One-Step Image-Function Generation via Consistency Training

Abstract

Diffusion models achieve remarkable image generation quality but suffer from slow inference due to hundreds of denoising steps. Consistency models alleviate this and enable one-step sampling by directly mapping noise to images with a U-Net generator, but they remain unstable and resource-hungry under small-batch training and can only generate images at fixed resolutions. These limitations motivate us to explore more efficient and flexible architectures. To this end, we propose a novel framework for efficient one-step flexible-resolution image generation. First, we represent images as continuous image functions based on implicit neural representations (INRs), which decouple image resolution f..