Seamlessly match visuals with audio for immersive content experiences.
Custom Styling
Fine-tune every aspect of your video with advanced customization options.
Example Showcase
Graceful Dance
The girl dances gracefully, with clear movements, full of charm.
City Timelapse
Modern city nightscape timelapse with neon lights and traffic trails.
Energetic Movement
The man dances energetically, leaping mid-air with fluid arm swings and quick footwork.
FramePack Tutorial
Complete guide from installation to generating high-quality AI videos
Installation Guide
- Download One-Click Installer (CUDA 12.6 + PyTorch 2.6)
- Extract the downloaded file
- Run
update.batto get the latest version (Very Important!) - Run
run.batto start the application
Note: On first run, it will automatically download model files from HuggingFace (over 30GB)
Recommended to use a separate Python 3.10 environment:
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu126
pip install -r requirements.txt
# Launch GUI
python demo_gradio.pySupports --share, --port, --server parameters.
GUI Usage Guide
FramePack's interface is clean and intuitive:
- Left Panel: Upload images and write prompts
- Right Panel: Generated video and latent space preview
As a next-frame prediction model, videos are generated progressively and grow longer. You can see the progress of each section on the progress bar, as well as the latent space preview of the next section.
Initial generation may be slower as your device needs to warm up. Subsequent generations will gradually speed up.
Prompt Writing Guide
Quality prompts are key to generating high-quality videos. Here's a guide to constructing effective prompts:
Prompt Formula
[Subject] [Action Description] [Action Details], [Environment/Background Description]
Quality Prompt Examples
The girl dances gracefully, with clear movements, full of charm.The man dances powerfully, striking sharp poses and gliding smoothly across the reflective floor.The woman dances elegantly among the blossoms, spinning slowly with flowing sleeves and graceful hand movements.
Prompt Tips
- Keep it Simple: Shorter prompts typically work better
- Action First: Prioritize large actions (dancing, jumping, running) over subtle ones
- Structured Description: Describe subject first, then action, then environment
- Avoid Complexity: Overly complex descriptions may lead to confused results
AI Prompt Assistant
You can use the following ChatGPT prompt template to generate quality prompts:
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.Parameter Optimization
| Parameter | Recommended Value | Description |
|---|---|---|
| Sampling Steps | 25-50 | Higher steps = better quality, but slower speed |
| TeaCache | Enable during development | 30% faster, but may slightly impact quality |
| Seed | Random or fixed | Fixed seed allows result reproducibility |
| CFG Scale | 7-9 | Controls prompt influence strength |
| Video Length | 5-60 seconds | Shorter videos typically maintain better coherence |
Important Note About TeaCache
TeaCache can speed up generation by about 30%, but may impact generation quality. Recommendation:
- Use TeaCache for creative exploration and rapid iteration
- Disable TeaCache for final high-quality rendering
This recommendation also applies to other optimization methods like sage-attention and bnb quantization.
Technical Principles
FramePack's core innovation lies in its "frame packing" technology, which uses a special neural network structure to compress the context information of generated frames to a fixed length.
Core Advantages
- Constant Workload: Regardless of video length, the complexity of generating each frame remains constant
- Efficient Memory Management: Only 6GB VRAM needed to generate videos up to one minute long
- Process Many Frames: Even laptop-grade GPUs can process videos with many frames
- 13B Large Model: Uses a 13B-parameter large model for precise rendering
"Video diffusion that feels as easy as image diffusion"
Citation Information
@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}
}Create Your Own Professional AI Video
Generate professional-quality video content with a simple text description. No technical skills required, ready in minutes.
