Opening the Black Box of Classifier-Free Guidance via Information Bottleneck

Key points

• In order to understand how the informational relevance between conditioning inputs and the score function evolves over the course of generation, we explore when guidance should be activated from an information-theoretic perspective.1
• In this work, we formulate the diffusion trajectory as a time-dependent information bottleneck and show that the effectiveness of conditional information is not uniform throughout sampling, but becomes significantly stronger after a critical stage characterized by a Fisher-information balance condition.1
• Motivated by this insight, we propose a training-free dynamic CFG strategy for inference.1
• Beyond empirical improvements, our framework provides an interpretation of dynamic guidance methods and a principled foundation for inference-time control in conditional diffusion models.1

Sources (1)

• [1]Opening the Black Box of Classifier-Free Guidance via Information Bottleneck
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 10:44 AM

  Classifier-free guidance (CFG) is a standard technique for improving conditional generation in diffusion models, yet its activation schedule is usually selected heuristically.

  In order to understand how the informational relevance between conditioning inputs and the score function evolves over the course of generation, we explore when guidance should be activated from an information-theoretic perspective.

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