  Current subject-driven image generation methods encounter significant
challenges in person-centric image generation. The reason is that they learn
the semantic scene and person generation by fine-tuning a common pre-trained
diffusion, which involves an irreconcilable training imbalance. Precisely, to
generate realistic persons, they need to sufficiently tune the pre-trained
model, which inevitably causes the model to forget the rich semantic scene
prior and makes scene generation over-fit to the training data. Moreover, even
with sufficient fine-tuning, these methods can still not generate high-fidelity
persons since joint learning of the scene and person generation also lead to
quality compromise. In this paper, we propose Face-diffuser, an effective
collaborative generation pipeline to eliminate the above training imbalance and
quality compromise. Specifically, we first develop two specialized pre-trained
diffusion models, i.e., Text-driven Diffusion Model (TDM) and Subject-augmented
Diffusion Model (SDM), for scene and person generation, respectively. The
sampling process is divided into three sequential stages, i.e., semantic scene
construction, subject-scene fusion, and subject enhancement. The first and last
stages are performed by TDM and SDM respectively. The subject-scene fusion
stage, that is the collaboration achieved through a novel and highly effective
mechanism, Saliency-adaptive Noise Fusion (SNF). Specifically, it is based on
our key observation that there exists a robust link between classifier-free
guidance responses and the saliency of generated images. In each time step, SNF
leverages the unique strengths of each model and allows for the spatial
blending of predicted noises from both models automatically in a saliency-aware
manner. Extensive experiments confirm the impressive effectiveness and
robustness of the Face-diffuser.
