LaVie

LaVie is a sophisticated integrated video generation framework based on cascaded latent diffusion models, developed by the V-Team. Architecturally, it utilizes a three-stage pipeline consisting of a Base T2V (Text-to-Video) model, a Temporal Interpolation model, and a Video Super-Resolution (SR) model. This modularity allows for the generation of high-definition, temporally coherent videos from text prompts. By 2026, LaVie has positioned itself as a critical open-source alternative to proprietary models like Sora and Kling, particularly for organizations requiring local deployment and data sovereignty. Its technical foundation leverages the Stable Diffusion pre-trained image models and adapts them for video through 3D-UNet structures, joint image-video fine-tuning, and RoPE (Rotary Positional Embeddings) for extended temporal sequences. The system is designed to handle high resolutions up to 1280x2048 and utilizes a T5-L11 text encoder for superior semantic understanding. It remains a cornerstone for developers seeking to build custom video-generation workflows without the constraints of centralized API costs or censorship filters typical of commercial SaaS platforms.