The open-source video generation ecosystem is evolving rapidly, and Diffusers — the popular Hugging Face library — is at the center of it. This article explores the current state of open video generation models available through Diffusers, highlighting key models, their capabilities, and limitations.
Key Models
- ModelScope Text-to-Video: One of the earliest open models, generating 16-frame videos at 256x256. It works but struggles with motion consistency.
- AnimateDiff: A popular approach that adds motion modules to existing image diffusion models. It supports longer videos and diverse styles.
- VideoFusion: Focuses on decomposed diffusion for better temporal coherence.
Challenges
Open video models face hurdles: high computational cost, limited video length, and flickering artifacts. However, community contributions are rapidly improving quality.
Getting Started
To experiment, install the latest diffusers and transformers libraries. Most models require a GPU with at least 16GB VRAM for reasonable inference times.
from diffusers import DiffusionPipeline
pipe = DiffusionPipeline.from_pretrained("damo-vilab/text-to-video-ms-1.7b")
video = pipe("a cat walking on a beach").frames[0]
Future Outlook
The community is converging on better evaluation metrics and shared benchmarks. Expect more powerful models with higher resolution and longer duration in coming months.