arXiv:2601. 19180v2 Announce Type: replace-cross Abstract: Inversion-free image editing using flow-based generative models challenges the prevailing inversion-based pipelines.
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Steering Fields introduce adaptive vector fields that re-estimate steering directions at each step of a flow-based text-to-image generation process, replacing the fixed global steering vectors traditionally used. By operating on noisy states, they provide a continuous trade-off between steering strength and content preservation, and allow simultaneous induction and inhibition of concepts without explicit spatial masks or object priors. The method achieves state-of-the-art safety steering benchmarks and can also function as a structure-preserving image-editing technique, delivering high semantic fidelity while remaining model-agnostic and inversion-free.
arXiv:2607. 19895v1 Announce Type: cross Abstract: Text-guided video editing with diffusion models is impractically slow, hindered by costly multi-step sampling and inversion.
arXiv:2603.20186v2 Announce Type: replace Abstract: In this work, we propose Image-to-Image Rectified Flow Reformulation (I2I-RFR), a practical plug-in reformulation that recasts standard I2I regress...
arXiv:2606. 17584v1 Announce Type: cross Abstract: Finding the initial noise that generates a given data sample, known as inversion, is a key component for downstream applications such as training-free image editing.
The paper introduces MS-Flow, a method that represents a flow-based generative model’s trajectory as a sequence of intermediate latent states instead of a single initial code. By enforcing local flow dynamics and coupling trajectory segments with matching penalties, the approach alternates between updating latent states and ensuring consistency with observed data. This strategy reduces memory usage and improves reconstruction quality on tasks such as image inpainting, super‑resolution, and computed tomography.
Despite remarkable progress in text-guided image editing, generative models frequently fail to preserve visual object consistency, defined as the preservation of a subject's key attributes throughout the editing process. We address this limitation through three contributions.