Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets

Source: Dev.to

Overview

Stable Video Diffusion is a new tool that generates short video clips from simple text prompts or a single image. The results are surprisingly smooth and realistic.

Training Pipeline

The model is trained on a large, carefully curated video dataset to learn realistic motion. The training proceeds in three stages:

1. Image pre‑training – learns visual concepts from still images.
2. Video pre‑training – learns temporal dynamics from a broad collection of videos.
3. Fine‑tuning – refines the model on high‑quality footage to improve fidelity.

This multi‑stage approach gives the model a strong grasp of both appearance and motion.

Capabilities

• Text‑to‑video generation with coherent motion and camera movements.
• Image‑to‑video expansion, turning a single picture into a moving scene.
• Ability to infer multiple viewpoints of an object, providing a simple 3‑D‑like multi‑view representation.
• Generates high‑quality, smooth video clips that can be reused in downstream applications.

Availability

The code and model checkpoints have been released publicly, allowing creators to experiment, fine‑tune, and build new tools on top of the system.

Further Reading

Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets – comprehensive review on Paperium.net.