SHIFT: Steering Hidden Intermediates in Flow Transformers
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. SHIFT learns steering vectors that are dynamically applied to selected layers and timesteps to suppress unwanted visual concepts while preserving the prompt's remaining content and overall image quality. Beyond suppression, the same mechanism can shif...
| Authors | Nina Konovalova, Andrey Kuznetsov, Aibek Alanov |
|---|---|
| Primary category | cs.CV |
| Published | 10 April 2026 |
| Last updated at source | 10 April 2026 |
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