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Stable Diffusion Videos

Stable Diffusion Videos is a Python library that uses a Stable Diffusion pipeline to interpolate between prompt-conditioned images and generate videos, with an option to use an audio file to pace the transitions.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Stable Diffusion Videos — OpenViST project profile in Image & Video AI.
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Understand the project

Practical overview

When it fits

Use it when you want a Python-based way to generate a video from a sequence of Stable Diffusion prompts, especially when you want to pace transitions to audio or control generation settings in a script.

Who it is for

  • Python developers building prompt-based video generation into scripts or tools.
  • Creators who want to make short videos that transition between multiple text prompts.
  • Developers exploring audio-paced prompt transitions or a simple interface around the generation pipeline.

Documented uses

  • Generate a video that morphs between two or more text prompts.
  • Use an audio segment to inform the pace of visual transitions.
  • Save generated frames and videos to a selected output directory.
Before you choose

Requirements & limits

  • It is a Python library, not a hosted video editing service.
  • The README’s main example loads CompVis/stable-diffusion-v1-4 and sends the pipeline to CUDA.
  • Apple M1/MPS users are advised to use torch.float32 rather than torch.float16.
  • The repository specifies the Apache-2.0 license.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Generate a video that transitions between prompts

Use the documented Python pipeline example to create a video from two text prompts.

Before you start

  • Install the library with pip.
  • Python and PyTorch, plus the Stable Diffusion model named in the example: CompVis/stable-diffusion-v1-4.
  • The README example uses CUDA with torch.float16; for Apple M1/MPS, it specifies torch.float32.

Steps

  1. Install the package with the command below.

    Documented command
    pip install stable_diffusion_videos

  2. Import StableDiffusionWalkPipeline and load CompVis/stable-diffusion-v1-4 with from_pretrained.

  3. Call pipeline.walk with prompt and seed lists, interpolation steps, image dimensions and an output directory.

Expected outcome

The pipeline returns a video path and saves generated images and videos in the specified output directory.

Guide limitations

  • The README shows CUDA for its float16 example; it notes that Apple M1/MPS requires float32 because float16 is unavailable there.
  • For dimensions above 512, the README recommends multiples of 64; below 512, multiples of 8.
Documentation 1 ↗
Source-based practical guide

Pace prompt transitions to an audio segment

Configure a prompt-walk video whose interpolation rate follows a selected portion of an audio file.

Before you start

  • The library and Stable Diffusion pipeline set up as in the README example.
  • A local audio file and its start and end times in seconds.

Steps

  1. Set the audio segment start and end times and choose a frame rate.

  2. Calculate interpolation steps from the segment duration multiplied by the frame rate, as shown in the README.

  3. Call pipeline.walk with the prompts, calculated interpolation steps, audio file path, audio start time and frame rate.

Expected outcome

The pipeline produces a prompt-transition video paced according to the supplied audio segment.

Guide limitations

  • The README demonstrates the audio input and start time, but does not describe broader audio-editing controls.
  • Its example uses 30 frames per second and suggests lower values such as 5 or 10 for testing and higher values such as 30 or 60 for better quality.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
10 October 2026
Original website title
nateraw/stable-diffusion-videos · Official homepage title · retained in its source language
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