Detailed Explanation of Embedding
Embedding is the process of converting different types of data (text, images, audio) into semantic-preserving numerical vectors in AI systems. These vectors represent the semantic features of the original data in a mathematical space, and data with similar meanings is expressed with nearby vectors.
Text embeddings convert words or sentences into fixed-size vectors. The words "cat" and "dog" have nearby vectors, while "cat" and "car" are represented with distant vectors. This property forms the basis of meaning-based search, classification, and recommendation systems.
In image generation, the embedding concept is used in several contexts. The CLIP text encoder converts the prompt into an embedding vector, and this vector guides the diffusion process. In the Textual Inversion technique, a new concept (person, style, object) can be taught to the model as a single embedding vector. This approach is a much lighter customization method than fine-tuning.
Embeddings are also widely used in semantic search engines, content recommendation systems, and finding similar images.
As a practical example, in Stable Diffusion, you can train an embedding through "textual inversion" to create a custom representation for a specific object, style, or concept. For instance, by training an embedding with 5-10 photos of your pet, you can then use that embedding in any prompt to generate your pet in different scenes and styles. Embedding files are very small (a few KB) and easy to share across different installations and with other users.
Tools on tasarim.ai that use embedding technology include Stable Diffusion (custom embeddings via textual inversion and CLIP embeddings) and Midjourney (internal embedding system for prompt processing). Tools like CLIP Interrogator also convert images into embedding space to suggest prompts for recreating similar visuals.
Tip for beginners: Think of embeddings as a way to add new concepts to the AI model's "vocabulary." You can download ready-made embeddings from the CivitAI platform for Stable Diffusion; negative prompt embeddings like EasyNegative in particular significantly improve output quality. Training your own embedding is simpler and faster than LoRA but more limited in flexibility.