Title: NÜWA-LIP: Language-guided Image Inpainting with Defect-free VQGAN. Abstract: Language-guided image inpainting aims to fill in the defective regions of an image under the guidance of text while keeping non-defective regions unchanged. However, the encoding process of existing models suffers from either receptive spreading of defective regions or information loss of non-defective regions, giving rise to visually unappealing inpainting results. To address the above issues, this paper proposes NÜWA-LIP by incorporating defect-free VQGAN (DF-VQGAN) with multi-perspective sequence to sequence (MP-S2S). In particular, DF-VQGAN introduces relative estimation to control receptive spreading and adopts symmetrical connections to protect information. MP-S2S further enhances visual information from complementary perspectives, including both low-level pixel and high-level token. Experiments show that DF-VQGAN performs much more robustly than VQGAN. To evaluate language-guided inpainting, we built up three open-domain benchmarks, where NÜWA-LIP is also superior to recent strong baselines.