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Where and How to Perturb: On the Design of Perturbation Guidance in Diffusion and Flow Models

arXiv — cs.LG•Tuesday, November 4, 2025 at 5:00:00 AM
NeutralArtificial Intelligence

Where and How to Perturb: On the Design of Perturbation Guidance in Diffusion and Flow Models

The article "Where and How to Perturb: On the Design of Perturbation Guidance in Diffusion and Flow Models" explores recent advancements in guidance methods for diffusion models, with a particular emphasis on attention perturbation techniques (F1, F2). These methods have shown effectiveness in steering reverse sampling processes and enhancing generation quality, especially in cases where traditional classifier-free guidance approaches are not applicable (F3). Despite these promising developments, the article underscores the necessity for more principled and systematic approaches to attention perturbation, indicating that current techniques may lack theoretical rigor or comprehensive frameworks (F4). This discussion aligns with ongoing research trends in the field of diffusion models, as reflected in related recent studies (connected coverage). Overall, the piece contributes to a deeper understanding of how perturbation guidance can be optimized to improve model performance in complex generative tasks.

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