Latent Diffusion
Noun · AI & Machine Learning
Definitions
A diffusion approach that operates in a compressed latent space instead of directly on pixels. It influences how models are trained, evaluated, or served, and it can materially change accuracy, robustness, latency, cost, or interpretability. Practitioners usually track it alongside data quality, compute limits, and validation results when moving models into production.
In plain English: Latent Diffusion is an AI concept that affects how a model learns, predicts, or gets deployed. It matters because it changes quality, speed, or reliability.
Example: "We revisited Latent Diffusion during model evaluation because the first run looked fine offline but behaved poorly in production, and the adjustment improved quality without breaking our latency or compute budget."