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Analysis of Classifier-Free Guidance Weight Schedulers

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arxiv 2404.13040 v2 pith:NTCTJ2K5 submitted 2024-04-19 cs.CV cs.LG

Xi Wang , Nicolas Dufour , Nefeli Andreou , Marie-Paule Cani , Victoria Fernandez Abrevaya , David Picard , Vicky Kalogeiton This is my paper · ORCID
classification cs.CVcs.LG
keywords schedulersweightanalysisclassifier-freediffusionguidancemodelsacross
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Classifier-Free Guidance (CFG) enhances the quality and condition adherence of text-to-image diffusion models. It operates by combining the conditional and unconditional predictions using a fixed weight. However, recent works vary the weights throughout the diffusion process, reporting superior results but without providing any rationale or analysis. By conducting comprehensive experiments, this paper provides insights into CFG weight schedulers. Our findings suggest that simple, monotonically increasing weight schedulers consistently lead to improved performances, requiring merely a single line of code. In addition, more complex parametrized schedulers can be optimized for further improvement, but do not generalize across different models and tasks.

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Cited by 5 Pith papers

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

  1. Classifier-Free Guidance: From High-Dimensional Analysis to Generalized Guidance Forms

    cs.LG 2025-02 conditional novelty 7.0 of 10

    CFG's distortion of the target distribution vanishes as data dimension grows, and a power-law generalization improves fidelity and diversity in high-dimensional generative models.

  2. Contrastive Conditional-Unconditional Alignment for Long-tailed Diffusion Model

    cs.CV 2025-07 conditional novelty 6.0 of 10

    A two-part training regularizer, an unconditional-only contrastive repulsion plus a large-timestep conditional-unconditional alignment, improves tail-class diversity and fidelity in diffusion models, cutting ImageNet-...

  3. FastFace: Tuning Identity Preservation in Distilled Diffusion via Guidance and Attention

    cs.CV 2025-05 conditional novelty 6.0 of 10

    An inference-time framework of decoupled classifier-free guidance and attention manipulation improves identity preservation and prompt alignment when pretrained face ID adapters are used with few-step distilled diffus...

  4. GDiffuSE: Diffusion-based speech enhancement with noise model guidance

    cs.SD 2025-10 reject novelty 5.0 of 10

    GDiffuSE steers a frozen speech-diffusion generator toward clean speech using a lightweight noise-model likelihood adapted from a short reference noise clip, improving PESQ/SI-SDR on mismatched BBC noise.

  5. DiffIER: Optimizing Diffusion Models with Iterative Error Reduction

    cs.CV 2025-08 reject novelty 4.0 of 10

    DiffIER claims that iteratively minimizing the distance between a diffusion model's predicted noise and a random Gaussian sample at each inference step improves generation quality.

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