  Few-shot image synthesis entails generating diverse and realistic images of
novel categories using only a few example images. While multiple recent efforts
in this direction have achieved impressive results, the existing approaches are
dependent only upon the few novel samples available at test time in order to
generate new images, which restricts the diversity of the generated images. To
overcome this limitation, we propose Conditional Distribution Modelling (CDM)
-- a framework which effectively utilizes Diffusion models for few-shot image
generation. By modelling the distribution of the latent space used to condition
a Diffusion process, CDM leverages the learnt statistics of the training data
to get a better approximation of the unseen class distribution, thereby
removing the bias arising due to limited number of few shot samples.
Simultaneously, we devise a novel inversion based optimization strategy that
further improves the approximated unseen class distribution, and ensures the
fidelity of the generated samples to the unseen class. The experimental results
on four benchmark datasets demonstrate the effectiveness of our proposed CDM
for few-shot generation.
