LaCon: Late-Constraint Controllable Visual Generation

Diffusion models have demonstrated impressive abilities in generating photo-realistic and creative images. To offer more controllability for the generation process of diffusion models, previous studies normally adopt extra modules to integrate condition signals by manipulating the intermediate features of the noise predictors, where they often fail in conditions not seen in the training. Although subsequent studies are motivated to handle multi-condition control, they are mostly resource-consuming to implement, where more generalizable and efficient solutions are expected for controllable visual generation. In this paper, we present a late-constraint controllable visual generation method, namely LaCon, which enables generalization across various modalities and granularities for each single-condition control. LaCon establishes an alignment between the external condition and specific diffusion timesteps, and guides diffusion models to produce conditional results based on this built alignment. Experimental results on prevailing benchmark datasets illustrate the promising performance and generalization capability of LaCon under various conditions and settings. Ablation studies analyze different components in LaCon, illustrating its great potential to offer flexible condition controls for different backbones.