  Subject-driven text-to-image diffusion models empower users to tailor the
model to new concepts absent in the pre-training dataset using a few sample
images. However, prevalent subject-driven models primarily rely on
single-concept input images, facing challenges in specifying the target concept
when dealing with multi-concept input images. To this end, we introduce a
textual localized text-to-image model (Texual Localization) to handle
multi-concept input images. During fine-tuning, our method incorporates a novel
cross-attention guidance to decompose multiple concepts, establishing distinct
connections between the visual representation of the target concept and the
identifier token in the text prompt. Experimental results reveal that our
method outperforms or performs comparably to the baseline models in terms of
image fidelity and image-text alignment on multi-concept input images. In
comparison to Custom Diffusion, our method with hard guidance achieves CLIP-I
scores that are 7.04%, 8.13% higher and CLIP-T scores that are 2.22%, 5.85%
higher in single-concept and multi-concept generation, respectively. Notably,
our method generates cross-attention maps consistent with the target concept in
the generated images, a capability absent in existing models.
