Be Yourself: Bounded Attention for Multi-Subject Text-to-Image Generation

arXiv:2403.16990v1 Announce Type: cross
Abstract: Text-to-image diffusion models have an unprecedented ability to generate diverse and high-quality images. However, they often struggle to faithfully capture the intended semantics of complex input prompts that include multiple subjects. Recently, numerous layout-to-image extensions have been introduced to improve user control, aiming to localize subjects represented by specific tokens. Yet, these methods often produce semantically inaccurate images, especially when dealing with multiple semantically or visually similar subjects. In this work, we study and analyze …

abstract arxiv attention control cs.ai cs.cv cs.gr cs.lg diffusion diffusion models diverse extensions generate however image image diffusion image generation images multiple prompts quality semantics struggle text text-to-image type