Researchers from the University of Southern California, the University of Washington, Bar-Ilan University, and Google Research introduced DreamSync, which addresses the problem of enhancing alignment and aesthetic appeal in diffusion-based text-to-image (T2I) models without the need for human annotation, model architecture modifications, or reinforcement learning. It achieves this by generating candidate images, evaluating them using…

In generative modeling, diffusion models (DMs) have assumed a pivotal role, facilitating recent progress in producing high-quality picture and video synthesis. Scalability and iterativeness are two of DMs’ main advantages; they enable them to do intricate tasks like picture creation from free-form text cues. Unfortunately, the many sample steps required for the iterative inference process…