Noise Consistency Training: A Native Approach for One-step Generator in Learning Additional Controls

The pursuit of efficient and controllable high-quality content generation stands as a pivotal challenge in artificial intelligence-generated content (AIGC). While one-step generators, refined through diffusion distillation techniques, offer excellent generation quality and computational efficiency, adapting them to new control conditions--such as structural constraints, semantic guidelines, or external inputs--poses a significant challenge.