The Etna model utilizes a Diffusion architecture combined with spatiotemporal convolution and attention layers, enabling it to process video data and comprehend temporal continuity, thus generating video content with a temporal dimension. This model is trained on a massive video dataset using deep learning techniques, including large-scale training, hyperparameter optimization, and fine-tuning, to ensure robust performance and generative capabilities.