Modern Generative AI for images

In this article, we explored how modern text-to-image models use Diffusion Models and meaning vectors to generate images from natural language prompts. We started by providing an overview of what text-to-image models are and why they are important. Then, we delved into the inner workings of such models, breaking them down into two primary components: a Text Encoder and a Diffusion Model. We began with understanding how Text Encoders work to extract meaning from natural language inputs. We discussed how vectors can be used as an interpretation schema for words and showed how we could generate consistent vectors for new words based on their meanings. This understanding of the role vectors play in capturing semantic information laid a foundation for us to understand how these vectors are then leveraged by our models. Next, we explained how Diffusion Models are used to generate images from vectors. We clarified the concept of conditioning and showed how it can be used within a text-to-image model to control the outcome of image generation based on semantic information in the form of meaning vectors. By doing so, we demonstrated how such models are capable of generating diverse yet coherent images that reflect the input text prompts. In conclusion, this article aimed at providing an intuitive understanding of how modern text-to-image models work to generate images from natural language inputs. We hope that this explanation has helped you gain a better grasp on this fascinating topic! ```