Non-Cross Diffusion for Semantic Consistency
Ziyang Zheng Ruiyuan Gao Qiang Xu
In diffusion models, deviations from a straight generative flow are a common
issue, resulting in semantic inconsistencies and suboptimal generations. To
address this challenge, we introduce `Non-Cross Diffusion', an innovative
approach in generative modeling for learning ordinary differential equation
(ODE) models. Our methodology strategically incorporates an ascending dimension
of input to effectively connect points sampled from two distributions with
uncrossed paths. This design is pivotal in ensuring enhanced semantic
consistency throughout the inference process, which is especially critical for
applications reliant on consistent generative flows, including various
distillation methods and deterministic sampling, which are fundamental in image
editing and interpolation tasks. Our empirical results demonstrate the
effectiveness of Non-Cross Diffusion, showing a substantial reduction in
semantic inconsistencies at different inference steps and a notable enhancement
in the overall performance of diffusion models.