  Score Distillation Sampling (SDS) has emerged as the de facto approach for
text-to-content generation in non-image domains. In this paper, we reexamine
the SDS process and introduce a straightforward interpretation that demystifies
the necessity for large Classifier-Free Guidance (CFG) scales, rooted in the
distillation of an undesired noise term. Building upon our interpretation, we
propose a novel Noise-Free Score Distillation (NFSD) process, which requires
minimal modifications to the original SDS framework. Through this streamlined
design, we achieve more effective distillation of pre-trained text-to-image
diffusion models while using a nominal CFG scale. This strategic choice allows
us to prevent the over-smoothing of results, ensuring that the generated data
is both realistic and complies with the desired prompt. To demonstrate the
efficacy of NFSD, we provide qualitative examples that compare NFSD and SDS, as
well as several other methods.
