NitroFusion: High-Fidelity Single-Step Diffusion through Dynamic
  Adversarial Training
Authors: name: Dar-Yen Chen, name: Hmrishav Bandyopadhyay, name: Kai Zou, name: Yi-Zhe Song

We introduce NitroFusion, a fundamentally different approach to single-step
diffusion that achieves high-quality generation through a dynamic adversarial
framework. While one-step methods offer dramatic speed advantages, they
typically suffer from quality degradation compared to their multi-step
counterparts. Just as a panel of art critics provides comprehensive feedback by
specializing in different aspects like composition, color, and technique, our
approach maintains a large pool of specialized discriminator heads that
collectively guide the generation process. Each discriminator group develops
expertise in specific quality aspects at different noise levels, providing
diverse feedback that enables high-fidelity one-step generation. Our framework
combines: (i) a dynamic discriminator pool with specialized discriminator
groups to improve generation quality, (ii) strategic refresh mechanisms to
prevent discriminator overfitting, and (iii) global-local discriminator heads
for multi-scale quality assessment, and unconditional/conditional training for
balanced generation. Additionally, our framework uniquely supports flexible
deployment through bottom-up refinement, allowing users to dynamically choose
between 1-4 denoising steps with the same model for direct quality-speed
trade-offs. Through comprehensive experiments, we demonstrate that NitroFusion
significantly outperforms existing single-step methods across multiple
evaluation metrics, particularly excelling in preserving fine details and
global consistency.