Existing score distillation methods are sensitive to classifier-free guidance (CFG) scale: manifested as over-smoothness or instability at small CFG scales, while over-saturation at large ones. To explain and analyze these issues, we revisit the derivation of Score Distillation Sampling (SDS) and decipher existing score distillation with the Wasserstein Generative Adversarial Network (WGAN) paradigm. With the WGAN paradigm, we find that existing score distillation either employs a fixed sub-optimal discriminator or conducts incomplete discriminator optimization, resulting in the scale-sensitive issue. We propose the Adversarial Score Distillation (ASD), which maintains an optimizable discriminator and updates it using the complete optimization objective. Experiments show that the proposed ASD performs favorably in 2D distillation and text-to-3D tasks against existing methods. Furthermore, to explore the generalization ability of our WGAN paradigm, we extend ASD to the image editing task, which achieves competitive results. The project page and code are at https://github.com/2y7c3/ASD.

现有的分数蒸馏方法对于分类器无关指导（CFG）的尺度非常敏感，表现为小的CFG尺度上的过度平滑或不稳定，而在大的尺度上过饱和。本文提出了对抗性分数蒸馏(ASD)方法，它使用可优化的鉴别器并使用完整的优化目标进行更新。实验证明，所提出的ASD在2D蒸馏和文本到3D任务中表现优异，并将ASD扩展到图像编辑任务，获得有竞争力的结果。

对抗性得分蒸馏：当得分蒸馏遇上GAN