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 approach strengthens the attention map,
allowing for precise attribute binding and feature injection for each subject.
Notably, our method exhibits exceptional zero-shot generation ability,
especially in the challenging task of compositional generation. Furthermore, we
propose a novel metric GroundingScore to evaluate subject alignment thoroughly.
The obtained quantitative results serve as compelling evidence showcasing the
effectiveness of our proposed method. The code will be released soon.

我们提出了一种主题驱动的生成框架，通过介入生成过程中的推理时间，强化注意力图，实现精确的属性绑定和特征注入，展示了卓越的零次生成能力，尤其在组合生成的挑战性任务中。