Stable Video Diffusion

Stable Video Diffusion is a content tool. Stability AI's open generative video model generates short clips from text prompts, with 14 or 25 frames at customizable frame rates and processing in 2 minutes or less. It is licensed for self-hosted deployment rather than hosted consumer use.

Stable Video Diffusion is Stability AI's first open generative video model, built on the same lineage as the Stable Diffusion image model. Instead of producing a single still frame, it accepts a text prompt and returns a short video clip. The model generates 14 or 25 frames per clip, and those frames can be played back at customizable frame rates between 3 and 30 frames per second. Stability AI states that processing completes in 2 minutes or less, which puts it in the range of quick iteration rather than overnight batch jobs. The distribution model is the defining characteristic. Stable Video Diffusion is not offered primarily as a hosted consumer app. It is licensed for self-hosted deployment, meaning teams download the model and run it inside their own environment. That appeals to studios, research groups and product teams that need to keep prompts and outputs on their own infrastructure, or that want to fine-tune and customize the model beyond what a closed API would allow. Stability AI positions the self-hosted license as the route for advanced customization. The tool fits a specific slot among generative video options. Users who want a point-and-click interface with no setup are better served by hosted video apps. Stable Video Diffusion is aimed at people who are comfortable with model weights, inference hardware and deployment work, and who value control and openness over convenience. Short clips, explicit frame counts and adjustable frame rate are the practical output parameters stated on the page. The page is lean on details: it does not list training data, resolution, codecs, GPU requirements or a step-by-step installation guide. Buyers should treat the license page as the starting point and expect to evaluate the model in their own stack before committing. It is a serious tool for a technical audience, not a finished consumer product.