Google frames image-model steering as a lightweight control problem
Google Research published Diffusion Controller, a control-theoretic framework that leaves a diffusion backbone frozen and trains a side network to steer its denoising trajectory. Google reports stronger prompt-alignment and quality tradeoffs than its gray-box baselines and LoRA in Stable Diffusion 1.4 experiments.
Why it matters: The method separates the control layer from the image model and can work with exposed intermediate outputs rather than full model weights. That could make preference or policy adaptation cheaper, but one dated paper and provider-run evaluation do not…