# Noise-Free Score Distillation Source: [Noise-Free Score Distillation]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:57:02.067Z Authors: Oren Katzir, Or Patashnik, Daniel Cohen-Or, Dani Lischinski Published: 2023-10-26T17:12:26.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Human Avatar Texture Editing on User Study 10 full-body and 3 localized human avatar edits](): Human Avatar Texture Editing; User Study 10 full-body and 3 localized human avatar edits; Visual Quality: 10.26. ## Original sources and implementation [Original paper]() [PDF]()