Update README.md

Author: swiftuiux

README.md

@@ -73,7 +73,7 @@ or with custom **ViewBuilder**
 ![OpenAI AsyncImage SwiftUI](https://github.com/The-Igor/openai-async-image-swiftui/blob/main/image/sun_11.png) 
 
 
-## Replicate toolkit for swift 
+## Replicate toolkit for swift. Set of diffusion models
 Announced in 2022, OpenAI's text-to-image model DALL-E 2 is a recent example of diffusion models. It uses diffusion models for both the model's prior (which produces an image embedding given a text caption) and the decoder that generates the final image.
 In machine learning, diffusion models, also known as diffusion probabilistic models, are a class of latent variable models. They are Markov chains trained using variational inference. The goal of diffusion models is to learn the latent structure of a dataset by modeling the way in which data points diffuse through the latent space.
 Diffusion models can be applied to a variety of tasks, including image denoising, inpainting, super-resolution, and image generation. For example, an image generation model would start with a random noise image and then, after having been trained reversing the diffusion process on natural images, the model would be able to generate new natural images.