Training-free Subject-Enhanced Attention Guidance for Compositional Text-to-image Generation

arXiv:2405.06948v1 Announce Type: new
Abstract: Existing subject-driven text-to-image generation models suffer from tedious fine-tuning steps and struggle to maintain both text-image alignment and subject fidelity. For generating compositional subjects, it often encounters problems such as object missing and attribute mixing, where some subjects in the input prompt are not generated or their attributes are incorrectly combined. To address these limitations, we propose a subject-driven generation framework and introduce training-free guidance to intervene in the generative process during inference time. This …

abstract alignment arxiv attention cs.cv fidelity fine-tuning free generated guidance image image generation image generation models object prompt struggle text text-image text-to-image training type