Joint Conditional Unconditional Model Training

via “joint conditional-unconditional model training”

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

Unique: Uses conditioning dropout (random signal masking during training) to force a single model to learn both conditional and unconditional score functions, avoiding the need for separate model architectures or training pipelines while maintaining shared parameter efficiency

vs others: More parameter-efficient than training separate conditional and unconditional models, but requires careful dropout tuning and may suffer from objective interference compared to dedicated single-purpose models