Generate a short video from a still photo and a text description using a home RTX laptop GPU
Experiment with long video generation without running out of GPU memory thanks to fixed-size context compression
Use the Gradio web GUI to create and preview videos without writing any Python code
Research next-frame video diffusion with FramePack-F1 and FramePack-P1 model variants included in the repo
| lllyasviel/framepack | binux/pyspider | exaloop/codon | |
|---|---|---|---|
| Stars | 16,810 | 16,810 | 16,769 |
| Language | Python | Python | Python |
| Setup difficulty | hard | moderate | moderate |
| Complexity | 4/5 | 3/5 | 4/5 |
| Audience | general | data | researcher |
Figures from each repo's GitHub metadata at analysis time.
Requires an Nvidia RTX 30/40/50 series GPU with at least 6 GB VRAM, CUDA-only with no CPU fallback.
FramePack is the official implementation of a research project on video generation by neural networks, specifically, a technique called "Frame Context Packing and Drift Prevention in Next-Frame-Prediction Video Diffusion Models." In plain terms, it is software that turns a still image and a text prompt into a video, generating the video one chunk of frames at a time. The headline promise on the README is "video diffusion, but feels like image diffusion." The technical idea is that FramePack compresses the past frames it has already generated into a fixed-size context, so the work needed to predict the next frame stays the same no matter how long the video gets. That means a single GPU can keep generating frames for a minute-long clip without running out of memory, and the project ships as functional desktop software with its own sampling system and memory management. The repository also documents follow-up versions FramePack-F1 and the upcoming FramePack-P1, which adds "Planned Anti-Drifting" and "History Discretization" to keep long generations from drifting away from the prompt. Anyone curious about generating short videos from a still image and prompt is the audience, particularly people who do not have access to a data-center GPU, the README states a 6 GB Nvidia laptop GPU is enough to generate a 60-second video at 30 frames per second using a 13-billion-parameter model. The software runs on Linux or Windows with an RTX 30, 40, or 50 series card, offers a one-click Windows package, and on Linux installs through pip on Python 3.10 with PyTorch. It exposes a Gradio web GUI. The full README is longer than what was provided.
Desktop software that turns a still image and a text prompt into a video by generating frames one chunk at a time, using a fixed-memory technique that lets a 6 GB laptop GPU produce 60-second clips at 30 fps.
Mainly Python. The stack also includes Python, PyTorch, CUDA.
Setup difficulty is rated hard, with roughly 1h+ to a first successful run.
Mainly general.
This repo across BitVibe Labs
Verify against the repo before relying on details.