AI Video Glossary

The attention mechanism lets AI models learn relationships between all parts of an input simultaneously. How it works and its role in video generation.

A benchmark is a standardized test used to measure and compare AI model performance. How video generation benchmarks work and where LTX-2 stands.

Classifier-free guidance controls how closely an AI generation follows its prompt. What the CFG scale parameter does and how to use it effectively.

ControlNet adds structural control to AI video generation using depth maps, pose skeletons, and other spatial signals. How it works and how LTX-2 uses it.

A diffusion model is the architecture powering modern AI video generation. This guide covers how denoising works, key model types, and LTX-2's approach.

An API endpoint is a specific URL that accepts requests and returns responses. How video generation endpoints work and how to use the LTX-2 API endpoints.

Fine-tuning adapts a pre-trained AI model to specific styles, subjects, or workflows by training on new data. How it works and how LTX-2 supports it.

Flow matching trains generative models to follow straight paths from noise to data, enabling fast, efficient inference. How it works and how LTX-2 uses it.

Generative video is AI-produced video created from text, images, or audio rather than filmed footage. How the technology works and where it's heading.

Image-to-video (I2V) animates a static image into a video clip using AI. How it works, what it's used for, and how LTX-2 handles I2V generation.

Latent space is the compressed representation where AI models learn to generate data. How it works and why modern video generation models depend on it.

Local inference runs an AI model entirely on your own hardware, with no cloud dependency, no per-generation fees, and full IP protection. How it works.

LoRA fine-tunes large AI models for specific styles or brand IP without touching the base weights. How it works, its variants, and how LTX-2 supports it.

Multimodal AI models process and generate across text, images, audio, and video in a single architecture. What that means and how LTX-2 implements it.

A noise schedule defines how noise is added and removed during diffusion model training and inference. How it affects generation quality and speed.

Open weights are the publicly released parameters of an AI model, enabling local inference, fine-tuning, and custom deployment without API dependency.

Pre-visualization is the creation of rough video representations before full production. How AI is replacing traditional pre-viz and what LTX-2 enables.

Quantization reduces AI model memory and inference cost by lowering the numerical precision of weights. How it works, its key types, and how LTX-2 uses it.

Rate limits cap how many API requests a client can make in a time window. Why they exist and how to build production pipelines that handle them correctly.

An SDK provides pre-built libraries and tools for integrating an API into your application. What the LTX-2 SDK includes and how to get started building.

Temporal consistency measures how stable objects, lighting, and motion remain across video frames. Why it's the central challenge of video generation.

The transformer is the neural network architecture behind most modern AI models, from LLMs to video generation. How it works and how LTX-2 builds on it.

U-Net was the dominant neural network architecture in diffusion models before transformers took over. What it is, how it works, and why DiT replaced it.

A VAE encodes video into a compact latent space for efficient generation, then decodes it back to pixels. Why it's essential to models like LTX-2.

A webhook sends an HTTP notification to your server when an event occurs, like a video generation job completing. How webhooks work and when to use them.

Zero marginal cost means each additional AI generation costs nothing beyond fixed hardware. Why it changes the economics of video production at scale.