High-fidelity Person-centric Subject-to-Image Synthesis

• Published in: Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) pp. 7675 - 7684
• Main Authors: Wang, Yibin, Zhang, Weizhong, Zheng, Jianwei, Jin, Cheng
• Format: Conference Proceeding
• Language: English
• Published: IEEE 16.06.2024
• Subjects: Collaboration; Diffusion; Image synthesis; Noise; Pipelines; Predictive models; Semantics; Subject-to-Image Synthesis; Text-to-Image Synthesis; Training
• DOI: 10.1109/CVPR52733.2024.00733

Current subject-driven image generation methods en-counter 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 imbal-ance 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, all of which can be seamlessly integrated into the DDIM sampling process. Extensive experiments confirm the impressive effectiveness and robustness of the Face-diffuser in gener-ating high-fidelity person images depicting multiple unseen persons with varying contexts. Code is available at https://github.com/CodeGoat24/Face-diffuser.