DynaFill: Dynamic Manifold-Consistent Flow Sampling for Localized Image Inpainting without Inversion

Image inpainting using pre-trained generative models typically relies on inversion—projecting the masked image back into the model’s noise space—which is computationally expensive and often struggles to preserve unmasked content while generating coherent structures in masked regions. In this paper, we introduce DynaFill, a novel inversion-free framework for localized image inpainting. Unlike existing inversion-based or fixed-anchor inversion-free editing methods, DynaFill is driven by a Dynamic Manifold-Consistent Sampling strategy. Specifically, we decouple the sampling trajectory from the static source prior by dynamically anchoring the noise perturbation to the current generation state rather than the corrupted original image. This allows the generative path to adaptively slide along the data manifold, ensuring that the filled content remains structurally faithful to the global context. To further enhance efficiency and locality, we integrate an enhanced ODE solver that accelerates sampling by nearly 2×, alongside a localized latent blending mechanism that strictly preserves unmasked pixels while enabling full generative freedom in masked holes. Extensive experimental results demonstrate that DynaFill achieves state-of-the-art performance in terms of structural coherence and texture fidelity, validating the effectiveness of the proposed method for inversion-free inpainting.