Beyond latent diffusion models, other types of generative models that could be explored for the information regeneration stage of the DiffBIR framework include Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Autoregressive Models. VAEs can be used to learn a latent space representation of the image content and generate realistic details based on this representation. GANs can generate high-quality images by training a generator network to produce images that are indistinguishable from real images. Autoregressive models, such as PixelCNN, can generate images pixel by pixel, capturing complex dependencies in the image data. By incorporating these different generative models into the information regeneration stage, DiffBIR can benefit from a diverse range of generative capabilities and potentially improve the quality and realism of the restored images.