MMD Guidance: Training-Free Distribution Adaptation for Diffusion Models via Maximum Mean Discrepancy Guidance

Researchers propose MMD Guidance, a training-free method that uses Maximum Mean Discrepancy to align pre-trained diffusion models with target data distributions during inference. The technique enables domain adaptation without retraining, working efficiently in both standard and latent diffusion models while maintaining sample quality.

MMD Guidance addresses a fundamental challenge in generative AI: adapting powerful pre-trained models to specific domains without expensive retraining. Traditional inference-time guidance methods optimize proxy objectives like classifier likelihoods, which don't directly measure distributional alignment. This research tackles the problem head-on by leveraging Maximum Mean Discrepancy, a statistical measure that quantifies differences between distributions. The approach proves particularly valuable in data-scarce scenarios where only a few reference examples exist, making it practical for real-world deployment.

The technical contribution centers on augmenting the reverse diffusion process with MMD gradients computed between generated samples and reference data. MMD's strength lies in its ability to provide reliable distributional estimates from limited samples while remaining efficiently differentiable—critical for iterative guidance during sampling. The framework's extension to conditional generation through product kernels and its applicability to latent diffusion models demonstrates generalizability across different architectures, expanding its utility across the generative AI ecosystem.

From a practical standpoint, this work enables practitioners to adapt sophisticated generative models without computational overhead typically associated with fine-tuning. For researchers and developers, it offers a principled, training-free alternative to existing methods, potentially accelerating adoption of diffusion models in specialized domains like medical imaging, industrial design, and domain-specific content generation. The open-source release further democratizes the approach.

Looking forward, the effectiveness of distribution-matching guidance in diffusion models could inspire similar approaches for other generative architectures and multimodal systems.

• →MMD Guidance enables diffusion model adaptation without retraining, using Maximum Mean Discrepancy to directly align outputs with target distributions.
• →The method works effectively with limited reference examples, making it practical for real-world domain adaptation scenarios.
• →Implementation in latent space maintains computational efficiency while preserving sample quality and fidelity.
• →The framework extends to conditional generation through product kernels, supporting prompt-aware adaptation.
• →Open-source code availability accelerates research adoption and practical deployment across generative AI applications.