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Noise-Free Score Distillation

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arxiv 2310.17590 v1 pith:JMHFNDJF submitted 2023-10-26 cs.CV

Oren Katzir , Or Patashnik , Daniel Cohen-Or , Dani Lischinski This is my paper · ORCID
classification cs.CV
keywords distillationnfsdscoreinterpretationnoise-freeprocessachieveallows
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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.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Lesson in Splats: Teacher-Guided Diffusion for 3D Gaussian Splats Generation with 2D Supervision

    cs.CV 2024-12 conditional novelty 7.0 of 10

    A 3D Gaussian Splat diffusion model trained with only 2D image supervision, using deterministic reconstruction models as noisy teachers, improves single-image 3D reconstruction over those teachers.

  2. PixSDS: Why Latent SDS Makes Noisy Pixels

    cs.CV 2026-08 conditional novelty 6.0 of 10

    Pixel artifacts in latent SDS are caused by VAE-induced pixel drift, and decoding the latent SDS lookahead provides a clean gradient direction that reduces them.

  3. StochSync: Stochastic Diffusion Synchronization for Image Generation in Arbitrary Spaces

    cs.CV 2025-01 conditional novelty 6.0 of 10

    StochSync generates images on arbitrary surfaces such as spheres and meshes by alternating non-overlapping denoised views, maximum stochasticity, and multi-step clean-image prediction from a pretrained diffusion model.

  4. DreamDPO: Aligning Text-to-3D Generation with Human Preferences via Direct Preference Optimization

    cs.CL 2025-02 conditional novelty 5.0 of 10

    Applying pairwise direct preference optimization to score distillation makes text-to-3D outputs better aligned with human preferences and more controllable.

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