---
title: "Stable Video Diffusion | Open-Source Video Generation."
canonical: "https://world-models.io/en/models/stable-video-diffusion/"
description: "Stable Video Diffusion is Stability AI's open-source foundation model for video generation from image conditioning."
author: "Tyler D."
generated: "2026-10-30"
---

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The Knowledge Hub for AI World Models

# Stable Video Diffusion

Stable Video Diffusion (SVD) is Stability AI's open-source foundation model for generative video, producing high-quality video from single image conditioning.

robotics

model-based-rl

simulation

embodied-ai

## What is a world model?

A world model is an AI system that learns a predictive representation of how an environment changes over time. It can use observations, actions, or language to estimate future states and possible outcomes before an agent acts. World models support planning in model-based reinforcement learning, robotics, embodied AI, autonomous driving, and interactive simulation. This site catalogs these systems with model records, research papers, benchmark context, and source links. Start with the [world models database](https://world-models.io/en/world-models-database/), compare systems on the [leaderboard](https://world-models.io/en/leaderboard/), or read the [Performance Index methodology](https://world-models.io/en/methodology/performance-index/).

## Key Attributes

Static HTML snapshot of the model record for crawlers and no-JS readers.

| Attribute | Value |
| --- | --- |
| Model | Stable Video Diffusion |
| Lab / Organization | Stability AI |
| Category | Generative World Model |
| Subtype | Video Diffusion Model |
| World Model Type | Image-to-video diffusion model |
| Primary Domain | Video Generation |
| Architecture | 3D UNet latent diffusion with temporal attention layers |
| Modality | Visual (Image → Video) |
| Training Method | Multi-stage training: image pretraining → video fine-tuning on curated dataset |
| Status | active |
| Year | 2023 |
| Performance Index | 57/100 (high confidence, v1.1) |

## About Stable Video Diffusion

Main editorial body preserved directly in static HTML.

Stable Video Diffusion (SVD) is an open-source latent video diffusion model built upon the Stable Diffusion image model. It generates short, temporally coherent video sequences from a single conditioning image. SVD demonstrates strong motion dynamics and physical plausibility, making it a foundational building block for researchers working on video generation, world simulation, and controllable content creation. Its open weights have enabled rapid community adoption and downstream applications.

Stable Video Diffusion is an image-to-video diffusion model developed by Stability AI in 2023 for video generation.

## Editorial Snapshot

Short extractable facts for answer engines and no-JS readers.

| Signal | Value |
| --- | --- |
| Definition | Stable Video Diffusion is an image-to-video diffusion model developed by Stability AI in 2023 for video generation. |
| Short Description | An open-source foundation model for video generation, producing temporally consistent video from a single image. |
| Benchmark Rows | 1 |
| FAQ Entries | 2 |
| Related Models | 4 |
| Related Guides | 0 |
| Related Research Topics | 1 |
| Last Updated | 2026-04-07 |

## Notable Features

Key capabilities associated with this model.

- • Open-source weights
- • Image-to-video generation
- • Temporally consistent motion
- • Multi-stage curation pipeline for training data

## Use Cases

Representative applications attached to this model record.

Creative video generation

Animation from stills

Research on video dynamics

Downstream video editing

## Strengths and Limitations

Balanced assessment surfaced in static HTML.

### Strengths

- • Open-source and widely adopted
- • Strong temporal coherence
- • Good physical plausibility for short clips
- • Efficient latent space architecture

### Limitations

- • Short video duration (2-4 seconds)
- • Limited resolution
- • No text-to-video (image conditioning only)
- • Occasional temporal artifacts

## Benchmarks

Published benchmark evidence attached to this model record.

| Benchmark | Metric | Result | Source |
| --- | --- | --- | --- |
| FVD (UCF-101) | Fréchet Video Distance | Competitive with closed-source models | [Source](https://arxiv.org/abs/2311.15127) |

## References and Citations

Primary references preserved in static HTML for citation extraction.

| Reference | Link |
| --- | --- |
| Blattmann et al., 2023. Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets. arXiv:2311.15127 | [Open source](https://arxiv.org/abs/2311.15127) |

