  The advent of open-source AI communities has produced a cornucopia of
powerful text-guided diffusion models that are trained on various datasets.
While few explorations have been conducted on ensembling such models to combine
their strengths. In this work, we propose a simple yet effective method called
Saliency-aware Noise Blending (SNB) that can empower the fused text-guided
diffusion models to achieve more controllable generation. Specifically, we
experimentally find that the responses of classifier-free guidance are highly
related to the saliency of generated images. Thus we propose to trust different
models in their areas of expertise by blending the predicted noises of two
diffusion models in a saliency-aware manner. SNB is training-free and can be
completed within a DDIM sampling process. Additionally, it can automatically
align the semantics of two noise spaces without requiring additional
annotations such as masks. Extensive experiments show the impressive
effectiveness of SNB in various applications. Project page is available at
https://magicfusion.github.io/.
