Stable Video Diffusion recorded 231,198 monthly downloads as of 2025, positioning itself as a leading open-source AI video generation model. Released by Stability AI in November 2023, the platform achieved 26,600+ GitHub stars and processed training data spanning 580 million video clips. The model operates within a global AI video generator market valued at $614.8 million in 2024, projected to reach $2.56 billion by 2032.
Stable Video Diffusion Key Statistics
- Stable Video Diffusion receives 231,198 monthly downloads on Hugging Face as of 2025
- The GitHub repository accumulated 26,600+ stars and 3,000+ forks from the developer community
- Training utilized 580 million video clips representing 212 years of content, filtered to 152 million clips
- Computational requirements reached 200,000 A100 GPU hours consuming 64,000 kWh of energy
- The global AI video generator market recorded $614.8 million in 2024, growing at 20% annually toward $2.56 billion by 2032
Stable Video Diffusion Adoption Metrics
The img2vid-xt model variant achieved 231,198 downloads per month on Hugging Face. This represents consistent utilization across research and creative professional communities.
The GitHub repository shows 26,600+ stars and 3,000+ forks. Community engagement extends to 3,200+ likes on Hugging Face and 125+ active discussions. The platform supports over 100 active Spaces utilizing the model for various applications.
Repository activity includes 273 watchers monitoring development updates. The project maintains 278 open issues reflecting active community feedback, with 50 open pull requests indicating ongoing contribution efforts.
Stable Video Diffusion Training Dataset
Stability AI developed the Large Video Dataset specifically for training purposes. The original collection contained 580 million annotated video clips totaling 212 years of content.
The curation process filtered approximately 74% of clips, removing content with insufficient motion or low aesthetic quality. The filtered dataset retained 152 million clips meeting quality standards for high-fidelity video generation.
| Dataset Metric | LVD Original | LVD Filtered |
|---|---|---|
| Total Video Clips | 580 Million | 152 Million |
| Total Duration | 212 Years | 50.64 Years |
| Average Clip Duration | 11.58 Seconds | 10.53 Seconds |
| Mean Frames Per Clip | 325 | 301 |
| Fine-Tuning Dataset | N/A | 250,000 Clips |
The fine-tuning phase utilized 250,000 high-quality clips selected from the filtered dataset. Average clip duration measured 10.53 seconds with 301 mean frames per clip.
Stable Video Diffusion Computational Requirements
Training consumed approximately 200,000 A100 80GB GPU hours. The primary configuration utilized 48 nodes, each equipped with 8 A100 GPUs.
Energy consumption reached 64,000 kWh during the training phase. This resulted in approximately 19,000 kg of CO2 equivalent emissions.
Generation times vary by model variant. The base SVD model requires approximately 100 seconds on an A100 GPU. The extended SVD-XT variant takes roughly 180 seconds per generation.
| Resource Category | Measurement |
|---|---|
| Training Compute Time | 200,000 A100 Hours |
| Primary Configuration | 48 × 8 A100 GPUs |
| Energy Consumption | 64,000 kWh |
| CO2 Emissions | 19,000 kg CO2 eq. |
| SVD Generation Time | ~100 Seconds |
| SVD-XT Generation Time | ~180 Seconds |
Stable Video Diffusion Model Specifications
The platform offers multiple model variants optimized for different output requirements. Each variant provides distinct technical capabilities for specific applications.
The base SVD model generates 14 frames at 576×1024 resolution. Frame rates range from 3 to 30 FPS, producing 2-4 seconds of video content.
SVD-XT extends output to 25 frames while maintaining the same resolution. The variant builds on the base 14-frame model architecture.
