MMD Guidance: Training-Free Method for Adapting Diffusion Models to Target Data Distributions
Researchers have developed MMD Guidance, a training-free technique that adjusts pre-trained diffusion models to match user-specific target data distributions using Maximum Mean Discrepancy gradients. The method works at inference time without requiring model retraining, making it practical for domain adaptation tasks with limited reference examples. This addresses a key limitation of existing diffusion model guidance approaches that optimize indirect objectives rather than directly aligning with target distributions.
MMD Guidance is a new inference-time mechanism that enhances pre-trained diffusion models' ability to generate samples matching specific target data characteristics. The approach augments the reverse diffusion process by incorporating gradients of Maximum Mean Discrepancy (MMD)—a statistical measure comparing generated samples against a reference dataset. MMD was selected because it provides reliable distributional estimates from limited data, maintains low variance, and is efficiently differentiable. The framework extends to conditional generation models through product kernels and operates efficiently in latent diffusion models by applying guidance in latent space rather than pixel space. Experimental validation on synthetic and real-world benchmarks demonstrates that the method achieves distributional alignment while maintaining sample quality, with code made publicly available for reproducibility.
What's missing
The study's own limitations and open questions are not detailed in the abstract provided. Specific benchmark datasets used, quantitative performance metrics compared against baseline methods, and computational overhead of the guidance mechanism are not described in the available excerpt.
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- arXiv cs.LGCenter
MMD Guidance: Training-Free Distribution Adaptation for Diffusion Models via Maximum Mean Discrepancy Guidance
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