## Related Models

Nearby models linked from the current editorial record.

| Model | Category | World Model Type | Index v1.1 |
| --- | --- | --- | --- |
| [Sora](https://world-models.io/en/models/sora/) | Generative World Model | Text-to-video world simulator | 63/100 |
| [Genie 2](https://world-models.io/en/models/genie-2/) | Generative World Model | Generative environment model | 79/100 |
| [Emu Video](https://world-models.io/en/models/emu-video/) | Generative World Model | Factorized text-to-video model | 49/100 |
| [NVIDIA Cosmos](https://world-models.io/en/models/nvidia-cosmos/) | Foundation World Model | Video world foundation model | 87/100 |

## Direct Comparisons

Side-by-side comparisons already connected to this model.

| Comparison | Matchup | Summary |
| --- | --- | --- |
| [Sora vs Gen-3 Alpha](https://world-models.io/en/compare/sora-vs-gen-3-alpha/) | Sora vs Gen-3 Alpha | The two leading commercial video generation models. Sora emphasizes physical world simulation and long-form coherence, while Gen-3 Alpha focuses on fine-grained creative control and production-ready tools. |
| [Stable Video Diffusion vs Emu Video](https://world-models.io/en/compare/stable-video-diffusion-vs-emu-video/) | Stable Video Diffusion vs Emu Video | Two image-to-video models: SVD is open-source and community-driven, while Emu Video is Meta's factorized approach that separates image and motion generation for better controllability. |

## Research Topics Referencing This Model

Connected research areas surfaced directly in static HTML.

| Topic | Summary |
| --- | --- |
| [Diffusion World Models](https://world-models.io/en/research/diffusion-world-models/) | How diffusion world models generate future states, preserve richer visual detail, and power video simulation systems such as DIAMOND, Sora, and Cosmos. |

## Frequently Asked Questions

FAQ answers rendered directly into static HTML for extractable responses.

### How does SVD compare to Sora?

SVD is open-source and generates short (2-4s) videos from images. Sora is closed-source, generates longer videos from text, and shows stronger physical reasoning. SVD is more accessible for research.

### Can SVD be used for world simulation?

While not designed as a world simulator, SVD's ability to generate physically plausible motion makes it a useful building block for world simulation research.

## Quick Answer

Short extractable summary preserved directly in static HTML.

- Stable Video Diffusion is an image-to-video diffusion model developed by Stability AI in 2023 for video generation.
- Use this page when you need a fast read on how Stable Video Diffusion fits into the generative world model landscape, then validate the details in the benchmarks, citations, and related pages.
- A key strength surfaced in the editorial record is open-source and widely adopted.

## Editorial Trust Signals

Editorial provenance and refresh policy preserved directly in static HTML.

**Published by** world-models.io editorial board.

**Lead editor** Tyler D. - Technical editor, methodology and benchmark analysis.

This model page synthesizes primary papers, official model pages, benchmark evidence, and related world-models.io context into a reference resource.

Each editorial page is assembled from primary sources, normalized into extractable summaries, checked for factual drift, and reviewed before publication or major refreshes. Last reviewed: 2026-04-07.

Pages are refreshed when a new paper, benchmark, release, architecture update, or stronger primary source materially changes the answer a reader or AI system should retrieve.

Each page links back to relevant primary sources and keeps a stable canonical URL so readers can verify claims, trace context, and reference the most up-to-date version. [See the editorial policy.](https://world-models.io/en/editorial-policy/)

Primary sources only

Last reviewed date visible

Methodology documented

Source links included

Correction channel published

Review date visible

## External Sources

Primary model and lab sources embedded in static HTML.

- [Stable Video Diffusion paper](https://arxiv.org/abs/2311.15127) Stability AI primary research source
- [Stable Video Diffusion official page](https://stability.ai/stable-video) Stability AI official page
- [Stability AI official website](https://stability.ai) Related lab

## References

1. [[1] Blattmann et al., 2023. Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets. arXiv:2311.15127](https://arxiv.org/abs/2311.15127)