AI RESEARCH
SHIFT: Steering Hidden Intermediates in Flow Transformers
arXiv CS.CV
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ArXi:2604.09213v1 Announce Type: new Diffusion models have become leading approaches for high-fidelity image generation. Recent DiT-based diffusion models, in particular, achieve strong prompt adherence while producing high-quality samples. We propose SHIFT, a simple but effective and lightweight framework for concept removal in DiT diffusion models via targeted manipulation of intermediate activations at inference time, inspired by activation steering in large language models.