arXiv:2602.11395v2 Announce Type: replace Abstract: Steering diffusion models toward conditions unseen during training typically requires either retraining with conditional inputs or per-step gradient computations, both of which incur substantial computational overhead. We present Noise-Aligned RFM Steering (NA-RFM), a general recipe for efficiently steering diffusion models without gradient guidance during inference, enabling fast controllable generation. The method combines two offline-computed signals: noise alignment, a high-noise correction from PCA statistics of the target examples and t
The rapid advancement of diffusion models has created a demand for more efficient and controllable generative AI, addressing limitations in current steering methods.
This breakthrough significantly reduces the computational overhead for controlling diffusion models, enabling their broader and more practical application in various fields.
Diffusion models can now be steered towards specific creative or functional conditions without extensive retraining or expensive per-step gradient calculations during inference.
- · AI developers
- · Creative industries
- · Generative AI platforms
- · Compute infrastructure providers
- · AI models requiring extensive fine-tuning
- · Companies relying on less efficient generative methods
More accessible and efficient controllable AI content generation becomes possible.
This democratizes advanced generative AI capabilities, fostering innovation across multiple sectors.
The reduced compute burden could accelerate the development of more complex and ambitious AI agentic systems.
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Read at arXiv cs.LG