How latent consistency models work

This post introduces Latent Consistency Models (LCMs) as a method to accelerate image generation. It explains how LCMs combine latent diffusion models with direct consistency models to operate in a lower-dimensional latent space, reducing inference steps from ~50 to 2-4. This results in significantly faster inference times (e.g., 0.5 seconds for PIXART-𝝳 vs. 3.8 seconds for SDXL on A100) for high-resolution images, making real-time generation feasible. The post contrasts LCMs with traditional diffusion models and direct consistency models, highlighting their advantages in speed and quality for image generation tasks.