2024
CADS: Unleashing the Diversity of Diffusion Models through Condition-Annealed Sampling
ICLR 2024spotlight
While conditional diffusion models are known to have good coverage of the data distribution, they still face limitations in output diversity, particularly when sampled with a high classifier-free guidance scale for optimal image quality or when trained on small datasets. We attribute this problem to…