Comparing few-step image generation models

This post details the technical approaches behind few-step image generation models (LCMs, SDXL Turbo, SDXL Lightning) to achieve faster inference and higher throughput. It explains the underlying techniques such as latent consistency training, adversarial diffusion distillation, and progressive distillation, and compares their trade-offs in terms of speed, image quality, and resolution. The post also discusses how these models circumvent traditional diffusion model bottlenecks by reducing iterative denoising steps.