Diffusion model approach tackles aspect ratio problem in generative AI images

The picture on the left was generated by a standard method while the picture on the right was generated by ElasticDiffusion. The prompt for both images was, "Photo of an athlete cat explaining its latest scandal at a press conference to journalists." Generative artificial intelligence (AI) has notoriously struggled to create consistent images, often getting details like fingers and facial symmetry wrong. Moreover, these models can completely fail when prompted to generate images at different image sizes and resolutions. Rice University computer scientists' new method of generating images with pre-trained diffusion models a class of generative AI models that "learn" by adding layer after layer of random noise to the images they are trained on and then generate new images by removing the added noise could help correct such issues.