  Recent progress in multi-modal conditioned face synthesis has enabled the
creation of visually striking and accurately aligned facial images. Yet,
current methods still face issues with scalability, limited flexibility, and a
one-size-fits-all approach to control strength, not accounting for the
differing levels of conditional entropy, a measure of unpredictability in data
given some condition, across modalities. To address these challenges, we
introduce a novel uni-modal training approach with modal surrogates, coupled
with an entropy-aware modal-adaptive modulation, to support flexible, scalable,
and scalable multi-modal conditioned face synthesis network. Our uni-modal
training with modal surrogate that only leverage uni-modal data, use modal
surrogate to decorate condition with modal-specific characteristic and serve as
linker for inter-modal collaboration , fully learns each modality control in
face synthesis process as well as inter-modal collaboration. The entropy-aware
modal-adaptive modulation finely adjust diffusion noise according to
modal-specific characteristics and given conditions, enabling well-informed
step along denoising trajectory and ultimately leading to synthesis results of
high fidelity and quality. Our framework improves multi-modal face synthesis
under various conditions, surpassing current methods in image quality and
fidelity, as demonstrated by our thorough experimental results.
