Trajectory Conditioned Solution Generation With Scoring Feedback

via “conditional-unconditional score function learning”

* ⭐ 08/2022: [Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation (DreamBooth)](https://arxiv.org/abs/2208.12242)

Unique: Uses conditioning dropout during training to force a single model to learn both conditional and unconditional score functions within shared parameters, rather than training separate models or using external classifiers for guidance

vs others: More parameter-efficient than separate conditional and unconditional models, and avoids external classifier dependencies compared to classifier guidance, but requires careful multi-objective training and may suffer from objective interference