TraDiffusion: Trajectory-Based Training-Free Image Generation
Authors: name: Mingrui Wu, name: Oucheng Huang, name: Jiayi Ji, name: Jiale Li, name: Xinyue Cai, name: Huafeng Kuang, name: Jianzhuang Liu, name: Xiaoshuai Sun, name: Rongrong Ji

In this work, we propose a training-free, trajectory-based controllable T2I
approach, termed TraDiffusion. This novel method allows users to effortlessly
guide image generation via mouse trajectories. To achieve precise control, we
design a distance awareness energy function to effectively guide latent
variables, ensuring that the focus of generation is within the areas defined by
the trajectory. The energy function encompasses a control function to draw the
generation closer to the specified trajectory and a movement function to
diminish activity in areas distant from the trajectory. Through extensive
experiments and qualitative assessments on the COCO dataset, the results reveal
that TraDiffusion facilitates simpler, more natural image control. Moreover, it
showcases the ability to manipulate salient regions, attributes, and
relationships within the generated images, alongside visual input based on
arbitrary or enhanced trajectories.