Stable Video Diffusion

Stable Video Diffusion is a cutting-edge generative AI model developed by Stability AI, designed to create short video clips from text and image inputs. This model represents a significant advancement in the field of video generation, building on the foundational principles of the widely used Stable Diffusion image model. It operates through image-to-video models that can generate 14 or 25 frames per clip, with configurable frame rates between 3 and 30 frames per second, producing videos that typically last 2-5 seconds.

Stability AI continues to expand the capabilities of Stable Video Diffusion, exploring multi-view synthesis and advanced video generation techniques. Share feedback and participate in ongoing research to shape the future of AI-powered video creation.

Frequently asked questions about Stable Video Diffusion

What is Stable Video Diffusion (SVD)?: Stable Video Diffusion (SVD) is Stability AI's first open-source generative AI video model built on the foundation of Stable Diffusion. Stable Video Diffusion transforms static images into dynamic, high-quality Stable Video Diffusion videos using advanced diffusion technology. The Stable Video Diffusion model excels at creating smooth, natural motion while preserving the original image's quality and details, making Stable Video Diffusion ideal for animation, design visualization, and creative video content generation.
How does Stable Video Diffusion work?: Stable Video Diffusion works by using a latent diffusion process specifically trained for Stable Video Diffusion video generation. Starting with a single input image, the Stable Video Diffusion model predicts and generates subsequent frames by understanding motion patterns and temporal consistency. The AI gradually removes noise from random data in latent space while being guided by the input image, creating coherent Stable Video Diffusion video sequences with realistic motion between frames.
What are the differences between SVD and SVD-XT models?: SVD and SVD-XT are two variants with different capabilities. The standard model generates 14 frames at 576x1024 resolution, while SVD-XT (extended) is fine-tuned to generate 25 frames at the same resolution. Both models support customizable frame rates between 3 and 30 frames per second, with SVD-XT offering longer video sequences ideal for more complex animations and smoother motion.
What video resolutions and frame rates does Stable Video Diffusion support?: Stable Video Diffusion generates videos at a resolution of 576x1024 pixels, optimized for both portrait and landscape orientations. The model supports customizable frame rates ranging from 3 to 30 frames per second (FPS), with optimal performance between 5-30 FPS. This flexibility allows you to create everything from slow-motion effects to standard video playback speeds depending on your creative needs.
How long are the videos generated by Stable Video Diffusion?: Videos generated are relatively short clips, typically lasting 1-4 seconds depending on the model variant and frame rate settings. The standard model produces 14 frames, while SVD-XT generates 25 frames. At a standard frame rate of 7-10 FPS, this output translates to approximately 2-4 seconds of video content, making it ideal for looping animations, GIFs, and short video clips.
What are the key parameters for controlling Stable Video Diffusion output?: The main parameters for controlling Stable Video Diffusion output include Motion Bucket ID (controls the amount of motion, with higher values creating more movement), Frames Per Second (optimal between 5-30 FPS), Noise Augmentation Strength (determines how much the video differs from the input image), and Seed (for reproducible results). Adjusting these parameters allows you to fine-tune motion intensity, video smoothness, and creative variation in your generated videos.
What are the hardware requirements for running Stable Video Diffusion?: Stable Video Diffusion is remarkably efficient for an AI video model. The default configuration uses less than 10GB of VRAM to generate 25 frames at 1024x576 resolution, making it possible to run on GPUs like the NVIDIA GTX 1080 with 8GB VRAM. For optimal performance, an NVIDIA A100 generates 14 frames in approximately 100 seconds and 25 frames in about 180 seconds. Most modern NVIDIA and AMD GPUs with 8GB+ VRAM can run the model effectively.
How does Stable Video Diffusion compare to other AI video models?: According to user preference studies, Stable Video Diffusion surpasses leading closed-source models like GEN-2 and PikaLabs in video quality and motion realism. It excels at creating smooth, natural-looking motion with superior temporal consistency. While competitors like CogVideoX may offer higher resolution output and models like Kling AI provide longer video generation, the model stands out for its motion quality, open-source accessibility, and efficient resource usage.
What are best practices for creating high-quality videos with Stable Video Diffusion?: For optimal results, start with high-quality input images featuring clear subjects and good composition. Images with dynamic elements like fire, smoke, water, or fabric tend to produce more interesting motion. Experiment with Motion Bucket ID settings to find the right amount of movement for your content. Generate multiple variations using different seeds to find the best output. Keep FPS between 5-30 for smooth playback, and consider using the SVD-XT model for longer, more complex animations.
Can I use Stable Video Diffusion for commercial purposes?: Yes, Stable Video Diffusion can be used for commercial purposes under the Stability AI Community License. If your organization generates less than $1 million USD in annual revenue, you can use it for commercial projects free of charge. Organizations exceeding this revenue threshold must obtain an Enterprise license from Stability AI. The model is also available for research and non-commercial use under a royalty-free license.
What are the limitations of Stable Video Diffusion?: Stable Video Diffusion has several limitations to consider: videos are relatively short (1-4 seconds), the model may struggle with perfect photorealism in certain scenarios, and it has known challenges rendering detailed faces, complex body movements, and text. Some input images may produce minimal or no motion despite parameter adjustments. The model is also limited to the 576x1024 resolution, which may require upscaling for higher-quality final outputs.
What are common use cases for Stable Video Diffusion?: Stable Video Diffusion is ideal for various creative and commercial applications including: animated social media content and marketing materials, product visualization and demonstration videos, concept art and storyboard animation, educational content creation, looping background videos for websites, artistic projects and digital installations, game asset animation, and design prototyping. The Stable Video Diffusion image-to-video capability makes it particularly valuable for bringing static designs and illustrations to life with Stable Video Diffusion.
How do I improve motion quality in Stable Video Diffusion outputs?: To enhance Stable Video Diffusion motion quality, start by selecting input images with elements that naturally suggest movement (flowing fabric, dynamic poses, environmental elements). Increase the Motion Bucket ID parameter gradually to add more motion without introducing artifacts. The Noise Augmentation Strength parameter can help - higher values allow more deviation from the input image, potentially creating more dynamic motion. Experiment with different seeds as motion quality can vary significantly between generations. Consider using images with clear depth and spatial relationships for better motion prediction.
Can Stable Video Diffusion generate videos from text prompts?: Stable Video Diffusion is primarily an image-to-video model, meaning it requires an input image to generate video output rather than working directly from text prompts. To create videos from text descriptions, you would first generate an image using a text-to-image model like Stable Diffusion, SDXL, or SD3, then use that generated image as input for SVD. This two-step workflow allows you to create videos from text by combining text-to-image and image-to-video capabilities.
Where can I try Stable Video Diffusion online for free?: You can try Stable Video Diffusion for free on various platforms including the official Stability AI website, Hugging Face Spaces, and community platforms like stable-diffusion-web.com. These online interfaces allow you to upload an image and generate videos without local installation. For more control and unlimited usage, you can also run SVD locally using the open-source code available on GitHub and model weights from Hugging Face, provided you have a compatible GPU with 8GB+ VRAM.