  Semantic image synthesis (SIS) is a task to generate realistic images
corresponding to semantic maps (labels). 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. Code
is available at https://github.com/mlvlab/SCDM.
