4 accepted papers
Layout-to-Image generation has significantly advanced content creation by enabling the rendering of visual text under predefined spatial layouts. Current approaches achieve training-free layout guidance by constructing attention-based energy functions to derive correction gradients. In this paper, w
Layout-to-Image generation aims to create complex scenes with precise control over the placement and arrangement of subjects. Existing works have demonstrated that pre-trained Text-to-Image diffusion models can achieve this goal without training on any specific data; however, they often face challen…
This paper focuses on face stylization with a single artistic target. Existing works for this task often fail to retain the source content while achieving geometry variation. Here, we present a novel StyO model, i.e., Stylize the face in only One-shot, to solve the above problem. In particular, StyO…
In this paper, we focus on analyzing and improving the dropout technique for self-attention layers of Vision Transformer, which is important while surprisingly ignored by prior works. In particular, we conduct researches on three core questions: First, what to drop in self-attention layers? Differen…