Fine-tuning Diffusion Models with Limited Data

Diffusion models have recently shown remarkable progress, demonstrating state-of-the-art image generation qualities. Like the other high-fidelity generative models, diffusion models require a large amount of data and computing time for stable training, which hinders the application of diffusion models for limited data settings. To overcome this issue, one can employ a pre-trained diffusion model built on a large-scale dataset and fine-tune it on a target dataset. Unfortunately, as we show empirically, this easily results in overfitting. In this paper, we propose an efficient fine-tuning algorithm for diffusion models that can efficiently and robustly train on limited data settings. We first show that fine-tuning only the small subset of the pre-trained parameters can efficiently learn the target dataset with much less overfitting. Then we further introduce a lightweight adapter module that can be attached to the pre-trained model with minimal overhead and show that fine-tuning with our adapter module significantly improves the image generation quality. We demonstrate the effectiveness of our method on various real-world image datasets.