  In this work, we address the challenge of multi-task image generation with
limited data for denoising diffusion probabilistic models (DDPM), a class of
generative models that produce high-quality images by reversing a noisy
diffusion process. We propose a novel method, SR-DDPM, that leverages
representation-based techniques from few-shot learning to effectively learn
from fewer samples across different tasks. Our method consists of a core meta
architecture with shared parameters, i.e., task-specific layers with exclusive
parameters. By exploiting the similarity between diverse data distributions,
our method can scale to multiple tasks without compromising the image quality.
We evaluate our method on standard image datasets and show that it outperforms
both unconditional and conditional DDPM in terms of FID and SSIM metrics.
