Researchers introduce LD-Pruner, a novel performance-preserving structured pruning method for compressing Latent Diffusion Models (LDMs). The method operates in the latent space, identifying and removing components with minimal contribution to the output, resulting in compressed models with faster inference speeds and fewer parameters. The proposed approach offers a nuanced understanding of LDM latent representations and demonstrates its effectiveness across multiple tasks.
