Latent Consistency Model-Efficient Image Synthesis

Introduction to Latent Consistency Models

Latent Consistency Models (LCMs) are an innovative approach to synthesizing high-resolution images with few-step inference, built on the foundation of latent diffusion models (LDMs). The core objective of LCMs is to address the computational intensity and slow generation speed of traditional iterative sampling processes in LDMs by enabling rapid, high-fidelity sampling in the latent space of pre-trained models such as Stable Diffusion. By viewing the guided reverse diffusion process as solving an augmented probability flow ODE (PF-ODE), LCMs are designed to directly predict the solution of such ODE in latent space, thus significantly reducing the need for numerous iterations. This direct approach allows for swift generation of high-quality images with minimal steps, leveraging the underlying structure of pre-existing models without the need for extensive retraining. For example, an LCM can distill a pre-trained Stable Diffusion model into a format that can generate 768×768 resolution images in 2∼4 steps or even one step, a process that originally might require thousands of iterations. Powered by ChatGPT-4o。