A New Technique to Train Diffusion Model in Latent Space Using Limited Computational Resources While Maintaining High-Resolution Quality

In recent years, image synthesis has experienced exponential growth in performance. The two main approaches to this task have been autoregressive transformers (ARs) and generative adversarial networks (GANs). The firsts are trained for sequence prediction and are able to generate images, token by token, starting from the first one. The seconds are based on the famous generator-discriminator method, where the generator tries to fool the discriminator into generating reliable samples. Nevertheless, both approaches have huge limitations: in particular, ARs require …

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