Fondamental Updated 2026-04
Latent Space
Definition
Latent space is a compressed mathematical representation where an AI model encodes the essential features of data, enabling content generation and manipulation.
See also in the glossary
E
Embedding
An embedding is a numerical representation (vector) of text or data, capturing its semantic meaning.
D
Diffusion Model
A diffusion model is an AI architecture that generates images starting from random noise and progressively refining it.
D
Deep Learning
Deep Learning is a subset of Machine Learning using multi-layered neural networks to learn complex representations from raw data.
G
Generative AI
Generative AI refers to artificial intelligence systems capable of creating original content: text, images, video, audio, code.
T
Text-to-Image
Text-to-Image refers to generating images from text descriptions using generative AI models.
N
Neural Network
A neural network is a computing model inspired by the human brain, composed of layers of interconnected nodes that process information to learn patterns.
Tools that use latent space
S
Stable Diffusion
The open source reference for AI image generation
4.4/5
M
Midjourney
The reference in AI image generation
4.4/5
D
DALL-E
The most used AI image generator, built into ChatGPT
4/5
F
Flux
The image generation model rivaling Midjourney
4.8/5
H
Hugging Face
The reference open source platform for AI models
4.6/5
Frequently Asked Questions
What's the difference between latent space and embedding?
An embedding is a vector representing a specific element (a word, a sentence) in a multidimensional space. Latent space is the complete continuous space in which these representations exist. Embeddings are points within latent space.
Why is latent space important for image generation?
Working in latent space allows manipulating images in a compact and efficient way. Stable Diffusion operates in a latent space 64x smaller than the final image, significantly reducing computation while preserving quality.
