ReState: Noise-Level-Aware State Correction for Training-Free Object Removal in Few-Step Diffusion Models

ReState is a training‑free method for object removal in few‑step diffusion models that corrects the masked clean prediction toward background evidence using a noise‑level‑aware weight that fades out at the final forward pass. It complements attention rerouting, requiring no additional denoiser evaluations, and outperforms existing training‑free techniques on two paired benchmarks and two distilled backbones, improving PSNR by an average of 3.17 dB. On the OBER‑Test set, ReState also surpasses a 50‑step attention‑forcing reference while running 6.7× faster.

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• diffusion
• benchmarks