MMD Guidance: Training-Free Distribution Adaptation for Diffusion Models via Maximum Mean Discrepancy Guidance
arXiv:2601.08379v2 Announce Type: replace Abstract: Pre-trained diffusion models have emerged as powerful generative priors for both unconditional and conditional sample generation, yet their outputs often deviate from the characteristics of user-specific target data. Such mismatches are especially problematic in domain adaptation tasks, where only a few reference examples are available and retraining the diffusion model is infeasible. Existing inference-time guidance methods can adjust sampling trajectories, but they typically optimize surrogate objectives such as classifier likelihoods rathe
The proliferation of pre-trained diffusion models necessitates improved methods for adapting them to specific user data without extensive retraining, especially for domain adaptation tasks with limited examples.
This development allows for more efficient and effective customization of powerful generative AI models, making them more versatile and accessible for niche applications and reducing the computational burden of fine-tuning.
Pre-trained diffusion models can now be adapted to user-specific data and characteristics more effectively and with less effort, even with few reference examples, enabling broader applicability and nuanced control.
- · AI researchers
- · Generative AI developers
- · Domain-specific AI applications
- · Small and medium enterprises using AI
- · Companies relying on extensive retraining
- · Generic diffusion model providers without adaptation features
Diffusion models become more practically usable across a wider array of specialized domains and tasks without prohibitive computational costs.
This capability democratizes high-quality generative AI, lowering the barrier for smaller teams to leverage powerful models for customized content creation and data synthesis.
The reduced need for large-scale retraining could shift investment priorities from pre-training monolithic models to developing more sophisticated and adaptable inference-time guidance mechanisms.
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