Diffusion models have recently exhibited remarkable abilities to synthesize
striking image samples since the introduction of denoising diffusion
probabilistic models (DDPMs). Their key idea is to disrupt images into noise
through a fixed forward process and learn its reverse process to generate
samples from noise in a denoising way. For conditional DDPMs, most existing
practices relate conditions only to the reverse process and fit it to the
reversal of unconditional forward process. We find this will limit the
condition modeling and generation in a small time window. In this paper, we
propose a novel and flexible conditional diffusion model by introducing
conditions into the forward process. We utilize extra latent space to allocate
an exclusive diffusion trajectory for each condition based on some shifting
rules, which will disperse condition modeling to all timesteps and improve the
learning capacity of model. We formulate our method, which we call
\textbf{ShiftDDPMs}, and provide a unified point of view on existing related
methods. Extensive qualitative and quantitative experiments on image synthesis
demonstrate the feasibility and effectiveness of ShiftDDPMs.

本研究提出了一种新的有条件扩散模型 ShiftDDPMs，通过将条件引入正向过程，为每个条件分配一个独特的扩散轨迹，并通过定向规则来实现条件建模，从而提高模型的学习能力和生成效果。在图像合成方面进行了广泛的实验，证明了 ShiftDDPMs 的可行性和有效性。