AI GitHub
HunyuanVideo for experimenting with text-to-video
Use Tencent's reference implementation and checkpoints for generating clips from text.
Key Takeaways
- The inference code connects text conditioning, the generation model and video decoding.
- Start with a documented inference configuration and known artifacts.
- Use a single shot with one clear motion and a fixed seed.
- Large video models can require substantial GPU memory.
How it works
The inference code connects text conditioning, the generation model and video decoding. The official instructions specify the available artifacts and execution options.
What you need
Start with a documented inference configuration and known artifacts. Track which text encoders and video components it expects before swapping individual files.
What to try first
Use a single shot with one clear motion and a fixed seed. Review frame-to-frame motion before adding multiple subjects or camera actions.
Log the chosen precision and output dimensions. A seemingly small configuration change can determine whether a short clip fits on the device.
Where to look for failures
Large video models can require substantial GPU memory. Follow hardware guidance for the chosen configuration and check the separate model terms.
Related Franklin pages
Compare complete clips through playback and frame inspection. Keep prompt, duration and dimensions as similar as supported, and inspect whether subject and camera motion match the task.
- Explore the official CogVideoX code and workflows for supported video tasks. CogVideo for experimenting with video diffusion models
- Experiment with the official Wan text-to-video and image-to-video workflows. Wan 2.1 for running video generation checkpoints
For video assembled from authored HTML and motion graphics, see Franklin’s HyperFrames profile. HyperFrames
Project documentation
Try it for your workflow
Explore the project’s documentation for setup and usage, or save this page to a collection for later.
Read the project documentation ↗