HyperTransport: Amortized Conditioning of T2I Generative Models

ArXi:2605.08254v1 Announce Type: cross As foundation models grow in capability, the ability to efficiently and reliably control their behavior becomes critical. Fine-tuning these models can be costly, and while prompting can be practical for controllability, it remains fragile due to models' high sensitivity to exact prompt wording and structure. This brittleness has driven interest in activation steering techniques that offer stable and predictable control over model behavior.