  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.
