  Text-to-image diffusion models have remarkably excelled in producing diverse,
high-quality, and photo-realistic images. This advancement has spurred a
growing interest in incorporating specific identities into generated content.
Most current methods employ an inversion approach to embed a target visual
concept into the text embedding space using a single reference image. However,
the newly synthesized faces either closely resemble the reference image in
terms of facial attributes, such as expression, or exhibit a reduced capacity
for identity preservation. Text descriptions intended to guide the facial
attributes of the synthesized face may fall short, owing to the intricate
entanglement of identity information with identity-irrelevant facial attributes
derived from the reference image. To address these issues, we present the novel
use of the extended StyleGAN embedding space $\mathcal{W}_+$, to achieve
enhanced identity preservation and disentanglement for diffusion models. By
aligning this semantically meaningful human face latent space with
text-to-image diffusion models, we succeed in maintaining high fidelity in
identity preservation, coupled with the capacity for semantic editing.
Additionally, we propose new training objectives to balance the influences of
both prompt and identity conditions, ensuring that the identity-irrelevant
background remains unaffected during facial attribute modifications. Extensive
experiments reveal that our method adeptly generates personalized text-to-image
outputs that are not only compatible with prompt descriptions but also amenable
to common StyleGAN editing directions in diverse settings. Our source code will
be available at \url{https://github.com/csxmli2016/w-plus-adapter}.
