One-Step Diffusion Inpainting via Reconstruction-Space Adaptation

Diffusion models have shown strong performance in image inpainting by leveraging powerful generative priors, but their iterative denoising process often incurs substantial inference cost. One-step diffusion models provide a promising alternative, yet removing iterative denoising also removes the opportunity for progressive error correction, allowing prediction errors produced in a single step to more directly affect the final reconstruction. In this work, we investigate how prediction errors propagate into reconstruction errors under different prediction targets and, more importantly, whether lower theoretical error propagation translates into better downstream adaptation. Although velocity prediction has lower theoretical error amplification than noise prediction under the adopted noise schedule, controlled experiments show that replacing the prediction target used during pretraining with velocity prediction degrades downstream adaptation performance under the evaluated training budget. This reveals a discrepancy between theoretical error propagation and practical adaptation performance. Motivated by this observation, we propose reconstruction-space adaptation for one-step diffusion inpainting. Instead of changing the pretrained prediction target, the proposed method preserves noise prediction while extending supervision from the prediction space to the reconstructed latent representation and decoded image, thereby aligning downstream optimization more directly with the final restoration objective. Independent-sample averaging and a lightweight residual refiner are further used as complementary components to reduce sampling-induced output fluctuations and correct remaining image-space errors. Extensive experiments on Places2 and CelebA-HQ validate the proposed adaptation strategy, showing consistent reconstruction improvements under controlled adaptation settings and competitive inpainting performance.