Diffusion models arise as a powerful generative tool recently. Despite the
great progress, existing diffusion models mainly focus on uni-modal control,
i.e., the diffusion process is driven by only one modality of condition. To
further unleash the users' creativity, it is desirable for the model to be
controllable by multiple modalities simultaneously, e.g., generating and
editing faces by describing the age (text-driven) while drawing the face shape
(mask-driven). In this work, we present Collaborative Diffusion, where
pre-trained uni-modal diffusion models collaborate to achieve multi-modal face
generation and editing without re-training. Our key insight is that diffusion
models driven by different modalities are inherently complementary regarding
the latent denoising steps, where bilateral connections can be established
upon. Specifically, we propose dynamic diffuser, a meta-network that adaptively
hallucinates multi-modal denoising steps by predicting the spatial-temporal
influence functions for each pre-trained uni-modal model. Collaborative
Diffusion not only collaborates generation capabilities from uni-modal
diffusion models, but also integrates multiple uni-modal manipulations to
perform multi-modal editing. Extensive qualitative and quantitative experiments
demonstrate the superiority of our framework in both image quality and
condition consistency.

本文提出了一种名为 Collaborative Diffusion 的模型，在不需要重新训练的情况下，利用多种单一模态扩展固有的单一模态扩散模型以实现多模态人脸生成和编辑。