Semantic image synthesis (SIS) is a task to generate realistic images
corresponding to semantic maps (labels). It can be applied to diverse
real-world practices such as photo editing or content creation. However, in
real-world applications, SIS often encounters noisy user inputs. To address
this, we propose Stochastic Conditional Diffusion Model (SCDM), which is a
robust conditional diffusion model that features novel forward and generation
processes tailored for SIS with noisy labels. It enhances robustness by
stochastically perturbing the semantic label maps through Label Diffusion,
which diffuses the labels with discrete diffusion. Through the diffusion of
labels, the noisy and clean semantic maps become similar as the timestep
increases, eventually becoming identical at $t=T$. This facilitates the
generation of an image close to a clean image, enabling robust generation.
Furthermore, we propose a class-wise noise schedule to differentially diffuse
the labels depending on the class. We demonstrate that the proposed method
generates high-quality samples through extensive experiments and analyses on
benchmark datasets, including a novel experimental setup simulating human
errors during real-world applications.

提出了一种鲁棒的条件扩散模型，用于语义图像合成，通过标签扩散处理噪声标签，同时引入类别权重的噪声时间表来增强鲁棒性。在实验中证明了该方法在生成高质量样本方面的有效性，并模拟了现实应用中的人类错误情况。