This modularization mimics a mixture-of-experts. Each adapter acts as a specialized “expert” for its task. Parallel pathways avoid conflicting objectives that would arise from entangled handling of all conditions. The model dynamically composites outputs from relevant adapters according to the input task.
The HyperNetwork enables dynamic modulation of UniControl based america phone number list on the specified task. It inputs instructions like “depth map to image” and outputs embedding vectors. These embeddings can specialize the model by modulating its convolution kernels based on the task.
For example, depth conditioning may modulate early layers to focus more on global layout and geometry. In the meanwhile, edge adaptation may emphasize higher-frequency details at later stages. The HyperNetwork allows UniControl to learn specialized understanding and processing of each task.