A generative model is a machine learning model, generally based on deep learning, that aims to learn the distribution of specific data (e.g. images or texts), so that it acquires the ability to generate new data according to the learned distribution. In simple words, it is a model that can generate new images or texts in a specific domain. We can understand generative models as artificial models focused on the synthesis of content. These models have evolved rapidly through variational models (VAE), adversarial networks (GAN) and currently diffusion-based models (DDPM). Our team focused on conditional generative models to produce realistic images guided by an input sketch.
