  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.
