IS-Diff: Improving Diffusion-Based Inpainting with Better Initial Seed
IS-Diff improves diffusion-based image inpainting by using initial seeds sampled from unmasked areas to better imitate the masked data distribution. It also introduces a dynamic selective refinement mechanism that adjusts how much the initial seed influences the final result based on distributional harmony, leading to more coherent and realistic inpaintings without needing training.