handles one specific visual condition, like edges or depth maps. Extensive retraining is needed to expand capabilities. Supporting diverse controllable inputs requires developing specialized models for each task. This bloats parameters, limits knowledge sharing, and hinders cross-modality adaptation or out-of-domain generalization.
Motivation
There is a pressing need for unified models that can handle diverse visual conditions for controllable generation. Consolidating capabilities in a single model would greatly improve training and deployment efficiency without needing multiple task-specific models. It also allows exploiting relationships across conditions, like depth and segmentation, to improve generation quality.
For example, depth estimation relies heavily on understanding america phone number list semantic segmentation and global scene layout. A unified model can better leverage these relationships compared to isolated task models. Furthermore, adding new modalities to individual models incurs massive retraining, while a consolidated approach could generalize more seamlessly.
The core challenge is to overcome the misalignment between diverse conditions like edges, poses, maps, etc. Each requires operations specialized to its characteristics. Trivially mixing diverse inputs in one model fails due to this feature mismatch. The goal is to develop a unified architecture that generalizes across tasks while adapting their conditioning components appropriately. Crucially, this needs to be achieved without requiring extensive retraining whenever expanding to new capabilities.