Stable Diffusion Videos

Stable Diffusion Videos is an open-source tool by Nate Raw that creates videos with Stable Diffusion by walking through latent space and morphing smoothly between text prompts. With roughly 4,700 stars on GitHub, it turned the classic latent-walk trick into an accessible Python package and command-line tool for generating hypnotic, music-video-style AI animations.

Overview

The idea is elegant: interpolate between the noise seeds and prompts of several keyframe images, render every intermediate frame with Stable Diffusion (via Hugging Face Diffusers), and stitch the frames into a video. The result is a continuous morph where scenes melt into one another. The tool handles prompt scheduling, interpolation curves, upscaling and audio muxing, and exposes everything both as a Python API and a simple CLI. In the first wave of open image models it was one of the most approachable ways to get motion out of Stable Diffusion, and its latent-walk technique is still used by artists who prefer its dreamy, morphing aesthetic to modern video models.

Key Features

• Generate morphing videos by interpolating between multiple text prompts
• Built on Hugging Face Diffusers, with support for community Stable Diffusion models
• Configurable frame counts, interpolation schedules and sampling settings
• Optional audio track support for finished music-video style clips
• Python API and command-line interface for scripting and batch creation
• Small, hackable codebase that is easy to extend with custom pipelines
• Example scripts and sample outputs included so you can reproduce results before customizing

💳 Pricing

Free and open source under the Apache 2.0 license. You run it on your own GPU (or a rented one); there is no hosted or paid edition.

Who Is It For

AI artists, musicians and creative coders who want to produce distinctive morphing animations and visuals for videos, loops and social content with open models.