Expanding Expressiveness of Diffusion Models with Limited Data via
  Self-Distillation based Fine-Tuning
Jiwan Hur Jaehyun Choi Gyojin Han Dong-Jae Lee Junmo Kim
Training diffusion models on limited datasets poses challenges in terms of
limited generation capacity and expressiveness, leading to unsatisfactory
results in various downstream tasks utilizing pretrained diffusion models, such
as domain translation and text-guided image manipulation. In this paper, we
propose Self-Distillation for Fine-Tuning diffusion models (SDFT), a
methodology to address these challenges by leveraging diverse features from
diffusion models pretrained on large source datasets. SDFT distills more
general features (shape, colors, etc.) and less domain-specific features
(texture, fine details, etc) from the source model, allowing successful
knowledge transfer without disturbing the training process on target datasets.
The proposed method is not constrained by the specific architecture of the
model and thus can be generally adopted to existing frameworks. Experimental
results demonstrate that SDFT enhances the expressiveness of the diffusion
model with limited datasets, resulting in improved generation capabilities
across various downstream tasks.