The core mechanism of CAG relies on text embeddings. In a traditional workflow, the input string "Dragon" is mapped to a sequence of glyph indices. In a CAG workflow, the string is processed by a language model (e.g., CLIP or BERT) to generate a semantic vector. This vector captures the abstract qualities of "Dragon" (scales, fire, myth, sharpness). This vector serves as the conditioning input for the generative visual model.
Whether you use a tool based on AGG, diffusion models, or GANs (Generative Adversarial Networks), the workflow generally follows these steps: cag generated font
Creating fonts used to be a painstaking process involving drawing every single letter (glyph) by hand in vector software. Today, AI can generate entire typefaces from a few examples or a text prompt. The core mechanism of CAG relies on text embeddings