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

The article introduces MMD Guidance, a training-free method for adapting pre-trained diffusion models to user-specific target distributions using Maximum Mean Discrepancy (MMD). This approach enhances the reverse diffusion process by integrating MMD gradients, allowing for effective distribution alignment without retraining, and is applicable to both unconditional and conditional generation tasks, including latent diffusion models. MMD Guidance shows promising results in maintaining sample fidelity while achieving distributional alignment, making it a valuable tool for practitioners facing domain adaptation challenges in generative modeling.