Google Research introduced Diffusion Controller. It is a lightweight steering damper network for image generation. It attaches to closed-source models without breaking stability. It reframes denoising as a continuous control problem. It dynamically adjusts generation to match user intent. It outperformed LoRA in HPS-v2 win rates. It works in both white-box and gray-box environments. It allows runtime adjustment of prompt alignment. It preserves base model image quality. It opens paths for personalization and safety controls.
What changed
Existing steering methods were disconnected and required guesswork to balance alignment and quality.
What it unlocks
Precisely steering closed-source image models without altering their core weights.
- 90% win rate over baseline
- HPS-v2 win rates
What you need to act on it
- a text-to-image model
- Stable Diffusion v1.4 backbone for experiments
Sources