Arxiv Preprint - LCM-LoRA: A Universal Stable-Diffusion Acceleration Module

In this episode we discuss LCM-LoRA: A Universal Stable-Diffusion Acceleration Module by Simian Luo, Yiqin Tan, Suraj Patil, Daniel Gu, Patrick von Platen, Apolinário Passos, Longbo Huang, Jian Li, Hang Zhao (Project page). The paper discusses the advancements in Latent Consistency Models (LCMs), which have shown great efficiency in text-to-image generation by being distilled from larger latent diffusion models, requiring only about 32 training hours on A100 GPUs. The research has successfully extended LCMs to work with larger models like Stable-Diffusion, resulting in higher-quality images and reduced memory usage through LoRA distillation. Additionally, the paper introduces LCM-LoRA, a universal acceleration module that can enhance various Stable-Diffusion models without additional training, outperforming traditional numerical solvers with its strong generalization capabilities.