🔥Diffusion Models Beat GANs on Image Synthesis

This research paper explores diffusion models for image synthesis, demonstrating superior image quality compared to Generative Adversarial Networks (GANs). The authors achieve this by refining model architecture and introducing "classifier guidance," a method to trade sample diversity for fidelity using classifier gradients. Experiments on ImageNet and LSUN datasets show state-of-the-art results, surpassing GANs in several metrics including FID (Fréchet Inception Distance). The study also investigates the effect of classifier guidance scaling and its combination with upsampling techniques. Finally, the paper discusses computational aspects and compares the efficiency of diffusion models to GANs.