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.

翻译：少样本图像合成旨在仅使用少量示例图像生成新颖类别的多样且逼真的图像。尽管该方向的近期多项研究取得了令人瞩目的成果，但现有方法仅依赖测试时可用的少数新颖样本来生成新图像，这限制了生成图像的多样性。为克服这一局限，我们提出条件分布建模（CDM）框架——该框架有效利用扩散模型进行少样本图像生成。通过对用于条件化扩散过程的潜在空间分布进行建模，CDM利用训练数据的学习统计量来更准确地逼近未知类别分布，从而消除因少样本数量有限而产生的偏差。同时，我们设计了一种基于反演的新型优化策略，该策略进一步改善了逼近的未知类别分布，并确保生成样本对未知类别的保真度。在四个基准数据集上的实验结果表明，我们提出的CDM在少样本生成任务中具有有效性。