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Analysis of Classifier-Free Guidance Weight Schedulers
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Analysis of Classifier-Free Guidance Weight Schedulers
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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.
Forward citations
Cited by 6 Pith papers
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Guidance Is Not a Hyperparameter: Learning Dynamic Control in Diffusion Language Models
Adaptive guidance trajectories learned via PPO outperform fixed-scale CFG on controllability-quality balance in three controlled NLP generation tasks with discrete diffusion models.
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VAGS: Velocity Adaptive Guidance Scale for Image Editing and Generation
VAGS adapts the CFG scale at each ODE step using velocity alignment signals to raise structural fidelity in editing and sample quality in generation over fixed-scale baselines.
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C$^2$FG: Control Classifier-Free Guidance via Score Discrepancy Analysis
C²FG provides a time-dependent exponential decay control for classifier-free guidance based on theoretical upper bounds on conditional-unconditional score discrepancies in diffusion processes.
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C$^2$FG: Control Classifier-Free Guidance via Score Discrepancy Analysis
C²FG provides a time-dependent guidance controller for diffusion models derived from score discrepancy upper bounds, implemented as an exponential decay function without retraining.
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GDiffuSE: Diffusion-based speech enhancement with noise model guidance
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.
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DiffIER: Optimizing Diffusion Models with Iterative Error Reduction
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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