Enhancing Diversity in Conditional Diffusion Models through Condition-Annealed Sampling

Conditional diffusion models can suffer from limited output diversity, especially when using high classifier-free guidance scales or trained on small datasets. The Condition-Annealed Diffusion Sampler (CADS) addresses this issue by annealing the conditioning signal during inference, leading to more diverse generations while maintaining high sample quality.