  Subject-driven text-to-image generation aims to generate customized images of
the given subject based on the text descriptions, which has drawn increasing
attention. Existing methods mainly resort to finetuning a pretrained generative
model, where the identity-relevant information (e.g., the boy) and the
identity-irrelevant information (e.g., the background or the pose of the boy)
are entangled in the latent embedding space. However, the highly entangled
latent embedding may lead to the failure of subject-driven text-to-image
generation as follows: (i) the identity-irrelevant information hidden in the
entangled embedding may dominate the generation process, resulting in the
generated images heavily dependent on the irrelevant information while ignoring
the given text descriptions; (ii) the identity-relevant information carried in
the entangled embedding can not be appropriately preserved, resulting in
identity change of the subject in the generated images. To tackle the problems,
we propose DisenBooth, an identity-preserving disentangled tuning framework for
subject-driven text-to-image generation. Specifically, DisenBooth finetunes the
pretrained diffusion model in the denoising process. Different from previous
works that utilize an entangled embedding to denoise each image, DisenBooth
instead utilizes disentangled embeddings to respectively preserve the subject
identity and capture the identity-irrelevant information. We further design the
novel weak denoising and contrastive embedding auxiliary tuning objectives to
achieve the disentanglement. Extensive experiments show that our proposed
DisenBooth framework outperforms baseline models for subject-driven
text-to-image generation with the identity-preserved embedding. Additionally,
by combining the identity-preserved embedding and identity-irrelevant
embedding, DisenBooth demonstrates more generation flexibility and
controllability
