Real data offers new path for aligning diffusion models

Researchers have explored using real-world images as a source for aligning diffusion models, moving beyond traditional methods that rely on model-generated preference pairs. This new approach constructs preference signals by contrasting real images with generated or perturbed samples, avoiding the need for manual annotations. The study indicates that this real-data-based supervision is effective, achieving performance comparable to existing preference-based alignment techniques and suggesting a practical, label-efficient alternative for guiding generative models. AI

影响 This research offers a more practical and label-efficient method for aligning diffusion models, potentially improving the quality and controllability of generated images.

排序理由 Academic paper detailing a new method for aligning diffusion models using real data. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• Diffusion Models

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