SVD 1.1 produces 25 frames at 1024×576 resolution with a fixed 6 FPS frame rate. This version prioritizes landscape orientation and consistent temporal output.
| Specification | SVD | SVD-XT | SVD 1.1 |
|---|---|---|---|
| Output Frames | 14 Frames | 25 Frames | 25 Frames |
| Resolution | 576×1024 | 576×1024 | 1024×576 |
| Frame Rate | 3-30 FPS | 3-30 FPS | 6 FPS |
| Video Duration | 2-4 Seconds | 2-4 Seconds | ~4 Seconds |
Stable Video Diffusion Extended Model Family
Development expanded beyond 2D video generation to 3D and 4D capabilities. SV3D launched in March 2024, generating 21 frames at 576×576 resolution for multi-view synthesis.
SV4D released in July 2024 with 40-frame output across 5×8 views. The latest SV4D 2.0 variant produces 48 frames distributed as 12×4 views, representing the current state-of-the-art for spatio-temporal consistency.
AI Video Generator Market Statistics
The global AI video generator market reached $614.8 million in 2024. Projections indicate growth to $2,562.9 million by 2032, representing a compound annual growth rate of 20%.
North America captured 40.61% market share in 2024. Asia-Pacific followed with 31.40% share, indicating significant regional adoption across major technology markets.
The solutions segment accounted for 63.31% of market distribution. Cloud deployment represented 78% of implementation approaches, reflecting preference for scalable infrastructure.
| Market Metric | Value |
|---|---|
| Global Market Size 2024 | $614.8 Million |
| Projected Size 2032 | $2,562.9 Million |
| CAGR 2025-2032 | 20.0% |
| North America Share | 40.61% |
| Asia-Pacific Share | 31.40% |
| Cloud Deployment | 78% |
Stable Video Diffusion Performance Benchmarks
Human preference studies evaluated SVD against commercial competitors. The model demonstrated superior video quality compared to GEN-2 by Runway in direct comparisons.
Independent evaluations showed SVD preferred over PikaLabs in video quality assessments. Evaluators received $12 per hour compensation, with primary assessment conducted in the USA, UK, and Canada.
Safety red-teaming achieved confidence levels exceeding 90%. Trustworthiness evaluations recorded scores above 95%, validating deployment readiness for production environments.
AI Video Startup Funding Statistics
Investment activity in the AI video generation sector exceeded $500 million during early 2025. Runway secured $308 million in funding from its New York headquarters.
Synthesia raised $180 million from London operations. California-based Hedra obtained $32 million in venture funding, while Gan.ai recorded $5.25 million in investment.
The combined capital allocation signals accelerating competitive dynamics across commercial platforms. These investments position proprietary alternatives against open-source solutions.
Stable Video Diffusion Repository Activity
The GitHub repository accumulated 80 total commits from 19 contributors. Development activity maintains 273 active watchers monitoring ongoing updates.
The codebase consists primarily of Python, representing 98.1% of the programming languages used. The MIT license enables broad commercial and research applications.
The model file size measures 32.6 GB for distribution. This represents the complete weights and architecture necessary for deployment across various computational environments.
FAQs
How many video clips were used to train Stable Video Diffusion?
Stable Video Diffusion trained on 580 million video clips from the Large Video Dataset, representing 212 years of content. After filtering for quality, 152 million clips remained, with 250,000 clips used for fine-tuning.
What are Stable Video Diffusion’s monthly download statistics?
The SVD-img2vid-xt model receives 231,198 downloads per month on Hugging Face as of 2025. The platform recorded 3,200+ likes and supports over 100 active Spaces utilizing the model.
How does Stable Video Diffusion compare to commercial alternatives?
Human preference studies show Stable Video Diffusion outperformed GEN-2 by Runway and PikaLabs in video quality assessments. Safety evaluations achieved 90%+ confidence and 95%+ trustworthiness scores.
What computational resources were required for training?
Training required 200,000 A100 80GB GPU hours using 48 nodes with 8 A100 GPUs each. Energy consumption reached 64,000 kWh, producing approximately 19,000 kg CO2 equivalent emissions.
What is the current AI video generator market size?
The global AI video generator market reached $614.8 million in 2024, projected to grow to $2,562.9 million by 2032 at a 20% compound annual growth rate.
